Multi-objective optimization scheduling method for optical storage energy system

Abstract

The embodiment of the invention discloses a multi-objective optimization scheduling method for an optical energy storage system. The method comprises the following steps: establishing a photovoltaic system generation power model and an energy storage system charging and discharging and state-of-charge model; according to the photovoltaic system generation power model, the energy storage system charging and discharging and state-of-charge model and other equipment models, an overall model of the light storage energy system is established to determine energy distribution; determining a flexibility objective function according to the fault condition of the optical storage energy system; based on the overall model of the optical storage energy system, optimal scheduling is carried out on the optical storage energy system by utilizing an optimal scheduling objective function, the optimal scheduling objective function comprises the flexibility objective function, the multi-objective optimal scheduling method of the optical storage energy system considering the flexibility is provided, and the operation flexibility of the system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic and energy storage system optimization scheduling, and in particular to a multi-objective optimization scheduling method for a photovoltaic energy storage system. Background Art

[0002] Energy is an important lifeline for national security and the cornerstone of economic development. In the energy consumption structure in 2022, fossil energy accounts for as much as 82%, causing serious problems such as pollutants and greenhouse gas emissions. In order to alleviate the problems of extreme weather and climate change caused by greenhouse gas emissions, it has become the current development consensus to achieve low-carbon goals such as "carbon neutrality" as soon as possible. To this end, the penetration rate of renewable energy needs to be further improved. However, the inherent randomness and volatility of renewable energy systems, and the uncertainty and inaccuracy of power generation forecasts have brought great challenges to their large-scale application and the improvement of their penetration rate in energy systems. As a key supporting equipment for peak shaving and valley filling in power grids, energy storage systems can play a role in improving system stability and achieving flexible operation.

[0003] The low reliability of photovoltaic systems is one of the main reasons that restrict their development. Although energy storage systems can effectively improve system reliability, their economic efficiency is poor. Therefore, if the system operation and scheduling plan is rough and unreasonable, it will weaken the feasibility of its operation plan and the role of energy storage. In the current energy system operation optimization process, most of them are single operation modes, and the operation mode of each device is fixed. Usually, during the operation process, the economic goal is considered as the optimization scheduling objective function.

[0004] However, the operation mode of the photovoltaic energy storage system is complex, and the reliability and safety requirements are high. The energy storage system is a system coupled across time and space. During the dispatching process, it is easy to "affect the whole body with a single hair". Therefore, how to achieve flexible, stable and safe operation of the system is the key issue at present. At present, natural disasters occur frequently, resulting in sudden situations such as failures and shutdowns of systems or equipment. During the operation process, how to balance the safe and stable operation of the system after a failure and the economic operation of the equipment under normal non-failure conditions is one of the key scientific issues at present. Summary of the invention

[0005] The main purpose of the present invention is to provide a multi-objective optimization scheduling method for a photovoltaic energy storage system to improve the flexibility of system operation.

[0006] To achieve the above objectives, the present application provides a multi-objective optimization scheduling method for a photovoltaic energy storage system, the method comprising:

[0007] Establish a photovoltaic system power generation model and an energy storage system charge-discharge and charge state model; establish an overall model of the photovoltaic energy storage system based on the photovoltaic system power generation model, the energy storage system charge-discharge and charge state model and other equipment models to determine energy distribution;

[0008] Determining a flexibility objective function according to a fault condition of the photovoltaic energy storage system;

[0009] Based on the overall model of the photovoltaic energy storage system, the photovoltaic energy storage system is optimally scheduled using an optimal scheduling objective function, and the optimal scheduling objective function includes the flexibility objective function.

[0010] Optionally, the establishing of a photovoltaic system power generation model includes:

[0011] Determine the photovoltaic system power generation model, according to the optimized scheduling time span and time interval, based on historical meteorological data and historical power generation data, and on the basis of current meteorological data, use extreme learning machine to predict the photovoltaic system power generation.

[0012] Optionally, determining the flexibility objective function according to the fault condition of the photovoltaic energy storage system includes:

[0013] Collect and analyze historical data of each energy system to obtain predicted fault types;

[0014] Analyze and obtain the failure occurrence rate of the predicted failure type;

[0015] Based on the predicted fault type, analyzing the change in equipment output after the fault occurs, and analyzing the load loss of the photovoltaic energy storage system;

[0016] According to the load loss situation of the photovoltaic energy storage system under different equipment and different fault conditions, combined with the fault occurrence rate, an energy system flexibility objective function model is established.

[0017] Optionally, the optimization scheduling objective function also includes an economic objective function;

[0018] The method further comprises:

[0019] According to the energy consumption of the photovoltaic energy storage system, the economic objective function is established;

[0020] Determining the flexibility objective function according to the fault condition of the photovoltaic energy storage system includes:

[0021] The flexibility objective function is established according to the load loss situation of the photovoltaic energy storage system in the event of a fault.

[0022] Optionally, establishing the economic objective function according to the energy consumption of the photovoltaic energy storage system includes:

[0023] According to the energy consumption of each device and the overall power consumption of the system during the optimization and scheduling of the photovoltaic energy storage system, the economic objective function is determined with the operating cost as the objective function;

[0024] The flexibility objective function is established according to the load loss condition of the photovoltaic energy storage system under a fault condition, including:

[0025] According to the predicted fault type, the change in equipment output after the fault occurs, and the ramp rate of other non-faulty equipment after the fault occurs, the minimum load loss of the photovoltaic energy storage system under the fault condition is analyzed and obtained;

[0026] According to the prediction results of photovoltaic system power generation, the kernel density estimation method is used to analyze its uncertainty interval, analyze the worst output of photovoltaic system under a certain confidence interval, and analyze the load loss under this scenario;

[0027] The flexibility objective function is determined by taking the failure occurrence rate of the predicted failure type, the probability of the worst renewable energy output, and the weighted average of the load loss under failure and poor output conditions.

[0028] Optionally, the step of optimizing the scheduling of the photovoltaic energy storage system based on the overall model of the photovoltaic energy storage system by using an optimization scheduling objective function includes:

[0029] By using the economic objective function and the flexibility objective function, and according to the energy distribution and equipment output characteristics of the photovoltaic energy storage system, system constraints are established. On this basis, a multi-objective grey wolf optimization algorithm is used to perform optimization and solve the problem, and the Pareto frontier is obtained.

[0030] Optionally, the photovoltaic system power generation model includes:

[0031]

[0032] Where P PV , P ref are the actual power generation and the power generation of the photovoltaic system under standard conditions; G and G ref are the actual irradiance and the standard state irradiance respectively; ΔT is the difference between the photovoltaic panel temperature and the standard state temperature; a, b, c are the model coefficients;

[0033] The model of the extreme learning machine includes:

[0034]

[0035] In the formula, g(x) is the activation function, W i is the input weight; X is the input data sample; b j is the bias of the jth node in the hidden layer, and β is the parameter to be determined.

[0036] On the other hand, the present application provides a multi-objective optimization scheduling device for a photovoltaic energy storage system, comprising:

[0037] A model building module is used to build a photovoltaic system power generation model and an energy storage system charge-discharge and charge state model; based on the photovoltaic system power generation model, the energy storage system charge-discharge and charge state model and other equipment models, an overall model of the photovoltaic energy storage system is built to determine the energy distribution;

[0038] A function determination module, used to determine a flexibility objective function according to a fault condition of the photovoltaic energy storage system;

[0039] An optimization scheduling module is used to optimize the scheduling of the photovoltaic energy storage system based on the overall model of the photovoltaic energy storage system using an optimization scheduling objective function, wherein the optimization scheduling objective function includes the flexibility objective function.

[0040] Optionally, the model building module is specifically used to:

[0041] Determine the photovoltaic system power generation model, according to the optimized scheduling time span and time interval, based on historical meteorological data and historical power generation data, and on the basis of current meteorological data, use extreme learning machine to predict the photovoltaic system power generation.

[0042] Optionally, the function determination module is specifically used to:

[0043] Collect and analyze historical data of each energy system to obtain predicted fault types;

[0044] Analyze and obtain the failure occurrence rate of the predicted failure type;

[0045] Based on the predicted fault type, analyzing the change in equipment output after the fault occurs, and analyzing the load loss of the photovoltaic energy storage system;

[0046] According to the load loss situation of the photovoltaic energy storage system under different equipment and different fault conditions, combined with the fault occurrence rate, an energy system flexibility objective function model is established.

[0047] Optionally, the optimization scheduling objective function also includes an economic objective function;

[0048] The function determination module is further used for:

[0049] According to the energy consumption of the photovoltaic energy storage system, the economic objective function is established;

[0050] And used to establish the flexibility objective function according to the load loss situation of the photovoltaic energy storage system in the event of a fault.

[0051] Optionally, the function determination module is specifically used to:

[0052] According to the energy consumption of each device and the overall power consumption of the system during the optimization and scheduling of the photovoltaic energy storage system, the economic objective function is determined with the operating cost as the objective function;

[0053] According to the predicted fault type, the change in equipment output after the fault occurs, and the ramp rate of other non-faulty equipment after the fault occurs, the minimum load loss of the photovoltaic energy storage system under the fault condition is analyzed and obtained;

[0054] According to the prediction results of photovoltaic system power generation, the kernel density estimation method is used to analyze its uncertainty interval, analyze the worst output of photovoltaic system under a certain confidence interval, and analyze the load loss under this scenario;

[0055] The flexibility objective function is determined by taking the failure occurrence rate of the predicted failure type, the probability of the worst renewable energy output, and the weighted average of the load loss under failure and poor output conditions.

[0056] Optionally, the optimization scheduling module is specifically used to:

[0057] By using the economic objective function and the flexibility objective function, and according to the energy distribution and equipment output characteristics of the photovoltaic energy storage system, system constraints are established. On this basis, a multi-objective grey wolf optimization algorithm is used to perform optimization and solve the problem, and the Pareto frontier is obtained.

[0058] Optionally, the photovoltaic system power generation model includes:

[0059]

[0060] Where P PV , P ref are the actual power generation and the power generation of the photovoltaic system under standard conditions; G and G ref are the actual irradiance and the standard state irradiance respectively; ΔT is the difference between the photovoltaic panel temperature and the standard state temperature; a, b, c are the model coefficients;

[0061] The model of the extreme learning machine includes:

[0062]

[0063] In the formula, g(x) is the activation function, W iis the input weight; X is the input data sample; b j is the bias of the jth node in the hidden layer, and β is the parameter to be determined.

[0064] On the other hand, the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the first aspect and any possible implementation method thereof.

[0065] On the other hand, the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes each step of the method described in the first aspect.

[0066] The present application provides a multi-objective optimization scheduling method for a photovoltaic energy storage system, by establishing a photovoltaic system power generation model and an energy storage system charging, discharging and charge state model; establishing an overall model of the photovoltaic energy storage system based on the photovoltaic system power generation model, the energy storage system charging, discharging and charge state model and other equipment models to determine the energy distribution; determining a flexibility objective function based on the fault conditions of the photovoltaic energy storage system; and optimizing and scheduling the photovoltaic energy storage system based on the overall model of the photovoltaic energy storage system using an optimization scheduling objective function, wherein the optimization scheduling objective function includes the flexibility objective function, thereby providing a multi-objective optimization scheduling method for a photovoltaic energy storage system taking flexibility into account, which can improve the flexibility of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] in:

[0069] Figure 1 A schematic diagram of a multi-objective optimization scheduling method for a photovoltaic energy storage system provided in an embodiment of the present application;

[0070] Figure 2 A flowchart of another multi-objective optimization scheduling method for a photovoltaic energy storage system provided in an embodiment of the present application;

[0071] Figure 3 A schematic diagram of the structure of a multi-objective optimization scheduling device for a photovoltaic energy storage system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0073] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0074] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0075] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0076] See also Figure 1 , is a flow chart of a multi-objective optimization scheduling method for a photovoltaic energy storage system provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0077] 101. Establish a photovoltaic system power generation model and an energy storage system charge-discharge and charge state model; based on the photovoltaic system power generation model, the energy storage system charge-discharge and charge state model and other equipment models, establish an overall model of the photovoltaic energy storage system to determine the energy distribution.

[0078] The method in the embodiment of the present application can be executed by a multi-objective optimization scheduling device for a photovoltaic energy storage system, which can specifically be a terminal device.

[0079] In the embodiment of the present application, a photovoltaic energy storage system model is first established, which may mainly include key equipment models such as photovoltaics, energy storage, and gas turbines.

[0080] The photovoltaic system power generation model mentioned in the embodiments of the present application may include: a characteristic model of power generation, photovoltaic irradiance and temperature; the energy storage system model may include: an energy storage system charging and discharging characteristic model and a state of charge model.

[0081] In an optional implementation, the above-mentioned establishment of a photovoltaic system power generation model includes:

[0082] Determine the photovoltaic system power generation model, according to the optimized scheduling time span and time interval, based on historical meteorological data and historical power generation data, and on the basis of current meteorological data, use extreme learning machine to predict the photovoltaic system power generation.

[0083] The above-mentioned photovoltaic energy storage system model is established, and the specific processing flow may include:

[0084] Step 1.1) Determine the characteristic model of the photovoltaic system power generation, and use an extreme learning machine to predict the photovoltaic system power generation based on the current meteorological data, based on the optimized scheduling time span and time interval, historical meteorological data, and historical power generation data;

[0085] Step 1.2) Determine the energy storage system model and establish the energy storage system charging, discharging and charge state model;

[0086] Step 1.3) Based on the above photovoltaic and energy storage models, establish the overall model of the energy system, determine the energy flow distribution, and analyze the uncertainty of the photovoltaic system.

[0087] 102. Determine the flexibility objective function based on the fault conditions of the above-mentioned photovoltaic energy storage system.

[0088] In the embodiment of the present application, the flexibility objective function is determined according to the fault conditions of the energy system, and the flexibility index is considered as the objective function for optimal scheduling.

[0089] In an optional implementation, the above step 102 includes:

[0090] Step 2.1) Establish an energy system fault model based on the failure rate of internal equipment in the energy system, possible failure conditions (predicted failure types), and output changes of the faulty equipment after the failure occurs;

[0091] Step 2.2) Analyze and determine the load loss situation based on the changes in each energy supply side after the fault occurs and the maximum climbing rate of the equipment;

[0092] Step 2.3) According to the load loss of the energy system under different equipment and different fault conditions, combined with the fault occurrence rate, the energy system flexibility objective function model is established.

[0093] 103. Based on the overall model of the above-mentioned photovoltaic energy storage system, the above-mentioned photovoltaic energy storage system is optimized and scheduled using an optimization scheduling objective function, and the above-mentioned optimization scheduling objective function includes the above-mentioned flexibility objective function.

[0094] In the embodiment of the present application, the above-mentioned flexibility objective function is taken into consideration when optimizing scheduling. In an optional implementation, the photovoltaic energy storage system can be optimized with economy and flexibility as the objective functions.

[0095] Optionally, the optimization scheduling of the photovoltaic energy storage system is carried out with economy and flexibility as the objective function, which may include:

[0096] Step 3.1) Establish an economic objective function based on the system energy consumption;

[0097] Step 3.2) Establish flexibility objective function according to the system load loss under fault conditions;

[0098] Step 3.3) Take economy and flexibility as the objective function, establish system constraints according to the energy distribution of the energy system and the output characteristics of the equipment, and on this basis, use the multi-objective grey wolf optimization algorithm to optimize and solve, and obtain the Pareto frontier.

[0099] The embodiment of the present application establishes a photovoltaic system power generation model and an energy storage system charging, discharging and charge state model; establishes an overall model of the photovoltaic energy storage system based on the photovoltaic system power generation model, the energy storage system charging, discharging and charge state model and other equipment models to determine the energy distribution; determines the flexibility objective function based on the fault conditions of the photovoltaic energy storage system; based on the overall model of the photovoltaic energy storage system, optimizes and schedules the photovoltaic energy storage system using an optimization scheduling objective function, the optimization scheduling objective function includes the flexibility objective function, and provides a multi-objective optimization scheduling method for a photovoltaic energy storage system that takes flexibility into account, which can improve the flexibility of system operation.

[0100] To more clearly illustrate the method in the embodiments of the present application, please refer to Figure 2 , Figure 2 A flow chart of another multi-objective optimization scheduling method for a photovoltaic energy storage system provided in an embodiment of the present application. Figure 2 The specific implementation of the present invention is described in more detail.

[0101] 1. Establish an energy model for the photovoltaic storage system, and establish key equipment models such as photovoltaics, energy storage, and gas turbines. The specific processing flow is as follows:

[0102] Step 1.1) Determine the characteristic model of the photovoltaic system power generation power, the specific model is:

[0103]

[0104] Where P PV , P ref are the actual power generation and the power generation of the photovoltaic system under standard conditions; G and G ref are the actual irradiance and the standard state irradiance respectively; ΔT is the difference between the photovoltaic panel temperature and the standard state temperature; a, b, c are the characteristic model coefficients;

[0105] Based on the extreme learning machine, the numerical weather forecast data such as ambient temperature, irradiance, humidity, and rainfall are used as influencing factors. According to historical data, the power generation is predicted. The extreme learning machine model is:

[0106]

[0107] In the formula, g(x) is the activation function, W i is the input weight; X is the input data sample; b j is the bias of the jth node in the hidden layer, and β is the parameter to be determined.

[0108] Step 1.2) Determine the charging, discharging and state of charge model of the energy storage system, and its characteristic model is:

[0109] SOC t =SOC t-Δt +S ESS,t P ESS,t Δtη ch +(1-S ESS,t ) ESS,t Δt / η dis

[0110] Where: S ESS,t is the battery charge and discharge state 0-1 variable at time t (charging is 1, discharging is 0); η ch , η dis It is the efficiency of energy storage charging and discharging.

[0111] Step 1.3) Based on the photovoltaic and energy storage system models, as well as the other equipment models, establish the overall energy flow distribution model of the system.

[0112] 2. According to the energy system failure situation, determine the flexibility objective function. The specific processing flow is as follows:

[0113] Step 2.1) Collect and analyze historical data of each energy system and analyze the possible fault types (predict fault types);

[0114] Step 2.2) Analyze the possible failure types and failure rates in step 2.1);

[0115] Step 2.3) Analyze the possible fault types in step 2.1) and the changes in the characteristics of the faulty equipment after the fault occurs. According to the changes in its output, analyze the load loss of the energy system.

[0116] 3. Optimize the scheduling of the photovoltaic energy storage system with economy and flexibility as the objective function. The specific processing flow is as follows:

[0117] Step 3.1) According to the energy consumption of each device and the overall power consumption of the system during the energy system optimization and scheduling process, the economic evaluation index is established with the operating cost as the objective function;

[0118] Step 3.2) Based on the possible fault types obtained in step 2), the changes in equipment output after the fault occurs, and the ramp rates of other non-faulty equipment after the fault occurs, analyze the minimum load loss of the energy system under the fault condition. Based on the power generation prediction results of the photovoltaic system, the kernel density estimation method is used to analyze its uncertainty interval, analyze the worst output of the photovoltaic system under a certain confidence interval, and analyze the load loss under this scenario;

[0119] Step 3.3) According to the probability of possible failure and the worst renewable energy output obtained in step 2), the weighted average of load loss under failure and poor output is used as the flexibility objective function. Based on the above two objective functions, the multi-objective grey wolf optimization algorithm is used to solve the multi-objective optimization scheduling Pareto frontier, thereby determining the optimal scheduling solution.

[0120] In the current energy system operation optimization process, most of them are in a single operation mode, and the operation mode of each device is fixed, or only a single objective function is considered during the operation, such as only taking the economic objective as the optimization scheduling objective function. The above scheduling scheme does not consider the impact of the flexibility objective function on the system optimization scheduling results, and fails to consider the system operation scheduling results under fault conditions. The method of the embodiment of the present application is mainly to provide a multi-objective optimization scheduling method for a photovoltaic energy storage system that takes into account flexibility, so as to improve the flexibility and economy of the system operation.

[0121] The embodiment of the present application mainly proposes a multi-objective optimization scheduling method for a photovoltaic energy storage system taking into account flexibility. The method is based on the photovoltaic energy storage system, and according to the economic and flexibility objective functions, a multi-objective optimization algorithm is used to obtain the Pareto frontier surface, and an operation plan is selected according to the load type. Specifically, the method described in the embodiment of the present application has the following beneficial effects:

[0122] Improve the economy and flexibility of the energy system. According to the load type and importance, select the operation plan based on the reduction of load loss in the system fault state and the improvement of system economic benefits in the non-fault state to ensure the economic benefits, reliability and flexibility of the system.

[0123] Based on the description of the aforementioned method embodiment, the embodiment of the present application also provides a multi-objective optimization scheduling device for a photovoltaic energy storage system.

[0124] Figure 3 A schematic diagram of the structure of a multi-objective optimization scheduling device for a photovoltaic energy storage system provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the device 300 includes:

[0125] Model building module 310, used to build a photovoltaic system power generation model and an energy storage system charge-discharge and charge state model; based on the photovoltaic system power generation model, the energy storage system charge-discharge and charge state model and other equipment models, build an overall model of the photovoltaic energy storage system to determine energy distribution;

[0126] A function determination module 320, used to determine a flexibility objective function according to the fault condition of the above-mentioned photovoltaic energy storage system;

[0127] The optimization scheduling module 330 is used to optimize the scheduling of the above-mentioned photovoltaic energy storage system based on the overall model of the above-mentioned photovoltaic energy storage system using the optimization scheduling objective function, and the above-mentioned optimization scheduling objective function includes the above-mentioned flexibility objective function.

[0128] Optionally, the model building module 310 is specifically used for:

[0129] Determine the photovoltaic system power generation model, according to the optimized scheduling time span and time interval, based on historical meteorological data and historical power generation data, and on the basis of current meteorological data, use extreme learning machine to predict the photovoltaic system power generation.

[0130] Optionally, the function determination module 320 is specifically configured to:

[0131] Collect and analyze historical data of each energy system to obtain predicted fault types;

[0132] Analyze and obtain the failure occurrence rate of the above-mentioned predicted failure types;

[0133] Based on the above predicted fault types, analyze the change in equipment output after the fault occurs, and analyze the load loss of the above photovoltaic energy storage system;

[0134] According to the load loss situation of the above-mentioned photovoltaic energy storage system under different equipment and different fault conditions, combined with the above-mentioned fault occurrence rate, an energy system flexibility objective function model is established.

[0135] Optionally, the above optimization scheduling objective function also includes an economic objective function;

[0136] The function determination module 320 is further used for:

[0137] According to the energy consumption of the above-mentioned photovoltaic energy storage system, the above-mentioned economic objective function is established;

[0138] And used to establish the above flexibility objective function according to the load loss situation of the above photovoltaic energy storage system in the event of a fault.

[0139] Optionally, the function determination module 320 is specifically configured to:

[0140] According to the energy consumption of each device and the overall power consumption of the system during the optimization and dispatching process of the above-mentioned photovoltaic energy storage system, the above-mentioned economic objective function is determined with the operating cost as the objective function;

[0141] According to the above-mentioned predicted fault type, the change in equipment output after the above-mentioned fault occurs, and the ramp rate of other non-faulty equipment after the fault occurs, the minimum load loss of the above-mentioned photovoltaic energy storage system under the fault condition is analyzed and obtained;

[0142] According to the prediction results of photovoltaic system power generation, the kernel density estimation method is used to analyze its uncertainty interval, analyze the worst output of photovoltaic system under a certain confidence interval, and analyze the load loss under this scenario;

[0143] The above flexibility objective function is taken as the failure rate of the predicted failure type, the probability of the worst renewable energy output, and the weighted average of the load loss under failure and poor output conditions.

[0144] Optionally, the above optimization scheduling module 330 is specifically used for:

[0145] By using the above-mentioned economic objective function and the above-mentioned flexibility objective function, according to the energy distribution and equipment output characteristics of the above-mentioned photovoltaic energy storage system, system constraints are established. On this basis, a multi-objective grey wolf optimization algorithm is used for optimization and solution to obtain the Pareto frontier.

[0146] Optionally, the photovoltaic system power generation model includes:

[0147]

[0148] Where P PV , P ref are the actual power generation and the power generation of the photovoltaic system under standard conditions; G and G ref are the actual irradiance and the standard state irradiance respectively; ΔT is the difference between the photovoltaic panel temperature and the standard state temperature; a, b, c are the model coefficients;

[0149] The model of the above extreme learning machine includes:

[0150]

[0151] In the formula, g(x) is the activation function, W i is the input weight; X is the input data sample; b j is the bias of the jth node in the hidden layer, and β is the parameter to be determined.

[0152] It can be understood that the relevant contents of each module in the above device have been described in detail in the aforementioned method embodiment, and the specific contents can be referred to the method embodiment; that is, a multi-objective optimization scheduling device for a photovoltaic energy storage system provided in this application can be executed as follows Figure 1 or Figure 2 Any steps in the illustrated embodiment will not be described in detail here.

[0153] In one embodiment of the present application, an electronic device is also provided. The electronic device may include a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the execution Figure 1 or Figure 2 Any step in the method embodiment shown. The electronic device may also include an input / output device, etc. In a specific implementation, the electronic device may be a terminal device, etc.

[0154] In one embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes any step in the above method embodiment.

[0155] 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 program can be stored in a non-volatile computer-readable storage medium. When the 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 in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0156] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0157] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-objective optimization scheduling method for a photovoltaic energy storage system, characterized in that: The method comprises: Establish a photovoltaic system power generation model and an energy storage system charge-discharge and charge state model; establish an overall model of the photovoltaic energy storage system based on the photovoltaic system power generation model, the energy storage system charge-discharge and charge state model and other equipment models to determine energy distribution; Determining a flexibility objective function according to a fault condition of the photovoltaic energy storage system; Based on the overall model of the photovoltaic energy storage system, the photovoltaic energy storage system is optimally scheduled using an optimal scheduling objective function, and the optimal scheduling objective function includes the flexibility objective function.

2. The multi-objective optimization scheduling method for a photovoltaic energy storage system according to claim 1 is characterized in that: The step of establishing a photovoltaic system power generation model comprises: Determine the photovoltaic system power generation model, according to the optimized scheduling time span and time interval, based on historical meteorological data and historical power generation data, and on the basis of current meteorological data, use extreme learning machine to predict the photovoltaic system power generation.

3. The multi-objective optimization scheduling method for a photovoltaic energy storage system according to claim 1 is characterized in that: Determining the flexibility objective function according to the fault condition of the photovoltaic energy storage system includes: Collect and analyze historical data of each energy system to obtain predicted fault types; Analyze and obtain the failure occurrence rate of the predicted failure type; Based on the predicted fault type, analyzing the change in equipment output after the fault occurs, and analyzing the load loss of the photovoltaic energy storage system; According to the load loss situation of the photovoltaic energy storage system under different equipment and different fault conditions, combined with the fault occurrence rate, an energy system flexibility objective function model is established.

4. The multi-objective optimization scheduling method for a photovoltaic energy storage system according to claim 3 is characterized in that: The optimization scheduling objective function also includes an economic objective function; The method further comprises: According to the energy consumption of the photovoltaic energy storage system, the economic objective function is established; Determining the flexibility objective function according to the fault condition of the photovoltaic energy storage system includes: The flexibility objective function is established according to the load loss situation of the photovoltaic energy storage system in the event of a fault.

5. The multi-objective optimization scheduling method for a photovoltaic energy storage system according to claim 4 is characterized in that: The establishing of the economic objective function according to the energy usage of the photovoltaic energy storage system includes: According to the energy consumption of each device and the overall power consumption of the system during the optimization and scheduling of the photovoltaic energy storage system, the economic objective function is determined with the operating cost as the objective function; The flexibility objective function is established according to the load loss condition of the photovoltaic energy storage system under a fault condition, including: According to the predicted fault type, the change in equipment output after the fault occurs, and the ramp rate of other non-faulty equipment after the fault occurs, the minimum load loss of the photovoltaic energy storage system under the fault condition is analyzed and obtained; According to the prediction results of photovoltaic system power generation, the kernel density estimation method is used to analyze its uncertainty interval, analyze the worst output of photovoltaic system under a certain confidence interval, and analyze the load loss under this scenario; The flexibility objective function is determined by taking the failure occurrence rate of the predicted failure type, the probability of the worst renewable energy output, and the weighted average of the load loss under failure and poor output conditions.

6. The multi-objective optimization scheduling method for a photovoltaic energy storage system according to claim 5 is characterized in that: The method of optimizing the scheduling of the photovoltaic energy storage system based on the overall model of the photovoltaic energy storage system by using the optimization scheduling objective function includes: By using the economic objective function and the flexibility objective function, and according to the energy distribution and equipment output characteristics of the photovoltaic energy storage system, system constraints are established. On this basis, a multi-objective grey wolf optimization algorithm is used to perform optimization and solve the problem, and the Pareto frontier is obtained.

7. The multi-objective optimization scheduling method for a photovoltaic energy storage system according to claim 2 is characterized in that: The photovoltaic system power generation model includes: Where P PV , P ref are the actual power generation and the power generation of the photovoltaic system under standard conditions; G and G ref are the actual irradiance and the standard state irradiance respectively; ΔT is the difference between the photovoltaic panel temperature and the standard state temperature; a, b, c are the model coefficients; The model of the extreme learning machine includes: In the formula, g(x) is the activation function, W i is the input weight; X is the input data sample; b j is the bias of the jth node in the hidden layer, and β is the parameter to be determined.

8. A multi-objective optimization scheduling device for a photovoltaic energy storage system, characterized in that: include: A model building module is used to build a photovoltaic system power generation model and an energy storage system charge-discharge and charge state model; based on the photovoltaic system power generation model, the energy storage system charge-discharge and charge state model and other equipment models, an overall model of the photovoltaic energy storage system is built to determine the energy distribution; A function determination module, used to determine a flexibility objective function according to a fault condition of the photovoltaic energy storage system; An optimization scheduling module is used to optimize the scheduling of the photovoltaic energy storage system based on the overall model of the photovoltaic energy storage system using an optimization scheduling objective function, wherein the optimization scheduling objective function includes the flexibility objective function.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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