CN115953012A - Multi-subject double-layer game-based optimized scheduling method for rural light storage system - Google Patents

Multi-subject double-layer game-based optimized scheduling method for rural light storage system Download PDF

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CN115953012A
CN115953012A CN202310233561.5A CN202310233561A CN115953012A CN 115953012 A CN115953012 A CN 115953012A CN 202310233561 A CN202310233561 A CN 202310233561A CN 115953012 A CN115953012 A CN 115953012A
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storage system
strategy
energy
agent
optical storage
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CN115953012B (en
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熊俊杰
张新鹤
何桂雄
刘铠诚
李升健
赵伟哲
匡德兴
贾晓强
陈洪银
王松岑
钟鸣
郭毅
黄伟
霍永峰
芋耀贤
金璐
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
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    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a rural light storage system optimal scheduling method based on a multi-subject double-layer game, which comprises the following steps: acquiring photovoltaic output and energy storage capacity of the optical storage system, load condition of the power system, real-time electricity price and the sum of capacities of all optical storage system devices within the jurisdiction range of an agent; constructing a first non-cooperative game model of a scheduling strategy of the optical storage system; establishing a second non-cooperative game model for the electricity market purchase and price competition with a plurality of agents; constructing a distributed response electric quantity model of the energy operator, and determining distributed response electric quantity of the energy operator to each agent; solving a scheduling-bidding double-layer game model consisting of a first non-cooperative game model and a second non-cooperative game model; and adjusting the optimal scheduling sub-strategy of the optical storage system in real time to obtain a target scheduling strategy of the optical storage system. The peak-valley regulation of the power system and the income balance of the light storage system are effectively coordinated, and the income of the light storage system of rural farmers can be ensured.

Description

Multi-subject double-layer game-based optimized scheduling method for rural light storage system
Technical Field
The invention belongs to the technical field of energy optimization, and particularly relates to a rural light storage system optimal scheduling method based on a multi-subject double-layer game.
Background
The single rural light storage system has the characteristics of relatively small capacity and dispersion. But in general, the total installed capacity of the rural light storage system in the same region is larger. Therefore, how to reasonably schedule the rural light storage system improves the operation income of the light storage system and enables the light storage system to participate in the optimization of the power system.
In order to optimize a multi-strategy set evolution game model or a non-cooperative game model (for example, a game model in a Chinese patent (CN 114662759A) of a large-scale electric vehicle charge-discharge optimization scheduling method named as a multi-main-body double-layer game) in the scheduling process of a rural optical storage system, the rural optical storage system optimization scheduling method adopting the models can consider economic benefits among an energy operator, an agent and a user, but when the power generation amount of the energy operator is larger than the demand, the energy utilization rate of the rural optical storage system is low.
Therefore, a rural light storage system optimal scheduling method based on a multi-subject double-layer game is urgently needed to be researched, and the method has important significance for improving the utilization efficiency of clean energy.
Disclosure of Invention
The invention provides a rural light storage system optimal scheduling method based on a multi-subject double-layer game, which is used for solving the technical problem of low energy utilization rate of a rural light storage system.
The invention provides a rural light storage system optimal scheduling method based on a multi-subject double-layer game, which comprises the following steps:
step 1, acquiring photovoltaic output and energy storage capacity of a light storage system, load condition of a power system, real-time electricity price and the sum of capacities of all light storage system devices in the range governed by an agent;
step 2, constructing a first non-cooperative game model and a constraint set of the optical storage system scheduling strategy, and solving the first non-cooperative game model based on a particle swarm algorithm to obtain the scheduling strategy of the optical storage system;
step 3, establishing a second non-cooperative game model for the electricity sale and price competition of the market with a plurality of agents;
step 4, constructing a distribution response electric quantity model of the energy operator, and determining the distribution response electric quantity of the energy operator to each agent;
step 5, solving a scheduling-bidding double-layer game model consisting of the first non-cooperative game model and the second non-cooperative game model, and determining the optimal strategies of the optical storage system, the agent and the energy operator, wherein the optimal strategies comprise an optimal scheduling sub-strategy of the optical storage system, an optimal bidding sub-strategy of the agent and an optimal allocation response electric quantity sub-strategy of the energy operator; the determination of the optimal strategies of the optical storage system, the agent and the energy operator specifically comprises the following steps:
policy set for energy provider's electric energy quotation to agents controlling an area
Figure SMS_1
Randomly selecting a certain quotation;
calculating the benefit of the current moment according to a certain offer by the energy operator and each agent, and outputting a target offer for increasing the benefit of the energy operator and the benefit of each agent respectively, wherein the variable quantity of each target offer is x;
calculating the response electric quantity distributed by the energy operator to each agent by utilizing a particle swarm algorithm according to a distributed response electric quantity model, and determining the charge-discharge strategy of each optical storage system according to a first non-cooperative game formed by a plurality of optical storage systems, wherein the speed updating formula of the particle swarm algorithm is as follows:
Figure SMS_2
，
in the formula:
Figure SMS_5
is the speed at time t>
Figure SMS_8
Is the speed at time t +1>
Figure SMS_10
Is an inertia factor; />
Figure SMS_4
、/>
Figure SMS_7
Are all acceleration constants->
Figure SMS_9
、/>
Figure SMS_12
Are all [0,1]Random number on a section, <' > based on>
Figure SMS_3
For the extreme value of the light-storage system i>
Figure SMS_6
For extreme values of all light-storage systems>
Figure SMS_11
Is a correction term;
calculating correction terms
Figure SMS_13
The expression of (a) is:
Figure SMS_14
，
in the formula (I), the compound is shown in the specification,
Figure SMS_15
is the position of the particle i at the time t;
if the benefits of the energy operator, the benefits of the agent and the benefits of the optical storage system are all increased, the target quotation is increased again, and otherwise, the previous quotation is output;
and 6, adjusting the optimal scheduling sub-strategy of the optical storage system in real time to obtain a target scheduling strategy of the optical storage system, wherein the adjusting the optimal scheduling sub-strategy of the optical storage system in real time specifically comprises the following steps:
when the power generation capacity of the energy operator is greater than the demand, that is
Figure SMS_16
And then, constructing an optimization function by taking the minimum sum of the SOC variation of the optical storage system as an optimization target, and obtaining the energy storage SOC of the optical storage system after real-time adjustment based on the optimization function, wherein the optimization function is as follows:
Figure SMS_17
，
in the formula (I), the compound is shown in the specification,
Figure SMS_18
for the generation of electricity by an energy operator>
Figure SMS_19
Is the sum of the energy storage SOC variation of the light storage system,
Figure SMS_20
the energy storage SOC (state of charge) of the ith light storage system is adjusted in real time>
Figure SMS_21
In order to adjust the energy storage SOC of the ith optical storage system before real time, n is the total number of the optical storage systems;
calculating the change of the charge and discharge quantity of the ith optical storage system due to consideration of promoting reasonable utilization of electric energy according to the energy storage SOC of the ith optical storage system after real-time adjustment
Figure SMS_22
The expression is:
Figure SMS_23
，
in the formula (I), the compound is shown in the specification,
Figure SMS_24
the charge-discharge quantity of the ith light storage system is changed in consideration of promoting reasonable utilization of electric energy, and then the charge-discharge quantity is changed>
Figure SMS_25
Is the energy storage capacity of the ith light storage system>
Figure SMS_26
The stored electric quantity of the ith optical storage system in the h time period;
calculating the charge and discharge amount of the ith light storage system after the reasonable utilization of electric energy is promoted
Figure SMS_27
The expression is:
Figure SMS_28
，/>
in the formula (I), the compound is shown in the specification,
Figure SMS_29
for adjusting the charge-discharge quantity of the ith light storage system in h period before real time, the charge-discharge quantity is adjusted according to the charge-discharge quantity of the ith light storage system>
Figure SMS_30
Is the optimal charge and discharge quantity of the ith light storage system in the h period>
Figure SMS_31
The charging and discharging quantity of the ith optical storage system is changed due to the consideration of promoting reasonable utilization of electric energy;
and adjusting the optimal scheduling sub-strategy of the optical storage system in real time by taking K =1 as an optimization target to obtain a target scheduling strategy of the optical storage system, wherein the expression of the optimization target is as follows:
Figure SMS_32
，
Figure SMS_33
，
in the formula (I), the compound is shown in the specification,
Figure SMS_34
for adjusting the charge-discharge quantity of the ith light storage system in h period after real time, the judgment is carried out>
Figure SMS_35
Is changed due to the change of the charge and discharge quantity caused by the change of the photovoltaic generator>
Figure SMS_36
The charge/discharge amount changes due to the peak shaving demand change.
Further, in step 2, the constructing a non-cooperative game model and a constraint set of the scheduling policy of the optical storage system includes:
assuming that a plurality of optical storage systems are an optical storage system population, the optical storage system will generate a plurality of power purchase and sale strategy sets within the scheduling time:
Figure SMS_37
，
Figure SMS_38
，
in the formula (I), the compound is shown in the specification,
Figure SMS_40
strategy set for buying and selling electric quantity>
Figure SMS_44
For the 1 st strategy, is>
Figure SMS_45
For the 2 nd strategy, in combination with a number of strategies>
Figure SMS_41
For the nth strategy, based on the number of combinations of a plurality of strategies>
Figure SMS_43
In the 1 st periodThe quantity of electricity purchased in the nth strategy is reserved and/or reserved>
Figure SMS_46
For buying and selling electricity in the nth strategy in the 2 nd time period,
Figure SMS_47
for purchasing electric quantity for sale in the nth strategy in the h period of time>
Figure SMS_39
Charging the light storage system and then selecting the light storage system>
Figure SMS_42
Discharging the light storage system;
the payment function of the optical storage system is as follows:
Figure SMS_48
，
in the formula (I), the compound is shown in the specification,
Figure SMS_49
is the first->
Figure SMS_50
The power purchased in the nth strategy in each time period is reserved and reserved>
Figure SMS_51
A charge and discharge price of the light storage system in the ith period, and>
Figure SMS_52
cost expense in the charge and discharge process of the optical storage system in the nth strategy in the ith time period is shown, and h is the time period;
the output of the photovoltaic generator set is constrained as follows:
Figure SMS_53
，
in the formula (I), the compound is shown in the specification,
Figure SMS_54
is the minimum output of the photovoltaic generator set and is greater or less than>
Figure SMS_55
For photovoltaic generator set on>
Figure SMS_56
A force acting at a moment of time>
Figure SMS_57
The maximum output of the photovoltaic generator set is obtained;
photovoltaic generating set all disposes the energy memory of certain capacity, and energy memory's restraint is as follows:
Figure SMS_58
，
in the formula (I), the compound is shown in the specification,
Figure SMS_60
is the minimum value of the SOC of the storage battery and is greater or less than>
Figure SMS_62
Is the SOC value at the time t of the battery,
Figure SMS_66
is the maximum value of the SOC of the storage battery and is greater or less than>
Figure SMS_61
For the minimum value of the charging power of the accumulator>
Figure SMS_64
For a maximum charging power of the battery>
Figure SMS_65
For the charging power of the battery at time t->
Figure SMS_67
For a minimum discharge power of the accumulator>
Figure SMS_59
For a maximum discharge power of the accumulator>
Figure SMS_63
The discharge power of the storage battery at the time t.
Further, the solving of the first non-cooperative game model based on the particle swarm optimization to obtain the scheduling strategy of the optical storage system comprises the following steps:
multiple electricity purchasing and selling strategy sets generated in scheduling time by referring to light storage system
Figure SMS_68
Initializing a scheduling strategy of each optical storage system;
each optical storage system optimizes the scheduling strategy according to the payment function of each optical storage system and the j-1 th scheduling strategy of other optical storage systems to obtain the j-th scheduling strategy;
and continuously optimizing and updating the scheduling strategy until the scheduling strategy of the jth optical storage system has the same benefit as that of the jth-1 optical storage system, so as to obtain the scheduling strategy of each optical storage system.
Further, in step 3, the establishing a second non-cooperative game model of the electricity market bid participated by a plurality of agents includes:
the final bid-winning electric quantity of each agent is determined by the bid electric quantity of all the agents participating in the bidding and the bid price, namely the benefit of the agents is influenced by decision behaviors of other participants, competition relationships exist among the agents, the energy operators finally distribute the final bid-winning electric quantity of each agent, and all managed optical storage systems can use total purchase electric quantity in the period i:
Figure SMS_69
，
in the formula (I), the compound is shown in the specification,
Figure SMS_70
for purchasing electric quantity for sale in the nth strategy in the h period of time>
Figure SMS_71
Is available for purchasing electricity for a number of time periods>
Figure SMS_72
Is agent quotient>
Figure SMS_73
All the managed light storage systems can use total purchased electricity in the h time period;
the strategy set of the energy operators for the electric energy quotation of the agents in the control area is as follows:
Figure SMS_74
，
in the formula (I), the compound is shown in the specification,
Figure SMS_75
，/>
Figure SMS_76
，/>
Figure SMS_77
，/>
Figure SMS_78
respectively providing an electric energy quotation strategy of a jth energy operator to an agent of the control area in a 1 st period, an electric energy quotation strategy of a jth energy operator to an agent of the control area in a 2 nd period, an electric energy quotation strategy of a jth energy operator to an agent of the control area in a 3 rd period, and an electric energy quotation strategy of a jth energy operator to an agent of the control area in an n th period;
the revenue function is the electricity purchase and sale price difference of each agent under the respective strategy:
Figure SMS_79
，
in the formula (I), the compound is shown in the specification,
Figure SMS_80
the contract contracted for the jth agent and the energy carrier for the period n responds to the amount of power, device for selecting or keeping>
Figure SMS_81
Scheduling penalty coefficients for agent merchants>
Figure SMS_82
Quote policy @ on energy carrier for agent j>
Figure SMS_83
Based on the gain function of->
Figure SMS_84
Power bid policy for a jth energy operator for agents in a control area during an nth time period>
Figure SMS_85
The total purchase and sale electric quantity can be used for all the light storage systems governed by the agent j in the h time period;
in the non-cooperative game process, all the agents continuously change the electricity price according to the income function until the non-cooperative game reaches balance, at the moment, the electricity price is not changed any more, and the charge and discharge prices provided by the agents to the light storage system are as follows:
Figure SMS_86
，
in the formula (I), the compound is shown in the specification,
Figure SMS_87
and providing the charge and discharge prices of all the light storage systems in the jurisdiction for the agent j in the n time period.
Further, in step 4, the constructing a distribution response electric quantity model of the energy provider, and determining distribution response electric quantity of the energy provider to each agent includes:
initializing the response capacity distributed by the energy operator to all agents;
the energy operator performs optimization updating according to the utility function of the energy operator and the updated purchase and sale electricity price to obtain a jth scheduling strategy, and obtains the distribution response electricity strategy of the energy operator to each agent until the jth scheduling strategy responding to the electricity distribution strategy has the same income as the jth-1 scheduling strategy, wherein the utility function of the energy operator for purchasing and selling electricity from the agents in the period of n is as follows:
Figure SMS_88
，
in the formula (I), the compound is shown in the specification,
Figure SMS_89
a contract response charge, based on a contract made by a jth agent and an energy provider for a period of n hours, is greater or less>
Figure SMS_90
Power bid policy for a jth energy operator for agents in a control area during an nth time period>
Figure SMS_91
Utility functions for buying and selling electricity for a jth utility operator over a period of n from an agent>
Figure SMS_92
And all the light storage systems governed by the agent j can be used for selling the electricity in the total time period of n.
Further, in step 5, the scheduling-bidding double-layer game model comprises a scheduling layer formed by a first non-cooperative game model and a distributed response electric quantity model and a bidding layer formed by a second non-cooperative game model.
According to the rural light storage system optimal scheduling method based on the multi-main-body double-layer game, the light storage system can participate in the peak-valley regulation of the power system by adopting the scheduling-bidding double-layer game model, rural energy is fully utilized, the game model is formed by two non-cooperative games, the peak-valley regulation of the power system and the income balance of the light storage system are effectively coordinated, and the income of the light storage system of rural farmers can be guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a rural light and storage system optimized scheduling method based on a multi-agent double-layer game according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flowchart of a rural optical storage system optimized scheduling method based on a multi-agent double-layer game according to the present application is shown.
As shown in fig. 1, step 1, obtaining photovoltaic output and energy storage capacity of the optical storage system, load condition of the power system, real-time electricity price, and total capacity of all optical storage system devices within the scope governed by the agent.
In this embodiment, parameters such as the capacity of the optical storage system, the system load, the real-time electricity price, and the capacity of the equipment governed by the agent need to be obtained, and the photovoltaic output and the energy storage capacity of each optical storage system, the load condition of the power system, the real-time electricity price, and the sum of the capacities of all the optical storage systems in the scope governed by the agent need to be obtained.
And 2, constructing a first non-cooperative game model and a constraint set of the optical storage system scheduling strategy, and solving the first non-cooperative game model based on a particle swarm algorithm to obtain the scheduling strategy of the optical storage system.
In this embodiment, assuming that a plurality of optical storage systems are an optical storage system population, the optical storage system will generate a plurality of power purchase and sale strategy sets within a scheduling time:
Figure SMS_93
，
Figure SMS_94
，
in the formula (I), the compound is shown in the specification,
Figure SMS_96
for purchasing and selling electric quantity strategy set, make and sell electric quantity strategy set>
Figure SMS_99
For the 1 st strategy, in>
Figure SMS_102
For the 2 nd strategy, is>
Figure SMS_95
For the nth strategy, is>
Figure SMS_98
For purchasing electricity in an nth strategy in a 1 st time period, based on a comparison of a plurality of parameters>
Figure SMS_101
For purchasing and selling the electric quantity in the nth strategy in the 2 nd time period,
Figure SMS_103
for purchasing electric quantity for sale in the nth strategy in the h period of time>
Figure SMS_97
Charging the light storage system and then selecting the light storage system>
Figure SMS_100
Discharging the light storage system;
the payment function of the optical storage system is as follows:
Figure SMS_104
，
in the formula (I), the compound is shown in the specification,
Figure SMS_105
is a first->
Figure SMS_106
The power purchased in the nth strategy in each time period is reserved and reserved>
Figure SMS_107
A charge and discharge price of the light storage system in the ith period, and>
Figure SMS_108
the cost in the charging and discharging process of the optical storage system in the nth strategy in the ith time period is h, and h is the time period;
the output of the photovoltaic generator set is constrained as follows:
Figure SMS_109
，
in the formula (I), the compound is shown in the specification,
Figure SMS_110
is the minimum output of the photovoltaic generator set and is greater or less than>
Figure SMS_111
Is on/off for the photovoltaic generator set>
Figure SMS_112
The force applied at the moment of time is,
Figure SMS_113
the maximum output of the photovoltaic generator set is obtained;
photovoltaic generator set all disposes the energy memory of certain capacity, and energy memory's restraint is as follows:
Figure SMS_114
，
in the formula (I), the compound is shown in the specification,
Figure SMS_116
is the minimum value of the SOC of the storage battery and is greater or less than>
Figure SMS_119
Is the SOC value at the time t of the battery,
Figure SMS_121
is the maximum value of the SOC of the storage battery and is greater or less than>
Figure SMS_117
For the minimum value of the charging power of the accumulator>
Figure SMS_120
For the maximum charging power of the accumulator>
Figure SMS_122
For the charging power of the battery at time t->
Figure SMS_123
Is the minimum value of the discharge power of the accumulator>
Figure SMS_115
For a maximum discharge power of the accumulator>
Figure SMS_118
The discharge power of the storage battery at the time t.
It should be noted that the solving of the first non-cooperative game model based on the particle swarm algorithm to obtain the scheduling policy of the optical storage system includes:
multiple electricity purchasing and selling strategy sets generated in scheduling time by referring to light storage system
Figure SMS_124
Initializing a scheduling strategy of each optical storage system;
each optical storage system optimizes the scheduling strategy according to the payment function of each optical storage system and the j-1 th scheduling strategy of other optical storage systems to obtain the j-th scheduling strategy;
and continuously optimizing and updating the scheduling strategy until the scheduling strategy of the jth optical storage system has the same benefit as that of the jth-1 optical storage system, so as to obtain the scheduling strategy of each optical storage system.
And 3, establishing a second non-cooperative game model for the electricity purchase and price competition of the market in which a plurality of agents participate.
In this embodiment, the final bid amount of each agent is determined by the bid amount of all agents participating in the bidding and the bid price, that is, the benefit of the agent is affected by decision behaviors of other participants, there is a competition relationship among the agents, the energy operator finally allocates the final bid amount of each agent, and all managed optical storage systems can use the total purchase electricity amount in the period i:
Figure SMS_125
，
in the formula (I), the compound is shown in the specification,
Figure SMS_126
for purchasing electric quantity for the nth strategy in the h-th time period, the method comprises the steps of>
Figure SMS_127
The number of the electricity selling time periods is the number of the electricity purchasing time periods,
Figure SMS_128
is a proxy>
Figure SMS_129
All the managed light storage systems can use total purchased electricity in the h time period;
the strategy set of the energy operator for the electric energy quotation of the agent in the control area is as follows:
Figure SMS_130
，
in the formula (I), the compound is shown in the specification,
Figure SMS_131
，/>
Figure SMS_132
，/>
Figure SMS_133
，/>
Figure SMS_134
electric energy of the agents of the control area of the jth energy operator in the 1 st periodA quotation strategy, an electric energy quotation strategy of a jth energy operator to an agent of a control area in a 2 nd period, an electric energy quotation strategy of the jth energy operator to an agent of the control area in a 3 rd period, and an electric energy quotation strategy of the jth energy operator to an agent of the control area in an n th period;
the revenue function is the electricity purchase and sale price difference of each agent under the respective strategy:
Figure SMS_135
，
in the formula (I), the compound is shown in the specification,
Figure SMS_136
a contract response charge, based on a contract made by a jth agent and an energy provider for a period of n hours, is greater or less>
Figure SMS_137
Scheduling a penalty factor for the agent, <' >>
Figure SMS_138
Quote policy @ on energy carrier for agent j>
Figure SMS_139
Based on the gain function of->
Figure SMS_140
Power bid policy for a jth energy operator for agents in a control area during an nth time period>
Figure SMS_141
The total purchase and sale electric quantity can be used for all the light storage systems governed by the agent j in the h time period;
in the non-cooperative game process, all the agents continuously change the electricity price according to the income function until the non-cooperative game reaches balance, at the moment, the electricity price is not changed any more, and the charge and discharge prices provided by the agents to the light storage system are as follows:
Figure SMS_142
，
in the formula (I), the compound is shown in the specification,
Figure SMS_143
and providing the charge and discharge prices of all the optical storage systems in the jurisdiction in the n time period for the agent j.
And 4, constructing a distribution response electric quantity model of the energy operator, and determining the distribution response electric quantity of the energy operator to each agent.
In this embodiment, the response capacity allocated by the energy operator to all agents is initialized;
the energy operator performs optimization updating according to the utility function of the energy operator and the updated purchase and sale electricity price to obtain a jth scheduling strategy, and obtains the distribution response electricity strategy of the energy operator to each agent until the jth scheduling strategy responding to the electricity distribution strategy has the same income as the jth-1 scheduling strategy, wherein the utility function of the energy operator for purchasing and selling electricity from the agents in the period of n is as follows:
Figure SMS_144
，
in the formula (I), the compound is shown in the specification,
Figure SMS_145
a contract response charge, based on a contract made by a jth agent and an energy provider for a period of n hours, is greater or less>
Figure SMS_146
Power bid policy for a jth energy operator for agents in a control area during an nth time period>
Figure SMS_147
Based on a utility function of the amount of power purchased and sold by the agent for the jth utility operator over a period of n hours, a->
Figure SMS_148
And all the light storage systems governed by the agent j can be used for selling the electricity in the total time period of n.
And 5, solving a scheduling-bidding double-layer game model consisting of the first non-cooperative game model and the second non-cooperative game model, and determining the optimal strategies of the light storage system, the agent and the energy operator.
In this embodiment, first, a first non-cooperative game model of a scheduling policy of an optical storage system is solved to obtain a scheduling policy of each optical storage system; secondly, solving a second non-cooperative game model of the market power purchase and sale price competition in which multiple agents participate to obtain the charge and discharge price of each agent; and finally, solving the distributed response electric quantity of the energy operator based on the evolutionary gaming and non-cooperative gaming results to obtain a response electric quantity distribution strategy of the energy operator.
Policy set for energy provider's electric energy quotation to agents controlling an area
Figure SMS_149
Randomly selecting a certain quotation;
the energy operator and each agent calculate the benefit at the current moment according to a certain quotation, and simultaneously respectively output target quotations for increasing the benefit of the energy operator and the benefit of each agent, wherein the variable quantity of each target quotation is x;
calculating the response electric quantity distributed by the energy operator to each agent by utilizing a particle swarm algorithm according to a distributed response electric quantity model, and determining the charge-discharge strategy of each optical storage system according to a first non-cooperative game formed by a plurality of optical storage systems, wherein the speed updating formula of the particle swarm algorithm is as follows:
Figure SMS_150
，
in the formula:
Figure SMS_152
at a speed of time t, based on the time t>
Figure SMS_154
At a speed of time t +1>
Figure SMS_158
Is an inertia factor; />
Figure SMS_153
、/>
Figure SMS_156
Are all acceleration constants->
Figure SMS_159
、/>
Figure SMS_160
Are all [0,1]A random number in a section, is greater or less>
Figure SMS_151
For the extreme value of the light storage system i>
Figure SMS_155
For the extreme values of all light storage systems>
Figure SMS_157
Is a correction term;
calculating correction terms
Figure SMS_161
The expression of (a) is:
Figure SMS_162
，
in the formula (I), the compound is shown in the specification,
Figure SMS_163
is the position of the particle i at the time t;
and if the benefits of the energy operators, the benefits of the agents and the benefits of the optical storage system are increased, increasing the target quotation again, and otherwise, outputting the previous quotation.
And 6, adjusting the optimal scheduling sub-strategy of the optical storage system in real time to obtain a target scheduling strategy of the optical storage system, wherein the adjusting the optimal scheduling sub-strategy of the optical storage system in real time specifically comprises the following steps:
when the power generation capacity of the energy operator is greater than the demand, that is
Figure SMS_164
And then, constructing an optimization function by taking the minimum sum of the SOC variation of the optical storage system as an optimization target, and obtaining the energy storage SOC of the optical storage system after real-time adjustment based on the optimization function, wherein the optimization function is as follows:
Figure SMS_165
，
in the formula (I), the compound is shown in the specification,
Figure SMS_166
for the generation of electricity by an energy operator>
Figure SMS_167
Is the sum of the SOC variation of the light storage system>
Figure SMS_168
The energy storage SOC (state of charge) of the ith light storage system is adjusted in real time>
Figure SMS_169
The energy storage SOC of the ith optical storage system before real-time adjustment is realized, and n is the total number of the optical storage systems;
calculating the change of the charge and discharge quantity of the ith optical storage system due to consideration of promoting reasonable utilization of electric energy according to the energy storage SOC of the ith optical storage system after real-time adjustment
Figure SMS_170
The expression is:
Figure SMS_171
，
in the formula (I), the compound is shown in the specification,
Figure SMS_172
the change of charge and discharge quantity for promoting reasonable utilization of electric energy for the ith light storage system>
Figure SMS_173
Is the energy storage capacity of the ith light storage system>
Figure SMS_174
The stored electric quantity of the ith optical storage system in the h time period;
calculating the charge and discharge amount of the ith light storage system after the reasonable utilization of electric energy is promoted
Figure SMS_175
The expression is:
Figure SMS_176
，
in the formula (I), the compound is shown in the specification,
Figure SMS_177
in order to adjust the charge and discharge amount of the ith optical storage system in the h period in real time, device for selecting or keeping>
Figure SMS_178
For an optimum charge and discharge quantity for the ith light storage system in a period of h>
Figure SMS_179
The charging and discharging quantity of the ith optical storage system is changed due to the consideration of promoting reasonable utilization of electric energy;
adjusting the optimal scheduling sub-strategy of the optical storage system in real time by taking K =1 as an optimization target to obtain a target scheduling strategy of the optical storage system, wherein the expression of the optimization target is as follows:
Figure SMS_180
，
Figure SMS_181
，
in the formula (I), the compound is shown in the specification,
Figure SMS_182
for adjusting the charge-discharge quantity of the ith light storage system in h period after real time, the judgment is carried out>
Figure SMS_183
Is changed due to the change of the charge and discharge quantity caused by the change of the photovoltaic generator>
Figure SMS_184
The charge/discharge amount changes due to the peak shaving demand change.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (6)

1. A rural light storage system optimal scheduling method based on a multi-subject double-layer game is characterized by comprising the following steps:
step 1, acquiring photovoltaic output and energy storage capacity of a light storage system, load condition of a power system, real-time electricity price and the sum of capacities of all light storage system devices in the range governed by an agent;
step 2, constructing a first non-cooperative game model and a constraint set of the optical storage system scheduling strategy, and solving the first non-cooperative game model based on a particle swarm algorithm to obtain the scheduling strategy of the optical storage system;
step 3, establishing a second non-cooperative game model for the electricity purchase and price competition of the market with the participation of a plurality of agents;
step 4, constructing a distribution response electric quantity model of the energy operator, and determining the distribution response electric quantity of the energy operator to each agent;
step 5, solving a scheduling-bidding double-layer game model consisting of the first non-cooperative game model and the second non-cooperative game model, and determining the optimal strategies of the optical storage system, the agent and the energy operator, wherein the optimal strategies comprise an optimal scheduling sub-strategy of the optical storage system, an optimal bidding sub-strategy of the agent and an optimal allocation response electric quantity sub-strategy of the energy operator; the determination of the optimal strategies of the optical storage system, the agent and the energy operator specifically comprises the following steps:
energy transportationPolicy set for electricity quotation of operators to agents controlling areas
Figure QLYQS_1
Randomly selecting a certain quotation;
the energy operator and each agent calculate the benefit at the current moment according to a certain quotation, and simultaneously respectively output target quotations for increasing the benefit of the energy operator and the benefit of each agent, wherein the variable quantity of each target quotation is x;
calculating the response electric quantity distributed by the energy operator to each agent by utilizing a particle swarm algorithm according to a distributed response electric quantity model, and determining the charge-discharge strategy of each optical storage system according to a first non-cooperative game formed by a plurality of optical storage systems, wherein the speed updating formula of the particle swarm algorithm is as follows:
Figure QLYQS_2
，
in the formula:
Figure QLYQS_5
is the speed at time t>
Figure QLYQS_7
Is the speed at time t +1>
Figure QLYQS_9
Is an inertia factor; />
Figure QLYQS_4
、/>
Figure QLYQS_6
Are all acceleration constants>
Figure QLYQS_11
、/>
Figure QLYQS_12
Are all [0,1]Random number over interval，/>
Figure QLYQS_3
For the extreme value of the light storage system i>
Figure QLYQS_8
For the extreme values of all the light storage systems,
Figure QLYQS_10
is a correction term;
calculating correction terms
Figure QLYQS_13
The expression of (a) is:
Figure QLYQS_14
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_15
is the position of the particle i at time t;
if the benefits of the energy operator, the benefits of the agent and the benefits of the optical storage system are increased, the target quotation is increased again, and if not, the previous quotation is output;
and 6, adjusting the optimal scheduling sub-strategy of the optical storage system in real time to obtain a target scheduling strategy of the optical storage system, wherein the adjusting the optimal scheduling sub-strategy of the optical storage system in real time specifically comprises:
when the power generation capacity of the energy operator is greater than the demand, that is
Figure QLYQS_16
And then, constructing an optimization function by taking the minimum sum of the energy storage SOC variation of the optical storage system as an optimization target, and obtaining the energy storage SOC of the optical storage system after real-time adjustment based on the optimization function, wherein the optimization function is as follows:
Figure QLYQS_17
，/>
in the formula (I), the compound is shown in the specification,
Figure QLYQS_18
is generated by an energy operator>
Figure QLYQS_19
Is the sum of the energy storage SOC variation of the light storage system>
Figure QLYQS_20
The energy storage SOC (state of charge) of the ith light storage system is adjusted in real time>
Figure QLYQS_21
The energy storage SOC of the ith optical storage system before real-time adjustment is realized, and n is the total number of the optical storage systems;
calculating the change of the charge and discharge quantity of the ith optical storage system due to consideration of promoting reasonable utilization of electric energy according to the energy storage SOC of the ith optical storage system after real-time adjustment
Figure QLYQS_22
The expression is:
Figure QLYQS_23
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_24
the charge-discharge quantity of the ith light storage system is changed in consideration of promoting reasonable utilization of electric energy, and then the charge-discharge quantity is changed>
Figure QLYQS_25
Is the energy storage capacity of the ith light storage system>
Figure QLYQS_26
The stored electric quantity of the ith light storage system in the h period;
calculating the charge and discharge amount of the ith light storage system after the reasonable utilization of electric energy is promoted
Figure QLYQS_27
The expression is:
Figure QLYQS_28
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_29
for adjusting the charge-discharge quantity of the ith light storage system in h period before real time, the charge-discharge quantity is adjusted according to the charge-discharge quantity of the ith light storage system>
Figure QLYQS_30
Is the optimal charge and discharge quantity of the ith light storage system in the h period>
Figure QLYQS_31
The charging and discharging quantity of the ith optical storage system is changed due to the consideration of promoting reasonable utilization of electric energy;
and adjusting the optimal scheduling sub-strategy of the optical storage system in real time by taking K =1 as an optimization target to obtain a target scheduling strategy of the optical storage system, wherein the expression of the optimization target is as follows:
Figure QLYQS_32
，
Figure QLYQS_33
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_34
for adjusting the charge-discharge quantity of the ith light storage system in h period after real time, the judgment is carried out>
Figure QLYQS_35
Is changed due to the change of the charge and discharge quantity caused by the change of the photovoltaic generator>
Figure QLYQS_36
The charge/discharge amount changes due to the peak shaving demand change.
2. The rural optical storage system optimization scheduling method based on multi-agent double-layer game of claim 1, wherein in step 2, the constructing of the first non-cooperative game model and the constraint set of the optical storage system scheduling strategy comprises:
assuming that a plurality of light storage systems are a light storage system population, the light storage system will generate a plurality of power purchase and sale strategy sets within the scheduling time:
Figure QLYQS_37
，
Figure QLYQS_38
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_40
for purchasing and selling electric quantity strategy set, make and sell electric quantity strategy set>
Figure QLYQS_43
For the 1 st strategy, is>
Figure QLYQS_45
For the 2 nd strategy, is>
Figure QLYQS_41
For the purpose of the n-th policy,
Figure QLYQS_44
for purchasing electric quantity for sale in the nth strategy in the 1 st time period>
Figure QLYQS_46
For purchasing and selling the electric quantity in the nth strategy in the 2 nd time period,
Figure QLYQS_47
for purchasing electric quantity for sale in the nth strategy in the h period of time>
Figure QLYQS_39
Charging the light storage system and then selecting the light storage system>
Figure QLYQS_42
Discharging the light storage system;
the payment function of the optical storage system is:
Figure QLYQS_48
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_49
is the first->
Figure QLYQS_50
The power purchased in the nth strategy in each time period is reserved and reserved>
Figure QLYQS_51
A charge and discharge price of the light storage system in the ith period, and>
Figure QLYQS_52
the cost in the charging and discharging process of the optical storage system in the nth strategy in the ith time period is h, and h is the time period;
the output of the photovoltaic generator set is constrained as follows:
Figure QLYQS_53
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_54
is the minimum output of the photovoltaic generator set and is greater or less than>
Figure QLYQS_55
Is on/off for the photovoltaic generator set>
Figure QLYQS_56
A force acting at a moment of time>
Figure QLYQS_57
The maximum output of the photovoltaic generator set is obtained;
photovoltaic generator set all disposes the energy memory of certain capacity, and energy memory's restraint is as follows:
Figure QLYQS_58
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_60
is the minimum value of the SOC of the storage battery and is greater or less than>
Figure QLYQS_63
Is the SOC value at the moment t of the storage battery>
Figure QLYQS_65
Is the maximum value of the SOC of the storage battery and is greater or less than>
Figure QLYQS_61
For the minimum value of the charging power of the accumulator>
Figure QLYQS_64
Is the maximum value of the charging power of the storage battery,
Figure QLYQS_66
for the charging power of the battery at time t->
Figure QLYQS_67
Is the minimum value of the discharge power of the accumulator>
Figure QLYQS_59
Is the maximum value of the discharge power of the accumulator>
Figure QLYQS_62
The discharge power of the storage battery at the time t.
3. The rural light storage system optimal scheduling method based on multi-agent double-layer game of claim 2, wherein the solving of the first non-cooperative game model based on the particle swarm optimization to obtain the scheduling strategy of the light storage system comprises:
multiple electricity purchasing and selling strategy sets generated in scheduling time by referring to light storage system
Figure QLYQS_68
Initializing a scheduling strategy of each optical storage system;
each optical storage system optimizes the scheduling strategy according to the payment function of each optical storage system and the j-1 th scheduling strategy of other optical storage systems to obtain the j-th scheduling strategy;
and continuously optimizing and updating the scheduling strategy until the scheduling strategy of the jth optical storage system has the same benefit as that of the jth-1 optical storage system, so as to obtain the scheduling strategy of each optical storage system.
4. The rural light-storage system optimized scheduling method based on multi-agent double-layer game as claimed in claim 1, wherein in step 3, the establishing of the second non-cooperative game model for the electricity-buying and selling price competition of a plurality of agents includes:
the final bid-winning electric quantity of each agent is determined by the bid electric quantity of all the agents participating in the bidding and the bid price, namely the benefit of the agents is influenced by decision behaviors of other participants, competition relationships exist among the agents, the energy operators finally distribute the final bid-winning electric quantity of each agent, and all managed optical storage systems can use total purchase electric quantity in the period i:
Figure QLYQS_69
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_70
for purchasing electric quantity for sale in the nth strategy in the h period of time>
Figure QLYQS_71
Is available for purchasing electricity for a number of time periods>
Figure QLYQS_72
Is agent quotient>
Figure QLYQS_73
All the managed light storage systems can use total purchased electricity in the h time period;
the strategy set of the energy operators for the electric energy quotation of the agents in the control area is as follows:
Figure QLYQS_74
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_75
，/>
Figure QLYQS_76
，/>
Figure QLYQS_77
，/>
Figure QLYQS_78
respectively providing an electric energy quotation strategy of a jth energy operator to an agent of the control area in a 1 st period, an electric energy quotation strategy of a jth energy operator to an agent of the control area in a 2 nd period, an electric energy quotation strategy of a jth energy operator to an agent of the control area in a 3 rd period, and an electric energy quotation strategy of a jth energy operator to an agent of the control area in an n th period;
the revenue function is the electricity purchase and sale price difference of each agent under the respective strategy:
Figure QLYQS_79
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_80
a contract response charge, based on a contract made by a jth agent and an energy provider for a period of n hours, is greater or less>
Figure QLYQS_81
Scheduling penalty coefficients for agent merchants>
Figure QLYQS_82
Quote policy @ on energy carrier for agent j>
Figure QLYQS_83
Based on the gain function of->
Figure QLYQS_84
The power bid policy for the agent in the control area for the nth time period, based on the jth energy operator>
Figure QLYQS_85
The total purchase and sale electric quantity can be used for all the light storage systems governed by the agent j in the h time period;
in the non-cooperative game process, all the agents continuously change the electricity price according to the income function until the non-cooperative game reaches balance, at the moment, the electricity price is not changed any more, and the charge and discharge prices provided by the agents to the light storage system are as follows:
Figure QLYQS_86
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_87
and providing the charge and discharge prices of all the light storage systems in the jurisdiction for the agent j in the n time period.
5. The method for optimizing and scheduling the village optical storage system based on the multi-agent double-layer game as claimed in claim 1, wherein in step 4, the constructing a distribution response electric quantity model of the energy provider and determining the distribution response electric quantity of the energy provider to each agent comprises:
initializing the response capacity distributed by the energy operator to all agents;
the energy operator performs optimization updating according to the utility function of the energy operator and the updated purchase and sale electricity price to obtain a jth scheduling strategy, and obtains the distribution response electricity strategy of the energy operator to each agent until the jth scheduling strategy responding to the electricity distribution strategy has the same income as the jth-1 scheduling strategy, wherein the utility function of the energy operator for purchasing and selling electricity from the agents in the period of n is as follows:
Figure QLYQS_88
，
in the formula (I), the compound is shown in the specification,
Figure QLYQS_89
a contract response charge, based on a contract made by a jth agent and an energy provider for a period of n hours, is greater or less>
Figure QLYQS_90
Power bid policy for a jth energy operator for agents in a control area during an nth time period>
Figure QLYQS_91
Based on a utility function of the amount of power purchased and sold by the agent for the jth utility operator over a period of n hours, a->
Figure QLYQS_92
And all the light storage systems managed by the agent j can be used for buying and selling electricity in the n time period.
6. The rural optical storage system optimized scheduling method based on the multi-agent double-layer game as claimed in claim 1, wherein in step 5, the scheduling-bidding double-layer game model comprises a scheduling layer consisting of a first non-cooperative game model and a distributed response electric quantity model, and a bidding layer consisting of a second non-cooperative game model.
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