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

Abstract

The present invention discloses an energy storage planning method for 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 the battery and the supercapacitor. The battery adopts a charge-discharge strategy of "low storage and high discharge", and the supercapacitor suppresses the fluctuations of wind and light based on model predictive control and tracks the planned power generation. Secondly, a two-layer optimal configuration model of 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 power station is allocated based on an improved Shapley value method considering the energy storage configuration effect. The energy storage planning method for the shared hybrid energy storage power station based on cooperative game provided by the present invention clarifies the operation strategy and profit-making method of the hybrid energy storage during planning, maximizes the annual comprehensive benefit of the energy storage power station, and reasonably allocates the income for the new energy power stations in the alliance, so that the shared hybrid energy storage power station obtains individual rationality and collective rationality.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning in power systems, and particularly to a method for energy storage planning of a shared hybrid energy storage power station based on cooperative game theory. Background Art

[0002] With the continuous increase in the penetration rate of new energy, the inherent volatility and randomness of new energy bring new challenges to the safe and stable operation of power systems. Therefore, how to scientifically configure the energy storage capacity to avoid waste of resources needs further discussion.

[0003] To reduce the construction cost of energy storage in new energy power stations and improve their utilization rate, Kang Chongqing et al. proposed the concept of "shared energy storage" in Automation of Electric Power Systems, 2017, 41(21): 2-8. "New form of energy storage in future power systems: cloud energy storage". Currently, the research on using shared energy storage to improve resource allocation efficiency mainly focuses on the distribution network side and solving the trading problems between energy storage operators and energy storage leasing users. Among them, energy storage operators are often provided by third parties, so non-cooperative game is mostly used. In recent years, relevant scholars have also studied shared energy storage on the power generation side, regarding new energy power stations as investors in energy storage power stations. Sun Si et al. established a shared energy storage planning model for new energy power plants based on cooperative game theory in Global Energy Interconnection, 2019, 2(04): 360-366. "Shared energy storage planning model for power generation side based on cooperative game theory". However, the alliance members are 5 wind power plants, and the addition of photovoltaic power plants is not considered. When distributing benefits, only the marginal benefits of members are considered, and the impact of the addition of members on the overall output effect is not considered. Therefore, at present, when new energy power stations are configured with energy storage systems alone, there are problems of low utilization rate and poor economy, which urgently need to be improved.

[0004] In addition, new energy power stations need to adapt to the diverse energy and power demands of modern power grids. A single type of energy storage technology often fails to meet the requirements. However, the combined configuration of two or more energy storage technologies can complement each other's strengths, give full play to the technical and economic advantages of all parties, and greatly expand the application scenarios of energy storage systems. The power distribution and capacity configuration of hybrid energy storage systems have an important impact on the technical and economic performance of the entire power grid. Zhang Qing et al. proposed a method for configuring the power and capacity of a hybrid energy storage system that uses moving average and empirical mode decomposition to obtain the reference power of the energy storage in "A Method for Configuring the Capacity of Hybrid Energy Storage for Smoothing Wind Power Fluctuations with the Maximum Net Benefit", Electric Engineering Journal, 2016, 31(14): 40-48. Ma Lan et al. obtained the reference power of the hybrid energy storage using the adaptive wavelet packet decomposition method and established an optimization model for the capacity of the hybrid energy storage based on the battery life quantification model in "A Strategy for Suppressing Wind Power Fluctuations Based on a Double-Layer Planning Model of Hybrid Energy Storage", Power System Technology, 2022, 46(03): 1016-1029. Yang Jun et al. constructed a double-layer planning model for suppressing and determining the capacity of a hybrid energy storage system based on a multi-step model algorithm control in "Optimal Configuration of the Capacity of Hybrid Energy Storage Systems in Grid-Connected Wind and Solar Power Generation", Power System Technology, 2013, 37(05): 1209-1216. Most of the above-mentioned literatures analyze the configuration of hybrid energy storage capacity in combination with methods for suppressing fluctuations. However, during periods of low wind power fluctuations, using the idle capacity of the energy storage to participate in peak shaving and provide standby can improve the utilization rate of the energy storage and increase the operating benefits of the energy storage. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for planning the energy storage of a shared hybrid energy storage power station based on cooperative game. When planning the shared energy storage for new energy power stations, the different operating characteristics of batteries and supercapacitors are fully considered, a control strategy for the hybrid energy storage is formulated, and the benefits of each new energy power station are allocated based on an improved Shapley value method considering the energy storage configuration effect, enabling the shared hybrid energy storage power station to achieve individual rationality and collective rationality.

[0006] To achieve the above object, the present invention provides the following solution:

[0007] A method for planning the energy storage of a shared hybrid energy storage power station based on cooperative game, the method comprising the following steps:

[0008] S1, formulating an operating strategy for the hybrid energy storage according to the different operating characteristics of the battery and the supercapacitor;

[0009] S2, establishing a double-layer optimization configuration model for the hybrid energy storage power station to maximize the annual income of the hybrid energy storage power station, and constructing an evaluation index for the effect of the hybrid energy storage configuration;

[0010] S3. Establish a cooperative game model and determine a comprehensive allocation strategy considering the configuration effect of hybrid energy storage based on the Shapley value method;

[0011] S4. Based on the operation strategy of the hybrid energy storage in step S1 and the two-layer optimal configuration model in step S2, obtain the hybrid energy storage configuration plan and annual income of the hybrid energy storage power station, and then use the cooperative game model in step S3 to allocate income to the new energy power stations in the alliance.

[0012] Further, in step S1, an operation strategy of the hybrid energy storage is formulated according to the different operation characteristics of the battery and the supercapacitor, which specifically includes:

[0013] S101. Based on the charge-discharge strategy of "low storage and high discharge", establish a mathematical model for the operation strategy of the battery to solve the reverse peak-shaving problem of the wind-solar power output;

[0014] S102. Based on model predictive control, establish a mathematical model for the operation strategy of the supercapacitor to suppress the wind-solar power fluctuations and track the planned power generation.

[0015] Further, in step S101, establishing the mathematical model for the operation strategy of the battery specifically includes:

[0016]

[0017] Where minJ bat represents the objective function of the battery operation strategy, which minimizes the gap between the output power of the new energy power station after peak shaving by the battery and the reference value; T is the number of time periods in a day; is the day-ahead predicted power of the new energy power station; p bat (t) is the output power of the battery; p ref (t) is the reference value of "low storage and high discharge" of the battery;

[0018] p ref (t) is calculated as follows:

[0019]

[0020] Where T1, T2, and T3 are the peak load period, normal period, and valley load period respectively, and median represents the median;

[0021] The corresponding constraint conditions:

[0022]

[0023] SOC bat (T) = SOC bat (0) (5)

[0024]

[0025] In the formula, SOC bat (t), are the state of charge of the battery and its upper and lower limits; η bat，c , η bat，d are the charge and discharge efficiencies of the battery respectively; p bat，c (t), p bat，d (t) are the charge and discharge powers of the battery respectively; SOC bat (t - 1) is the state of charge of the battery at time t - 1; E bat is the rated capacity of the battery; ΔT is the scheduling time interval; SOC bat (T) is the state of charge of the battery at time T; SOC bat (0) is the state of charge of the battery at the starting time; p bat (t) is the output power of the battery, with charging being negative and discharging being positive; is the maximum charge and discharge power of the battery;

[0026] Among them, the above formula (3) is the state of charge constraint of the battery, and formula (5) is the constraint that the energy state of the battery needs to be equal at the beginning and end of the scheduling period.

[0027] Further, in step S102, a mathematical model for the operation strategy of the supercapacitor is established, specifically including:

[0028] Select the vector composed of the state of charge of the supercapacitor, the charge and discharge power, and the sum of the real-time output of wind and light and the battery power as the state variable; among them, SOC sc (t) is the state of charge of the supercapacitor at time t, p sc,c (t - 1) is the charging power of the supercapacitor at time t - 1, p sc,d (t - 1) is the discharging power of the supercapacitor at time t - 1, is the sum of the real-time output of wind and light and the battery power at time t - 1;

[0029] Take the vector u(t) = [Δp sc,c (t), Δp sc,d (t)] T composed of the increments of the charge and discharge powers of the supercapacitor as the control variable; among them, Δp sc,c (t), Δp sc,d (t) respectively represent the increment of the charging power of the supercapacitor at time t and the increment of the discharging power of the supercapacitor at time t;

[0030] Take the vector composed of the increment of the sum of the real-time output of wind and light and the battery power as the disturbance input; The meaning of

[0031] is the increment of the sum of the real-time output of wind and light and the battery power at time t; sc (t), p d (t)] T is the output variable; where the meaning of p d (t) is the grid-connected power of wind and light at time t;

[0032] The state-space equations are established as shown in equations (7) and (8); based on equation (8), iteration is performed to predict the control commands within the future t + n time periods, and the specific equation is as shown in equation (9):

[0033]

[0034] Among them, in equation (7), x(t + 1) is the state variable of the system at time t + 1; A is the system matrix; B1 is the control input matrix; B2 is the external disturbance input matrix; η sc and E sc are respectively the charge and discharge efficiency and rated capacity of the supercapacitor;

[0035] In equation (8), y(t + 1) is the output variable of the system at time t + 1; C is the coefficient matrix;

[0036] In equation (9), K, L1, and L2 are respectively the coefficient matrices of the state variable, control variable, and disturbance input; and respectively represent the output variable, control variable, and disturbance input within the prediction range starting from time t;

[0037] Take the vector composed of the mean value of the predicted wind and light power and the planned value of the supercapacitor SOC within the n time periods forward from the current time as the tracking control target; then, with the goal of minimizing the error between the grid-connected power of wind and light and the supercapacitor SOC and the tracking control target, and at the same time minimizing the increment of the charge and discharge power of the supercapacitor as much as possible, the following loss function is obtained:

[0038]

[0039] In the formula, Ω is the weighted matrix of the grid-connected power tracking error of wind and light and the supercapacitor SOC tracking error, and Ψ and λ are respectively the weighted matrix and weighted coefficient of the control quantity;

[0040] Substitute equation (9) into equation (10) and expand the loss function, and the model for suppressing the fluctuation of wind and light output based on MPC can be transformed into the following quadratic programming:

[0041]

[0042] Corresponding constraint conditions:

[0043]

[0044] Among them, and are the maximum charging power and the maximum discharging power of the supercapacitor respectively; and are the upper and lower limits of the state of charge of the supercapacitor respectively;

[0045] Furthermore, in step S2, a two-layer optimization configuration model of the hybrid energy storage power station is established to maximize the annual income of the hybrid energy storage power station, and evaluation indexes for the configuration effect of the hybrid energy storage are constructed, specifically including:

[0046] S201. The upper-layer optimization of the two-layer optimization configuration model of the hybrid energy storage power station aims to maximize the annual comprehensive benefit of the hybrid energy storage during the planning period, determine the hybrid energy storage configuration scheme, and optimize the rated capacity of the hybrid energy storage; the lower-layer optimization is based on the determined hybrid energy storage configuration scheme, combined with the operation strategy of the hybrid energy storage described in step S1, aims to maximize the annual operation income of the hybrid energy storage power station, and feeds back the respective costs and incomes of the battery and the supercapacitor to the upper-layer optimization to realize the mutual iteration of the upper and lower-layer optimizations;

[0047] S202. Taking the wind-solar grid-connected power and the operation effect of the hybrid energy storage power station after configuring the hybrid energy storage power station with the hybrid energy storage configuration scheme as the object, evaluation indexes for the configuration effect of the hybrid energy storage are constructed, including the grid-connected power volatility index, the peak shaving effect index, and the energy storage system utilization rate index.

[0048] Furthermore, in step S201, the upper-layer optimization of the two-layer optimization configuration model of the hybrid energy storage power station aims to maximize the annual comprehensive benefit of the hybrid energy storage during the planning period, determine the hybrid energy storage configuration scheme, and optimize the rated capacity of the hybrid energy storage; the lower-layer optimization is based on the determined hybrid energy storage configuration scheme, combined with the operation strategy of the hybrid energy storage described in step S1, aims to maximize the annual operation income of the hybrid energy storage power station, and feeds back the respective costs and incomes of the battery and the supercapacitor to the upper-layer optimization to realize the mutual iteration of the upper and lower-layer optimizations; specifically including:

[0049] S2011. Set the upper-layer optimization configuration model:

[0050] maxC total =C income -C inv -C op (13)

[0051] In the formula, C totalis the annual comprehensive benefit of the shared hybrid energy storage power station; C income is the annual operating income of the hybrid energy storage power station; C inv is the equivalent annual value investment cost of the hybrid energy storage system; C op is the annual comprehensive operating cost of the hybrid energy storage power station, where, C income and C op are the objective functions of the lower-layer optimization, which are transmitted from the lower-layer optimization. The calculation method of the equivalent annual value investment cost of the energy storage is as follows:

[0052]

[0053] In the formula: E bat and E sc are the rated capacities of the battery and the supercapacitor configurations respectively; r is the discount rate; y bat and y sc are the operating lives of the battery and the supercapacitor respectively; c bat and c sc are the investment costs per unit capacity of the battery and the supercapacitor respectively;

[0054] The corresponding constraint conditions include the upper and lower limit constraints of the rated capacities of the battery and the supercapacitor to be planned:

[0055]

[0056] In the formula: E bat max and E bat min are the upper and lower bounds of the rated capacity of the battery; E sc max and E sc min are the upper and lower bounds of the rated capacity of the supercapacitor respectively;

[0057] S2012, set the lower-layer optimization configuration model:

[0058] max(C income -C op ) = C pr +C ar +C p -C dod -C om (16)

[0059] In the formula, C pr is the income obtained by the battery through peak-valley electricity price difference arbitrage; C ar is the auxiliary income of the battery participating in peak shaving; C p is the electricity benefit brought by the supercapacitor after suppressing the fluctuations of wind and light; C dodis the replacement cost of the hybrid energy storage power station due to cycle aging; C om is the annual average operation and maintenance cost of the hybrid energy storage power station, and its magnitude is independent of the energy storage capacity;

[0060] The calculation methods of each cost and benefit are as follows:

[0061]

[0062] In the formula, c e is the time-of-use electricity price for grid connection; c ar is the auxiliary peak shaving service cost per unit capacity; f conserve is the grid connection ratio of wind and light in the conservative dispatching scheme considering the maximum prediction error of wind and light; f(α) is the grid connection ratio after suppressing the fluctuations of wind and light; T d is the number of days in a year; T is the number of time periods in a day; T1 is the peak time period in a day; f bdc and f scdc are respectively the functions of the aging costs of the battery and the supercapacitor; d bat (Δt) is the depth of discharge of the battery; p re (t) and p d (t) are respectively the actual output power and the grid connection power of the new energy power station at time t.

[0063] f bdc ,f scdc ,e(t) and d bat (Δt) are calculated as follows:

[0064]

[0065] In the formula: L bat (d bat (Δt)) is the cycle life of the battery at the depth of charge and discharge d bat (Δt); a, b, c are fitting parameters; p bat (t) is the output power of the battery at time t; e bat (t) is the actual capacity of the battery at time t; L sc is the service life of the supercapacitor;

[0066] The constraint condition of the lower-layer optimization is the operation constraints of the battery and the supercapacitor in the operation strategy of the hybrid energy storage described in step S1.

[0067] Furthermore, in step S202, the grid connection power volatility index, the peak shaving effect index, and the energy storage system utilization index are specifically:

[0068] S2021, grid connection power volatility index I1:

[0069]

[0070] Where: p d (t1) is the wind-solar grid-connected power within 1 h; p re is the installed capacity of renewable energy;

[0071] S2022, peak shaving effect index I2:

[0072]

[0073] Wherein, p d,ref (T1), p d,ref (T2), p d,ref (T3) are the medians of the wind-solar grid-connected power at the peak, flat, and valley periods of the load respectively;

[0074] S2023, energy storage system utilization rate index I3:

[0075]

[0076] Wherein, n is the number of times when the charge-discharge power of the battery or supercapacitor is 0 within a day, and SOC(t), SOC max and SOC min respectively represent the state of charge of the battery or supercapacitor at time t, the upper limit of the state of charge, and the lower limit of the state of charge.

[0077] Furthermore, in step S3, a cooperative game model is established, and based on the Shapley value method, a comprehensive distribution strategy considering the configuration effect of hybrid energy storage is determined, which specifically includes:

[0078] S301, Shapley value distribution strategy:

[0079] Let m be the total number of members participating in the cooperative game; M is the set composed of m members; S represents different cooperative coalitions composed of members, and S is a subset of M; s is the number of members in the coalition S, and the Shapley value of member i is the income distribution obtained by i in the cooperation M, and its calculation method is:

[0080]

[0081] Wherein, V S -V S\{i} represents the marginal contribution of member i in participating in the cooperative coalition S; V S is the income obtained by the cooperative coalition S including member i; V S\{i} is the income obtained by the cooperative coalition S after removing member i; (s - 1)!(m - s)! / m! represents the probability that the cooperative coalition S including member i appears;

[0082]

[0083] In the formula, is the Shapley value of wind farm 1; V w1 , V w2 , V pv are the revenues when energy storage is separately configured for wind farm 1, wind farm 2, and the PV power station, respectively; V w1，w2 is the revenue when energy storage is configured through the cooperation of wind farm 1 and wind farm 2; V w1，pv is the cooperative revenue of wind farm 1 and the PV power station; V w2，pv is the cooperative revenue of wind farm 2 and the PV power station; V w1，w2，pv is the cooperative revenue of these three new energy power stations;

[0084] Similarly, the Shapley values of wind farm 2 and the PV power station are calculated;

[0085] S302. Allocation strategy considering the configuration effect of hybrid energy storage:

[0086] The weighted TOPSIS method is used to calculate the comprehensive evaluation value of each cooperation alliance. Referring to the Shapley value allocation strategy, the contribution degree of member i to the configuration effect of energy storage in cooperation M is calculated:

[0087]

[0088] In the formula, I S is the comprehensive evaluation value of the configuration effect of hybrid energy storage obtained by cooperation alliance S containing member i; I S\{i} is the comprehensive evaluation value after removing member i from cooperation alliance S;

[0089] Then, using the contribution degree of member i to the configuration effect of energy storage obtained, the revenue obtained by this member is calculated:

[0090]

[0091] Among them, x i is the contribution degree of member i to the configuration effect of energy storage in cooperation alliance M; V M is the revenue obtained by cooperation alliance M;

[0092] S303. Improved Shapley value allocation strategy:

[0093] Now, the above two allocation strategies are denoted as M1 and M2. The analytic hierarchy process is used to determine the weight of each strategy; score the importance degree of the above strategies M1 and M2 in revenue allocation to obtain the judgment matrix A, and check the consistency of the judgment matrix. The weights of M1 and M2 are α1 and α2, and the revenue of member i is:

[0094]

[0095] According to the specific embodiments provided by the present invention, the following technical effects are disclosed: Compared with the prior art, the energy storage planning method of the shared hybrid energy storage power station based on cooperative game provided by the present invention has the following beneficial effects: When planning, the operation strategy and profit-making method of the hybrid energy storage power station are clarified, and the annual comprehensive income maximization of the energy storage power station is taken as the planning goal, and the energy storage capacity is scientifically configured, avoiding problems such as low utilization rate of the energy storage system and production shelving caused by new energy power stations configuring energy storage in order to give priority to grid connection; The business model of "shared energy storage" is selected, and the income of each new energy power station is allocated based on the improved Shapley score method, reducing the investment cost of the new energy power station for the energy storage system, and making the shared hybrid energy storage power station obtain individual rationality and collective rationality. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0097] Figure 1 The power generation structure of the new energy power station sharing the hybrid energy storage power station in the embodiment of the present invention;

[0098] Figure 2 The hierarchical structure model of the improved Shapley value allocation strategy proposed in the embodiment of the present invention;

[0099] Figure 3a The effect diagram of using a battery for peak shaving before cooperation of the new energy power station proposed in the embodiment of the present invention;

[0100] Figure 3b The effect diagram of using a battery for peak shaving after cooperation of the new energy power station proposed in the embodiment of the present invention;

[0101] Figure 4a The effect diagram of using a supercapacitor to suppress output power fluctuations before cooperation of the new energy power station proposed in the embodiment of the present invention;

[0102] Figure 4b The effect diagram of using a supercapacitor to suppress output power fluctuations after cooperation of the new energy power station proposed in the embodiment of the present invention;

[0103] Figure 5 The flowchart of the energy storage planning method of the shared hybrid energy storage power station based on cooperative game in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0104] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0105] The purpose of the present invention is to provide a storage energy planning method for a shared hybrid energy storage power station based on cooperative game. When planning shared energy storage for new energy power stations, the different operating characteristics of batteries and supercapacitors are fully considered, a control strategy for hybrid energy storage is formulated, and the benefits of each new energy power station are allocated 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 collective rationality.

[0106] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0107] Figure 1 For the power generation structure of the shared hybrid energy storage power station of the new energy power station of the present invention, for this structure, the storage energy planning method for the shared hybrid energy storage power station based on cooperative game provided by the present invention fully considers the different operating characteristics of batteries and supercapacitors when planning shared energy storage for new energy power stations, formulates a control strategy for hybrid energy storage, and allocates the benefits of each new energy power station based on an improved Shapley score method considering the energy storage configuration effect. As Figure 5 shown, the method includes the following steps:

[0108] S1. Formulate an operating strategy for hybrid energy storage according to the different operating characteristics of batteries and supercapacitors;

[0109] S2. Establish a two-layer optimal configuration model for the hybrid energy storage power station to maximize the annual income of the hybrid energy storage power station, and construct an evaluation index for the hybrid energy storage configuration effect;

[0110] S3. Establish a cooperative game model, and determine a comprehensive allocation strategy considering the hybrid energy storage configuration effect based on the Shapley score method;

[0111] S4. Based on the operating strategy of the hybrid energy storage in step S1 and the two-layer optimal configuration model in step S2, obtain the hybrid energy storage configuration plan and the annual income of the hybrid energy storage power station, and then use the cooperative game model in step S3 to allocate benefits to the new energy power stations in the alliance.

[0112] Among them, in the step S1, according to the characteristics that the energy density of the storage battery is relatively large, but the power density is relatively small and the response time is relatively long, while the power density of the supercapacitor is large, the response time is short, and it can be charged and discharged frequently but the energy density is relatively low, the storage battery is used to solve the reverse peak regulation problem of wind and light, and the supercapacitor is used to suppress the fluctuation of wind and light output.

[0113] Therefore, in the step S1, an operation strategy for hybrid energy storage is formulated according to the different operation characteristics of the storage battery and the supercapacitor, which specifically includes:

[0114] S101, based on the charge and discharge strategy of "low storage and high discharge", establish a mathematical model for the operation strategy of the storage battery to solve the reverse peak regulation problem of wind and light output; establishing a mathematical model for the operation strategy of the storage battery specifically includes:

[0115]

[0116] In the formula, minJ bat represents the objective function of the operation strategy of the storage battery, making the gap between the output of the new energy power station after peak regulation by the storage battery and the reference value the smallest; T is the number of time periods in a day; is the day-ahead predicted power of the new energy power station; p bat (t) is the output power of the storage battery; p ref (t) is the reference value of "low storage and high discharge" of the storage battery;

[0117] p ref (t) The calculation method is as follows:

[0118]

[0119] In the formula, T1, T2, and T3 are the peak load period, normal period, and valley period respectively, and median represents the median;

[0120] The corresponding constraint conditions:

[0121]

[0122] SOC bat (T) = SOC bat (0) (5)

[0123]

[0124] In the formula, SOC bat (t), is the state of charge of the storage battery and its upper and lower limits; η bat，c , η bat，d are the charge and discharge efficiencies of the storage battery respectively; p bat，c (t), p bat，d(t) is the charging and discharging power of the battery; SOC bat (t - 1) is the state of charge of the battery at time t - 1; E bat is the rated capacity of the battery; ΔT is the scheduling time interval; SOC bat (T) is the state of charge of the battery at time T; SOC bat (0) is the state of charge of the battery at the starting time; p bat (t) is the output power of the battery, with charging being negative and discharging being positive; is the maximum charging and discharging power of the battery;

[0125] Among them, the above formula (3) is the state of charge constraint of the battery, and formula (5) is the constraint that the energy state of the battery needs to be equal at the beginning and end of the scheduling period;

[0126] S102, based on model predictive control, establish a mathematical model for the operation strategy of the supercapacitor to suppress the fluctuations of wind and light and track the planned power generation; establishing a mathematical model for the operation strategy of the supercapacitor specifically includes:

[0127] Select the vector composed of the state of charge of the supercapacitor, the charging and discharging power, and the sum of the real-time output of wind and light and the battery power as the state variable; among them, SOC sc (t) is the state of charge of the supercapacitor at time t, p sc,c (t - 1) is the charging power of the supercapacitor at time t - 1, p sc,d (t - 1) is the discharging power of the supercapacitor at time t - 1, is the sum of the real-time output of wind and light and the battery power at time t - 1;

[0128] Take the vector composed of the increment of the charging and discharging power of the supercapacitor u(t) = [Δp sc,c (t), Δp sc,d (t)] T as the control variable; among them, Δp sc,c (t), Δp sc,d (t) respectively represent the increment of the charging power of the supercapacitor at time t and the increment of the discharging power of the supercapacitor at time t;

[0129] Take the vector composed of the increment of the sum of the real-time output of wind and light and the battery power as the disturbance input; means the increment of the sum of the real-time output of wind and light and the battery power at time t;

[0130] Take the vector composed of the state of charge of the supercapacitor and the grid-connected power of wind and light y(t) = [SOC sc (t), p d (t)]T is the output variable; where, p d (t) represents the grid-connected power of wind and light at time t:

[0131] The state space equations are established as shown in Equations (7) and (8); based on Equation (8), iteration is performed to predict the control instructions within the future t + n time moments, and the specific equations are as shown in Equation (9):

[0132]

[0133]

[0134] Among them, in Equation (7), x(t + 1) is the state variable of the system at time t + 1; A is the system matrix; B1 is the control input matrix; B2 is the external disturbance input matrix; η sc and E sc are the charge-discharge efficiency and rated capacity of the supercapacitor, respectively.

[0135] In Equation (8), y(t + 1) is the output variable of the system at time t + 1; C is the coefficient matrix;

[0136] In Equation (9), K, L1, and L2 are the coefficient matrices of the state variable, control variable, and disturbance input, respectively; and represent the output variable, control variable, and disturbance input within the prediction range starting from time t, respectively;

[0137] To cope with the fluctuations of renewable energy and the error between the predicted output and the real-time output, ensure that the grid-connected power of wind and light tracks the day-ahead planned value, and at the same time ensure that the SOC of the supercapacitor meets the state of charge constraint, take the vector composed of the average value of the predicted power of wind and light and the planned value of the SOC of the supercapacitor within the previous n time periods from the current moment as the tracking control target; then, with the goal of minimizing the error between the grid-connected power of wind and light and the SOC of the supercapacitor and the tracking control target, and at the same time making the increment of the charge-discharge power of the supercapacitor as small as possible, the following loss function is obtained:

[0138]

[0139] In the formula, Ω is the weighted matrix of the grid-connected power tracking error of wind and light and the SOC tracking error, and Ψ and λ are the weighted matrix and weighted coefficient of the control quantity, respectively;

[0140] Substitute Equation (9) into Equation (10) and expand the loss function, and the model for suppressing the fluctuations of wind and light output based on MPC can be transformed into the following quadratic programming:

[0141]

[0142] Corresponding constraint conditions:

[0143]

[0144] Among them, and are the maximum charging power and maximum discharging power of the supercapacitor respectively; and are the upper and lower limits of the state of charge of the supercapacitor respectively;

[0145] In step S2, a two-layer optimization configuration model of the hybrid energy storage power station is established to maximize the annual income of the hybrid energy storage power station, and evaluation indexes for the configuration effect of the hybrid energy storage are constructed, specifically including:

[0146] S201. The upper-layer optimization of the two-layer optimization configuration model of the hybrid energy storage power station aims to maximize the annual comprehensive benefit of the hybrid energy storage during the planning period, determine the hybrid energy storage configuration scheme, and optimize the rated capacity of the hybrid energy storage;

[0147] Under the premise that the hybrid energy storage configuration scheme is determined, the lower-layer optimization combines the operation strategy of the hybrid energy storage described in step S1, aims to maximize the income of the annual operation of the hybrid energy storage power station, and feeds back the respective costs and incomes of the battery and the supercapacitor to the upper-layer optimization to realize the mutual iteration of the upper and lower-layer optimizations; among them, it is specifically divided into:

[0148] S2011. Set the upper-layer optimization configuration model:

[0149] maxC total =C income -C inv -C op (13)

[0150] In the formula, C total is the annual comprehensive benefit of the shared hybrid energy storage power station; C income is the annual operation income of the hybrid energy storage power station; C inv is the equal annual value investment cost of the hybrid energy storage system; C op is the annual comprehensive operation cost of the hybrid energy storage power station, among which, C income and C op are the objective functions of the lower-layer optimization, which are transmitted from the lower-layer optimization. The calculation method of the equal annual value investment cost of the energy storage is as follows:

[0151]

[0152] In the formula: E bat and E sc are the rated capacities of the battery and the supercapacitor configured respectively; r is the discount rate; y bat and ysc are the operating lives of the battery and the supercapacitor, respectively; c bat and c sc are the investment costs per unit capacity of the battery and the supercapacitor, respectively;

[0153] The corresponding constraint conditions include the upper and lower bound constraints on the rated capacities of the battery and the supercapacitor to be planned:

[0154]

[0155] In the formula: E bat max and E bat min are the upper and lower bounds of the rated capacity of the battery, respectively; E sc max and E sc min are the upper and lower bounds of the rated capacity of the supercapacitor, respectively;

[0156] S2012, set up the lower-layer optimal configuration model:

[0157] max(C income -C op ) = C pr +C ar +C p -C dod -C om (16)

[0158] In the formula, C pr is the income obtained by the battery through peak-valley electricity price difference arbitrage; C ar is the auxiliary income of the battery participating in peak shaving; C p is the electricity benefit brought by the supercapacitor after suppressing the fluctuations of wind and light; C dod is the renewal and replacement cost generated by the hybrid energy storage power station due to cyclic aging. In actual operation, a certain amount of capacity needs to be supplemented annually according to the annual aging rate of the hybrid energy storage system to ensure the available capacity of the energy storage system. The main factors affecting the service life of the battery are the depth of discharge and the battery capacity. The life of the supercapacitor mainly depends on the evaporation rate of the liquid electrolyte. Under normal operating conditions, the aging cost of the supercapacitor can be regarded as a linear function varying with time; C om is the average annual operation and maintenance cost of the hybrid energy storage power station, and its magnitude is independent of the energy storage capacity;

[0159] The calculation methods of each cost and income are as follows:

[0160]

[0161]

[0162] Wherein, c e is the time-of-use electricity price for accessing the grid;

[0163] c ar is the auxiliary peak shaving service cost per unit capacity;

[0164] f conserve is the grid connection ratio of wind and light in the conservative dispatching scheme considering the maximum prediction error of wind and light;

[0165] f(α) is the grid connection ratio after suppressing the fluctuations of wind and light, and its value is related to the capacity of the configured supercapacitor;

[0166] T d is the number of days in a year;

[0167] T is the number of time periods in a day;

[0168] T1 is the peak time period in a day;

[0169] f bdc and f scdc are respectively functions of the aging costs of the battery and the supercapacitor;

[0170] d bat (Δt) is the depth of discharge of the battery;

[0171] p re (t) and p d (t) are respectively the actual output power and the grid-connected power of the new energy power station at time t.

[0172] f bdc ,f scdc ,e(t) and d bat (Δt) are calculated as follows:

[0173]

[0174] Wherein: L bat (d bat (Δt)) is the cycle life of the battery at the depth of charge and discharge d bat (Δt); a, b, c are fitting parameters, which can be obtained by fitting the relationship curve between the depth of discharge and the number of cycles of the battery provided by the manufacturer; p bat (t) is the output power of the battery at time t; e bat (t) is the actual capacity of the battery at time t; L sc is the service life of the supercapacitor;

[0175] The constraint conditions for the lower-layer optimization are the operation constraints of the battery and the supercapacitor in the operation strategy of the hybrid energy storage described in step S1, namely, equations (3)-(6) and equation (12).

[0176] S202. After configuring a hybrid energy storage power station using a hybrid energy storage configuration scheme, taking the grid-connected power of wind and light and the operation effect of the hybrid energy storage power station as the object, evaluation indexes for the configuration effect of the hybrid energy storage are constructed, including a grid-connected power volatility index, a peak shaving effect index, and an energy storage system utilization rate index.

[0177] The specific definitions of the grid-connected power volatility index, the peak shaving effect index, and the energy storage system utilization rate index are as follows:

[0178] S2021. Grid-connected power volatility index I1:

[0179] After the access of a high proportion of renewable energy, due to its inherent volatility and randomness, conventional units are adjusted more frequently, even started and stopped, and the adjustment depth also increases significantly. That is, the system needs to have stronger flexibility to match the access and operation of wind and light. Therefore, using a supercapacitor to suppress the output power fluctuations of wind and light can reduce the flexibility requirements of the system for wind and light. In this invention, the volatility of the grid-connected power of wind and light within 1 hour is used to evaluate the suppression effect of the supercapacitor.

[0180]

[0181] In the formula: p d (t1) is the grid-connected power of wind and light within 1 hour; p re is the installed capacity of renewable energy;

[0182] S2022. Peak shaving effect index I2:

[0183] The reverse peak shaving characteristic of new energy output is also a reason restricting its development. During the low load period, the downward adjustment capacity of the power grid is limited. Excessive acceptance of new energy will cause the grid peak shaving units to enter an unconventional output mode, and may even lead to the start and stop of units for peak shaving, which will seriously affect the safe and economic operation of the power grid and further restrict the scale of new energy acceptance in the existing power grid. In this invention, the comparison of the magnitudes of the grid-connected power of wind and light during the peak, flat, and valley periods of the load in a day is used to evaluate the peak shaving effect of the battery.

[0184]

[0185] In the formula, p d,ref (T1), p d,ref (T2), p d,ref (T3) are the medians of the grid-connected power of wind and light during the peak, flat, and valley periods of the load respectively;

[0186] S2023. Energy storage system utilization rate index I3:

[0187] Although many new energy power stations are currently equipped with energy storage systems, they often only use them as tools for priority grid connection, and the energy storage is not called upon during actual operation. Therefore, in the present invention, the product of the charge-discharge depth and the energy storage utilization hours of the energy storage system within one day is used as its utilization rate index.

[0188]

[0189] In the formula, n is the number of times when the charge-discharge power of the battery or supercapacitor is 0 within one day, SOC(t), SOC max and SOC min respectively represent the state of charge of the battery or supercapacitor at time t, the upper limit and the lower limit of the state of charge.

[0190] Traditional cooperative games are based on the marginal contributions of coalition members for profit distribution, but it is not the most reasonable method for the problem of cooperative configuration of hybrid energy storage power stations in new energy power stations. The effect of grid connection after different coalitions configure energy storage should also be considered when distributing benefits.

[0191] Therefore, in step S3 of the present invention, a cooperative game model is established, and based on the Shapley value method, a comprehensive distribution strategy considering the configuration effect of hybrid energy storage is determined; Figure 2 For the hierarchical structure model of the improved Shapley value distribution strategy proposed by the present invention, it can be seen that the main content of the cooperative game distribution strategy is as follows:

[0192] S301, Shapley value distribution strategy:

[0193] Let m be the total number of members participating in the cooperative game; M is the set composed of m members; S represents different cooperative coalitions composed of members, and S is a subset of M; s is the number of members in coalition S, and the Shapley value of member i is the income distribution obtained by i in cooperation M, and its calculation method is:

[0194]

[0195] In the formula, V S -V S\{i} represents the marginal contribution of member i in participating in coalition S; V S is the income obtained by coalition S including member i; V S\{i} is the income obtained by coalition S after removing member i; (s - 1)!(m - s)! / m! represents the probability of the occurrence of coalition S including member i;

[0196]

[0197]

[0198] Wherein, is the Shapley value of Wind Farm 1; V w1 , V w2 , V pv are the revenues of Wind Farm 1, Wind Farm 2, and the PV power station when energy storage is configured separately; V w1，w2 is the revenue of Wind Farm 1 and Wind Farm 2 when they cooperate to configure energy storage; V w1，pv is the cooperation revenue of Wind Farm 1 and the PV power station; V w2，pv is the cooperation revenue of Wind Farm 2 and the PV power station; V w1，w2，pv is the cooperation revenue of these three new energy power stations;

[0199] Similarly, the Shapley values of Wind Farm 2 and the PV power station are calculated;

[0200] S302, Allocation strategy considering the configuration effect of hybrid energy storage:

[0201] Simply allocating based on the revenue contribution of members to the cooperation alliance is not the most reasonable method for the problem of new energy power stations cooperating to configure a hybrid energy storage power station. Because the configuration of energy storage in new energy power stations is to solve the fluctuations in new energy output and reverse peak shaving problems, the revenue should also be allocated based on the grid connection effect after energy storage configuration. If the overall energy storage configuration effect is better after a certain member joins, then that member should obtain a higher revenue. In step S22 of the present invention, three indicators for evaluating the energy storage configuration effect are proposed. The weighted Topsis method is used to calculate the comprehensive evaluation value of each cooperation alliance. Referring to the Shapley value allocation strategy, the contribution degree of member i to the energy storage configuration effect in cooperation M is calculated:

[0202]

[0203] Wherein, I S is the comprehensive evaluation value of the hybrid energy storage configuration effect obtained by cooperation alliance S containing member i; I S\{i} is the comprehensive evaluation value of cooperation alliance S after removing member i;

[0204] Then, using the contribution degree of member i to the energy storage configuration effect obtained, the revenue obtained by this member is calculated:

[0205]

[0206] Wherein, x i is the contribution degree of member i to the energy storage configuration effect in cooperation alliance M; V M is the revenue obtained by cooperation alliance M;

[0207] S303, Improved Shapley value allocation strategy:

[0208] Now, the above two allocation strategies are denoted as M1 and M2. Each strategy reflects its rationality in a certain income allocation principle, so they should be considered comprehensively. Therefore, after calculating the income of each member using these two strategies in the present invention, the Analytic Hierarchy Process (AHP) is adopted to determine the weight of each strategy; experts are invited to score the importance degree of the above strategies M1 and M2 in income allocation to obtain the judgment matrix A, and the consistency of the judgment matrix is tested. Suppose the weights of M1 and M2 obtained are α1 and α2, then the income of member i is:

[0209]

[0210] In step S4 described above, energy storage configuration strategies and evaluation indicators of different coalitions can be obtained, and cooperative game is used to allocate income to new energy power stations in the coalition.

[0211] Figure 3(a) - Figure 3(b) FIG. 3 is a comparison diagram of the effect of using a storage battery for peak shaving before and after the cooperation of new energy power stations according to the embodiment of the present invention. It can be seen from FIGS. 3(a) and 3(b) that the output characteristic of wind power is large output at night, while the peak periods of the load are 8:00-11:00 and 18:00-22:00. Therefore, if the wind farm is configured with energy storage alone, the energy storage will charge at night and discharge during the two peak periods during the day, and the utilization rate of the energy storage is very low; after cooperating with the photovoltaic power station, the wind power stored by the energy storage at night can be discharged at 8:00-11:00, and the photovoltaic output stored by the energy storage at noon can be discharged at 18:00-22:00, which not only improves the peak shaving effect, but also improves the utilization rate of the energy storage.

[0212] Figure 4(a) - 4(b) FIG. 4 is a comparison diagram of the effect of using a super capacitor to suppress output power fluctuations before and after the cooperation of new energy power stations according to the embodiment of the present invention. From Figure 4(a) 、 4(b) the actual power curves in, it can be seen that when the same energy storage ratio is configured, the suppression effect after the cooperation of the wind farm and the photovoltaic power station is better than the effect of configuring the super capacitor alone. This is because although there are prediction errors in both the wind farm and the photovoltaic power station, the prediction errors are complementary, reducing the required energy storage capacity.

[0213] Under the mode of cooperative configuration and shared hybrid energy storage for new energy power stations, the income situations of different coalition scales are summarized in Table 1 as follows:

[0214] Table 1 Summary of energy storage configuration results and evaluation indicators under different coalition scales

[0215]

[0216]

[0217] As can be seen from Table 1, the cooperative game under the shared energy storage configuration mode for new energy power stations satisfies collective rationality, that is, for the cooperation alliance, the total revenue is greater than the sum of the revenues when each new energy power station configures energy storage separately. In addition, the energy storage configuration effect of the cooperation alliance is also better than that when new energy power stations configure energy storage separately. From the perspective of economy, when new energy power stations configure energy storage separately, the economy of the wind farm in configuring energy storage is better than that of the photovoltaic power station. This is because the revenue in the objective function mainly comes from the power generation revenue after peak shaving and suppressing fluctuations. However, whether it is the degree of reverse peak shaving or the volatility of the output, the photovoltaic power station is better than the wind farm. Therefore, the wind farm can benefit from configuring energy storage. However, the revenue after the cooperation between the photovoltaic power station and the wind farm is much higher than the revenue when the power stations configure energy storage separately. In addition, its revenue is also higher than the cooperation revenue between two wind farms. From Figure 3(a) and 3(b) it can be seen that during peak shaving, since the operation state of the battery is improved after the cooperation between the photovoltaic power station and the wind farm, and the power of the battery participating in peak shaving is increased, the peak shaving revenue and the arbitrage revenue of the electricity price obtained after the cooperation are both higher than those before the cooperation; from Figure 4(a) and 4(b) it can be seen that when suppressing the output fluctuation, due to the complementarity of the output prediction errors of wind and light, the capacity of the supercapacitor can be reduced. From the perspective of the energy storage configuration effect, the cooperation between wind farms has little impact on the energy storage configuration effect, but the cooperation between the wind farm and the photovoltaic power station greatly improves all indicators of the energy storage configuration effect.

[0218] Table 2 Analysis of the Revenues of Participants under Different Allocation Strategies

[0219]

[0220] As can be seen from Table 2, the cooperative game under the shared energy storage configuration mode for new energy power stations satisfies individual rationality, that is, for new energy power stations, the revenue obtained after cooperation is greater than the revenue when they configure energy storage separately. Under the Shapely value allocation strategy, the photovoltaic power station obtains the most revenue. Because the current cost of energy storage is still very high, when the wind farm configures energy storage separately, even though it can obtain peak shaving revenue and power generation benefits, after removing the configuration cost, the total revenue is still not high. However, after the cooperation between the wind farm and the photovoltaic power station, due to the complementarity of wind and light, the utilization rate of energy storage is improved, and to a certain extent, the configuration ratio of energy storage is reduced. Therefore, the marginal revenue of the photovoltaic power station is higher than that of the wind farm. Under the allocation strategy considering the energy storage configuration effect of hybrid energy storage, the revenue obtained by the photovoltaic power station is also higher than that of the wind farm. Because after the photovoltaic power station joins the alliance, the evaluation index of the alliance is better than that before the cooperation. Therefore, in terms of the configuration effect, the contribution degree of the photovoltaic power station is higher, and the revenue obtained is more.

[0221] The energy storage planning method of the shared hybrid energy storage power station based on cooperative game provided by the present invention is as follows. First, a control strategy for hybrid energy storage is formulated according to the different operating characteristics of the battery and the supercapacitor. The battery adopts a charge-discharge strategy of "low storage and high discharge", while the supercapacitor suppresses the fluctuations of wind and light based on model predictive control and tracks the planned power generation. Secondly, a two-layer planning model of the 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 an improved Shapley score method considering the energy storage configuration effect. By comparing with the individual energy storage configuration of new energy stations, the results show that this trading mode can not only improve the utilization rate of energy storage devices, but also reduce the investment cost of new energy stations for the energy storage system.

[0222] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method section.

[0223] In this application, specific examples are used to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for energy storage planning of a shared hybrid energy storage power station based on cooperative game, characterized in that It includes the following steps: S1. Develop an operation strategy for hybrid energy storage according to the different operation characteristics of the battery and the supercapacitor, including the operation strategy of the battery and the operation strategy of the supercapacitor. Specifically: S101. Based on the charge-discharge strategy of "low storage and high output", establish a mathematical model for the operation strategy of the battery to solve the reverse peak-shaving problem of wind and light output. Specifically: Establish a mathematical model for the operation strategy of the battery, which specifically includes: where minJ bat represents the objective function of the battery operation strategy, which minimizes the difference between the output power of the new energy power station after peak shaving by the battery and the reference value; T is the number of time periods in a day; is the day-ahead predicted power of the new energy power station; p bat (t) is the output power of the battery; p ref (t) is the reference value of "low storage and high output" of the battery; p ref (t) The calculation method is as follows: In the formula, T1, T2, and T3 are the peak load period, normal period, and valley period respectively, and median represents the median; The corresponding constraint conditions: SOC bat (T) = SOC bat (0)(5) Where, SOC bat (t), is the state of charge of the battery and its upper and lower limits; η bat，c , η bat，d are the charge and discharge efficiencies of the battery respectively; p bat，c (t), p bat，d (t) are the charge and discharge powers of the battery respectively; SOC bat (t - 1) is the state of charge of the battery at time t - 1; E bat is the rated capacity of the battery; ΔT is the scheduling time interval; SOC bat (T) is the state of charge of the battery at time T; SOC bat (0) is the state of charge of the battery at the starting time; p bat (t) is the output power of the battery, with charging being negative and discharging being positive; is the maximum charge and discharge power of the battery; Among them, the above formula (3) is the state of charge constraint of the battery, and formula (5) is the constraint that the energy state of the battery needs to be equal at the beginning and end of the scheduling period; S102. Based on model predictive control, establish a mathematical model for the operation strategy of the supercapacitor to suppress wind and light fluctuations and track the planned power generation. Specifically: Establish a mathematical model for the operation strategy of the supercapacitor, which specifically includes: Select a vector composed of the state of charge of the supercapacitor, the charge and discharge power, and the sum of the real-time output of wind and light and the battery power as the state variable; where SOC sc (t) is the state of charge of the supercapacitor at time t, p sc,c (t - 1) is the charging power of the supercapacitor at time t - 1, p sc,d (t - 1) is the discharging power of the supercapacitor at time t - 1, is the sum of the real-time output of wind and light and the battery power at time t - 1; The vector u(t) = [Δp sc,c (t), Δp sc,d (t)] composed of the charging and discharging power increments of the supercapacitor T is the control variable; where, Δp sc,c (t) and Δp sc,d (t) are the increment of the charging power of the supercapacitor at time t and the increment of the discharging power of the supercapacitor at time t, respectively; A vector formed by the increment of the sum of the real-time output of wind and light and the battery power is used as the disturbance input, where the increment is the sum of the real-time output of wind and light and the battery power at time t; The vector y(t)=[SOC sc (t), p d (t)] T is the output variable; where p d (t) is the grid-connected power of wind and light at time t; Establish state space equations as shown in formulas (7) and (8); based on formula (8), perform iteration to predict the control instructions within the future t + n moments, and the specific equation is as shown in formula (9): Among them, in formula (7), x(t + 1) is the state variable of the system at time t + 1; A is the system matrix; B1 is the control input matrix; B2 is the external disturbance input matrix; η sc and E sc are the charge-discharge efficiency and rated capacity of the supercapacitor, respectively; In formula (8), y(t + 1) is the output variable of the system at the moment of t + 1, and C is the coefficient matrix; In Equation (9), K, L1, and L2 are the coefficient matrices of the state variable, control variable, and disturbance input, respectively; and represent the output variable, control variable, and disturbance input within the prediction range starting from time t, respectively; Take the vector composed of the mean value of the predicted wind-solar power and the planned value of the supercapacitor SOC within n time periods forward from the current moment as the tracking control target; then, aiming at minimizing the error between the wind-solar grid-connected power and the supercapacitor SOC and the tracking control target, and at the same time minimizing the increment of the supercapacitor charge-discharge power as much as possible, the following loss function is obtained: In the formula, Ω is the weighted matrix of the tracking error of the wind and light grid-connected power and the tracking error of the supercapacitor SOC, and Ψ and λ are the weighted matrix and weighted coefficient of the control quantity respectively; Substitute formula (9) into formula (10) and expand the loss function, and the model for suppressing wind and light output fluctuations based on MPC can be transformed into the following quadratic programming: The corresponding constraint conditions: Among them, and are the maximum charging power and the maximum discharging power of the supercapacitor respectively; and are the upper and lower limits of the state of charge of the supercapacitor respectively; S2. Establish a two-layer optimal configuration model for the hybrid energy storage power station to maximize the annual income of the hybrid energy storage power station, and construct an evaluation index for the configuration effect of the hybrid energy storage. Specifically: S201. The upper-layer optimization of the two-layer optimal configuration model of the hybrid energy storage power station aims to maximize the annual comprehensive benefit of the hybrid energy storage during the planning period, determine the hybrid energy storage configuration plan, and optimize the rated capacity of the hybrid energy storage; the lower-layer optimization is based on the determined hybrid energy storage configuration plan, combined with the operation strategy of the hybrid energy storage described in step S1, aiming to maximize the income of the annual operation of the hybrid energy storage power station, and feedback the respective costs and incomes of the battery and the supercapacitor to the upper-layer optimization to realize the mutual iteration of the upper and lower-layer optimizations; S202. Take the wind and light grid-connected power and the operation effect of the hybrid energy storage power station after adopting the hybrid energy storage configuration plan as the object, and construct an evaluation index for the configuration effect of the hybrid energy storage, including the grid-connected power volatility index, the peak-shaving effect index, and the energy storage system utilization rate index; S3. Establish a cooperative game model, and based on the Shapley score method, determine the comprehensive distribution strategy considering the configuration effect of the hybrid energy storage. Specifically: S301. Shapley value distribution strategy: Let m be the total number of members participating in the cooperative game; M is the set composed of m members; S represents different cooperative coalitions composed of members, and S is a subset of M; s is the number of members in the coalition S, and the Shapley value of member i is the income distribution obtained by i in the cooperation M, and its calculation method is: where V S -V S\{i} represents the marginal contribution of member i in the participating cooperation alliance S; V S is the profit obtained by the cooperation alliance S that includes member i; V S\{i} is the profit obtained by the cooperation alliance S after removing member i; (s - 1)!(m - s)! / m! represents the probability of the cooperation alliance S that includes member i appearing; In the formula, is the Shapley value of Wind Farm 1; V w1 , V w2 , V pv are the benefits when energy storage is configured separately for Wind Farm 1, Wind Farm 2, and the PV power station respectively; V w1，w2 is the benefit when energy storage is configured cooperatively by Wind Farm 1 and Wind Farm 2; V w1，pv is the cooperative benefit between Wind Farm 1 and the PV power station; V w2，pv is the cooperative benefit between Wind Farm 2 and the PV power station; V w1，w2，pv is the cooperative benefit of these three new energy power stations; Similarly, the Shapley values of Wind Farm 2 and the PV power station are calculated; S302, Allocation strategy considering the configuration effect of the hybrid energy storage: Use the weighted Topsis method to calculate the comprehensive evaluation value of each cooperation alliance. Referring to the Shapley value allocation strategy, calculate the contribution degree of member i to the configuration effect of the energy storage in cooperation M: Where, I S is the comprehensive evaluation value of the hybrid energy storage configuration effect obtained by the cooperation alliance S containing member i; I S\{i} is the comprehensive evaluation value after removing member i from the cooperation alliance S; Then, use the obtained contribution degree of member i to the configuration effect of the energy storage to calculate the income obtained by this member: Among them, x i is the contribution degree of member i to the energy storage configuration effect in the cooperation alliance M; V M is the revenue obtained by the cooperation alliance M; S303, Improve the Shapley value allocation strategy: Now, record the above two allocation strategies as M1 and M2, and use the analytic hierarchy process to determine the weight of each strategy; score the importance degree of the above strategies M1 and M2 in income distribution to obtain the judgment matrix A, and check the consistency of the judgment matrix to obtain the weights of M1 and M2 as α1 and α2. The income of member i is: S4, Based on the operation strategy of the hybrid energy storage in step S1 and the two-layer optimal configuration model in step S2, obtain the hybrid energy storage configuration plan and the annual income of the hybrid energy storage power station, and then use the cooperative game model in step S3 to allocate income to the new energy power stations in the alliance.

2. The energy storage planning method of the shared hybrid energy storage power station based on cooperative game according to claim 1, wherein In the step S201, the upper-layer optimization of the two-layer optimal configuration model of the hybrid energy storage power station aims to maximize the annual comprehensive benefit of the hybrid energy storage within the planning period, determine the hybrid energy storage configuration plan, and optimize the rated capacity of the hybrid energy storage; Under the premise that the hybrid energy storage configuration plan is determined in the lower-layer optimization, combined with the operation strategy of the hybrid energy storage described in step S1, aim to maximize the income of the annual operation of the hybrid energy storage power station, and feedback the respective costs and incomes of the battery and the supercapacitor to the upper-layer optimization to realize the mutual iteration of the upper and lower layers of optimization; specifically include: S2011, Set the upper-layer optimal configuration model: maxC total = C income - C inv - C op (13) Where, C total is the annual comprehensive benefit of the shared hybrid energy storage power station; C income is the annual operating income of the hybrid energy storage power station; C inv is the equivalent annual value investment cost of the hybrid energy storage system; C op is the annual comprehensive operating cost of the hybrid energy storage power station, where C income and C op are the objective functions of the lower-layer optimization, which are transmitted from the lower-layer optimization. The calculation method of the equivalent annual value investment cost of the energy storage is as follows: Where: E bat and E sc are the rated capacities of the battery and supercapacitor configurations respectively; r is the discount rate; y bat and y sc are the operating lives of the battery and supercapacitor respectively; c bat and c sc are the investment costs per unit capacity of the battery and supercapacitor respectively; The corresponding constraint conditions include the upper and lower limit constraints of the rated capacities of the battery and the supercapacitor to be planned: Where: E bat max and E bat min are respectively the upper and lower bounds of the rated capacity of the storage battery; E sc max and E sc min are respectively the upper and lower bounds of the rated capacity of the supercapacitor; S2012, Set the lower-layer optimal configuration model: max(C income -C op )=C pr +C ar +C p -C dod -C om (16) Where C pr is the revenue obtained by the battery through arbitrage of peak-valley electricity price differences; C ar is the auxiliary revenue of the battery participating in peak shaving; C p is the power benefit brought by the supercapacitor after suppressing the fluctuations of wind and light; C dod is the renewal and replacement cost generated by the hybrid energy storage power station due to cyclic aging; C om is the annual average operation and maintenance cost of the hybrid energy storage power station, and its magnitude is independent of the energy storage capacity. The calculation methods of each cost and income are as follows: where c e is the time-of-use electricity price for accessing the Internet; c ar is the auxiliary peak shaving service cost per unit capacity; f conserve is the grid connection ratio of wind and light in the conservative scheduling plan considering the maximum prediction error of wind and light; f(α) is the grid connection ratio after suppressing the fluctuations of wind and light. T d is the number of days in a year; T is the number of time periods in a day; T1 is the peak time period in a day; f bdc and f scdc are functions of the aging costs of the battery and the supercapacitor, respectively; d bat (Δt) is the depth of discharge of the storage battery; p re (t) and p d (t) are respectively the actual output power and grid-connected power of the new energy power station at time t; Among them, f bdc , f scdc and d bat (Δt) is calculated as follows: Where: L bat (d bat (Δt)) is the cycle life of the battery at the charge-discharge depth d bat (Δt); a, b, and c are fitting parameters; p bat (t) is the output power of the battery at time t; e bat (t) is the actual capacity of the battery at time t; L sc is the service life of the supercapacitor; The constraint condition of the lower-layer optimization is the operation constraint of the battery and the supercapacitor in the operation strategy of the hybrid energy storage described in step S1.

3. The energy storage planning method of the shared hybrid energy storage power station based on cooperative game according to claim 2, wherein In the step S202, the grid-connected power volatility index, the peak shaving effect index, and the energy storage system utilization rate index are specifically: S2021, Grid-connected power volatility index I1: where: p d (t1) is the wind-solar grid-connected power within 1 hour; p re is the installed capacity of renewable energy; S2022, Peak shaving effect index I2: where p d,ref (T1), p d,ref (T2), p d,ref (T3) are the medians of the grid-connected power of wind and light during peak, flat, and valley periods of the load, respectively; S2023, Energy storage system utilization rate index I3: where n is the number of times the charge and discharge power of the battery or supercapacitor is 0 within a day, and SOC(t), SOC max and SOC min represent the state of charge of the battery or supercapacitor at time t, the upper limit and the lower limit of the state of charge, respectively.

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