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CN114819373B - A method for energy storage planning of a shared hybrid energy storage power station based on cooperative game - Google Patents

A method for energy storage planning of a shared hybrid energy storage power station based on cooperative game

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CN114819373B
CN114819373B CN202210500640.3A CN202210500640A CN114819373B CN 114819373 B CN114819373 B CN 114819373B CN 202210500640 A CN202210500640 A CN 202210500640A CN 114819373 B CN114819373 B CN 114819373B
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朱晓荣
山雨琦
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North China Electric Power University
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

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Abstract

本发明公开了一种基于合作博弈的共享混合储能电站的储能规划方法，首先，针对蓄电池和超级电容器不同的运行特性制定混合储能的控制策略，蓄电池采用“低储高发”的充放电策略，超级电容器基于模型预测控制平抑风光波动，跟踪计划发电；其次，建立混合储能电站的双层优化配置模型，并构建储能配置结果的评价指标；最后，基于考虑储能配置效果的改进Shapley分值法分配各新能源场站的收益。本发明提供的基于合作博弈的共享混合储能电站的储能规划方法，规划时明确混合储能的运行策略及获利方式，最大化储能电站的年综合效益，并为联盟中的新能源场站合理分配收益，使共享混合储能电站获得个体合理性和集体合理性。

The present invention discloses a method for energy storage planning of a shared hybrid energy storage power station based on cooperative game. First, a control strategy for hybrid energy storage is formulated according to the different operating characteristics of batteries and supercapacitors. The batteries adopt a "low storage and high generation" charging and discharging strategy. The supercapacitors use model-based predictive control to smooth wind and solar fluctuations and track planned power generation. Secondly, a two-layer optimization configuration model for the hybrid energy storage power station is established, and an evaluation index for the energy storage configuration result is constructed. Finally, the income of each new energy station is allocated based on the improved Shapley scoring method considering the energy storage configuration effect. The energy storage planning method for a shared hybrid energy storage power station based on cooperative game provided by the present invention clarifies the operating strategy and profit-making method of hybrid energy storage during planning, maximizes the annual comprehensive benefits of the energy storage power station, and reasonably allocates income to the new energy stations in the alliance, so that the shared hybrid energy storage power station obtains individual rationality and collective rationality.

Description

Energy storage planning method of shared hybrid energy storage power station based on cooperative game
Technical Field
The invention relates to the technical field of energy storage planning of power systems, in particular to an energy storage planning method of a shared hybrid energy storage power station based on cooperative game.
Background
With the continuous improvement of the permeability of new energy, the inherent volatility and randomness of the new energy bring new challenges to the safe and stable operation of the power system. Therefore, how to scientifically configure the energy storage capacity to avoid resource waste needs to be further discussed.
In order to reduce the energy storage construction cost of a new energy station and improve the utilization rate of the energy storage construction cost, kangchongqing et al put forward the concept of 'shared energy storage' in the power system automation 2017,41 (21): 2-8. 'New form of future power system energy storage: cloud energy storage'. Currently, research on improving resource allocation efficiency by utilizing shared energy storage is mainly focused on the side of a power distribution network, solving the transaction problems of an energy storage operator and an energy storage leasing user and the like, wherein the energy storage operator is often provided by a third party, so that non-cooperative game is adopted. In recent years, the shared energy storage has also been studied by the relevant scholars on the power generation side, and the new energy station is used as an investor in the energy storage power station. Sun et al establish a new energy power plant shared energy storage planning model based on cooperative game in the global energy Internet 2019,2 (04): 360-366 in the "power generation side shared energy storage planning model based on cooperative game", but the alliance members are 5 wind power plants, the addition of the photovoltaic power station is not considered, and the addition of the members is only from the marginal benefit angle of the members during benefit distribution, and the influence of the addition of the members on the overall output effect is not considered. Therefore, the new energy station is independently configured with the energy storage system at present, so that the problems of low utilization rate and poor economy exist, and improvement is needed.
In addition, the new energy station is suitable for the diversified demands of modern power grids on energy and power, the single type of energy storage technology is difficult to meet the demands, and the combined configuration of 2 or more energy storage technologies can make the best of the advantages, fully exert the technical and economic advantages of all parties, and greatly expand the application scenes of the energy storage system. The power distribution and capacity allocation of the hybrid energy storage system has a significant impact on the technical and economic efficiency of the overall grid. Zhang Qing et al in the technical science of electrician, 2016,31 (14): 40-48, "hybrid energy storage capacity configuration method for stabilizing wind power fluctuation with maximum net benefit" propose a hybrid energy storage power and capacity configuration method for obtaining energy storage reference power by means of moving average and empirical mode decomposition. Malan et al in the power grid technology 2022,46 (03): 1016-1029, "wind power fluctuation stabilizing strategy based on hybrid energy storage double-layer planning model" adopts a self-adaptive wavelet packet decomposition method to obtain the reference power of hybrid energy storage, and establishes a hybrid energy storage capacity optimization model based on a battery life quantization model. Yang et al in power grid technology, 2013,37 (05): 1209-1216, "capacity optimization configuration of hybrid energy storage system in grid-connected wind-solar power generation" constructed hybrid energy storage stabilization-constant volume double-layer planning model document based on multi-step model algorithm control. The above-mentioned document mostly combines the configuration of the hybrid energy storage capacity with the control method of stabilizing fluctuation to analyze, however, in the period of small wind power fluctuation, the energy storage idle capacity is utilized to participate in peak regulation and provide standby, so that the energy storage utilization rate can be improved, and the energy storage operation benefit can be increased.
Disclosure of Invention
The invention aims to provide an energy storage planning method of a shared hybrid energy storage power station based on a cooperative game, when planning shared energy storage for a new energy station, different operation characteristics of a storage battery and a super capacitor are fully considered, a hybrid energy storage control strategy is formulated, and the benefits of each new energy station are distributed based on an improved Shapley score method considering the energy storage configuration effect, so that the shared hybrid energy storage power station obtains individual rationality and integrated rationality.
In order to achieve the above object, the present invention provides the following solutions:
An energy storage planning method of a shared hybrid energy storage power station based on cooperative game, comprising the following steps:
s1, setting an operation strategy of hybrid energy storage according to different operation characteristics of a storage battery and a super capacitor;
s2, establishing a double-layer optimal configuration model of the hybrid energy storage power station, maximizing annual income of the hybrid energy storage power station, and constructing an evaluation index of the hybrid energy storage configuration effect;
S3, establishing a cooperative game model, and determining a comprehensive allocation strategy considering the hybrid energy storage configuration effect based on a Shapley score method;
S4, obtaining a hybrid energy storage configuration scheme and annual benefits of the hybrid energy storage power station based on the operation strategy of the hybrid energy storage in the step S1 and the double-layer optimal configuration model in the step S2, and then distributing benefits to new energy stations in the alliance by utilizing the cooperative game model in the step S3.
Further, the step S1 of formulating an operation strategy of hybrid energy storage for different operation characteristics of the storage battery and the supercapacitor specifically includes:
S101, based on a charge-discharge strategy of 'low storage and high emission', establishing a mathematical model of a storage battery operation strategy, and solving the problem of anti-peak regulation of wind and light output;
s102, based on model predictive control, establishing a mathematical model of an operation strategy of the supercapacitor, stabilizing wind and light fluctuation, and tracking and planning power generation.
Further, in the step S101, a mathematical model of the battery operation strategy is established, which specifically includes:
Wherein minJ bat represents an objective function of a battery operation strategy, so that the difference between the output of a new energy station after peak shaving of the battery and a reference value is minimized; The method comprises the steps of predicting power for a new energy station day before, wherein p bat (t) is the output power of a storage battery, and p ref (t) is the reference value of low storage and high emission of the storage battery;
the calculation method of p ref (t) is as follows:
wherein T 1、T2、T3 is a load peak period, a normal period and a valley period respectively, and medium represents a median;
Corresponding constraint conditions:
SOCbat(T)＝SOCbat(0) (5)
in the formula, SOC bat (t), The method comprises the steps of determining the charge state and the upper and lower limits of a storage battery, wherein eta bat,c,ηbat,d is the charge and discharge efficiency of the storage battery respectively, p bat,c(t)、pbat,d (T) is the charge and discharge power of the storage battery respectively, SOC bat (T-1) is the charge state of the storage battery at the time T-1, E bat is the rated capacity of the storage battery, deltaT is a scheduling time interval, SOC bat (T) is the charge state of the storage battery at the time T, SOC bat (0) is the charge state of the storage battery at the initial time, and p bat (T) is the output power of the storage battery, and the charge is negative and the discharge is positive; Maximum charge and discharge power of the storage battery;
the above formula (3) is a state of charge constraint of the storage battery, and the formula (5) is a constraint that the energy states of the storage battery need to be equal at the beginning and the end of a dispatching cycle.
Further, in the step S102, a mathematical model of the supercapacitor operation strategy is established, which specifically includes:
selecting vectors formed by the sum of the charge state, charge and discharge power, wind-light real-time output and storage battery power of the super capacitor Is a state variable, wherein SOC sc (t) is the state of charge of the super capacitor at the time t, p sc,c (t-1) is the charging power of the super capacitor at the time t-1, p sc,d (t-1) is the discharging power of the super capacitor at the time t-1,The sum of the real-time wind-light output and the power of the storage battery at the time t-1;
Taking a vector u (t) = [ delta p sc,c(t),Δpsc,d(t)]T ] formed by the increment of the charging and discharging power of the super capacitor as a control variable, wherein the meaning of delta p sc,c(t),Δpsc,d (t) is the increment of the charging power of the super capacitor at the moment t and the increment of the discharging power of the super capacitor at the moment t respectively;
Vector formed by increment of sum of wind-light real-time output and power of storage battery Is a disturbance input; meaning of t-moment wind-light real-time output an increment of the sum of the power of the storage battery;
Vector y (t) = [ SOC sc(t),pd(t)]T ] formed by the charge state of the super capacitor and wind-solar grid-connected power is taken as an output variable, wherein p d (t) means wind-solar grid-connected power at the moment t;
The state space equations are established as shown in formulas (7) and (8), iteration is carried out on the basis of the formula (8), and a control instruction in a future time t+n is predicted, wherein the specific equation is shown in formula (9):
In the formula (7), x (t+1) is a state variable of the system at the moment t+1, A is a system matrix, B 1 is a control input matrix, B 2 is an external interference input matrix, and eta sc and E sc are respectively the charge and discharge efficiency and rated capacity of the supercapacitor;
in the formula (8), y (t+1) is an output variable of the system at the moment t+1, and C is a coefficient matrix;
In the formula (9), K, L 1,L2 are respectively a state variable, a control variable and a disturbance input coefficient matrix; And Respectively representing an output variable, a control variable and a disturbance input in a prediction range from the time t;
Taking a vector formed by the average value of wind-solar predicted power and the SOC planned value of the super capacitor in n time periods forward of the current moment And then, taking the wind-solar grid-connected power and the minimum error between the SOC of the super capacitor and the tracking control target as targets, and simultaneously enabling the increment of the charge and discharge power of the super capacitor to be as small as possible to obtain the following loss function:
Wherein omega is a weighting matrix of wind-solar grid-connected power tracking error and supercapacitor SOC tracking error, and ψ and lambda are a weighting matrix and a weighting coefficient of control quantity respectively;
taking equation (9) into equation (10), developing a loss function, and converting the MPC-based stabilized wind-solar power output fluctuation model into a quadratic programming as follows:
Corresponding constraint conditions:
Wherein, the AndThe maximum charging power and the maximum discharging power of the super capacitor are respectively; And The upper limit and the lower limit of the charge state of the super capacitor are respectively;
further, step S2 is described, where a double-layer optimization configuration model of the hybrid energy storage power station is built, so that annual income of the hybrid energy storage power station is maximized, and an evaluation index of the hybrid energy storage configuration effect is built, and the method specifically includes:
S201, optimizing the rated capacity of the hybrid energy storage by using an upper layer optimization of a double-layer optimization configuration model of the hybrid energy storage power station with the aim of maximizing the annual comprehensive benefit of the hybrid energy storage in a planning period, determining a hybrid energy storage configuration scheme, optimizing the rated capacity of the hybrid energy storage, combining the operation strategy of the hybrid energy storage in the step S1 on the premise of determining the hybrid energy storage configuration scheme, maximizing the benefit of the operation of the hybrid energy storage power station all the year round, feeding back the respective cost and benefit of a storage battery and a supercapacitor to the upper layer optimization, and realizing the mutual iteration of the upper layer optimization and the lower layer optimization;
s202, after the hybrid energy storage power station is configured by adopting the hybrid energy storage configuration scheme, wind-solar grid-connected power and the operation effect of the hybrid energy storage power station are taken as objects, and evaluation indexes of the hybrid energy storage configuration effect are constructed, wherein the evaluation indexes comprise grid-connected power fluctuation rate indexes, peak regulation effect indexes and energy storage system utilization indexes.
Further, in the step S201, the upper optimization of the double-layer optimization configuration model of the hybrid energy storage power station aims at maximizing the annual comprehensive benefit of the hybrid energy storage in the planning period, determines the hybrid energy storage configuration scheme, optimizes the rated capacity of the hybrid energy storage, and the lower optimization aims at maximizing the benefit of the operation of the hybrid energy storage power station all the year around by combining the operation strategy of the hybrid energy storage in the step S1 on the premise of determining the hybrid energy storage configuration scheme, and feeds back the respective cost and benefit of the storage battery and the supercapacitor to the upper optimization, thereby realizing the mutual iteration of the upper optimization and the lower optimization, and the method specifically comprises the following steps:
s2011, setting an upper layer optimization configuration model:
maxCtotal＝Cincome-Cinv-Cop (13)
Wherein, C total is the annual comprehensive benefit of the shared hybrid energy storage power station, C income is the annual income of the hybrid energy storage power station, C inv is the annual investment cost of the hybrid energy storage system, C op is the annual comprehensive operation cost of the hybrid energy storage power station, wherein, C income and C op are objective functions of lower-layer optimization and are transmitted by the lower-layer optimization, and the calculation method of the annual investment cost of energy storage is as follows:
Wherein E bat and E sc are rated capacities configured for the storage battery and the super capacitor respectively, r is the discount rate, y bat and y sc are the service lives of the storage battery and the super capacitor respectively, and c bat and c sc are the investment cost of the unit capacities of the storage battery and the super capacitor respectively;
The corresponding constraint conditions comprise upper and lower limit constraints of rated capacities of the storage battery and the super capacitor to be planned:
Wherein E bat max and E bat min are respectively the upper and lower limits of the rated capacity of the storage battery, and E sc max and E sc min are respectively the upper and lower limits of the rated capacity of the super capacitor;
S2012, setting a lower-layer optimal configuration model:
max(Cincome-Cop)＝Cpr+Car+Cp-Cdod-Com (16)
Wherein, C pr is the benefit obtained by the secondary battery through peak-to-valley electricity price difference arbitrage, C ar is the auxiliary benefit of the secondary battery in peak regulation, C p is the electric quantity benefit brought by the super capacitor after stabilizing wind and light fluctuation, C dod is the update replacement cost of the hybrid energy storage power station due to cyclic aging, C om is the annual average operation maintenance cost of the hybrid energy storage power station, and the size of the annual average operation maintenance cost is irrelevant to the energy storage capacity;
The calculation method of each cost and benefit is as follows:
Wherein c e is the online time-sharing electricity price, c ar is the auxiliary peak regulation service cost of unit capacity, f conserve is the wind-light dispatching network access proportion of a conservative dispatching scheme under the consideration of the maximum wind-light prediction error, f (alpha) is the dispatching network access proportion after stabilizing wind-light fluctuation, T d is the number of days in the whole year, T is the number of time periods of one day, T 1 is the peak time period in one day, f bdc and f scdc are functions of the ageing cost of a storage battery and a super capacitor respectively, d bat (delta T) is the discharge depth of the storage battery, and p re (T) and p d (T) are the actual output power and the grid-connected power of a new energy field station at the moment T respectively.
The calculation method of f bdc,fscdc, e (t) and d bat (Δt) is as follows:
Wherein L bat(dbat (delta t)) is the cycle life of the storage battery under the charge and discharge depth d bat (delta t), a, b and c are fitting parameters, p bat (t) is the output power of the storage battery at the moment t, e bat (t) is the actual capacity of the storage battery at the moment t, and L sc is the service life of the supercapacitor;
and the constraint condition of lower-layer optimization is the operation constraint of the storage battery and the super capacitor in the operation strategy of the hybrid energy storage in the step S1.
Further, in the step S202, the grid-connected power fluctuation rate index, the peak shaving effect index and the energy storage system utilization index are specifically:
S2021, grid-connected power fluctuation index I 1:
wherein p d(t1) is wind-solar grid-connected power within 1h, and p re is the installed capacity of renewable energy sources;
S2022, peak shaving effect index I 2:
wherein, p d,ref(T1),pd,ref(T2),pd,ref(T3) is the median of the wind-solar grid-connected power in the load peak, flat and valley periods respectively;
s2023, the utilization index I 3 of the energy storage system:
In the formula, n is the number of times that the charge and discharge power of the storage battery or the super capacitor is 0 in one day, and the SOC (t), the SOC max and the SOC min respectively represent the state of charge and the upper limit and the lower limit of the state of charge of the storage battery or the super capacitor at the time t.
Further, step S3 is to establish a cooperative game model, and determine a comprehensive allocation strategy considering the hybrid energy storage configuration effect based on a Shapley score method, which specifically includes:
s301, shapley value allocation strategy:
Let M be the total number of members participating in the cooperative game, M be the set of M members, S represent the cooperative alliance of different members, S be the subset of M, S be the number of members in the alliance S, the Shapley value of the member i is the profit distribution obtained by i in the cooperative M, the calculation method is as follows:
Wherein V S-VS\{i} represents the marginal contribution of the member i in participating in the cooperative federation S, V S is the benefit obtained by the cooperative federation S containing the member i, V S\{i} is the benefit obtained by the cooperative federation S after removing the member i, (S-1) | (m-S) |/m| represents the probability of occurrence of the cooperative federation S containing the member i;
in the formula, V w1、Vw2、Vpv is the income of wind power plant 1, wind power plant 2 and photovoltaic power station when energy storage is configured independently, V w1,w2 is the income of wind power plant 1 and wind power plant 2 when energy storage is configured cooperatively, V w1,pv is the income of wind power plant 1 and photovoltaic power station, V w2,pv is the income of wind power plant 2 and photovoltaic power station, and V w1,w2,pv is the income of three new energy stations;
The Shapley values of the wind power plant 2 and the photovoltaic power station are obtained through similar calculation;
S302, considering an allocation strategy of the hybrid energy storage configuration effect:
calculating the comprehensive evaluation value of each cooperative alliance by using a weighted Topsis method, and calculating the contribution degree of the member i to the energy storage configuration effect in the cooperation M by referring to a Shapley value allocation strategy:
Wherein, I S is the comprehensive evaluation value of the hybrid energy storage configuration effect obtained by the cooperative alliance S containing the member I, I S\{i} is the comprehensive evaluation value of the cooperative alliance S after the member I is removed;
And then calculating the income obtained by the member by using the contribution degree of the member i to the energy storage configuration effect:
Wherein x i is the contribution degree of the member i to the energy storage configuration effect in the cooperative alliance M, V M is the income obtained by the cooperative alliance M;
S303, improving a Shapley value allocation strategy:
The two allocation strategies are marked as M1 and M2, the weight of each strategy is determined by adopting a hierarchical analysis method, the importance degree of the strategies M1 and M2 in the profit allocation is scored to obtain a judgment matrix A, the consistency of the judgment matrix is checked, the weights of the M1 and M2 are alpha 1 and alpha 2, and the profit of the member i is:
Compared with the prior art, the energy storage planning method of the shared hybrid energy storage power station based on the cooperative game has the advantages that the operation strategy and the profit mode of the hybrid energy storage power station are defined during planning, annual comprehensive benefits of the energy storage power station are maximized as a planning target, the energy storage capacity is scientifically configured, the problems that the new energy station is configured for energy storage for grid connection with priority, the utilization rate of an energy storage system is low, the productivity is shelved and the like are avoided, the business mode of 'shared energy storage' is selected, the benefits of each new energy station are distributed based on an improved Shapley score method, the investment cost of the new energy station on the energy storage system is reduced, and the shared hybrid energy storage power station is enabled to obtain individual rationality and integrated rationality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a power generation structure of a new energy station sharing hybrid energy storage power station according to an embodiment of the present invention;
FIG. 2 is a hierarchical structure model of an improved Shapley allocation strategy according to an embodiment of the present invention;
FIG. 3a is a graph showing the effect of using a storage battery to adjust the peak value before the new energy stations are cooperated;
fig. 3b is an effect diagram of peak shaving by using a storage battery after cooperation of the new energy station according to the embodiment of the present invention;
FIG. 4a is a graph showing the effect of stabilizing the fluctuation of the output by using the super capacitor before the cooperation of the new energy station provided by the embodiment of the invention;
FIG. 4b is a graph showing the effect of stabilizing the fluctuation of the output by using the super capacitor after the cooperation of the new energy station according to the embodiment of the invention;
Fig. 5 is a flowchart of an energy storage planning method of a shared hybrid energy storage power station based on a cooperative game according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide an energy storage planning method of a shared hybrid energy storage power station based on a cooperative game, when planning shared energy storage for a new energy station, different operation characteristics of a storage battery and a super capacitor are fully considered, a hybrid energy storage control strategy is formulated, and the benefits of each new energy station are distributed based on an improved Shapley score method considering the energy storage configuration effect, so that the shared hybrid energy storage power station obtains individual rationality and integrated rationality.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a power generation structure of a new energy station sharing hybrid energy storage power station, and according to the structure, when the energy storage planning method of the new energy station sharing hybrid energy storage power station based on cooperative game provided by the invention is used for planning shared energy storage for the new energy station, different operation characteristics of a storage battery and a super capacitor are fully considered, a control strategy of hybrid energy storage is formulated, and benefits of each new energy station are distributed based on an improved shape score method considering energy storage configuration effect. As shown in fig. 5, the method comprises the steps of:
s1, setting an operation strategy of hybrid energy storage according to different operation characteristics of a storage battery and a super capacitor;
s2, establishing a double-layer optimal configuration model of the hybrid energy storage power station, maximizing annual income of the hybrid energy storage power station, and constructing an evaluation index of the hybrid energy storage configuration effect;
S3, establishing a cooperative game model, and determining a comprehensive allocation strategy considering the hybrid energy storage configuration effect based on a Shapley score method;
S4, obtaining a hybrid energy storage configuration scheme and annual benefits of the hybrid energy storage power station based on the operation strategy of the hybrid energy storage in the step S1 and the double-layer optimal configuration model in the step S2, and then distributing benefits to new energy stations in the alliance by utilizing the cooperative game model in the step S3.
In the step S1, according to the characteristics that the energy density of the storage battery is high, but the power density is low, the response time is long, the power density of the super capacitor is high, the response time is short, the super capacitor can be charged and discharged frequently, but the energy density is low, the storage battery is used for solving the problem of the reverse peak regulation of wind and light, and the super capacitor is used for stabilizing the fluctuation of wind and light output.
Therefore, the step S1 of formulating an operation strategy of hybrid energy storage for different operation characteristics of the storage battery and the supercapacitor specifically includes:
S101, based on a charge-discharge strategy of low storage and high emission, establishing a mathematical model of a storage battery operation strategy, solving the problem of anti-peak regulation of wind-light output, wherein the establishing the mathematical model of the storage battery operation strategy specifically comprises the following steps:
Wherein minJ bat represents an objective function of a battery operation strategy, so that the difference between the output of a new energy station after peak shaving of the battery and a reference value is minimized; The method comprises the steps of predicting power for a new energy station day before, wherein p bat (t) is the output power of a storage battery, and p ref (t) is the reference value of low storage and high emission of the storage battery;
the calculation method of p ref (t) is as follows:
wherein T 1、T2、T3 is a load peak period, a normal period and a valley period respectively, and medium represents a median;
Corresponding constraint conditions:
SOCbat(T)＝SOCbat(0) (5)
in the formula, SOC bat (t), The method comprises the steps of determining the charge state and the upper and lower limits of a storage battery, wherein eta bat,c,ηbat,d is the charge and discharge efficiency of the storage battery respectively, p bat,c(t)、pbat,d (T) is the charge and discharge power of the storage battery respectively, SOC bat (T-1) is the charge state of the storage battery at the time T-1, E bat is the rated capacity of the storage battery, deltaT is a scheduling time interval, SOC bat (T) is the charge state of the storage battery at the time T, SOC bat (0) is the charge state of the storage battery at the initial time, and p bat (T) is the output power of the storage battery, and the charge is negative and the discharge is positive; Maximum charge and discharge power of the storage battery;
Wherein, the formula (3) is the state of charge constraint of the storage battery, and the formula (5) is the constraint that the energy states of the storage battery need to be equal at the beginning and the end of a dispatching period;
S102, establishing a mathematical model of an operation strategy of the super capacitor based on model predictive control, stabilizing wind and light fluctuation, tracking plan power generation, and establishing the mathematical model of the operation strategy of the super capacitor, wherein the mathematical model specifically comprises the following steps:
selecting vectors formed by the sum of the charge state, charge and discharge power, wind-light real-time output and storage battery power of the super capacitor Is a state variable, wherein SOC sc (t) is the state of charge of the super capacitor at the time t, p sc,c (t-1) is the charging power of the super capacitor at the time t-1, p sc,d (t-1) is the discharging power of the super capacitor at the time t-1,The sum of the real-time wind-light output and the power of the storage battery at the time t-1;
Taking a vector u (t) = [ delta p sc,c(t),Δpsc,d(t)]T ] formed by the increment of the charging and discharging power of the super capacitor as a control variable, wherein the meaning of delta p sc,c(t),Δpsc,d (t) is the increment of the charging power of the super capacitor at the moment t and the increment of the discharging power of the super capacitor at the moment t respectively;
Vector formed by increment of sum of wind-light real-time output and power of storage battery Is a disturbance input; meaning of t-moment wind-light real-time output an increment of the sum of the power of the storage battery;
Vector y (t) = [ SOC sc(t),pd(t)]T ] formed by the charge state of the super capacitor and wind-light grid-connected power is taken as an output variable, wherein p d (t) means wind-light grid-connected power at the moment t:
The state space equations are established as shown in formulas (7) and (8), iteration is carried out on the basis of the formula (8), and a control instruction in a future time t+n is predicted, wherein the specific equation is shown in formula (9):
In the formula (7), x (t+1) is a state variable of the system at the moment t+1, A is a system matrix, B 1 is a control input matrix, B 2 is an external interference input matrix, and eta sc and E sc are respectively the charge and discharge efficiency and rated capacity of the supercapacitor.
In the formula (8), y (t+1) is an output variable of the system at the moment t+1, and C is a coefficient matrix;
In the formula (9), K, L 1,L2 are respectively a state variable, a control variable and a disturbance input coefficient matrix; And Respectively representing an output variable, a control variable and a disturbance input in a prediction range from the time t;
In order to cope with renewable energy fluctuation and errors between predicted output and real-time output, the wind-solar grid-connected power is ensured to track a planned value before the day, meanwhile, in order to ensure that the super capacitor SOC meets the state of charge constraint, a vector formed by the average value of wind-solar predicted power and the planned value of the super capacitor SOC in a period n forward of the current moment is taken And then, taking the wind-solar grid-connected power and the minimum error between the SOC of the super capacitor and the tracking control target as targets, and simultaneously enabling the increment of the charge and discharge power of the super capacitor to be as small as possible to obtain the following loss function:
Wherein omega is a weighting matrix of wind-solar grid-connected power tracking error and supercapacitor SOC tracking error, and ψ and lambda are a weighting matrix and a weighting coefficient of control quantity respectively;
taking equation (9) into equation (10), developing a loss function, and converting the MPC-based stabilized wind-solar power output fluctuation model into a quadratic programming as follows:
Corresponding constraint conditions:
Wherein, the AndThe maximum charging power and the maximum discharging power of the super capacitor are respectively; And The upper limit and the lower limit of the charge state of the super capacitor are respectively;
step S2, a double-layer optimization configuration model of the hybrid energy storage power station is established, annual income of the hybrid energy storage power station is maximized, and an evaluation index of the hybrid energy storage configuration effect is established, and the method specifically comprises the following steps:
s201, optimizing an upper layer of a double-layer optimizing configuration model of the hybrid energy storage power station by taking annual comprehensive benefits of hybrid energy storage in a planning period as targets, determining a hybrid energy storage configuration scheme, and optimizing rated capacity of the hybrid energy storage;
The lower layer optimization aims at maximizing the operation income of the annual hybrid energy storage power station by combining the operation strategy of the hybrid energy storage in the step S1 on the premise of determining the configuration scheme of the hybrid energy storage, and feeds back the respective cost and income of the storage battery and the super capacitor to the upper layer optimization to realize the mutual iteration of the upper layer optimization and the lower layer optimization, wherein the method comprises the following steps:
s2011, setting an upper layer optimization configuration model:
maxCtotal＝Cincome-Cinv-Cop (13)
Wherein, C total is the annual comprehensive benefit of the shared hybrid energy storage power station, C income is the annual income of the hybrid energy storage power station, C inv is the annual investment cost of the hybrid energy storage system, C op is the annual comprehensive operation cost of the hybrid energy storage power station, wherein, C income and C op are objective functions of lower-layer optimization and are transmitted by the lower-layer optimization, and the calculation method of the annual investment cost of energy storage is as follows:
Wherein E bat and E sc are rated capacities configured for the storage battery and the super capacitor respectively, r is the discount rate, y bat and y sc are the service lives of the storage battery and the super capacitor respectively, and c bat and c sc are the investment cost of the unit capacities of the storage battery and the super capacitor respectively;
The corresponding constraint conditions comprise upper and lower limit constraints of rated capacities of the storage battery and the super capacitor to be planned:
Wherein E bat max and E bat min are respectively the upper and lower limits of the rated capacity of the storage battery, and E sc max and E sc min are respectively the upper and lower limits of the rated capacity of the super capacitor;
S2012, setting a lower-layer optimal configuration model:
max(Cincome-Cop)＝Cpr+Car+Cp-Cdod-Com (16)
Wherein, C pr is the gain of the accumulator obtained by the peak-valley electricity price difference arbitrage, C ar is the auxiliary gain of the accumulator participating in peak regulation, C p is the electric quantity benefit brought by the super capacitor after stabilizing the wind and light fluctuation, C dod is the update replacement cost of the hybrid energy storage power station due to cyclical aging, in actual operation, a certain capacity is required to be added annually according to the aging rate of the hybrid energy storage system to ensure the available capacity of the energy storage system, the main factors influencing the service life of the accumulator are the discharge depth and the battery capacity, the service life of the super capacitor mainly depends on the evaporation rate of liquid electrolyte, the aging cost of the super capacitor can be regarded as a linear function changing with time under normal operation conditions, and C om is the annual average operation maintenance cost of the hybrid energy storage power station, and the size of the cost is irrelevant to the energy storage capacity;
The calculation method of each cost and benefit is as follows:
Wherein c e is the online time-of-use electricity price;
c ar is auxiliary peak shaving service cost of unit capacity;
f conserve is the wind-light dispatching network-access proportion of a conservative dispatching scheme under the consideration of wind-light maximum prediction error;
f (alpha) is a dispatching network access proportion after stabilizing wind and light fluctuation, and the value of f (alpha) is related to the capacity of the configured super capacitor;
T d is the number of days of the whole year;
t is the number of time periods of a day;
T 1 is the peak period of the day;
f bdc and f scdc are functions of battery and supercapacitor aging costs, respectively;
d bat (Δt) is the depth of discharge of the battery;
p re (t) and p d (t) are the actual output power and grid-connected power of the new energy station at the moment t respectively.
The calculation method of f bdc,fscdc, e (t) and d bat (Δt) is as follows:
Wherein L bat(dbat (delta t)) is the cycle life of the storage battery under the charge and discharge depth d bat (delta t), a, b and c are fitting parameters which can be obtained through curve fitting of the relation between the discharge depth and the cycle times of the storage battery provided by manufacturers, p bat (t) is the output power of the storage battery at the moment t, e bat (t) is the actual capacity of the storage battery at the moment t, and L sc is the service life of the super capacitor;
The lower-layer optimization constraint conditions are the operation constraint of the storage battery and the super capacitor in the operation strategy of the hybrid energy storage in the step S1, namely the operation constraint of the formula (3) -the formula (6) and the formula (12).
S202, after the hybrid energy storage power station is configured by adopting the hybrid energy storage configuration scheme, wind-solar grid-connected power and the operation effect of the hybrid energy storage power station are taken as objects, and evaluation indexes of the hybrid energy storage configuration effect are constructed, wherein the evaluation indexes comprise grid-connected power fluctuation rate indexes, peak regulation effect indexes and energy storage system utilization indexes.
The grid-connected power fluctuation rate index, the peak shaving effect index and the energy storage system utilization rate index are specifically as follows:
S2021, grid-connected power fluctuation index I 1:
After the high-proportion renewable energy is connected, due to inherent volatility and randomness, the conventional unit is regulated more frequently, even started and stopped, and the regulating depth is also greatly increased, namely the system is required to have stronger flexibility to match the wind-solar connected operation. Therefore, the fluctuation of the wind and light output can be stabilized by using the super capacitor, so that the flexibility requirement of wind and light on the system can be reduced, and the stabilization effect of the super capacitor on the wind and light grid-connected power fluctuation rate within 1h is used for evaluating.
Wherein p d(t1) is wind-solar grid-connected power within 1h, and p re is the installed capacity of renewable energy sources;
S2022, peak shaving effect index I 2:
the anti-peak regulation characteristic of new energy output is also a reason for restricting the development of the anti-peak regulation characteristic, the power grid in the load valley period is limited in downward regulation capacity, excessive receiving of new energy can lead a power grid peak regulating unit to enter an unconventional output mode, even the unit start-up and the peak regulation can be caused, the safe and economic operation of the power grid can be seriously influenced, and the new energy acceptance scale of the existing power grid is further restricted.
Wherein, p d,ref(T1),pd,ref(T2),pd,ref(T3) is the median of the wind-solar grid-connected power in the load peak, flat and valley periods respectively;
s2023, the utilization index I 3 of the energy storage system:
although many new energy stations are provided with energy storage systems at present, the energy storage systems are often only used as a tool for preferentially grid connection, and energy storage is not called in actual operation, so that the invention uses the product of the charge and discharge depth of the energy storage systems in one day and the number of energy storage utilization hours as an index of the utilization rate of the energy storage systems.
In the formula, n is the number of times that the charge and discharge power of the storage battery or the super capacitor is 0 in one day, and the SOC (t), the SOC max and the SOC min respectively represent the state of charge and the upper limit and the lower limit of the state of charge of the storage battery or the super capacitor at the time t.
The traditional cooperative game takes marginal contribution of alliance members as a profit distribution basis, but the problem of cooperative configuration of a hybrid energy storage power station for a new energy station is not the most reasonable method, and the effect of grid connection after energy storage is configured by different alliances should be considered when the profit is distributed.
Therefore, in the step S3 of the invention, a cooperative game model is established, and a comprehensive allocation strategy considering the hybrid energy storage configuration effect is determined based on a shape score method, and FIG. 2 is a hierarchical structure model of the improved shape allocation strategy provided by the invention, and the main contents of the cooperative game allocation strategy are as follows:
s301, shapley value allocation strategy:
Let M be the total number of members participating in the cooperative game, M be the set of M members, S represent the cooperative alliance of different members, S be the subset of M, S be the number of members in the alliance S, the Shapley value of the member i is the profit distribution obtained by i in the cooperative M, the calculation method is as follows:
Wherein V S-VS\{i} represents the marginal contribution of the member i in participating in the cooperative federation S, V S is the benefit obtained by the cooperative federation S containing the member i, V S\{i} is the benefit obtained by the cooperative federation S after removing the member i, (S-1) | (m-S) |/m| represents the probability of occurrence of the cooperative federation S containing the member i;
in the formula, V w1、Vw2、Vpv is the income of wind power plant 1, wind power plant 2 and photovoltaic power station when energy storage is configured independently, V w1,w2 is the income of wind power plant 1 and wind power plant 2 when energy storage is configured cooperatively, V w1,pv is the income of wind power plant 1 and photovoltaic power station, V w2,pv is the income of wind power plant 2 and photovoltaic power station, and V w1,w2,pv is the income of three new energy stations;
The Shapley values of the wind power plant 2 and the photovoltaic power station are obtained through similar calculation;
S302, considering an allocation strategy of the hybrid energy storage configuration effect:
The problem of the cooperation configuration of the hybrid energy storage power station is not the most reasonable method for the problem of the cooperation configuration of the new energy station, and because the new energy station configures the energy storage to solve the problem of fluctuation and inverse peak shaving of the new energy output, the benefits are also allocated from the grid-connected effect after the energy storage is configured, and if the overall configuration energy storage effect is better after a certain member is added, the member can obtain higher benefits. In the invention, three indexes for evaluating the energy storage configuration effect are provided in the step S22, the Topsis method with weight is used for calculating the comprehensive evaluation value of each cooperative alliance, and the contribution degree of the member i to the energy storage configuration effect in the cooperation M is calculated by referring to the Shapley value allocation strategy:
Wherein, I S is the comprehensive evaluation value of the hybrid energy storage configuration effect obtained by the cooperative alliance S containing the member I, I S\{i} is the comprehensive evaluation value of the cooperative alliance S after the member I is removed;
And then calculating the income obtained by the member by using the contribution degree of the member i to the energy storage configuration effect:
Wherein x i is the contribution degree of the member i to the energy storage configuration effect in the cooperative alliance M, V M is the income obtained by the cooperative alliance M;
S303, improving a Shapley value allocation strategy:
The two allocation strategies described above are now denoted M1 and M2, each reflecting its rationality in certain revenue allocation principles and should therefore be considered in combination. The invention calculates the profits of each member by using the two strategies and then adopts an Analytic Hierarchy Process (AHP) to determine the weight of each strategy, please the expert to score the importance degree of the strategies M1 and M2 in the profit allocation to obtain a judgment matrix A, and check the consistency of the judgment matrix, and the obtained weights of M1 and M2 are alpha 1 and alpha 2, wherein the profits of member i are:
In the step S4, energy storage configuration strategies and evaluation indexes of different alliances can be obtained, and benefits are distributed to new energy stations in the alliances by utilizing the cooperative game.
Fig. 3 (a) -3 (b) are graphs comparing peak regulation effects of storage batteries before and after cooperation of a new energy station provided by the embodiment of the invention, and as can be seen from fig. 3 (a) and 3 (b), the output characteristics of wind power are that the output is large at night, and peak periods of load are 8:00-11:00 and 18:00-22:00, so if the wind power station is independently configured with energy storage, the energy storage can be charged at night, discharge is carried out at two peak periods of daytime, the utilization rate of the energy storage is very low, and after cooperation with the photovoltaic power station, wind power stored at night can be discharged at 8:00-11:00, and photovoltaic output stored at the midday of the energy storage is discharged at 18:00-22:00, thereby not only improving the peak regulation effect, but also improving the utilization rate of the energy storage.
Fig. 4 (a) -4 (b) are graphs comparing effects of stabilizing force fluctuation by using the super capacitor before and after cooperation of the new energy station provided by the embodiment of the invention, and it can be seen from actual power curves in fig. 4 (a) and 4 (b) that when the same energy storage proportion is configured, stabilizing effects of the wind power plant and the photovoltaic power station after cooperation are better than effects of independently configuring the super capacitor. This is because although there is a prediction error for both the wind farm and the photovoltaic power plant, there is complementarity of the prediction error, reducing the required energy storage capacity.
Under the mode of cooperative configuration of the new energy station and shared hybrid energy storage, the revenue conditions of different alliance scales are summarized as shown in table 1:
TABLE 1 aggregation of energy storage configuration results and evaluation indicators at different alliance scales
As can be seen from table 1, the cooperative game in the new energy station configuration shared energy storage mode satisfies the collective rationality that the total profit is greater for the cooperative league than the sum of the profits of each new energy station configuration energy storage alone. In addition, the energy storage configuration effect of the cooperation alliance is better than the effect when the new energy station is independently configured for energy storage. From the economical point of view, when the new energy station is configured to store energy alone, the energy storage economical efficiency of the wind power station configuration is better than that of the photovoltaic power station, because the benefits in the objective function are mainly derived from the electric quantity benefits after peak shaving and surge stabilization, but the photovoltaic power station is better than the wind power station in terms of the degree of peak shaving and the fluctuation rate of output, so that the wind power station can obtain benefits after the energy storage is configured. However, the benefits of the photovoltaic power station and the wind farm after cooperation are far higher than those of the station for independently configuring energy storage, in addition, the benefits are also higher than those of cooperation between the two wind farms, and when peak shaving is seen from fig. 3 (a) and 3 (b), the operation state of the storage battery is improved after the photovoltaic power station and the wind farm are cooperated, the power of the storage battery participating in peak shaving is increased, so that the peak shaving benefits and the benefit of electricity price obtained after cooperation are both higher than those before cooperation, and the complementation of the wind and light output prediction errors exists when the stable output fluctuation can be seen from fig. 4 (a) and 4 (b), so that the capacity of the supercapacitor can be reduced. From the aspect of energy storage configuration effect, the influence of cooperation among wind power plants on the energy storage configuration effect is small, but after the wind power plants cooperate with the photovoltaic power station, various indexes of the energy storage configuration effect are greatly improved.
TABLE 2 participation in subject revenue analysis under different allocation policies
As can be seen from table 2, the cooperative game in the shared energy storage mode of the new energy station configuration satisfies individual rationality, that is, the gain obtained after cooperation is greater for the new energy station than when the new energy station is configured to store energy alone. Under Shapely value distribution strategies, the photovoltaic power station obtains the most benefit, and because the current energy storage cost is still high, when the wind power station is independently configured for energy storage, even if peak regulation benefit and electric quantity benefit can be obtained, the total benefit is still not high after the configuration cost is removed. However, after the wind power station and the photovoltaic power station cooperate, the wind and the light complement each other, so that the utilization rate of energy storage is improved, and the configuration proportion of the energy storage is reduced to a certain extent, so that the marginal benefit of the photovoltaic power station is higher than that of the wind power station. Under the distribution strategy considering the configuration effect of the hybrid energy storage, the obtained benefits of the photovoltaic power station are higher than those of the wind power station, and the evaluation index of the alliance is better than that before cooperation after the photovoltaic power station is added into the alliance, so that the contribution degree of the photovoltaic power station is higher and the obtained benefits are more on the configuration effect.
The energy storage planning method for the shared hybrid energy storage power station based on the cooperative game comprises the steps of firstly, formulating a hybrid energy storage control strategy aiming at different operation characteristics of a storage battery and a super capacitor, enabling the storage battery to adopt a low-storage high-emission charge-discharge strategy, enabling the super capacitor to stabilize wind and light fluctuation based on model prediction control and track planned power generation, secondly, establishing a double-layer planning model of the energy storage power station, constructing an evaluation index of an energy storage configuration result, and finally, distributing benefits of all new energy stations based on an improved shape score method considering the energy storage configuration effect. The result shows that the transaction mode not only can improve the utilization rate of the energy storage device, but also can reduce the investment cost of the new energy station on the energy storage system by independently configuring the energy storage with the new energy station for comparison.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present application have been described in detail with reference to specific examples, which are provided herein to facilitate understanding of the principles and embodiments of the present application and to provide further advantages and practical applications for those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.

Claims (3)

1. The energy storage planning method of the shared hybrid energy storage power station based on the cooperative game is characterized by comprising the following steps of:
S1, formulating a hybrid energy storage operation strategy aiming at different operation characteristics of a storage battery and a super capacitor, wherein the hybrid energy storage operation strategy comprises the storage battery operation strategy and the super capacitor operation strategy and specifically comprises the following steps:
S101, based on a charge-discharge strategy of 'low storage and high emission', establishing a mathematical model of a storage battery operation strategy, solving the problem of anti-peak shaving of wind-light output, specifically comprising the following steps:
establishing a mathematical model of a battery operation strategy, which specifically comprises the following steps:
Wherein minJ bat represents an objective function of a battery operation strategy, so that the difference between the output of a new energy station after peak shaving of the battery and a reference value is minimized; The method comprises the steps of predicting power for a new energy station day before, wherein p bat (t) is the output power of a storage battery, and p ref (t) is the reference value of low storage and high emission of the storage battery;
the calculation method of p ref (t) is as follows:
wherein T 1、T2、T3 is a load peak period, a normal period and a valley period respectively, and medium represents a median;
Corresponding constraint conditions:
SOCbat(T)＝SOCbat(0) (5)
in the formula, SOC bat (t), The method is characterized in that the method comprises the steps of determining the charge state and the upper and lower limits of a storage battery, wherein eta bat,c、ηbat,d is the charge and discharge efficiency of the storage battery respectively, p bat,c(t)、pbat,d (T) is the charge and discharge power of the storage battery respectively, SOC bat (T-1) is the charge state of the storage battery at the time T-1, E bat is the rated capacity of the storage battery, deltaT is a scheduling time interval, SOC bat (T) is the charge state of the storage battery at the time T, SOC bat (0) is the charge state of the storage battery at the initial time, and p bat (T) is the output power of the storage battery, and the charge is negative and the discharge is positive; Maximum charge and discharge power of the storage battery;
Wherein, the formula (3) is the state of charge constraint of the storage battery, and the formula (5) is the constraint that the energy states of the storage battery need to be equal at the beginning and the end of a dispatching period;
s102, based on model predictive control, establishing a mathematical model of an operation strategy of the supercapacitor, stabilizing wind and light fluctuation, tracking and planning power generation, wherein the method specifically comprises the following steps of:
establishing a mathematical model of an operation strategy of the supercapacitor, which specifically comprises the following steps:
selecting vectors formed by the sum of the charge state, charge and discharge power, wind-light real-time output and storage battery power of the super capacitor Is a state variable, wherein SOC sc (t) is the state of charge of the super capacitor at the time t, p sc,c (t-1) is the charging power of the super capacitor at the time t-1, p sc,d (t-1) is the discharging power of the super capacitor at the time t-1,The sum of the real-time wind-light output and the power of the storage battery at the time t-1;
Taking a vector u (t) = [ delta p sc,c(t),Δpsc,d(t)]T ] formed by the increment of the charging power and the discharging power of the super capacitor as a control variable, wherein delta p sc,c(t)、Δpsc,d (t) is the increment of the charging power of the super capacitor at the moment t and the increment of the discharging power of the super capacitor at the moment t respectively;
Vector formed by increment of sum of wind-light real-time output and power of storage battery In order to perturb the input,The increment of the sum of the real-time wind-light output and the power of the storage battery at the moment t;
vector y (t) = [ SOC sc(t),pd(t)]T ] formed by the charge state of the super capacitor and wind-solar grid-connected power is taken as an output variable, wherein p d (t) is the wind-solar grid-connected power at the moment t;
The state space equations are established as shown in formulas (7) and (8), iteration is carried out on the basis of the formula (8), and a control instruction in a future time t+n is predicted, wherein the specific equation is shown in formula (9):
In the formula (7), x (t+1) is a state variable of the system at the moment t+1, A is a system matrix, B 1 is a control input matrix, B 2 is an external interference input matrix, and eta sc and E sc are respectively the charge and discharge efficiency and rated capacity of the supercapacitor;
in the formula (8), y (t+1) is an output variable of the system at the moment t+1, and C is a coefficient matrix;
in the formula (9), K, L 1、L2 is a coefficient matrix of state variable, control variable and disturbance input respectively; And Respectively representing an output variable, a control variable and a disturbance input in a prediction range from the time t;
Taking a vector formed by the average value of wind-solar predicted power and the SOC planned value of the super capacitor in n time periods forward of the current moment And then, taking the wind-solar grid-connected power and the minimum error between the SOC of the super capacitor and the tracking control target as targets, and simultaneously enabling the increment of the charge and discharge power of the super capacitor to be as small as possible to obtain the following loss function:
Wherein omega is a weighting matrix of wind-solar grid-connected power tracking error and supercapacitor SOC tracking error, and ψ and lambda are a weighting matrix and a weighting coefficient of control quantity respectively;
taking equation (9) into equation (10), developing a loss function, and converting the MPC-based stabilized wind-solar power output fluctuation model into a quadratic programming as follows:
Corresponding constraint conditions:
Wherein, the AndThe maximum charging power and the maximum discharging power of the super capacitor are respectively; And The upper limit and the lower limit of the charge state of the super capacitor are respectively;
S2, establishing a double-layer optimization configuration model of the hybrid energy storage power station, maximizing annual income of the hybrid energy storage power station, and establishing an evaluation index of the hybrid energy storage configuration effect, wherein the evaluation index specifically comprises the following steps:
S201, optimizing the rated capacity of the hybrid energy storage by using an upper layer optimization of a double-layer optimization configuration model of the hybrid energy storage power station with the aim of maximizing the annual comprehensive benefit of the hybrid energy storage in a planning period, determining a hybrid energy storage configuration scheme, optimizing the rated capacity of the hybrid energy storage, combining the operation strategy of the hybrid energy storage in the step S1 on the premise of determining the hybrid energy storage configuration scheme, maximizing the benefit of the operation of the hybrid energy storage power station all the year round, feeding back the respective cost and benefit of a storage battery and a supercapacitor to the upper layer optimization, and realizing the mutual iteration of the upper layer optimization and the lower layer optimization;
S202, after a hybrid energy storage power station is configured by adopting a hybrid energy storage configuration scheme, wind-solar grid-connected power and the operation effect of the hybrid energy storage power station are taken as objects, and evaluation indexes of the hybrid energy storage configuration effect are constructed, wherein the evaluation indexes comprise grid-connected power fluctuation rate indexes, peak regulation effect indexes and energy storage system utilization indexes;
S3, establishing a cooperative game model, and determining a comprehensive allocation strategy considering the hybrid energy storage configuration effect based on a Shapley score method, wherein the comprehensive allocation strategy specifically comprises the following steps:
s301, shapley value allocation strategy:
Let M be the total number of members participating in the cooperative game, M be the set of M members, S represent the cooperative alliance of different members, S be the subset of M, S be the number of members in the alliance S, the Shapley value of the member i is the profit distribution obtained by i in the cooperative M, the calculation method is as follows:
Wherein V S-VS\{i} represents the marginal contribution of the member i in participating in the cooperative federation S, V S is the benefit obtained by the cooperative federation S containing the member i, V S\{i} is the benefit obtained by the cooperative federation S after removing the member i, (S-1) | (m-S) |/m| represents the probability of occurrence of the cooperative federation S containing the member i;
in the formula, V w1、Vw2、Vpv is the income of wind power plant 1, wind power plant 2 and photovoltaic power station when energy storage is configured independently, V w1,w2 is the income of wind power plant 1 and wind power plant 2 when energy storage is configured cooperatively, V w1,pv is the income of wind power plant 1 and photovoltaic power station, V w2,pv is the income of wind power plant 2 and photovoltaic power station, and V w1,w2,pv is the income of three new energy stations;
Similarly, calculating to obtain Shapley values of the wind power plant 2 and the photovoltaic power station;
S302, considering an allocation strategy of the hybrid energy storage configuration effect:
calculating the comprehensive evaluation value of each cooperative alliance by using a weighted Topsis method, and calculating the contribution degree of the member i to the energy storage configuration effect in the cooperation M by referring to a Shapley value allocation strategy:
Wherein, I S is the comprehensive evaluation value of the hybrid energy storage configuration effect obtained by the cooperative alliance S containing the member I, I S\{i} is the comprehensive evaluation value of the cooperative alliance S after the member I is removed;
And then calculating the income obtained by the member by using the contribution degree of the member i to the energy storage configuration effect:
Wherein x i is the contribution degree of the member i to the energy storage configuration effect in the cooperative alliance M, V M is the income obtained by the cooperative alliance M;
S303, improving a Shapley value allocation strategy:
The two allocation strategies are marked as M1 and M2, the weight of each strategy is determined by adopting a hierarchical analysis method, the importance degree of the strategies M1 and M2 in the profit allocation is scored to obtain a judgment matrix A, the consistency of the judgment matrix is checked, the weights of the M1 and M2 are alpha 1 and alpha 2, and the profit of the member i is:
S4, obtaining a hybrid energy storage configuration scheme and annual benefits of the hybrid energy storage power station based on the operation strategy of the hybrid energy storage in the step S1 and the double-layer optimal configuration model in the step S2, and then distributing benefits to new energy stations in the alliance by utilizing the cooperative game model in the step S3.
2. The energy storage planning method of the shared hybrid energy storage power station based on the cooperative game as claimed in claim 1, wherein in the step S201, the upper layer optimization of the double-layer optimization configuration model of the hybrid energy storage power station aims at maximizing the annual comprehensive benefit of the hybrid energy storage in the planning period, determines the hybrid energy storage configuration scheme, optimizes the rated capacity of the hybrid energy storage, and the lower layer optimization combines with the operation strategy of the hybrid energy storage in the step S1 on the premise of determining the hybrid energy storage configuration scheme, aims at maximizing the benefit of the operation of the hybrid energy storage power station all the year, and feeds the respective cost and benefit of the storage battery and the supercapacitor back to the upper layer optimization to realize the mutual iteration of the upper layer optimization and the lower layer optimization, and specifically comprises:
s2011, setting an upper layer optimization configuration model:
maxCtotal＝Cincome-Cinv-Cop (13)
Wherein, C total is the annual comprehensive benefit of the shared hybrid energy storage power station, C income is the annual income of the hybrid energy storage power station, C inv is the annual investment cost of the hybrid energy storage system, C op is the annual comprehensive operation cost of the hybrid energy storage power station, wherein, C income and C op are objective functions of lower-layer optimization and are transmitted by the lower-layer optimization, and the calculation method of the annual investment cost of energy storage is as follows:
Wherein E bat and E sc are rated capacities configured for the storage battery and the super capacitor respectively, r is the discount rate, y bat and y sc are the service lives of the storage battery and the super capacitor respectively, and c bat and c sc are the investment cost of the unit capacities of the storage battery and the super capacitor respectively;
The corresponding constraint conditions comprise upper and lower limit constraints of rated capacities of the storage battery and the super capacitor to be planned:
Wherein E bat max and E bat min are respectively the upper and lower limits of the rated capacity of the storage battery, and E sc max and E sc min are respectively the upper and lower limits of the rated capacity of the super capacitor;
S2012, setting a lower-layer optimal configuration model:
max(Cincome-Cop)＝Cpr+Car+Cp-Cdod-Com (16)
Wherein, C pr is the benefit obtained by the secondary battery through peak-to-valley electricity price difference arbitrage, C ar is the auxiliary benefit of the secondary battery in peak regulation, C p is the electric quantity benefit brought by the super capacitor after stabilizing wind and light fluctuation, C dod is the update replacement cost of the hybrid energy storage power station due to cyclic aging, C om is the annual average operation maintenance cost of the hybrid energy storage power station, and the size of the annual average operation maintenance cost is irrelevant to the energy storage capacity;
The calculation method of each cost and benefit is as follows:
Wherein, c e is the online time-sharing electricity price, c ar is the auxiliary peak regulation service cost of unit capacity;
f conserve is the wind-light dispatching network access proportion of a conservative dispatching scheme under the maximum prediction error of wind light, and f (alpha) is the dispatching network access proportion after wind-light fluctuation is stabilized;
T d is the number of days of the whole year, T is the number of times of the day, and T 1 is the peak time of the day;
f bdc and f scdc are functions of battery and supercapacitor aging costs, respectively;
d bat (Δt) is the depth of discharge of the battery;
p re (t) and p d (t) are the actual output power and grid-connected power of the new energy station at the moment t respectively;
Wherein, the calculation method of f bdc、fscdc and d bat (delta t) is as follows:
Wherein L bat(dbat (delta t)) is the cycle life of the storage battery under the charge and discharge depth d bat (delta t), a, b and c are fitting parameters, p bat (t) is the output power of the storage battery at the moment t, e bat (t) is the actual capacity of the storage battery at the moment t, and L sc is the service life of the supercapacitor;
and the constraint condition of lower-layer optimization is the operation constraint of the storage battery and the super capacitor in the operation strategy of the hybrid energy storage in the step S1.
3. The energy storage planning method of the shared hybrid energy storage power station based on the cooperative game as claimed in claim 2, wherein in the step S202, the grid-connected power fluctuation rate index, the peak shaving effect index and the energy storage system utilization index are specifically:
S2021, grid-connected power fluctuation index I 1:
wherein p d(t1) is wind-solar grid-connected power within 1h, and p re is the installed capacity of renewable energy sources;
S2022, peak shaving effect index I 2:
wherein, p d,ref(T1),pd,ref(T2),pd,ref(T3) is the median of the wind-solar grid-connected power in the load peak, flat and valley periods respectively;
s2023, the utilization index I 3 of the energy storage system:
In the formula, n is the number of times that the charge and discharge power of the storage battery or the super capacitor is 0 in one day, and the SOC (t), the SOC max and the SOC min respectively represent the state of charge and the upper limit and the lower limit of the state of charge of the storage battery or the super capacitor at the time t.
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