Optical storage micro-grid capacity configuration method and system oriented to various application requirements

Abstract

The invention discloses an optical storage micro-grid capacity configuration method and system oriented to multiple application requirements, and relates to the technical field of power grids, and the multiple application requirements comprise an economic index, an environmental protection index and a reliability index. The various application requirements pay attention to power output, power fluctuation and extreme conditions of uncertain variables including photovoltaic power generation and net load power; based on the multiple application requirements, a scene set comprising a normal scene, a fluctuation scene and an extreme scene is constructed through scene generation and clustering; an energy storage capacity configuration model of various application requirements is constructed, the economic index is set as a target, other application requirement modeling is set as constraints, the energy storage performance in an extreme scene is considered, the target is to minimize the total planning and operation cost of the system, and the constraints comprise power system operation constraints and application requirement constraints. The method has the beneficial effects that the total economic cost in an energy storage operation period is effectively reduced through efficient decision-making on the energy storage optimal capacity and the output power.

Description

Technical Field

[0001] The present invention relates to power grid technology, and in particular to a method and system for configuring the capacity of a photovoltaic storage microgrid for various application requirements. Background Art

[0002] Photovoltaic power generation, as a promising renewable energy source, brings green energy to the system and supports the growing load demand, but it also brings great uncertainty to the safety and stability of the power system. Through effective management strategies, energy storage can achieve economic benefits, improve the operating performance of photovoltaic power stations, and provide power support for a variety of system application requirements. Therefore, the optimal allocation of energy storage capacity has become a research hotspot.

[0003] Many methods have been proposed to deal with the uncertainty in energy storage capacity allocation models. Stochastic optimization uses a set of normal scenarios to describe the uncertainty of photovoltaic power generation, and on this basis establishes and solves the planning model. However, it often directly uses the actual power of photovoltaic loads to obtain normal scenarios, ignoring a few low-probability extreme scenarios. Robust optimization focuses on the robustness of the model in extreme scenarios, and the results are usually highly conservative and insufficiently optimized. However, different application requirements focus on different data characteristics of photovoltaic power generation. For example, the safety of photovoltaic grid connection has high requirements on the volatility of grid-connected nodes, while the focus on the output size of photovoltaic power generation is relatively low. Ignoring application requirements on data may lead to significant planning errors. Therefore, it is necessary to build a set of scenarios based on application requirements. Summary of the invention

[0004] The purpose of the present invention is to provide a photovoltaic energy storage microgrid capacity configuration method for various application requirements, which can solve the problem of insufficient consideration of scenarios in the prior art.

[0005] The present invention also aims to provide a system for realizing a method for configuring the capacity of a photovoltaic microgrid for various application requirements.

[0006] The method of the present invention comprises:

[0007] Analyze multiple application requirements, including economic indicators, environmental protection indicators, and reliability indicators; wherein the system total planning and operation cost are used as the economic indicators, the abandonment rate of photovoltaic power generation is used as the environmental indicator, and the volatility of grid-connected nodes, system spinning reserve capacity, and algorithm performance in extreme scenarios are used as the reliability indicators;

[0008] The various application requirements focus on power output, power fluctuation and extreme situations of uncertain variables including photovoltaic power generation and net load power; based on the various application requirements, a scene set including normal scenes, fluctuation scenes and extreme scenes is constructed through scene generation and clustering; the scene set contains multiple different scenes, each of which can describe the operating state of the system and serve the system state analysis and optimization decision; wherein scene generation is generally performed by collecting historical observation data, and scene clustering is performed by Kmeans clustering method;

[0009] A storage capacity configuration model for various application requirements is constructed to determine the optimal capacity (MWh) and maximum output power (MW) of the energy storage equipment in the system. The model sets the economic indicators as the target, models other application requirements as constraints, and considers the energy storage performance under extreme scenarios. The goal is to minimize the total planning and operating costs of the system. The constraints include power system operation constraints and application demand constraints.

[0010] The normal scenario described in the present invention is obtained based on the actual output size of photovoltaic power generation and net load power, and the fluctuation scenario is obtained based on the fluctuation change of photovoltaic power generation and net load power.

[0011] The extreme scenarios described in the present invention are obtained by selecting extreme evaluation indicators, and the extreme evaluation indicators include: a photovoltaic surplus situation when the average photovoltaic power generation is maximum and the average net load power is minimum, a photovoltaic shortage situation when the average photovoltaic power generation is minimum and the average net load power is maximum, a situation when the photovoltaic power generation drops suddenly and the net load power suddenly rises, a situation when the net load power drops suddenly and the photovoltaic power generation suddenly rises, a situation with the maximum average fluctuation of photovoltaic power generation and net load power, and a situation with the maximum average fluctuation of photovoltaic power generation fluctuation exceeding the grid-connected fluctuation threshold.

[0012] In the present invention, in the normal scenario and the fluctuation scenario, the energy storage capacity configuration model of the multiple application requirements needs to satisfy both the power system operation constraints and the application requirement constraints;

[0013] In the extreme scenario, the energy storage capacity configuration model for the multiple application requirements only needs to meet the power system operation constraints. In the extreme scenario, the satisfaction of the application requirement constraints is reflected by adding a penalty cost that exceeds the application requirement limit in the target.

[0014] The objective function of the energy storage capacity configuration model for multiple application requirements of the present invention includes system total planning and operating costs; the system total planning and operating costs include energy storage planning costs, operating costs within the energy storage operation cycle, and penalty costs for exceeding demand limits, wherein the system total planning costs include energy storage planning costs, the total operating costs include generator operating costs within the energy storage operation cycle, energy storage operating costs, load shedding costs, and light abandonment costs, and the penalty costs for exceeding demand limits are penalties for not meeting application requirements for grid-connected photovoltaic power fluctuations, light abandonment rates, and system rotating reserve capacity;

[0015] The total system planning and operating costs are as follows:

[0016]

[0017] The energy storage planning cost is expressed as:

[0018]

[0019] The operating cost during the energy storage operation cycle is expressed as:

[0020]

[0021] The penalty cost for exceeding the demand limit during the energy storage operation cycle is expressed as:

[0022]

[0023] Among them, C plan Indicates the total system planning and operating costs; represents the energy storage planning cost; Represents the operating cost during the energy storage operation cycle, represents the penalty cost of exceeding the demand limit during the energy storage operation cycle; are the cost coefficients of power and capacity of energy storage planning, respectively; are non-negative integer decision variables for the maximum charge and discharge power and maximum energy capacity of energy storage, respectively; γ is the discount rate, a is the service life of energy storage; S is the number of scenarios, w s is the probability of scene s appearing, They are the operating cost under scenario s at time t and the penalty cost for exceeding the demand limit.

[0024] It's a scene s The operating cost at time t, including the operating cost of the generator C G,s,t , energy storage operation cost C ES,s,t , load shedding cost And the cost of abandoned light The operating cost is expressed as:

[0025]

[0026] The energy storage operating cost is expressed as:

[0027]

[0028] The generator operating cost is expressed as:

[0029]

[0030] The cost of abandoned light is expressed as:

[0031]

[0032] The load shedding cost is expressed as:

[0033]

[0034] in, are the cost coefficients of energy storage, solar curtailment, and load shedding respectively. G2,g / λ G1,g / λ G0,g Fuel cost factor for generator, is the charging and discharging efficiency of energy storage. is the charging and discharging power of the energy storage. G,g,s,t Active output of generator g. is the abandoned optical power, is the load shedding power at node l. G / N L are the node sets containing generators and loads, respectively;

[0035] The penalty cost for exceeding the demand limit is expressed as:

[0036]

[0037] in:

[0038] It is the amount of grid-connected photovoltaic fluctuations, abandoned photovoltaic power, and system spinning reserve capacity that exceeds the demand limit. is the corresponding cost penalty coefficient;

[0039] The algorithm performance in extreme scenarios is as follows:

[0040] The net load power balance constraint of the photovoltaic grid-connected node is expressed as:

[0041]

[0042] The power fluctuation range constraint of photovoltaic grid-connected nodes is expressed as:

[0043]

[0044] The photovoltaic maximum power fluctuation constraint is expressed as:

[0045]

[0046] The light abandonment range constraint is expressed as:

[0047]

[0048] The spinning reserve constraint is expressed as:

[0049]

[0050] The calculation method is as follows:

[0051]

[0052] The calculation method is as follows:

[0053]

[0054] The calculation method is as follows:

[0055]

[0056] is the grid-connected power, PPV,s,t is the actual photovoltaic output power; is the abandoned optical power, is the maximum fluctuation rate of grid-connected power, is the maximum fluctuating power of the grid-connected power, The rated capacity of the photovoltaic power station, △t represents the operation step length, Indicates the maximum light abandonment rate, P G,g,s,t is the output power of generator g, represents the maximum output power of generator g, is the energy storage charging and discharging power,

[0057] The energy storage capacity configuration model for various application requirements described in the present invention uses the maximum charge and discharge power and maximum energy capacity of the energy storage as decision variables, the discount rate, the service life and occurrence probability of the energy storage as parameters, and the node set including the generator and the load as a constraint condition.

[0058] The constraints of the energy storage capacity configuration model for various application requirements described in the present invention also include grid-connected power, abandoned light power, maximum grid-connected power fluctuation rate, rated capacity of photovoltaic power station, output power of generator, energy storage charging and discharging power and upper and lower system rotating standby capacity.

[0059] The present invention provides a photovoltaic storage microgrid capacity configuration system for various application requirements, including:

[0060] The first unit is used to analyze multiple application requirements, including economic indicators, environmental protection indicators and reliability indicators; wherein the system total planning and operation economic cost are used as the economic indicator, the photovoltaic power generation abandonment rate is used as the environmental indicator, and the volatility of the grid-connected node, the system spinning reserve capacity and the algorithm performance in extreme scenarios are used as the reliability indicator;

[0061] The second unit is used for the various application requirements to focus on power output, power fluctuation and extreme situations of uncertain variables including photovoltaic power generation and net load power; based on the various application requirements, a scenario set including normal scenarios, fluctuation scenarios and extreme scenarios is constructed through scenario generation and clustering;

[0062] The third unit is used to build an energy storage capacity configuration model for multiple application requirements, setting the economic indicators as the target, modeling other application requirements as constraints, considering the performance under extreme scenarios, and the goal is to minimize the total system planning and operation costs, including the penalty costs for exceeding application requirements in extreme scenarios. The constraints include power system operation constraints and application demand constraints.

[0063] The present invention provides an electronic device, comprising:

[0064] processor;

[0065] a memory for storing processor-executable instructions;

[0066] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0067] The present invention provides a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0068] Compared with the prior art, the beneficial effect of the present invention is that the total economic cost during the energy storage operation cycle is effectively reduced through efficient decision-making on the optimal energy storage capacity and output power. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of a process for configuring the capacity of a photovoltaic and energy storage microgrid for various application requirements according to an embodiment of the present invention;

[0070] Figure 2 This is a schematic diagram of the structure of a photovoltaic and energy storage microgrid capacity configuration system for various application requirements according to an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0073] like Figure 1 As shown, the method of the present invention comprises:

[0074] S101: Analyze multiple application requirements, including economic indicators, environmental protection indicators, and reliability indicators; wherein the system total planning and operation economic cost are used as the economic indicator, the abandonment rate of photovoltaic power generation is used as the environmental indicator, and the volatility of grid-connected nodes, system spinning reserve capacity, and algorithm performance in extreme scenarios are used as the reliability indicator;

[0075] S102: The multiple application requirements focus on power output, power fluctuation and extreme situations of uncertain variables including photovoltaic power generation and net load power; based on the multiple application requirements, a scenario set including normal scenarios, fluctuation scenarios and extreme scenarios is constructed through scenario generation and clustering;

[0076] S103: Construct an energy storage capacity configuration model for multiple application requirements, set the economic indicators as the target, model other application requirements as constraints, consider the performance under extreme scenarios, and aim to minimize the total system planning and operating costs, including penalty costs for exceeding application requirements under extreme scenarios. Constraints include power system operation constraints and application demand constraints.

[0077] In an optional embodiment,

[0078] The normal scenario is obtained based on the actual output size of photovoltaic power generation and net load power, and the fluctuation scenario is obtained based on the fluctuation change of photovoltaic power generation and net load power.

[0079] In an optional embodiment,

[0080] The extreme scenarios are obtained by selecting extreme evaluation indicators, and the extreme evaluation indicators include: a photovoltaic surplus situation when the average photovoltaic power generation is maximum and the average net load power is minimum, a photovoltaic shortage situation when the average photovoltaic power generation is minimum and the average net load power is maximum, a situation when the photovoltaic power generation drops suddenly and the net load power suddenly increases, a situation when the net load power drops suddenly and the photovoltaic power generation suddenly increases, a situation with the maximum average fluctuation of photovoltaic power generation and net load power, and a situation with the maximum average fluctuation of photovoltaic power generation that exceeds the grid-connected fluctuation threshold.

[0081] In an optional embodiment,

[0082] In the normal scenario and the fluctuating scenario, the energy storage capacity configuration model for the various application requirements needs to satisfy both the power system operation constraints and the application requirement constraints, while in the extreme scenario, the energy storage capacity configuration model for the various application requirements only needs to satisfy the power system operation constraints. In the extreme scenario, the satisfaction of the application requirement constraints is reflected by adding the penalty cost of exceeding the application requirement limit to the target.

[0083] In an optional embodiment,

[0084] The objective function of the energy storage capacity configuration model for the various application requirements includes system planning cost, operating cost and penalty cost for exceeding demand limit, wherein the system planning cost includes the planning cost of energy storage, the operating cost mainly includes the operating cost of the generator, the energy storage operating cost, the load shedding cost and the abandonment cost, and whether the penalty cost for exceeding the demand limit exceeds the application requirements of grid-connected photovoltaic fluctuations, abandonment of light, and system rotating reserve capacity.

[0085] In an optional embodiment,

[0086] The energy storage capacity configuration model for various application requirements uses the maximum charge and discharge power and maximum energy capacity of the energy storage as decision variables, the discount rate, the service life and occurrence probability of the energy storage as parameters, and the node set including the generator and the load as a constraint condition.

[0087] In an optional embodiment,

[0088] The constraints of the energy storage capacity configuration model for the various application requirements also include grid-connected power, abandoned light power, maximum grid-connected power fluctuation rate, rated capacity of the photovoltaic power station, output power of the generator, energy storage charging and discharging power, and upper and lower system rotating reserve capacity.

[0089] Another embodiment of the present application is as follows:

[0090] First, we analyze various application requirements, including economic indicators, environmental protection indicators, and reliability indicators. This paper takes the system's total planning and operating economic costs as economic indicators, the photovoltaic power generation abandonment rate as an environmental indicator, and the volatility of grid-connected nodes, system spinning reserve capacity, and algorithm performance in extreme scenarios as reliability indicators. The above application requirements mainly focus on three important characteristics of uncertain variables (mainly including photovoltaic power generation and net load power): 1) power output, 2) power fluctuations, and 3) extreme situations.

[0091] Based on application requirements, a scenario set including normal scenarios, fluctuating scenarios and extreme scenarios is constructed through scenario generation and clustering. Normal scenarios are obtained based on the actual output size of photovoltaic power generation and net load power, while fluctuating scenarios are obtained based on the fluctuation changes of photovoltaic power generation and net load power. Extreme scenarios are selected through extreme evaluation indicators:

[0092] 1) Photovoltaic surplus when average photovoltaic power generation is maximum and average net load power is minimum

[0093] 2) Photovoltaic shortage when average photovoltaic power generation is minimum and average net load power is maximum

[0094] 3) When the photovoltaic power generation drops suddenly and the net load power increases suddenly

[0095] 4) When the net load power drops suddenly and the photovoltaic power generation suddenly increases,

[0096] 5) Maximum average fluctuation of photovoltaic power generation and net load power,

[0097] 6) The maximum average fluctuation of photovoltaic power generation exceeding the grid-connected fluctuation threshold.

[0098] A storage capacity configuration model for multiple application requirements is constructed. We set the economic indicators as the objectives and model other application requirements as constraints instead of setting a multi-objective model. In order to strike a balance between economic performance and model robustness, the model considers the performance under extreme scenarios. The goal is to minimize the total system planning and operation costs, including the penalty costs for exceeding application requirements in extreme scenarios. The constraints include power system operation constraints and application demand constraints. In normal and fluctuating scenarios, the model needs to satisfy both, while in extreme scenarios, the model only needs to satisfy the operation constraints. The satisfaction of application demand constraints in extreme scenarios is reflected by adding the penalty costs for exceeding the application demand limits in the objectives.

[0099] The total system planning and operating costs are as follows:

[0100]

[0101] The energy storage planning cost is expressed as:

[0102]

[0103] The operating cost during the energy storage operation cycle is expressed as:

[0104]

[0105] The penalty cost for exceeding the demand limit during the energy storage operation cycle is expressed as:

[0106]

[0107] Among them, C plan Indicates the total system planning and operating costs; represents the energy storage planning cost; Represents the operating cost during the energy storage operation cycle, represents the penalty cost of exceeding the demand limit during the energy storage operation cycle; are the cost coefficients of power and capacity of energy storage planning, respectively; are non-negative integer decision variables for the maximum charge and discharge power and maximum energy capacity of energy storage, respectively; γ is the discount rate, a is the service life of energy storage; S is the number of scenarios, w s is the probability of scene s appearing, They are the operating cost under scenario s at time t and the penalty cost for exceeding the demand limit.

[0108] is the operating cost at time t in scenario s, including the operating cost of the generator C G,s,t , energy storage operation cost C ES,s,t , load shedding cost And the cost of abandoned light The operating cost is expressed as:

[0109]

[0110] The energy storage operating cost is expressed as:

[0111]

[0112] The generator operating cost is expressed as:

[0113]

[0114] The cost of abandoned light is expressed as:

[0115]

[0116] The load shedding cost is expressed as:

[0117]

[0118] in, are the cost coefficients of energy storage, solar curtailment, and load shedding respectively. G2,g / λ G1,g / λ G0,g Fuel cost factor for generator, is the charging and discharging efficiency of energy storage. is the charging and discharging power of the energy storage. G,g,s,t Active output of generator g. is the abandoned optical power, is the load shedding power at node l. G / N L are the node sets containing generators and loads, respectively;

[0119] The penalty cost for exceeding the demand limit is expressed as:

[0120]

[0121] in:

[0122] It is the amount of grid-connected photovoltaic fluctuations, abandoned photovoltaic power, and system spinning reserve capacity that exceeds the demand limit. is the corresponding cost penalty coefficient;

[0123] The algorithm performance in extreme scenarios is as follows:

[0124] The net load power balance constraint of the photovoltaic grid-connected node is expressed as:

[0125]

[0126] The power fluctuation range constraint of photovoltaic grid-connected nodes is expressed as:

[0127]

[0128] The photovoltaic maximum power fluctuation constraint is expressed as:

[0129]

[0130] The light abandonment range constraint is expressed as:

[0131]

[0132] The spinning reserve constraint is expressed as:

[0133]

[0134] The calculation method is as follows:

[0135]

[0136] The calculation method is as follows:

[0137]

[0138] The calculation method is as follows:

[0139]

[0140] is the grid-connected power, PPV,s,t is the actual photovoltaic output power; is the abandoned optical power, is the maximum fluctuation rate of grid-connected power, is the maximum fluctuating power of the grid-connected power, The rated capacity of the photovoltaic power station, △t represents the operation step length, Indicates the maximum light abandonment rate, P G,g,s,t is the output power of generator g, represents the maximum output power of generator g, is the energy storage charging and discharging power,

[0141] Figure 2 This is a schematic diagram of the structure of a photovoltaic storage microgrid capacity configuration system for various application requirements according to an embodiment of the present invention, wherein the system comprises:

[0142] The first unit is used to analyze multiple application requirements, including economic indicators, environmental protection indicators and reliability indicators; wherein the system total planning and operation economic cost are used as the economic indicator, the photovoltaic power generation abandonment rate is used as the environmental indicator, and the volatility of the grid-connected node, the system spinning reserve capacity and the algorithm performance in extreme scenarios are used as the reliability indicator;

[0143] The second unit is used for the various application requirements to focus on power output, power fluctuation and extreme situations of uncertain variables including photovoltaic power generation and net load power; based on the various application requirements, a scenario set including normal scenarios, fluctuation scenarios and extreme scenarios is constructed through scenario generation and clustering;

[0144] The third unit is used to build an energy storage capacity configuration model for multiple application requirements, setting the economic indicators as the target, modeling other application requirements as constraints, considering the performance under extreme scenarios, and the goal is to minimize the total system planning and operation costs, including the penalty costs for exceeding application requirements in extreme scenarios. The constraints include power system operation constraints and application demand constraints.

[0145] According to a third aspect of an embodiment of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0146] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0147] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for configuring the capacity of a photovoltaic and energy storage microgrid for various application requirements, characterized in that: include: Analyze multiple application requirements, including economic indicators, environmental protection indicators, and reliability indicators; wherein the system total planning and operation cost are used as the economic indicators, the abandonment rate of photovoltaic power generation is used as the environmental indicator, and the volatility of grid-connected nodes, system spinning reserve capacity, and algorithm performance in extreme scenarios are used as the reliability indicators; The various application requirements focus on power output, power fluctuation and extreme situations of uncertain variables including photovoltaic power generation and net load power; based on the various application requirements, a scene set including normal scenes, fluctuation scenes and extreme scenes is constructed through scene generation and clustering; A storage capacity configuration model for various application requirements is constructed. The model sets the economic indicators as the target, models other application requirements as constraints, considers the energy storage performance under extreme scenarios, and aims to minimize the total planning and operating costs of the system. The constraints include power system operation constraints and application demand constraints.

2. The method according to claim 1, characterized in that The normal scenario is obtained by performing Kmeans clustering based on the actual output size of photovoltaic power generation and net load power, and the fluctuation scenario is obtained by performing Kmeans clustering based on the fluctuation change of photovoltaic power generation and net load power.

3. The method according to claim 1, characterized in that The extreme scenarios are obtained by selecting extreme evaluation indicators, and the extreme evaluation indicators include: a photovoltaic surplus situation when the average photovoltaic power generation is maximum and the average net load power is minimum, a photovoltaic shortage situation when the average photovoltaic power generation is minimum and the average net load power is maximum, a situation when the photovoltaic power generation drops suddenly and the net load power suddenly increases, a situation when the net load power drops suddenly and the photovoltaic power generation suddenly increases, a situation with the maximum average fluctuation of photovoltaic power generation and net load power, and a situation with the maximum average fluctuation of photovoltaic power generation that exceeds the grid-connected fluctuation threshold.

4. The method according to claim 1, characterized in that: In the normal scenario and the fluctuation scenario, the energy storage capacity configuration model for the multiple application requirements needs to satisfy both the power system operation constraints and the application requirement constraints; In the extreme scenario, the energy storage capacity configuration model for the multiple application requirements only needs to meet the power system operation constraints. In the extreme scenario, the satisfaction of the application requirement constraints is reflected by adding a penalty cost that exceeds the application requirement limit in the target.

5. The method according to claim 1, characterized in that: The objective function of the energy storage capacity configuration model for the various application requirements includes the total system planning and operating costs; the total system planning and operating costs include the energy storage planning cost, the operating costs within the energy storage operation cycle, and the penalty costs for exceeding the demand limit, wherein the total system planning cost includes the planning cost of energy storage, the total operating cost includes the operating cost of the generator within the energy storage operation cycle, the energy storage operating cost, the load shedding cost, and the abandoned light cost, and the penalty cost for exceeding the demand limit is a penalty for not meeting the application requirements of grid-connected photovoltaic power fluctuations, abandoned light rate, and system rotating reserve capacity; The total system planning and operating costs are as follows: The energy storage planning cost is expressed as: The operating cost during the energy storage operation cycle is expressed as: The penalty cost for exceeding the demand limit during the energy storage operation cycle is expressed as: Among them, C plan Indicates the total system planning and operating costs; represents the energy storage planning cost; Represents the operating cost during the energy storage operation cycle, represents the penalty cost of exceeding the demand limit during the energy storage operation cycle; are the cost coefficients of power and capacity of energy storage planning respectively; are non-negative integer decision variables for the maximum charge and discharge power and maximum energy capacity of energy storage, respectively; γ is the discount rate, a is the service life of energy storage; S is the number of scenarios, w s is the probability of scene s appearing, They are the operating cost under scenario s at time t and the penalty cost for exceeding the demand limit. is the operating cost at time t in scenario s, including the operating cost of the generator C G,s,t , energy storage operation cost C ES,s,t , load shedding cost And the cost of abandoned light The operating cost is expressed as: The energy storage operating cost is expressed as: The generator operating cost is expressed as: The cost of abandoned light is expressed as: The load shedding cost is expressed as: in, are the cost coefficients of energy storage, solar curtailment, and load shedding respectively. G2,g / λ G1,g / λ G0,g Fuel cost factor for generator, is the charging and discharging efficiency of energy storage. is the charging and discharging power of the energy storage. G,g,s,t Active output of generator g. is the abandoned optical power, is the load shedding power at node l. G / N L are the sets of nodes containing generators and loads, respectively; The penalty cost for exceeding the demand limit is expressed as: in: It is the amount of grid-connected photovoltaic fluctuations, abandoned photovoltaic power, and system spinning reserve capacity that exceeds the demand limit. is the corresponding cost penalty coefficient; The algorithm performance in extreme scenarios is as follows: The net load power balance constraint of the photovoltaic grid-connected node is expressed as: The power fluctuation range constraint of photovoltaic grid-connected nodes is expressed as: The photovoltaic maximum power fluctuation constraint is expressed as: The light abandonment range constraint is expressed as: The spinning reserve constraint is expressed as: The calculation method is as follows: The calculation method is as follows: The calculation method is as follows: is the grid-connected power, PPV,s,t is the actual photovoltaic output power; is the abandoned optical power, is the maximum fluctuation rate of grid-connected power, is the maximum fluctuating power of the grid-connected power, The rated capacity of the photovoltaic power station, △t represents the operation step length, Indicates the maximum light abandonment rate, P G,g,s,t is the output power of generator g, represents the maximum output power of generator g, is the energy storage charging and discharging power, is the upper and lower system spinning reserve capacity, Indicates the maximum output power of the energy storage. represents the maximum value function, and || represents the absolute value function.

6. The method according to claim 1, characterized in that The energy storage capacity configuration model for various application requirements uses the maximum charge and discharge power and maximum energy capacity of the energy storage as decision variables, the discount rate, the service life and occurrence probability of the energy storage as parameters, and the node set including the generator and the load as a constraint condition.

7. The method according to claim 6, characterized in that The constraints of the energy storage capacity configuration model for the various application requirements also include grid-connected power, abandoned light power, maximum grid-connected power fluctuation rate, rated capacity of the photovoltaic power station, output power of the generator, energy storage charging and discharging power, and upper and lower system rotating reserve capacity.

8. A photovoltaic energy storage microgrid capacity configuration system for multiple application requirements, used to implement any of the methods in claims 1-7, characterized in that: include: The first unit is used to analyze multiple application requirements, including economic indicators, environmental protection indicators and reliability indicators; wherein the system total planning and operation economic cost are used as the economic indicator, the photovoltaic power generation abandonment rate is used as the environmental indicator, and the volatility of the grid-connected node, the system spinning reserve capacity and the algorithm performance in extreme scenarios are used as the reliability indicator; The second unit is used for the various application requirements to focus on power output, power fluctuation and extreme situations of uncertain variables including photovoltaic power generation and net load power; based on the various application requirements, a scenario set including normal scenarios, fluctuation scenarios and extreme scenarios is constructed through scenario generation and clustering; The third unit is used to build an energy storage capacity configuration model for multiple application requirements, setting the economic indicators as the target, modeling other application requirements as constraints, considering the performance under extreme scenarios, and the goal is to minimize the total system planning and operation costs, including the penalty costs for exceeding application requirements in extreme scenarios. The constraints include power system operation constraints and application demand constraints.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

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

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