CN114255076A - Capacity subsidy optimization pricing method for new energy power station shared energy storage - Google Patents

Capacity subsidy optimization pricing method for new energy power station shared energy storage Download PDF

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CN114255076A
CN114255076A CN202111519807.2A CN202111519807A CN114255076A CN 114255076 A CN114255076 A CN 114255076A CN 202111519807 A CN202111519807 A CN 202111519807A CN 114255076 A CN114255076 A CN 114255076A
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陈来军
李春来
梅生伟
周万鹏
马恒瑞
杨立滨
司杨
李正曦
刘庭响
陈晓弢
安娜
张海宁
李志青
杜锡力
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State Grid Qinghai Electric Power Co Clean Energy Development Research Institute
Qinghai University
State Grid Qinghai Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Qianghai Electric Power Co Ltd
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State Grid Qinghai Electric Power Co Clean Energy Development Research Institute
Qinghai University
State Grid Qinghai Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Qianghai Electric Power Co Ltd
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Abstract

The invention discloses a capacity subsidy optimization pricing method for shared energy storage of a new energy power station, which comprises the following steps of: s1: determining game elements of a power grid company and a new energy power station through behavior analysis of the power grid company and the new energy power station, constructing two-party evolution game models, analyzing evolution curves of the two-party evolution game models by using a replicator dynamic equation, and calculating and balancing to obtain an evolution stability strategy expression; s2, researching the implementation effect of a dynamic patch punishment mechanism, and considering the evolution dynamic balance of the game problem under four conditions of static capacity patch static error punishment, static capacity patch dynamic error punishment, dynamic capacity patch static error punishment and dynamic capacity patch dynamic error punishment; and S3, providing an optimal parameter design algorithm optimization evolution process curve, verifying existence and accuracy of balance by combining simulation data, and providing a reference suggestion by combining actual conditions. The invention reduces the economic loss of a power grid company and effectively improves the enthusiasm of new energy power stations for sharing energy storage.

Description

Capacity subsidy optimization pricing method for new energy power station shared energy storage
Technical Field
The invention belongs to the technical field of new energy power stations, and particularly relates to a capacity subsidy optimization pricing method for shared energy storage of a new energy power station.
Background
The randomness and intermittence of new energy power generation cause the pressure of stable operation of a power grid to increase day by day. Energy storage can realize the space-time transfer of energy, and the flexibility of a power grid is enhanced. Due to the high investment and use cost of energy storage, the large-scale energy storage is still in the initial stage. In order to improve the utilization rate and the income of the energy storage of the new energy station, the new energy station can obtain profit through energy storage sharing, and at present, a power grid company improves the enthusiasm of the new energy station for energy storage to participate in sharing by setting incentive and punishment policies, and how to set corresponding energy storage capacity subsidies plays a crucial role in the method. Therefore, researching how to make reasonable capacity subsidies by the power grid company is an effective means for realizing development of shared energy storage.
At present, parameters for related calculation are usually set to be static, however, the acceptance degree and the benefit degree of a new energy power station for energy storage sharing change along with the increase and decrease of the generated energy, and the excitation effect and the rationality cannot be guaranteed by a fixed static patch method. Therefore, the dynamic capacity subsidy method is more beneficial to promoting the sharing of the stored energy of the new energy power station. However, there is a lack in the prior art of optimized pricing for capacity patches for making reasonable dynamic capacity patches.
Disclosure of Invention
The invention aims to provide a capacity subsidy optimization pricing method for shared energy storage of a new energy power station, which is used for solving the problem that a subsidy optimization pricing method for making a reasonable dynamic capacity subsidy is lacked in the prior art, and the enthusiasm of the new energy power station for sharing in the shared energy storage development process cannot be improved to the maximum extent.
The capacity subsidy optimization pricing method for the shared energy storage of the new energy power station comprises the following steps:
s1: determining game elements of a power grid company and a new energy power station through behavior analysis of the power grid company and the new energy power station, constructing two-party evolution game models, analyzing evolution curves of the two-party evolution game models by using a replicator dynamic equation, and calculating and balancing to obtain an evolution stability strategy expression;
s2, researching the implementation effect of a dynamic patch punishment mechanism, and considering the evolution dynamic balance of the game problem under four conditions of static capacity patch static error punishment, static capacity patch dynamic error punishment, dynamic capacity patch static error punishment and dynamic capacity patch dynamic error punishment;
and S3, providing an optimal parameter design algorithm optimization evolution process curve, verifying existence and accuracy of balance by combining simulation data, and providing a reference suggestion by combining actual conditions.
Preferably, in the step S1, the new energy power station of the participant in the capacity subsidized evolving game model has two strategies of selectively storing energy and participating in sharing or not storing energy and sharing, and the grid company of the participant in the evolving game model has two strategies of adopting a strict policy or adopting a loose policy; when a power grid company adopts a loose policy, the development of shared energy storage is not interfered, and the expenditure of capacity subsidy excitation and the income of error punishment do not exist; when a power grid company adopts a strict policy, the development of the shared energy storage of the new energy power station is controlled and intervened through a capacity subsidy mechanism and an error punishment mechanism;
if the new energy power station chooses to adopt the stored energy to participate in sharing, a power grid company obtains the income S brought by the impact of the randomness and the volatility of power generation on the power grid due to the stored energy sharing of the new energy power station, and provides a capacity subsidy C for the new energy power station; if the new energy power station selects to adopt stored energy and does not participate in sharing, the error punishment cost caused by electricity abandonment or power failure can be paid, and a power grid company can obtain the payment P and is responsible for making up the power failure cost M of the new energy power station;
when a power grid company adopts a loose policy, the corresponding benefits of the new energy power station adopting the energy storage participation sharing strategy and the energy storage non-participation sharing strategy are respectively recorded as IessAnd Iness(ii) a Wherein, IessThe electricity selling income W of the sharing strategy is participated by the stored energy of the new energy power stationessCost C of self-distribution energy storage equipment shared by energy storageessForming; i isnessThe stored energy of the new energy power station does not participate in the sharing of the electricity selling income WnessCost C of self-distribution energy storage equipment not sharing with energy storagenessForming;
Iessand InessThe formula of (c) is as follows:
Figure BDA0003408313580000021
when a power grid company adopts strict policies, the gains of the new energy power station selecting energy storage to participate in sharing and energy storage not to participate in sharing are respectively Iess+ C and Iness-P。
Preferably, x is a proportion that the new energy power station adopts stored energy to participate in sharing, and 1 is a proportion that the new energy power station adopts stored energy to not participate in sharing; assuming that y is the intention degree of the power grid company adopting a strict policy, and 1 is the intention degree of the power grid company adopting a loose policy;
is provided with a Uess，Uness，UnepsRespectively selecting energy storage participation sharing, energy storage non-participation sharing income and expected income between the two for the new energy power station; the expression is as follows:
Figure BDA0003408313580000031
is provided with a Usp，Ulp，UpgcRespectively selecting the gains of a strict policy and a loose policy and the expected gains between the strict policy and the loose policy for a power grid company; the expression is as follows:
Figure BDA0003408313580000032
and (3) balancing the evolution process and stability of the income expression analysis model based on the power grid company and the new energy power station.
Preferably, in step S2, the evolution stabilization strategy under four different conditions is solved under each scene, the policies of the power grid company and the new energy power station under four conditions, namely, the static capacity subsidy static error punishment, the static capacity subsidy dynamic error punishment, the dynamic capacity subsidy static error punishment and the dynamic capacity subsidy dynamic error punishment, are obtained respectively, the equilibrium points of the power grid company and the new energy power station under the condition are solved based on the replicator dynamic equation, the game evolution equilibrium and the stability under the condition are analyzed, the stability determination conditions of each equilibrium point under the four different conditions are determined, and when the power grid company sets the target of how much shared energy storage is desired to be achieved, the optimal parameters are determined by constructing the feature vectors according to the feature values determined by table three, and include the corresponding capacity subsidy and error punishment of the new energy power station.
Preferably, C and P are constants in case of static capacity subsidy and static error penalty; the replicator dynamic equation of the new energy power station selecting the energy storage participation sharing strategy and the replicator dynamic equation of the power grid company selecting the strict strategy are specifically expressed as follows:
Figure BDA0003408313580000033
the replicator dynamic equations in the formula (4) are all 0, four fixed equilibrium points (0,0), (0,1), (1,0), (1,1) can be obtained by solving, and the four fixed equilibrium point stability conditions under the condition are determined;
since both the capacity subsidy and the error penalty are positive numbers, asymptotic stability at the equilibrium points (0,0) and (1,1) cannot be satisfied, and (0,0) and (1,1) are not evolution stability strategies; when the asymptotic steady condition of the equilibrium point (0,1) is satisfied, (0,1) is an evolving steady policy, and when the asymptotic steady condition of the equilibrium point (1,0) is satisfied, (1,0) is an evolving steady policy.
Preferably, in case of static capacity subsidization and dynamic error penalty, P is assumeduThe upper limit of the error punishment coefficient of the power grid company for the new energy power station stored energy not participating in sharing is the upper limit, the error punishment cost is in positive correlation with the error proportion of the new energy power station selected stored energy not participating in sharing, and the specific expression is as follows:
P＝Pu*(1-x) (9)
solving according to the formula (9) to obtain the replicator dynamic equation under the condition, and obtaining the equilibrium point
Figure BDA0003408313580000041
The expression of (a) is as follows:
Figure BDA0003408313580000042
at the equilibrium point
Figure BDA0003408313580000043
Jacobian matrix J of (a)2The following were used:
Figure BDA0003408313580000044
based on the above formula, when
Figure BDA0003408313580000045
Namely, the proportion of the energy storage participation of the new energy power station is reduced along with the increase of the capacity subsidy;
Figure BDA0003408313580000046
namely, the proportion of the energy storage participation of the new energy power station in sharing is increased along with the increase of the upper limit of the error punishment coefficient;
Figure BDA0003408313580000047
that is, the willingness of grid companies to adopt strict policies decreases as capacity subsidies increase;
Figure BDA0003408313580000048
that is, the willingness of grid companies to adopt strict policies decreases as the upper limit of the error penalty factor increases.
Preferably, in case of dynamic capacity subsidization and static error penalty, C is assumeduThe capacity subsidy is the upper limit of the capacity subsidy, the capacity subsidy is gradually reduced along with the increase of the proportion of the energy storage participation of the new energy power station in sharing, and the capacity subsidy is specifically represented as follows:
C＝Cu*(1-x) (12)
solving according to the formula (12) to obtain the replicator dynamic equation under the condition, and obtaining the equilibrium point
Figure BDA0003408313580000049
The expression of (a) is as follows:
Figure BDA0003408313580000051
at the equilibrium point
Figure BDA0003408313580000052
Jacobian matrix J of (a)3The following were used:
Figure BDA0003408313580000053
based on the above formula, when
Figure BDA0003408313580000054
Namely, the proportion of the energy storage participation of the new energy power station is increased along with the increase of the error punishment coefficient;
Figure BDA0003408313580000055
namely, the proportion of the energy storage participation of the new energy power station in sharing is reduced along with the increase of the upper limit of the capacity subsidy;
Figure BDA0003408313580000056
that is, the willingness of grid companies to adopt strict policies decreases with increasing error penalty coefficients;
Figure BDA0003408313580000057
i.e. the willingness of grid companies to adopt strict policies decreases with increasing upper limit of capacity subsidies.
Preferably, under the condition of dynamic capacity subsidy and dynamic error punishment, the expression of the equilibrium point can be obtained according to the dynamic equation of the replicator under the condition
Figure BDA0003408313580000058
As follows below, the following description will be given,
Figure BDA0003408313580000059
at the equilibrium point
Figure BDA00034083135800000510
Jacobian matrix J of (a)4The following were used:
Figure BDA00034083135800000511
the analysis is performed based on the above-mentioned formula,
Figure BDA00034083135800000512
namely, the proportion of the energy storage of the new energy power station participating in sharing is reduced along with the increase of the upper limit of the capacity subsidy;
Figure BDA00034083135800000513
namely, the proportion of the energy storage participation of the new energy power station in sharing is increased along with the increase of the upper limit of the error punishment coefficient;
Figure BDA00034083135800000514
that is, the intention degree of the power grid company adopting strict policies is reduced along with the increase of the upper limit of the capacity subsidy; at the same time
Figure BDA00034083135800000515
Namely, the intention degree of the power grid company adopting strict policy is not influenced by the upper limit of the error penalty coefficient.
Preferably, in the step of determining the approximate stability of the four equilibrium points based on the jacobian matrices in the four cases, the jacobian matrices in the four cases are: j. the design is a square1、J2、J3And J4The characteristic values in four cases are shown in Table 3, where Δ is a positive number,
Figure BDA0003408313580000061
the number under the root number is a positive number, and whether the balance point is stable and balanced in evolution or not is judged by combining the positive and negative of the value in the front of i; specifically analyzing to obtain stability judgment conditions corresponding to each equilibrium point under four different conditions; wherein the balance point under static capacity subsidy and static error penalty is removed
Figure BDA0003408313580000062
Balance points under the three conditions of other static capacity subsidies and dynamic error punishments, dynamic capacity subsidies and static error punishments, and dynamic capacity subsidies and dynamic error punishments all meet the approximate stability of Lyapunov, and the relation between the capacity subsidies and the energy storage participation sharing proportion of the new energy power station can be accurately defined through an equation of a stability judgment condition; when a power grid company sets the target of how much shared energy storage is expected to be achieved, the characteristic value is determined according to the stability judgment condition of each balance point, and the characteristic vector is constructed to determine the optimal parameter.
The invention has the following advantages: (1) according to the invention, a new energy power station group distributed shared energy storage capacity subsidy model is established, the influence of different decision behaviors of a power grid company and a new energy power station on shared energy storage development is comprehensively considered under the model, the economic loss of the power grid company is reduced, and the development efficiency of shared energy storage is improved.
(2) The invention provides a capacity subsidy method for researching shared energy storage development based on the idea of an evolutionary game.
(3) The invention provides a dynamic capacity subsidy and error punishment policy mechanism. The mechanism comprehensively considers the influences of the new energy power station on the change of the acceptance degree of the energy storage participation sharing and the like, and the dynamic policy is more favorable for promoting the development of the shared energy storage.
Drawings
Fig. 1 is a structure diagram of an evolutionary game of a power grid company and a new energy power station in the capacity subsidy optimization pricing method for shared energy storage of the new energy power station.
Detailed Description
The following detailed description of the present invention will be given in conjunction with the accompanying drawings, for a more complete and accurate understanding of the inventive concept and technical solutions of the present invention by those skilled in the art.
As shown in fig. 1, the present invention provides a capacity subsidy optimization pricing method for shared energy storage of a new energy power station, which includes the following steps:
s1, determining game elements of the power grid company and the new energy power station through behavior analysis of the power grid company and the new energy power station, constructing a two-party evolution game model, analyzing an evolution curve of the two-party evolution game model by using a replicator dynamic equation, and calculating balance to obtain an evolution stability strategy expression.
Specifically, the method comprises the following steps: the participants in the capacity subsidized evolutionary game model provided by the invention are a network company nesp and a new energy power station pgc, so that the set of the participants is as follows: n ═ { nesp, pgc }. The new energy power station of the participator in the evolution game model with capacity subsidy has two strategies, namely a strategy S for selecting stored energy to participate in sharingessOr strategy S that stored energy does not participate in sharingnessIs denoted as { Sess,Sness}. When the new energy power station adopts energy storage to participate in sharing, capacity subsidies of a power grid company can be obtained; when the new energy power station adopts the stored energy and does not participate in sharing, the error punishment cost caused by electricity abandonment or power failure needs to be paid. Suppose x is the proportion of the new energy power station adopting the stored energy to participate in sharing, and 1 is the proportion of the new energy power station adopting the stored energy not to participate in sharing.
The strategy of a participant power grid company in the evolutionary game model has two strategies, namely adopting a strict strategy SspOr adopting a loose policy SlpIs denoted as { Ssp,Slp}. When a power grid company adopts a loose policy, the development of shared energy storage is not interfered, and the expenditure of capacity subsidy excitation and the income of error punishment do not exist; when a power grid company adopts strict policies, namely a capacity subsidy mechanism and an error punishment mechanism are used for sharing storage of the new energy power stationThe development of energy makes control and intervention; suppose y is the intention degree of the power grid company adopting the strict policy, and 1y is the intention degree of the power grid company adopting the loose policy.
Considering the situation that whether the new energy power station selects energy storage to participate in sharing or not, the new energy power station, namely a participant of the game model learns the decision of obtaining the highest income individual in the group by comparing the income of the new energy power station with that of other participants, and in the evolutionary game theory, the learning process is represented by a replicator dynamic equation, namely when a certain strategy can bring higher income, the proportion of selecting the strategy in the group is correspondingly increased. The new energy power station can obtain the capacity subsidy of the new energy power station which is shared by the power grid company for the stored energy, and the dynamic capacity subsidy is in inverse proportion to the stored energy sharing proportion of the new energy power station; the power grid company can obtain the error punishment caused by the electricity abandonment or the power failure of the new energy power station with the stored energy not participating in the sharing, and the dynamic error punishment is in direct proportion to the proportion that the stored energy of the new energy power station does not participate in the sharing. New energy power station energy storage does not participate in income I of sharingnessHigher than income before capacity subsidyesssI.e. Iness-Iesss＞0。
The participant payment matrix in the capacity subsidized evolutionary game model provided by the invention is shown in table 1, and the specific analysis is as follows: the income of the new energy power station mainly comes from the profit of electricity selling for users, and is determined by selling price, cost, capacity subsidy and error punishment. If the new energy power station chooses to adopt the stored energy to participate in sharing, a power grid company obtains the income S brought by the impact of the randomness and the volatility of power generation on the power grid due to the stored energy sharing of the new energy power station, and provides a capacity subsidy C for the new energy power station. If the new energy power station chooses to adopt stored energy and does not participate in sharing, the error punishment cost caused by electricity abandonment or power failure can be paid, and the power grid company can obtain the payment P and is responsible for making up the power failure cost M of the new energy power station.
Table 1: the power grid company and the new energy power station evolution game payment matrix in the invention
Figure BDA0003408313580000081
From the perspective of the revenue of the grid company: when a power grid company adopts strict policies and a new energy power station adopts energy storage to participate in sharing, the yield function is S-C; when a power grid company adopts strict policies and the new energy power station adopts stored energy and does not participate in sharing, the revenue function is P-M. When a power grid company adopts a loose policy and a new energy power station adopts energy storage to participate in sharing, the yield function is S; when a power grid company adopts a loose policy and a new energy power station adopts stored energy and does not participate in sharing, a yield function-M is obtained.
And (3) analyzing from the perspective of the yield of the new energy power station: in order to simplify calculation, the invention provides that when a power grid company adopts a loose policy, the gains corresponding to the strategy that new energy power stations adopt energy storage to participate in sharing and the strategy that energy storage does not participate in sharing are respectively recorded as IessAnd Iness. Wherein, IessThe electricity selling income W of the sharing strategy is participated by the stored energy of the new energy power stationessCost C of self-distribution energy storage equipment shared by energy storageessForming; i isnessThe stored energy of the new energy power station does not participate in the sharing of the electricity selling income WnessCost C of self-distribution energy storage equipment not sharing with energy storagenessAnd (4) forming. The equipment cost mainly comprises the initial investment cost and the operation and maintenance cost of the energy storage device of the new energy power station. When a power grid company adopts strict policies, the gains of the new energy power station selecting energy storage to participate in sharing and energy storage not to participate in sharing are respectively Iess+ C and Iness-P。IessAnd InessThe formula of (c) is as follows:
Figure BDA0003408313580000082
is provided with a Uess，Uness，UnepsAnd respectively selecting the energy storage participation sharing, the energy storage non-participation sharing income and the expected income between the energy storage participation sharing and the energy storage non-participation sharing income for the new energy power station. The expression is as follows:
Figure BDA0003408313580000091
is provided with a Usp，Ulp，UpgcThe gains of the strict policy, the loose policy, and the expected gains between the two are selected for the grid company, respectively. The expression is as follows:
Figure BDA0003408313580000092
the evolution process and stability of the income expression analysis model based on the power grid company and the new energy power station are balanced.
S2, researching the implementation effect of a dynamic subsidy punishment mechanism, and considering the evolution dynamic balance of the game problem under four conditions of static capacity subsidy static error punishment, static capacity subsidy dynamic error punishment, dynamic capacity subsidy static error punishment and dynamic capacity subsidy dynamic error punishment.
In the invention, the capacity subsidy and the error punishment change along with the sharing proportion of the energy storage of the new energy power station in the development process of the shared energy storage. Respectively solving the evolution stability strategy under each scene, and carrying out comprehensive analysis according to results under four conditions; strategies and benefits of power grid companies and new energy power stations under four different conditions are analyzed specifically.
1) The strategy of the power grid company and the new energy power station under the condition of static capacity subsidy and static error punishment is obtained, the balance point of the power grid company and the new energy power station under the condition is solved based on a replicator dynamic equation, and the game evolution balance and stability under the condition are analyzed.
In the case of static capacity subsidies and static error penalties, C and P are constants. The replicator dynamic equation of the new energy power station selecting the energy storage participation sharing strategy and the replicator dynamic equation of the power grid company selecting the strict strategy are specifically expressed as follows:
Figure BDA0003408313580000093
let the replicator dynamic equations of equation (4) be all 0, four fixed equilibrium points (0,0), (0,1), (1,0), (1,1) can be solved.
Because not all the equilibrium points are evolution stability strategies of the game, the asymptotic stability of the equilibrium points is evaluated by constructing the Jacobian matrix J to analyze whether the equilibrium points are the evolution stability strategies of the game. According to the Lyapunov stability condition, if the characteristic value of J is actually negative at the balance point, the J is asymptotically stable, namely the evolution stable strategy of the game.
Figure BDA0003408313580000101
Under the condition of static capacity subsidy and static error punishment, the method provided by the invention has the following conditions:
Figure BDA0003408313580000102
Figure BDA0003408313580000103
is a stable equilibrium, whose expression is:
Figure BDA0003408313580000104
at the equilibrium point
Figure BDA0003408313580000105
Jacobian matrix J of (a)1Characteristic value of
Figure BDA0003408313580000106
Specifically, the following are shown:
Figure BDA0003408313580000107
it is noted that
Figure BDA0003408313580000108
Is an imaginary number and the asymptotic stability of the equilibrium point cannot be determined directly from theory.
The four fixed equilibrium point stabilization conditions of the present invention in this case are shown in table 2. Since both the capacity patch and the error penalty are positive numbers, asymptotic stabilization at the equalization points (0,0) and (1,1) cannot be satisfied, and (0,0) and (1,1) are not evolutionary stabilization strategies. When the asymptotic stability condition for equilibrium point (0,1) is satisfied, (0,1) is an evolving stability strategy, and similarly for equilibrium point (1, 0).
Table 2: the progressive stable condition of each balance point under the penalty of static capacity subsidy and static error in the invention
Figure BDA0003408313580000109
Figure BDA0003408313580000111
2) The strategy of the power grid company and the new energy power station under the static capacity subsidy and the dynamic error punishment is obtained, the balance point of the power grid company and the new energy power station under the condition is solved based on the replicator dynamic equation, and the game evolution balance and stability under the condition are analyzed.
In the case of static capacity subsidization and dynamic error penalty, P is assumeduThe upper limit of the error punishment coefficient of the power grid company for the new energy power station stored energy not participating in sharing is the upper limit, the error punishment cost is in positive correlation with the error proportion of the new energy power station selected stored energy not participating in sharing, and the specific expression is as follows:
P＝Pu*(1-x) (9)
solving according to the formula (9) to obtain the replicator dynamic equation under the condition, and obtaining the mean valueWeighing point
Figure BDA0003408313580000112
The expression of (a) is as follows:
Figure BDA0003408313580000113
at the equilibrium point
Figure BDA0003408313580000114
Jacobian matrix J of (a)2The following were used:
Figure BDA0003408313580000115
based on the above formula, when
Figure BDA0003408313580000116
Namely, the proportion of the energy storage participation of the new energy power station is reduced along with the increase of the capacity subsidy;
Figure BDA0003408313580000117
namely, the proportion of the energy storage participation of the new energy power station in sharing is increased along with the increase of the upper limit of the error punishment coefficient;
Figure BDA0003408313580000118
that is, the willingness of grid companies to adopt strict policies decreases as capacity subsidies increase;
Figure BDA0003408313580000119
that is, the willingness of grid companies to adopt strict policies decreases as the upper limit of the error penalty factor increases.
3) The strategy of the power grid company and the new energy power station under the condition of dynamic capacity subsidy and static error punishment is obtained, the balance point of the power grid company and the new energy power station under the condition is solved based on a replicator dynamic equation, and the game evolution balance and stability under the condition are analyzed.
For dynamicsCapacity subsidy and static error punishment, a power grid company provides a capacity subsidy policy to promote the energy storage participation sharing of the new energy power station, and in the initial development stage, the proportion of the energy storage participation sharing of the new energy power station is small, so that the amount of the given capacity subsidy is large; when the proportion of the new energy power station energy storage participating in sharing is increased, subsidies are reduced, and therefore the subsidies provided by the power grid company should change along with the proportion of the new energy power station energy storage participating in sharing. Hypothesis CuThe capacity subsidy is the upper limit of the capacity subsidy, the capacity subsidy is gradually reduced along with the increase of the proportion of the energy storage participation of the new energy power station in sharing, and the capacity subsidy is specifically represented as follows:
C＝Cu*(1-x) (12)
solving according to the formula (12) to obtain the replicator dynamic equation under the condition, and obtaining the equilibrium point
Figure BDA0003408313580000121
The expression of (a) is as follows:
Figure BDA0003408313580000122
at the equilibrium point
Figure BDA0003408313580000123
Jacobian matrix J of (a)3The following were used:
Figure BDA0003408313580000124
based on the above formula, when
Figure BDA0003408313580000125
Namely, the proportion of the energy storage participation of the new energy power station is increased along with the increase of the error punishment coefficient;
Figure BDA0003408313580000126
namely, the proportion of the energy storage participation of the new energy power station in sharing is reduced along with the increase of the upper limit of the capacity subsidy;
Figure BDA0003408313580000127
that is, the willingness of grid companies to adopt strict policies decreases with increasing error penalty coefficients;
Figure BDA0003408313580000128
i.e. the willingness of grid companies to adopt strict policies decreases with increasing upper limit of capacity subsidies.
4) The strategy of the power grid company and the new energy power station under the condition of dynamic capacity subsidy and dynamic error punishment is obtained, the balance point of the power grid company and the new energy power station under the condition is solved based on a replicator dynamic equation, and the game evolution balance and stability under the condition are analyzed.
Under the condition of dynamic capacity subsidy and dynamic error punishment, the expression of the balance point can be obtained according to the replicator dynamic equation under the condition
Figure BDA0003408313580000129
As follows below, the following description will be given,
Figure BDA00034083135800001210
at the equilibrium point
Figure BDA0003408313580000131
Jacobian matrix J of (a)4The following were used:
Figure BDA0003408313580000132
the analysis is performed based on the above-mentioned formula,
Figure BDA0003408313580000133
namely, the proportion of the energy storage of the new energy power station participating in sharing is reduced along with the increase of the upper limit of the capacity subsidy;
Figure BDA0003408313580000134
namely the energy storage of the new energy power station is involved inThe share proportion is increased along with the increase of the upper limit of the error penalty coefficient;
Figure BDA0003408313580000135
that is, the intention degree of the power grid company adopting strict policies is reduced along with the increase of the upper limit of the capacity subsidy; at the same time
Figure BDA0003408313580000136
Namely, the intention degree of the power grid company adopting strict policy is not influenced by the upper limit of the error penalty coefficient.
5) In the step of determining the approximate stability of the four equalization points according to the jacobian matrices in the four cases, the jacobian matrices in the four cases are: j. the design is a square1、J2、J3And J4The characteristic values in four cases are shown in Table 3, where Δ is a positive number,
Figure BDA0003408313580000137
and judging whether the equalization point is evolution stable equalization or not by combining the positive and negative of the value in the front of i. The specific analysis is as follows:
table 3: stability determination conditions for each equilibrium point in four different cases in step S2 of the present invention
Figure BDA0003408313580000138
In the invention, the balance point under static capacity subsidy and static error penalty is removed
Figure BDA0003408313580000139
The balance points under the three conditions of other static capacity subsidies and dynamic error punishments, dynamic capacity subsidies and static error punishments, and dynamic capacity subsidies and dynamic error punishments all meet the approximate stability of Lyapunov, and the relation between the capacity subsidies and the new energy power station energy storage participation sharing proportion can be accurately defined through an equation of a stability judgment condition. Thus, when the grid company sets the goal of how much shared energy storage is desired, the grid company follows the tableAnd 3, constructing a characteristic vector to determine an optimal parameter according to the characteristic value determined by the step 3, namely determining a corresponding capacity subsidy and an error punishment of the new energy power station according to an equation in the table 3.
And S3, an optimal parameter design algorithm is provided to optimize an evolution process curve, existence and accuracy of balance are verified through simulation data, and a reference suggestion of shared energy storage development is provided in combination with actual conditions. The optimal parameters refer to capacity subsidy and error penalty, and an algorithm is designed to perform an optimal evolution process by using the optimal parameters (capacity subsidy and error penalty), and the optimal parameters are finally determined in step S2. In the calculation process, the evolutionary game model constructed in the step S2 is converted into a programming language for solving, and the evolutionary dynamic trajectory is adjusted according to the optimal parameters.
The invention is described above with reference to the accompanying drawings, it is obvious that the specific implementation of the invention is not limited by the above-mentioned manner, and it is within the scope of the invention to adopt various insubstantial modifications of the inventive concept and solution of the invention, or to apply the inventive concept and solution directly to other applications without modification.

Claims (9)

1. A capacity subsidy optimization pricing method for shared energy storage of a new energy power station is characterized by comprising the following steps: comprises the following steps:
s1: determining game elements of a power grid company and a new energy power station through behavior analysis of the power grid company and the new energy power station, constructing two-party evolution game models, analyzing evolution curves of the two-party evolution game models by using a replicator dynamic equation, and calculating and balancing to obtain an evolution stability strategy expression;
s2: researching the implementation effect of a dynamic subsidy punishment mechanism, and considering the evolution dynamic balance of the game problem under four conditions of static capacity subsidy static error punishment, static capacity subsidy dynamic error punishment, dynamic capacity subsidy static error punishment and dynamic capacity subsidy dynamic error punishment;
s3: and (3) providing an optimal parameter design algorithm optimization evolution process curve, verifying existence and accuracy of balance by combining simulation data, and providing a reference suggestion by combining actual conditions.
2. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: in the step S1, the new energy power station of the participant in the capacity subsidized evolving game model has two strategies of selectively storing energy and sharing or not storing energy and sharing, and the grid company of the participant in the evolving game model has two strategies of adopting a strict policy or adopting a loose policy; when a power grid company adopts a loose policy, the development of shared energy storage is not interfered, and the expenditure of capacity subsidy excitation and the income of error punishment do not exist; when a power grid company adopts a strict policy, the development of the shared energy storage of the new energy power station is controlled and intervened through a capacity subsidy mechanism and an error punishment mechanism;
if the new energy power station chooses to adopt the stored energy to participate in sharing, a power grid company obtains the income S brought by the impact of the randomness and the volatility of power generation on the power grid due to the stored energy sharing of the new energy power station, and provides a capacity subsidy C for the new energy power station; if the new energy power station selects to adopt stored energy and does not participate in sharing, the error punishment cost caused by electricity abandonment or power failure can be paid, and a power grid company can obtain the payment P and is responsible for making up the power failure cost M of the new energy power station;
when a power grid company adopts a loose policy, the corresponding benefits of the new energy power station adopting the energy storage participation sharing strategy and the energy storage non-participation sharing strategy are respectively recorded as IessAnd Iness(ii) a Wherein, IessThe electricity selling income W of the sharing strategy is participated by the stored energy of the new energy power stationessCost C of self-distribution energy storage equipment shared by energy storageessForming; i isnessThe stored energy of the new energy power station does not participate in the sharing of the electricity selling income WnessCost C of self-distribution energy storage equipment not sharing with energy storagenessForming;
Iessand InessThe formula of (c) is as follows:
Figure FDA0003408313570000021
when a power grid company adopts strict policies, the gains of the new energy power station selecting energy storage to participate in sharing and energy storage not to participate in sharing are respectively Iess+ C and Iness-P。
3. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: assuming that x is the proportion of the new energy power station adopting stored energy to participate in sharing, and 1-x is the proportion of the new energy power station adopting stored energy not to participate in sharing; supposing that y is the intention degree of the power grid company adopting a strict policy, and 1-y is the intention degree of the power grid company adopting a loose policy;
is provided with a Uess，Uness，UnepsRespectively selecting energy storage participation sharing, energy storage non-participation sharing income and expected income between the two for the new energy power station; the expression is as follows:
Figure FDA0003408313570000022
is provided with a Usp，Ulp，UpgcRespectively selecting the gains of a strict policy and a loose policy and the expected gains between the strict policy and the loose policy for a power grid company; the expression is as follows:
Figure FDA0003408313570000023
and (3) balancing the evolution process and stability of the income expression analysis model based on the power grid company and the new energy power station.
4. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: in the step S2, under each scene, the evolution stabilization strategy under four different conditions is respectively solved, the strategies of the power grid company and the new energy power station under four conditions of static capacity subsidy static error punishment, static capacity subsidy dynamic error punishment, dynamic capacity subsidy static error punishment and dynamic capacity subsidy dynamic error punishment are respectively obtained, the equilibrium points of the power grid company and the new energy power station under the condition are solved based on the replicator dynamic equation, the game evolution equilibrium and the stability under the condition are analyzed, the stability judgment conditions of each equilibrium point under the four different conditions are determined, when the power grid company sets the target of how much shared energy storage is expected to be achieved, the optimal parameters are determined by constructing the characteristic vectors according to the characteristic values determined by the table three, and the optimal parameters include the corresponding capacity subsidy and error punishment of the new energy power station.
5. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: under the condition of static capacity subsidy and static error punishment, C and P are constants; the replicator dynamic equation of the new energy power station selecting the energy storage participation sharing strategy and the replicator dynamic equation of the power grid company selecting the strict strategy are specifically expressed as follows:
Figure FDA0003408313570000031
the replicator dynamic equations in the formula (4) are all 0, four fixed equilibrium points (0,0), (0,1), (1,0), (1,1) can be obtained by solving, and the four fixed equilibrium point stability conditions under the condition are determined;
since both the capacity subsidy and the error penalty are positive numbers, asymptotic stability at the equilibrium points (0,0) and (1,1) cannot be satisfied, and (0,0) and (1,1) are not evolution stability strategies; when the asymptotic steady condition of the equilibrium point (0,1) is satisfied, (0,1) is an evolving steady policy, and when the asymptotic steady condition of the equilibrium point (1,0) is satisfied, (1,0) is an evolving steady policy.
6. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: in the case of static capacity subsidization and dynamic error penalty, P is assumeduThe power grid company does not participate in the energy storage of the new energy power stationThe upper limit of the shared error punishment coefficient, the error punishment cost and the error proportion of the new energy power station selecting the stored energy not to participate in sharing are positively correlated, and are specifically represented as follows:
P＝Pu*(1-x) (9)
solving according to the formula (9) to obtain the replicator dynamic equation under the condition, and obtaining the equilibrium point
Figure FDA0003408313570000035
The expression of (a) is as follows:
Figure FDA0003408313570000032
at the equilibrium point
Figure FDA0003408313570000033
Jacobian matrix J of (a)2The following were used:
Figure FDA0003408313570000034
D2＝(Iness-Iess)[(1-x)Pu+C-Iness+Iess]{PuC-[(1-x)Pu+C]2}
based on the above formula, when
Figure FDA0003408313570000041
Namely, the proportion of the energy storage participation of the new energy power station is reduced along with the increase of the capacity subsidy;
Figure FDA0003408313570000042
namely, the proportion of the energy storage participation of the new energy power station in sharing is increased along with the increase of the upper limit of the error punishment coefficient;
Figure FDA0003408313570000043
namely electricityThe willingness of web companies to adopt strict policies decreases as capacity subsidies increase;
Figure FDA0003408313570000044
that is, the willingness of grid companies to adopt strict policies decreases as the upper limit of the error penalty factor increases.
7. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: in case of dynamic capacity subsidization and static error penalty, assume CuThe capacity subsidy is the upper limit of the capacity subsidy, the capacity subsidy is gradually reduced along with the increase of the proportion of the energy storage participation of the new energy power station in sharing, and the capacity subsidy is specifically represented as follows:
C＝Cu*(1-x) (12)
solving according to the formula (12) to obtain the replicator dynamic equation under the condition, and obtaining the equilibrium point
Figure FDA00034083135700000412
The expression of (a) is as follows:
Figure FDA0003408313570000045
at the equilibrium point
Figure FDA0003408313570000046
Jacobian matrix J of (a)3The following were used:
Figure FDA0003408313570000047
D3＝(Iness-Iess)[(1-x)Cu+P-Iness+Iess]{CuP-[(1-x)Cu+P]2}
based on the above formula, when
Figure FDA0003408313570000048
Namely, the proportion of the energy storage participation of the new energy power station is increased along with the increase of the error punishment coefficient;
Figure FDA0003408313570000049
namely, the proportion of the energy storage participation of the new energy power station in sharing is reduced along with the increase of the upper limit of the capacity subsidy;
Figure FDA00034083135700000410
that is, the willingness of grid companies to adopt strict policies decreases with increasing error penalty coefficients;
Figure FDA00034083135700000411
i.e. the willingness of grid companies to adopt strict policies decreases with increasing upper limit of capacity subsidies.
8. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: under the condition of dynamic capacity subsidy and dynamic error punishment, the expression of the balance point can be obtained according to the replicator dynamic equation under the condition
Figure FDA0003408313570000051
As follows below, the following description will be given,
Figure FDA0003408313570000052
at the equilibrium point
Figure FDA0003408313570000053
Jacobian matrix J of (a)4The following were used:
Figure FDA0003408313570000054
the analysis is performed based on the above-mentioned formula,
Figure FDA0003408313570000055
namely, the proportion of the energy storage of the new energy power station participating in sharing is reduced along with the increase of the upper limit of the capacity subsidy;
Figure FDA0003408313570000056
namely, the proportion of the energy storage participation of the new energy power station in sharing is increased along with the increase of the upper limit of the error punishment coefficient;
Figure FDA0003408313570000057
that is, the intention degree of the power grid company adopting strict policies is reduced along with the increase of the upper limit of the capacity subsidy; at the same time
Figure FDA0003408313570000058
Namely, the intention degree of the power grid company adopting strict policy is not influenced by the upper limit of the error penalty coefficient.
9. The capacity subsidy optimization pricing method for the new energy power station sharing energy storage is characterized by comprising the following steps of: in the step of determining the approximate stability of the four equalization points according to the jacobian matrices in the four cases, the jacobian matrices in the four cases are: j. the design is a square1、J2、J3And J4The characteristic values in four cases are shown in Table 3, where Δ is a positive number,
Figure FDA0003408313570000059
the number under the root number is a positive number, and whether the balance point is stable and balanced in evolution or not is judged by combining the positive and negative of the value in the front of i; specifically analyzing to obtain stability judgment conditions corresponding to each equilibrium point under four different conditions; wherein the balance point under static capacity subsidy and static error penalty is removed
Figure FDA00034083135700000510
Other static capacity subsidies and movesThe balance points under three conditions of state error punishment, dynamic capacity subsidy, static error punishment, dynamic capacity subsidy and dynamic error punishment all meet the approximate stability of Lyapunov, and the relation between the capacity subsidy and the energy storage participation sharing proportion of the new energy power station can be accurately defined through an equation of a stability judgment condition; when a power grid company sets the target of how much shared energy storage is expected to be achieved, the characteristic value is determined according to the stability judgment condition of each balance point, and the characteristic vector is constructed to determine the optimal parameter.
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