U.S. patent application number 10/863145 was filed with the patent office on 2004-12-30 for method of distributing energy for a building, energy management system, system for satisfying the energy requirements of a building, and micro-cogeneration system.
Invention is credited to Kramer, Robert A..
Application Number | 20040267408 10/863145 |
Document ID | / |
Family ID | 22841382 |
Filed Date | 2004-12-30 |
United States Patent
Application |
20040267408 |
Kind Code |
A1 |
Kramer, Robert A. |
December 30, 2004 |
Method of distributing energy for a building, energy management
system, system for satisfying the energy requirements of a
building, and micro-cogeneration system
Abstract
An energy management system is provided for managing the
generation and distribution of energy from an energy source to a
building. The building has a desired building environment and a
total energy profile including a thermal energy requirement and an
electrical energy requirement. The energy management system
comprises an energy generator arranged to convert energy from the
energy source to thermal energy and electrical energy, a heat
recovery unit arranged to recover byproduct heat from the energy
generator, a cooling unit arranged to use a first portion of the
thermal energy to drive a refrigeration unit, a heating unit
arranged to use a second portion of the thermal energy to drive a
heating unit, a heat storage unit arranged to store excess heat,
and an energy optimizing controller. The energy optimizing
controller includes a thermal flow controller and an electrical
flow controller, with the thermal flow controller being arranged to
distribute the thermal energy and the recovered byproduct heat to
at least one of the cooling unit, the heating unit, and the heat
storage unit. The electrical flow controller is arranged to
distribute electrical energy to at least one of a plurality of
electrical components, with the energy optimizing controller being
arranged to establish a target total energy cost, calculate an
index of performance indicative of an actual energy cost based on
an actual electrical load and an actual thermal load, compare the
actual energy cost to the target total energy cost, and adjust the
distribution of the thermal and electrical energy to thereby obtain
a minimum total cost.
Inventors: |
Kramer, Robert A.; (Crown
Point, IN) |
Correspondence
Address: |
MARSHALL, GERSTEIN & BORUN LLP
6300 SEARS TOWER
233 S. WACKER DRIVE
CHICAGO
IL
60606
US
|
Family ID: |
22841382 |
Appl. No.: |
10/863145 |
Filed: |
June 8, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10863145 |
Jun 8, 2004 |
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09928040 |
Aug 10, 2001 |
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6757591 |
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60224606 |
Aug 11, 2000 |
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Current U.S.
Class: |
700/291 ;
700/288 |
Current CPC
Class: |
F24D 19/1042 20130101;
Y02B 70/3225 20130101; H02J 2310/64 20200101; H02J 3/46 20130101;
H02J 3/383 20130101; Y04S 20/222 20130101; H02J 3/0075 20200101;
Y02B 10/10 20130101; H02J 2203/20 20200101; H02J 3/005 20130101;
F24D 19/1009 20130101; Y02E 10/56 20130101; H02J 3/00 20130101;
H02J 2300/24 20200101; H02J 3/004 20200101; H02J 3/381 20130101;
Y04S 50/10 20130101 |
Class at
Publication: |
700/291 ;
700/288 |
International
Class: |
G06F 019/00 |
Claims
What is claimed:
1. A method of distributing energy for a building, the method
comprising the steps of: A. generating energy including electrical
energy, generated heat, and byproduct heat from energy sources
selected from the group consisting of natural gas, solar energy
assemblies, and an electric grid; B. recovering at least a portion
of the byproduct heat; C. modeling a total energy profile for the
building, the total energy profile including a thermal energy
requirement and an electrical energy requirement; D. assessing a
total available heat stream, the total available heat stream
including a stream of the generated heat and a stream of the
byproduct heat; E. routing the electrical energy and the streams of
generated heat and byproduct heat to the building for use; F.
establishing a target total energy cost for a discrete time
interval; G. calculating an index of performance indicative of an
actual total energy cost for the discrete time interval; and H.
optimizing the usage of the electrical energy and the streams of
generated heat and byproduct heat over the discrete time interval
through an optimizing scheme to thereby maintain a desired building
environment at a minimum total energy cost, wherein the optimizing
scheme comprises the steps of: a. calculating a minimum electrical
energy requirement; b. setting an electrical energy output to meet
the calculated minimum electrical energy requirement; c. comparing
the total available heat stream to the thermal requirement for the
building; d. if the total available heat stream exceeds the thermal
requirement for the building, then one of: i) storing an excess
portion of the total available heat stream in a storage device; ii)
dumping the excess portion to the atmosphere; or iii) decreasing
the total available heat stream; e. if the total available heat
stream is less than the thermal requirement for the building, then
at least one of: i) extracting the stored excess portion from the
storage device; and ii) increasing the generated heat stream; and
f. optimizing the system by comparing the actual total energy cost
to the target total energy cost and adjusting the usage of the
electrical energy and the total available heat stream to thereby
provide the minimum actual total energy cost.
2. The method of claim 1, wherein the index of performance is
defined as a function of N independent variables within a given
region of N space or dimension as follows: IP=IP (x1, x2, . . . ,
xn), with the region being defined by .psi. (x1, x2, . . . ,
xn).ltoreq.0, and wherein the index of performance is calculated by
the following equation:IP=(x1+x2+x3+x4+x5)/.- xi.,wherein
.xi.=kilowatts of energy (electric and heat), and further wherein:
x1=an actual cost of natural gas over the discrete time interval;
x2=an actual cost of grid supplied electrical energy over the
discrete time interval; x3=an actual cost of solar energy assembly
supplied electrical energy over the discrete time interval; x4=an
actual cost of solar energy supplied heat energy over the discrete
time interval; x5=an actual cost of grid supplied electrical backup
energy; and wherein x1+x2+x3+x4+x5.ltoreq.non-combined heat, power,
gas and electrical energy cost.
3. The method of claim 2, wherein the energy is generated using at
least one of a microturbine or a fuel cell assembly, and wherein
the index of performance is further calculated by considering the
following variables and constraints: x6=turbine efficiency; x7=fuel
cell efficiency; x8=turbine/fuel cell heat recovery efficiency;
x9=solar heating or cooling efficiency; x10=heat storage cost;
x11=heat storage capacity; x12=heat storage time constant;
x13=solar heat incident on building; x14=heat needed by building to
maintain desired temperature; x15=desired building temperature for
occupied regions; x16=electrical energy usage, including
alternating current usage and direct current usage; x17=reliability
of the grid-supplied electrical energy; x18=reliability of the
total available heat stream; x19=power quality service cost,
x20=actual building thermal energy usage; x21=actual building
electrical usage; x22=fraction of the electrical energy requirement
supplied by the microturbine and the fuel cell; x23=fraction of the
electrical energy requirement supplied by the electric grid;
x24=fraction of the electrical energy requirement supplied in the
form of direct current; x25=fraction of byproduct heat used to
satisfy a portion of the thermal energy requirement for heating the
building; x26=fraction of byproduct heat used for dehumidification
of the heat storage device; x27=fraction of byproduct heat used to
satisfy a portion of the thermal energy requirement for cooling the
building; x28=temperature of the heat storage device; x29=the heat
storage capacity for a first set of designated regions of the
building; and x30=maximum .DELTA. temperature of the first set of
designated regions of the building; wherein heat storage minimum
temperature.ltoreq.x28.ltoreq.heat storage maximum temperature;
wherein minimum temperature.ltoreq.x31.ltoreq.maximum temperature,
and further wherein: x24.ltoreq.1; x25.ltoreq.1; x26.ltoreq.1;
x27.ltoreq.1; x30.ltoreq.a predetermined level; and wherein a
minimum solar input.ltoreq.x13.ltoreq.a maximum solar input; and
wherein x10.ltoreq.direct heat production cost, x11.ltoreq.maximum
allowable physical size, x12.gtoreq.minimum possible value.
4. The method of claim 1, wherein the index of performance is
defined as a function of N fuzzy variables within a given region of
N space as follows: IP=IP (x1, x2, . . . , xn), with the region
being defined by .psi. (x1, x2, . . . , xn).ltoreq.0, and wherein
the index of performance is calculated by the following
equation:IP=(x1+x2+x3+x4+x5)/.xi.,wherein .xi.=kilowatts of energy
(electric and heat), and further wherein: x1=an actual cost of
natural gas over the discrete time interval; x2=an actual cost of
grid supplied electrical energy over the discrete time interval;
x3=an actual cost of solar energy assembly supplied electrical
energy over the discrete time interval; x4=an actual cost of solar
energy supplied heat energy over the discrete time interval; x5=an
actual cost of grid supplied electrical backup energy; wherein
x1+x2+x3+x4+x5.ltoreq.non-combined heat, power, gas and electrical
energy cost; and including the additional steps of: determining a
degree of membership in each of at least two of the fuzzy
variables; mapping the at least two degrees of membership onto a
control surface adapted to optimize the energy usage; and assigning
a priority to at least one of the variables in response to the
control surface mapping.
5. The method of claim 1, wherein the group of energy sources
further includes at least one of refuse-generated methane gas and
biomass-generated methane gas.
6. The method of claim 1, wherein the storage device is a fast
response thermal storage unit.
7. The method of claim 1, wherein the electrical energy requirement
includes a direct current requirement and an alternating current
requirement, and calculating an optimal fraction of at least one of
the direct or alternating current requirements relative to the
electrical energy requirement.
8. The method of claim 1, wherein the step of modeling the total
energy profile for the building includes the steps of assessing an
occupancy pattern of the building and a usage pattern of the
building.
9. The method of claim 1, wherein the step of modeling the total
energy profile for the building includes the step of calculating
the effect of environmental factors on the building.
10. The method of claim 1, including the step of using a
dehumidification system to alter the total energy profile.
11. The method of claim 10, wherein the dehumidification system is
a dessicant dehumidification system.
12. The method of claim 11, including the step of using a
fractional portion of the byproduct heat to recondition the
dessicant dehumidification system.
13. The method of claim 1, including routing the total available
heat stream to the building using at least one of a heat exchanger
or an absorption cooling device.
14. The method of claim 1, wherein the byproduct heat is recovered
using a heat exchanger.
15. The method of claim 1, wherein the byproduct heat is dumped
using a heat exchanger.
16. The method of claim 1, wherein the byproduct heat is dumped to
the atmosphere using a bypass valve.
17. An energy management system for managing the generation and
distribution of energy from an energy source to a building, the
building having a desired building environment and a total energy
profile including a thermal energy requirement and an electrical
energy requirement, the energy management system comprising: an
energy generator arranged to convert energy from the energy source
to thermal energy and electrical energy; a heat recovery unit
arranged to recover byproduct heat from the energy generator; a
cooling unit arranged to use a first portion of the thermal energy
to drive a refrigeration unit; a heating unit arranged to use a
second portion of the thermal energy to drive a heating unit; a
heat storage unit arranged to store excess heat; and an energy
optimizing controller, the energy optimizing controller including a
thermal flow controller and an electrical flow controller, the
thermal flow controller being arranged to distribute the thermal
energy and the recovered byproduct heat to at least one of the
cooling unit, the heating unit, and the heat storage unit, the
electrical flow controller being arranged to distribute electrical
energy to at least one of a plurality of electrical components, the
energy optimizing controller being arranged to establish a target
total energy cost, calculate an index of performance indicative of
an actual energy cost based on an actual electrical load and an
actual thermal load, compare the actual energy cost to the target
total energy cost, and adjust the distribution of the thermal and
electrical energy to thereby obtain a minimum total cost.
18. The system of claim 17, including a dessicant dehumidification
system to remove moisture from the building.
19. The system of claim 17, wherein the heating unit includes a
heating heat exchanger.
20. The system of claim 17, wherein the cooling unit includes a
cooling heat exchanger.
21. The system of claim 17, including a bypass valve to dump an
excess portion of the thermal energy to the atmosphere.
22. The system of claim 17, wherein the energy generator can be
selected from the group consisting of a micro turbine, a fuel cell,
an electric grid, and solar cells.
23. The system of claim 17, including a power converter for
converting a portion of the electrical energy to at least one of
direct current or alternating current, and further wherein the
energy optimizing controller also includes a converter controller
for controlling the power converter.
24. A system for satisfying the energy requirements of a building
comprising the steps of: generating electrical energy and byproduct
heat from one or more energy sources selected from the group
consisting of natural gas, solar heating assemblies, and an
electric grid; determining the available amount of electrical
energy; determining the available amount of byproduct heat;
determining the energy requirements of the building, the energy
requirements of the building including building heating
requirements and building electrical energy requirements; using the
electrical energy and the byproduct heat to meet the energy
requirements of the building; and optimizing the using of the
electrical energy and the byproduct heat through an optimizing
scheme to minimize a total energy cost, wherein the optimizing
scheme comprises the steps of: establishing a target total energy
cost; calculating an index of performance indicative of an actual
energy cost; comparing the actual energy cost to the target total
energy cost; and adjusting the usage of the electrical energy and
the byproduct heat.
25. A micro-cogeneration system comprising: a gas fired
micro-turbine for providing electrical and thermal energy; means
for recovering byproduct heat from the micro-turbine; a heat
exchanger system for converting a first portion of the thermal
energy into useable heat; an absorption cooler system for
converting a second portion of the thermal energy into useable
energy to drive a refrigeration cycle; and means for distributing
the electrical and thermal energy to a building for use.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This is a division of U.S. patent application Ser. No.
09/928,040 filed Aug. 10, 2001, which claims the priority benefit
of U.S. provisional application Ser. No. 60/224,606 filed Aug. 11,
2000, the respective disclosures of which are incorporated herein
by reference in their entirety for all purposes.
TECHNICAL FIELD
[0002] The disclosure relates generally to distributed generation
energy systems and, more specifically, to a distributed generation
energy system having an energy management system that optimizes and
controls the generation and usage of energy to maintain a desired
building environment at minimum cost.
BACKGROUND
[0003] Conventional power generation is typically based on a
process in which fuel is burned as a source of energy to turn a
turbine. For example, in the case of a base load power plant, coal
is burned to obtain heat, the heat is converted to steam, and steam
pressure is used to turn one or more turbines. The turbines in turn
generate electrical power. Unfortunately, conventional power
generation is an inherently inefficient process, with much of the
total energy value of the fuel source being lost in the form of
wasted heat. Typically, only a small fraction of the total energy
value of the fuel is extracted from the fuel and ultimately useable
as electrical power.
[0004] Cogeneration processes have been developed in order to
increase the overall efficiency of the power generation process. In
contrast to conventional power generation, cogeneration makes use
of at least some of the waste heat, usually by extracting a portion
of the waste heat from the low-temperature steam exhausted from the
turbines. The recovered heat may be used directly in the form of a
hot air stream. Alternatively, the recovered heat may be used to
create additional steam, which is then used to generate still more
electrical power.
[0005] Other processes have been developed in the continuing
attempts to maximize the overall efficiency of power generation and
energy extraction. For example, natural gas is favored as a fuel
source because it is clean burning with low emissions. Thus,
gas-fired turbines are often used to generate electricity. In some
cogeneration power plants, these clean-burning gas-fired turbines
are coupled with heat exchangers which extract heat from the stream
of hot exhaust gases. The exhaust gases are not released until a
maximum amount of thermal energy has been recovered. These
gas-fired power plants, which are often manufactured as
independent, stand alone units, are therefore more efficient
producers of electricity, hot water, or steam, and have the added
benefit of producing negligible emissions.
[0006] Although cogeneration systems offer increased efficiency
relative to more conventional power plants, further increases in
overall energy efficiency are desirable.
SUMMARY
[0007] In accordance with an aspect of the disclosure, an energy
management system is provided for managing the generation and
distribution of energy from an energy source to a building. The
building has a desired building environment and a total energy
profile including a thermal energy requirement and an electrical
energy requirement. The energy management system includes an energy
generator arranged to convert energy from the energy source to
thermal energy and electrical energy, a heat recovery unit arranged
to recover byproduct heat from the energy generator, a cooling unit
and/or a dessicant unit arranged to use a first portion of the
thermal energy to provide cooling and/or dehumidification, a
heating unit arranged to use a second portion of the thermal energy
to drive a heating unit, a heat storage unit arranged to store
excess heat, and an energy optimizing controller. The energy
optimizing controller includes a thermal flow controller and an
electrical flow controller, with the thermal flow controller being
arranged to distribute the thermal energy and the recovered
byproduct heat to at least one of the cooling unit, the heating
unit, and the heat storage unit. The electrical flow controller is
arranged to distribute electrical energy to at least one of a
plurality of electrical components, with the energy optimizing
controller being arranged to establish a target total energy cost,
calculate an index of performance indicative of an actual energy
cost based on an actual electrical load and an actual thermal load,
compare the actual energy cost to the target total energy cost, and
adjust the distribution of the thermal and electrical energy to
thereby obtain a minimum total cost.
[0008] In further accordance with a preferred embodiment, a
thermally driven dessicant dehumidification system may be employed
in order to reduce the humidity within the building, which thus
lowers the amount of energy required for cooling purposes. Further,
the electrical flow controller may be arranged to distribute
electrical energy in the form of alternating current, direct
current, or an optimized combination of alternating current and
direct current.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a schematic diagram of an energy management system
constructed in accordance with the teachings of the disclosure;
[0010] FIG. 2 is a schematic diagram of an optimization scheme in
accordance with the teachings of the disclosure for use with the
energy management system of FIG. 1;
[0011] FIG. 3 is a fragmentary cross-sectional schematic view of a
dessicant wheel disposed in a duct system;
[0012] FIG. 4 is a schematic diagram of a preferred embodiment of
the energy management system in accordance with the teachings of
the disclosure;
[0013] FIG. 5 is a schematic diagram of another preferred
embodiment of the energy management system in accordance with the
teachings of the disclosure;
[0014] FIG. 6 is an exemplary fuzzy logic control surface;
[0015] FIG. 7 is an exemplary fuzzy logic rule matrix used to
construct the control surface of FIG. 6;
[0016] FIG. 8 is an exemplary interpolation map used in conjunction
with the rule matrix of FIG. 7 to construct the control surface of
FIG. 6;
[0017] FIG. 9 is an exemplary triangular membership function;
[0018] FIG. 10 is another exemplary triangular membership
function;
[0019] FIG. 11 is another exemplary triangular membership function;
and
[0020] FIG. 12 is another exemplary triangular membership
function.
DETAILED DESCRIPTION
[0021] The following description of the disclosed embodiments is
not intended to limit the scope of the invention to the precise
form or forms detailed herein. Instead, the following description
is intended to be illustrative of the principles of the invention
so that others may follow its teachings.
[0022] Referring to FIG. 1, an energy management system, generally
designated 10, in accordance with the teachings of the disclosure,
includes one or more energy sources, such as, by way of example
rather than limitation, a solar energy source 14 and a natural gas
energy source 16. The energy management system 10 also includes a
connection to an electric grid 18. The energy management system 10,
along with the electric grid 18, are configured to meet the
heating, cooling, and electrical needs of a building 20.
[0023] The solar energy source 14 includes solar cells 22 that
produce thermal energy 24 and/or electrical energy 26. The solar
energy source 14 may also include a solar heat assembly 28 that
produces thermal energy 30. The thermal energy 24 and 30 are routed
to heating and cooling equipment 32. The electrical energy 26 is
routed to electric control/conversion equipment 34.
[0024] The natural gas energy source 16 includes a micro turbine 36
(or a number of micro turbines 36) and/or a system of fuel cells
38. A suitable micro turbine 36 may be obtained from Capstone
Turbine Corporation of Chatsworth, Calif. Both 30 kw and 60 kw
micro turbines have been tested. Other sizes may be chosen based
upon the overall power requirements of the building 20.
Alternatively, one or more reciprocating engines may be used in
place of the micro turbine 36. The micro turbines 36 produce
thermal energy 40 that is routed to the heating and cooling
equipment 32, and electrical energy 42 that is routed to the
electric control/conversion equipment 34. A conventional thermal
heat producer, such as a gas furnace 44, may also be provided to
produce a thermal energy stream 46. Further, the fuel cells 38
produce thermal energy 48 and electrical energy 50. The thermal
energy 48 from the fuel cells 38 is routed to the heating and
cooling equipment 32, while the electrical energy 50 from the fuel
cells 38 is routed to the electric control/conversion equipment 34.
The electric grid connection 18 supplies additional electrical
energy 52 that is routed to or from the electric control/conversion
equipment 34.
[0025] The energy management system 10 includes an optimization
system 54, which optimizes the generation and usage of the
electrical and/or thermal energy from all of the available sources.
The optimization system 54 optimizes the usage of electrical energy
from the electric control/conversion equipment 34, and optimizes
the usage of the thermal energy from the heating and cooling
equipment 32, thus optimizing the overall energy usage. The
optimization process is explained in greater detail below.
[0026] In the event there is a surplus of available electrical
energy, the excess electrical energy can be stored within a
flywheel 56. As an alternative, excess electrical energy can be
sold back to the electric grid 18 and/or stored in a conventional
cell or battery. The available thermal energy within the heating
and cooling equipment 32 is then assessed and the total energy cost
is optimized. The thermal requirements for heating and/or cooling
the building 20 for a desired building environment are then
calculated based upon the occupancy and usage patterns of the
building. The difference between the thermal output (available
thermal energy) and the thermal requirements for the desired
building environment is then calculated. If the thermal output of
the heating and cooling equipment 32 is in excess of the thermal
requirements for the building 20, the excess thermal energy may be
stored in a heating and cooling storage unit 58 in a conventional
manner. If there is insufficient thermal output from the heating
and cooling equipment 32 to meet the thermal requirements of the
building 20, additional thermal energy may extracted from the
heating and cooling storage unit 58. The heating and cooling
storage unit 58 may take a variety of forms. For example, the
heating and cooling storage unit 58 may be a eutectic salt system
which stores thermal energy as the eutectic salt changes phases
between a liquid and a solid. Other forms of thermal storage
battery systems are known.
[0027] Referring now to FIG. 2, the optimization system 54 from
FIG. 1 is described in more detail. The optimization system 54
continually optimizes the usage of energy and byproduct heat from
the various energy sources in order to maintain a desired building
environment at a minimum cost. This is shown by an energy balance
and comparison step at 56. The electric demand for the building is
calculated at 58. Local electrical energy sources 60, such as the
solar energy source 14 and the micro turbine 36, provide an
electric output 61 that is set to meet a calculated minimum cost of
electric energy. The electrical energy utilization is controlled by
a subsystem that optimizes its value with respect to the local
energy usage, the generation of electrical energy from the local
electrical energy source 60, and electrical energy from the
electric grid 18 provided to the building. This subsystem also
provides a combination of alternating current and direct current 62
for various applications, as it may be advantageous to utilize at
least a portion of the generated electrical energy in the form of
direct current rather than converting to alternating current. The
subsystem may also optimize the portion of the electrical energy
used as direct current.
[0028] The various heat sources 64 within the building are then
considered, such as the thermal output from the micro turbine(s)
36, fuel cells 38, the thermal output of the solar energy source
14, and/or any auxiliary furnaces that might be present in the
building. The thermal usage patterns of the building (which are
typically based on occupancy patterns) are then observed at 66, and
a thermal requirement for heating and/or cooling of the building to
maintain a desired building environment is calculated at 68. The
difference between the thermal output of the heat sources 64 and
the thermal requirement for the desired building environment 68 is
then calculated. If there is excess thermal output, the excess
thermal output is preferably allocated to fast response thermal
storage at 70, 72. If a maximum temperature is reached within the
fast response thermal storage unit 70, 72, heat may be dumped to
the atmosphere at 74 through a bypass valve, the generation of heat
may be decreased, or the heat may be cooled by means of an
absorption cooling device 76, thereby decreasing the need for
electric air conditioning (shown at 78). In addition, if the level
of humidity within the building needs to be decreased, the heat may
be routed to a dessicant dehumidification device 80.
[0029] As shown schematically in FIG. 3, the dessicant
dehumidification device 80 includes a dessicant wheel 80a (shown in
cross section in FIG. 3) which rotates about a central axis 80b. A
lower duct 82a routes warm, humid air past the lower portion of the
dessicant wheel 80, while an upper duct 82b routes heated air, such
as waste heat, past an upper portion of the dessicant wheel 80. The
dessicant wheel 80 absorbs humidity in the lower duct 82a, thus
saturating that portion of the dessicant wheel 80. The dry air
leaving the dessicant wheel 80 is returned to the building at A. As
the dessicant wheel 80 rotates about its axis 80b, the saturated
portion of the wheel travels to the upper duct 82b, where the
saturated portion is exposed to the hot air. The hot air
regenerates the dessicant, and the humid air is exhausted at B.
[0030] Because approximately half or more of the air conditioning
in a typical heating, ventilating, and air conditioning (HVAC)
application is used for dehumidification, the use of a dessicant
dehumidification system 80 thereby decreases the need for electric
air conditioning (shown at 84 in FIG. 2). The choice is made based
upon a calculation of the optimal cost based on a comparison
between the cost of generating additional heat for use in the
dehumidification system 80, and the cost of using additional
electric air conditioning 84. If the calculation for the difference
between the thermal output of the heat sources 64 and the thermal
requirement for the desired building environment 68 results in an
insufficient amount of heat to meet the thermal requirements for
the desired building environment, heat is preferably extracted from
the fast response thermal storage unit 70, 72 or an auxiliary
furnace (not shown), based upon a cost optimization of the value of
the energy.
[0031] FIG. 4 illustrates a distributed generation system 100
assembled in accordance with the teachings of another disclosed
embodiment. A micro turbine 102 produces thermal energy, which is
recovered by a heat recovery unit 104, and also produces electrical
energy. A connection to an electric grid 106 provides additional
electrical energy. The amount of electrical energy needed for a
building 108 is set to meet a minimum cost of energy at 110. An
energy flow control and optimization unit 112 controls and
optimizes the required amounts of electrical energy at 114 and the
required amounts of thermal energy for the building 108 by
monitoring the amounts of each form of energy that is used or
needed. A flow control device 116 then routes the thermal energy
and/or electrical energy to various portions of the building 108 as
needed based upon the occupancy and usage patterns of the building
108. If there is excess thermal energy, it can be stored in a heat
storage unit 118 or, if the heat is not needed the heat may be
dumped into the atmosphere, through a bypass valve 120. The excess
thermal energy may take the form of a thermal stream exiting the
micro turbine 102. Should the excess thermal energy be needed (for
example, in the form of hot air or to recharge a dessicant
dehumidification system 122), the thermal energy can be routed to
the appropriate component. If there is a sufficient amount of
thermal energy that needs to be allocated to the building, the
thermal energy can be converted into heat through use of a heat
exchanger 124.
[0032] Depending on the building temperature monitoring that occurs
at 126, the thermal energy might need to be converted into cooler
air rather than heat. In this case the thermal energy can be sent
to an absorption cooling unit 128 and a cooling heat exchanger 130.
The resulting heated or cooled air is then directed to a building
air handler 132, which thereby directs the heated or cooled air to
various portions of the building 108.
[0033] Another preferred embodiment is designated generally 150 in
FIG. 5. The system 150 optimizes the usage of energy and heat from
various energy sources to maintain a desired building environment
at minimum cost. This is shown by an energy balance and comparison
step at 152. The electric demand for the building is calculated and
an electric output 154 is set to meet a calculated minimum cost of
electric energy. Various heat sources 156 within the building are
then assessed to determine the amount of thermal energy available.
The heat sources may include micro turbines, fuel cells, solar
heating sources, and/or any auxiliary furnaces that might be
present in the building. The thermal usage patterns of the building
are then observed at 158, and a thermal requirement for heating
and/or cooling of the building to maintain a desired building
environment is calculated. The difference between the thermal
output of the heat sources 156 and the thermal requirement for the
desired building environment is then calculated. If there is excess
thermal output, the excess thermal output is preferably allocated
to fast response thermal storage at 160. If a maximum temperature
is reached within the fast response thermal storage unit 160,
either heat is dumped to the atmosphere or the generation of heat
is decreased based upon an optimal calculation of cost for either
action. Additionally, or alternatively, heat may be stored in
regions of the building. If the calculation for the difference
between the thermal output of the heat sources 156 and the thermal
requirement for the desired building environment results in an
insufficient amount of heat to meet the thermal requirements for
the desired building environment, heat is preferably extracted from
the fast response thermal storage unit 160 or an auxiliary furnace
(not shown), based upon a cost optimization of the value of the
energy.
[0034] Distributed combined heat and power systems assembled in
accordance with the disclosed embodiments offer further increases
in overall fuel efficiency in conjunction with offering increased
service reliability and lower costs to the consumer. In a
distributed combined heat and power system, a stand alone power
generation system provides electrical energy and thermal energy to
a building. The power generation system may generate electrical and
thermal energy from a number of sources, including, for example,
natural gas, waste gas such as refuse generated methane gas, solar
energy, and other sources.
[0035] In a distributed combined heat and power system, the usage
of the combined thermal and electrical energy from the variety of
energy sources is controlled and optimized so as to achieve a
minimum overall energy cost. In an optimized distributed combined
heat and power system, an entire building may be modeled as a
system that can be controlled, regulated and interconnected with
the external electric grid. The system may have the capacity to
generate electrical energy in a number of ways, including from
sources such as solar energy assemblies, micro turbines, fuel
cells, reciprocating engines, flywheels, or other electricity
storage devices.
[0036] The system may also have the capacity to capture and use
thermal energy form a variety of sources, including streams of
recovered byproduct heat from micro turbines and fuel cell
reformers. The system may also obtain solar heat directly via
active and passive solar heat assemblies and may extract heat from
the surrounding atmosphere. The system may also achieve additional
efficiencies by optimizing the use of a building's inherent heat
storage capacity, and/or by using a fast response thermal storage
unit based upon eutectic salt or thermal mass storage technology.
Thus, it is possible to reduce the total energy cost for the chosen
building application while simultaneously increasing the
reliability and quality of the supplied energy streams.
[0037] By utilizing energy sources in a localized fashion, (i.e.,
at the building), which may be used in conjunction with the
conventional electric grid, the inventive system may provide
electricity and/or thermal energy of specifiable reliability and
quality. Additional efficiencies may be gained by recovering a
maximum amount of normally wasted heat energy. Further, by
controlling the flow and usage of energy, both thermal and
electrical, within the building, the overall efficiency of the
energy usage is thus further increased, thus reducing the overall
cost.
[0038] Preferably, the system includes a series of subsystems that
optimize the energy usage at the building location. By including
building considerations such as building electrical and thermal
load patterns and other building energy demand considerations and
variables as part of the overall energy usage profile, the system
may achieve increased efficiency of between 75% to 80% compared to
other systems. As a result, many building systems such as HVAC
systems and various electrical systems can be downsized, again
reducing cost.
[0039] Widespread implementation of such improved and more
efficient distributed combined heat and power systems may save
ratepayers great amounts in energy costs, and may on an annual
basis, greatly reduce carbon dioxide, sulfur dioxide, and nitrogen
oxide emissions.
[0040] An integrated optimizing control system optimizes the usage
of the energy sources and optimizes the usage of the generated
energy (i.e., both thermal and electrical energy) within a building
resulting in decreased net energy costs, increased reliability, and
increased power quality. On a continuous basis, the system
evaluates the price of the electric grid, the price of locally
generated electricity, and the cost and the value of heat from the
various available sources. The system then continually adjusts the
usage to achieve an optimal cost. The system also provides an
environmentally friendly solution for energy management because of
the use of waste heat, and further provides the end user with the
ability to customize the desired power quality and reliability
options.
[0041] The system utilizes a building's inherent heat storage
capacity and other available energy sources to reduce the total
energy cost for the building while at the same time increasing the
reliability and quality of the supplied energy streams.
[0042] Solar energy assemblies in the form of solar cells and/or
active or passive solar heat assemblies may also be provided in
order to provide electrical and thermal energy. Passive solar
energy may be configured into the basic design of the building such
that there is an optimization of this thermal energy source as well
as a consideration of the amount of heat storage within the
building. Preferably, a fast response thermal storage system is
employed, which allows for the rapid supply of thermal energy
during changing environmental and economic conditions of the
building.
[0043] Further, artificial intelligence systems based on fuzzy
logic and neural networks may be used to further optimize and
control the system. A neural network is an information processing
system that may include an array of highly interconnected processor
nodes that have a similarity to living neurons. These artificial
neurons are arranged in interconnected layers. The network is
programmed, in the simplest case, by comparing actual output values
with desired output values and adjusting the weighting of the
interconnections between the layers to produce the desired output
values for one or more sets of training input values.
[0044] Neural networks may be used for two purposes. First, the
networks may be used as part of a feed forward predictive system.
This system will allow for characterization of the building energy
system and its subsequent optimization. Second, the networks may be
used to predict building occupancy patterns based on regional
energy usage, and may change the energy content of these regions by
changing either the sensible or possibly a portion of the latent
heat of the particular region.
[0045] The neural networks preferably divide the building into
zones and define usage patterns of each of the zones within the
building. Then, low usage portions of the building may be used to
store heat for use by high usage portions of the building. These
occupancy patterns shift throughout the day, and the system adapts
dynamically to these patterns with the intent of minimizing the net
thermal energy use of the building. Such a predictive scheme will
allow the building itself to become an energy storage system. Such
energy storage will reduce the total energy costs by shifting
energy from where it is not needed to regions of the building where
it can be of most value. This will allow for optimization of the
building energy system and will provide information for potential
reductions in the HVAC capital expenditures anticipated for smaller
buildings. The fast response thermal storage option will greatly
enhance the value of the waste heat and overall efficiency of the
energy system.
[0046] Natural gas energy sources include distributed generation
sources of heat or electricity such as micro turbines,
reciprocating engines, fuel cells, and/or conventional HVAC
components that are driven by natural gas. For the distributed
generation options, electricity and heat may be produced by micro
turbines, reciprocating engines, fuel cells, and/or other
electricity producing devices.
[0047] The heat produced as a byproduct of the electric generation
process may be recovered by means of a system that heats a flowing
heat transfer medium, such as propylene glycol. Additional heat may
also be recovered from passive solar heating assemblies in the
building. This heat is then utilized for direct heating, cooling by
means of absorption cooling devices, and/or stored for later
use.
[0048] Electricity and heat may be produced by one or more of a
micro turbine(s), a reciprocating engine(s), and/or fuel cells.
Renewable resources including photoelectric generation, active
solar heating and lighting, and passive solar heating and lighting
may also provide energy. By actively integrating the produced
electricity with thermal energy sources and fast response thermal
storage, it is possible to provide multiple levels of power quality
and reliability as well as reduced overall energy costs.
[0049] The electrical energy utilization is controlled by a
subsystem that optimizes its value with respect to the local energy
usage as well as competitive electric energy sources such as energy
from the electric grid of the building. This subsystem may also
provide a combination of alternating current and direct current for
various applications contingent upon optimal usage profiles. One of
the benefits of the system described herein is the ability to use
direct current (DC) electricity directly, rather than converting
the electricity into alternating current (AC). This provides a
savings in electricity because normally a loss results when direct
current is converted into alternating current using a conventional
inverter such as an oscillator or chopper followed by a step
transformer. By using the direct current directly, efficiency is
increased. This also greatly enhances the reliability of the system
since the inverter is typically a point of higher failure rate for
those systems that contain standby or uninterruptible power
supplies. The direct use of direct current also provides a cost
savings because DC to AC conversion equipment is not needed.
[0050] The optimization system optimizes the usage of the energy
and byproduct heat from the various energy sources to maintain a
desired building environment at minimum cost. This optimization
system first considers the electric demand for the building and
sets an electric output to meet a calculated minimum cost of
electric energy. Next, the various heat streams are considered,
such as the thermal output from micro turbines and/or fuel cells,
solar heating sources, or any auxiliary furnaces that might be
present in the building. The occupancy and usage patterns of the
building are then observed, and a thermal requirement for heating
and/or cooling of the building is calculated. This thermal
requirement is based upon a model of the building at various
weather conditions and the geographic orientation of the building.
The difference between the thermal output of the heat streams and
the thermal requirement for the desired building environment is
then calculated. If there is an excess thermal output, the excess
thermal output is preferably allocated to fast response or other
appropriate thermal storage until a maximum temperature is reached.
If the maximum temperature is reached within the appropriate
thermal storage unit, excess heat may be dumped to the atmosphere,
or the generation of heat may be decreased based on an optimal
calculation of cost for either action. If the calculation for the
difference between the thermal output of the heat streams and the
thermal requirement for the desired building environment results in
an insufficient amount of heat to meet the heating requirements of
the building, heat may be extracted from the fast response thermal
storage or an auxiliary furnace, based upon a cost optimization of
the value of the energy. The calculation of the difference between
the thermal output of the heat streams and the thermal requirement
for the desired building environment is crucial, because the
thermal or time constant limits of the thermal storage cannot be
exceeded.
[0051] Two approaches to the time response of the thermal storage
can be used. The first approach establishes a dead band above and
below the acceptable operating limits. The second approach
continually estimates the time constant associated with the
particular operating point. Both approaches or methods may be used
within the optimizing system described herein and can be selected
by user or system preference dependent upon operating
conditions.
[0052] At this level the building itself is considered a thermal
storage entity. A feed forward, fuzzy logic, and or neural network
system may be utilized to learn the occupancy and usage patterns of
the building and allocate thermal energy in the building as part of
the cost optimization system. Various active building energy
schemes, such as physical space reconfiguration (movable walls,
lighting schemes and water walls) may be included. This level of
optimization then may be included in an overall cost optimization
system that considers external grid power as part of the
optimization system. The index of performance for this calculation
is the total energy cost (e. g., the total fuel cost and the total
electrical energy cost), divided by the amount of useable energy
(e.g., kilowatts of thermal and electrical energy).
[0053] To allocate thermal energy to the building appropriately,
the particular characteristics for each energy source that provide
the best economic and reliability value are preferably
investigated. Each of these individual aspects may then become a
degree of freedom for formulation of a global index of performance.
This global index may then be optimized given the constraints
within the physical and environmental aspects of the building. By
doing this, the individual energy sources may be controlled in a
manner that produces the maximum economic value within the
constraints for the economics, reliability, power quality and
environmental considerations.
[0054] In the preferred embodiment, the index of performance is
defined as the cost per kilowatt of energy for a specific instant
in time, where one kilowatt equals both electrical energy and
thermal energy:
IP=(x1+x2+x3+x4+x5)/.xi.,
[0055] wherein .xi.=kilowatts of energy (electric and heat);
[0056] wherein
[0057] x1=natural gas cost;
[0058] x2=grid electric cost;
[0059] x3=solar electric cost;
[0060] x4=solar heat cost;
[0061] x5=grid electric backup cost;
[0062] wherein x1+x2+x3+x4+x5.ltoreq.non combined heat and power
gas and electric energy cost.
[0063] There are also numerous variables that are considered in the
calculations of the index of performance, and they are as
follows:
[0064] x6=turbine efficiency;
[0065] x7=fuel cell efficiency;
[0066] x8=turbine/fuel cell heat recovery efficiency;
[0067] x9=solar heating or cooling efficiency;
[0068] x10=fast response heat storage cost;
[0069] x11=fast response heat storage size;
[0070] x12=fast response heat storage time constant;
[0071] x13=solar heat incident on building;
[0072] x14=heat needed by building to maintain desired
temperature;
[0073] x15=desired building temperature for occupied regions;
[0074] x16=electric usage (AC+DC);
[0075] x17=reliability of electric service;
[0076] x18=reliability of thermal service;
[0077] x19=power quality service cost;
[0078] x20=building thermal usage;
[0079] x21=building electric usage;
[0080] x22=fraction of electric supplied by gas distributed
generation;
[0081] x23=fraction of electric supplied by grid;
[0082] x24=fraction of electric supplied as direct current;
[0083] x25=fraction of gas generation waste heat used for building
heating;
[0084] x26=fraction of gas generation waste heat used for dessicant
dehumidification;
[0085] x27=fraction of gas generation waste heat used for
absorption cooling;
[0086] x28=temperature of heat storage;
[0087] x29=heat storage capacity for unoccupied regions of
building;
[0088] x30=maximum .DELTA. temperature of unoccupied region of
building;
[0089] wherein heat storage minimum temperature
.ltoreq.x28.ltoreq.heat storage maximum temperature;
[0090] wherein minimum temperature.ltoreq.x31.ltoreq.maximum
temperature;
[0091] wherein
[0092] x24.ltoreq.1,
[0093] x25.ltoreq.1,
[0094] x26.ltoreq.1,
[0095] x27.ltoreq.1,
[0096] x30.ltoreq.accepted level;
[0097] wherein minimum solar input .ltoreq.x13.ltoreq.maximum solar
input; and
[0098] wherein
[0099] x10.ltoreq.direct heat production cost,
[0100] x11.ltoreq.maximum allowable physical size,
[0101] x12.gtoreq.minimum possible value.
[0102] Preferably, the analysis for the optimization of the index
of performance is done in the time domain. Various frequency domain
considerations and calculations may be performed during and/or
after the optimization to supplement the optimization process.
[0103] To visualize the fuzzy control system, a multi-dimensional
control surface may be used. Illustrated in FIG. 6 is one potential
three-dimensional control surface. The value for a particular
output variable is calculated by determining the output variable's
degree of membership in each fuzzy input variable and mapping the
input variables on to the multi-dimensional control surface. The
final dimension of the control surface (the height in this case) is
the value of the output variable. In the example shown, there are
two input variables and one output variable. However, it should be
understood that any number of different input and output variables
could be used.
[0104] In practice, only a few points on the control surface are
design coordinates that are stored in the system, with the
remainder of the points on the control surface being determined via
interpolation. In this example, nine design coordinates are used to
define the three dimensional control surface, one for each
combination of low, medium, and high of the two input variables.
However, it should be understood that any number of design
coordinates could be used to define the control surface, and that
the control surface could have any number of dimensions.
[0105] Illustrated in FIG. 7 are rules, depicted in matrix and
textual forms, associated with the design coordinates used to
construct the control surface of FIG. 6. For example, the "center"
rule states that if input variable 1 is "high" and input variable 2
is "medium," then set the output variable's value to 4. In another
example, the following three rules may be used to define a portion
of an exemplary control surface: 1) If the temperature differential
is a large positive, then the heat bypass is large; 2) If the
temperature differential is a medium positive; and 3) if the
temperature differential is a small positive, then the heat bypass
is small. A matrix may be considered a very coarse view of the
control surface looking straight down from above. The values of the
design coordinates are the outputs of the rules. The weight each of
these rules carries can be adjusted by modifying the value of its
output, thereby altering the shape of the control surface.
[0106] Illustrated in FIG. 8 is another downward view of the
control surface. The rule numbers associated with the design
coordinates are indicated in nine of the locations. A single rule
governs the priority in these areas. The arrows indicate regions of
the control surface where more than one rule applies and
interpolation is used. Trapezoidal membership functions along the
axes allegorize overlapping areas where more than one label of an
input is partially true at the same time. The centroids of these
trapezoids can be adjusted to alter the shape of the control
surface. Of course, shapes other than trapezoids could be used to
describe the membership functions. For example, triangular
membership functions are shown in FIGS. 9-12, which correspond to
the exemplary rules given above.
[0107] Given the dynamic environment in which such a system is
deployed, determining the best shape for the control surface is a
moving target. Therefore, the control surface can be continually
adjusted during operation to home in on the optimal shape and
optimize the efficiency of the system. In a first embodiment, each
input axis is scaled to distribute the range of values. Of course,
any number of moving average and scaling functions could be used to
adapt the control surface according to this method.
[0108] In a second embodiment, the weights of the rules and/or the
centroids of the trapezoidal membership functions are adjusted (in
effect the shape of the control surface is adjusted) in response to
one or more performance metrics. Performance metrics are any type
of feedback the system receives. In one embodiment, the system
adjusts one or more of the weights and/or centroids in a random
direction. If the performance metric improves, the adjustment
continues in the same direction. If the performance metric
degrades, another random direction is chosen. Eventually, in a
stable environment, the system will converge on a small set of near
optimal control surfaces (i.e., a circle of convergence). In a
dynamic environment it will continue to chase the optimal control
surface. Large adjustments will converge faster, but will also
create a large circle of convergence. Of course, the size of the
adjustment need not be constant. The system could use large
adjustments when necessary to converge and then small adjustments
to linger in a tight circle of convergence.
[0109] The modeled system is preferably continuous. Under various
circumstances discrete models are used as appropriate to either
enhance the calculation or decrease the time needed for
calculations. Generally, a lumped parameter representation is used
in the analysis in a well known manner. This allows for the use of
standard differential equations as opposed to partial differential
equations. The initial optimization is done assuming the system is
deterministic rather than stochastic. After the initial
optimization is completed, a probabilistic optimization is done to
account for uncertainties in data and forecasts.
[0110] Preferably, the index of performance is based upon a generic
system that is simulated using a computer simulation modeling
system. Input and output from this simulation is done through an
interface with a numeric computational computer program. System
optimization is done through computer routines that utilize custom
designed functions as well as those available in the numeric
computational computer program. A building model is interfaced with
the numeric computational computer program to provide building
thermal requirements as a function of time.
[0111] This methodology is not specific to any particular computer
product. In one embodiment, MATHCAD.RTM., a general purpose
modeling program, is used for the modeling of the building. The
standard program has been modified to account for various building
requirements, including but not limited to thermal output, solar
heating output, geographic orientation, and insulation. This
program then outputs to MATLAB.RTM. from Mathsoft, Inc., which is
used for the numeric computational computer program. This is a
broad modeling system that was programmed for the energy management
package, which accounts for the various heat sources, the
capability of heat storage, the maximum temperature within the heat
storage, and what to do with excess heat once the maximum heat
storage temperature is reached. SIMULINK.RTM., also from Mathsoft,
interfaces with MATLAB.RTM. and is used for the simulation. This
program provides a prediction of the amount of BTUs produced within
the system at any given time, depending on the amount of energy
available. OPTIMIZATION TOOLBOX.RTM., also from Mathsoft, is used
for the optimization routines.
[0112] The net effect of this multi-dimensional optimization scheme
is to choose the blend of energy sources that produces the desired
energy configuration of the building and minimizes the total energy
cost and any negative environmental impact of the building.
[0113] The global energy control system optimizes the sources of
electrical and thermal energy so as to minimize the overall energy
costs of the building to less than that provided by conventional
energy source options. This system continually regulates the
balance between energy production and storage so as to minimize
cost within the constraints of the energy reliability, quality and
usage patterns of the building. As a result of this optimization,
it is possible to run the combined heat and power system in a base
loaded mode of operation rather than a peaking mode. This allows
the system to run continuously because it is less weather
dependent. This, in turn, greatly enhances the economic and
environmental benefits of the system.
[0114] The foregoing description is for the purpose of teaching
those of ordinary skill in the art the best mode of carrying out
the invention and is illustrative only. Modifications and
alternative embodiments of the invention will be apparent to those
skilled in the art in view of this detailed description of the
exemplary embodiments. The details of the disclosed structure may
be varied substantially without departing from the scope or spirit
of the invention, and the exclusive use of all modifications within
the scope of the appended claims is reserved.
* * * * *