CN111080014A - Load curve optimization method based on load aggregator non-cooperative game - Google Patents

Load curve optimization method based on load aggregator non-cooperative game Download PDF

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CN111080014A
CN111080014A CN201911316578.7A CN201911316578A CN111080014A CN 111080014 A CN111080014 A CN 111080014A CN 201911316578 A CN201911316578 A CN 201911316578A CN 111080014 A CN111080014 A CN 111080014A
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load
aggregator
formula
cooperative game
time period
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唐昊
王晓东
吕凯
谭琦
张千里
管金昱
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Hefei University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention belongs to the field of power system scheduling and power markets, and particularly relates to a load curve optimization method based on a non-cooperative game of a load aggregator. The method comprises the steps of taking a scheduling mechanism and a load aggregator as main bodies in an optimization problem, and establishing an interaction mechanism of the scheduling mechanism and the load aggregator; establishing a non-cooperative game model for the problem of the reduction of the competitive bidding load of the load aggregators; and (3) carrying out strategy solution on the problem by adopting a cooperative immune quantum particle swarm optimization algorithm, wherein the obtained optimization strategy is used for guiding a load aggregator to select an optimal scheme in the actual strategy reporting process so as to realize optimization of a load curve. The invention can effectively optimize the load curve through the non-cooperative game among the load aggregators, is beneficial to ensuring the benefits of the load aggregators, simultaneously embodies the guiding function of a dispatching mechanism on the reduction behavior of the load aggregators, reduces the load at the peak time, relieves the side pressure of a power generation system, and further improves the operation efficiency of a power grid.

Description

Load curve optimization method based on load aggregator non-cooperative game
Technical Field
The invention belongs to the field of power system scheduling and power markets, and particularly relates to a load curve optimization method based on a load aggregation merchant non-cooperative game.
Background
In recent years, the energy crisis is increasingly prominent, the environmental deterioration is increasingly serious, the new energy power generation curve is influenced by natural conditions and is an uncontrollable factor, and the situation that the new energy power generation curve and the load curve are reversely distributed brings great difficulty to scheduling, how to make the load side participate in scheduling and release the power generation side pressure by optimizing and adjusting the load curve is increasingly emphasized by scheduling workers. On the load side, although the cardinality of the resident user is large, the electricity consumption of the resident user also has great scheduling potential, but the elasticity level of a single user is low and can not reach the minimum level of the scheduling of the load side, the electricity utilization efficiency is low, and the electricity waste is serious, so that the resident user is difficult to directly participate in the scheduling of the load side, and the resident flexible load resources can be gathered by a load aggregator to form a regional large load group to further participate in the power grid scheduling. The load aggregator obtains profits from the power grid company by selling the gathered user flexible load resources, and meanwhile, dispatches the resident flexible load resources, so that the load curve can be optimized, the power generation side pressure of the power grid is relieved, and the overall dispatching efficiency of the power grid is improved.
The existing method for optimizing the load curve through the load aggregator is mainly divided into two types, the first type is that a scheduling mechanism or a power grid company directly makes load adjustment plans of all time periods for the load aggregator, and then the load aggregator controls the micro-layer load to respond to the scheduling plans. In this mode, the dispatching organization stands in the leading position, the benefit of the load aggregator cannot be guaranteed, and then the load aggregator can only reduce the micro-layer dispatching cost for obtaining profits, which can bring negative influence to the electricity consumption of residents and further influence the satisfaction degree of users. And the second type is that the load aggregators interact with the dispatching mechanism to jointly determine the load dispatching amount in each time period and compensate the information such as the electricity price. In this mode, the load aggregator reports the load scheduling policy and the load reduction electricity price, and the scheduling mechanism selects to adopt the policy and the quotation of the load aggregator from the benefit of the scheduling mechanism, or provides adjustment for the policy and the quotation, so that the benefits of the scheduling mechanism and the load aggregator are guaranteed simultaneously. However, when there are multiple load aggregators, the scheduling entity needs to evaluate each reported load amount, and adopt or adjust the policies and quotations of the load aggregators, which brings great difficulty to the decision of the scheduling entity.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a load curve optimization method based on a non-cooperative game of load aggregators, so that the load curve is optimized through the non-cooperative game decision among a plurality of load aggregators, and the goals of slowly demodulating decision pressure of a mechanism and pressure of a power generation side of a power grid are realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
the load curve optimization method based on the non-cooperative game of the load aggregator is characterized by comprising the following steps,
s1, obtaining an initial load curve at a certain time from a scheduling mechanism, and determining the peak load time and the number N thereofHDetermining a market electricity price calculation formula and parameters;
s2, the load aggregator decides to report the load reduction amount in each time period according to the information obtained in the step S1;
s3, calculating the profit which can be obtained in a certain period of time by the load aggregator;
s4, establishing a non-cooperative game model of the load aggregator;
and S5, performing strategy solution on the non-cooperative game model in the step S4 by adopting a cooperative quantum immune particle swarm optimization algorithm, searching for Nash equilibrium points, and obtaining the load reduction amount reported by the load aggregators in peak time.
In the further optimization of the technical scheme, the market electricity price formula in the step S1 is as follows:
ph＝ah(Lh-Lsh)+bh(1)，
in the formula (1), phFor a time period h ∈ {1, 2.,. NHMarket price of electricity; l ishInitial load for time period h; l isshThe total load reduction amount bid in the time period h for all the load aggregators; a ish,bhIs the electricity price function parameter for time period h.
The further optimization of the technical solution is characterized in that, in the step S2, the load reduction amount is calculated, and the total load reduction amount bid in the time period h is:
Figure BDA0002325982960000021
in the formula (2), NLAThe number of the load aggregation quotient; l issh,nAnd (4) reducing the load reported by the nth load aggregator in the time period h.
In the further optimization of the technical scheme, the load aggregator bidding strategy in the step 2 has the following relevant constraints:
Figure BDA0002325982960000022
Figure BDA0002325982960000023
wherein L issh,nThe load reduction amount reported by the nth load aggregator in the time period h,
Figure BDA0002325982960000024
for the nth load aggregate, a reduction of 0 maximum load in time period h, LshIs NLAThe total load reduction amount reported by each load aggregator in the time period h,
Figure BDA0002325982960000025
the maximum amount of load the mechanism allows to curtail for period h.
In the further optimization of the technical solution, the profit calculation formula in step S3 is:
Figure BDA0002325982960000031
in the formula (3), unProfit for the nth load aggregator; c. Ch,nScheduling cost for the nth load aggregator, phThe market price of electricity; l issh,nAnd (4) reducing the load reported by the nth load aggregator in the time period h.
In the further optimization of the technical solution, the step of establishing the non-cooperative game model of the load aggregator in the step S4 is as follows,
s4.1, determining the participants of the non-cooperative game model: all load aggregators;
s4.2, determining a strategy of the non-cooperative game model: the load reduction amount reported by each load aggregator in peak hours;
s4.3, determining the income function of each load aggregator of the non-cooperative game model:
Rn(Ls,n,Ls,n′)＝un(4)，
in the formula (4), the reaction mixture is,
Figure BDA0002325982960000032
a load shedding strategy for the nth load aggregator at each load peak time period;
Figure BDA0002325982960000033
to other NLA-policy of 1 load aggregator;
s4.4, determining a non-cooperative game model game mechanism: all game participants continuously change the strategy according to the self income function until the profit is maximum, and the state is called a Nash equilibrium state, namely:
Figure BDA0002325982960000034
in the formula (5), the reaction mixture is,
Figure BDA0002325982960000035
referred to as nash equilibrium points.
In a further optimization of the present technical solution, the solving of the nash equilibrium point in the step S5 includes the following steps,
step S5.1, setting a strategy space phi of each participantnAnd total policy space
Figure BDA0002325982960000036
Step S5.2, setting a fitness function,
Figure BDA0002325982960000037
in formula (6):
Figure BDA0002325982960000038
means that others do not change the policy, only the policy
Figure BDA0002325982960000039
Replacement of original policy Ls,n∈ΦnProfit of the last nth aggregator;
s5.3, defining and initializing particle dimension N in cooperative quantum immunization particle swarm optimization algorithmLA×NHThe iteration number T is 1, and the maximum iteration number TmaxPopulation size M, each particle at the t-th iteration is recorded as
Figure BDA00023259829600000310
S5.4, randomly initializing M particles in a strategy space range phi;
s5.5, calculating and recording the fitness of each particle according to a formula (6); according to the fitness value, the best position of the individual under the t-th iteration is updated and recorded
Figure BDA0002325982960000041
Best position of population gbesttMean best position mbesttThe average best position calculation formula is:
Figure BDA0002325982960000042
s5.6, randomly generating Q new particles in a strategy space range phi, and calculating the fitness of the Q new particles;
step S5.7, calculating the concentration of M + Q particles according to the following formula:
Figure BDA0002325982960000043
in the formula (8), i ∈ {1, 2.., M + Q },
Figure BDA0002325982960000044
the concentration of the ith particle at the t iteration;
Figure BDA0002325982960000045
the fitness values of the ith particle and the jth particle in the tth iteration are respectively;
step S5.8, calculating the probability of selecting each particle as an update particle according to the following formula:
Figure BDA0002325982960000046
s5.9, selecting M particles with higher probability for updating the population, and using the gbesttReplacing the particle with the worst fitness in the updated population;
step S5.10, calculating attractors of each particle in the t iteration
Figure BDA0002325982960000047
Figure BDA0002325982960000048
In formula (10), α1Is a random number;
step S5.11, updating the population according to the following formula:
Figure BDA0002325982960000049
in formula (11), α2,α3Random number, β coefficient of contraction-expansion, generally in a linear decreasing manner:
Figure BDA00023259829600000410
step S5.12, if t<TmaxIf t is t +1, returning to step S5.5 for iteration; otherwise, the loop is ended.
According to the technical scheme, the load aggregator aggregates one or more of the loads of the bottom layer resident users, the air conditioner and the electric automobile.
In a further optimization of the present technical solution, the load curve in step S1 is a total load curve, which includes a rigid load and/or a flexible load.
Different from the prior art, the technical scheme has the beneficial effects that:
1. aiming at the load curve optimization problem based on the non-cooperative game of the load aggregators, the problem is solved through an improved particle swarm optimization algorithm, so that the load at the peak time can be effectively reduced, the load curve is optimized, the side pressure of the power generation side of a power grid is effectively relieved, and the benefit of the load aggregators is guaranteed;
2. the invention takes the load aggregator as the game main body and gives consideration to the guiding function of the scheduling mechanism, the decision-making basis information of the load aggregator is issued by the scheduling mechanism, the market price calculation formula has an initial load item, and the set price is positively correlated with the initial load, thereby fully embodying the guiding intention that the scheduling mechanism hopes the load aggregator to reduce more loads at a higher load period;
3. the cooperative immune quantum particle swarm optimization algorithm used by the invention is based on the particle swarm optimization algorithm, simultaneously introduces the ideas of the quantum algorithm and the immune algorithm, has high accuracy compared with the particle swarm optimization algorithm, the quantum particle swarm optimization algorithm and the immune particle swarm optimization algorithm, can effectively avoid the algorithm from falling into local optimization, has high algorithm searching speed, and well meets the real-time requirement in power grid dispatching.
Drawings
FIG. 1 is a schematic diagram of a scheduling architecture and load aggregator interaction;
FIG. 2 is a schematic flow chart of a load curve optimization method;
FIG. 3 is a flow chart of a cooperative immunization quantum particle swarm optimization algorithm;
Detailed Description
To explain technical contents, structural features, and objects and effects of the technical solutions in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.
The invention provides a load curve optimization method based on a non-cooperative game of a load aggregator, which is applied to a scheduling mechanism-load aggregator interaction structure, and is shown in figure 1, and is a schematic diagram of the scheduling structure and the load aggregator interaction, and the method comprises the following steps: the system comprises a scheduling mechanism, a plurality of load aggregators and a non-cooperative game link. Considering that the profit of each load aggregator is related to the strategies of all participants, but not mutually independent, a non-cooperative game model is established, and a cooperative immune quantum particle group optimization algorithm is adopted to solve the problem in a strategy manner, so that the obtained peak period load reduction bidding strategy can not only protect the benefit of the load aggregator, but also optimize a load curve and relieve the side pressure of the power generation.
A preferred embodiment of the present invention, referring to fig. 2, is a schematic flow chart of a load curve optimization method based on a non-cooperative game of load aggregators. The method comprises the following steps of,
step S1, the dispatching mechanism obtains the all-day initial load curve and determines the peak time of the load and the number N thereofHDetermining a market electricity price calculation formula and parameters, wherein the market electricity price formula used in the embodiment is as follows:
ph＝ah(Lh-Lsh)+bh(1)，
in the formula (1), phFor a time period h ∈ {1, 2.,. NHMarket price of electricity; l ishIs the beginning of period hA load; l isshThe total load reduction amount bid in the time period h for all the load aggregators is reduced; a ish,bhIs the electricity price function parameter for time period h.
Step S2, all load aggregators make a decision to report the load reduction amount of each time segment according to the information issued by the scheduling mechanism in step S1, and the total load reduction amount bid in the time segment h is:
Figure BDA0002325982960000061
in the formula (2), NLAThe number of the load aggregation quotient; l issh,nAnd (4) reducing the load reported by the nth load aggregator in the time period h.
The relevant constraints of the system in actual operation in step S2 are:
Figure BDA0002325982960000062
Figure BDA0002325982960000063
wherein:
Lsh,nthe load reduction amount reported by the nth load aggregator in the time period h,
Figure BDA0002325982960000064
reducing the maximum load of the nth load aggregator in the time period h;
Lshis NLAThe total load reduction amount reported by each load aggregator in the time period h,
Figure BDA0002325982960000065
the maximum amount of load the mechanism allows to curtail for period h.
Step S3, each load aggregator calculates the profit they can obtain all day, the profit calculation formula is:
Figure BDA0002325982960000066
in the formula (3), unProfit for the nth load aggregator; c. Ch,nThe scheduling cost of the nth load aggregator is related to the types of flexible loads managed by the nth load aggregator, including electric vehicles, air-conditioning loads, residential electricity flexible loads and the like;
step S4, establishing a non-cooperative game model of the load aggregators, wherein the market electricity price is related to the total load reduction amount, so the profit of each load aggregator can be influenced by other load aggregators, and the non-cooperative game model belongs to a typical non-cooperative game problem.
The steps for establishing the non-cooperative gaming model of the load aggregator are as follows,
s4.1, determining the participants of the non-cooperative game model: all load aggregators;
s4.2, determining a strategy of the non-cooperative game model: the load reduction amount reported by each aggregator in peak hours;
s4.3, determining the income function of each participant of the non-cooperative game model:
Rn(Ls,n,Ls,n′)＝un(4)，
in the formula (4), the reaction mixture is,
Figure BDA0002325982960000071
a load shedding strategy for the nth load aggregator at each load peak time period;
Figure BDA0002325982960000072
to other NLA-policy of 1 load aggregator;
s4.4, determining a non-cooperative game model game mechanism: all game participants continuously change their own strategy according to the own income function until the profit is maximum, and once the maximum value is reached, any participant can not change its own strategy to obtain larger profit, and the state is called Nash equilibrium state, namely:
Figure BDA0002325982960000073
in the formula (5), the reaction mixture is,
Figure BDA0002325982960000074
referred to as nash equilibrium points.
And step S5, performing strategy solution on the non-cooperative game model by adopting a cooperative quantum immune particle swarm optimization algorithm according to the following mode, and searching for Nash equilibrium points. Referring to fig. 3, which is a flow chart of the cooperative immune quantum particle swarm optimization algorithm, the solving steps of the nash equilibrium point are as follows:
step S5.1, setting a strategy space phi of each participantnAnd total policy space
Figure BDA0002325982960000075
Step S5.2, setting a fitness function,
Figure BDA0002325982960000076
in formula (6):
Figure BDA0002325982960000077
means that others do not change the policy, only the policy
Figure BDA0002325982960000078
Replacement of original policy Ls,n∈ΦnProfit of the last nth aggregator;
s5.3, defining and initializing particle dimension N in cooperative quantum immunization particle swarm optimization algorithmLA×NHThe iteration number T is 1, and the maximum iteration number TmaxPopulation size M, each particle at the t-th iteration is recorded as
Figure BDA0002325982960000079
S5.4, randomly initializing M particles in a strategy space range phi;
s5.5, calculating and recording the fitness of each particle according to a formula (6); according to the applicationStress value, updating and recording the best position of the individual under the t-th iteration
Figure BDA00023259829600000710
Best position of population gbesttMean best position mbesttThe average best position calculation formula is:
Figure BDA00023259829600000711
s5.6, randomly generating Q new particles in a strategy space range phi, and calculating the fitness of the Q new particles;
step S5.7, calculating the concentration of M + Q particles according to the following formula:
Figure BDA0002325982960000081
in the formula (8), i ∈ {1, 2.., M + Q },
Figure BDA0002325982960000082
the concentration of the ith particle at the t iteration;
Figure BDA0002325982960000083
the fitness values of the ith particle and the jth particle in the tth iteration are respectively;
step S5.8, calculating the probability of selecting each particle as an update particle according to the following formula:
Figure BDA0002325982960000084
s5.9, selecting M particles with higher probability for updating the population, and using the gbesttReplacing the particle with the worst fitness in the updated population;
step S5.10, calculating attractors of each particle in the t iteration
Figure BDA0002325982960000085
Figure BDA0002325982960000086
In formula (10), α1Is a random number;
step S5.11, updating the population according to the following formula:
Figure BDA0002325982960000087
in formula (11), α2,α3Random number, β coefficient of contraction-expansion, generally in a linear decreasing manner:
Figure BDA0002325982960000088
step S5.12, if t<TmaxIf t is t +1, returning to step S5.5 for iteration; otherwise, the loop is ended.
The invention can effectively optimize the load curve through the non-cooperative game among the load aggregators, is beneficial to ensuring the benefits of the load aggregators, simultaneously embodies the guiding function of a dispatching mechanism on the reduction behavior of the load aggregators, reduces the load at the peak time, relieves the side pressure of a power generation system, and further improves the operation efficiency of a power grid.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrases "comprising … …" or "comprising … …" does not exclude the presence of additional elements in a process, method, article, or terminal that comprises the element. Further, herein, "greater than," "less than," "more than," and the like are understood to exclude the present numbers; the terms "above", "below", "within" and the like are to be understood as including the number.
Although the embodiments have been described, once the basic inventive concept is obtained, other variations and modifications of these embodiments can be made by those skilled in the art, so that the above embodiments are only examples of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes using the contents of the present specification and the attached drawings, or other related technical fields, are included in the scope of the present invention.

Claims (9)

1. The load curve optimization method based on the non-cooperative game of the load aggregator is characterized by comprising the following steps,
s1, obtaining an initial load curve at a certain time from a scheduling mechanism, and determining the peak load time and the number N thereofHDetermining a market electricity price calculation formula and parameters;
s2, the load aggregator decides to report the load reduction amount in each time period according to the information obtained in the step S1;
s3, calculating the profit which can be obtained in a certain period of time by the load aggregator;
s4, establishing a non-cooperative game model of the load aggregator;
and S5, performing strategy solution on the non-cooperative game model in the step S4 by adopting a cooperative quantum immune particle swarm optimization algorithm, searching for Nash equilibrium points, and obtaining the load reduction amount reported by the load aggregators in peak time.
2. The method for optimizing a load curve based on a non-cooperative game of load aggregators according to claim 1, wherein the market electricity price formula in step S1 is:
ph＝ah(Lh-Lsh)+bh(1)，
in the formula (1), phFor a time period h ∈ {1, 2.,. NHMarket price of electricity; l ishInitial load for time period h; l isshThe total load reduction amount bid in the time period h for all the load aggregators; a ish,bhIs the electricity price function parameter for time period h.
3. The load curve optimization method based on the load aggregator non-cooperative game as claimed in claim 1, wherein the load reduction amount is calculated in step S2, and the total load reduction amount bid in time period h is:
Figure FDA0002325982950000011
in the formula (2), NLAThe number of the load aggregation quotient; l issh,nAnd (4) reducing the load reported by the nth load aggregator in the time period h.
4. The load curve optimization method based on the load aggregator non-cooperative game as claimed in claim 3, wherein the relevant constraints in the bid strategy of the load aggregator in the step 2 are:
Figure FDA0002325982950000012
Figure FDA0002325982950000013
wherein L issh,nThe load reduction amount reported by the nth load aggregator in the time period h,
Figure FDA0002325982950000014
for the nth load aggregate, a reduction of 0 maximum load in time period h, LshIs NLAThe total load reduction amount reported by each load aggregator in the time period h,
Figure FDA0002325982950000015
when isThe segment h scheduling mechanism allows for a curtailed maximum amount of load.
5. The load curve optimization method based on the non-cooperative game of load aggregators as claimed in claim 1, wherein the profit calculation formula of step S3 is:
Figure FDA0002325982950000021
in the formula (3), unProfit for the nth load aggregator; c. Ch,nScheduling cost for the nth load aggregator, phThe market price of electricity; l issh,nAnd (4) reducing the load reported by the nth load aggregator in the time period h.
6. The method for optimizing a load curve based on a load aggregator non-cooperative game as claimed in claim 1, wherein the step of building a load aggregator non-cooperative game model at step S4 is as follows,
s4.1, determining the participants of the non-cooperative game model: all load aggregators;
s4.2, determining a strategy of the non-cooperative game model: the load reduction amount reported by each load aggregator in peak time period;
s4.3, determining the income function of each load aggregator of the non-cooperative game model:
Rn(Ls,n,Ls,n′)＝un(4)，
in the formula (4), the reaction mixture is,
Figure FDA0002325982950000022
load shedding strategy for the nth load aggregator at each load peak time period;
Figure FDA0002325982950000023
to other NLA-policy of 1 load aggregator;
s4.4, determining a non-cooperative game model game mechanism: all game participants continuously change the strategy according to the self income function until the profit is maximum, and the state is called a Nash equilibrium state, namely:
Figure FDA0002325982950000024
in the formula (5), the reaction mixture is,
Figure FDA0002325982950000025
referred to as nash equilibrium points.
7. The method for load curve optimization based on load aggregator non-cooperative gaming as claimed in claim 5, wherein said step S5 of solving Nash equilibrium points comprises the steps of,
step S5.1, setting a strategy space phi of each participantnAnd total policy space
Figure FDA0002325982950000026
Step S5.2, setting a fitness function,
Figure FDA0002325982950000027
in formula (6):
Figure FDA0002325982950000028
means that others do not change the policy, only the policy
Figure FDA0002325982950000029
Replacement of original policy Ls,n∈ΦnProfit of the last nth aggregator;
s5.3, defining and initializing particle dimension N in cooperative quantum immunization particle swarm optimization algorithmLA×NHThe iteration number T is 1, and the maximum iteration number TmaxPopulation size M, each particle at the t-th iteration is recorded as
Figure FDA00023259829500000210
S5.4, randomly initializing M particles in a strategy space range phi;
s5.5, calculating and recording the fitness of each particle according to a formula (6); according to the fitness value, the best position of the individual under the t-th iteration is updated and recorded
Figure FDA0002325982950000031
Best position of population gbesttMean best position mbesttThe average best position calculation formula is:
Figure FDA0002325982950000032
s5.6, randomly generating Q new particles in a strategy space range phi, and calculating the fitness of the Q new particles;
step S5.7, calculating the concentration of M + Q particles according to the following formula:
Figure FDA0002325982950000033
in the formula (8), i ∈ {1, 2.., M + Q },
Figure FDA0002325982950000034
the concentration of the ith particle at the t iteration;
Figure FDA0002325982950000035
respectively the fitness values of the ith particle and the jth particle in the tth iteration;
step S5.8, calculating the probability of selecting each particle as an update particle according to the following formula:
Figure FDA0002325982950000036
step S5.9, selecting M particles with high probabilityIn updating the population, and using gbesttReplacing the particle with the worst fitness in the updated population;
step S5.10, calculating attractors of each particle in the t iteration
Figure FDA0002325982950000037
Figure FDA0002325982950000038
In formula (10), α1Is a random number;
step S5.11, updating the population according to the following formula:
Figure FDA0002325982950000039
in formula (11), α2,α3Random number, β coefficient of contraction-expansion, generally in a linear decreasing manner:
Figure FDA00023259829500000310
step S5.12, if t<TmaxIf t is t +1, returning to step S5.5 for iteration; otherwise, the loop is ended.
8. The load curve optimization method based on the load aggregator non-cooperative game as claimed in claim 1, wherein the load aggregator aggregates one or more of an underlying residential user load, an air conditioning load, an electric vehicle load.
9. The method for optimizing a load curve based on a non-cooperative game of load aggregators according to claim 1, wherein the load curve in step S1 is a total load curve comprising a rigid load and/or a flexible load.
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