Photovoltaic energy storage planning method, system and equipment for power distribution network and medium

Abstract

The invention belongs to the technical field of energy storage planning, and discloses a photovoltaic energy storage planning method, system and equipment for a power distribution network, and a medium, and the method comprises the steps: obtaining a photovoltaic comprehensive consumption capability evaluation value of the power distribution network and an energy storage planning cost evaluation value of the power distribution network through calculation; constructing a power distribution network energy storage planning multi-objective optimization model; fuzzy clustering is carried out on influence factor indexes beneficial to all game parties in the game form, and a strategy set of the photovoltaic comprehensive consumption capacity and the energy storage planning cost of the power distribution network is obtained; performing game iteration optimization on each game party by using a differential evolution solving algorithm to obtain an optimized objective function with the maximum photovoltaic comprehensive consumption capability and an optimized objective function with the minimum energy storage planning cost of the power distribution network; and planning photovoltaic energy storage of the power distribution network based on each optimized objective function. According to the method, a multi-target planning model considering the maximum photovoltaic comprehensive absorption capacity and the minimum economic cost is established, and a multi-target model solving method is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning, and in particular to an energy storage planning method, system, device and medium for photovoltaic power distribution networks. Background Art

[0002] In recent years, clean energy power generation such as wind and light will show a rapid development momentum. With the promotion and implementation of relevant measures such as user-side photovoltaics, high-penetration distributed photovoltaic access has become an important trend in the development of distribution networks. Due to the inherent volatility and randomness of distributed photovoltaics, its high-proportion access will affect the safe and stable operation of the distribution network. Distributed energy storage can effectively smooth out the strong randomness of distributed photovoltaics. At present, it has become a trend to build a certain proportion of distributed energy storage systems in photovoltaic distribution networks. The coordinated development of photovoltaic-storage in distribution networks will become an inevitable choice to promote the consumption of distributed photovoltaics. However, the access of distributed photovoltaic storage to the distribution network has changed the original operating characteristics of the system and increased the difficulty of energy storage planning in the distribution network. For this reason, it is urgently necessary to carry out research on energy storage planning methods for efficient consumption of photovoltaics in distribution networks.

[0003] Existing related technologies include:

[0004] Aiming at the capacity configuration problem of grid-connected photovoltaic and energy storage distribution network energy storage system, an energy storage optimization configuration strategy considering demand response and source-load uncertainty is proposed. The operation scenarios are reduced by clustering, and the costs such as power purchase, energy storage configuration and loss, and interconnection line power fluctuation penalty are comprehensively considered to construct a distribution network energy storage system planning and operation collaborative optimization model considering long-term uncertainty. However, the main purpose of the existing distribution network energy storage planning is to improve the absorption capacity of renewable energy and the economy of distribution network operation. There is no distribution network energy storage planning method that considers the dual objectives of distributed photovoltaic comprehensive absorption and planning economy.

[0005] The battery degradation cost is calculated using a linear approximation of the rainflow cycle counting method, and a piecewise linear cost function is used to model the cycle aging of battery energy storage to provide an approximate cycle aging mechanism. However, the existing mathematical model of battery energy storage degradation cost does not consider the health state (SOH) of energy storage, and the existing distribution network energy storage planning model does not consider the health state (SOH) of energy storage.

[0006] Therefore, how to provide a method, system, equipment and medium for energy storage planning for photovoltaic power distribution networks is an urgent problem to be solved. Summary of the invention

[0007] The embodiments of the present invention provide a method, system, device and medium for energy storage planning for photovoltaic power distribution networks, so as to solve the problems in the prior art that the planning target is single and the energy storage planning model of the distribution network does not consider the health status of energy storage.

[0008] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0009] According to a first aspect of an embodiment of the present invention, a method for energy storage planning for photovoltaic power distribution networks is provided.

[0010] In one embodiment, a method for energy storage planning for photovoltaic power distribution networks includes:

[0011] Based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network, the photovoltaic comprehensive absorption capacity assessment value of the distribution network is calculated; based on the distribution network investment cost, distribution network operation cost and energy storage degradation cost, the distribution network energy storage planning cost assessment value is calculated;

[0012] According to the evaluation value of photovoltaic comprehensive absorption capacity and the evaluation value of distribution network energy storage planning cost, a multi-objective optimization model for distribution network energy storage planning is constructed with the maximum photovoltaic comprehensive absorption capacity and the minimum distribution network energy storage planning cost.

[0013] The game theory is used to construct the game form of the multi-objective optimization model of distribution network energy storage planning, and the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost is obtained by fuzzy clustering the influencing factor indicators of the benefits of each player in the game form.

[0014] According to the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, the differential evolution algorithm is used to iteratively optimize the game of each player, and the optimized photovoltaic comprehensive absorption capacity maximum objective function and distribution network energy storage planning cost minimum objective function are obtained;

[0015] Based on the optimized maximum objective function of photovoltaic comprehensive absorption capacity and the minimum objective function of distribution network energy storage planning cost, the photovoltaic energy storage of the distribution network is planned.

[0016] In one embodiment, based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network, the photovoltaic comprehensive absorption capacity evaluation value of the distribution network is calculated to include:

[0017] The voltage qualification rate, carbon emission reduction rate and photovoltaic penetration rate are added together to obtain the first index; the voltage fluctuation value, harmonic distortion rate and line loss rate are added together to obtain the second index;

[0018] Add the first indicator to the adjustment parameter and subtract the second indicator to obtain the evaluation value of the comprehensive photovoltaic absorption capacity.

[0019] In one embodiment, based on the distribution network investment cost, the distribution network operation cost and the energy storage degradation cost, the distribution network energy storage planning cost evaluation value is calculated to include:

[0020] The distribution network investment cost, distribution network operation cost and energy storage degradation cost are calculated separately, and the calculated distribution network investment cost, distribution network operation cost and energy storage degradation cost are added together to obtain the distribution network energy storage planning cost assessment value.

[0021] In one embodiment, the calculation formula of energy storage degradation cost is:

[0022]

[0023] In the formula, represents the degradation cost at each stage of the battery life cycle, Represents the unit capacity investment cost of the battery, E bat Indicates battery capacity, L R Indicates the cycle life under standard cycle conditions;

[0024] k1 represents the fitting coefficient, α represents the battery power coefficient, E max Indicates battery capacity, D R and Indicates DoD and discharge power under standard cycle conditions;

[0025] n represents the number of health stages, Indicates the maximum discharge depth, u1, u2 are fitting coefficients, SOH i It represents the ratio of the maximum capacity to the rated capacity in the i-th health stage.

[0026] In one embodiment, when constructing a multi-objective optimization model for distribution network energy storage planning with maximum photovoltaic comprehensive absorption capacity and minimum distribution network energy storage planning cost, distribution network operation constraints and energy storage operation constraints are configured;

[0027] The distribution network operation constraints include distribution network power balance constraints, photovoltaic power constraints and grid power constraints:

[0028] The energy storage operation constraints include energy storage charging and discharging power constraints and energy storage charge state constraints.

[0029] In one embodiment, the game theory is used to construct the game form of the multi-objective optimization model of the distribution network energy storage planning, and the influencing factor indicators of the benefits of each player in the game form are fuzzy clustered to obtain the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, including:

[0030] Based on the Stackelberg game theory, the game form of the multi-objective optimization model of power grid energy storage planning is constructed, and the game form includes the photovoltaic comprehensive absorption capacity game party and the distribution network energy storage planning cost game party, as well as the strategy set of each game party;

[0031] Multi-objective optimization is performed on the photovoltaic comprehensive consumption capacity game player and the distribution network energy storage planning cost game player to obtain the optimal solution for each game player.

[0032] Based on the optimization solutions of each player, the influence factor set of the design variables on the PV comprehensive absorption capacity player and the distribution network energy storage planning cost player is calculated.

[0033] By performing fuzzy clustering on the influencing factor sets of the photovoltaic comprehensive absorption capacity game player and the distribution network energy storage planning cost game player, the design variable set is classified into the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set.

[0034] In one embodiment, according to the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, the differential evolution solution algorithm is used to perform game iterative optimization on each game player, and the optimized photovoltaic comprehensive absorption capacity maximum objective function and distribution network energy storage planning cost minimum objective function are obtained, including:

[0035] The players who are betting on the comprehensive photovoltaic consumption capacity are regarded as the game leaders, and the players who are betting on the distribution network energy storage planning cost are regarded as the game followers;

[0036] The distribution network energy storage planning cost strategy set is optimized according to the photovoltaic comprehensive absorption capacity strategy set, and the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set are optimized through multiple rounds of game optimization through the differential evolution solution algorithm. When the game equilibrium is reached, the optimized photovoltaic comprehensive absorption capacity maximum objective function and the distribution network energy storage planning cost minimum objective function are obtained.

[0037] According to a second aspect of an embodiment of the present invention, a photovoltaic energy storage planning system for a distribution network is provided.

[0038] In one embodiment, the energy storage planning system for distribution network photovoltaics includes:

[0039] The evaluation value calculation module is used to calculate the evaluation value of the comprehensive photovoltaic absorption capacity of the distribution network based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network; based on the distribution network investment cost, distribution network operation cost and energy storage degradation cost, calculate the distribution network energy storage planning cost evaluation value;

[0040] The optimization model building module is used to build a multi-objective optimization model for distribution network energy storage planning with the maximum comprehensive photovoltaic absorption capacity and the minimum distribution network energy storage planning cost based on the evaluation value of the comprehensive photovoltaic absorption capacity and the evaluation value of the distribution network energy storage planning cost;

[0041] The game strategy set acquisition module is used to construct the game form of the multi-objective optimization model of the distribution network energy storage planning using game theory, and obtain the strategy set of the comprehensive photovoltaic absorption capacity and the distribution network energy storage planning cost by fuzzy clustering the influencing factor indicators of the benefits of each player in the game form;

[0042] The game iterative optimization module is used to iteratively optimize the game for each player based on the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, and use the differential evolution solution algorithm to obtain the optimized maximum objective function of photovoltaic comprehensive absorption capacity and the minimum objective function of distribution network energy storage planning cost;

[0043] The energy storage planning module is used to plan the photovoltaic energy storage of the distribution network based on the optimized maximum objective function of photovoltaic comprehensive absorption capacity and the minimum objective function of distribution network energy storage planning cost.

[0044] In one embodiment, based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network, the photovoltaic comprehensive absorption capacity evaluation value of the distribution network is calculated to include:

[0045] The voltage qualification rate, carbon emission reduction rate and photovoltaic penetration rate are added together to obtain the first index; the voltage fluctuation value, harmonic distortion rate and line loss rate are added together to obtain the second index;

[0046] Add the first indicator to the adjustment parameter and subtract the second indicator to obtain the evaluation value of the comprehensive photovoltaic absorption capacity.

[0047] In one embodiment, based on the distribution network investment cost, the distribution network operation cost and the energy storage degradation cost, the distribution network energy storage planning cost evaluation value is calculated to include:

[0048] The distribution network investment cost, distribution network operation cost and energy storage degradation cost are calculated separately, and the calculated distribution network investment cost, distribution network operation cost and energy storage degradation cost are added together to obtain the distribution network energy storage planning cost assessment value.

[0049] In one embodiment, the calculation formula of energy storage degradation cost is:

[0050]

[0051] In the formula, represents the degradation cost at each stage of the battery life cycle, Represents the unit capacity investment cost of the battery, Ebat Indicates battery capacity, L R Indicates the cycle life under standard cycle conditions;

[0052] k1 represents the fitting coefficient, α represents the battery power coefficient, E max Indicates battery capacity, D R and Indicates DoD and discharge power under standard cycle conditions;

[0053] n represents the number of health stages, Indicates the maximum discharge depth, u1, u2 are fitting coefficients, SOH i It represents the ratio of the maximum capacity to the rated capacity in the i-th health stage.

[0054] In one embodiment, when constructing a multi-objective optimization model for distribution network energy storage planning with maximum photovoltaic comprehensive absorption capacity and minimum distribution network energy storage planning cost, distribution network operation constraints and energy storage operation constraints are configured;

[0055] The distribution network operation constraints include distribution network power balance constraints, photovoltaic power constraints and grid power constraints:

[0056] The energy storage operation constraints include energy storage charging and discharging power constraints and energy storage charge state constraints.

[0057] In one embodiment, the game theory is used to construct the game form of the multi-objective optimization model of the distribution network energy storage planning, and the influencing factor indicators of the benefits of each player in the game form are fuzzy clustered to obtain the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, including:

[0058] Based on the Stackelberg game theory, the game form of the multi-objective optimization model of power grid energy storage planning is constructed, and the game form includes the photovoltaic comprehensive absorption capacity game party and the distribution network energy storage planning cost game party, as well as the strategy set of each game party;

[0059] Multi-objective optimization is performed on the photovoltaic comprehensive consumption capacity game player and the distribution network energy storage planning cost game player to obtain the optimal solution for each game player.

[0060] Based on the optimization solutions of each player, the influence factor set of the design variables on the PV comprehensive absorption capacity player and the distribution network energy storage planning cost player is calculated.

[0061] By performing fuzzy clustering on the influencing factor sets of the photovoltaic comprehensive absorption capacity game player and the distribution network energy storage planning cost game player, the design variable set is classified into the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set.

[0062] In one embodiment, according to the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, the differential evolution solution algorithm is used to perform game iterative optimization on each game player, and the optimized photovoltaic comprehensive absorption capacity maximum objective function and distribution network energy storage planning cost minimum objective function are obtained, including:

[0063] The players who are betting on the comprehensive photovoltaic consumption capacity are regarded as the game leaders, and the players who are betting on the distribution network energy storage planning cost are regarded as the game followers;

[0064] The distribution network energy storage planning cost strategy set is optimized according to the photovoltaic comprehensive absorption capacity strategy set, and the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set are optimized through multiple rounds of game optimization through the differential evolution solution algorithm. When the game equilibrium is reached, the optimized photovoltaic comprehensive absorption capacity maximum objective function and the distribution network energy storage planning cost minimum objective function are obtained.

[0065] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0066] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0067] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0068] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0069] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0070] The present invention firstly establishes a distribution network photovoltaic efficient absorption capacity evaluation model integrating multi-dimensional indicators based on the voltage qualification rate, voltage fluctuation, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate. Secondly, a distribution network energy storage planning cost model considering the energy storage degradation cost is established, in which an innovative calculation method of the energy storage degradation cost coefficient is introduced, and the mathematical relationship between the energy storage health state (SOH) and the energy storage degradation cost coefficient is established. For the first time, the energy storage battery SOH is introduced into the distribution network energy storage planning model. Finally, a multi-objective planning model considering the maximum photovoltaic comprehensive absorption capacity and the minimum economic cost is established, and a multi-objective model solution method based on the MO-SG-DE algorithm is proposed.

[0071] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0073] Figure 1 It is a flow chart of a method for energy storage planning for photovoltaic power distribution networks according to an exemplary embodiment;

[0074] Figure 2 It is a principle block diagram of a photovoltaic energy storage planning system for distribution network according to an exemplary embodiment;

[0075] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0076] Figure 4 is a flow chart in a specific application according to an exemplary embodiment;

[0077] Figure 5 It is a flow chart of a multi-objective model solving algorithm based on MO-SG-DE according to an exemplary embodiment. DETAILED DESCRIPTION

[0078] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0079] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0080] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0081] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0082] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0083] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0084] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0085] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0086] Figure 1An embodiment of a photovoltaic energy storage planning method for a distribution network of the present invention is shown.

[0087] In this optional embodiment, the energy storage planning method for photovoltaic power distribution network includes:

[0088] Step S101: Based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network, the photovoltaic comprehensive absorption capacity evaluation value of the distribution network is calculated; based on the distribution network investment cost, distribution network operation cost and energy storage degradation cost, the distribution network energy storage planning cost evaluation value is calculated;

[0089] Step S103: construct a multi-objective optimization model for distribution network energy storage planning with the maximum photovoltaic comprehensive absorption capacity and the minimum distribution network energy storage planning cost according to the photovoltaic comprehensive absorption capacity evaluation value and the distribution network energy storage planning cost evaluation value;

[0090] Step S105, using game theory to construct a game form of a multi-objective optimization model for distribution network energy storage planning, and by fuzzy clustering the influencing factor indicators of the benefits of each player in the game form, a strategy set for the comprehensive photovoltaic absorption capacity and the distribution network energy storage planning cost is obtained;

[0091] Step S107: Based on the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, the differential evolution algorithm is used to perform game iterative optimization on each game player to obtain the optimized maximum objective function of photovoltaic comprehensive absorption capacity and the minimum objective function of distribution network energy storage planning cost;

[0092] Step S109: Planning the photovoltaic energy storage of the distribution network based on the optimized maximum objective function of the photovoltaic comprehensive absorption capacity and the minimum objective function of the distribution network energy storage planning cost.

[0093] Figure 2 An embodiment of a photovoltaic energy storage planning system for distribution network of the present invention is shown.

[0094] In this optional embodiment, the energy storage planning system for distribution network photovoltaics includes:

[0095] The evaluation value calculation module 201 is used to calculate the evaluation value of the photovoltaic comprehensive absorption capacity of the distribution network based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network; based on the investment cost of the distribution network, the operation cost of the distribution network and the energy storage degradation cost, calculate the distribution network energy storage planning cost evaluation value;

[0096] The optimization model construction module 203 is used to construct a multi-objective optimization model for distribution network energy storage planning with the maximum photovoltaic comprehensive absorption capacity and the minimum distribution network energy storage planning cost according to the photovoltaic comprehensive absorption capacity evaluation value and the distribution network energy storage planning cost evaluation value;

[0097] The game strategy set acquisition module 205 is used to construct the game form of the multi-objective optimization model of the distribution network energy storage planning by using game theory, and obtain the strategy set of the photovoltaic comprehensive absorption capacity and the distribution network energy storage planning cost by fuzzy clustering the influencing factor indicators of the benefits of each game party in the game form;

[0098] The game iterative optimization module 207 is used to perform game iterative optimization on each player according to the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, and use the differential evolution solution algorithm to obtain the optimized photovoltaic comprehensive absorption capacity maximum objective function and distribution network energy storage planning cost minimum objective function;

[0099] The energy storage planning module 209 is used to plan the photovoltaic energy storage of the distribution network based on the optimized maximum objective function of the photovoltaic comprehensive absorption capacity and the minimum objective function of the distribution network energy storage planning cost.

[0100] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are further described below from the perspective of architecture and principle, as follows:

[0101] 1. Photovoltaic comprehensive absorption capacity assessment model

[0102] 1. Evaluation indicators of photovoltaic comprehensive absorption capacity

[0103] (1) Voltage qualification rate

[0104] The voltage level qualification rate refers to the ratio of the number of nodes that meet the voltage level requirements in the distribution network to the total number of nodes in the distribution network. It is used to evaluate whether the voltage level of the photovoltaic distribution network reaches a technically reasonable level after energy storage is connected. Its expression is:

[0105]

[0106] Where N v and N represent the number of nodes and the total number of nodes in the distribution network that meet the voltage level requirements, respectively.

[0107] (2) Voltage fluctuation index

[0108] Voltage fluctuation refers to the change in voltage amplitude at the same node in the distribution network between two adjacent sampling periods. The calculation formula is as follows:

[0109]

[0110] Where Vi t represents the voltage at time t, It represents the voltage at time t+T, where T represents the time interval.

[0111] (3) Harmonic distortion index

[0112] The harmonic distortion rate reflects the proportion of the higher harmonics of the distribution network voltage relative to the fundamental wave. The voltage harmonic distortion rate calculation formula is:

[0113]

[0114] In the formula, V0 represents the fundamental voltage; V h Indicates the hth harmonic of voltage; H max Indicates the highest harmonic taken into account.

[0115] (4) Line loss rate index

[0116] The line loss rate refers to the percentage of active power loss of the distribution network feeder to the input power at the beginning of the feeder. The calculation formula of the line loss rate is as follows:

[0117]

[0118] Where, L represents the branch set; I i represents the current amplitude of the i-th branch of the distribution network; r i represents the resistance of the i-th branch of the distribution network; P l max Indicates the maximum power supply value of the distribution network line.

[0119] (5) Carbon emission reduction rate indicator

[0120] The carbon emission reduction rate refers to the ability to reduce the carbon-containing electricity input from the power grid after the energy storage is connected and the local photovoltaic absorption capacity of the distribution network is improved. The calculation formula is as follows:

[0121]

[0122] In the formula, It represents the carbon-containing electricity input from the power grid by the distribution network before energy storage is connected. It indicates the carbon-containing electricity input from the power grid to the distribution network after energy storage is connected.

[0123] (6) Photovoltaic penetration index

[0124] The photovoltaic penetration rate is the ratio of the actual photovoltaic output to the load, and the calculation formula is as follows:

[0125]

[0126] Where P tpv Indicates the actual photovoltaic output, P t load represents the electric load of the distribution network, and t represents the time.

[0127] 2. Photovoltaic comprehensive absorption capacity assessment model

[0128] Taking into account the voltage qualification rate, voltage fluctuation, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate indicators, a photovoltaic comprehensive absorption capacity assessment model is established.

[0129] W=W1+(1-W2)+(1-W3)+(1-W4)+W5+W6=3+W1+W5+W6-(W2+W3+W4) (6)

[0130] Where W represents the objective function for evaluating the comprehensive photovoltaic absorption capacity of the distribution network. The larger the W value is, the higher the comprehensive photovoltaic absorption capacity of the distribution network is.

[0131] 2. Distribution Network Energy Storage Planning Cost Model

[0132] A distribution network energy storage planning cost model is established. The energy storage planning cost includes investment cost, distribution network operation cost and energy storage degradation cost.

[0133] C=C in +C op +C de (7)

[0134] In the formula, C in represents the energy storage investment cost, C op represents the operating cost of the distribution network, C de Represents the energy storage degradation cost.

[0135] (1) Investment cost

[0136]

[0137] In the formula, C in represents the energy storage investment cost, R represents the discount rate, N represents the planned period, represents the investment cost of energy storage unit energy capacity, represents the investment cost of energy storage unit power capacity, E max Represents the energy storage capacity, P max It represents the energy storage power capacity, and n represents the full life cycle of the energy storage.

[0138] (2) Operating costs

[0139]

[0140] In the formula, and Represent the unit power operation cost of energy storage and photovoltaic respectively, P t ES Represents the energy storage operating power, P t pv Indicates the photovoltaic operating power, It indicates the power that the distribution network buys from the power grid. Indicates the electricity selling price of the power grid.

[0141] (3) Degradation costs

[0142]

[0143] The health status of a battery is characterized by the ratio of the remaining cycle life of the battery to the cycle life of a new battery of the same type. The actual cycle life of the battery and the depth of discharge (DoD) satisfy the following relationship.

[0144]

[0145]

[0146] In the formula, Indicates the actual cycle life of the battery; D R and Indicates DoD and discharge power under standard cycle conditions; L R represents the cycle life under standard cycle conditions; u1 and u2 represent the fitting coefficients respectively; Indicates the average charge / discharge power; E max represents the battery capacity; α represents the battery power correction factor; represents the degradation cost coefficient, d b Indicates the actual discharge depth, k c Indicates the battery cycle life correction factor.

[0147] During the battery charging and discharging process, there is an approximate proportional relationship between the discharge power and the current, and a linear relationship between the discharge current and the battery cycle life.

[0148]

[0149] In the formula, k1 represents the fitting coefficient.

[0150] By substituting equations (12)-(13) into equation (11), a mathematical expression for the actual cycle life of the battery can be derived.

[0151]

[0152] Under normal circumstances, the remaining capacity of the battery will gradually decay. The degradation cost per charge / discharge power of the battery at different health stages can be expressed by equations (15)-(16), where the degradation cost per charge / discharge power at each stage is a function of the DoD of that stage.

[0153]

[0154] In the formula, represents the degradation cost of each stage of the battery life cycle; n represents the number of healthy stages; Represents the unit capacity investment cost of the battery; represents the actual cycle life of the battery in the i-th life stage, It indicates the actual discharge depth of the battery when it is running in the i-th life stage.

[0155] According to battery characteristics, DoD is strictly limited by battery capacity, which is related to battery degradation. Therefore, it can be concluded that the maximum DoD of a battery at different health stages is a function of the battery degradation coefficient.

[0156]

[0157] in, Indicates the maximum storage capacity of the battery; represents the battery degradation coefficient; Indicates the maximum discharge depth.

[0158] In addition, according to the definition of battery SOH, SOH is the ratio of the maximum capacity of the battery at each health stage to the rated capacity.

[0159]

[0160] The relationship between the maximum DoD and SOH of the battery can be obtained by equations (17)-(19).

[0161]

[0162] In the initial health stage, the battery is in the best health state, with large capacity and deep discharge potential, which also causes the degradation cost coefficient of the battery in the initial health stage to be higher than that in the later health stage. As the battery continues to operate, the capacity of the battery begins to decline and the maximum DOD also becomes smaller, reducing the degradation cost of the battery in the later stages of its life cycle. Therefore, the maximum DoD can be used to clearly distinguish the degradation of the battery at different stages of its life cycle.

[0163]

[0164] in, represents the degradation cost of each stage of the battery life cycle, Ebat Indicates the battery capacity.

[0165] The present invention uses the middle value of the maximum and minimum SOH in each health stage to represent the average value of SOH.

[0166]

[0167] in, and Indicates the minimum and maximum values ​​of the battery SOH at each stage of the battery life cycle.

[0168] Equations (21)-(22) are joint modeling, which can obtain the degradation cost coefficient of ES in each health stage:

[0169]

[0170] 3. Multi-objective planning model for energy storage in distribution network

[0171] 1. Distribution network energy storage planning objective function

[0172] The present invention comprehensively considers two factors, namely, the photovoltaic comprehensive absorption capacity after energy storage access and the economy of energy storage planning, and constructs a multi-objective optimization model for distribution network energy storage planning with the maximum photovoltaic comprehensive absorption capacity and the minimum economic cost.

[0173]

[0174] Where F1 represents the maximum objective function of photovoltaic comprehensive absorption capacity, and F2 represents the minimum objective function of economic cost.

[0175] 2. Constraints

[0176] (1) Distribution network operation constraints

[0177] The operation constraints of the distribution network mainly include distribution network power balance constraints, photovoltaic power constraints and grid power constraints.

[0178]

[0179] In the formula, Represents the electrical load in the distribution network, P pv,max Indicates the maximum power of photovoltaic, Indicates the maximum power supplied by the power grid to the distribution network.

[0180] (2) Energy storage operation constraints

[0181] Energy storage operation constraints include energy storage charging and discharging power and energy storage charge state constraints.

[0182]

[0183] Where P t ES Represents the energy storage operating power, η t represents the charging and discharging efficiency of the energy storage device at time t, η d and η c Respectively represent the discharge efficiency and charging efficiency, SOC t Indicates the state of charge of the energy storage device at time t, SOC min and SOC max They represent the minimum and maximum state of charge of the energy storage device respectively. SOC0 represents the initial state of charge of the energy storage device. Indicates the maximum charge and discharge power of the battery, u E,t Indicates the battery charge / discharge status.

[0184] 4. Multi-objective model solution based on MO-SG-DE

[0185] The present invention proposes a Multiple Objectives-Stackelberg Game-Differential Evolution (MO-SG-DE) algorithm for solving the multi-objective model established in the third part. Stackelberg game is Stackelberg game.

[0186] (1) Constructing the Stackelberg game form of the distribution network energy storage multi-objective planning model:

[0187] G = {S1, S2; F1, F2} (27)

[0188] In the formula, F1 and F2 represent two target game parties, S1 = {x i ...x j},S2={x k ...x l} represents the strategy set of two objective function game players. Among them, x i represents the i-th design variable; x j represents the jth design variable; x k represents the kth design variable; x l represents the lth design variable.

[0189] (2) Calculation of the objective function game strategy set

[0190] The present invention calculates the influencing factor index of the design variables on the benefits of the game parties, and performs fuzzy clustering on the index to obtain the strategy set belonging to each game party.

[0191] 1) Multi-objective optimization is performed on the maximum objective function F1 of the photovoltaic comprehensive absorption capacity and the minimum objective function F2 of the economic cost to obtain the optimal solution of each objective. and in,

[0192] 2) For any design variable x of the objective function F1 j , in its feasible space, with a step size of Δx j Divided into T segments, the jth design variable x j The influencing factor Δx on the objective function F2 of the game player j2 for:

[0193]

[0194] In the formula, represents the optimal solution at time t; represents the optimal solution at time t-1.

[0195] 3) Let the classification sample be Δj={Δj1,Δj2}, where Δj is the set of factors affecting the maximum bearing capacity objective function F1 and the minimum economic cost objective function F2 of any j-th design variable. The entire classification sample is Δ={Δ1,Δ1,...Δ n}, perform fuzzy clustering on it and classify the design variable set X into the strategy sets S1 and S2 of each player, which is the result after fuzzy clustering.

[0196] (3) Game solution of multi-objective model based on DE

[0197] Assume that the maximum photovoltaic comprehensive absorption capacity objective function F1 is the game leader and is optimized first; the economic cost minimum objective function F2 is the follower of the game, and its strategy set S2 is optimized according to the strategy set S1 of the maximum carrying capacity objective function F1. Through the DE algorithm, multiple rounds of game optimization are performed, and finally the game equilibrium solution is obtained, and the results of the maximum carrying capacity objective function F1 and the minimum economic cost objective function F2 are calculated.

[0198] like Figure 4 As shown, it is a flow chart of an embodiment of the present invention in a specific application.

[0199] like Figure 5 As shown in the figure, it is a flow chart of the multi-objective model solving algorithm based on MO-SG-DE, which is as follows:

[0200] Initialize the strategy sets S1 and S2 of targets F1 and F2 respectively; calculate the objective function matrices of targets F1 and F2; calculate the Nash equilibrium of targets F1 and F2 to obtain the optimal strategy for each target; calculate the fitness values ​​of all evolved individuals and obtain the best individual; construct a new mutation operator to perform multi-objective evolutionary iterative optimization; obtain the offspring population and calculate the objective function matrices of F1 and F2; update the evolution probability according to the updated objective function matrix.

[0201] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0202] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0203] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0204] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0205] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0206] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for energy storage planning for photovoltaic power distribution networks, characterized in that: include: Based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network, the photovoltaic comprehensive absorption capacity assessment value of the distribution network is calculated; based on the distribution network investment cost, distribution network operation cost and energy storage degradation cost, the distribution network energy storage planning cost assessment value is calculated; According to the evaluation value of photovoltaic comprehensive absorption capacity and the evaluation value of distribution network energy storage planning cost, a multi-objective optimization model for distribution network energy storage planning is constructed with the maximum photovoltaic comprehensive absorption capacity and the minimum distribution network energy storage planning cost. The game theory is used to construct the game form of the multi-objective optimization model of distribution network energy storage planning, and the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost is obtained by fuzzy clustering the influencing factor indicators of the benefits of each player in the game form. According to the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, the differential evolution algorithm is used to iteratively optimize the game of each player, and the optimized photovoltaic comprehensive absorption capacity maximum objective function and distribution network energy storage planning cost minimum objective function are obtained; Based on the optimized maximum objective function of photovoltaic comprehensive absorption capacity and the minimum objective function of distribution network energy storage planning cost, the photovoltaic energy storage of the distribution network is planned.

2. A method for energy storage planning for photovoltaic power distribution networks according to claim 1, characterized in that: The photovoltaic comprehensive absorption capacity assessment value of the distribution network calculated based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network includes: The voltage qualification rate, carbon emission reduction rate and photovoltaic penetration rate are added together to obtain the first index; the voltage fluctuation value, harmonic distortion rate and line loss rate are added together to obtain the second index; Add the first indicator to the adjustment parameter and subtract the second indicator to obtain the evaluation value of the comprehensive photovoltaic absorption capacity.

3. The energy storage planning method for photovoltaic power distribution network according to claim 1, characterized in that: The distribution network energy storage planning cost evaluation value calculated based on the distribution network investment cost, distribution network operation cost and energy storage degradation cost includes: The distribution network investment cost, distribution network operation cost and energy storage degradation cost are calculated separately, and the calculated distribution network investment cost, distribution network operation cost and energy storage degradation cost are added together to obtain the distribution network energy storage planning cost assessment value.

4. A method for energy storage planning for photovoltaic power distribution networks according to claim 3, characterized in that: The calculation formula of the energy storage degradation cost is: In the formula, represents the degradation cost at each stage of the battery life cycle, Represents the unit capacity investment cost of the battery, E bat Indicates battery capacity, L R Indicates the cycle life under standard cycle conditions; k1 represents the fitting coefficient, α represents the battery power coefficient, E max Indicates battery capacity, D R and Indicates DoD and discharge power under standard cycle conditions; n represents the number of health stages, Indicates the maximum discharge depth, u1, u2 are fitting coefficients, SOH i It represents the ratio of the maximum capacity to the rated capacity in the i-th health stage.

5. The energy storage planning method for photovoltaic power distribution network according to claim 1, characterized in that: When constructing the multi-objective optimization model for distribution network energy storage planning with the maximum photovoltaic comprehensive absorption capacity and the minimum distribution network energy storage planning cost, the distribution network operation constraints and energy storage operation constraints are configured; The distribution network operation constraints include distribution network power balance constraints, photovoltaic power constraints and grid power constraints: The energy storage operation constraints include energy storage charging and discharging power constraints and energy storage charge state constraints.

6. The energy storage planning method for photovoltaic power distribution network according to claim 1, characterized in that: The game form of the multi-objective optimization model of distribution network energy storage planning is constructed by using game theory, and the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost is obtained by fuzzy clustering the influencing factor indicators of the benefits of each player in the game form, including: Based on the Stackelberg game theory, the game form of the multi-objective optimization model of power grid energy storage planning is constructed, and the game form includes the photovoltaic comprehensive absorption capacity game party and the distribution network energy storage planning cost game party, as well as the strategy set of each game party; Multi-objective optimization is performed on the photovoltaic comprehensive consumption capacity game player and the distribution network energy storage planning cost game player to obtain the optimal solution for each game player. Based on the optimization solutions of each player, the influence factor set of the design variables on the PV comprehensive absorption capacity player and the distribution network energy storage planning cost player is calculated. By performing fuzzy clustering on the influencing factor sets of the photovoltaic comprehensive absorption capacity game player and the distribution network energy storage planning cost game player, the design variable set is classified into the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set.

7. The method for energy storage planning for photovoltaic power distribution network according to claim 1, characterized in that: The strategy set based on the photovoltaic comprehensive absorption capacity and the distribution network energy storage planning cost, and using the differential evolution solution algorithm to iteratively optimize the game of each game party, obtains the optimized photovoltaic comprehensive absorption capacity maximum objective function and the distribution network energy storage planning cost minimum objective function including: The players who are betting on the comprehensive photovoltaic consumption capacity are regarded as the game leaders, and the players who are betting on the distribution network energy storage planning cost are regarded as the game followers; The distribution network energy storage planning cost strategy set is optimized according to the photovoltaic comprehensive absorption capacity strategy set, and the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set are optimized through multiple rounds of game optimization through the differential evolution solution algorithm. When the game equilibrium is reached, the optimized photovoltaic comprehensive absorption capacity maximum objective function and the distribution network energy storage planning cost minimum objective function are obtained.

8. A photovoltaic energy storage planning system for distribution networks, characterized in that: include: The evaluation value calculation module is used to calculate the evaluation value of the comprehensive photovoltaic absorption capacity of the distribution network based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network; based on the distribution network investment cost, distribution network operation cost and energy storage degradation cost, calculate the distribution network energy storage planning cost evaluation value; The optimization model building module is used to build a multi-objective optimization model for distribution network energy storage planning with the maximum comprehensive photovoltaic absorption capacity and the minimum distribution network energy storage planning cost based on the evaluation value of the comprehensive photovoltaic absorption capacity and the evaluation value of the distribution network energy storage planning cost; The game strategy set acquisition module is used to construct the game form of the multi-objective optimization model of the distribution network energy storage planning using game theory, and obtain the strategy set of the comprehensive photovoltaic absorption capacity and the distribution network energy storage planning cost by fuzzy clustering the influencing factor indicators of the benefits of each player in the game form; The game iterative optimization module is used to iteratively optimize the game for each player based on the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost, and use the differential evolution solution algorithm to obtain the optimized maximum objective function of photovoltaic comprehensive absorption capacity and the minimum objective function of distribution network energy storage planning cost; The energy storage planning module is used to plan the photovoltaic energy storage of the distribution network based on the optimized maximum objective function of photovoltaic comprehensive absorption capacity and the minimum objective function of distribution network energy storage planning cost.

9. The energy storage planning system for photovoltaic power distribution network according to claim 8, characterized in that: The photovoltaic comprehensive absorption capacity assessment value of the distribution network calculated based on the voltage qualification rate, voltage fluctuation value, harmonic distortion rate, line loss rate, carbon emission reduction rate and photovoltaic penetration rate of the distribution network includes: The voltage qualification rate, carbon emission reduction rate and photovoltaic penetration rate are added together to obtain the first index; the voltage fluctuation value, harmonic distortion rate and line loss rate are added together to obtain the second index; Add the first indicator to the adjustment parameter and subtract the second indicator to obtain the evaluation value of the comprehensive photovoltaic absorption capacity.

10. The energy storage planning system for photovoltaic power distribution network according to claim 8, characterized in that: The distribution network energy storage planning cost evaluation value calculated based on the distribution network investment cost, distribution network operation cost and energy storage degradation cost includes: The distribution network investment cost, distribution network operation cost and energy storage degradation cost are calculated separately, and the calculated distribution network investment cost, distribution network operation cost and energy storage degradation cost are added together to obtain the distribution network energy storage planning cost assessment value.

11. The energy storage planning system for photovoltaic power distribution network according to claim 10, characterized in that: The calculation formula of the energy storage degradation cost is: In the formula, represents the degradation cost at each stage of the battery life cycle, Represents the unit capacity investment cost of the battery, E bat Indicates battery capacity, L R Indicates the cycle life under standard cycle conditions; k1 represents the fitting coefficient, α represents the battery power coefficient, E max Indicates battery capacity, D R and Indicates DoD and discharge power under standard cycle conditions; n represents the number of health stages, Indicates the maximum discharge depth, u1, u2 are fitting coefficients, SOH i It represents the ratio of the maximum capacity to the rated capacity in the i-th health stage.

12. The energy storage planning system for photovoltaic power distribution network according to claim 8, characterized in that: When constructing the multi-objective optimization model for distribution network energy storage planning with the maximum photovoltaic comprehensive absorption capacity and the minimum distribution network energy storage planning cost, the distribution network operation constraints and energy storage operation constraints are configured; The distribution network operation constraints include distribution network power balance constraints, photovoltaic power constraints and grid power constraints: The energy storage operation constraints include energy storage charging and discharging power constraints and energy storage charge state constraints.

13. The energy storage planning system for photovoltaic power distribution network according to claim 8, characterized in that: The game form of the multi-objective optimization model of distribution network energy storage planning is constructed by using game theory, and the strategy set of photovoltaic comprehensive absorption capacity and distribution network energy storage planning cost is obtained by fuzzy clustering the influencing factor indicators of the benefits of each player in the game form, including: Based on the Stackelberg game theory, the game form of the multi-objective optimization model of power grid energy storage planning is constructed, and the game form includes the photovoltaic comprehensive absorption capacity game party and the distribution network energy storage planning cost game party, as well as the strategy set of each game party; Multi-objective optimization is performed on the photovoltaic comprehensive consumption capacity game player and the distribution network energy storage planning cost game player to obtain the optimal solution for each game player. Based on the optimization solutions of each player, the influence factor set of the design variables on the PV comprehensive absorption capacity player and the distribution network energy storage planning cost player is calculated. By performing fuzzy clustering on the influencing factor sets of the photovoltaic comprehensive absorption capacity game player and the distribution network energy storage planning cost game player, the design variable set is classified into the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set.

14. The energy storage planning system for photovoltaic power distribution network according to claim 8, characterized in that: The strategy set based on the photovoltaic comprehensive absorption capacity and the distribution network energy storage planning cost, and using the differential evolution solution algorithm to iteratively optimize the game of each game party, obtains the optimized photovoltaic comprehensive absorption capacity maximum objective function and the distribution network energy storage planning cost minimum objective function including: The players who are betting on the comprehensive photovoltaic consumption capacity are regarded as the game leaders, and the players who are betting on the distribution network energy storage planning cost are regarded as the game followers; The distribution network energy storage planning cost strategy set is optimized according to the photovoltaic comprehensive absorption capacity strategy set, and the photovoltaic comprehensive absorption capacity strategy set and the distribution network energy storage planning cost strategy set are optimized through multiple rounds of game optimization through the differential evolution solution algorithm. When the game equilibrium is reached, the optimized photovoltaic comprehensive absorption capacity maximum objective function and the distribution network energy storage planning cost minimum objective function are obtained.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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