U.S. patent application number 12/895780 was filed with the patent office on 2012-04-05 for adaptive load management: a system for incorporating customer electrical demand information for demand and supply side energy management.
This patent application is currently assigned to ROBERT BOSCH GmbH. Invention is credited to Burton Warren Andrews, Diego Benitez, Marija Dragoljub Ilic, Jhi Young Joo, Felix Maus, Badri Raghunathan.
Application Number | 20120083930 12/895780 |
Document ID | / |
Family ID | 44863220 |
Filed Date | 2012-04-05 |
United States Patent
Application |
20120083930 |
Kind Code |
A1 |
Ilic; Marija Dragoljub ; et
al. |
April 5, 2012 |
ADAPTIVE LOAD MANAGEMENT: A SYSTEM FOR INCORPORATING CUSTOMER
ELECTRICAL DEMAND INFORMATION FOR DEMAND AND SUPPLY SIDE ENERGY
MANAGEMENT
Abstract
A method for determining an amount of electricity to purchase
includes determining electrical power consumption characteristics
of an electrical load at an end user of the electricity. A
preference of the end user for an output of the electrical load is
ascertained. The output varies with a rate of electrical power
consumption by the load. A mathematical model is created of an
amount of electrical power to be consumed by the load as a function
of time and of monetary cost of the electricity. The model is
dependent upon the electrical power consumption characteristics of
the electrical load and the preference of the end user for an
output of the electrical load. An amount of electricity is
purchased based on the mathematical model of an amount of
electrical power to be consumed by the load, and based on the
monetary cost of the electricity.
Inventors: |
Ilic; Marija Dragoljub;
(Sudbury, MA) ; Joo; Jhi Young; (Pittsburgh,
PA) ; Andrews; Burton Warren; (Pittsburgh, PA)
; Raghunathan; Badri; (San Jose, CA) ; Benitez;
Diego; (Pittsburgh, PA) ; Maus; Felix;
(Pittsburgh, PA) |
Assignee: |
ROBERT BOSCH GmbH
Stuttgart
DE
|
Family ID: |
44863220 |
Appl. No.: |
12/895780 |
Filed: |
September 30, 2010 |
Current U.S.
Class: |
700/287 ;
700/291; 703/2; 705/37; 705/412 |
Current CPC
Class: |
G06Q 10/04 20130101;
Y04S 50/14 20130101; Y04S 10/50 20130101; G06Q 40/04 20130101; G06Q
50/06 20130101; G06Q 30/06 20130101; Y04S 50/10 20130101; G06Q
30/0206 20130101 |
Class at
Publication: |
700/287 ; 703/2;
700/291; 705/412; 705/37 |
International
Class: |
G06F 1/26 20060101
G06F001/26; G06F 17/00 20060101 G06F017/00; G06Q 40/00 20060101
G06Q040/00; G06F 17/10 20060101 G06F017/10 |
Claims
1. A method for determining an amount of electricity to purchase,
the method comprising: determining electrical power consumption
characteristics of an electrical load, the electrical load being
used by an end user of the electricity; ascertaining a preference
of the end user for an output of the electrical load, the output
varying with a rate of electrical power consumption by the load;
creating a mathematical model of an amount of electrical power to
be consumed by the load as a function of time and of monetary cost
of the electricity, the model being dependent upon the electrical
power consumption characteristics of the electrical load and the
preference of the end user for an output of the electrical load;
and purchasing an amount of electricity based on: the mathematical
model of an amount of electrical power to be consumed by the load;
and the monetary cost of the electricity.
2. The method of claim 1 comprising the further step of sensing a
condition of an environment associated with the electrical load,
the determining of the electrical power consumption characteristics
of the electrical load being dependent upon the sensed
condition.
3. The method of claim 2 wherein the sensed condition comprises
temperature, humidity, wind speed, motion and/or lighting.
4. The method of claim 1 wherein the electrical power consumption
characteristics of the electrical load are determined as a function
of a level of output of the load.
5. The method of claim 1 wherein the preference of the end user for
an output of the electrical load is received via manual and/or oral
inputs from the end user.
6. The method of claim 1 wherein the preference of the end user for
an output of the electrical load is deduced from prior settings
entered into the load by the end user, the output being dependent
upon the settings.
7. The method of claim 1 wherein the load comprises an HVAC system,
and the preference of the end user for an output of the electrical
load comprises an environmental temperature produced by the HVAC
system.
8. The method of claim 1 wherein the load comprises an HVAC system,
and the electricity consumption is determined based on thermal
models of the building or of a thermal storage device.
9. The method of claim 1 wherein the load comprises electric
vehicle charging infrastructure, the electric power consumption
characteristics being provided by a battery management system or
being determined by models.
10. The method of claim 1 wherein the preference of the end user
for an output of the electrical load is determined from inputs
concerning comfort, costs and environmental impact.
11. The method of claim 1 comprising the further step of
determining an amount of electricity that can be produced by a
distributed generator and that is available for consumption by the
load, wherein the amount of electricity purchased is dependent upon
the amount of electricity that can be produced by the distributed
generator and that is available for consumption by the load.
12. The method of claim 11 wherein the mathematical model is
dependent upon a forecasted amount of excess heat produced by the
distributed generator.
13. The method of claim 11 comprising the further step of
determining an amount of electricity that is to be produced by the
distributed generator for consumption by the load, the amount of
electricity that is to be produced by the distributed generator
being determined dependent upon the monetary cost of the purchased
electricity.
14. A method for distributing electricity among a plurality of end
users, the method comprising the computer-implemented steps of:
determining electrical energy consumption characteristics of each
of a plurality of electrical loads, each of the electrical loads
being used by a corresponding one of the end users of the
electricity; for each of the electrical loads, ascertaining a
preference of the corresponding end user for an output of the
electrical loads, the output varying with an amount of electrical
energy consumed by the load; creating a mathematical model of an
amount of electrical energy to be consumed by each of the
electrical loads as a function of time and of monetary cost of the
electrical energy, each of the models being dependent upon the
electrical energy consumption characteristics of the electrical
load and the preference of the end user for an output of the
electrical load; aggregating together the mathematical models of
electrical energy consumption; preparing a bid for an amount of
electrical energy to be delivered during a specified period of
time, the bid being prepared based on: the aggregation of the
mathematical models of electrical energy consumption; and a known
current market price and/or a future expected market price of the
electrical energy; and submitting the bid to a supplier of
electrical energy or electricity market.
15. The method of claim 14 wherein the aggregation of the
mathematical models of electrical energy consumption is a function
of time and of monetary cost of the electrical energy.
16. The method of claim 14 wherein the bid specifies an amount of
electrical energy to be delivered during a specified period of time
as a function of monetary cost of the electrical energy.
17. The method of claim 14 comprising the further step of sensing a
condition of an environment associated with at least one of the
electrical loads, the determining of the electrical power
consumption characteristics of the at least one electrical load
being dependent upon the sensed condition, the sensed condition
comprising temperature, humidity, wind speed, motion and/or
lighting.
18. The method of claim 14 wherein the electrical power consumption
characteristics of the electrical loads are determined as a
function of a level of output of the loads.
19. The method of claim 14 wherein the preferences of the end user
for outputs of the electrical loads are received via manual and/or
oral inputs from the end user.
20. The method of claim 14 wherein the preferences of the end user
for outputs of the electrical loads are deduced from prior settings
entered into the loads by the end user, the outputs being dependent
upon the settings.
21. The method of claim 14 wherein the load comprises an HVAC
system, and the electricity consumption is determined based on
thermal models of the building or of a thermal storage device.
22. The method of claim 14 wherein the load comprises electric
vehicle charging infrastructure, the electric power consumption
characteristics being provided by a battery management system or
being determined by models.
23. The method of claim 14 wherein the preference of the end user
for an output of the electrical load is determined from inputs
concerning comfort, costs and/or environmental impact.
24. An adaptive load management system, comprising: a plurality of
local modules, each of the local modules being disposed in a
respective building and configured to: determine electrical energy
consumption characteristics of each of a plurality of electrical
loads associated with the respective building; for each of the
corresponding electrical loads, ascertain a preference of a
corresponding end user of the load for an output of the electrical
load, the output varying with an amount of electrical energy
consumed by the load; and create a mathematical model of an amount
of electrical energy to be consumed by each of the corresponding
electrical loads as a function of time and of monetary cost of the
electrical energy, each of the models being dependent upon the
electrical energy consumption characteristics of the electrical
load and the preference of the corresponding end user for an output
of the electrical load; and an aggregator module communicatively
coupled to each of the local modules and configured to: receive
price data regarding a market for electricity; receive the
mathematical models from each of the local modules; and purchase an
amount of electrical energy to be delivered to the electrical loads
during a specified period of time, the amount of energy being
purchased being based on: the mathematical models of electrical
energy consumption; and a known current market price and/or a
future expected market price of the electrical energy.
25. The system of claim 24 wherein the aggregator module is
configured to prepare and submit a bid to a supplier of electrical
energy for the purchased amount of electrical energy, a monetary
value of the bid being dependent upon: the mathematical models of
electrical energy consumption; and a known current market price
and/or a future expected market price of the electrical energy.
26. The system of claim 24 wherein the aggregator module is
configured to aggregate together the mathematical models of
electrical energy consumption, the aggregation of the mathematical
models of electrical energy consumption being a function of time
and of monetary cost of the electrical energy.
27. The system of claim 24 further comprising a sensor
communicatively coupled to at least one of the local modules and
configured to: sense a condition of an environment associated with
at least one of the electrical loads, the sensed condition
comprising temperature, humidity, wind speed, motion and/or
lighting; and transmit sensor data to the at least one local
module, the at least one local module being configured to determine
the electrical power consumption characteristics of the at least
one electrical load dependent upon the sensor data.
28. The system of claim 24 wherein the electrical power consumption
characteristics of at least one of the electrical loads are
determined by at least one of the local modules as a function of a
level of output of the load.
29. The system of claim 24 wherein the preference of the end user
for an output of at least one of the electrical loads is: received
by at least one of the local modules via manual and/or oral inputs
from the end user; and/or deduced by at least one of the local
modules from prior settings entered into the at least one load by
the end user, the output being dependent upon the settings.
30. The system of claim 24 wherein the load comprises an HVAC
system, and the electricity consumption is determined based on
thermal models of the building or of a thermal storage device.
31. The system of claim 24 wherein the load comprises electric
vehicle charging infrastructure, the electric power consumption
characteristics being provided by a battery management system or
being determined by models.
32. The system of claim 24 wherein the preference of the end user
for an output of the electrical load is determined from inputs
concerning comfort, costs and/or environmental impact.
33. A method for determining an amount of electricity to purchase,
the method comprising: determining electrical power consumption
characteristics of an electrical load, the electrical load being
used by an end user of the electricity; ascertaining a preference
of the end user for an output of the electrical load, the output
varying with a rate of electrical power consumption by the load;
creating a mathematical model of an amount of electrical power to
be consumed by the load as a function of time and of monetary cost
of the electricity, the model being dependent upon the electrical
power consumption characteristics of the electrical load and the
preference of the end user for an output of the electrical load;
reserving an amount of electricity to be delivered by an
electricity provider at a future point in time, the amount of
electricity that is reserved being based on: the mathematical model
of an amount of electrical power to be consumed by the load; and
the monetary cost of the electricity; repeating the determining,
ascertaining, creating and reserving steps a plurality of times
before the future point in time; and receiving delivery of the most
recently reserved amount of electricity at the future point in
time.
34. The method of claim 33 wherein the load comprises an HVAC
system, and the electricity consumption is determined based on
thermal models of the building or of a thermal storage device.
35. The method of claim 33 wherein the load comprises electric
vehicle charging infrastructure, the electric power consumption
characteristics being provided by a battery management system or
being determined by models.
36. The method of claim 33 wherein the preference of the end user
for an output of the electrical load is determined from inputs
concerning comfort, costs and environmental impact.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention relates to methods for managing the
use of electricity, and, more particularly, to methods for managing
the use of electricity by consumers wherein the methods may be at
least partially based on values the consumers place on the
electricity usage.
[0003] 2. Description of the Related Art
[0004] The price and consumption of energy throughout the world has
been increasing dramatically over recent years and is expected to
continue along this trend in the years to come. For example,
according to the U.S. Department of Energy Annual Energy Outlook,
total residential energy consumption is expected to increase by
approximately twenty percent from 2007 to 2030 despite efficiency
improvements. This is attributed to a number of factors including a
projected twenty-four percent increase in the number of households
and an approximately seven percent increase in the share of
electricity attributed to "other" appliances such as home
electronics. Increases in residential electricity consumption are
accompanied by a projected 1.4 percent increase per year in
commercial electricity consumption. Given these figures, and the
fact that residential and commercial buildings comprise the largest
energy consumer segment in the U.S., accounting for seventy-two
percent of U.S. electricity consumption and forty percent of all
energy use in the U.S., the recent push for technological solutions
that increase energy awareness and efficiency are of no
surprise.
[0005] The smart electric grid has been a vision for quite some
time now, and rising energy prices and climate change have recently
strengthened the urgency of this topic. Mandated by the U.S. Energy
Independence and Security Act, The National Institute of Standards
and Technology (NIST) is stewarding the development of a standards
framework to accelerate the deployment of the smart grid. Buildings
constitute a key part of the smart grid picture on the demand side;
residential and commercial buildings comprise the largest energy
consumer segment in the United States. Together, residential and
commercial buildings account for 40% of all energy use in the U.S.
Buildings account for 72% of U.S. electricity consumption and 36%
of natural gas consumption. Without action, U.S. energy consumption
is projected to grow about 25% over the next two decades, and
buildings are expected to play a large part in that growth.
[0006] To address the above problems, electric power systems around
the globe are faced with needs for fundamental changes, which have
brought about the concept of smart grids. The ongoing changes of
the system have called for demand to become smarter as well in ways
that comply better with the volatility of the supply side arising
from increased penetration of intermittent and distributed
resources. The curtailment of the peak demands has also become
important to reduce the needs for additional electricity generation
capacity. Driven by government mandated spending, there are huge
commercial initiatives to deploy Advanced Metering Infrastructure
(AMI) and other technologies for the smart grid. Current smart grid
standardization activities are attempting to address the issues of
protocols and information models needed to enable decision making
throughout the grid. These protocols need to bring values captured
from the smart grid technologies to the complete ecosystem, i.e.,
customers, Load Serving Entities (LSEs), utilities, and society as
a whole.
[0007] Until very recently, there has been an active skepticism
concerning benefits from retail competition. To the contrary, many
years ago a vision was put forward that if all customers, small and
large, responded to the changing system conditions locally, the
system would in a homeostatic way keep itself in a healthy
sustainable equilibrium. A concept was put forward that the law of
large numbers will result in significant economic savings provided
even the smallest users respond. These concepts have never
materialized for a variety of reasons, perhaps the key reason being
a lack of adequate incentives to encourage users to respond.
[0008] The past decade has seen a revival of electricity customer
choice. Most recently, it has become accepted that active demand
side response might be the key to overall energy efficiency and
sustainability. The role of timely demand side response has become
even more recognized as large-scale penetration of intermittent
electricity generation is planned. Consequently, there has been
significant research on thermal modeling of buildings,
non-intrusive load monitoring, economic characterization of demand
response, and the like. There have been renewed efforts to serve
large customers efficiently. However, hardly any frameworks have
attempted to systematically integrate large-scale responses from,
and preferences of, the individual building users in residential
and commercial buildings. It is also known for buildings including
residential, commercial and public buildings to make use of
distributed generation in order to provide at least a portion of
the electricity that they consume. Distributed generation, which is
also referred to as distributed energy, decentralized energy,
decentralized generation, embedded generation, dispersed generation
or on-site generation, involves the generation of electricity from
many small energy sources. Such small energy sources may include
renewable energy sources such as sunlight, wind and geothermal, but
may also include non-renewable energy sources such as natural gas
or propane powered generators. Distributed generation systems are
small-scale electricity generators (typically in the range of 3 kW
to 10,000 kW) used to provide an alternative to or an enhancement
of the traditional electric power system.
[0009] Distributed generation may reduce the amount of energy lost
in transmitting electricity because the electricity is generated
very near the location where the electricity is used, perhaps even
in the same building. Thus, the size and number of power lines that
must be constructed in also reduced. Distributed generation systems
may include technologies including combined heat power (CHP) and
photovoltaic (PV) systems.
[0010] Combined heat power (CHP), which is also referred to as
cogeneration, may include the use of a heat engine or a power
station to simultaneously generate both electricity and useful
heat. In addition to small-scale natural gas or propane powered
generators for residential use, cogeneration plants are commonly
utilized in district heating systems of hospitals, prisons, and
industrial facilities with large heating needs. CHP may include
natural gas or propane powered electricity generators that are
disposed in the basement of a residence. Such generators may
typically be used to provide electricity primarily during time
periods of peak demand, when the cost of electricity may be the
highest. In addition to the electricity produced by these
generators, excess heat produced by these generators may be used in
space heating within the same residence.
[0011] Photovoltaics (PV) is a method of generating electrical
power by converting solar radiation into direct current electricity
using semiconductors that exhibit the photovoltaic effect.
Photovoltaic power generation may employ solar panels comprising a
number of cells containing photovoltaic material. In contrast to
the CHP generators described above, solar panels are used, and may
only be used, whenever the sun is shining.
[0012] What is neither disclosed nor suggested in the art is a
system and method for managing the use and generation of
electricity by consumers that takes advantage of voluntary
behaviors and preferences of the consumers.
SUMMARY OF THE INVENTION
[0013] The present invention may provide a system and method for
managing the use and generation of electricity at multiple levels
of the electrical infrastructure while explicitly accounting for
the value that consumers place on the supply of electricity. The
system may include two components that may operate independently or
in a joint manner. The first component is a local Adaptive Load
Management (ALM) module at the level of the consumer that may
manage electricity use decisions for a building operator. The
second component is an ALM module at the level of an electricity
aggregator such as a Load Serving Entity (LSE), Power Marketer,
Virtual Power Plant operator (VPP) or Demand Response Operator
(DRO) that may manage electricity resource allocation, pricing,
and/or market decisions. The ALM module at the local consumer level
may take, as input, models of electrical loads and/or distributed
generators in the building. These models of electrical loads and/or
distributed generators may be automatically obtained through
sensing, electricity pricing information, and customer preference
information. The ALM module at the local consumer level may yield
an optimal electricity purchasing strategy for the building
operator as well as a prediction of electricity consumption. The
ALM module at the aggregator level may take as input the local
purchasing strategies, viewed as demand functions, along with
pricing information from the wholesale electricity market, from
bilateral supply contracts or from distributed generation at the
consumer level, to arrive at an optimal electric grid operation and
energy purchase strategy. The optimal electric grid operation and
energy purchase strategy may include, for example, customer
pricing, plans for utilization of distributed energy storage, lists
of resources, bulk electricity buy decision-points, etc. While each
of these ALM modules may be used independently, a system employing
ALM modules at each of at least two levels that communicate, as
enabled by smart meters and/or smart grid IT systems, may enable
achievement of the many goals of next generation electrical
infrastructures by coupling optimal decision making with customer
value.
[0014] Described above is a first of two ALM steps, namely,
obtaining the user preferences and purchasing energy accordingly.
In a second ALM step, the loads and/or distributed generators are
controlled according to the amount of energy that has been
purchased.
[0015] The process of identifying user preferences and purchasing
corresponding amounts of energy may be performed repeatedly and
iteratively as the time of energy delivery approaches. By doing so,
the predictions of electricity prices as well as other environment
variables (e.g., ambient temperature, building electricity usage)
can be refined for the short-term steps. Thus, the more recent
calculations may produce more accurate predictions for the steps in
the near future. Different market mechanisms that have different
time scales (e.g., one day ahead vs. real-time markets) can be
addressed by utilizing each of the market mechanisms in long term
calculations and/or short term calculations as appropriate.
[0016] As described above, the invention applies the process of
characterizing the load and/or distributed generators, modeling and
aggregating the models, and purchasing an amount of electricity
from several electricity markets through, for example, long-term
bilateral contracts, day-ahead energy procurement, balancing
capacity markets, and/or real-time markets. Re-applying this
process may lead to increasingly accurate predictions as the time
of energy delivery gets closer. The distributed loads and/or
generators can thus react to the overall system state. In this
setup, the ALM may implement a closed-loop control of distributed
loads and/or generators with respect to the state of the power
supply system.
[0017] Determining the electricity consumption based on the end
user preferences can include the use of thermal models. Such
thermal models may describe the thermal behavior of a building or
of a thermal storage device.
[0018] The ALM system can be used to control the connected devices,
e.g., by updating the first pricing prediction with newer and more
accurate price profiles, and may even do so multiple times. This
may provide the aggregator with a quickly responding system,
closing the loop between the aggregator and the connected
devices.
[0019] There may be various benefits for controlling the connected
devices. First, the devices may be adjusted in order to minimize
the real-time deviation from the previous energy purchase. That is,
the electricity consumption of the devices may be adjusted in order
to more closely match an amount of electricity that is purchased
and available.
[0020] Second, the devices may be adjusted to minimize the cost of
the aggregator. The cost of the aggregator may include long-term
energy procurement at variable prices, so load-shifting (e.g.,
re-scheduling loads from times of high prices to times of lower
prices) or peak-shaving (e.g., reducing the peak demand) can be
achieved.
[0021] Third, the devices may be adjusted according to the system
real-time deviation. For example, the load may be reduced if the
electricity system is lacking of generation, and the load may be
increased if too much generation has been procured. This is
especially useful with respect to volatile/fluctuating energy
sources, e.g., generation from wind or solar. Thus, the ALM can
help to integrate higher fractions of renewable energy sources into
the supply system.
[0022] Fourth, the devices may be adjusted in order to participate
in power system ancillary services, such as balancing energy
provision. Thus, the load may be reduced if energy is to be
transferred to another load, and the load may be increased if
energy is to be procured from another load.
[0023] The ALM system may be most effectively used with loads that
have a certain degree of flexibility in the time and magnitude of
their energy consumption, e.g., HVAC devices and electric vehicles.
With regard to heating or air conditioning (HVAC) devices, hot
water tanks or the building structure itself can be considered
thermal storages that may provide a certain degree of flexibility
in the time and magnitude of electricity consumption. The heating
system can pre-heat a water tank of the building in order to reduce
electricity consumption at other times. The ALM system may use
thermal models of water tanks and/or buildings in order to
determine the energy consumption characteristics.
[0024] With regard to electric vehicle or plug-in hybrid electric
vehicle charging, the boundary condition may be for the car to be
in a charged state by a certain time in the morning, but the actual
time period(s) for charging can be flexibly determined sometime
during the previous night. The ALM system may use models describing
the electric vehicle battery in order to determine the consumption
characteristics.
[0025] The invention comprises, in one form thereof, a method for
determining an amount of electricity to purchase, including
determining electrical power consumption characteristics of an
electrical load at an end user of the electricity. A preference of
the end user for an output of the electrical load is ascertained.
The output varies with a rate of electrical power consumption by
the load. A mathematical model is created of an amount of
electrical power to be consumed by the load as a function of time
and of monetary cost of the electricity. The model is dependent
upon the electrical power consumption characteristics of the
electrical load and the preference of the end user for an output of
the electrical load. An amount of electricity is purchased based on
the mathematical model of an amount of electrical power to be
consumed by the load, and based on the monetary cost of the
electricity. The local load may then be controlled according to the
purchased energy and/or the preferences retrieved in a previous
step.
[0026] The invention comprises, in another form thereof, a method
for distributing electricity among a plurality of end users,
including the following computer-implemented steps. Electrical
energy consumption characteristics of each of a plurality of
electrical loads are determined. Each of the electrical loads is
used by a corresponding one of the end users of the electricity.
For each of the electrical loads, a preference of the corresponding
end user for an output of the electrical loads is ascertained. The
output varies with an amount of electrical energy consumed by the
load. A mathematical model of an amount of electrical energy to be
consumed by each of the electrical loads is created as a function
of time and of monetary cost of the electrical energy. Each of the
models is dependent upon the electrical energy consumption
characteristics of the electrical load and the preference of the
end user for an output of the electrical load. The mathematical
models of electrical energy consumption are aggregated together. A
bid is prepared for an amount of electrical energy to be delivered
during a specified period of time. The bid is prepared based upon
the aggregation of the mathematical models of electrical energy
consumption, as well as upon a known current market price and/or a
future expected market price of the electrical energy. The bid is
submitted to a supplier of electrical energy. The local load may
then be controlled according to the purchased energy and/or the
preferences retrieved in a previous step.
[0027] The invention comprises, in yet another form thereof, an
adaptive load management system including a plurality of load
modules. Each of the load modules is disposed in a respective
building and determines electrical energy consumption
characteristics of each of a plurality of electrical loads
associated with the respective building. For each of the
corresponding electrical loads, each of the load modules ascertains
a preference of a corresponding end user of the load for an output
of the electrical load. The output varies with an amount of
electrical energy consumed by the load. Each of the load modules
creates a mathematical model of an amount of electrical energy to
be consumed by each of the corresponding electrical loads as a
function of time and of monetary cost of the electrical energy.
Each of the models is dependent upon the electrical energy
consumption characteristics of the electrical load and the
preference of the corresponding end user for an output of the
electrical load. An aggregator module is communicatively coupled to
each of the load modules. The aggregator module receives price data
regarding a market for electricity, and receives the mathematical
models from each of the electrical loads. The aggregator module
purchases an amount of electrical energy to be delivered to the
electrical loads during a specified period of time. The amount of
energy being purchased is based on the mathematical models of
electrical energy consumption, as well as a known current market
price and/or a future expected market price of the electrical
energy.
[0028] The invention comprises, in a further form thereof, a method
for determining an amount of electricity to purchase, including
determining electrical power consumption characteristics of an
electrical load at an end user of the electricity. A preference of
the end user for an output of the electrical load is ascertained.
The output varies with a rate of electrical power consumption by
the load. A mathematical model is created of an amount of
electrical power to be consumed by the load as a function of time
and of monetary cost of the electricity. The model is dependent
upon the electrical power consumption characteristics of the
electrical load and the preference of the end user for an output of
the electrical load. An amount of electricity to be delivered by an
electricity provider at a future point in time is reserved. The
amount of electricity that is reserved is based on the mathematical
model of an amount of electrical power to be consumed by the load,
and the monetary cost of the electricity. The determining,
ascertaining, creating and reserving steps are repeated a plurality
of times before the future point in time. Delivery of the most
recently reserved amount of electricity is received at the future
point in time.
[0029] An advantage of the present invention is that it provides
efficient allocation of electricity resources based on consumer
preferences and the consumers' willingness to pay for different
levels of electricity during different time periods.
BRIEF DESCRIPTION OF THE DRAWINGS
[0030] The above mentioned and other features and objects of this
invention, and the manner of attaining them, will become more
apparent and the invention itself will be better understood by
reference to the following description of embodiments of the
invention taken in conjunction with the accompanying drawings,
wherein:
[0031] FIG. 1 is a block diagram of information flow in a direct
load control scheme of the prior art.
[0032] FIG. 2 is a block diagram of one embodiment of a
non-intrusive load monitoring system of the present invention.
[0033] FIG. 3 is a block diagram of one embodiment of an adaptive
load management system of the present invention.
[0034] FIG. 4 is a block diagram of one embodiment of a local
module of the adaptive load management system of FIG. 3 along with
various sources of input to the local module.
[0035] FIG. 5 is a block diagram of one embodiment of an aggregator
module of the adaptive load management system of FIG. 3 along with
various sources of input to the aggregator module.
[0036] FIG. 6 is a flow chart of one embodiment of a method of the
present invention for determining an amount of electricity to
purchase.
[0037] FIG. 7 is a flow chart of one embodiment of a method of the
present invention for distributing electricity among a plurality of
end users.
[0038] FIG. 8 is a flow chart of another embodiment of a method of
the present invention for determining an amount of electricity to
purchase.
[0039] Corresponding reference characters indicate corresponding
parts throughout the several views. Although the exemplification
set out herein illustrates embodiments of the invention, in several
forms, the embodiments disclosed below are not intended to be
exhaustive or to be construed as limiting the scope of the
invention to the precise forms disclosed.
DETAILED DESCRIPTION
[0040] In the following detailed description, reference is made to
the accompanying drawings, which form a part hereof. In the
drawings, similar symbols typically identify similar components,
unless context dictates otherwise. The illustrative embodiments
described in the detailed description, drawings, and claims are not
meant to be limiting. Other embodiments may be utilized, and other
changes may be made, without departing from the spirit or scope of
the subject matter presented here. It will be readily understood
that the aspects of the present disclosure, as generally described
herein, and illustrated in the Figures, may be arranged,
substituted, combined, and designed in a wide variety of different
configurations, all of which are explicitly contemplated and make
part of this disclosure.
[0041] While the role that demand is expected to play in the new
changing electricity industry is larger than ever, the information
flow from demand to the system has been very limited: the system
operator usually has the whole aggregated demand prediction based
on the historical data and the weather conditions. Various demand
response programs under experiment fail to enable interactive
online adaptation by the users in response to price changes.
Instead, some form of direct load control in a top-down manner by
the system/market operator is in place, as illustrated in FIG.
1.
[0042] In contrast to known operations, true system equilibrium may
be achieved by the present invention as a result of interactions
between end users, power producers, load serving entities (or
utilities) and the system/market operator. It is conceptually
difficult to arrive at this equilibrium unilaterally without
constantly and bi-directionally exchanging information between the
participants within the electric power system. In other words, the
system/market operator may not be able to clear the market without
the full knowledge of the demand by the end-users and the supply
characteristics of the power producers. Similarly, realistic demand
and supply functions may not be arrived at without active
adaptation by the demand and supply to the expected electricity
prices. Therefore, a multi-layered, multi-directional adaptive
system may be called for in order to enable the necessary online
information exchange. Such a system may be referred to herein as an
adaptive load management (ALM) system.
[0043] The exchange of information between market participating
units and the operator is, of course, not new. However, prior to
the present invention, there has been no development of technology
that obtains and leverages needed information from the demand side,
especially in the case of individual end-users. In the prior art,
the demand is usually taken as an exogenous factor that the supply
has to meet. Even the most recently developed demand-side
management programs either control demand in a centralized manner
(e.g., direct load control) or take price signals as a factor for
individual control. System demand is basically taken as inelastic
and unresponsive to the price when generation bids are cleared.
This is because the demand is considered to be by and large
inelastic to the price in the short-term, and/or because there is
little information known about the price elasticity of electricity
demand. The lack of information about the price elasticity of
electricity demand may be due to the limited experience and data
regarding the response of demand to varying prices.
[0044] The present invention addresses these above-described
shortcomings of the prior art via the employment of an ALM system
that may have two components. The first component is a module that
resides at the level of the individual consumer and optimally
obtains each end user's economic value with respect to the price of
electricity without violating their comfort level. To achieve this,
sensing and embedded intelligence is used to capture models of
relevant electrical loads in the building and to translate the
comfort and physical specifications at each end user's premises
into a demand function for each pricing interval. As part of the
second component of the system, these demand functions may be
aggregated at a higher layer by an entity such as a load
aggregator. The aggregation of the demand functions may be sent to
the market and may be used in aggregator decision making
strategies.
[0045] Some of the potential benefits and possible implementations
of the present invention are summarized below. First, the inventive
system may include a software module that resides at the local
building level (which hereinafter may be referred to as a "local
module"). This local module may take as input (1) a model of the
electrical loads and/or distributed generators in the building; (2)
information regarding the price of electricity for an upcoming
period of time (e.g., 24 hours); and (3) customer preference
information such as temperature preferences or minimum energy
consumption needs. These inputs may be used by an optimization
algorithm in the software which determines the optimal demand
function for the user (e.g., how much electricity to purchase from
the electricity provider for each of a number of upcoming time
intervals). For instance, the demand function may specify, for each
hour of the upcoming day, how much energy should be purchased at a
given price.
[0046] Second, the local module may be connected to a smart meter
which receives electricity pricing information from the utility. In
addition, the demand function computed by the local module may be
communicated to the utility company through a device such as a
smart meter in order to make demand information available for
utilities to incorporate into their operation strategies.
[0047] Third, the local module may include or communicate with a
user interaction device that obtains preference information
directly from the household user. For instance, a remote handheld
device with connectivity to the local module (e.g., via wireless
communication, power line communication, direct connection, etc.)
may prompt the user for his preferences (e.g., temperature comfort
bounds, energy cost budget, etc.), or for a list of the typical
types of devices that are used in the building. Other possible
manifestations of such a user interaction device may include a
thermostat interface, smart phone, or internet portal.
[0048] Fourth, the model of the electrical loads used by the local
module may be obtained via the user interaction device by, for
example, the user specifying the type of load or loads used in the
house. For instance, the user may specify the model of the heating
system used in the building. The load model may comprise in its
entirety only a single device (e.g., a heating system) or a
multitude of devices that make up the entire building load.
[0049] Fifth, the electrical load model used by the local module
may be obtained automatically through an intelligent sensing system
in the building that is connected to the local module. This
intelligent sensing system may include numerous types of sensors
distributed throughout the building, such as motion sensors,
ambient sensors (temperature, humidity, lighting, etc.), or sensors
to measure electricity consumption. Electricity consumption sensors
may be directly connected to individual loads in the building
(e.g., to detect individual device consumption) or may measure
aggregated electrical information, such as from the main electrical
feed to the building or at the circuit level. Individual appliance
use may be obtained from aggregate sensor information via a
non-intrusive load monitoring system. The intelligent sensing
system may process all of the sensor data to thereby ascertain
patterns in electricity use to construct a model of electricity
consumption behavior for the building which can be input to the
local module.
[0050] Shown in FIG. 2 is one embodiment of such a non-intrusive
load monitoring system 8 of the present invention including
appliances 1-4 disposed within a building 10. Appliances 1-4 are
powered by a voltage source 12, which may be power lines or an
electrical grid provided by a public utility company. Disposed in
association with the main power line leading into building 12 are a
voltage meter 14 and a current meter 16. Voltage meter 14 may
continuously measure the voltage being supplied to building 10.
Current meter 16 may continuously measure the electrical current
flowing into building 10. The voltage and current readings from
meters 14, 16 may be transmitted to an electrical processor 18,
such as a microprocessor, which may include memory. Although meters
14, 16 and processor 18 are shown as being disposed outside of
building 10, any or all of these components may be disposed inside
building 10. Processor 18 may be communicatively coupled to a local
or remote central database using any wired or wireless
communication protocol such as Wi-Fi, Bluetooth, power line
communication, the Internet, etc. 20, from which processor 18 may
receive mathematical models of the electrical characteristics of
appliances 1-4 or other information about appliances 1-4.
[0051] In addition, appliances 1-4 may also be powered by an
optional distributed generator 22, which may be in the form of
combined heat and power (CHP) or a photovoltaic (PV) system, for
example, that is located on the premises of building 10. Disposed
in association with the power line connecting distributed generator
22 with appliances 1-4 are a voltage meter 24 and a current meter
26. Although meters 14, 16 are shown in the simplified view of FIG.
2 as being directly connected to meters 24, 26, it will be
appreciated by those skilled in the art that meters 14, 16 may be
electrically isolated from meters 24, 26 such that the two sides do
not share a common node with a shared voltage. Voltage meter 24 may
continuously measure the voltage being supplied to building 10 by
distributed generator 22. Current meter 26 may continuously measure
the electrical current flowing into building 10 from distributed
generator 22. The voltage and current readings from meters 24, 26
may be transmitted to an electrical processor 28, such as a
microprocessor, which may include memory. Processor 28 may be
communicatively coupled to processor 18 using any wired or wireless
communication protocol such as Wi-Fi, Bluetooth, power line
communication, the Internet, etc. 20. Processor 18 may inform
processor 28 of the current or future costs of externally procured
electricity, and processor 28 may inform processor 18 of the
present power generating capacity of distributed generator 22. One
or both of processors 18, 28 may determine, based on the
electricity cost and the power generating capacity of generator 22,
how much external electricity should be bought, and how much power
should be generated by generator 22, and when such power should be
generated. Based on this determination, processor 28 may control
the operation of generator 22 via line 30. However, in the case of
generator 22 being a photovoltaic device, control of the operation
of the photovoltaic device may not be called for. In the case of
generator 22 being a combined heat and power device, any excess
heat produced by generator 22 may be used for space heating within
building 10.
[0052] Processors 18, 28 may take into account the excess heat
produced by generator 22 and used for space heating and
correspondingly reduce the forecasted electricity needs due to the
space heating. More particularly, processors 18, 28 may have a
table stored in memory that specifies an amount of electrical
energy that would be needed to achieve the same level of space
heating as produced by the excess heat of generator 22. This amount
of equivalent electrical energy may be subtracted from the
forecasted electricity needs of building 10. Although only one
distributed generator 22 is shown in association with building 10,
it is to be understood that any number of distributed generators
may be associated with the building.
[0053] Sixth, the local module may use a variety of different
optimization algorithms to determine, based on the price of
electricity, the model of the electrical load and/or distributed
generators, the customer preferences, and the optimal demand
function for the building for each of a number of upcoming time
intervals.
[0054] Seventh, the local module may include, or be connected to,
any of a number of different types of actuation devices that allow
for implementation of the optimal decision making functions
computed by the local module. An example of such an actuation
device may be an arbitrator device at the main electrical feed of
the building that governs how much electricity is to be purchased
from the utility for use within the building. Other possibilities
include a device connected to the heating system to govern
operation of the heating system as allowed by the optimal policy
computed by the local module.
[0055] Eighth, the local module software may reside on a variety of
different computing platforms in the building. This may include a
computer, a device with embedded software (e.g., connected to the
main electrical panel or sitting elsewhere in the building), the
smart meter itself, or a remote server.
[0056] Ninth, the system may include a software module at the level
of the electricity aggregator (which hereinafter may be referred to
as the "aggregator module") such as an LSE or VPP which takes as
input (1) the demand functions computed from each of the local
modules; (2) electricity price information from the wholesale
market; and (3) resource availability of the aggregator (e.g.,
available generation and storage) and associated costs.
[0057] Tenth, the aggregator module may use any of a variety of
optimization algorithms that compute business decisions for the
aggregator. Such business decisions may include the price of
electricity to set for each of a number of upcoming time intervals,
or how much of which source of electricity generation to use at
which time interval.
[0058] Eleventh, the aggregator module may be a standalone device
that makes decisions automatically and integrates with other
systems used by the aggregator to automatically implement business
decisions. Alternatively, the aggregator module may be a module
that works interactively with aggregator operators `in the loop` to
provide assistance with business decisions. For example, the
aggregator module may be used as an option evaluation or simulation
tool to project or predict effects of policy implementation.
[0059] Twelfth, the aggregator module and the local module may
exist as separate, independent entities that accept only the basic
inputs and compute the basic outputs as described in the above
possible implementations.
[0060] Thirteenth, the aggregator and local modules may be deployed
by a common provider, in which case additional functionality may be
included. For instance, the local modules may provide to the
aggregator module information pertaining to projected load
forecasts from the electrical load models and stochastic
characterizations of projected demand functions. Such information
may then be used by the aggregator module to incorporate risk
management into business decision making.
[0061] One embodiment of an adaptive load management (ALM) system
30 of the present invention is illustrated in FIG. 3. As shown,
there may be three levels of the electrical infrastructure: the
primary level includes the end users of electricity (e.g.,
residential and commercial buildings); the secondary level includes
local aggregators of electricity such as Load Serving Entities
(LSEs) and/or Virtual Power Plant Operators (VPPs); and the
tertiary level is the wholesale electricity market. ALM 30 may be
implemented via optimal decision making mechanisms at the primary
and secondary levels. More specifically, the local modules at the
primary level may take pricing information as input from the
aggregators at the secondary level, as well as local inputs
pertaining to electrical loads of the building, preferences of the
user, and possibly power generating capabilities of a local
distributed generator, and derive optimal demand functions
therefrom. These demand functions may specify how much electricity
the customer is willing to purchase at each of a range of
particular cost rates at particular times of day.
[0062] One or more load aggregators at the secondary level may
employ an Aggregator Module that may take the demand functions from
the customers as input along with wholesale electricity market
prices to arrive at an optimal business strategy for determining
future customer prices and resource allocation decisions. More
details of the modules at the primary and secondary levels are
described hereinbelow.
[0063] The Local Module at the primary level may determine, for
each customer, a demand function (e.g., customer demand for
electricity as a function of cost and time of day) that truly
reflects the value that that particular user places on the
electricity commodity. As such, the Local Module may account for
the specific preferences and needs of the user.
[0064] Although other possible implementations as described above
are within the scope of the invention, a Local Module may be an
embedded device, and may be installed at the main electrical feed
of the building. The Local Module may be connected to a smart meter
which receives electricity pricing information from the utility
company. The Local Module may also be connected to several sensing
devices distributed throughout the building, such as motion sensors
and electricity monitoring or measurement sensors. The Local Module
may further be connected to any distributed generator(s) associated
with the building, as well as to any processor(s) in communication
with the distributed generator(s).
[0065] FIG. 4 illustrates a possible implementation of a Local
Module of adaptive load management system 30 of FIG. 3. A smart
meter or other communication source (e.g., internet connection) may
provide electricity price information to the Local Module.
[0066] Distributed sensors may capture data regarding the building
environment and provide this sensor data information to the Local
Module to aid in constructing a model of the entire building load.
One example of data regarding the building environment is human
occupancy information from motion sensors. Such human occupancy
information may be used by the Local Module in learning and
constructing a model of the building occupants' presence and
absence patterns. Such a model of the ingress of people into the
building and egress of people out of the building may be used by
the Local Module in making decisions pertaining to when to purchase
electricity for heating and cooling needs. For example, upon
recognizing a consistent pattern of there being an unusually high
number of people present in the building beginning at 6 p.m. on
Monday nights, the Local Module may recognize that an increased
level of electricity will need to be purchased on future Monday
nights beginning at 6 p.m.
[0067] As another example of the Local Module using sensor data to
decide how much electricity to purchase and when, data from motion
sensors, indoor thermometers and/or electricity consumption meters
may be used to establish a pattern that a convention center
requires less electricity for heating when a relatively large
number of people are present in the convention center and providing
body heat. Thus, an amount of heat that is purchased for use in the
next few hours for heating may be inversely related to the number
of people that are sensed entering the convention center, such as
by turnstiles and/or motion sensors. In one embodiment, motion
sensors that are conventionally provided on toilets and/or urinals
may provide occupancy data that is used to create an electricity
demand model.
[0068] As yet another example of the Local Module using sensor data
to create an electricity demand model, sensors may collect
electricity consumption data from individual appliances and
devices, or may collect data regarding the total electricity
consumption of the building. Such electricity consumption data may
be used by the Local Module in constructing a model of the typical
electricity consumption behavior of the building occupants. Such a
load profile may provide valuable information to the Local Module
to determine, for instance, times of peak consumption so that
demand can reflect such preferences. Additional load information
such as more detailed models of appliances and devices in the
building (e.g., an HVAC system) may be obtained from other sources
such as an internet connection to an appliance manufacturer.
Additional remote data may also be input to the electricity demand
model, such as weather forecast information obtained from the
internet.
[0069] The weather forecast information may also be used by the
Local Module to forecast the power generating capacity of a
distributed generator in the form of solar panels. The Local Module
may then subtract this local power generation forecast from the
local demand function to arrive at a level of power that needs to
be purchased. A processor associated with the distributed generator
may measure the current actual power output of the distributed
generator, and share this capacity information with the Local
Module. To the extent that the power output of the distributed
generator can be controlled, the Local Module may control the
operation of the distributed generator in order to match the
generator's power output with the power needs forecasted by the
Local Module.
[0070] While the patterns learned from the available sensor data
may provide some indication of customer preference as reflected in
the customer's behavioral patterns, additional preference
information may be obtained directly from the user via a user
interaction device. For instance, the user may input his
preferences via an internet portal on a personal computer, a smart
phone, a user interface on a thermostat, or a separate handheld
device somewhere in the building. Examples of relevant user
preference information may be preferred maximum and minimum
temperatures.
[0071] The Local Module may take the above-described inputs along
with market pricing information from the utility, and derive
relevant load and/or generator models through pattern detection
algorithms, for example, by using the available sensor data. The
Local Module may compute an optimal demand function that minimizes
the cost of electricity to be purchased by the consumer without
sacrificing or deviating from the consumer's comfort temperature
bounds which are provided by the consumer or derived from data. One
simple example of such an optimization is a scenario wherein the
load model is for a cooling system for a building, and the customer
preferences are the maximum and minimum temperature set-points. The
optimization problem may be where
T i [ k + 1 ] = A i T i [ k ] + B i x i [ k ] subject to T i min
.ltoreq. T i [ h ] .ltoreq. T i max ##EQU00001##
for all h. Here, T is the temperature inside the building, A and B
parameterize a linear system model of the temperature dynamics, x
is energy consumed, h is the price of electricity, and k is a time
interval (e.g., hours).
[0072] The result of this optimization is a function x(h) that
specifies the amount of energy the customer is willing to purchase
at price h for a particular hour of the day. Such an optimization
may be readily extended to other load models that are derived from
the sensing information and corresponding preferences. The
temperature dynamics may be replaced with dynamics of the load
behavior such as dynamics characterizing occupant consumption
patterns or patterns of human presence in the building and absence
from the building. Such dynamics may be time dependent to reflect
behavioral dependencies on the time of day.
[0073] The resulting optimal demand function can be used in several
ways. For example, the resulting optimal demand function can be
communicated back to entities at the secondary level, provided to
the user as a feedback mechanism to enable the user to make better
energy management decisions, or input to building control system
actuators that may control appliances and devices in the building
so as to satisfy the demand function. One example may be a heating
system that is controlled so as to use only as much energy as
specified by the demand function.
[0074] The Aggregator Module at the secondary level may obtain
information from the primary level consumers regarding their
electrical load demands and the consumers' willingness to pay for
their electrical load demands. The Aggregator Module may further
make decisions for upcoming electricity price points and use of
electricity resources. A diagram of a possible architecture for the
Aggregator Module is shown in FIG. 5.
[0075] The Aggregator Module may accept the various demand
functions from the primary level as input. These demand functions
may be a product of the Local Modules or from an assumed inelastic
demand (or other sources or assumptions) if Local Modules are not
present. These demand functions may be aggregated together to
formulate a complete picture of the local market for electricity.
Other inputs may include information about other available energy
generation resources such as renewable energy sources (e.g., solar,
wind, etc.), energy storage systems, micro combined heat and power
(micro-CHPs), biomass, and natural gas turbines as well as
associated costs and prices. Other inputs may include business
strategies such as risk tolerance levels. These inputs may be fed
into an optimization function that decides, based on the wholesale
electricity market price, how much energy to allocate from which
generation sources and at which price on the local market.
[0076] In the embodiment of FIG. 5, the resource information 500
may provide information regarding the current availability of
energy from volatile/fluctuating electricity sources such as solar
and wind. In response, the aggregator module outputs real time
energy consumption commands 502 to the local modules which instruct
the local modules how much external energy is available to consume.
The energy consumption commands 502 may be at least partially based
upon the current rate of electricity production by solar and wind,
as well as the amount of energy currently available in storage
devices. The energy consumption commands 502 may be particularly
useful in embodiments in which solar/wind production and the
storage devices are the only available sources of electricity
(i.e., electricity is not available on the wholesale or retail
market, or is available but prohibitively expensive).
[0077] A water heater or building HVAC system may be considered a
form of thermal energy storage, which may be more efficient than
storing the energy in electrical form and then converting the
electrical energy to thermal energy when the thermal energy is
actually needed. Thus, any available electrical energy that is
locally produced by wind and/or solar energy may be immediately
used by the water heater and/or building HVAC system. The water
heater and/or HVAC system then converts the electrical energy into
thermal energy in the form of heat (or possibly in the form of a
drop in building temperature in the case of an air conditioning
HVAC system). Depending upon how well the water heater and/or
building is thermally insulated, the water heater and/or building
may store the thermal energy for a period of time until it is
actually needed. For example, solar energy collected during the day
may be thermally stored in the water heater for use in the evening
when residents return to their homes and begin using hot water. As
another example, wind energy collected at night (by another form of
distributed generator) may be thermally stored in the thermal mass
of an office building as a result of HVAC system operation. Thus,
the HVAC system does not need to use as much electricity that must
be bought on the open market in order to achieve a desired building
temperature when office workers return to their offices later in
the morning.
[0078] While the Aggregator Module may operate independently of the
existence of Local Modules, additional benefits may be obtained if
modules at both primary and secondary levels are in place. For
instance, if a single company or partnering company installs
systems at multiple levels, it may be beneficial to have both an
aggregator module and a local module. In this case, the value of
electricity in the market may be truly obtained from the primary
levels and therefore the value of electricity may be reflected in
the market price as information flows across the primary and
secondary levels. Furthermore, additional input information that
may be obtained from the Local Modules such as stochastic load
projections, may enable secondary level operators to manage risk in
a effective way.
[0079] In FIG. 6, there is shown one embodiment of a method 600 of
the present invention for determining an amount of electricity to
purchase. In a first step 602, electrical power consumption
characteristics of an electrical load are determined. The
electrical load is used by an end user of the electricity. For
example, various types of sensors may be provided on premises, such
as motion sensors, temperature sensors, humidity sensors, lighting
sensors, and sensors to measure electricity consumption.
Electricity consumption sensors may be directly connected to
individual loads on the premises in order to detect the electricity
consumption of individual devices. Such electricity consumption
sensors may also measure aggregated electrical information, such as
from the main electrical feed to the premises or at the circuit
level. Data regarding the use of individual appliance may be
obtained from aggregate sensor information via a non-intrusive load
monitoring system, such as shown in FIG. 2. Sensors may also sense
the state or position of user controls for the electrical loads.
For instance, sensors may detect the set temperature of a
thermostat, and such set temperature data may be recorded in
correspondence to measured electrical consumption of the
corresponding HVAC system. In another instance, sensors may detect
the operating speed of a piece of machinery, and such operating
speed data may be recorded in correspondence to measured electrical
consumption of the corresponding piece of machinery. All of the
sensor data may be processed to thereby ascertain patterns in
electricity use and to construct a model of electricity consumption
behavior for the premises which can be input to the local
module.
[0080] The above-described sensors may collect electricity
consumption data from individual appliances, machines, devices, and
electrical systems. Alternatively, the sensors may collect data
regarding the total electricity consumption on the premises, and
this data may be recorded in conjunction with corresponding
environmental sensor data regarding machine settings provided by
the user, performance of the machines, temperatures and other
environmental conditions on the premises that may affect
electricity consumption. The data may also be time-stamped such
that times of peak and lowest electricity consumption may be
identified.
[0081] In addition to the electrical power consumption
characteristics that are empirically measured by the inventive
system as described above, electrical power consumption
characteristic data associated with the individual machines,
appliances, devices, etc. on the premises (e.g., a robotic system)
may be obtained from the manufacturers of the apparatuses or from a
third party data provider. Such electrical performance
specifications may be automatically obtained via the internet.
Other pertinent data may be obtained from remote sources, such as
past weather condition data from a web site of the National Weather
Service.
[0082] In a next step 604, a preference of the end user for an
output of the electrical load is ascertained. The output varies
with a rate of electrical power consumption by the load. As one
example, the set temperatures that a user inputs into a thermostat
through multiple day-long cycles may be recorded. Also recorded may
be the power consumption of the HVAC system corresponding to the
set temperature variations. Other pertinent data that may affect
the electrical power consumption by the load may be recorded in
conjunction with the other data. For example, ambient weather
conditions may be recorded in conjunction with the data regarding
the power consumption of the HVAC system and the user set
temperatures.
[0083] Behavioral patterns of the end user may be learned from the
available sensor data, and these patterns may indicate one or more
preferences of the end user. Additional end user preference data
may be obtained directly from manual or oral inputs from the end
user via a user interface on the premises, such as an internet
portal, a smart phone, a thermostat, or a personal electronic
device. Examples of relevant end user preference information may be
a machine speed and/or output force as preferred by a human factory
manager.
[0084] Next, in step 606, a mathematical model of an amount of
electrical power to be consumed by the load as a function of time
and of monetary cost of the electricity is created. The model is
dependent upon the electrical power consumption characteristics of
the electrical load and the preference of the end user for an
output of the electrical load. For example, in one embodiment, from
the above-described inputs (electrical power consumption
characteristics of the electrical apparatuses and the preference of
the end user for outputs of the electrical apparatuses) along with
market pricing information from the utility company or a commodity
exchange, relevant load models may be derived through pattern
detection algorithms, for example. As described above, electrical
power consumption characteristics of the electrical apparatuses and
the preference of the end user for outputs of the electrical
apparatuses may be obtained or derived by analysis of the available
sensor data. The mathematical model may be in the form of an
optimal demand function that minimizes the cost of electricity to
be purchased by the end user while still staying within the ranges
or limits of the end user's preferred outputs of the electrical
apparatuses. One simple example of such an optimization is a
scenario wherein the load model is for a machine in a factory, and
the end user preferences are the maximum and minimum machine
speeds. The optimization problem may be where
S i [ k + 1 ] = A i S i [ k ] + B i x i [ k ] subject to S i min
.ltoreq. S i [ h ] .ltoreq. S i max ##EQU00002##
for all h. Here, S is the speed of the machine, A and B
parameterize a linear system model of the speed dynamics, x is
energy consumed, h is the price of electricity, and k is a time
interval (e.g., seconds).
[0085] The result of this optimization is a function x(h) that
specifies the amount of energy the end user desires to purchase at
price h for a particular time of the day. Such an optimization may
be readily extended to other load models that are derived from the
sensing information and corresponding preferences. The speed
dynamics may be replaced with dynamics of the load behavior such as
dynamics characterizing the work piece or substance that the
machine is operating on. For example, a slow hardening of a liquid
that a machine is working on (e.g., stirring) may result in the
machine using more power as the liquid hardens. Such dynamics may
be time dependent to reflect changes in the work piece or substance
with time.
[0086] In a final step 608, an amount of electricity is purchased
based on the mathematical model of an amount of electrical power to
be consumed by the load, and based on the monetary cost of the
electricity. For example, the mathematical model may specify an
optimal amount of electrical energy that should be purchased during
a particular time period as a function of the cost of electricity.
It is possible that the actual cost of the electricity is not known
with precision until the electricity is actually bought or bid for.
In this case, the actual price of the electricity may be input into
the mathematical model, and the model may output an optimal amount
of electrical energy to purchase at that particular known
price.
[0087] As one example, an electric vehicle may be re-charged over
night while the end user sleeps. Data regarding the end user's
driving needs the following day may be ascertained through pattern
recognition or via direct user input. An amount of electricity to
re-charge the vehicle may be purchased dependent upon the cost of
the electricity on that particular night, or on an hour-by-hour
basis. For instance, the user may specify his preference that if
the cost of electricity is above a threshold price, then he wants
enough electricity that there is 95% certainty that he can get
through the following day without recharging away from home; and if
the cost of electricity is below the threshold price, then he wants
enough electricity that there is 99% certainty that he can get
through the following day without recharging away from home.
[0088] If the price of the electricity changes on an hour-by-hour
basis, the user may specify that the rate of electrical re-charging
be inversely related to the price of electricity early in the
available time period for recharging (e.g., early being 10 p.m. to
11 p.m. during an available re-charging time period extending from
10 p.m. to 6 a.m). For example, during this 10-11 p.m. hour,
recharging may be performed at 6 kilowatts if the electricity cost
is in the lowest 20th percentile of historic costs, but recharging
may be performed at only 1 kilowatt if the electricity cost is in
the highest 20th percentile. Recharging may be performed at 3
kilowatts at any other cost. In contrast, if it is 5 a.m. (i.e.,
the final 5 a.m. to 6 a.m. recharging period is beginning), and the
vehicle has still not been fully charged, or has not been charged
to an acceptable level, then recharging may be performed at the
maximum rate of 6 kilowatts regardless of the cost of electricity
at that hour. The amount of electricity to be purchased at a given
price may increase throughout the night as the need to re-charge to
an acceptable level gets more urgent closer to the end of the 10
p.m. to 6 a.m. re-charging window.
[0089] In FIG. 7, there is shown one embodiment of a method 700 of
the present invention for distributing electricity among a
plurality of end users. In a first step 702, electrical energy
consumption characteristics of each of a plurality of electrical
loads are determined. Each of the electrical loads is used by a
corresponding one of the end users of the electricity. For example,
sensors may detect human motion, environmental conditions such as
temperature, humidity, and light levels, and electricity
consumption by each and/or all of the electrical loads together.
Electricity consumption sensors may be placed in association with
individual loads on the premises in order to detect the electricity
consumption of the individual devices. The sensor may measure the
electricity consumption directly, or the sensors may measure the
electricity consumption indirectly, such as inductively. Aggregated
electrical information may also be measured, such as from the main
electrical feed to the premises or by summing the electricity
consumption of all of the individual loads. Data regarding the
electricity of individual appliances may be obtained via ammeters
and voltmeters, such as shown in FIG. 2. The state or position of
user controls for the electrical loads may also be known by the
control electronics, and this information may be provided to the
inventive system. For instance, re-charging parameters for each of
a plurality of vehicle recharging stations may be known by a
central recharging controller, and such electrical re-charging
parameters may be recorded in correspondence to measured electrical
consumption of each of the re-charging stations. In another
instance, dryness settings (e.g., damp, normal dry, more dry) for
each of a plurality of dryers operating in a Laundromat may be
known by a central controller, and such dryness settings may be
recorded in correspondence to measured electrical consumption of
the individual dryers. All of the sensor data may be processed to
thereby ascertain patterns in electricity use and to construct a
model of electricity consumption behavior for the premises which
can be input to the local module.
[0090] The above-described sensors and/or central digital
controllers may collect electricity consumption data regarding
individual appliances, machines, devices, and electrical systems.
Alternatively, the sensors and/or central digital controllers may
collect data regarding the total electricity consumption on the
premises, and this data may be recorded in conjunction with
corresponding environmental sensor data regarding machine settings
provided by the user, performance of the machines, temperatures,
humidity, wind speed and other environmental conditions on the
premises that may affect electricity consumption. The data may also
be time-stamped such that times of peak and lowest electricity
consumption may be identified, and repeating cycles in the
electricity consumption may be identified.
[0091] In addition to the electrical power consumption
characteristics that are empirically measured by the inventive
system as described above, electrical power consumption
characteristic data associated with the individual machines,
appliances, devices, etc. on the premises (e.g., vehicle recharging
stations) may be obtained from a central database that
automatically collects historic electrical power consumption
characteristic data for the apparatuses via the internet. Such
electrical power consumption characteristic data may be
automatically measured at various geographically dispersed
locations for similar apparatus models, such that electricity
consumption data from a large number of similar apparatuses may be
leveraged to benefit each of the individual apparatuses.
[0092] In a next step 704, for each of the electrical loads, a
preference of the corresponding end user for an output of the
electrical loads is ascertained. The output varies with an amount
of electrical energy consumed by the load. As one example, the
dryness settings that corresponding users input into a group of
clothes dryers may be recorded. Also recorded may be the power
consumption of each of the individual dryers corresponding to the
dryness settings. Other pertinent data that may affect the
electrical power consumption by the load may be recorded in
conjunction with the other data. For example, weights of the
clothes in each individual dryer may be recorded in conjunction
with the data regarding the power consumption of the dryers and the
users' preferred dryness settings.
[0093] Behavioral patterns of previous groups of end user may be
learned from historic recorded data, and these patterns may
indicate one or more preferences of the end user. For instance,
historic data may show that a certain percentage of users turn off
the dryer and remove their clothes before the clothes have reached
the set dryness condition. Additional end user preference data may
be obtained directly from manual or oral inputs from the end user
via a user interface on the premises, such as an internet portal, a
smart phone, a kiosk, user interfaces on the dryers, or a personal
electronic device. Examples of relevant end user preference
information manually or orally provided by users may include
whether the actual dryness of their clothes is wetter or dryer than
the setting the users requested. Amounts of electrical energy
purchased in the future may then be compensated for the feedback
thus provided by the users as to whether the previously purchased
amounts of electrical energy were insufficient or more than
sufficient to achieve the users' desired dryness settings.
[0094] Next, in step 706, a mathematical model of an amount of
electrical energy to be consumed by each of the electrical loads as
a function of time and of monetary cost of the electrical energy is
created. Each of the models is dependent upon the electrical energy
consumption characteristics of the electrical loads and the
preference of the end user for an output of the electrical loads.
In one embodiment, from the above-described inputs (electrical
power consumption as a function of time and of the end user's
desired output of the electrical apparatuses) along with a known
pricing schedule for the electricity, relevant load models may be
derived with the benefit of analysis of historical electricity
usage patterns, for example.
[0095] As described above, electrical power consumption functions
of the electrical apparatuses and the end user's preferred outputs
of the electrical apparatuses may be obtained or derived by
analysis of the available current and historical sensor data. In
one embodiment, the mathematical model may be in the form of an
optimal demand function that determines an amount of electricity to
be purchased by the end user that achieves the best trade-off
between the monetary cost of the electricity and the end user's
varying satisfaction (e.g., utility) from each level of output of
the electrical apparatuses. One simple example of such an
optimization is a scenario wherein the load models are for a fleet
of clothes dryers in a Laundromat, and the end user preferences are
desired levels of dryness for the batches (i.e., loads) of
clothing. Each level of dryness may correspond to a maximum and
minimum percentage reduction in weight of the batches of clothing
during the drying process. Each clothes dryer may have a built-in
scale for weighing the batch of clothes inside the dryer. The
optimization problem may be where
W i [ k + 1 ] = A i W i [ k ] + B i x i [ k ] subject to W i min
.ltoreq. W i [ h ] .ltoreq. W i max ##EQU00003##
for all h. Here, W is the weight of the batch of clothes, A and B
parameterize a linear system model of the drying dynamics, x is
energy consumed, h is the price of electricity, and k is a time
interval (e.g., minutes).
[0096] The mathematical model of an amount of electrical energy to
be consumed by a particular electrical load may be dependent upon
the amount of electrical energy to be consumed by one or more other
electrical loads. For example, in the case of a Laundromat, the
amount of electricity needed by the HVAC system load to heat the
Laundromat itself may be dependent upon how much excess heat is
produced by the clothes dryers and released into the ambient
environment. A table in memory may equate an amount of electricity
that can be saved in running the HVAC with a number of clothes
dryers operating; particular heat settings of the dryers; clothing
weights in each of the dryers; times at which the dryers are each
scheduled to complete the drying cycle and release excess heat upon
opening of the dryer door; and even locations of the running dryers
within the Laundromat.
[0097] In step 708, the mathematical models of electrical energy
consumption are aggregated together. As described above, a
respective mathematical optimization model may be performed for
each dryer in the Laundromat, and these optimizations may be
aggregated (e.g., summed together). The result of this summed
optimization may be a function x.sub.total(h) that specifies the
amount of energy the end user desires to purchase at price h for a
particular time of the day. Such an optimization may be readily
extended to other load models that are derived from the sensing
information and corresponding preferences. The weight dynamics may
be extended to reflect the interaction between the changing weight
of the batch and the amount of electricity needed to dry the
clothes. For example, greater power may be needed to rotate the
clothes in the dryer at the beginning of the drying cycle when the
clothes are heavier, and this level of power for turning the
clothes may slowly decrease as the clothes dry and become lighter.
Thus, such dynamics may be time dependent to reflect changes in the
weight of the clothes with time.
[0098] In a next step 710, a bid is prepared for an amount of
electrical energy to be delivered during a specified period of
time. The bid is prepared based upon the aggregation of the
mathematical models of electrical energy consumption, and upon a
known current market price and/or a future expected market price of
the electrical energy. For example, the aggregation of the
mathematical models may specify an optimal amount of electrical
energy that should be purchased during a particular time period as
a function of a current cost of electricity and/or an expected
future cost of electricity. It is possible that the actual cost of
the electricity is not known with precision until the electricity
is actually bought or a bid is accepted. In this case, the actual
price of the electricity may be input into the mathematical models,
and the aggregation of the models may output an optimal amount of
electrical energy to purchase at that particular known price.
[0099] The bidding process may be iterative wherein the system
submits a bid for a certain amount of electricity at a certain
price, and, if the bid is rejected, the system continues to re-
submit bids until one of the bids is accepted. Alternatively, the
system may submit a bid in the form of an amount of electricity
that the system commits to purchase at each of a range of possible
unit prices for the electricity.
[0100] As one example, a Laundromat may be able to purchase or
otherwise obtain electricity from a variety of sources. For
example, the Laundromat may be able to selectively purchase
electricity from a number of utilities, the Laundromat may be able
to provide its own electricity internally such as from solar
collectors, generators, and/or storage devices for storing such
internally-generated electricity, and/or the Laundromat may be able
to purchase excess electricity from other businesses who may
internally produce more electricity than they need.
[0101] The amount of electricity needed by the Laundromat in the
next couple of hours may be ascertained from the aggregation of the
electricity consumption models for each of the dryers. These
individual models may be dependent upon a dryness setting selected
by the user as well as a weight of the clothes in the batch. In one
embodiment, the weight of the dry clothes as put into and measured
by the washing machine is subtracted from the weight of the wet
clothes as put into the dryer to thereby ascertain a total water
weight, or percentage water weight, of the clothes put into the
dryer. Thus, the amount of electricity needed, as reflected in the
model, may depend upon the total water weight, or percentage water
weight, of the clothes put into the dryer. An amount of electricity
to operate the fleet of dryers may be purchased dependent upon both
the cost of the electricity at that particular time, or on an
hour-by-hour basis, and the availability and cost of electricity
from other internal and external sources. For instance, the user
may specify to the system his preference that if the cost of
electricity is above a threshold price, then he wants to use all of
the electricity that he can internally produce, and purchase as
little electricity as necessary to operate the dryers. Conversely,
if the cost of electricity is below the threshold price, then the
user may specify that he wants to purchase as much electricity as
is necessary to operate the dryers, and he will not internally
produce electricity. The user may then store whatever electricity
that is internally collected.
[0102] If the price of the electricity changes on an hour-by-hour
basis, the user may internally produce some electricity with a
distributed generator and store this electricity as backup in case
the price of electricity exceeds the threshold price some time
within the next several hours. The system may assume that the
closer the present price of electricity is to the threshold price,
the more likely it is that the price within the next several hours
will exceed the threshold price. If the price does happen to rise
above the threshold price, then it may be desirable to have a
backup supply of electricity stored, or to have bought an option to
purchase the electricity from another source. Thus, the user may
specify that the rate of internal electricity production and
storage, and/or whether options to purchase electricity from other
providers are bought by the user from the other providers or on the
open market, be related to the current price of electricity
relative to the threshold price.
[0103] In determining the degree to which the system should make
arrangements for obtaining electricity from a backup source, the
system may also take into account any trend there is in the current
price of electricity and extrapolate the trend. Alternatively, or
in addition, the system may take into account historic trends in
the price of electricity during that particular period of the day,
on that particular day of the week, season of the year, etc.
[0104] In a final step 712, the bid is submitted to a supplier of
electrical energy. For example, the system may automatically and
electronically submit its bid for electricity to the utility
company or to some go-between party that manages and responds to
such bids.
[0105] In FIG. 8, there is shown another embodiment of a method 800
of the present invention for determining an amount of electricity
to purchase. In a first step 802, electrical power consumption
characteristics of an electrical load are determined. The
electrical load is used by an end user of the electricity. In the
specific example, the electrical load maintains a temperature of a
thermal body such as a water heater, a building, or a swimming
pool. Sensors such as temperature sensors may be provided in
association with the thermal body. Electricity consumption sensors
may be directly connected to the individual loads, such as
resistance heaters or air conditioning units, in order to detect
the electricity consumption of the resistance heaters or air
conditioning units. Such electricity consumption sensors may also
measure aggregated electrical information, such as from the main
electrical feed to the premises or at the circuit level. Data
regarding the use of individual appliance may be obtained from
aggregate sensor information via a non-intrusive load monitoring
system, such as shown in FIG. 2. Sensors may also sense the state
or position of user controls for the electrical loads. For
instance, sensors may detect the set temperature of a thermostat,
and such set temperature data may be recorded in correspondence to
measured electrical consumption of the corresponding resistance
heaters or air conditioning units. All of the sensor data may be
processed to thereby ascertain patterns in electricity use and to
construct a model of electricity consumption behavior for the
resistance heaters or air conditioning units, which can be input to
the local module.
[0106] In addition to the electrical power consumption
characteristics that are empirically measured by the inventive
system as described above, electrical power consumption
characteristic data associated with the individual the resistance
heaters or air conditioning units may be obtained from the
manufacturers of the apparatuses or from a third party data
provider. Such electrical performance specifications may be
automatically obtained via the internet. Other pertinent data may
be obtained from remote sources, such as past weather condition
data from a web site of the National Weather Service.
[0107] In a next step 804, a preference of the end user for an
output of the electrical load is ascertained. The output varies
with a rate of electrical power consumption by the load. As one
example, the set temperatures that a user inputs into a thermostat
through multiple day-long cycles may be recorded. Also recorded may
be the power consumption of the resistance heaters or air
conditioning units corresponding to the set temperature variations.
Other pertinent data that may affect the electrical power
consumption by the load may be recorded in conjunction with the
other data. For example, ambient weather conditions may be recorded
in conjunction with the data regarding the power consumption of the
resistance heaters or air conditioning units and the user set
temperatures.
[0108] Behavioral patterns of the end user may be learned from the
available sensor data, and these patterns may indicate one or more
preferences of the end user. Additional end user preference data
may be obtained directly from manual or oral inputs from the end
user via a user interface on the premises, such as an internet
portal, a smart phone, a thermostat, or a personal electronic
device. Examples of relevant end user preference information may be
air temperature or water temperature. These temperature preferences
may vary according to a user-specified time schedule.
[0109] Next, in step 806, a mathematical model of an amount of
electrical power to be consumed by the load as a function of time
and of monetary cost of the electricity is created. The model is
dependent upon the electrical power consumption characteristics of
the electrical load and the preference of the end user for an
output of the electrical load. For example, in one embodiment, from
the above-described inputs (electrical power consumption
characteristics of the resistance heaters or air conditioning units
and the preference of the end user for air temperature or water
temperatures) along with market pricing information from the
utility company or a commodity exchange, relevant load models may
be derived through pattern detection algorithms, for example. As
described above, electrical power consumption characteristics of
the resistance heaters or air conditioning units and the preference
of the end user for air and/or water temperatures may be obtained
or derived by analysis of the available sensor data. The
mathematical model may be in the form of an optimal demand function
that minimizes the cost of electricity to be purchased by the end
user while still staying within the ranges or limits of the end
user's preferred air/water temperatures. One simple example of such
an optimization is a scenario wherein the load model is for
resistance heaters or air conditioning units, and the end user
preferences are the maximum and minimum air/water temperatures. The
optimization problem may be where
T i [ k + 1 ] = A i T i [ k ] + B i x i [ k ] subject to T i min
.ltoreq. T i [ h ] .ltoreq. T i max ##EQU00004##
for all h. Here, T is the air/water temperature, A and B
parameterize a linear system model of the speed dynamics, x is
energy consumed, h is the price of electricity, and k is a time
interval (e.g., seconds).
[0110] The result of this optimization is a function x(h) that
specifies the amount of energy the end user desires to purchase at
price h for a particular time of the day. Such an optimization may
be readily extended to other load models that are derived from the
sensing information and corresponding preferences.
[0111] In step 808, an amount of electricity is reserved based on
the mathematical model of an amount of electrical power to be
consumed by the load, and based on the monetary cost of the
electricity. For example, the mathematical model may specify an
optimal amount of electrical energy that should be reserved during
a particular time period as a function of the cost of electricity.
As used herein, "reserve" or "reserved" may mean informing a
provider of electricity of the user's forecasted need for a certain
amount of electricity at a certain time or time period so that the
electricity provider may make arrangements to be able to provide
the requested amount of electricity at the requested time.
Reserving electricity may or may not involve a legal obligation to
buy the electricity. It is possible that the actual cost of the
electricity is not known with precision until the electricity is
actually bought or delivered. In this case, the actual price of the
electricity may be input into the mathematical model, and the model
may output an optimal amount of electrical energy to reserve at
that particular known price.
[0112] As one example, an electric water heater of an apartment
building may need to provide large volumes of electricity starting
at 6 a.m. on weekdays, but may need to provide much smaller amounts
between midnight and 6 a.m. Data regarding the building occupants'
hot water needs the following day may be ascertained through
pattern recognition or via direct user input. An amount of
electricity to heat the water may be purchased dependent upon the
cost of the electricity on that particular night, or on an
hour-by-hour basis.
[0113] Next, in step 810, it is determined whether the most
recently reserved amount of electricity has been delivered to the
user. In the above example, the amount of reserved electricity may
be delivered beginning at 6 a.m. Before the reserved amount of
electricity has actually been delivered, the system may
periodically update or adjust the amount of electricity that is
reserved. For example, the desired amount of electricity may be
first reserved at 1 a.m. based on steps 802-808. However, new input
data received after the initial reservation may change how much
electricity the user wants to reserve. Such new input data may
result from the system sensing a change in the number of residents
present in the building and likely to want hot water in the
morning. Thus, if the electricity has not yet been actually
delivered (e.g., it is not yet 6 a.m.), then steps 802-808 may be
periodically repeated to ensure that the amount of electricity
reserved is based on the most recent input data. In one embodiment,
steps 802-808 may be repeated hourly (e.g., at 2 a.m., 3 a.m., 4
a.m. and 5 a.m.).
[0114] While this invention has been described as having an
exemplary design, the present invention may be further modified
within the spirit and scope of this disclosure. This application is
therefore intended to cover any variations, uses, or adaptations of
the invention using its general principles.
* * * * *