U.S. patent application number 15/939238 was filed with the patent office on 2018-11-29 for systems and methods for monitoring and controlling an energy plant.
The applicant listed for this patent is Johnson Controls Technology Company. Invention is credited to Peter A. Craig, Collin W. Eggert, Michael F. Jaeger, Nicole A. Madison, Carol T. Tumey, Robert D. Turney, Tricia J. Valentine.
Application Number | 20180340702 15/939238 |
Document ID | / |
Family ID | 62977355 |
Filed Date | 2018-11-29 |
United States Patent
Application |
20180340702 |
Kind Code |
A9 |
Turney; Robert D. ; et
al. |
November 29, 2018 |
SYSTEMS AND METHODS FOR MONITORING AND CONTROLLING AN ENERGY
PLANT
Abstract
An energy plant includes a plurality of subplants, a high level
optimizer, a low level optimizer, and a controller. The plurality
of subplants include a cogeneration subplant configured to generate
steam and electricity and a chiller subplant electrically coupled
to the cogeneration subplant and configured to consume the
electricity generated by the cogeneration subplant. The high level
optimizer is configured to determine recommended subplant loads for
each of the plurality of subplants. The recommended subplant loads
include a rate of steam production and a rate of electricity
production of the cogeneration subplant and a rate of electricity
consumption of the chiller subplant. The low level optimizer is
configured to determine recommended equipment setpoints for
equipment of the plurality of subplants based on the recommended
subplant loads. The controller is configured to operate the
equipment of the plurality of subplants based on the recommended
equipment setpoints.
Inventors: |
Turney; Robert D.;
(Watertown, WI) ; Valentine; Tricia J.; (Glendale,
WI) ; Madison; Nicole A.; (Milwaukee, WI) ;
Eggert; Collin W.; (Milwaukee, WI) ; Tumey; Carol
T.; (Wauwatosa, WI) ; Jaeger; Michael F.;
(Thiensville, WI) ; Craig; Peter A.; (Pewaukee,
WI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Johnson Controls Technology Company |
Auburn Hills |
MI |
US |
|
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20180216842 A1 |
August 2, 2018 |
|
|
Family ID: |
62977355 |
Appl. No.: |
15/939238 |
Filed: |
March 28, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15387512 |
Dec 21, 2016 |
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15939238 |
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62286287 |
Jan 22, 2016 |
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62476510 |
Mar 24, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
F24F 11/46 20180101;
G05B 15/02 20130101; G05B 19/41865 20130101; F24F 2110/40 20180101;
F24F 2221/54 20130101; G05B 2219/25011 20130101; F24F 2110/10
20180101; F24F 11/58 20180101; G06F 3/04847 20130101; Y02P 80/114
20151101; Y02P 80/10 20151101; F24F 11/47 20180101; F24F 11/62
20180101; F24F 2110/20 20180101; F24F 11/30 20180101; F24F 11/52
20180101; F24F 11/54 20180101; F24F 2140/50 20180101 |
International
Class: |
F24F 11/30 20060101
F24F011/30; F24F 11/54 20060101 F24F011/54; F24F 11/58 20060101
F24F011/58; F24F 11/62 20060101 F24F011/62; G06F 3/0484 20060101
G06F003/0484; G05B 15/02 20060101 G05B015/02 |
Claims
1. An energy plant comprising: a plurality of subplants configured
to serve energy loads of a campus, the plurality of subplants
comprising: a cogeneration subplant configured to generate steam
and electricity; and a chiller subplant electrically coupled to the
cogeneration subplant and configured to consume the electricity
generated by the cogeneration subplant; a high level optimizer
configured to determine recommended subplant loads for each of the
plurality of subplants, the recommended subplant loads comprising a
rate of steam production and a rate of electricity production of
the cogeneration subplant and a rate of electricity consumption of
the chiller subplant; a low level optimizer configured to determine
recommended equipment setpoints for equipment of the plurality of
subplants based on the recommended subplant loads; and a controller
configured to operate the equipment of the plurality of subplants
based on the recommended equipment setpoints.
2. The energy plant of claim 1, wherein: the chiller subplant is
fluidly coupled to the cogeneration subplant and configured to
chill the steam generated by the cogeneration subplant; and the
recommended subplant loads comprise a rate of steam consumption and
a rate of chilled steam production of the chiller subplant.
3. The energy plant of claim 1, further comprising a user interface
configured to receive manual subplant loads specified by a user;
wherein the low level optimizer configured to determine the
recommended equipment setpoints for the equipment of the plurality
of subplants based on the manual subplant loads.
4. The energy plant of claim 3, further comprising a dispatch
graphical user interface (GUI) generator configured to generate a
dispatch GUI and present the dispatch GUI via the user interface,
the dispatch GUI comprising the recommended subplant loads and the
manual subplant loads.
5. The energy plant of claim 4, wherein the dispatch GUI comprises
an operating mode selector configured to display a plurality of
operating modes and to receive a user selection of one of the
operating modes; the plurality of operating modes comprising an
automatic operating mode, an advisory operating mode, and a manual
operating mode.
6. The energy plant of claim 3, wherein the low level optimizer is
configured to operate in: an automatic operating mode in which the
low level optimizer determines a first set of recommended equipment
setpoints to achieve the recommended subplant loads; and a manual
operating mode in which the low level optimizer determines a second
set of recommended equipment setpoints to achieve the manual
subplant loads.
7. The energy plant of claim 3, wherein the high level optimizer is
configured to determine an amount of overproduction or
underproduction resulting from the manual subplant loads by
comparing the manual subplant loads to a campus energy load.
8. The energy plant of claim 7, wherein: the plurality of subplants
comprise an energy storage subplant; and the user interface
comprises an indication of an amount of time until the energy
storage subplant is (1) fully depleted based on the amount of
underproduction or (2) filled to capacity based on the amount of
overproduction.
9. The energy plant of claim 1, further comprising a subplant
monitor configured to monitor the energy plant and identify actual
subplant loads for each of the plurality of subplants.
10. The energy plant of claim 9, further comprising a dispatch
graphical user interface (GUI) generator configured to generate a
dispatch GUI comprising the recommended subplant loads and the
actual subplant loads.
11. A method for monitoring and controlling an energy plant
comprising a plurality of subplants that operate to serve energy
loads of a campus, the method comprising: operating a cogeneration
subplant of the plurality of subplants to generate steam and
electricity; and operating a chiller subplant of the plurality of
subplants to consume the electricity generated by the cogeneration
subplant; determining recommended subplant loads for each of the
plurality of subplants, the recommended subplant loads comprising a
rate of steam production and a rate of electricity production of
the cogeneration subplant and a rate of electricity consumption of
the chiller subplant; determining recommended equipment setpoints
for equipment of the plurality of subplants based on the
recommended subplant loads; and operating the equipment of the
plurality of subplants based on the recommended equipment
setpoints.
12. The method of claim 11, further comprising operating the
chiller subplant to chill the steam generated by the cogeneration
subplant; wherein the recommended subplant loads comprise a rate of
steam consumption and a rate of chilled steam production of the
chiller subplant.
13. The method of claim 11, further comprising receiving manual
subplant loads specified by a user via a user interface; and
determining the recommended equipment setpoints for the equipment
of the plurality of subplants based on the manual subplant
loads.
14. The method of claim 13, further comprising generating a
dispatch GUI and presenting the dispatch GUI via the user
interface, the dispatch GUI comprising the recommended subplant
loads and the manual subplant loads.
15. The method of claim 14, further comprising receiving a user
selection of one of a plurality of operating modes via an operating
mode selector of the dispatch GUI, the plurality of operating modes
comprising an automatic operating mode, an advisory operating mode,
and a manual operating mode.
16. The method of claim 13, further comprising: operating in an
automatic operating mode to determine a first set of recommended
equipment setpoints to achieve the recommended subplant loads; and
operating in a manual operating mode to determine a second set of
recommended equipment setpoints to achieve the manual subplant
loads.
17. The method of claim 13, further comprising determining an
amount of overproduction or underproduction resulting from the
manual subplant loads by comparing the manual subplant loads to a
campus energy load.
18. The method of claim 17, wherein: the plurality of subplants
comprise an energy storage subplant; and the user interface
comprises an indication of an amount of time until the energy
storage subplant is (1) fully depleted based on the amount of
underproduction or (2) filled to capacity based on the amount of
overproduction.
19. The method of claim 11, further comprising monitoring the
energy plant to identify actual subplant loads for each of the
plurality of subplants.
20. The method of claim 19, further comprising generating a
dispatch GUI comprising the recommended subplant loads and the
actual subplant loads.
Description
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS
[0001] This application is a continuation-in-part of U.S. patent
application Ser. No. 15/387,512 filed Dec. 21, 2016, which claims
the benefit of and priority to U.S. Provisional Patent Application
No. 62/286,287 filed Jan. 22, 2016. This application also claims
the benefit of and priority to U.S. Provisional Patent Application
No. 62/476,510 filed Mar. 24, 2017. The entire disclosures of each
of these patent applications are incorporated by reference
herein.
BACKGROUND
[0002] The present disclosure relates generally to an plant that
includes multiple subplants configured to serve the energy loads of
a building or campus. The present disclosure relates more
particularly to systems and methods for monitoring and controlling
multiple subplants and the equipment within each of the
subplants.
[0003] An energy plant may include a number of subplants configured
to serve the heating, cooling, electric, and other energy loads of
a building or campus. For example, the energy plant may include a
heater subplant, a chiller subplant, a heat recovery chiller
subplant, a cogeneration subplant, a steam subplant, a hot thermal
energy storage (TES) subplant, and/or a cold TES subplant. The
subplants may include a variety of HVAC equipment configured to
serve heating, cooling, and electric loads (e.g., heaters, boilers,
chillers, heat recovery chillers, electric generators, cooling
towers, etc.). The energy plant may be controlled by an energy
plant controller that distributes a requested or predicted energy
load (e.g., a heating load, a cooling load, an electric load, etc.)
across the plurality of subplants. It can be difficult and
challenging to monitor and control the operation of an energy
plant.
SUMMARY
[0004] One implementation of the present disclosure is an energy
plant. The energy plant includes a plurality of subplants, a high
level optimizer, a low level optimizer, and a controller. The
plurality of subplants are configured to serve energy loads of a
campus and include a cogeneration subplant and a chiller subplant.
The cogeneration subplant is configured to generate steam and
electricity. The chiller subplant is electrically coupled to the
cogeneration subplant and configured to consume the electricity
generated by the cogeneration subplant. The high level optimizer is
configured to determine recommended subplant loads for each of the
plurality of subplants. The recommended subplant loads include a
rate of steam production and a rate of electricity production of
the cogeneration subplant and a rate of electricity consumption of
the chiller subplant. The low level optimizer is configured to
determine recommended equipment setpoints for equipment of the
plurality of subplants based on the recommended subplant loads. The
controller is configured to operate the equipment of the plurality
of subplants based on the recommended equipment setpoints.
[0005] In some embodiments, the chiller subplant is fluidly coupled
to the cogeneration subplant and configured to chill the steam
generated by the cogeneration subplant. The recommended subplant
loads may include a rate of steam consumption and a rate of chilled
steam production of the chiller subplant.
[0006] In some embodiments, the energy plant includes a user
interface configured to receive manual subplant loads specified by
a user. The low level optimizer can be configured to determine the
recommended equipment setpoints for the equipment of the plurality
of subplants based on the manual subplant loads.
[0007] In some embodiments, the energy plant includes a dispatch
graphical user interface (GUI) generator configured to generate a
dispatch GUI and present the dispatch GUI via the user interface.
The dispatch GUI may include the recommended subplant loads and the
manual subplant loads.
[0008] In some embodiments, the dispatch GUI includes an operating
mode selector configured to display a plurality of operating modes
and to receive a user selection of one of the operating modes. The
plurality of operating modes may include an automatic operating
mode, an advisory operating mode, and a manual operating mode.
[0009] In some embodiments, the low level optimizer is configured
to operate in (1) an automatic operating mode in which the low
level optimizer determines a first set of recommended equipment
setpoints to achieve the recommended subplant loads and (2) a
manual operating mode in which the low level optimizer determines a
second set of recommended equipment setpoints to achieve the manual
subplant loads.
[0010] In some embodiments, the high level optimizer is configured
to determine an amount of overproduction or underproduction
resulting from the manual subplant loads by comparing the manual
subplant loads to a campus energy load.
[0011] In some embodiments, the plurality of subplants include an
energy storage subplant. The user interface may include an
indication of an amount of time until the energy storage subplant
is (1) fully depleted based on the amount of underproduction or (2)
filled to capacity based on the amount of overproduction.
[0012] In some embodiments, the energy plant includes a subplant
monitor configured to monitor the energy plant and identify actual
subplant loads for each of the plurality of subplants. In some
embodiments, the energy plant includes a dispatch graphical user
interface (GUI) generator configured to generate a dispatch GUI
comprising the recommended subplant loads and the actual subplant
loads.
[0013] Another implementation of the present disclosure is a method
for monitoring and controlling an energy plant including a
plurality of subplants that operate to serve energy loads of a
campus. The method includes operating a cogeneration subplant of
the plurality of subplants to generate steam and electricity,
operating a chiller subplant of the plurality of subplants to
consume the electricity generated by the cogeneration subplant, and
determining recommended subplant loads for each of the plurality of
subplants. The recommended subplant loads include a rate of steam
production and a rate of electricity production of the cogeneration
subplant and a rate of electricity consumption of the chiller
subplant. The method includes determining recommended equipment
setpoints for equipment of the plurality of subplants based on the
recommended subplant loads and operating the equipment of the
plurality of subplants based on the recommended equipment
setpoints.
[0014] In some embodiments, the method includes operating the
chiller subplant to chill the steam generated by the cogeneration
subplant. The recommended subplant loads may include a rate of
steam consumption and a rate of chilled steam production of the
chiller subplant.
[0015] In some embodiments, the method includes receiving manual
subplant loads specified by a user via a user interface and
determining the recommended equipment setpoints for the equipment
of the plurality of subplants based on the manual subplant
loads.
[0016] In some embodiments, the method includes generating a
dispatch GUI and presenting the dispatch GUI via the user
interface. The dispatch GUI may include the recommended subplant
loads and the manual subplant loads.
[0017] In some embodiments, the method includes receiving a user
selection of one of a plurality of operating modes via an operating
mode selector of the dispatch GUI. The plurality of operating modes
may include an automatic operating mode, an advisory operating
mode, and a manual operating mode.
[0018] In some embodiments, the method includes operating in an
automatic operating mode to determine a first set of recommended
equipment setpoints to achieve the recommended subplant loads and
operating in a manual operating mode to determine a second set of
recommended equipment setpoints to achieve the manual subplant
loads.
[0019] In some embodiments, the method includes determining an
amount of overproduction or underproduction resulting from the
manual subplant loads by comparing the manual subplant loads to a
campus energy load.
[0020] In some embodiments, the plurality of subplants include an
energy storage subplant. The user interface may include an
indication of an amount of time until the energy storage subplant
is (1) fully depleted based on the amount of underproduction or (2)
filled to capacity based on the amount of overproduction.
[0021] In some embodiments, the method includes monitoring the
energy plant to identify actual subplant loads for each of the
plurality of subplants. In some embodiments, the method includes
generating a dispatch GUI comprising the recommended subplant loads
and the actual subplant loads.
[0022] Those skilled in the art will appreciate that the summary is
illustrative only and is not intended to be in any way limiting.
Other aspects, inventive features, and advantages of the devices
and/or processes described herein, as defined solely by the claims,
will become apparent in the detailed description set forth herein
and taken in conjunction with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] FIG. 1 is a drawing of a building equipped with a building
automation system and served by an energy plant, according to an
exemplary embodiment.
[0024] FIG. 2 is a schematic diagram of an energy plant including a
number of subplants configured to serve the energy loads of the
building of FIG. 1, according to an exemplary embodiment.
[0025] FIG. 3 is a block diagram of an energy optimization system
(EOS) configured to monitor and control the energy plant of FIG. 2,
according to an exemplary embodiment.
[0026] FIG. 4 is a block diagram of a plant system illustrating a
cascaded optimization process which may be performed by the EOS of
FIG. 3, according to an exemplary embodiment.
[0027] FIG. 5 is a block diagram illustrating the operation of the
EOS of FIG. 3 in an automatic operating mode, according to an
exemplary embodiment.
[0028] FIG. 6 is a flow diagram illustrating the operation of the
EOS of FIG. 3 in the automatic operating mode, according to an
exemplary embodiment.
[0029] FIG. 7 is a block diagram illustrating the operation of the
EOS of FIG. 3 in an advisory operating mode, according to an
exemplary embodiment.
[0030] FIG. 8 is a flow diagram illustrating the operation of the
EOS of FIG. 3 in the advisory operating mode, according to an
exemplary embodiment.
[0031] FIG. 9 is a block diagram illustrating the operation of the
EOS of FIG. 3 in a manual operating mode, according to an exemplary
embodiment.
[0032] FIG. 10 is a flow diagram illustrating the operation of the
EOS of FIG. 3 in the manual operating mode, according to an
exemplary embodiment.
[0033] FIG. 11 is a drawing of a site summary interface which may
be generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0034] FIG. 12 is a drawing of a chilled water loop summary
interface which may be generated by the EOS of FIG. 3, according to
an exemplary embodiment.
[0035] FIG. 13 is a drawing of another chilled water loop summary
interface which may be generated by the EOS of FIG. 3, according to
an exemplary embodiment.
[0036] FIG. 14 is a drawing of an equipment summary interface which
may be generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0037] FIG. 15 is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0038] FIG. 16 is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0039] FIG. 17 is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0040] FIG. 18 is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0041] FIG. 19 is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0042] FIG. 20 is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0043] FIG. 21 is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0044] FIG. 22A is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0045] FIG. 22B is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0046] FIG. 22C is a drawing of another equipment summary interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0047] FIG. 23A is a drawing of a dispatch schedule interface which
may be generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0048] FIG. 23B is a drawing of another dispatch schedule interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0049] FIG. 23C is a drawing of another dispatch schedule interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0050] FIG. 24 is a drawing of a dispatch chart interface which may
be generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0051] FIG. 25 is a drawing of another dispatch chart interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0052] FIG. 26A is a drawing of another dispatch chart interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0053] FIG. 26B is a drawing of another dispatch chart interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0054] FIG. 27A is a drawing of another dispatch chart interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0055] FIG. 27B is a drawing of another dispatch chart interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0056] FIG. 28A is a drawing of another dispatch chart interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0057] FIG. 28B is a drawing of another dispatch chart interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0058] FIG. 29 is a drawing of a weather forecast input interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0059] FIG. 30 is a drawing of a dry bulb temperature forecast
input interface which may be generated by the EOS of FIG. 3,
according to an exemplary embodiment.
[0060] FIG. 31 is a drawing of an override interface which may be
generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0061] FIG. 32 is a drawing of a utility pricing forecast input
interface which may be generated by the EOS of FIG. 3, according to
an exemplary embodiment.
[0062] FIG. 33 is a drawing of an out-of-service schedule interface
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0063] FIG. 34 is a drawing of a campus schedule interface which
may be generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0064] FIG. 35A is a drawing of a reporting interface which may be
generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0065] FIG. 35B is a drawing of another reporting interface which
may be generated by the EOS of FIG. 3, according to an exemplary
embodiment.
[0066] FIGS. 36A and 36B are drawings of a site summary report
which may be generated by the EOS of FIG. 3, according to an
exemplary embodiment.
[0067] FIG. 37 is a drawing of an audit log interface which may be
generated by the EOS of FIG. 3, according to an exemplary
embodiment.
DETAILED DESCRIPTION
[0068] Referring generally to the FIGURES, systems and methods for
monitoring and controlling an energy plant are shown, according to
an exemplary embodiment. The energy plant may include a plurality
of subplants configured to serve the heating, cooling, and electric
loads of a building or campus. For example, the energy plant may
include a heater subplant, one or more chiller subplants, a heat
recovery chiller subplant, a cogeneration subplant, a steam
subplant, a hot thermal energy storage (TES) subplant, and/or a
cold TES subplant. The subplants may be located within the same
building or distributed across multiple buildings. The energy plant
may be controlled by an energy optimization system (EOS) that
distributes a requested or predicted energy load (e.g., a heating
load, a cooling load, an electric load, etc.) across the plurality
of subplants. In some embodiments, the EOS performs an optimization
process to determine an optimal load distribution across the
plurality of subplants for each of a plurality of time steps within
a prediction window.
[0069] The EOS may predict the loads on each of the subplants
throughout the prediction window. In some embodiments, the
predicted subplant loads include a heating load served by the
heating subplant, a heating load served by the heat recovery
chiller subplant, a heating load served by the steam subplant, a
cooling load served by the heat recovery chiller subplant, a
cooling load served by the chiller subplant(s), an electric load
served by the cogeneration subplant, a heating load served or
stored by the hot TES subplant (e.g., a predicted rate at which the
hot TES subplant will be charging or discharging), and/or a cooling
load served or stored by the cold TES subplant (e.g., a predicted
rate at which the cold TES subplant will be charging or
discharging). In some embodiments, the EOS predicts a charge state
for the TES subplants defining an amount of thermal energy stored
in each of the TES subplants during each of the dispatch intervals.
The predicted subplant loads and TES charge states for each of the
future time steps may be stored in a subplant utilization database.
The actual operation of the energy plant may be monitored to
determine the actual subplant loads and TES charge stages during
each of the time steps. The actual subplant loads and TES charge
states may be stored in the subplant utilization database for each
of the past and current dispatch intervals.
[0070] A dispatch graphical user interface (GUI) generator may
access the data stored in the subplant utilization database and use
the stored data to generate a dispatch GUI. The dispatch GUI may be
a graph or chart that illustrates the actual subplant utilization
data (e.g., past and current subplant loads) and the predicted
subplant utilization data (e.g., future subplant loads) in a
graphical format. In some embodiments, the dispatch GUI indicates
the actual and predicted subplant loads for each of the subplants
at each of the dispatch intervals represented in the dispatch GUI.
The dispatch GUI may be configured to receive manual subplant loads
specified by a user.
[0071] The dispatch GUI generator may provide the dispatch GUI to a
user interface of a client device (e.g., a computer terminal, a
workstation, a laptop, a tablet, a smartphone, etc.). In some
embodiments, the dispatch GUI generator is a component of the
energy plant controller. In other embodiments, the dispatch GUI
generator may be a component of the client device. For example, the
dispatch GUI generator may be a web browser or a specialized
application running on the client device. In some embodiments, the
dispatch GUI is rendered using specialized viewing software (e.g.,
an energy plant monitoring application) installed on the client
device. In other embodiments, the dispatch GUI is provided via a
web interface which allows the dispatch GUI to be rendered and
viewed using a web browser without requiring any specialized
applications or software to be installed on the client device.
[0072] The dispatch GUI may present recommended (i.e., optimal)
subplant loads, manual subplant loads, and actual subplant loads in
a variety of visual formats (e.g., diagrams, charts, graphs, etc.)
that illustrate the operation of the energy plant. The dispatch GUI
may include a first set of recommended equipment setpoints that
result from the recommended subplant loads, a second set of
recommended equipment setpoints that result from the manual
subplant loads, and the actual equipment setpoints to provide
details regarding the operation of each subplant. The dispatch GUI
may include past dispatch data (e.g., past subplant loads, past
equipment setpoints) and current dispatch data (e.g., current
subplant loads, current equipment setpoints) to allow a user to
view a history of energy plant operations. The dispatch GUI may
also include future dispatch data (e.g., planned subplant loads,
planned equipment setpoints) to allow the user to view planned
energy plant operations into the future. Additional features and
advantages of the dispatch GUI are described in detail below.
[0073] In some embodiments, the dispatch GUI generator and/or the
dispatch GUI include some or all of the features described in U.S.
Design patent application No. 29/515,801 filed Jan. 27, 2015, U.S.
Provisional Patent Application No. 62/286,287 filed Jan. 22, 2017,
and/or U.S. patent application Ser. No. 15/387,512 filed Dec. 21,
2016. The entire disclosure of each of these patent applications is
incorporated by reference herein.
Building with HVAC System
[0074] Referring now to FIG. 1, a perspective view of a building 10
is shown. Building 10 is served by a building automation system
(BAS). A BAS is, in general, a system of devices configured to
control, monitor, and manage equipment in or around a building or
building area. A BAS can include, for example, a HVAC system, a
security system, a lighting system, a fire alerting system, any
other system that is capable of managing building functions or
devices, or any combination thereof.
[0075] The BAS that serves building 10 includes an HVAC system 100.
HVAC system 100 may include a plurality of HVAC devices (e.g.,
heaters, chillers, air handling units, pumps, fans, thermal energy
storage, etc.) configured to provide heating, cooling, ventilation,
or other services for building 10. For example, HVAC system 100 is
shown to include a waterside system 120 and an airside system 130.
Waterside system 120 may provide a heated or chilled fluid to an
air handling unit of airside system 130. Airside system 130 may use
the heated or chilled fluid to heat or cool an airflow provided to
building 10. In some embodiments, waterside system 120 is replaced
with a central energy plant such as energy plant 200, described
with reference to FIG. 2.
[0076] Still referring to FIG. 1, HVAC system 100 is shown to
include a chiller 102, a boiler 104, and a rooftop air handling
unit (AHU) 106. Waterside system 120 may use boiler 104 and chiller
102 to heat or cool a working fluid (e.g., water, glycol, etc.) and
may circulate the working fluid to AHU 106. In various embodiments,
the HVAC devices of waterside system 120 may be located in or
around building 10 (as shown in FIG. 1) or at an offsite location
such as an energy plant (e.g., a chiller plant, a steam plant, a
heat plant, etc.). The working fluid may be heated in boiler 104 or
cooled in chiller 102, depending on whether heating or cooling is
required in building 10. Boiler 104 may add heat to the circulated
fluid, for example, by burning a combustible material (e.g.,
natural gas) or using an electric heating element. Chiller 102 may
place the circulated fluid in a heat exchange relationship with
another fluid (e.g., a refrigerant) in a heat exchanger (e.g., an
evaporator) to absorb heat from the circulated fluid. The working
fluid from chiller 102 and/or boiler 104 may be transported to AHU
106 via piping 108.
[0077] AHU 106 may place the working fluid in a heat exchange
relationship with an airflow passing through AHU 106 (e.g., via one
or more stages of cooling coils and/or heating coils). The airflow
may be, for example, outside air, return air from within building
10, or a combination of both. AHU 106 may transfer heat between the
airflow and the working fluid to provide heating or cooling for the
airflow. For example, AHU 106 may include one or more fans or
blowers configured to pass the airflow over or through a heat
exchanger containing the working fluid. The working fluid may then
return to chiller 102 or boiler 104 via piping 110.
[0078] Airside system 130 may deliver the airflow supplied by AHU
106 (i.e., the supply airflow) to building 10 via air supply ducts
112 and may provide return air from building 10 to AHU 106 via air
return ducts 114. In some embodiments, airside system 130 includes
multiple variable air volume (VAV) units 116. For example, airside
system 130 is shown to include a separate VAV unit 116 on each
floor or zone of building 10. VAV units 116 may include dampers or
other flow control elements that can be operated to control an
amount of the supply airflow provided to individual zones of
building 10. In other embodiments, airside system 130 delivers the
supply airflow into one or more zones of building 10 (e.g., via air
supply ducts 112) without using intermediate VAV units 116 or other
flow control elements. AHU 106 may include various sensors (e.g.,
temperature sensors, pressure sensors, etc.) configured to measure
attributes of the supply airflow. AHU 106 may receive input from
sensors located within AHU 106 and/or within the building zone and
may adjust the flow rate, temperature, or other attributes of the
supply airflow through AHU 106 to achieve setpoint conditions for
the building zone.
Energy Plant and Control System
[0079] Referring now to FIG. 2, a block diagram of an energy plant
200 is shown, according to an exemplary embodiment. In brief
overview, energy plant 200 may include various types of equipment
configured to serve the thermal and electrical energy loads of a
building or campus (i.e., a system of buildings). For example,
energy plant 200 may include heaters, chillers, heat recovery
chillers, cooling towers, or other types of equipment configured to
serve the heating and/or cooling loads of a building or campus.
Energy plant 200 may consume resources from a utility (e.g.,
electricity, water, natural gas, etc.) to heat or cool a working
fluid that is circulated to one or more buildings or stored for
later use (e.g., in thermal energy storage tanks) to provide
heating or cooling for the buildings. In various embodiments,
energy plant 200 may supplement or replace waterside system 120 in
building 10 or may be implemented separate from building 10 (e.g.,
at an offsite location, distributed across multiple buildings,
etc.).
[0080] Energy plant 200 is shown to include a plurality of
subplants 202-210 including a first chiller subplant 202, a second
chiller subplant 204, a third chiller subplant 206, a steam
subplant 208, and a cogeneration subplant 210. Subplants 202-210
consume resources from utilities to serve the thermal and
electrical energy loads (e.g., hot water, cold water, heating,
cooling, steam, electricity, etc.) of a building or campus. For
example, chiller subplants 202-206 may be configured to chill water
in a cold water loop that circulates the cold water between chiller
subplants 202-206 campus 216. Steam subplant 208 may be configured
to generate steam and circulate the steam in a steam loop between
steam subplant 208 and campus 216. Cogeneration subplant 210 can be
configured to cogenerate both steam and electricity (e.g., by
operating one or more combustion turbines 230).
[0081] Each of subplants 202-210 may include a variety of equipment
configured to facilitate the functions of the subplant. For
example, chiller subplants 202-206 are shown to include a plurality
of electric chillers 218, 222, and 226 and steam chillers 220, 224,
and 228. Electric chillers 218, 222, and 226 can be configured to
remove heat from the cold water in the cold water loop, whereas
steam chillers 220, 224, and 228 can be configured to cool the
steam generated by boilers 232 and/or combustion turbines 230.
Steam subplant 208 may include one or more boilers 232 configured
to boil water to produce steam. Cogeneration subplant 210 may
include one or more combustion turbines 230 configured to generate
both steam and electricity. In some embodiments, steam subplant 208
burns gas from a gas supply 214. Chiller subplants 202-206 may
consume electricity from an electricity supply 212 to generate the
chilled water.
[0082] Referring now to FIG. 3, a block diagram illustrating an
energy plant system 300 is shown, according to an exemplary
embodiment. System 300 is shown to include an energy optimization
system (EOS) 302, a building automation system (BAS) 308, and a
plurality of subplants 202-210. Subplants 202-210 may be the same
as previously described with reference to FIG. 2. For example,
subplants 202-210 are shown to include a first chiller subplant
202, a second chiller subplant 204, a third chiller subplant 206, a
steam subplant 208, and a cogeneration subplant 210.
[0083] Each of subplants 202-210 is shown to include equipment 340
that can be controlled by EOS 302 and/or building automation system
308 to optimize the performance of energy plant 200. Equipment 340
may include, for example, heating devices, chillers, heat recovery
heat exchangers, cooling towers, thermal energy storage devices,
pumps, valves, and/or other devices of subplants 202-210 (e.g.,
electric chillers 218, 222, and 226, steam chillers 220, 224, and
228, combustion turbines 230, and/or boilers 232). Individual
devices of equipment 340 can be turned on or off to adjust the
thermal or electrical energy load served by each of subplants
202-210. In some embodiments, individual devices of equipment 340
can be operated at variable capacities (e.g., operating a chiller
at 10% capacity or 60% capacity) according to an operating setpoint
received from EOS 302.
[0084] In some embodiments, one or more of subplants 202-210
includes a subplant level controller configured to control the
equipment 340 of the corresponding subplant. For example, EOS 302
may determine an on/off configuration and global operating
setpoints for equipment 340. In response to the on/off
configuration and received global operating setpoints, the subplant
controllers may turn individual devices of equipment 340 on or off,
and implement specific operating setpoints (e.g., damper position,
vane position, fan speed, pump speed, etc.) to reach or maintain
the global operating setpoints.
[0085] In some embodiments, the subplant level controllers receive
subplant load setpoints from EOS 302. Each subplant level
controller may use the subplant load setpoint for the corresponding
subplant to select one or more devices of the equipment 340 within
the subplant to activate or deactivate in order to meet the
subplant load setpoint in an energy-efficient manner. In other
embodiments, the equipment selection and staging decisions (i.e.,
deciding which devices to turn on/off) are performed by a low level
optimizer 332 within EOS 302.
[0086] BAS 308 may be configured to monitor conditions within a
controlled building or building zone. For example, BAS 308 may
receive input from various sensors (e.g., temperature sensors,
humidity sensors, airflow sensors, voltage sensors, etc.)
distributed throughout the building and may report building
conditions to EOS 302. Building conditions may include, for
example, a temperature of the building or a zone of the building, a
power consumption (e.g., electric load) of the building, a state of
one or more actuators configured to affect a controlled state
within the building, or other types of information relating to the
controlled building. BAS 308 may operate subplants 202-210 to
affect the monitored conditions within the building and/or to serve
the thermal/electrical energy loads of the building.
[0087] BAS 308 may receive control signals from EOS 302 specifying
on/off states and/or setpoints for equipment 340. BAS 308 may
control equipment 340 (e.g., via actuators, power relays, etc.) in
accordance with the control signals provided by EOS 302. For
example, BAS 308 may operate equipment 340 using closed loop
control to achieve the setpoints specified by EOS 302. In various
embodiments, BAS 308 may be combined with EOS 302 or may be part of
a separate building automation system. According to an exemplary
embodiment, BAS 308 is a METASYS.RTM. brand building automation
system, as sold by Johnson Controls, Inc. In some embodiments, BAS
308 is a controller that operates to control equipment 340 and/or
operates equipment 340 to achieve the setpoints specified by EOS
302 (i.e., the setpoints generated by low level optimizer 332).
[0088] EOS 302 may monitor the status of the controlled building
using information received from BAS 308. EOS 302 may be configured
to predict the thermal/electrical energy loads (e.g., heating
loads, cooling loads, electric, etc.) of the building for plurality
of time steps in a prediction window (e.g., using weather forecasts
from a weather service 324). EOS 302 may generate on/off decisions
and/or setpoints for equipment 340 to minimize the cost of energy
consumed by subplants 202-210 to serve the predicted heating and/or
cooling loads for the duration of the prediction window. In various
embodiments, EOS 302 may be integrated within a single computer
(e.g., one server, one housing, etc.) or distributed across
multiple servers or computers. In some embodiments, EOS 302 is
integrated with a smart building manager that manages multiple
building systems and/or combined with BAS 308.
[0089] EOS 302 may be configured to operate in multiple different
operating modes including an automatic mode, an advisory mode, and
a manual mode. In the automatic mode, EOS 302 may automatically
control energy plant 200. For example, EOS 302 may automatically
determine optimal subplant loads for each of subplants 202-210. EOS
302 may use the optimal subplant loads to determine optimal on/off
decisions and operating setpoints for individual devices of
equipment 340. In the automatic mode, EOS 302 may provide the
control signals to BAS 308 and/or equipment 340 to control
equipment 340 without requiring user intervention or input. BAS 308
may relay the control signals to subplants 202-210 or may be
bypassed entirely. In other words, EOS 302 may automatically
control energy plant 200 in accordance with the optimized load
setpoints and/or equipment setpoints when operated in the automatic
mode. The automatic mode is described in greater detail with
reference to FIGS. 5-6.
[0090] In the advisory mode, BAS 308 may control energy plant 200,
whereas EOS 302 may provide advice. EOS 302 may still determine
optimal subplant loads for each of subplants 202-210 and optimal
on/off decisions and operating setpoints for equipment 340.
However, the optimal values determined by EOS 302 may be provided
to BAS 308 as recommended subplant loads and recommended setpoints.
The recommended loads and setpoints may also be presented to a user
via a user interface. BAS 308 may determine whether to use the
recommended loads and setpoints to control energy plant 200. In
some embodiments, BAS 308 uses input from a user to determine
whether to apply the recommended loads and setpoints provided by
EOS 302. In some embodiments, EOS 302 displays the recommended
loads/setpoints to a user and the user enters the recommended
loads/setpoints as an input to BAS 308. In other embodiments, BAS
308 receives the recommended loads/setpoints directly from EOS 302.
The advisory mode is described in greater detail with reference to
FIGS. 7-8.
[0091] In the manual mode, EOS 302 may control energy plant 200
based on user input. Similar to the advisory mode, EOS 302 may
determine optimal subplant loads for each of subplants 202-210 and
optimal on/off decisions and operating setpoints for equipment 340.
The optimal values determined by EOS 302 may be presented to a user
(e.g., via a user interface) as recommended subplant loads and
recommended setpoints. A user can accept the optimized values or
enter user-defined (i.e., "manual") values for the subplant loads,
equipment on/off decisions, and/or operating setpoints via the user
interface. In some embodiments, the user-defined values override
the optimized values when EOS 302 is operated in the manual mode.
For example, EOS 302 may determine on/off decisions and control
setpoints for equipment 340 based on the user-defined load
setpoints for each of subplants 302-312. The manual mode is
described in greater detail with reference to FIGS. 9-10.
[0092] Still referring to FIG. 3, EOS 302 is shown to include a
communications interface 304 and a processing circuit 306.
Communications interface 304 may include wired or wireless
interfaces (e.g., jacks, antennas, transmitters, receivers,
transceivers, wire terminals, etc.) for conducting data
communications with various systems, devices, or networks. For
example, communications interface 304 may include an Ethernet card
and port for sending and receiving data via an Ethernet-based
communications network and/or a Wi-Fi transceiver for communicating
via a wireless communications network. Communications interface 304
may be configured to communicate via local area networks or wide
area networks (e.g., the Internet, a building WAN, etc.) and may
use a variety of communications protocols (e.g., BACnet, IP, LON,
etc.).
[0093] Communications interface 304 may be a network interface
configured to facilitate electronic data communications between EOS
302 and various external systems or devices (e.g., BAS 308,
subplants 202-210, etc.). For example, EOS 302 may receive
information from BAS 308 indicating one or more measured states of
the controlled building (e.g., temperature, humidity, electric
loads, etc.) and one or more states of subplants 202-210 (e.g.,
equipment status, power consumption, equipment availability, etc.).
Communications interface 304 may receive inputs from BAS 308 and/or
subplants 202-210 and may provide operating parameters (e.g.,
on/off decisions, setpoints, etc.) to subplants 202-210 via BAS
308. The operating parameters may cause subplants 202-210 to
activate, deactivate, or adjust a setpoint for various devices of
equipment 340.
[0094] Still referring to FIG. 3, processing circuit 306 is shown
to include a processor 310 and memory 312. Processor 310 may be a
general purpose or specific purpose processor, an application
specific integrated circuit (ASIC), one or more field programmable
gate arrays (FPGAs), a group of processing components, or other
suitable processing components. Processor 310 may be configured to
execute computer code or instructions stored in memory 312 or
received from other computer readable media (e.g., CDROM, network
storage, a remote server, etc.).
[0095] Memory 312 may include one or more devices (e.g., memory
units, memory devices, storage devices, etc.) for storing data
and/or computer code for completing and/or facilitating the various
processes described in the present disclosure. Memory 312 may
include random access memory (RAM), read-only memory (ROM), hard
drive storage, temporary storage, non-volatile memory, flash
memory, optical memory, or any other suitable memory for storing
software objects and/or computer instructions. Memory 312 may
include database components, object code components, script
components, or any other type of information structure for
supporting the various activities and information structures
described in the present disclosure. Memory 312 may be communicably
connected to processor 310 via processing circuit 306 and may
include computer code for executing (e.g., by processor 310) one or
more processes described herein.
[0096] Still referring to FIG. 3, memory 312 is shown to include a
building status monitor 334. EOS 302 may receive data regarding the
overall building or building space to be heated or cooled with
energy plant 200 via building status monitor 334. In an exemplary
embodiment, building status monitor 334 may include a graphical
user interface component configured to provide graphical user
interfaces to a user for selecting building requirements (e.g.,
overall temperature parameters, selecting schedules for the
building, selecting different temperature levels for different
building zones, etc.).
[0097] EOS 302 may determine on/off configurations and operating
setpoints to satisfy the building requirements received from
building status monitor 334. In some embodiments, building status
monitor 334 receives, collects, stores, and/or transmits cooling
load requirements, building temperature setpoints, occupancy data,
weather data, energy data, schedule data, and other building
parameters. In some embodiments, building status monitor 334 stores
data regarding energy costs, such as pricing information available
from utilities 326 (energy charge, demand charge, etc.).
[0098] Still referring to FIG. 3, memory 312 is shown to include a
load/rate predictor 322. Load/rate predictor 322 may be configured
to predict the energy loads ({circumflex over (l)}.sub.k) of the
building or campus for each time step k (e.g., k=1 . . . n) of an
optimization period. Load/rate predictor 322 is shown receiving
weather forecasts from a weather service 324. In some embodiments,
load/rate predictor 322 predicts the energy loads {circumflex over
(l)}.sub.k as a function of the weather forecasts. In some
embodiments, load/rate predictor 322 uses feedback from BAS 308 to
predict loads {circumflex over (l)}.sub.k. Feedback from BAS 308
may include various types of sensory inputs (e.g., temperature,
flow, humidity, enthalpy, etc.) or other data relating to the
controlled building (e.g., inputs from a HVAC system, a lighting
control system, a security system, a water system, etc.).
[0099] In some embodiments, load/rate predictor 322 receives a
measured electric load and/or previous measured load data from BAS
308 (e.g., via building status monitor 334). Load/rate predictor
322 may predict loads {circumflex over (l)}.sub.k as a function of
a given weather forecast ({circumflex over (.PHI.)}.sub.w), a day
type (day), the time of day (t), and previous measured load data
(Y.sub.k-1). Such a relationship is expressed in the following
equation:
{circumflex over (l)}.sub.k=f({circumflex over
(.PHI.)}.sub.w,day,t|Y.sub.k-1)
[0100] In some embodiments, load/rate predictor 322 uses a
deterministic plus stochastic model trained from historical load
data to predict loads {circumflex over (l)}.sub.k. Load/rate
predictor 322 may use any of a variety of prediction methods to
predict loads {circumflex over (l)}.sub.k (e.g., linear regression
for the deterministic portion and an AR model for the stochastic
portion). Load/rate predictor 322 may predict one or more different
types of loads for the building or campus. For example, load/rate
predictor 322 may predict a hot water load {circumflex over
(l)}.sub.Hot,k and a cold water load {circumflex over
(l)}.sub.Cold,k for each time step k within the prediction
window.
[0101] Load/rate predictor 322 is shown receiving utility rates
from utilities 326. Utility rates may indicate a cost or price per
unit of a resource (e.g., electricity, natural gas, water, etc.)
provided by utilities 326 at each time step k in the prediction
window. In some embodiments, the utility rates are time-variable
rates. For example, the price of electricity may be higher at
certain times of day or days of the week (e.g., during high demand
periods) and lower at other times of day or days of the week (e.g.,
during low demand periods). The utility rates may define various
time periods and a cost per unit of a resource during each time
period. Utility rates may be actual rates received from utilities
326 or predicted utility rates estimated by load/rate predictor
322.
[0102] In some embodiments, the utility rates include demand
charges for one or more resources provided by utilities 326. A
demand charge may define a separate cost imposed by utilities 326
based on the maximum usage of a particular resource (e.g., maximum
energy consumption) during a demand charge period. The utility
rates may define various demand charge periods and one or more
demand charges associated with each demand charge period. In some
instances, demand charge periods may overlap partially or
completely with each other and/or with the prediction window.
Advantageously, energy plant optimizer 328 may be configured to
account for demand charges in the high level optimization process
performed by high level optimizer 330. Utilities 326 may be defined
by time-variable (e.g., hourly) prices, a maximum service level
(e.g., a maximum rate of consumption allowed by the physical
infrastructure or by contract) and, in the case of electricity, a
demand charge or a charge for the peak rate of consumption within a
certain period.
[0103] Load/rate predictor 322 may store the predicted loads
{circumflex over (l)}.sub.k and the utility rates in memory 312
and/or provide the predicted loads {circumflex over (l)}.sub.k and
the utility rates to energy plant optimizer 328. Energy plant
optimizer 328 may use the predicted loads {circumflex over
(l)}.sub.k and the utility rates to determine an optimal load
distribution for subplants 202-210 and to generate on/off decisions
and setpoints for equipment 340.
[0104] Still referring to FIG. 3, memory 312 is shown to include an
energy plant optimizer 328. Energy plant optimizer 328 may perform
a cascaded optimization process to optimize the performance of
energy plant 200. For example, energy plant optimizer 328 is shown
to include a high level optimizer 330 and a low level optimizer
332. High level optimizer 330 may control an outer (e.g., subplant
level) loop of the cascaded optimization. High level optimizer 330
may determine an optimal distribution of energy loads across
subplants 202-210 for each time step in the prediction window in
order to optimize (e.g., minimize) the cost of energy consumed by
subplants 202-210. Low level optimizer 332 may control an inner
(e.g., equipment level) loop of the cascaded optimization. Low
level optimizer 332 may determine how to best run each subplant at
the load setpoint determined by high level optimizer 330. For
example, low level optimizer 332 may determine on/off states and/or
operating setpoints for various devices of equipment 340 in order
to optimize (e.g., minimize) the energy consumption of each
subplant while meeting the energy load setpoint for the subplant.
The cascaded optimization process is described in greater detail
with reference to FIG. 4.
[0105] Still referring to FIG. 3, memory 312 is shown to include a
subplant monitor 338. Subplant monitor 338 may collect and store
information regarding the past, current, and future (e.g., planned)
utilization of subplants 202-210. For example, subplant monitor 338
may receive actual utilization data from BAS 308 and/or energy
plant 200 indicating the actual energy loads served by subplants
202-210. The actual utilization data may be current utilization
data (e.g., the actual energy loads currently being served) or past
utilization data (e.g., the actual energy loads served at a
previous time). In some embodiments, the actual utilization data
indicates a total heating load and/or a total cooling load
requested to be served by energy plant 200 at a past or current
time. The actual utilization data may also indicate any unmet
heating and/or cooling load that is requested but not met by energy
plant 200 at a past or current time. In some embodiments, the
actual utilization data indicates a past or current rate of utility
consumption (e.g., water consumption, electricity consumption,
natural gas consumption, photovoltaic energy consumption,
etc.).
[0106] The actual utilization data may be provided at various
levels of granularity. For example, the actual utilization data for
a given subplant (e.g., chiller subplant 202) may include an
aggregate value that represents the total energy load served by the
subplant (e.g., the total load served by all of the chillers). In
other embodiments, the actual utilization data may be provided for
each of the individual devices within subplants 202-210 (e.g., the
cooling load served by each of the chillers individually).
[0107] In some embodiments, subplant monitor 338 receives the
actual utilization data as a continuous data signal. In other
embodiments, subplant monitor 338 receives the actual utilization
data at regular intervals (e.g., every minute, every fifteen
minutes, every hour, etc.). Subplant monitor 338 may store the
actual utilization data in memory 312 or in a separate subplant
utilization database. In some embodiments, subplant monitor 338
stores the actual utilization data at regular intervals such that
the stored utilization data represents a history of the relevant
operating information for energy plant 200 over time.
[0108] Subplant monitor 338 may receive predicted future
utilization data indicating the thermal and electrical energy loads
to be served by subplants 202-210 at a future time. In some
embodiments, the predicted utilization data for subplants 202-210
is generated by energy plant optimizer 328 for multiple time steps
during a prediction window. For example, the predicted utilization
data may include the optimal subplant loads predicted by high level
optimizer 330 and/or the optimal equipment on/off states predicted
by low level optimizer 332 for each time step during the prediction
window.
[0109] In some embodiments, the predicted utilization data
indicates a total heating load and/or a total cooling load
predicted by load/rate predictor 322. The predicted utilization
data may also indicate any unmet heating and/or cooling load that
is predicted to be requested but not met by energy plant 200. In
some embodiments, the predicted utilization data indicates a
predicted rate of utility consumption (e.g., water consumption,
electricity consumption, natural gas consumption, photovoltaic
energy consumption, etc.).
[0110] The predicted utilization data may be provided at various
levels of granularity. For example, the predicted utilization data
for a given subplant (e.g., chiller subplant 202) may include an
aggregate value that represents the total energy load estimated to
be served by the subplant (e.g., the total predicted load served by
all of the chillers). In other embodiments, the predicted
utilization data may be provided for each of the individual devices
within subplants 202-210 (e.g., the predicted cooling load served
by each of the chillers individually).
[0111] In some embodiments, subplant monitor 338 receives the
predicted utilization data for each of a plurality of time steps
during a prediction window. For example, energy plant optimizer 328
may perform an optimization process (described in greater detail
with reference to FIG. 4) to generate subplant load values for each
time step during a prediction window that extends from the current
time to a predetermined prediction horizon. Each time step may have
a defined duration (e.g., fifteen minutes, one hour, etc.). The
predicted subplant load values may be updated each time the
optimization process is performed. Subplant monitor 338 may store
the predicted utilization data in memory 312 or in a separate
subplant utilization database. In some embodiments, subplant
monitor 338 stores the predicted utilization data at regular
intervals (e.g., hourly intervals) such that the stored utilization
data represents a planned dispatch schedule for energy plant 200
over time.
[0112] Data and processing results from energy plant optimizer 328,
subplant monitor 338, or other modules of EOS 302 may be accessed
by (or pushed to) monitoring and reporting applications 336.
Monitoring and reporting applications 336 may be configured to
generate real time system health dashboards that can be viewed and
navigated by a user (e.g., an energy plant engineer). For example,
monitoring and reporting applications 336 may include a web-based
monitoring application with several graphical user interface (GUI)
elements (e.g., widgets, dashboard controls, windows, etc.) for
displaying key performance indicators (KPI) or other information to
users of a GUI. In some embodiments, the GUI elements include a
chart or graph (e.g., a dispatch bar chart) that represents the
actual and predicted utilization data provided by subplant monitor
338. GUI elements or reports may be generated and shown based on
actual and predicted utilization data that allow users to monitor
the performance of subplants 202-210 and energy plant 200 as a
whole using a single screen. An exemplary dispatch GUI that may be
generated by monitoring and reporting applications 336 is described
in greater detail with reference to FIGS. 11-37.
[0113] Still referring to FIG. 3, EOS 302 may include one or more
GUI servers, web services 314, or GUI engines 316 to support
monitoring and reporting applications 336. In various embodiments,
applications 336, web services 314, and GUI engine 316 may be
provided as separate components outside of EOS 302 (e.g., as part
of a smart building manager). EOS 302 may be configured to maintain
detailed historical databases (e.g., relational databases, XML
databases, etc.) of relevant data and includes computer code
modules that continuously, frequently, or infrequently query,
aggregate, transform, search, or otherwise process the data
maintained in the detailed databases. EOS 302 may be configured to
provide the results of any such processing to other databases,
tables, XML files, or other data structures for further querying,
calculation, or access by, for example, external monitoring and
reporting applications.
[0114] EOS 302 is shown to include configuration tools 318.
Configuration tools 318 can allow a user to define (e.g., via
graphical user interfaces, via prompt-driven wizards, etc.) how EOS
302 should react to changing conditions in the energy plant
subsystems. In an exemplary embodiment, configuration tools 318
allow a user to build and store condition-response scenarios that
can cross multiple energy plant devices, multiple building systems,
and multiple enterprise control applications (e.g., work order
management system applications, entity resource planning
applications, etc.). For example, configuration tools 318 can
provide the user with the ability to combine data (e.g., from
subsystems, from event histories) using a variety of conditional
logic. In varying exemplary embodiments, the conditional logic can
range from simple logical operators between conditions (e.g., AND,
OR, XOR, etc.) to pseudo-code constructs or complex programming
language functions (allowing for more complex interactions,
conditional statements, loops, etc.). Configuration tools 318 can
present user interfaces for building such conditional logic. The
user interfaces may allow users to define policies and responses
graphically. In some embodiments, the user interfaces may allow a
user to select a pre-stored or pre-constructed policy and adapt it
or enable it for use with their system.
[0115] Referring now to FIG. 4, a block diagram illustrating a
portion of energy plant system 300 in greater detail is shown,
according to an exemplary embodiment. FIG. 4 illustrates the
cascaded optimization process performed by energy plant optimizer
328 to optimize the performance of energy plant 200. In the
cascaded optimization process, high level optimizer 330 performs a
subplant level optimization that determines an optimal distribution
of thermal and electrical energy loads across subplants 202-210 for
each time step in the prediction window in order to minimize the
cost of energy consumed by subplants 202-210. Low level optimizer
332 performs an equipment level optimization that determines how to
best run each subplant at the subplant load setpoint determined by
high level optimizer 330. For example, low level optimizer 332 may
determine on/off states and/or operating setpoints for various
devices of equipment 340 in order to optimize the energy
consumption of each subplant while meeting the thermal and/or
electrical energy load setpoint for the subplant.
[0116] One advantage of the cascaded optimization process performed
by energy plant optimizer 328 is the optimal use of computational
time. For example, the subplant level optimization performed by
high level optimizer 330 may use a relatively long time horizon due
to the operation of the thermal energy storage. However, the
equipment level optimization performed by low level optimizer 332
may use a much shorter time horizon or no time horizon at all since
the low level system dynamics are relatively fast (compared to the
dynamics of the thermal energy storage) and the low level control
of equipment 340 may be handled by BAS 308. Such an optimal use of
computational time makes it possible for energy plant optimizer 328
to perform the energy plant optimization in a short amount of time,
allowing for real-time predictive control. For example, the short
computational time enables energy plant optimizer 328 to be
implemented in a real-time planning tool with interactive
feedback.
[0117] Another advantage of the cascaded optimization performed by
energy plant optimizer 328 is that the energy plant optimization
problem can be split into two cascaded subproblems. The cascaded
configuration provides a layer of abstraction that allows high
level optimizer 330 to distribute the energy loads across subplants
202-210 without requiring high level optimizer 330 to know or use
any details regarding the particular equipment configuration within
each subplant. The interconnections between equipment 340 within
each subplant may be hidden from high level optimizer 330 and
handled by low level optimizer 332. For purposes of the subplant
level optimization performed by high level optimizer 330, each
subplant may be completely defined by one or more subplant curves
342.
[0118] Low level optimizer 332 may generate and provide subplant
curves 342 to high level optimizer 330. Subplant curves 342 may
indicate the rate of utility use by each of subplants 202-210
(e.g., electricity use measured in kW, water use measured in L/s,
etc.) as a function of the subplant load. In some embodiments, low
level optimizer 332 generates subplant curves 342 based on
equipment models 320 (e.g., by combining equipment models 320 for
individual devices into an aggregate curve for the subplant). Low
level optimizer 332 may generate subplant curves 342 by running the
low level optimization process for several different loads and
weather conditions to generate multiple data points. Low level
optimizer 332 may fit a curve to the data points to generate
subplant curves 342. In other embodiments, low level optimizer 332
provides the data points to high level optimizer 330 and high level
optimizer 330 generates the subplant curves using the data
points.
[0119] High level optimizer 330 may receive the load and rate
predictions from load/rate predictor 322 and the subplant curves
342 from low level optimizer 332. The load predictions may be based
on weather forecasts from weather service 324 and/or information
from building automation system 308 (e.g., a current electric load
of the building, measurements from the building, a history of
previous loads, a setpoint trajectory, etc.). The utility rate
predictions may be based on utility rates received from utilities
326 and/or utility prices from another data source. High level
optimizer 330 may determine the optimal load distribution for
subplants 202-210 (e.g., a subplant load for each subplant) for
each time step the prediction window and may provide the subplant
loads as setpoints to low level optimizer 332. In some embodiments,
high level optimizer 330 determines the subplant loads by
minimizing the total operating cost of energy plant 200 over the
prediction window. In other words, given a predicted load and
utility rate information from load/rate predictor 322, high level
optimizer 330 may distribute the predicted load across subplants
202-210 over the optimization period to minimize operating
cost.
[0120] In some instances, the optimal load distribution may include
using thermal energy storage to store thermal energy during a first
time step for use during a later time step. Thermal energy storage
may advantageously allow thermal energy to be produced and stored
during a first time period when energy prices are relatively low
and subsequently retrieved and used during a second time period
when energy proves are relatively high. The high level optimization
may be different from the low level optimization in that the high
level optimization has a longer time constant due to the thermal
energy storage provided by thermal energy storage. The high level
optimization may be described by the following equation:
.theta. HL * = arg min .theta. HL J HL ( .theta. HL )
##EQU00001##
where .theta.*.sub.HL contains the optimal high level decisions
(e.g., the optimal load for each of subplants 202-210) for the
entire optimization period and J.sub.HL is the high level cost
function.
[0121] To find the optimal high level decisions .theta.*.sub.HL,
high level optimizer 330 may minimize the high level cost function
J.sub.HL. The high level cost function J.sub.HL may be the sum of
the economic (e.g., monetary) costs of each utility consumed by
each of subplants 202-210 for the duration of the optimization
period. In some embodiments, the high level cost function J.sub.HL
may be described using the following equation:
J HL ( .theta. HL ) = k = 1 n h i = 1 n s [ j = 1 n u t s c jk u
jik ( .theta. HL ) ] ##EQU00002##
where n.sub.h is the number of time steps k in the optimization
period, n.sub.s is the number of subplants, t.sub.s is the duration
of a time step, c.sub.jk is the economic cost of utility j at a
time step k of the optimization period, and u.sub.jik is the rate
of use of utility j by subplant i at time step k.
[0122] In some embodiments, the cost function J.sub.HL includes an
additional demand charge term such as:
w d c demand max n h ( u elec ( .theta. HL ) , u max , ele )
##EQU00003##
where w.sub.d is a weighting term, c.sub.demand is the demand cost,
and the max( ) term selects the peak electricity use during the
applicable demand charge period. Accordingly, the high level cost
function J.sub.HL may be described by the equation:
J HL ( .theta. HL ) = k = 1 n h i = 1 n s [ j = 1 n u t s c jk u
jik ( .theta. HL ) ] + w d c demand max n h ( u elec ( .theta. HL )
, u max , ele ) ##EQU00004##
[0123] The decision vector .theta..sub.HL may be subject to several
constraints. For example, the constraints may require that the
subplants not operate at more than their total capacity, that the
thermal storage not charge or discharge too quickly or under/over
flow for the tank, and that the energy loads for the building or
campus are met. These restrictions may lead to both equality and
inequality constraints on the high level optimization problem.
[0124] In some embodiments, the high level optimization performed
by high level optimizer 330 is the same or similar to the high
level optimization process described in U.S. patent application
Ser. No. 14/634,609 filed Feb. 27, 2015 and titled "High Level
Energy plant Optimization," the entire disclosure of which is
incorporated by reference herein. High level optimizer 330 may
include some or all of the features and/or functionality of the
high level optimization module described in U.S. patent application
Ser. No. 14/634,609.
[0125] Still referring to FIG. 4, low level optimizer 332 may use
the subplant loads determined by high level optimizer 330 to
determine optimal low level decisions .theta.*.sub.LL (e.g. binary
on/off decisions, flow setpoints, temperature setpoints, etc.) for
equipment 340. The low level optimization process may be performed
for each of subplants 202-210. In various embodiments, the low
level optimization process may be performed by centralized low
level optimizer 332 that performs a separate low level optimization
for each of subplants 202-210 or by a set of subplant level
controllers that operate within each subplant (e.g., each subplant
controller running an instance of low level optimizer 332). Low
level optimizer 332 may be responsible for determining which
devices of the subplant to use and/or the operating setpoints for
such devices that will achieve the subplant load setpoint while
minimizing energy consumption. The low level optimization may be
described using the following equation:
.theta. LL * = arg min .theta. LL J LL ( .theta. LL )
##EQU00005##
where .theta.*.sub.LL contains the optimal low level decisions and
J.sub.LL is the low level cost function.
[0126] To find the optimal low level decisions .theta.*.sub.LL, low
level optimizer 332 may minimize the low level cost function
J.sub.LL. The low level cost function J.sub.LL may represent the
total energy consumption for all of equipment 340 in the applicable
subplant. The low level cost function J.sub.LL may be described
using the following equation:
J LL ( .theta. LL ) = j = 1 N t s b j u j ( .theta. LL )
##EQU00006##
where N is the number of devices of equipment 340 in the subplant,
t.sub.s is the duration of a time step, b.sub.j is a binary on/off
decision (e.g., 0=off, 1=on), and u.sub.j is the energy used by
device j as a function of the setpoint .theta..sub.LL. Each device
may have continuous variables which can be changed to determine the
lowest possible energy consumption for the overall input
conditions.
[0127] Low level optimizer 332 may minimize the low level cost
function J.sub.LL subject to inequality constraints based on the
capacities of equipment 340 and equality constraints based on
energy and mass balances. In some embodiments, the optimal low
level decisions .theta.*.sub.LL are constrained by switching
constraints defining a short horizon for maintaining a device in an
on or off state after a binary on/off switch. The switching
constraints may prevent devices from being rapidly cycled on and
off. In some embodiments, low level optimizer 332 performs the
equipment level optimization without considering system dynamics.
The optimization process may be slow enough to safely assume that
the equipment control has reached its steady-state. Thus, low level
optimizer 332 may determine the optimal low level decisions
.theta.*.sub.LL at an instance of time rather than over a long
horizon.
[0128] In some embodiments, the optimal low level decisions
.theta.*.sub.LL are constrained by an equipment staging coordinator
344 that prevents two or more of subplants 202-210 from reacting to
a change in the subplant load too quickly after a change in another
of subplants 202-210 has occurred. For example, equipment staging
coordinator 344 is shown receiving equipment status information
from BAS 308. Equipment staging coordinator 344 may use the
equipment status information to determine when the equipment 340
within a given subplant has been staged (e.g., turned on/off).
Equipment staging coordinator 344 may prevent multiple subplants
from staging their equipment 340 simultaneously or within a
predetermined time period after another subplant has staged its
equipment 340. Advantageously, this functionality reduces the peak
energy consumption for energy plant 200 at any instant in time and
results in a lower demand charge.
[0129] In some embodiments, equipment staging coordinator 344
causes low level optimizer 332 to halt its efficiency calculations
and hold the optimal low level decisions .theta.*.sub.LL for a
given subplant at previously-determined values shortly after the
equipment 340 for another subplant has been staged (e.g., by
providing a freeze signal to low level optimizer 332). Once a
threshold time has passed since the equipment 340 for the other
subplant has been staged, equipment staging coordinator 344 may
allow low level optimizer 332 to resume its efficiency calculations
and to determine new values for the optimal low level decisions
.theta.*.sub.LL (e.g., by withdrawing the freeze signal).
[0130] Low level optimizer 332 may determine optimum operating
statuses (e.g., on or off) for a plurality of devices of equipment
340. According to an exemplary embodiment, the on/off combinations
may be determined using binary optimization and quadratic
compensation. Binary optimization may minimize a cost function
representing the power consumption of devices in the applicable
subplant. In some embodiments, non-exhaustive (i.e., not all
potential combinations of devices are considered) binary
optimization is used. Quadratic compensation may be used in
considering devices whose power consumption is quadratic (and not
linear). Low level optimizer 332 may also determine optimum
operating setpoints for equipment using nonlinear optimization.
Nonlinear optimization may identify operating setpoints that
further minimize the low level cost function J.sub.LL. Low level
optimizer 332 may provide the on/off decisions and setpoints to
building automation system 308 for use in controlling the energy
plant equipment 340.
[0131] In some embodiments, the low level optimization performed by
low level optimizer 332 is the same or similar to the low level
optimization process described in U.S. patent application Ser. No.
14/634,615 filed Feb. 27, 2015 and titled "Low Level Energy plant
Optimization," the entire disclosure of which is incorporated by
reference herein. Low level optimizer 332 may include some or all
of the features and/or functionality of the low level optimization
module described in U.S. patent application Ser. No.
14/634,615.
Automatic, Advisory, and Manual Operating Modes
[0132] Referring now to FIGS. 5-10, several block diagrams and flow
diagrams illustrating the operation of energy optimization system
(EOS) 302 in an automatic operating mode (FIGS. 5-6), advisory
operating mode (FIGS. 7-8), and manual operating mode (FIGS. 9-10)
are shown, according to an exemplary embodiment. In brief overview,
EOS 302 may determine optimal subplant loads and optimal equipment
setpoints in each of the operating modes. In the automatic
operating mode, EOS 302 may provide the optimal equipment setpoints
to energy plant 200. In the advisory operating mode, EOS 302 may
provide the optimal subplant loads and equipment setpoints to a
user interface as recommended setpoints for energy plant 200. A
user may view the recommended setpoints and provide user-defined
setpoints for building automation system 308, which may or may not
be based on the recommended setpoints. In the manual operating
mode, EOS 302 may control energy plant 200 based on manual (i.e.,
user-defined) subplant loads received via a user interface of EOS
302. EOS 302 may use the manual subplant loads to generate
recommended equipment setpoints and equipment control signals that
achieve the manual subplant loads. The equipment control signals
may then be provided to energy plant 200. Each of these operating
modes is discussed in greater detail below.
Automatic Operating Mode
[0133] Referring now to FIG. 5, a block diagram illustrating a
system 500 for monitoring and controlling an energy plant in an
automatic operating mode is shown, according to an exemplary
embodiment. In the automatic operating mode, EOS 302 automatically
determines optimal subplant loads for each of subplants 202-210 and
optimal equipment setpoints for the energy plant equipment 340. EOS
302 provides the optimal equipment setpoints to energy plant 200,
which uses the optimal equipment setpoints to control the energy
plant equipment 340. EOS 302 monitors actual subplant utilization
via subplant monitor 338 and stores planned and actual subplant
utilization data in a subplant utilization database 502. A dispatch
GUI generator 506 uses the planned and actual subplant utilization
data to generate a dispatch GUI 902. Dispatch GUI 902 may be
presented to a user via a user interface 504 to allow the user to
monitor the operation of energy plant 200.
[0134] System 500 is shown to include high level optimizer 330, low
level optimizer 332, and energy plant 200, which may be the same or
similar as described with reference to FIGS. 2-4. For example, high
level optimizer 330 may perform an optimization process to generate
a set of optimal subplant loads for each of subplants 202-210. In
some embodiments, the optimal subplant loads include a total cold
requested load and a total hot requested load. In some embodiments,
the optimal subplant loads include an unmet heating load and/or an
unmet cooling load. The unmet heating load may be defined as the
amount by which the total requested heating load exceeds the sum of
the heating loads produced by subplants 202-210. Similarly, the
unmet cooling load may be defined as the amount by which the total
requested cooling load exceeds the sum of the cooling loads
produced by subplants 202-210. In some embodiments, the optimal
subplant loads include an optimal amount of one or more resources
(e.g., water, electricity, natural gas, etc.) consumed by subplants
202-210 to generate the optimal subplant loads.
[0135] High level optimizer 330 may determine the optimal subplant
loads for a plurality of time steps between the current time and a
future time horizon. Each of the time steps may have a defined or
predetermined duration (e.g., fifteen minutes, one hour, etc.). The
optimal subplant loads may be updated with each iteration of the
high level optimization process. High level optimizer 330 may
provide the optimal subplant loads to low level optimizer 332 and
to subplant monitor 338. Subplant monitor 338 may store the optimal
subplant loads as planned subplant utilization data in a subplant
utilization database 502.
[0136] Low level optimizer 332 may use the optimal subplant loads
to generate a set of optimal equipment setpoints for the equipment
of subplants 202-210. The optimal equipment setpoints may include
on/off states, operating setpoints, and/or other setpoints defining
the utilization of individual devices of the subplants 202-210. The
optimal equipment setpoints may include setpoints for individual
devices of subplants 202-210 in order for each subplant to meet the
optimal subplant load. In some embodiments, low level optimizer 332
performs an optimization process to generate the optimal equipment
setpoints. Low level optimizer 332 may provide the optimal
equipment setpoints to energy plant 200 and to subplant monitor
338. Subplant monitor 338 may store the optimal equipment setpoints
as planned subplant utilization data in subplant utilization
database 502. In various embodiments, subplant utilization database
502 may be a component of EOS 302 (e.g., memory 312) or a separate
database outside EOS 302.
[0137] Energy plant 200 may use the optimal equipment setpoints to
operate the equipment of subplants 202-210. Energy plant 200 may
monitor the utilization of subplants 202-210 and generate a set of
actual subplant utilization data. The actual subplant utilization
data may include actual values (rather than optimal values) for
each of the subplant loads optimized by high level optimizer 330.
In some embodiments, the actual subplant utilization data include a
total cold requested load and a total hot requested load. In some
embodiments, the actual subplant utilization data include an actual
unmet heating load and/or unmet cooling load. In some embodiments,
the actual subplant utilization data include an actual amount of
one or more resources (e.g., water, electricity, natural gas, etc.)
consumed by subplants 202-210 to generate the actual subplant
loads.
[0138] Energy plant 200 may report the actual subplant utilization
data to subplant monitor 338. In some embodiments, energy plant 200
reports the actual subplant utilization data at regular intervals
(e.g., once every fifteen minutes, once per hour, etc.) Subplant
monitor 338 may store the actual subplant utilization data in
subplant utilization database 502. In some embodiments, subplant
monitor 338 overwrites or replaces the planned subplant utilization
data for a given time step with the actual subplant utilization
data for the time step once the actual subplant utilization data
for the time step is available (e.g., once the current time
advances to the time step). Similarly, subplant monitor 338 may
overwrite or replace the planned subplant data for a given time
step with an updated optimal value for the time step each time a
new set of optimal subplant utilization data is generated by high
level optimizer 330. In other embodiments, subplant monitor 338
supplements the existing planned subplant utilization data with the
updated optimal subplant utilization data and/or the actual
subplant utilization data without replacing or overwriting the
exiting subplant utilization data. For example, the existing
subplant utilization data may be stored as a first version of the
subplant utilization data and the updated subplant utilization data
may be stored as a second version of the subplant utilization
data.
[0139] Still referring to FIG. 5, system 500 is shown to include a
dispatch GUI generator 506. Dispatch GUI generator 506 may access
the data stored in subplant utilization database 502 and use the
stored data to generate a dispatch GUI. Dispatch GUI 902 may
include various graphics (e.g., graphs, charts, flow diagrams,
etc.) that illustrate the actual subplant utilization data (e.g.,
past and current subplant loads) and the planned subplant
utilization data (e.g., future subplant loads) in a graphical
format. Exemplary dispatch GUIs which may be generated by dispatch
GUI generator 506 are described in greater detail with reference to
FIGS. 11-37. Dispatch GUI generator 506 may provide dispatch GUI
902 to a user interface 504 for presentation to a user.
[0140] In various embodiments, dispatch GUI generator 506 and user
interface 504 may be components of BAS 308 or components of EOS 302
(e.g., monitoring and reporting applications 336). Dispatch GUI
generator 506 may generate dispatch GUI 902 and provide dispatch
GUI 902 to user interface 504 via a communications network. In
other embodiments, dispatch GUI generator 506 and user interface
504 are components of a user device (e.g., a computer terminal, a
workstation, a laptop, a tablet, a smartphone, etc.). For example,
dispatch GUI generator 506 may be a web browser or a specialized
application running on the user device. In some embodiments,
dispatch GUI 902 is rendered using specialized viewing software
(e.g., an energy plant monitoring application) installed on the
user device. In other embodiments, dispatch GUI 902 is provided via
a web interface which allows dispatch GUI 902 to be rendered and
viewed using a web browser without requiring any specialized
applications or software to be installed on the user device.
[0141] Referring now to FIG. 6, a flow diagram illustrating a
process 600 for monitoring and controlling an energy plant in the
automatic operating mode is shown, according to an exemplary
embodiment. Process 600 may be performed by one or more components
of energy plant system 300 and/or system 500 as previously
described. For example, process 600 may be performed by EOS 302,
BAS 308, dispatch GUI generator 506, and/or other devices of energy
plant system 300 or system 500.
[0142] Process 600 is shown to include determining optimal subplant
loads for a plurality of subplants of an energy plant (step 602).
Step 602 may be performed by high level optimizer 330. Step 602 may
include determining the optimal subplant loads for a plurality of
time steps between the current time and a future time horizon. Each
of the time steps may have a defined or predetermined duration
(e.g., fifteen minutes, one hour, etc.). The optimal subplant loads
may be updated with each iteration of the high level optimization
process. High level optimizer 330 may provide the optimal subplant
loads to low level optimizer 332 and to subplant monitor 338.
Subplant monitor 338 may store the optimal subplant loads as
planned subplant utilization data in a subplant utilization
database 502.
[0143] Process 600 is shown to include determining optimal
setpoints for equipment of each subplant to achieve the optimal
subplant loads (step 604). Step 604 may be performed by low level
optimizer 332. The optimal equipment setpoints may include on/off
states, operating setpoints, and/or other setpoints defining the
utilization of individual devices of the subplants 202-210. The
optimal equipment setpoints may include setpoints for individual
devices of subplants 202-210 in order for each subplant to meet the
optimal subplant load. Low level optimizer 332 may provide the
optimal equipment setpoints to energy plant 200 and to subplant
monitor 338. Subplant monitor 338 may store the optimal equipment
setpoints as planned subplant utilization data in subplant
utilization database 502.
[0144] Process 600 is shown to include automatically providing the
optimal equipment setpoints to the energy plant to control the
equipment (step 606). Step 606 may include providing the optimal
equipment setpoints from low level optimizer 332 to energy plant
200. Energy plant 200 may use the optimal equipment setpoints as
control inputs for individual devices within each subplant (e.g.,
individual chillers, individual hot water generators, individual
thermal energy storage tanks, etc.). In other words, energy plant
200 may operate controllable devices within energy plant 200 (e.g.,
equipment 340) in accordance with the optimal equipment setpoints.
For example, energy plant 200 may activate a device, deactivate a
device, change a setpoint for a device, or otherwise control
individual devices of the energy plant equipment 340 based on the
optimal equipment setpoints. Energy plant 200 may provide actual
subplant utilization data to subplant monitor 338. Subplant monitor
338 may store the actual subplant utilization data in subplant
utilization database 502.
[0145] Process 600 is shown to include presenting planned and
actual subplant utilization data to a user via a graphical user
interface (step 608). Step 608 may be performed by dispatch GUI
generator 506. In some embodiments, step 608 includes accessing the
data stored in subplant utilization database 502 and using the
stored data to generate a dispatch GUI. Dispatch GUI 902 may
include various graphics (e.g., graphs, charts, flow diagrams,
etc.) that illustrate the actual subplant utilization data (e.g.,
past and current subplant loads) and the planned subplant
utilization data (e.g., future subplant loads) in a graphical
format. Dispatch GUI generator 506 may provide dispatch GUI 902 to
a user interface 504 for presentation to a user.
[0146] In the automatic operating mode, the optimal subplant loads
and equipment setpoints are used to control energy plant 200. The
optimal values determined by EOS 302 may be automatically provided
as inputs to energy plant 200. For example, low level optimizer 332
is shown providing the optimal equipment setpoints directly to
energy plant 200. In other embodiments, the optimal equipment
setpoints may be provided to BAS 308, which may use the optimal
equipment setpoints to generate control signals for the energy
plant equipment 340. Advantageously, the automatic operating mode
allows EOS 302 to control energy plant 200 without requiring user
intervention by automatically providing the optimal equipment
setpoints to energy plant 200.
Advisory Operating Mode
[0147] Referring now to FIG. 7, a block diagram illustrating a
system 700 for monitoring and controlling an energy plant in an
advisory operating mode is shown, according to an exemplary
embodiment. In the advisory operating mode, EOS 302 determines
recommended subplant loads for each of subplants 202-210 and
recommended equipment setpoints for the energy plant equipment 340.
The recommended subplant loads and recommended equipment setpoints
may be the same or similar to the optimal subplant loads and
optimal equipment setpoints as previously described. However,
unlike the automatic operating mode, EOS 302 does not automatically
provide the recommended equipment setpoints as control inputs to
energy plant 200. Instead, energy plant 200 receives equipment
control signals from BAS 308, which may generate the equipment
control signals based in part on user-defined setpoints received
from a user 702 via a BAS user interface 309.
[0148] System 700 is shown to include high level optimizer 330, low
level optimizer 332, energy plant 200, subplant monitor 338,
subplant utilization database 502, dispatch GUI generator 506, and
BAS 308, which may be the same or similar as described with
reference to FIGS. 2-6. For example, high level optimizer 330 may
perform an optimization process to generate a set of recommended
subplant loads for each of subplants 202-210. High level optimizer
330 may determine the recommended subplant loads for a plurality of
time steps between the current time and a future time horizon. Each
of the time steps may have a defined or predetermined duration
(e.g., fifteen minutes, one hour, etc.). High level optimizer 330
may provide the recommended subplant loads to low level optimizer
332 and to subplant monitor 338. Subplant monitor 338 may store the
recommended subplant loads as recommended subplant utilization data
in a subplant utilization database 502.
[0149] Low level optimizer 332 may use the recommended subplant
loads to generate a set of recommended equipment setpoints for the
equipment of subplants 202-210. The recommended equipment setpoints
may include on/off states, operating setpoints, and/or other
setpoints defining the utilization of individual devices of
subplants 202-210. The recommended equipment setpoints may include
setpoints for individual devices of subplants 202-210 in order for
each subplant to meet the recommended subplant load. In some
embodiments, low level optimizer 332 performs an optimization
process to generate the recommended equipment setpoints. Low level
optimizer 332 may provide the recommended equipment setpoints to
subplant monitor 338. Subplant monitor 338 may store the
recommended equipment setpoints as recommended subplant utilization
data in subplant utilization database 502.
[0150] Energy plant 200 may monitor the utilization of subplants
202-210 and generate a set of actual subplant utilization data. The
actual subplant utilization data may include actual values (rather
than recommended values) for each of the subplant loads produced by
subplants 202-210. The actual subplant utilization data may also
include actual equipment operating states (e.g., on/off states,
load production, operating capacities, etc.) for individual devices
of energy plant equipment 340. Energy plant 200 may report the
actual subplant utilization data to subplant monitor 338. In some
embodiments, energy plant 200 reports the actual subplant
utilization data at regular intervals (e.g., once every fifteen
minutes, once per hour, etc.) Subplant monitor 338 may store the
actual subplant utilization data in subplant utilization database
502.
[0151] Dispatch GUI generator 506 may access the data stored in
subplant utilization database 502 and use the stored data to
generate a dispatch GUI. Dispatch GUI 902 may include various
graphics (e.g., graphs, charts, flow diagrams, etc.) that
illustrate the actual subplant utilization data (e.g., past and
current subplant loads) and the recommended subplant utilization
data (e.g., future subplant loads) in a graphical format. Dispatch
GUI generator 506 may provide dispatch GUI 902 to EOS user
interface 704 for presentation to a user 702.
[0152] In system 700, EOS 302 and energy plant 200 may be decoupled
such that the output of EOS 302 (i.e., recommended subplant loads
and equipment setpoints) is not automatically provided as an input
to energy plant 200. User 702 can view the recommended setpoints
(e.g., recommended subplant load setpoints, recommended equipment
setpoints, etc.) and monitor actual subplant operation via EOS user
interface 704. In some instances, user 702 may use the recommended
setpoints to generate the user-defined setpoints provided to BAS
308; however, such use is not automatic or required when EOS 302 is
operated in the advisory operating mode. Instead, EOS 302 may
provide the recommended setpoints as suggestions or advice, which
the user 702 can choose to accept or reject when generating the
user-defined setpoints.
[0153] Referring now to FIG. 8, a flow diagram illustrating a
process 800 for monitoring and controlling an energy plant in the
advisory operating mode is shown, according to an exemplary
embodiment. Process 800 may be performed by one or more components
of energy plant system 300 and/or system 700 as previously
described. For example, process 800 may be performed by EOS 302,
BAS 308, dispatch GUI generator 506, and/or other devices of energy
plant system 300 or system 700.
[0154] Process 800 is shown to include determining recommended
subplant loads for a plurality of subplants of an energy plant
(step 802). Step 802 may be performed by high level optimizer 330.
Step 802 may include determining the recommended subplant loads for
a plurality of time steps between the current time and a future
time horizon. Each of the time steps may have a defined or
predetermined duration (e.g., fifteen minutes, one hour, etc.). The
recommended subplant loads may be updated with each iteration of
the high level optimization process. High level optimizer 330 may
provide the recommended subplant loads to low level optimizer 332
and to subplant monitor 338. Subplant monitor 338 may store the
recommended subplant loads as planned subplant utilization data in
a subplant utilization database 502.
[0155] Process 800 is shown to include determining recommended
setpoints for equipment of each subplant to achieve the recommended
subplant loads (step 804). Step 804 may be performed by low level
optimizer 332. The recommended equipment setpoints may include
on/off states, operating setpoints, and/or other setpoints defining
the utilization of individual devices of the subplants 202-210. The
recommended equipment setpoints may include setpoints for
individual devices of subplants 202-210 in order for each subplant
to meet the recommended subplant load. Low level optimizer 332 may
provide the recommended equipment setpoints to energy plant 200 and
to subplant monitor 338. Subplant monitor 338 may store the
recommended equipment setpoints as planned subplant utilization
data in subplant utilization database 502.
[0156] Process 800 is shown to include presenting recommended and
actual subplant utilization data to a user via an EOS user
interface (e.g., EOS user interface 704) (step 806). Step 806 may
be performed by dispatch GUI generator 506 and/or EOS user
interface 704. The recommended subplant utilization data may
include the recommended subplant loads generated by high level
optimizer 330 and/or the recommended equipment setpoints generated
by low level optimizer 332. The actual subplant utilization data
may include data describing the actual operation of energy plant
200 (e.g., actual subplant loads, actual equipment setpoints,
etc.), which may be collected by subplant monitor 338 and stored in
subplant utilization database 502.
[0157] In some embodiments, step 806 includes accessing the data
stored in subplant utilization database 502 and using the stored
data to generate a dispatch GUI. Dispatch GUI 902 may include
various graphics (e.g., graphs, charts, flow diagrams, etc.) that
illustrate the actual subplant utilization data (e.g., past and
current subplant loads) and the planned subplant utilization data
(e.g., future subplant loads) in a graphical format. Dispatch GUI
generator 506 may provide dispatch GUI 902 to EOS user interface
704 for presentation to a user 702.
[0158] Process 800 is shown to include receiving user-defined
setpoints at a BAS user interface (e.g., BAS user interface 309)
(step 808) and generating control signals for the subplant
equipment based on the user-defined setpoints (step 810). In some
embodiments, BAS user interface 309 is separate and distinct from
EOS user interface 704. For example, EOS user interface 704 may be
coupled to EOS 302 and configured to present information stored in
subplant utilization database 502, whereas BAS user interface 309
may be coupled to BAS 308 and configured to allow user 702 to
provide control inputs to BAS 308. In some embodiments, user 702 is
the only link between EOS user interface 704 and BAS user interface
309.
[0159] In some instances, user 702 may view the recommended
setpoints provided via EOS user interface 704 and generate the
user-defined setpoints provided to BAS user interface 309 based on
the recommended setpoints. In other instances, user 702 may
generate the user-defined setpoints independent of the recommended
setpoints. Advantageously, the advisory mode allows user 702 to
exercise a greater level of control over energy plant 200 relative
to the automatic mode by allowing user 702 to choose whether to use
the recommended setpoints to control energy plant 200 or provide
different user-defined setpoints.
Manual Operating Mode
[0160] Referring now to FIG. 9, a block diagram illustrating a
system 900 for monitoring and controlling an energy plant in a
manual operating mode is shown, according to an exemplary
embodiment. In the manual operating mode, EOS 302 determines
recommended subplant loads for each of subplants 202-210 and
recommended equipment setpoints for the energy plant equipment 340.
The recommended subplant loads and recommended equipment setpoints
may be the same or similar to the optimal subplant loads and
optimal equipment setpoints as previously described. However, EOS
302 does not automatically provide the recommended equipment
setpoints as control inputs to energy plant 200 when operating in
the manual mode. Instead, EOS 302 receives manual (i.e.,
user-defined) subplant loads from a user via EOS user interface
704. EOS 302 uses the manual subplant loads to determine
recommended (e.g., optimal) equipment setpoints to achieve the
manual subplant loads. EOS 302 may generate equipment control
signals for subplant equipment 340 based on the recommended
equipment setpoints to achieve the manual subplant loads.
[0161] System 900 is shown to include high level optimizer 330, low
level optimizer 332, energy plant 200, subplant monitor 338,
subplant utilization database 502, dispatch GUI generator 506, EOS
user interface 704, and BAS 308, which may be the same or similar
as described with reference to FIGS. 2-8. For example, high level
optimizer 330 may perform an optimization process to generate a set
of recommended subplant loads for each of subplants 202-210. High
level optimizer 330 may determine the recommended subplant loads
for a plurality of time steps between the current time and a future
time horizon. Each of the time steps may have a defined or
predetermined duration (e.g., fifteen minutes, one hour, etc.).
High level optimizer 330 may provide the recommended subplant loads
to low level optimizer 332 and to subplant monitor 338. Subplant
monitor 338 may store the recommended subplant loads as recommended
subplant utilization data in a subplant utilization database
502.
[0162] Low level optimizer 332 may use the recommended subplant
loads to generate a first set of recommended equipment setpoints
for the equipment of subplants 202-210. The first set of
recommended equipment setpoints may include on/off states,
operating setpoints, and/or other setpoints defining the
utilization of individual devices of subplants 202-210. The first
set of recommended equipment setpoints may include setpoints for
individual devices of subplants 202-210 in order for each subplant
to meet the recommended subplant load. In some embodiments, low
level optimizer 332 performs an optimization process to generate
the first set of recommended equipment setpoints. Low level
optimizer 332 may provide the first set of recommended equipment
setpoints to subplant monitor 338. Subplant monitor 338 may store
the first set of recommended equipment setpoints as recommended
subplant utilization data in subplant utilization database 502.
[0163] Low level optimizer 332 may also receive manual subplant
loads from EOS user interface 704. The manual subplant loads may
include user-defined loads for each of subplants 202-210. A user
can specify the manual subplant loads via EOS user interface 704
(e.g., by entering values for the manual subplant loads, by
adjusting sliders or dials, etc.). The manual subplant loads may be
provided from EOS user interface 704 to low level optimizer 332.
The manual subplant loads may also be provided to subplant monitor
338. Subplant monitor 338 may store the manual subplant loads as
manual subplant utilization data in subplant utilization database
502.
[0164] Low level optimizer 332 may use the manual subplant loads to
generate a second set of recommended equipment setpoints for the
equipment of subplants 202-210. The second set of recommended
equipment setpoints may include setpoints for individual devices of
subplants 202-210 in order for each subplant to meet the manual
subplant load. In some embodiments, low level optimizer 332
performs an optimization process to generate the second set of
recommended equipment setpoints. Low level optimizer 332 may
provide the second set of recommended equipment setpoints to
subplant monitor 338. Subplant monitor 338 may store the
recommended equipment setpoints as manual subplant utilization data
in subplant utilization database 502.
[0165] In some embodiments, low level optimizer 332 uses the second
set of recommended equipment setpoints to generate equipment
control signals for energy plant 200. The equipment control signals
may include on/off signals, load production signals, power
consumption signals, or other types of control signals which may be
used to control individual devices of subplant equipment 340. The
equipment control signals may cause each of subplants 202-210 to
operate the individual devices of the subplant in a manner that
achieves the manual subplant load for the subplant. For example,
the equipment control signals for a chiller subplant may specify
that a first chiller of the subplant operate at 75% capacity, a
second chiller of the subplant operate at 40% capacity, and a third
chiller of the subplant operate at 60% capacity. In some
embodiments, the equipment control signals are provided directly
from low level optimizer 332 to energy plant 200 (as shown in FIG.
9). In other embodiments, the equipment control signals may be
provided to energy plant 200 via BAS 308 or generated by BAS 308
based on the second set of recommended equipment setpoints.
[0166] Energy plant 200 may monitor the utilization of subplants
202-210 and generate a set of actual subplant utilization data. The
actual subplant utilization data may include actual values (rather
than recommended values) for each of the subplant loads produced by
subplants 202-210. The actual subplant utilization data may also
include actual equipment operating states (e.g., on/off states,
load production, operating capacities, etc.) for individual devices
of energy plant equipment 340. Energy plant 200 may report the
actual subplant utilization data to subplant monitor 338. In some
embodiments, energy plant 200 reports the actual subplant
utilization data at regular intervals (e.g., once every fifteen
minutes, once per hour, etc.) Subplant monitor 338 may store the
actual subplant utilization data in subplant utilization database
502.
[0167] Dispatch GUI generator 506 may access the data stored in
subplant utilization database 502 and use the stored data to
generate a dispatch GUI. Dispatch GUI 902 may include various
graphics (e.g., graphs, charts, flow diagrams, etc.) that
illustrate the recommended subplant utilization data (e.g.,
recommended subplant loads and the first set of recommended
equipment setpoints, etc.) manual subplant utilization data (e.g.,
manual subplant loads and the second set of recommended equipment
setpoints), and actual subplant utilization data (e.g., actual
subplant loads and actual equipment setpoints) in a graphical
format. Dispatch GUI generator 506 may provide dispatch GUI 902 to
EOS user interface 704 for presentation to a user 702. A user can
view dispatch GUI 902 and provide the manual subplant loads via EOS
user interface 704.
[0168] In some embodiments, EOS user interface 704 is a
comprehensive monitoring and control interface that allows the user
to monitor the operation of energy plant 200 and provide control
inputs (e.g., manual subplant loads) that affect the operation of
energy plant 200. In addition to displaying dispatch GUI 902
generated by dispatch GUI generator 506, EOS user interface 704 may
be configured to receive input from the user via dispatch GUI 902.
For example, dispatch GUI 902 presented via user interface 504 may
include interface options that allow a user to change the load
setpoints for subplants 202-210, change the on/off states or
operating setpoints for equipment 340, activate or deactivate
entire subplants or individual devices of equipment 340, adjust the
configuration of BAS 308 or energy plant 200, or otherwise monitor
and control the operation of energy plant 200 and/or equipment 340.
EOS 302 may control energy plant 200 and/or equipment 340 (e.g.,
via actuators, power relays, etc.) in accordance with the user
input received via EOS user interface 704.
[0169] In some embodiments, the user input received via EOS user
interface 704 overrides automated control decisions made by EOS
302. For example, the manual subplant loads provided via EOS user
interface 704 may override the recommended subplant loads
determined by high level optimizer 330. In some embodiments, EOS
302 determines whether the manual subplant loads would cause EOS
302 to violate an optimization constraint (e.g., building
temperature out of range, thermal energy storage tank fully
depleted or filled to capacity, etc.). If the manual subplant loads
would not cause EOS 302 to violate an optimization constraint, EOS
302 may override the recommended subplant loads determined by high
level optimizer 330 with the manual subplant loads. However, if the
manual subplant loads would cause EOS 302 to violate an
optimization constraint, EOS 302 may not override the recommended
subplant loads determined by high level optimizer 330. In some
embodiments, EOS 302 causes dispatch GUI 902 to display a warning
for the user if the manual subplant loads would cause EOS 302 to
violate an optimization constraint within a predetermined time
window. The user can then choose to override the recommended
subplant loads and violate the optimization constraint or use the
recommended subplant loads and satisfy the optimization
constraint.
[0170] In some embodiments, dispatch GUI generator 506 updates
dispatch GUI 902 based on the manual subplant loads before the
manual subplant loads are used to affect the operation of energy
plant 200. For example, dispatch GUI generator 506 may display the
second set of recommended equipment setpoints that would result
from the manual subplant loads before the manual subplant loads are
used to generate the equipment control signals. In some
embodiments, EOS 302 uses the manual subplant loads to calculate an
amount of time before the manual subplant loads would cause EOS 302
to violate an optimization constraint (e.g., time until thermal
energy storage tanks are filled or depleted). Advantageously, this
allows the user to view the predicted effects of the manual
subplant loads before the manual subplant loads take effect.
[0171] Referring now to FIG. 10, a flow diagram illustrating a
process 1000 for monitoring and controlling an energy plant in the
manual operating mode is shown, according to an exemplary
embodiment. Process 1000 may be performed by one or more components
of energy plant system 300 and/or system 900 as previously
described. For example, process 1000 may be performed by EOS 302,
BAS 308, dispatch GUI generator 506, and/or other devices of energy
plant system 300 or system 900.
[0172] Process 1000 is shown to include determining recommended
subplant loads for a plurality of subplants of an energy plant
(step 1002). Step 1002 may be performed by high level optimizer
330. Step 802 may include determining the recommended subplant
loads for a plurality of time steps between the current time and a
future time horizon. Each of the time steps may have a defined or
predetermined duration (e.g., fifteen minutes, one hour, etc.). The
recommended subplant loads may be updated with each iteration of
the high level optimization process. High level optimizer 330 may
provide the recommended subplant loads to low level optimizer 332
and to subplant monitor 338. Subplant monitor 338 may store the
recommended subplant loads as planned subplant utilization data in
a subplant utilization database 502.
[0173] Process 1000 is shown to include determining recommended
setpoints for equipment of each subplant to achieve the recommended
subplant loads (step 1004). Step 1004 may be performed by low level
optimizer 332. The recommended equipment setpoints may include
on/off states, operating setpoints, and/or other setpoints defining
the utilization of individual devices of the subplants 202-210. The
recommended equipment setpoints may include setpoints for
individual devices of subplants 202-210 in order for each subplant
to meet the recommended subplant load. Low level optimizer 332 may
provide the recommended equipment setpoints to energy plant 200 and
to subplant monitor 338. Subplant monitor 338 may store the
recommended equipment setpoints as planned subplant utilization
data in subplant utilization database 502.
[0174] Process 1000 is shown to include presenting recommended and
actual subplant utilization data to a user via an EOS user
interface (e.g., EOS user interface 704) (step 1006). Step 1006 may
be performed by dispatch GUI generator 506 and/or EOS user
interface 704. The recommended subplant utilization data may
include the recommended subplant loads generated by high level
optimizer 330 and/or the first set of recommended equipment
setpoints generated by low level optimizer 332. The actual subplant
utilization data may include data describing the actual operation
of energy plant 200 (e.g., actual subplant loads, actual equipment
setpoints, etc.), which may be collected by subplant monitor 338
and stored in subplant utilization database 502.
[0175] In some embodiments, step 1006 includes accessing the data
stored in subplant utilization database 502 and using the stored
data to generate a dispatch GUI. Dispatch GUI 902 may include
various graphics (e.g., graphs, charts, flow diagrams, etc.) that
illustrate the actual subplant utilization data and the recommended
subplant utilization data in a graphical format. Dispatch GUI
generator 506 may provide dispatch GUI 902 to EOS user interface
704 for presentation to a user 702.
[0176] Process 1000 is shown to include receiving manual subplant
loads at the EOS user interface (step 1008) and determining
recommended setpoints for equipment of each subplant to achieve the
manual subplant loads (step 1010). The manual subplant loads may be
user-specified subplant loads and may override the recommended
subplant loads determined by high level optimizer 330. Step 1010
may be the same or similar to step 1004, with the exception that
the manual subplant loads are used instead of the recommended
subplant loads to determine the recommended setpoints for the
subplant equipment. In some embodiments, dispatch GUI 902 is
updated to include the manual subplant loads and the set of
recommended setpoints that result from the manual load
setpoints.
[0177] Process 1000 is shown to include generating control signals
for the subplant equipment based on the recommended setpoints to
achieve the manual subplant loads (step 1012). The equipment
control signals may include on/off signals, load production
signals, power consumption signals, or other types of control
signals which may be used to control individual devices of subplant
equipment 340. The equipment control signals may cause each of
subplants 202-210 to operate the individual devices of the subplant
in a manner that achieves the manual subplant load for the
subplant. In some embodiments, the equipment control signals are
provided directly from low level optimizer 332 to energy plant 200.
In other embodiments, the equipment control signals may be provided
to energy plant 200 via BAS 308 or generated by BAS 308 based on
the recommended equipment setpoints determined in step 1010.
Dispatch GUIs
[0178] Referring now to FIGS. 11-37 several drawings of a dispatch
GUI 902 which may be generated by dispatch GUI generator 506 are
shown, according to an exemplary embodiment. Dispatch GUI 902 may
present the recommended (i.e., optimal) subplant loads, manual
subplant loads, and actual subplant loads in a variety of visual
formats (e.g., diagrams, charts, graphs, etc.) that illustrate the
operation of energy plant 200. Dispatch GUI 902 may also present
the first set of recommended equipment setpoints (i.e., the
setpoints that result from the recommended subplant loads), the
second set of recommended equipment setpoints (i.e., the setpoints
that result from the manual subplant loads), and the actual
equipment setpoints to provide details regarding the operation of
each subplant. Dispatch GUI 902 may include past dispatch data
(e.g., past subplant loads, past equipment setpoints) and current
dispatch data (e.g., current subplant loads, current equipment
setpoints) to allow a user to view a history of energy plant
operations. Dispatch GUI 902 may also include future dispatch data
(e.g., planned subplant loads, planned equipment setpoints) to
allow the user to view planned energy plant operations into the
future.
Plant and Subplant Summary Interfaces
[0179] Referring particularly to FIG. 11, a plant summary interface
1100 is shown, according to an exemplary embodiment. Plant summary
interface 1100 may include graphics depicting each of the subplants
within energy plant 200. For example, plant summary interface 1100
is shown to include a plurality of chilled water loops 1102-1106
(i.e., Loop A 1102, Loop B 1104, and Loop C 1106). Chilled water
loops 1102-1106 are configured to provide cold thermal energy
(e.g., measured in tons) to satisfy the cooling load of campus
1108. Similarly, the steam subplant 1111 within plant B is
configured to provide steam (measured in KPPH) to satisfy the
heating load of campus 1108. The cogeneration subplant 1110 within
plant B can be configured to provide electricity (measured in MW)
to satisfy the electric load of campus 1108.
[0180] Plant summary interface 1100 is shown to include paths
1112-1120 connecting each of the chilled water loops 1102-1106 and
subplants 1110-1111 to campus 1108. If a subplant or chilled water
loop is currently providing energy to campus 1108, the path
connecting the subplant or chilled water loop to campus 1108 may be
highlighted or otherwise marked to indicate the flow of energy
along the path. For example, path 1120 may be colored red to
indicate the flow of steam along path 1120, whereas paths 1112-1116
may be colored blue to indicate the flow of cold thermal energy
along paths 1112-1116, path 1118 may be colored orange to indicate
the flow of electricity along path 1118, or a path may be colored
green to indicate the flow of gas along the path. In some
embodiments, paths 1112-1120 are animated to indicate the flow of
thermal energy. For example, the arrows along each of paths
1112-1120 may move along each path 1112-1120 to indicate the flow
of thermal energy. Similarly, plant summary interface 1100 is shown
to include paths 1122-1124 connecting electricity supply 1126 and
gas supply 1128 to campus 1108. Like paths 1112-1120 connecting the
subplants to campus 1108, paths 1122-1124 may be highlighted or
otherwise marked to indicate the flow of electricity or gas energy
along each path. The values below each of paths 1112-1124 within
campus 1108 indicate the amount of each resource (i.e.,
electricity, chilled water, steam, and gas) being provided to
campus 1108 by the corresponding subplant or supply.
[0181] Percentage indicators next to subplants 1110-1111 and
chilled water loops 1102-1106 indicate the percentage of
utilization for each subplant 1110-1111 or chilled water loop
1102-1106 (i.e., the percentage of the total subplant or chilled
water loop capacity being utilized). For example, FIG. 11 is shown
to include a 37% indicator next to Loop A 1102, a 37% indicator
next to Loop B 1104, a 37% indicator next to the Loop C 1106, a 37%
indicator next to the plant B cogeneration subplant 1110, and a 37%
indicator next to the plant B steam subplant 1111. This indicates
that each subplant and chilled water loop is being utilized at 37%
of its maximum capacity. The utilization rings associated with each
subplant and chilled water loop may be filled to visually indicate
the utilization percentage.
[0182] Referring now to FIG. 12, a chilled water loop summary
interface 1200 for Loop B 1104 is shown, according to an exemplary
embodiment. Interface 1200 illustrates the percent utilization of
each chilled water subplant within Loop B (e.g., chilled water
subplant 1202, chilled water subplant 1204, and chilled water
subplant 1206) as well as the utilized capacity for the entire Loop
B 1104. The values below each of the paths 1208, 1210, and 1212
connecting chilled water subplants 1202-1206 to campus 1108
indicate the chilled water production of each chilled water
subplant 1202-1206.
[0183] Referring now to FIG. 13, a chilled water loop summary
interface 1300 for Loop A 1102 is shown, according to an exemplary
embodiment. Interface 1300 illustrates the percent utilization of
each chilled water subplant within Loop A (e.g., chilled water
subplant 1302, chilled water subplant 1304, and chilled water
subplant 1306) as well as the utilized capacity for the entire Loop
A 1102. The values below each of the paths 1308, 1310, and 1312
connecting chilled water subplants 1302-1306 to campus 1108
indicate the chilled water production of each chilled water
subplant 1302-1306.
[0184] Referring now to FIG. 14, a plant summary interface 1400 for
Loop C 1106 is shown, according to an exemplary embodiment.
Interface 1400 illustrates the equipment within Loop C 1106 and
provides key performance indicators (KPIs) for each of the
equipment. For example, interface 1400 is shown to include a
plurality of electric chillers (i.e., electric chiller 1, electric
chiller 2, electric chiller 3, and steam chiller 1). The KPIs may
include efficiency (e.g., COP, kW/ton, etc.), cooling production
(tons), and rate of electric consumption (kW). Interface 1400 may
display the percent utilization and cooling production of each
device within Loop C 1106 as well as the overall percent
utilization and cooling production of Loop C 1106. In some
embodiments, the equipment images will appear dimmed if the
equipment is out of service.
[0185] Referring now to FIG. 15, an equipment information page 1500
for the equipment of Plant F within Loop A 1102 is shown, according
to an exemplary embodiment. Interface 1500 illustrates the
equipment within Plant F and provides KPIs for each of the
equipment. For example, interface 1500 is shown to include a
plurality of steam chillers (i.e., steam chiller 1 and steam
chiller 2). The KPIs may include efficiency (e.g., COP, KPPH/ton,
etc.), cooling production (tons), and rate of steam consumption
(KPPH). Interface 1500 may display the percent utilization and
cooling production of each device within Plant F as well as the
overall percent utilization and cooling production of Plant F. In
some embodiments, the equipment images will appear dimmed if the
equipment is out of service.
[0186] Referring now to FIG. 16, an equipment information page 1600
for the equipment of Plant E within Loop B 1104 is shown, according
to an exemplary embodiment. Interface 1600 illustrates the
equipment within Plant E and provides KPIs for each of the
equipment. For example, interface 1600 is shown to include a
plurality of electric chillers (i.e., electric chiller 1 and
electric chiller 2). The KPIs may include efficiency (e.g., COP,
kW/ton, etc.), cooling production (tons), and rate of electric
consumption (kW). Interface may display the percent utilization and
cooling production of each device within Plant E as well as the
overall percent utilization and cooling production of Plant E. In
some embodiments, the equipment images will appear dimmed if the
equipment is out of service.
[0187] Referring now to FIG. 17, an equipment information page 1700
for the equipment of Plant D within Loop A 1102 is shown, according
to an exemplary embodiment. Interface 1700 illustrates the
equipment within Plant D and provides KPIs for each of the
equipment. For example, interface 1700 is shown to include a
plurality of chillers (i.e., electric chiller 1 and steam chiller
1). The KPIs may include efficiency (e.g., COP, kW/ton, KPPH/ton,
etc.), cooling production (tons), rate of electric consumption
(kW), and rate of steam consumption (KPPH). Interface 1700 may
display the percent utilization and cooling production of each
device within Plant D as well as the overall percent utilization
and cooling production of Plant D. In some embodiments, the
equipment images will appear dimmed if the equipment is out of
service.
[0188] Referring now to FIG. 18, an equipment information page 1800
for the equipment of Plant A within Loop B 1104 is shown, according
to an exemplary embodiment. Interface 1800 illustrates the
equipment within Plant A and provides KPIs for each of the
equipment. For example, interface 1800 is shown to include a steam
chiller (i.e., steam chiller 1). The KPIs may include efficiency
(e.g., COP, KPPH/ton, etc.), cooling production (tons), and rate of
steam consumption (KPPH). Interface 1800 may display the percent
utilization and cooling production of each device within Plant A as
well as the overall percent utilization and cooling production of
Plant A. In some embodiments, the equipment images will appear
dimmed if the equipment is out of service.
[0189] Referring now to FIG. 19, an equipment information page 1900
for the equipment of Plant C within Loop B 1104 is shown, according
to an exemplary embodiment. Interface 1900 illustrates the
equipment within Plant C and provides KPIs for each of the
equipment. For example, interface 1900 is shown to include an
electric chiller (i.e., electric chiller 1). The KPIs may include
efficiency (e.g., COP, kW/ton, etc.), cooling production (tons),
and rate of electric consumption (kW). Interface 1900 may display
the percent utilization and cooling production of each device
within Plant C as well as the overall percent utilization and
cooling production of Plant C. In some embodiments, the equipment
images will appear dimmed if the equipment is out of service.
[0190] Referring now to FIG. 20, an equipment information page 2000
for the equipment of Plant B within Loop B 1104 is shown, according
to an exemplary embodiment. Interface 2000 illustrates the
equipment within Plant B and provides KPIs for each of the
equipment. For example, interface 2000 is shown to include a
plurality of chillers (i.e., electric chiller 1, electric chiller
2, and steam chiller 1). The KPIs may include efficiency (e.g.,
COP, kW/ton, KPPH/ton, etc.), cooling production (tons), rate of
electric consumption (kW), and rate of steam consumption (KPPH).
Interface 2000 may display the percent utilization and cooling
production of each device within Plant B as well as the overall
percent utilization and cooling production of Plant B. In some
embodiments, the equipment images will appear dimmed if the
equipment is out of service.
[0191] Referring now to FIG. 21, an equipment information page 2100
for the steam equipment of Plant B is shown, according to an
exemplary embodiment. Interface 2100 illustrates the steam
equipment within Plant B and provides KPIs for each of the
equipment. For example, interface 2100 is shown to include a
plurality of boilers (i.e., boiler 4, boiler 5, boiler 6, and
boiler 7). The KPIs may include steam production (KPPH), rate of
natural gas consumption (MMBtu), and rate of electric consumption
(kW). Interface 2100 may display the percent utilization and steam
production of each steam production device within Plant B as well
as the overall percent utilization and steam production of Plant B.
In some embodiments, the equipment images will appear dimmed if the
equipment is out of service.
[0192] Referring now to FIG. 22A, an equipment information page
2200 for the electric equipment of Plant B is shown, according to
an exemplary embodiment. Interface 2200 illustrates the electric
equipment within Plant B and provides KPIs for each of the
equipment. For example, interface 2200 is shown to include a
plurality of combustion turbines (i.e., combustion turbine 1 and
combustion turbine 2). The KPIs may include efficiency (e.g., COP,
kW/kW), electric production (MW), and rate of electric consumption
(kW). Interface 2200 may display the percent utilization and
electricity production of each electricity production device within
Plant B as well as the overall percent utilization and electricity
production of Plant B. In some embodiments, the equipment images
will appear dimmed if the equipment is out of service.
[0193] Referring now to FIG. 22B, another equipment information
page 2210 for the electric equipment of Plant B is shown, according
to an exemplary embodiment. Interface 2210 illustrates the electric
equipment within Plant B and provides KPIs for each of the
equipment. For example, interface 2210 is shown to include a
plurality of gas turbines (i.e., gas turbine 1 and gas turbine 2).
The KPIs may include efficiency (e.g., COP, kW/kW), electric
production (MW), and rate of natural gas consumption (mcf/hr).
Interface 2210 may display the percent utilization and electricity
production of each electricity production device within Plant B as
well as the overall percent utilization, electricity production,
heat rate, and electricity import of Plant B. In some embodiments,
the equipment images will appear dimmed if the equipment is out of
service.
[0194] Referring now to FIG. 22C, another equipment information
page 2220 for the electric equipment of Plant B is shown, according
to an exemplary embodiment. Interface 2220 illustrates the electric
equipment within Plant B and provides KPIs for each of the
equipment. For example, interface 2220 is shown to include a
plurality of gas turbines (i.e., gas turbine 1 and gas turbine 2).
The KPIs may include efficiency (e.g., COP, kW/kW), electric
production (MW), and rate of natural gas consumption (mcf/hr). Both
the actual and target values of the electricity production and rate
of natural gas consumption may be indicated when operating in the
automatic mode. Interface 2220 may display the percent utilization
and electricity production of each electricity production device
within Plant B as well as the overall percent utilization,
electricity production, heat rate, and electricity import of Plant
B. In some embodiments, the equipment images will appear dimmed if
the equipment is out of service.
Dispatch Schedule Interface
[0195] Referring now to FIGS. 23A-23B, a dispatch schedule
interface 2300 which may be generated by dispatch GUI generator 506
is shown, according to an exemplary embodiment. Dispatch schedule
interface 2300 may include detailed dispatch information for each
of the subplants within energy plant 200. For example, dispatch
schedule interface 2300 may include detailed dispatch information
for each chiller subplant 202-206, steam subplant 208, and
cogeneration subplant 210. Dispatch schedule interface 2300 may
also include detailed dispatch information for each of the
individual devices within each subplant 202-210. For example,
dispatch schedule interface 2300 may include detailed dispatch
information 2302 for each of chillers 218-228 within each chiller
subplant 202-206, each of boilers 232 within steam subplant 208,
and each of combustion turbines 230 within cogeneration subplant
210.
[0196] Detailed dispatch information 2302 for each chiller may
include the chiller's status (e.g., on or off), the amount of
cooling energy (i.e., "Cooling Q") produced by the chiller
(measured in tons), the power consumption of each chiller (measured
in kW), the temperature of the cold water produced by each chiller,
the flow rate of the cold water produced by each chiller, and the
dispatched condenser flow setpoint for each chiller. Detailed
dispatch information 2302 for each boiler may include the boiler's
status (e.g., on or off), the amount of heating energy (i.e.,
"Heating Q") produced by the boiler (measured in mmBTU/h), the
amount of fuel usage of each boiler (measured in mmBTU/h), the
power consumption of each boiler (measured in kW), and the
temperature of the hot water or steam produced by each boiler.
Detailed dispatch information 2302 for the cogeneration subplant
may include the amount of electricity produced by each of the
combustion turbines.
[0197] In some embodiments, dispatch schedule interface 2300
includes multiple values for each of the variables included in
detailed dispatch information 2302. For example, dispatch schedule
interface 2300 may include an optimal value 2304 for each variable
(i.e., the "EOS" value), a building automation system (BAS) value
2306 for each variable (i.e., the "BAS" value), and an actual value
2308 for each variable. Optimal values 2304 may be determined by
EOS 302 as previously described. In some instances, optimal values
2304 are based on the optimal or recommended subplant loads
determined by high level optimizer 330. In other instances, optimal
values 2304 may be based on the manual subplant loads received via
EOS user interface 704. When EOS 302 is operated in the automatic
mode, optimal values 2304 may be the same as the BAS values 2306
since optimal values 2304 are automatically provided as inputs to
the BAS. However, when EOS 302 is operated in the advisory mode,
optimal values 2304 may be different from BAS values 2306 since
optimal values 2304 are provided as recommendations but not
automatically applied as inputs to the BAS. Actual values 2308 may
represent measured or observed values of the variables obtained
from energy plant 200, as indicated by the actual subplant
utilization data collected by subplant monitor 338.
[0198] In some embodiments, dispatch schedule interface 2300
presents snapshots of detailed dispatch information 2302 for
multiple different dispatch intervals. For example, FIG. 23A shows
a snapshot of detailed dispatch information 2302 for the present
dispatch interval 2310. Other pages of dispatch schedule interface
2300 may show snapshots of detailed dispatch information 2302 for a
past dispatch interval or a future dispatch interval. A user can
navigate between the past, present, and future dispatch intervals
by selecting arrows 2312-2314 located on the sides of each window.
For example, clicking arrow 2312 may cause dispatch schedule
interface 2300 to transition from displaying the snapshot for the
present dispatch interval 2310 to the snapshot for the past
dispatch interval. Similarly, clicking arrow 2314 may cause
dispatch schedule interface 2300 to transition from displaying the
snapshot for the present dispatch interval 2310 to the snapshot for
the future dispatch interval. Advantageously, this allows a user to
navigate between past, present, and future snapshots of detailed
dispatch information 2302 to view subplant loads and equipment
setpoints for a series of dispatch intervals.
[0199] Referring now to FIG. 23C, another dispatch schedule
interface 2350 which may be generated by dispatch GUI generator 506
is shown, according to an exemplary embodiment. Dispatch schedule
interface 2350 is shown displaying dispatch information for several
devices of equipment. In some embodiments, the equipment are
grouped into corresponding plants and/or loops in dispatch schedule
interface 2350. A user can expand or collapse various sections of
dispatch schedule interface 2350 to show or hide information for
equipment of each plant and/or loop. For each device of equipment,
dispatch schedule interface 2350 may indicate whether EOS 302 is on
or off, whether the device is out of service, and whether no
dispatch is provided for each time interval. These status
indications may be provided in the form of colored bars, where each
color represents a different status indication.
Dispatch Chart Interfaces
[0200] Referring now to FIGS. 24-28B, several dispatch chart
interfaces 2400-2850 which may be generated by dispatch GUI
generator 506 is shown, according to an exemplary embodiment.
Dispatch chart interfaces 2400-2850 show high-level energy flows
for past, present and future dispatch schedules in stacked bar
charts 2410, 2510, 2610, 2660, 2710, 2760, 2810, and 2860. The
lines 2402, 2502, 2602, 2652, 2702, 2752, 2802, and 2852 that span
vertically across each chart 2410, 2510, 2610, 2660, 2710, 2760,
2810, and 2860 represent the last dispatch time. When a new
dispatch schedule is generated, each chart 2410, 2510, 2610, 2660,
2710, 2760, 2810, and 2860 is updated and shifted to the left.
[0201] To hide or unhide specific data on each chart 2410, 2510,
2610, 2660, 2710, 2760, 2810, and 2860, a user can click the
checkboxes 2404, 2504, 2604, 2654, 2704, 2754, 2804, and 2854 in
the legends 2406, 2506, 2606, 2656, 2706, 2756, 2806, and 2856. To
focus on a specific area of data, a user can click a point on
charts 2410, 2510, 2610, 2660, 2710, 2760, 2810, and 2860 and drag
to the right. The chart 2410, 2510, 2610, 2660, 2710, 2760, 2810,
and 2860 then zooms in on the selected range. The user can continue
zooming in until the desired area of focus is reached. To pan, the
user can use chart controls 2408, 2508, 2608, 2658, 2708, 2758,
2808, and 2858 below each chart. To return to the original view,
the user can select Reset zoom. The user can point to any point of
data for more information about the date, time, and energy output
flows.
[0202] In some embodiments, dispatch chart interfaces 2400-2850
include some or all of the features described in U.S. patent
application Ser. No. 14/815,845 filed Jul. 31, 2015, and titled
"Systems and Methods for Visualizing Equipment Utilization in an
energy plant." The entire disclosure of U.S. patent application
Ser. No. 14/815,845 is incorporated by reference herein.
[0203] Referring particularly to FIG. 24, dispatch chart interface
2400 is shown displaying the chilled water production for Loop B.
The total chilled water production of Loop B consists of the
production 2412 of the Plant A, the production 2414 of Plant B, and
the production 2416 of Plant C. The chilled water production of
each plant at each time step is indicated by the height of the
corresponding stacked bar in stacked bar chart 2410. The total
height of the stacked bars at each time step indicate the total
chilled water production of Loop B at that time step.
[0204] Referring now to FIG. 25, dispatch chart interface 2500 is
shown displaying the chilled water production for Loop A. The total
chilled water production of Loop A consists of the production 2512
of Plant D, the production 2514 of Plant E, and the production 2516
of Plant F. The chilled water production of each plant at each time
step is indicated by the height of the corresponding stacked bar in
stacked bar chart 2510. The total height of the stacked bars at
each time step indicate the total chilled water production of Loop
A at that time step.
[0205] Referring now to FIG. 26A, dispatch chart interface 2600 is
shown displaying the chilled water production for Loop C. The total
chilled water production of Loop C consists of only the production
2612 of Plant G. The chilled water production of Plant G at each
time step is indicated by the height of the stacked bar in stacked
bar chart 2610, which is equal to the total chilled water
production of Loop A at that time step.
[0206] Referring now to FIG. 26B, dispatch chart interface 2650 is
shown displaying the production and storage of Loop C at each time
step. The total production of Loop C consists of the production
2662 of electricity and steam within Loop C and the amount of
electricity and steam discharged from storage within Loop C. Line
2664 indicates the load on Loop C. The difference between the total
production of Loop C and the load on Loop C signifies the unmet
load. The storage of Loop C consists of the amount of return water
stored within Loop C at each time step, designated by line
2666.
[0207] Referring now to FIG. 27A, dispatch chart interface 2700 is
shown displaying the total steam production and consumption for
energy plant 200. The total steam production of energy plant 200
consists of the production 2712 of the boilers of Plant B and the
production 2714 of the cogenerators of Plant B. The steam
production of each of the boilers and the cogenerators at each time
step is indicated by the height of the corresponding stacked bars
2712 and 2714 above the horizontal axis (i.e., KPPH=0) in stacked
bar chart 2710. The total height of the stacked bars 2712 and 2714
above the horizontal axis at each time step indicate the total
steam production at that time step.
[0208] The total steam consumption of energy plant 200 consists of
the consumption of Plant A, campus 1108, Plant F, Plant G, Plant B,
and Plant D. The steam consumption of each of the plants at each
time step is indicated by the height of the corresponding stacked
bars 2716, 2718, 2720, 2722, 2724, and 2726 below the horizontal
axis (i.e., KPPH=0) in the stacked bar chart. The total height of
the stacked bars 2716-2726 below the horizontal axis at each time
step indicate the total steam consumption at that time step.
[0209] Referring now to FIG. 27B, dispatch chart interface 2750 is
shown displaying the total steam production and consumption of
Plant B at each time step within a selected time period. The total
steam production of Plant B consists of the production 2762 of the
boilers of Plant B, the production 2764 of a first set of
cogenerators of Plant B, and the production 2766 of a second set of
cogenerators of Plant B. The steam production of each of the
boilers and the cogenerators at each time step is indicated by the
height of the corresponding stacked bars 2762-2766 above the
horizontal axis in stacked bar chart 2760. The total height of the
stacked bars 2762-2766 above the horizontal axis at each time step
indicate the total steam production at that time step. Line 2768
represents the total load on Plant B. The difference between the
total load on Plant B and the total production of Plant B
represents the unmet load.
[0210] The total steam consumption of Plant B consists of the
consumption 2770 of Building A, the consumption 2772 of Building B,
the consumption 2774 of Building C, the consumption 2776 of the
chillers within Plant B, and the consumption 2778 of Building D.
The steam consumption of each of the buildings and chillers at each
time step is indicated by the height of the corresponding stacked
bars 2770-2778 below the horizontal axis in stacked bar chart 2760.
The total height of the stacked bars 2770-2770 below the horizontal
axis at each time step indicate the total steam consumption at that
time step.
[0211] Referring now to FIG. 28A, dispatch chart interface 2800 is
shown displaying the total electricity production and consumption
for energy plant 200. The total electricity production of energy
plant 200 consists of the production 2812 of Plant B. The
electricity production at each time step is indicated by the height
of the corresponding stacked bar 2812 above the horizontal axis
(i.e., KPPH=0) in stacked bar chart 2810.
[0212] The total electricity consumption of energy plant 200
consists of the consumption 2814 of Plant A, the consumption 2816
of campus 1108, the consumption 2818 of Plant F, the consumption
2820 of Plant G, the consumption 2822 of Plant C, the consumption
2824 of Plant E, the consumption 2826 of the cooling equipment of
Plant B, the consumption 2828 of the electric equipment of Plant B,
the consumption 2830 of the steam equipment of Plant B, and the
consumption 2832 of Plant D. The electricity consumption of each of
the plants at each time step is indicated by the height of the
corresponding stacked bars 2814-2832 below the horizontal axis
(i.e., KPPH=0) in stacked bar chart 2810. The total height of
stacked bars 2814-2832 below the horizontal axis at each time step
indicate the total electricity consumption at that time step.
[0213] Referring now to FIG. 28B, dispatch chart interface 2850 is
shown displaying the total electricity production and consumption
of Plant B at each time step within a selected time period. The
total electricity production of Plant B consists of the production
2862 of a first set of cogenerators of Plant B, the production 2864
of a second set of cogenerators of Plant B, and amount 2866 of
electricity imported by Plant B. The electricity production of each
of the cogenerators and the amount of electricity imported at each
time step is indicated by the height of the corresponding stacked
bars 2862-2866 above the horizontal axis in stacked bar chart 2860.
The total height of the stacked bars 2862-2866 above the horizontal
axis at each time step indicate the total electricity production at
that time step. Line 2768 represents the total load on Plant B.
Line 2868 represents the total electric load on Plant B. The
difference between the total electric load on Plant B and the total
production of Plant B represents the unmet load.
[0214] The total electric consumption of Plant B consists of the
consumption 2870 of Building A, the consumption 2872 of Building B,
the consumption 2874 of Building C, the consumption 2876 of
Building D, the consumption 2878 of Building E, the consumption
2880 of the chillers within Plant B, the consumption 2882 of the
steam generators within Plant B, and the consumption 2884 of
Building F. The electric consumption of each of the buildings,
chillers, and steam generators at each time step is indicated by
the height of the corresponding stacked bars 2870-2884 below the
horizontal axis in stacked bar chart 2760. The total height of the
stacked bars 2870-2884 below the horizontal axis at each time step
indicate the total electric consumption at that time step.
Input Data Interfaces
[0215] Referring now to FIGS. 29-34, several input data interfaces
2900-3400 which may be generated by dispatch GUI generator 506 is
shown, according to an exemplary embodiment. In some embodiments,
input data interfaces 2900-3400 allow a user to manage inputs used
by high level optimizer 330. Such inputs can include, for example,
campus electric and thermal energy load forecasts, utility pricing
forecasts, weather forecasts, campus schedules, equipment
out-of-service schedules, and manual utility pricing. Manual inputs
can be maintained solely by the user, whereas forecast inputs give
the user the ability to override weather, pricing, loads, etc.
forecasted by EOS 302.
[0216] Referring particularly to FIG. 29, a weather forecast input
interface 2900 is shown, according to an exemplary embodiment. The
weather forecast inputs may predict the weather in the geographic
region in which the building or campus 1108 is located for each of
the plurality of time steps. Weather forecasts may include, for
example, cloud coverage, dry bulb temperature, wet bulb
temperature, humidity, precipitation, wind, or any other type of
weather forecast. In some embodiments, weather forecast input
interface 2900 includes a graph 2902, 2904, and 2906 for each of
the weather variables. Each graph 2902-2906 may illustrate the
predicted values for the corresponding weather variable over time.
Weather forecasts can be predicted for the duration of the
optimization period. In some embodiments, EOS 302 uses the weather
forecasts to predict the thermal and electric energy loads of
campus 1108.
[0217] Referring now to FIG. 30, a dry bulb temperature forecast
input interface 3000 is shown, according to an exemplary
embodiment. Dry bulb temperature forecast input interface 3000 is
shown to include a graph 3002 of the predicted dry bulb temperature
as a function of time. Interface 3000 is also shown to include a
table 3004 of the predicted dry bulb temperature values. Table 3004
may include a cell for each hour of the optimization period. For
example, table 3004 is shown to include a row for each day and a
column for each hour. The cells at the intersections of the rows
and columns contain the predicted dry bulb temperature values for
the corresponding days and hours.
[0218] Referring now to FIG. 31, an override interface 3100 is
shown, according to an exemplary embodiment. Override interface
3100 may be displayed in response to a user clicking override
button 3006 in forecast input interface 3000. In some embodiments,
a user can override the predicted values by selecting override
button 3006 and entering a manual value for the corresponding
variable. The manual override can be applied to a user-defined time
period beginning at a start time/date and ending at an end
time/date. The forecasted values can be changed by a user defined
offset (e.g., increased or decrease by a user-defined amount) or
set to a specific user-defined value. In some embodiments, override
interface 3100 prompts the user to specify a reason for the
override.
[0219] Referring now to FIG. 32, a utility pricing forecast input
interface 3200 is shown, according to an exemplary embodiment.
Utility pricing forecast input interface 3200 may include a
forecast of utility prices for each time step of the optimization
period. The forecasted utility prices can be defined for
electricity (e.g., $/kWh), natural gas ($/mmBtu), water ($/L), or
any other resource consumed by energy plant 200. In some
embodiments, utility pricing forecast input interface 3200 allows a
user to define one or more utility pricing rates by selecting add
rate button 3202 and specifying the rate 3204 and time period 3206
during which the rate 3202 applies. In some embodiments, EOS 302
uses the utility pricing forecasts to predict the cost associated
with resource purchases over the optimization period.
[0220] Referring now to FIG. 33, an equipment out-of-service
interface 3300 is shown, according to an exemplary embodiment.
Equipment out-of-service interface 3300 allows a user to define the
time periods during which each of the subplants and/or the pieces
of equipment within each subplant will be out-of-service. When a
subplant or piece equipment is out-of-service, EOS 302 may exclude
that subplant/equipment from the dispatches.
[0221] Referring now to FIG. 34, a campus schedule interface 3400
is shown, according to an exemplary embodiment. Campus schedule
interface 3400 allows a user to define the holiday and
out-of-season calendar 3402 for campus 1108. During holidays, the
occupancy of campus 1108 may be lower than usual, which can affect
the load forecast. In some embodiments, EOS 302 uses the campus
schedule to generate the load forecasts.
Reporting Interfaces
[0222] Referring now to FIGS. 35A-36B, several reporting interfaces
which may be generated by dispatch GUI generator 506 is shown,
according to an exemplary embodiment. FIG. 35A is a drawing of a
reporting landing page 3500. Reporting landing page 3500 shows high
level KPIs 3502-3510 to provide the most relevant performance data
to the user. For example, reporting landing page 3500 is shown to
include the total utility cost 3502, the peak demand 3504, the
performance 3506, the unmet chilled water load 3508, and the system
efficiency 3510. KPIs 3502-3510 can include the data for the
current week, day, month, or any other time period. In some
embodiments, KPIs 3502-3510 indicate a percent change relative to
the previous time period (e.g., 14% decrease this week, 9% increase
this week) for each of KPIs 3502-3510. In some embodiments,
reporting landing page 3500 allows a user to generate and/or view
detailed reports for various dispatch schedules, performance
monitoring, and a site summary.
[0223] FIG. 35B is a drawing of a performance monitoring interface
3550. Performance monitoring interface 3550 allows a user to
visualize various performance measurements over time. The user can
select a performance measurement via dropdown menu 3552 (e.g.,
consumption, production, etc.), a type of energy via dropdown 3554
(e.g., steam, electricity, hot water, cold water, etc.), and a type
of equipment via dropdown 3556. The user can specify a start date
3558 and an end date 3560 and click the "Generate Chart" button
3562 to create a chart 3570 or 3580 that plots the selected
performance metric for the selected energy type and equipment for
the specified date range. Each of the lines in chart 3570
represents the value of the steam consumption performance metric
for the equipment of campus 1108, Loop A 1102, Loop B 1104, or Loop
C 1106. Each of the lines in chart 3580 represents the value of the
electricity consumption performance metric for the equipment of
campus 108, Plant C, Plant B, or Loop B 1104.
[0224] Referring now to FIGS. 36A-36B, an example of a site summary
report 3600 which can be accessed via the reporting interfaces is
shown. Site summary report 3600 may provide information such as
utility cost; economic load demand response (ELDR) revenue;
production by site, loop, plant, or equipment; and consumption by
site, loop, plant, or equipment. Such information can be provided
for a user-defined time period.
Audit Log Interface
[0225] Referring now to FIG. 37, an audit log interface 3700 which
may be generated by dispatch GUI generator 506 is shown, according
to an exemplary embodiment. The audit log is a running list of user
edits and changes to operating mode used by EOS 302. User edits can
include, for example, overrides to forecasted inputs, manual input
entries, schedule changes, and other manual overrides. Changes to
the operating mode used by EOS 302 can include manually switching
among the advisory mode, the automatic mode, and the manual mode.
In some embodiments, each edit requires the user to log a reason
for their change. Each of these edits and the reason for the edit
can be displayed in the audit report.
Configuration of Exemplary Embodiments
[0226] The construction and arrangement of the systems and methods
as shown in the various exemplary embodiments are illustrative
only. Although only a few embodiments have been described in detail
in this disclosure, many modifications are possible (e.g.,
variations in sizes, dimensions, structures, shapes and proportions
of the various elements, values of parameters, mounting
arrangements, use of materials, colors, orientations, etc.). For
example, the position of elements may be reversed or otherwise
varied and the nature or number of discrete elements or positions
may be altered or varied. Accordingly, all such modifications are
intended to be included within the scope of the present disclosure.
The order or sequence of any process or method steps may be varied
or re-sequenced according to alternative embodiments. Other
substitutions, modifications, changes, and omissions may be made in
the design, operating conditions and arrangement of the exemplary
embodiments without departing from the scope of the present
disclosure.
[0227] The present disclosure contemplates methods, systems and
program products on any machine-readable media for accomplishing
various operations. The embodiments of the present disclosure may
be implemented using existing computer processors, or by a special
purpose computer processor for an appropriate system, incorporated
for this or another purpose, or by a hardwired system. Embodiments
within the scope of the present disclosure include program products
comprising machine-readable media for carrying or having
machine-executable instructions or data structures stored thereon.
Such machine-readable media can be any available media that can be
accessed by a general purpose or special purpose computer or other
machine with a processor. By way of example, such machine-readable
media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical
disk storage, magnetic disk storage or other magnetic storage
devices, or any other medium which can be used to carry or store
desired program code in the form of machine-executable instructions
or data structures and which can be accessed by a general purpose
or special purpose computer or other machine with a processor.
Combinations of the above are also included within the scope of
machine-readable media. Machine-executable instructions include,
for example, instructions and data which cause a general purpose
computer, special purpose computer, or special purpose processing
machines to perform a certain function or group of functions.
[0228] Although the figures show a specific order of method steps,
the order of the steps may differ from what is depicted. Also two
or more steps may be performed concurrently or with partial
concurrence. Such variation will depend on the software and
hardware systems chosen and on designer choice. All such variations
are within the scope of the disclosure. Likewise, software
implementations could be accomplished with standard programming
techniques with rule based logic and other logic to accomplish the
various connection steps, processing steps, comparison steps and
decision steps.
* * * * *