U.S. patent application number 13/621195 was filed with the patent office on 2014-03-20 for decision support system based on energy markets.
This patent application is currently assigned to HONEYWELL INTERNATIONAL INC.. The applicant listed for this patent is Radek Fisera, Karel Macek, Martin Strelec. Invention is credited to Radek Fisera, Karel Macek, Martin Strelec.
Application Number | 20140081704 13/621195 |
Document ID | / |
Family ID | 50275400 |
Filed Date | 2014-03-20 |
United States Patent
Application |
20140081704 |
Kind Code |
A1 |
Strelec; Martin ; et
al. |
March 20, 2014 |
DECISION SUPPORT SYSTEM BASED ON ENERGY MARKETS
Abstract
A system for purchasing and selling power that fairly
accommodates sellers and buyers. For instance, a submarket may be
formed between a utility company or retailer and its consumer or
customer. The utility or retailer may eliminate differences between
generated or purchased power and demanded power. Mechanisms used
for elimination of power differences may incorporate utilizing
power from ancillary services, purchasing or selling power on the
spot market, and affecting a demand for power with demand response
programs. A difference between purchased power and demanded power
may be minimized by forming an optimal power stack having a mix of
power of the demand response program, power at the spot market
and/or power of ancillary services. An optimization sequence may be
implemented to minimize the difference between the purchased power
and demanded power, and to maximize profit.
Inventors: |
Strelec; Martin; (Chodov,
CZ) ; Macek; Karel; (Prague, CZ) ; Fisera;
Radek; (Mnichovice, CZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Strelec; Martin
Macek; Karel
Fisera; Radek |
Chodov
Prague
Mnichovice |
|
CZ
CZ
CZ |
|
|
Assignee: |
HONEYWELL INTERNATIONAL
INC.
Morristown
NJ
|
Family ID: |
50275400 |
Appl. No.: |
13/621195 |
Filed: |
September 15, 2012 |
Current U.S.
Class: |
705/7.31 ;
705/7.29 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 40/04 20130101; G06Q 50/06 20130101; G06Q 30/0202
20130101 |
Class at
Publication: |
705/7.31 ;
705/7.29 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A system for optimizing a balance of power, comprising: a first
mechanism that decides about purchasing power from ancillary
services; a second mechanism that purchases or sells power at a
spot market; a third mechanism that purchases or sells power
according to a demand response program; and a processor having a
connection to the first, second and third mechanisms; and wherein
the processor processes a reduction of a difference between
purchased power of a supplier and demanded power of a consumer, by
determining an amount of power bought and/or sold with one or more
of the first, second and third mechanisms.
2. The system of claim 1, wherein the difference between purchased
power and demanded power is minimized by forming an optimal power
stack.
3. The system of claim 2, wherein the power stack comprises a mix
having power via the demand response program, power at the spot
market, and/or power from ancillary services.
4. The system of claim 3, wherein the processor determines the mix
of the power stack to minimize the difference between purchased
power and demanded power.
5. The system of claim 1, further comprising: an optimization
sequence; and wherein the optimization sequence comprises
maximizing profit and/or minimizing the difference between the
purchased power and the demanded power.
6. The system of claim 1, wherein: P.sub.Load is demanded power;
P.sub.Purchased is purchased power; and .DELTA.P is the difference
between P.sub.Load and P.sub.Purchased.
7. The system of claim 6, wherein: P.sub.DR=.alpha..DELTA.P;
P.sub.Spot.beta..DELTA.P; P.sub.AS=.chi..DELTA.P;
.DELTA.P.sub.Correction comprises P.sub.DR, P.sub.Spot and
P.sub.AS; .DELTA.P+.DELTA.P.sub.Correction=0; and
.alpha.+.beta.+.chi..apprxeq.1.
8. The system of claim 6, wherein a could be a discrete variable
representing a magnitude of acceptance of the consumer in the
demand response program.
9. The system of claim 6, wherein: a power difference is
.DELTA.P=P.sub.Load-P.sub.Purchased; for a load greater than
supply, .DELTA.P=P.sub.DR-P.sub.Spot-P.sub.AS=0 and
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)+R.sub.Spot(.beta..DEL-
TA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased;
-P.sub.DR-P.sub.Spot-P.sub.AS=.DELTA.P.sub.Correction; R(.DELTA.P)
is profit; R.sub.Load(P) is a price of the load;
R.sub.DR(.alpha..DELTA.P) is a price of power determined between
the utility and the consumer in a demand response relationship;
R.sub.Spot(.beta..DELTA.P) is a price of power on an open market;
R.sub.AS(.chi..DELTA.P) is a price of power from a system operator
providing ancillary services at a set price;
.alpha.+.beta.+.chi..apprxeq.1; and max .alpha. , .beta. , .chi.
.di-elect cons. 0 ; 1 R ( P , .DELTA. P ) ##EQU00005## for
optimization.
10. The system of claim 6, wherein: a power difference is
.DELTA.P=P.sub.Load-P.sub.Purchased; for a load greater than
supply, .DELTA.P=P.sub.DR+P.sub.Spot+P.sub.AS=0 and
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)-R.sub.Spot(.beta..DEL-
TA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased;
-P.sub.DR+P.sub.Spot+P.sub.AS=.DELTA.P.sub.Correction; R(.DELTA.P)
is profit; R.sub.Load(P) is a price of the load;
R.sub.DR(.alpha..DELTA.P) is a price of power determined between
the utility and the consumer in a demand response relationship;
R.sub.Spot(.beta..DELTA.P) is a price of power on an open market;
R.sub.AS(.chi..DELTA.P) is a price of power from a system operator
providing ancillary services at a set price;
.alpha.+.beta.+.chi..apprxeq.1; and max .alpha. , .beta. , .chi.
.di-elect cons. 0 ; 1 R ( P , .DELTA. P ) ##EQU00006## for
optimization.
11. A system for managing energy, comprising: a server; a virtual
energy marketing (VEM) module connected to the server; a utility
energy source connected to the VEM module; a meter data management
(MDM) database connected to the VEM module; an energy consumer
connected to the server and the MDM database; and an energy market
source connected to the VEM module.
12. The system of claim 11, wherein the VEM module comprises: a
decision engine connected to the utility energy source and the
server; a scenario generator connected to the decision engine; a
forecaster mechanism connected to the scenario generator and the
MDM database; and a probability distribution generator connected to
the forecaster, the energy market database and the weather forecast
database.
13. The system of claim 12, further comprising a weather forecast
database connected to the probability distribution generator.
14. The system of claim 12, wherein: the utility provides
information about power unbalance between loaded power and
purchased power and/or grid status to the decision engine; demand
response signals and business information are exchanged between the
consumer and the server; the decision engine provides optimal
timing and selection of demand response resources to the server;
the consumer provides energy consumption data to the MDM database;
the forecaster receives selected relevant data from the MDM
database; and the energy market source provides a market price to
the probability distribution generator.
15. The system of claim 14, further comprising: a weather forecast
database connected to the probability distribution generator; and
wherein the weather forecast database provides weather parameters
to the probability distribution generator.
16. The system of claim 14, wherein the server is a demand response
automation server.
17. A method for coordinating power transactions, comprising:
finding out an amount of purchased power of a utility; finding out
an amount of demanded power by a consumer; minimizing a power
difference between an amount of purchased power of the utility and
an amount of demanded power by the consumer; minimizing the power
difference that depends on, at least in part, purchasing power from
a system operator providing ancillary services at a set price,
selling or purchasing power on the open market at market price,
and/or selling or purchasing power at a price determined between
the utility and the consumer in a demand response relationship.
18. The method of claim 17, wherein: the power difference is
between an amount of purchased power of the utility and an amount
of demanded power by the consumer, with optimizing a combination of
P.sub.Spot, P.sub.AS and P.sub.DR; and goals of optimizing the
combination comprise maximizing profit to the utility and
minimizing the power difference.
19. The method of claim 18, wherein: .alpha..DELTA.P,
.beta..DELTA.P and .chi..DELTA.P represent portions of the
respective power that constitute the power difference between the
amount of purchased power of the utility and the amount of demanded
power by the consumer; parameters .alpha., .beta. and .chi. are
determined to minimize the power difference; and
.alpha.+.beta.+.chi..apprxeq.1.
20. The method of claim 17, wherein: a power difference is
.DELTA.P=P.sub.Load-P.sub.Purchased; for a load greater than
supply, .DELTA.P=P.sub.DR-P.sub.Spot-P.sub.AS=0 and
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)+R.sub.Spot(.beta..DEL-
TA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased;
-P.sub.DR-P.sub.Spot-P.sub.AS=.DELTA.P.sub.Correction; R(.DELTA.P)
is profit; R.sub.Load(P) is a price of the load;
R.sub.DR(.alpha..DELTA.P) is a price of power determined between
the utility and the consumer in a demand response relationship;
R.sub.Spot(.beta..DELTA.P) is a price of power on an open market;
R.sub.AS(.chi..DELTA.P) is a price of power from a distribution
company providing ancillary services at a set price; and
.alpha.+.beta.+.chi..apprxeq.1.
21. The method of claim 17, wherein: a power difference is
.DELTA.P=P.sub.Load-P.sub.Purchased; for a load greater than
supply, .DELTA.P=P.sub.DR+P.sub.Spot+P.sub.AS=0 and
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)-R.sub.Spot(.beta..DEL-
TA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased;
-P.sub.DR+P.sub.Spot+P.sub.AS=.DELTA.P.sub.Correction; R(.DELTA.P)
is profit; R.sub.Load(R) is a price of the load;
R.sub.DR(.alpha..DELTA.P) is a price of power determined between
the utility and the consumer in a demand response relationship;
R.sub.Spot(.beta..DELTA.P) is a price of power on an open market;
R.sub.AS(.chi..DELTA.P) is a price of power from a distribution
company providing ancillary services at a set price; and
.alpha.+.beta.+.chi..apprxeq.1.
Description
BACKGROUND
[0001] The present disclosure pertains to power and particularly to
stabilization of power grids. More particularly, the disclosure
pertains to buying and selling power.
SUMMARY
[0002] The disclosure reveals a system for purchasing and selling
power that fairly accommodates sellers and buyers. For instance, a
submarket may be formed between a utility company or retailer and
its consumer or customer. The utility or retailer may eliminate
differences between generated or purchased power based on day-ahead
predictions and demanded power in a given day. Mechanisms used for
elimination of power differences may incorporate purchasing or
selling power on the spot market, and affecting a demand for power
with demand response programs. A difference between purchased power
and demanded power may be minimized by forming an optimal power
stack having a mix of power of the demand response program, power
at the spot market and/or power of ancillary services. A
transmission and system operator (TSO) may operate a distribution
grid and maintain grid stability through a use of ancillary
services. The utility may pay a fee for elimination of its eventual
power imbalance to the TSO. An optimization sequence may be
implemented to minimize the difference between the purchased power
and demanded power, and to maximize profit.
BRIEF DESCRIPTION OF THE DRAWING
[0003] FIG. 1 is a diagram illustrative of the components and
dynamics among an energy market, a retailer and consumers;
[0004] FIG. 2 is a diagram of a graph of generated power and load
versus time;
[0005] FIG. 3 is a diagram of a graph of a power difference between
generated power and load versus time;
[0006] FIG. 4 is a diagram of graphs relevant to consumers' load
reduction relative to a demand response program;
[0007] FIG. 5 is a diagram of plots of spot market price versus
power and for supply and load;
[0008] FIG. 6 is a diagram of a plot of ancillary service penalty
versus power;
[0009] FIG. 7 is a diagram of a set of equations concerning
elimination of a difference in power balance equation for a load
greater than supply;
[0010] FIG. 8 is a diagram of a set of equations concerning
elimination of a difference in power balance equation for a load
less than supply;
[0011] FIG. 9 is a diagram of balance and profit equations for a
load less than supply;
[0012] FIG. 10 is a diagram of balance and profit equations for a
load greater than supply;
[0013] In FIG. 11, is a diagram of symbols representing
optimization of a power differential and profit;
[0014] FIG. 12 is a diagram of a graph involving an aggregation of
cost function values in selected scenarios with fixed
parameters;
[0015] FIG. 13 is a diagram of a graph showing considered bids for
various values of a demand response power parameter;
[0016] FIG. 14 is a diagram of a deployment scheme of the present
system relative to a virtual energy market;
[0017] FIG. 15 is a diagram of an example infrastructure portion of
demand response layout;
[0018] FIG. 16 is a diagram of an illustrative example of various
power loads and their relationships;
[0019] FIG. 17 is a diagram of an example optimization procedure;
and
[0020] FIG. 18 is a diagram about evaluation of acceptance using
various scenarios.
DESCRIPTION
[0021] A utility company may be responsible for stabilization of
the power grid and for this purpose can use several stabilization
mechanisms. The utility company or companies may have made an
effort to reduce usage of the ancillary services because of their
high prices. A demand response (DR) program may be another option
to ensure a stability of the grid by influencing the demand. With
an application of the program, the utility company may change
customers' loads if a change is beneficial. Utility companies may
offer various demand response programs to their customers and each
customer can participate in a DR program in its own way. DR
programs may be divided into programs with discrete decisions and
real-time pricing.
[0022] However, DR programs may have considerable drawbacks for
both sides--consumers and utility companies. First, the utility
company may face a rather difficult decision. In a case of programs
with discrete decisions, a DR adjustment is not necessarily smooth
and may represent a complex combinatory issue. In case of real-time
pricing, it may be difficult to determine an appropriate price as
well as the reactions of the consumers that are of a stochastic
nature. "Stochastic" may be of or pertain to a process involving a
randomly determined sequence of observations each of which is
considered as a sample of one element from a probability
distribution.
[0023] On the other hand, the consumers may have to consider their
reactions to a DR event with respect to changing prices (in the
case of real-time pricing). Alternatively, the customers may face
more or less discrete decisions.
[0024] Demand bidding programs may just exploit fixed incentives
(e.g., 0.50 cents/kW in day-ahead mode and 0.60 cents/kW in day-of
mode). The participants may then just decide whether they should
submit their bids and determine what amount of power they are
willing to curtail. Bids may be gathered by a utility or at a
demand response automation server (DRAS) and evaluated when the
time for bid-sending is over. Such an approach may have some
disadvantages. First, the fixed discount rate may not necessarily
be always beneficial because of its inherent inability to react on
current conditions (e.g., real time price, actual demand, and so
on). It is simply not necessarily a result of continuous trading
but may be rather of an apparent long-term over-designed estimate.
Second, the programs may count just with the demand reduction on
the participants' side. However, when a utility is facing a power
surplus, it may be beneficial for the utility to provide an
incentive payment to a customer who commits to move some power
required operation (i.e., a re-schedulable load) to a time interval
with a surplus.
[0025] The present approach may provide a business model for
utilities and their consumers that copes with above-mentioned
issues, and also be a related decision support tool for utilities
for bringing in significant savings.
[0026] One goal may be to create a virtual submarket between a
utility company (retailer) and its customer. A customer may
actively participate in a DR program and supply bids for a load
increase or reduction to the virtual submarket (located on utility
side).
[0027] Customers may evaluate and submit bids that consist of an
energy amount and a corresponding price. A price may depend on the
particular case and can be categorized as revenue in the case of
load reduction, or as a discount price for an additional load in
the case of a load increase.
[0028] A utility company may need to eliminate differences between
generated (purchased) and demanded power. Three kinds of mechanisms
may be utilized for elimination of a power difference. The
mechanisms may 1) use ancillary services, 2) purchase or sell power
at the spot market, or 3) influence the demand via DR program
events, respectively. Each mechanism may have its advantages and
disadvantages.
[0029] 1) Ancillary services may represent an ample power source
with deterministic prices, but these prices can be high. 2) On the
spot market, the power may be sold or bought under market prices
which are of a stochastic nature and unknown until trading time. 3)
In the present DR mechanism, prices for DR may be given by
customers and the prices may be nondeterministic, but known. The
utility may have full control of acceptance of the customer's bids
(DR power). The first two mechanisms may affect the supply side and
the last mechanism (DR) may affect the demand side. The utility may
make a decision about an optimal structure of a power stack used
for elimination of a power difference. The power may be considered
as a mix of DR power, power bought on spot market, and ancillary
services power.
[0030] A present decision support system may be provided in a form
of, e.g., a web service residing on a cloud, on an automated demand
response server and help to find an optimal ratio of power mixing.
The system may use a scenario approach for overcoming the
uncertainty that is included in customer's loads and in final spot
market prices. Advanced optimization algorithms (i.e., mixed
integer stochastic optimization) may be employed for optimal power
mixing and optimal customer bids selection. Probabilistic measures
may be exploited for an evaluation of risk. A level of risk may be
specified by the utility (e.g., conservative behavior versus
aggressive behavior).
[0031] There may be basically two major features of the present
approach. 1) The customers may have the opportunity to influence
the final incentives (which are fixed in a current demand bidding
program (DBP)) as they are allowed to send the bids that consist
not only of a power reduction/increase amount but also an expected
price for each particular amount. Furthermore, not only may load
reduction bids be requested but also bids may be made for load
increases. The utility may then decide whether it is economically
beneficial to exploit these bids or, e.g., sell the power surplus
back to the market. 2) A present decision support tool may help the
utility to make the most beneficial decisions in every step of an
operation. For example, the tool may suggest an optimum amount of
power to be taken from accepted bids (besides the decision about
which bids should be accepted), an optimum amount of power that
should be traded at the market, and so forth (e.g., from ancillary
services). The decisions may be generated by the optimization tool
that considers the stochastic nature of involved variables by using
a so-called scenario approach (i.e., a significant principle from
stochastic optimization).
[0032] There may be devices placed on the customer site that can
communicate with a DR server. Each customer may be allowed to
submit bids that consist of a provided amount of demand
reduction/increase, time interval and price offer. The bid may also
have a form of a function (price=function(power increase or
reduction)). Bids may be generated either manually or automatically
whenever they are requested by the DRAS (demand response automation
server) hosting the virtual sub-market application. Similarly with
respect to demand bidding programs, which may already be run by
most of the utilities supporting a demand response, the bids may be
requested the day before a particular event (i.e., day-ahead mode)
or directly during the event day (i.e., day-of mode, near future or
real time DR).
[0033] When an electricity demand forecaster, running at the DRAS,
indicates that, on the next day (a day-ahead mode), there may be a
high probability of a mismatch between purchased/generated power
and a forecasted demand, the request for bids may be sent to
virtually all DR participants. The participants may be requested to
send their bids up to some fixed deadline on the day before event.
The participants of the particular DR program may then submit their
bids. The utility may evaluate the most profitable composition of a
power stack needed for overcoming the purchased/generated power
versus demand discrepancy. Nearly, the same mechanism may be
utilized for the day-of events. The events may be generated when
more accurate (forecast horizon in order of hours) predictions are
available. The participants may then have, of course, less time to
submit their bids; however, they can expect higher payments as the
final price should be more influenced by the spot market and the
ancillary services price. In both modes (the day-ahead and day-of),
the virtual sub-market application running at DRAS may be
responsible for generating recommendations for a utility in how to
compound the power from different sources (e.g., the market,
ancillary services, DR bids, and so on) in the final corrective
action that matches the demand and supply with each other.
[0034] A decision support system may be based on a virtual energy
market (VEM). Accomplishments may incorporate establishing a new
mechanism for demand responses, creating a virtual submarket
between a retailer (utility) and its customers, and bringing
benefits to the retailer (i.e., higher profit) and to consumers
(i.e., more savings).
[0035] FIG. 1 may be a basic diagram illustrative of the components
and dynamics between an energy market 11, a retailer (e.g.,
utility), and consumption 13 (e.g., customers). A two-way
relationship may exist between market 11 and retailer 12. A two-way
relationship may also exist between consumption 13 and retailer 12.
For instance, bids of consumption may be either accepted or
denied.
[0036] FIG. 2 is a diagram of a graph of power versus time. In an
example, curve 14 may represent generated power and curve 15 may
represent a load. Load may be understood as power utilized by a
consumer (e.g., customer). FIG. 3 is a diagram of a graph of power
difference and time. Curve 16 may represent a power difference
between the generated/purchased power and the load. Customers may
participate actively in a DR program, supply bids for load
increases or reductions, and send these bids to the retailer
(utility, ADR Server). These bids may be used to reduce the power
difference.
[0037] FIG. 4 is a diagram of graphs relevant to customers' overall
load reduction (DR). Graph 21 for a customer #1 may be R.sub.DR
(revenue--demand response) versus P.sub.DR (power--demand response)
where there is a bar for R.sub.DR at each P.sub.DR increment. An
R.sub.Spot (revenue--spot market) is shown in graph 21. Graph 22
for customer #2 may be R.sub.DR versus P.sub.DR where there is a
bar for R.sub.DR at each P.sub.DR increment. An R.sub.Spot is shown
in graph 22. Graph 23 may be R.sub.DR versus P.sub.DR where the
information of customers #1 and #2 are combined to reveal an
aggregated load for DR. There is a bar of customer #1 or #2 for
R.sub.DR at each P.sub.DR increment, as indicated by a shading of
the bar. A .DELTA.P (e.g., a difference between generated power and
demand power) is indicated in graph 23. An R.sub.Spot level is also
shown in graph 23.
[0038] FIG. 5 is a diagram of a graph of the spot market. The graph
may be plots of R.sub.Spot (price for power unit on the spot
market) versus P.sub.Spot (power--spot market) for supply as shown
by plot 25 and for load as shown by plot 26.
[0039] FIG. 6 is a diagram of a graph of ancillary services.
[0040] The graph may be plot R.sub.Penalty (revenue--penalty)
versus P.sub.AS (power--ancillary services) as indicated by lines
27.
[0041] Particular bids may consist of an amount of energy, duration
time and incentives. The incentives may depend of the particular
case which may provide revenue in the event of load reduction and a
discount of price for additional loads in the event of a load
increase.
[0042] A retailer (utility) may eliminate differences between
purchased and load power or energy. The difference may be
eliminated via DR, spot market or ancillary power. The retailer may
make a decision about accepting bids from customers.
[0043] A retailer may purchase an energy or power profile for the
next day in the day-a-head market. The purchase may depend on a
load forecast for the next day. Generally, power differences may
occur because of load uncertainties. A retailer may want to
eliminate the differences in an economically optimal way.
[0044] Difference elimination possibilities may incorporate: 1)
Letting a system operator making use of ancillary services to
eliminate the difference (i.e., an expensive way); 2) Selling or
purchasing, for instance, electricity on the market (i.e., price is
given by market--stochastic approach); and 3) Making a demand
response action (i.e., price is given by relationship of a
trader-consumer--deterministic approach).
[0045] One may note an optimization at a one time instance of a
.DELTA.P elimination with a set 31 of equations indicated in FIG. 7
for a load greater than supply. A balance equation may be
indicating .DELTA.P=P.sub.load-P.sub.Purchased.
"-P.sub.DR-P.sub.Spot-P.sub.AS" may indicate specifics of a
.DELTA.P correction, as ".DELTA.P-P.sub.DR-P.sub.Spot-P.sub.AS=0".
A profit equation may be
R=R.sub.Load-R.sub.DR-R.sub.Spot-R.sub.AS-R.sub.Purchased.
R.sub.Load may indicate that paid by customers, a deterministic
variable (given by contracts) and reflect an actual load. R.sub.DR
may indicate a price given by a DR mechanism, a stochastic variable
and a decision about a price made on the side of a retailer.
R.sub.SPOT may indicate unit costs given by a spot market, a
stochastic variable and involve a risk of increased costs. R.sub.AS
may indicate a price given by a central authority, a deterministic
variable and having a risk of a possible penalty. R.sub.Purchased
may indicate costs for power already bought.
[0046] One may note an optimization of a .DELTA.P elimination with
a set 32 of equations indicated in FIG. 8 for a load less than
supply. A balance equation may be indicated by
.DELTA.P=P.sub.load-P.sub.Purchased.
"+P.sub.DR+P.sub.Spot+P.sub.AS" may indicate specifically of a
.DELTA.P correction, as ".DELTA.P+P.sub.DR+P.sub.Spot+P.sub.AS=0".
A profit equation may be
R=R.sub.Load-R.sub.DR+R.sub.Spot-R.sub.AS-R.sub.Purchased.
R.sub.Load may indicate that paid by customers, a deterministic
variable (given by contracts) and reflect an actual load. R.sub.DR
may indicate a price given by a DR mechanism, a stochastic variable
and a decision about a price made on the side of a retailer.
R.sub.SPOT may indicate a price given by a spot market, a
stochastic variable and involve a chance of a lower price or bids
that could not be accepted. R.sub.AS may indicate a price given by
a central authority, a deterministic variable and having a risk of
a possible penalty. R.sub.Purchased may indicate costs for power
already bought.
[0047] FIGS. 9 and 10 reveal balance and cost equations. For load
less than supply in FIG. 9, a set 33 of equations, as noted herein,
may incorporate a balance equation of
.DELTA.P=P.sub.load-P.sub.Purchased, where
.DELTA.P+P.sub.DR+P.sub.Spot+P.sub.AS=0.
"+P.sub.DR+P.sub.Spot+P.sub.AS" may indicate specifics of a
.DELTA.P correction. Also in set 33 may be a profit equation of
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)-R.sub.Spot(.beta..DE-
LTA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased.
[0048] R.sub.Load(P) may indicate a deterministic price (known) and
load reflection. R.sub.DR(.alpha..DELTA.P) may indicate a
stochastic price (known) and limited power.
R.sub.Spot(.beta..DELTA.P) may indicate a stochastic price
(unknown) and partially limited power. R.sub.AS(.chi..DELTA.P) may
indicate a deterministic price (penalty) and "unlimited" power.
R.sub.Purchased may indicate purchased power and is not necessarily
important for optimization.
[0049] For a load greater than supply, a set 34 of equations, as
shown in FIG. 10, may incorporate a balance equation of
.DELTA.P=P.sub.load-P.sub.Purchased, where
.DELTA.P=P.sub.DR+P.sub.Spot+P.sub.AS=0
"+P.sub.DR+P.sub.Spot+P.sub.AS" may indicate specifics of a
.DELTA.P correction. Also in set 34, may be a profit equation
of
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)+R.sub.Spot(.beta..DE-
LTA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased.
[0050] R.sub.Load(P) may indicate a deterministic price (known) and
load reflection. R.sub.DR(.alpha..DELTA.P) may indicate a
stochastic price (known) and limited power.
R.sub.Spot(.beta..DELTA.P) may indicate a stochastic price
(unknown) and partially limited power. R.sub.AS(.chi..DELTA.P) may
indicate a deterministic price (penalty) and "unlimited" power.
R.sub.Purchased may indicate purchased power and is not necessarily
important for optimization.
[0051] In FIG. 11, symbols 35 representing optimization may
incorporate
max .alpha. , .beta. , .chi. .di-elect cons. 0 ; 1 R ( P , .DELTA.
P ) ##EQU00001##
where .alpha.+.beta.+.chi.=1. A scenario approach may be used to
overcome an uncertainty. Solving the optimization task may be done
with a presently selected approach. Probabilistic measures may lead
to a determination of risk.
[0052] An optimization sequence may incorporate: 1) Reading
historical data from a database; 2) Constructing one or more load
forecasting models; 3) Retrieving external information about prices
and weather trends; 4) Retrieving bids from consumers; 5) Using the
models, generating scenarios and considered parameter combinations
(.alpha., .beta., .chi.) (e.g., 0.6, 0.3, 0.1); 6) For each
parameter combination (.alpha., .beta., .chi.), a) evaluating a
cost function for particular settings (.alpha., .beta., .chi.) over
virtually all scenarios, and b) using probabilistic measures, e.g.,
a combination the brings a highest revenue at a given risk level
(such as revenue achieved with 95 percent probability), as an
aggregation function for virtually all scenarios in determination
of a final value of the cost function for the combination (.alpha.,
.beta., .chi.); 7) Finding optimal values of parameters .alpha.*,
.beta.*, .chi.* from aggregated values; 8) Informing consumers
about acceptance; and 9) Measuring a real operation and saving the
operation to a database.
[0053] As to step 2, concerning model construction, models may be
needed for: a) Distributions P(Weather), P(Prices),
P(Behavior|Weather) and/or P(Behavior); b) Mapping
Consumption(Weather, Behavior, Acceptance); and c) Mapping
Profit(Consumption, Acceptance, Prices). One may note profit as
revenue and cost but as also involving accepted and fulfilled
incentives.
[0054] The models may be obtained or construed from historical data
(i.e., a black box), possibly with use of: a) Some apparent
relationships such as summing up the total consumption of
particular consumers; b) External information such as weather
forecasts, public holidays, and so forth; and c) Behavior to be
modeled as a function of time explaining modeling residuals of
black box models of the consumption conditioned by weather and
acceptance.
[0055] As to step 5, concerning application of a scenario approach,
a scenario may represent uncertain information in the system.
Knowing the scenario and making a decision, a next evolution of the
system may be determined. In a case of DR programs, scenarios may
involve: a) Weather (temperature, humidity, solar radiation) which
may not necessarily depend on decisions; b) Consumer behavior
patterns (daily, weekly, yearly trends) which may be affected by
acceptance of demand response bids; c) Spot market (prices); and d)
Impact of DR (e.g., in the hope that the consumer is able to
fulfill the bid).
[0056] As to step 5, concerning generating scenarios, items to be
noted may incorporate: 1) Sampling a trajectory of weather, prices;
2) Conditioned in this trajectory, sampling a trajectory of
behavior; and 3) Determining consumption as a function of
acceptance. The steps may be repeated in that many scenarios are
generated. Thus, each scenario "s" may produce
mapping--Consumption.sub.s(Acceptance). Consumption and prices may
directly determine utility profit--Profit.sub.s(Acceptance).
[0057] As to step 6, an aim of optimization may be to maximize
profit with ensuring an elimination of a power
difference--.DELTA.P+.DELTA.P.sub.CORR=0 and
.DELTA.P.sub.CORR=(a.DELTA.P)+(.beta..DELTA.P)+(.chi..DELTA.P),
where (.alpha..DELTA.P) pertains to R.sub.DR, (.beta..DELTA.P)
pertains to R.sub.SPOT, and (.chi..DELTA.P) pertains to R.sub.AS.
An optimization algorithm may find an optimal combination of a spot
market power 13 and an ancillary services power .chi.. A demand
response power a may be a discrete variable determined by
acceptance. An impact of individual consumers may be assumed to be
reasonably independent. With respect to a search algorithm, genetic
algorithms may be proposed because of a discontinuous objective
function. Other heuristics may be applicable if a set of accepted
bids does not depend on a simple sort.
[0058] FIGS. 12 and 13 reveal an optimization example, relative to
step 6. FIG. 12 is a diagram of a graph involving an aggregation of
cost function values in two selected scenarios with parameters
.beta., .chi.. The graph shows a profit function with R versus an
optimized parameter .alpha.. 1.sub.o represents scenario 1. 2.sub.o
represents scenario 2. Line 37 indicates a mean value and line 38
indicates a minimum value. It may be noted that
.alpha.+.beta.+.chi.=1, .alpha.={0.1, 0.25, 0.4, 0.65, 0.82, 0.97},
and .beta.,.chi..epsilon.<0;1>.
[0059] FIG. 13 is a diagram of a graph 39 showing received unit
price bids with an amount versus a for considered bids.
Unconsidered bids are also noted. The light and dark bars may
represent customers #1 and #2, respectively.
[0060] As to step 6, concerning risk measures, the following
factors may be considered. Using a selected measure may determine
an aggregation function. Parameter a may represent an optimal
combination of supplied bids (discretized value). A selected
combination may optimize an objective function (e.g., profit) over
virtually all scenarios with a consideration of risk. Possible
aggregation approaches may incorporate: 1) Mean--a combination may
maximize expected profit over virtually all scenarios; 2) Worst
case--a combination may maximize a minimal profit over virtually
all scenarios; and 3) Percentile--a combination may maximize a
profit that is given by N-th percentile of the objective functions
for virtually all scenarios.
[0061] FIG. 14 is a diagram of a deployment scheme of the present
system relative to a virtual energy market (VEM). A VEM 41
incorporating a probability distribution generator/estimator 42, a
forecaster module 43, a scenario generation module 44 and a
decision (optimization) engine 45. The probability distribution
generator/estimator 42, forecaster module 43, scenario generation
module 44 and decision (optimization) engine 45 may be
interconnected to one another. A utility/system operator (SO) 46
may provide information such as renewable generation and grid
status to engine 45. Customers involved in auto-DR may incorporate
one or more residential customers 47, commercial customers 48 and
industrial customers 49. Customers 47, 48 and 49 may provide
electricity consumption data to a database such as a meter data
management (MDM) system database 51. Database 51 may provide
selected relevant data to VEM 41. A weather forecast database 52
may provide temperature, humidity, solar radiation and related
information to VEM 41. An energy market database 53 may provide
market prices and related information to VEM 41. There may also be
ancillary services and related information available for VEM 41.
Other databases may provide pertinent information to VEM 41.
Decision engine 45 may take information from, for instance,
scenario generator module 44, forecaster module 43,
generator/estimator 42, utility/ISO 46, and databases 51-53, to
provide an output such as optimal timing and selection of DR
resources. The output from engine 45 may go to DRAS 54 for
processing. DRAS 54 may provide DR signals and business information
to customers 47-49. Customers 47-49 may provide business
information (e.g., bids) to DRAS 54.
[0062] FIG. 15 is a diagram of an example infrastructure portion,
e.g., demand response, of the present system. A DRAS server 61 at a
utility may be connected to a customer bids manager 62 via a
connection 63 such as, for example, an internet. Connection 63 may
be one or more of various wire and wireless connections. Load
information such as that of curtailable loads 64 and reschedulable
loads 65 may be provided to customer bids manager 62. Curtailable
loads 64 may incorporate, for example, actual loads such as 5 kW
and 10 kW. The loads may be of various amounts, number and type.
For each such load may be a schedule or graph 66 showing DR dollars
($.sub.DR) versus load reduction in terms of percent such as, for
illustration, instances of 5, 7, 10, 20, 25, 40, 50, 60, 75 and 100
percent, relative to an actual load of 5 or 10 kW. The instances
may be any other percentages.
[0063] Reschedulable loads 65 may incorporate, for example, loads
such as 1 kW for 2 hours and 20 kW for one hour. The loads may be
of any other amounts and durations. For each of such loads may be a
schedule or graph 67 showing DR dollars versus time with increments
of price along the time line. There may be a time deadline where
the price ($.sub.DR) is stopped at a fixed level. The price at the
fixed level may be, for instance, a normal price.
[0064] FIG. 16 is a diagram of a graph showing load (e.g., kW)
versus time. The graph is an illustrative example various loads and
their relationships. The graph may show load curves for critical
loads 71, curtailable loads 72 and reschedulable loads 73. A
maximal load line 74 indicates a level that no load should exceed.
Other graphs of various loads and their relationships may be
had.
[0065] Before the each optimization procedure run, the algorithm
should have the following items at its disposal. It may be noted
that the solution described herein may just deal with static
optimization, i.e., the optimization task is solved separately for
each time slot (e.g., one hour) or a several time slots in row but
with the no correlation between the slots being assumed. The
present approach may be easily extended to solve a dynamic
optimization problem (allowing inter-slot dependencies).
[0066] The items may incorporate the following. 1) A weather
forecast for each DR participant (sharing weather resources may be
exploited in advance). The probability distribution function, e.g.,
for OAT, may be estimated for a given time-slot. 2) Spot market
price prediction for given time slot in a form of probability
distribution function. It may be estimated based on the historical
data. 3) Individual electrical energy demand models for each DR
participating load. An example of global multivariate regression
model (ToD=Time-of-Day could simulate the occupancy level which is
not usually available) may be:
L=a.sub.0+a.sub.1OAT+a.sub.2ToD+a.sub.3ToD.sup.2+AccpDR
, where Accep is a bid acceptance status (binary) and DR is a
general term representing the influence of demand response action.
4) Bids (nominations) from all potential DR participants, where
each bid (load [kW] reduction/increase) may be a function of time
slot and incentive expected. It may mean that multiple bids for the
same time slot are allowed and participants are allowed to offer
their own price in contrast to current incentive politics produced
exclusively by utilities.
[0067] FIG. 17 is a flow chart of an example optimization
procedure. The optimization may be solved as a one time-slot (one
step) or several time-slots ahead (multi-step). Symbol 81 indicates
creating testing acceptance vectors. A set of eligible acceptance
vectors/matrices may be created which is a subset of all possible
combinations (2N for N DR participants). This process may select
just vectors that are worthy to test, i.e., there is a high
probability that would maximize the profit. A number of selection
criteria may be generated, e.g., one may sort bids according to the
price or participant reliability.
[0068] Symbol 82 indicates generating a sufficient number of
scenarios. Create set of test scenarios (say 1000). Every scenario
can be described by a vector (or matrix for multi-step) of values
generated based on estimated (historical data based) probability
distribution functions. Following the example load model, the
random variables of the 3-participants scenario vector are
generated based on distributions
[P(price,P(OAT.sub.1),P(OAT.sub.2),P(OAT.sub.3),P(DR.sub.1|OAT.sub.1),P(-
DR.sub.2|OAT.sub.2),P(DR.sub.3|OAT.sub.3)]
where first term is the spot market price distribution, next three
terms are distributions of outdoor air temperatures for given
time-slot and last three terms are conditional distributions of
load reduction/increase capabilities for given outdoor air
temperatures.
[0069] Symbol 83 indicates evaluating expected demands. For every
acceptance vector (selected in the first step) the expected total
demand (sum of individual participants' demands) now may be
evaluated against virtually all (e.g., 1000) scenarios
(scenario=vector of realizations of random variables).
[0070] Symbol 84 indicates evaluating an expected profit
distribution. Having the spot market price for each scenario and
known penalty politics for exploiting the ancillary services (i.e.,
excessive power consumption), the expected profit may be evaluated
for each scenario given the acceptance vector. It may be seen as a
profit distribution over all testing scenarios for given acceptance
vector.
[0071] Symbol 85 indicates finding an optimum acceptance vector.
Profit distributions may then be evaluated for all testing
acceptance vectors. The optimization may search for such an
acceptance vector that maximizes the profit with the given required
level of confidence (i.e., risk level). Note the set of testing
acceptance vectors was found by the search procedure based on the
bid ordering or on some other search approach (genetic algorithm)
in the first step.
Acc * = argmax accep , vectors ( Profit ( Demand ( Acc ) , Spot
Market Price , Penalty ) ) ##EQU00002##
[0072] Symbol 86 indicates exploiting the acceptance vector. The
optimum acceptance vector may then support the decisions about whom
to accept the DR bid and when.
[0073] FIG. 18 is a diagram 94 about evaluation of acceptance using
various scenarios. A utility company 90 may receive bids relative
to a load 1 in column 91, load 2 in column 92 and load 3 in column
93. Acceptances 1, 2 and 3 may be provided by utility company 90
for loads 1, 2 and 3, respectively, in columns 91, 92 and 93,
respectively. For each acceptance and scenario, the loads and
profit may be known deterministically. Deterministic mappings 99
may consist of: Load=Load(Weather, Occupancy, Acceptance) and
Profit=Profit(Profit(Load, Price, Acceptance). There may be a
histogram of profit for each acceptance.
[0074] Each column may have graphs 95 and 96 of P(Weather) versus
Weather and of P(Occupancy) versus Occupancy, respectively. The
energy market may be represented with a graph 97 of P(Price) versus
Price.
[0075] A table 98 may show data for a number of scenarios for three
situations with indications of Toa1, Toa2, Toa3, Occ1, Occ2 and
Occ3. Price may be indicated for each scenario. In this example,
weather may be determined by outside temperature Toa. If occupancy
is not available, then it may be replaced by a time-of-day variable
that is able to capture the occupancy profile sufficiently.
[0076] To recap, a system for optimizing a balance of power, may
incorporate a first mechanism that decides about purchasing power
from ancillary services, a second mechanism that purchases or sells
power at a spot market, a third mechanism that purchases or sells
power according to a demand response program, and a processor
having a connection to the first, second and third mechanisms. The
processor may process a reduction of a difference between purchased
power of a supplier and demanded power of a consumer, by
determining an amount of power bought and/or sold with one or more
of the first, second and third mechanisms.
[0077] The difference between purchased power and demanded power
may be minimized by forming an optimal power stack. The power stack
may incorporate a mix having power via the demand response program,
power at the spot market, and/or power from ancillary services. The
processor may determine the mix of the power stack to minimize the
difference between purchased power and demanded power.
[0078] The system may further incorporate an optimization sequence.
The optimization sequence may incorporate maximizing profit and/or
minimizing the difference between the purchased power and the
demanded power.
[0079] Some terms may incorporate P.sub.Load as demanded power,
P.sub.Purchased as purchased power, and .DELTA.P as the difference
between P.sub.Load and P.sub.Purchased. Also, there may
P.sub.DR=.alpha..DELTA.P, P.sub.Spot.beta..DELTA.P, and
P.sub.AS=.chi..DELTA.P .DELTA.P.sub.Correction may incorporate
P.sub.DR, P.sub.Spot and P.sub.AS.
.DELTA.P+.DELTA.P.sub.Correction=0 and
.alpha.+.beta.+.chi..apprxeq.1 may be applicable.
[0080] .alpha. could be a discrete variable representing a
magnitude of acceptance of the consumer in the demand response
program. A power difference may be
.DELTA.P=P.sub.Load-P.sub.Purchased. For a load greater than
supply, .DELTA.P-P.sub.DR-P.sub.Spot-P.sub.AS=0 and
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)+R.sub.Spot(.beta..DEL-
TA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased. Also,
-P.sub.DR-P.sub.Spot-P.sub.AS=.DELTA.P.sub.Correction, R(.DELTA.P)
may be profit, R.sub.Load(P) may be a price of the load, and
R.sub.DR(.alpha..DELTA.P) may be a price of power determined
between the utility and the consumer in a demand response
relationship. Also, R.sub.Spot(.beta..DELTA.P) may be a price of
power on an open market, and R.sub.AS(.chi..DELTA.P) may be a price
of power from a system operator providing ancillary services at a
set price. One may have .alpha.+.beta.+.chi..apprxeq.1, and
max .alpha. , .beta. , .chi. .di-elect cons. 0 ; 1 R ( P , .DELTA.
P ) ##EQU00003##
for optimization.
[0081] A power difference may be
.DELTA.P=P.sub.Load-P.sub.Purchased. For a load less than supply,
there may be .DELTA.P+P.sub.DR+P.sub.Spot+P.sub.AS=0 and
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)-R.sub.Spot(.beta..DEL-
TA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased. There may be
P.sub.DR+P.sub.Spot+P.sub.AS=.DELTA.P.sub.Correction. R(.DELTA.P)
may be profit, and R.sub.Load(P) may be a price of the load.
R.sub.DR(.alpha..DELTA.P) may be a price of power determined
between the utility and the consumer in a demand response
relationship. R.sub.Spot(.beta..DELTA.P) may be a price of power on
an open market, and R.sub.AS(.chi..DELTA.P) may be a price of power
from a system operator providing ancillary services at a set price.
One may have .alpha.+.beta.+.chi..apprxeq.1, and
max .alpha. , .beta. , .chi. .di-elect cons. 0 ; 1 R ( P , .DELTA.
P ) ##EQU00004##
for optimization.
[0082] A system for managing energy, may incorporate a server, a
virtual energy marketing (VEM) module connected to the server, a
utility energy source connected to the VEM module, a meter data
management (MDM) database connected to the VEM module, an energy
consumer connected to the server and the MDM database, and an
energy market source connected to the VEM module.
[0083] The VEM module may incorporate a decision engine connected
to the utility energy source and the server, a scenario generator
connected to the decision engine, a forecaster mechanism connected
to the scenario generator and the MDM database, and a probability
distribution generator connected to the forecaster, the energy
market database and the weather forecast database. The system for
managing energy may further incorporate a weather forecast database
connected to the probability distribution generator.
[0084] The utility may provide information about power unbalance
between loaded power and purchased power and/or grid status to the
decision engine. Demand response signals and business information
may be exchanged between the consumer and the server. The decision
engine may provide optimal timing and selection of demand response
resources to the server. The consumer may provide energy
consumption data to the MDM database. The forecaster may receive
selected relevant data from the MDM database. The energy market
source may provide a market price to the probability distribution
generator.
[0085] The system may further incorporate a weather forecast
database connected to the probability distribution generator. The
weather forecast database may provide weather parameters to the
probability distribution generator. The server may be a demand
response automation server.
[0086] An approach for coordinating power transactions, may
incorporate finding out an amount of purchased power of a utility,
finding out an amount of demanded power by a consumer, minimizing a
power difference between an amount of purchased power of the
utility and an amount of demanded power by the consumer, minimizing
the power difference that depends on, at least in part, purchasing
power from a system operator providing ancillary services at a set
price, selling or purchasing power on the open market at market
price, and/or selling or purchasing power at a price determined
between the utility and the consumer in a demand response
relationship.
[0087] The power difference may be between an amount of purchased
power of the utility and an amount of demanded power by the
consumer, with optimizing a combination of P.sub.Spot, P.sub.AS and
P.sub.DR Goals of optimizing the combination may incorporate
maximizing profit to the utility and minimizing the power
difference.
[0088] .alpha..DELTA.P, .beta..DELTA.P and .chi..DELTA.P may
represent portions of the respective power that constitute the
power difference between the amount of purchased power of the
utility and the amount of demanded power by the consumer.
Parameters .alpha., .beta. and .chi. may be determined to minimize
the power difference, where .alpha.+.beta.+.chi..apprxeq.1.
[0089] In the approach, the power difference may be
.DELTA.P=P.sub.Load-P.sub.Purchased. For a load greater than
supply, .DELTA.P=P.sub.DR.sym.P.sub.Spot-P.sub.AS=0 and
R(.DELTA.P).beta.R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)+R.sub.Spot(.beta-
..DELTA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased. There may be
-P.sub.DR-P.sub.Spot-P.sub.AS=.DELTA.P.sub.Correction. R(.DELTA.P)
may be profit, and R.sub.Load(P) may be a price of the load.
R.sub.DR(.alpha..DELTA.P) may be a price of power determined
between the utility and the consumer in a demand response
relationship. R.sub.Spot(.beta..DELTA.P) may be a price of power on
an open market. R.sub.AS(.chi..DELTA.P) may be a price of power
from a distribution company providing ancillary services at a set
price, and .alpha.+.beta.+.chi..apprxeq.1.
[0090] The power difference may be
.DELTA.P=P.sub.Load-P.sub.Purchased. For a load less than supply,
.DELTA.P+P.sub.DR+P.sub.Spot+P.sub.AS=0 and
R(.DELTA.P)=R.sub.Load(P)-R.sub.DR(.alpha..DELTA.P)-R.sub.Spot(.beta..DEL-
TA.P)-R.sub.AS(.chi..DELTA.P)-R.sub.Purchased. There may be
P.sub.DR+P.sub.Spot+P.sub.AS=.DELTA.P.sub.Correction. R(.DELTA.P)
may be profit. R.sub.Load(P) may be a price of the load.
R.sub.DR(.alpha..DELTA.P) may be a price of power determined
between the utility and the consumer in a demand response
relationship. R.sub.Spot(.beta..DELTA.P) may be a price of power on
an open market. R.sub.AS(.chi..DELTA.P) may be a price of power
from a distribution company providing ancillary services at a set
price, and .alpha.+.beta.+.chi..apprxeq.1.
[0091] In the present specification, some of the matter may be of a
hypothetical or prophetic nature although stated in another manner
or tense.
[0092] Although the present system and/or approach has been
described with respect to at least one illustrative example, many
variations and modifications will become apparent to those skilled
in the art upon reading the specification. It is therefore the
intention that the appended claims be interpreted as broadly as
possible in view of the related art to include all such variations
and modifications.
* * * * *