Wind-solar-storage combined system capacity configuration method considering time-of-use electricity price

Abstract

The invention relates to a wind-light-storage combined system capacity configuration method and device considering time-of-use electricity price, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of electric power. The method comprises the following steps: establishing a structure model of the wind-solar-storage combined system; according to the structure model of the wind-light-storage combined system, establishing a capacity configuration model taking resources and a wind-light complementation rate as optimization targets, and taking the capacity configuration model as a capacity configuration model of the wind-light-storage combined system; utilizing the capacity configuration model to output evaluation index information of the wind-light-storage combined system; the evaluation index information comprises a power supply loss rate, an energy waste rate and a system contribution rate of the wind-solar-storage combined system; and performing iterative analysis on the capacity configuration model according to the evaluation index information and preset energy information based on the time-of-use electricity price to obtain a capacity configuration scheme of the wind-solar-storage combined system. By adopting the method, the efficiency of determining the capacity configuration scheme of the wind-solar-storage combined system can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for configuring the capacity of a wind-solar-storage combined system taking into account time-of-use electricity prices. Background Art

[0002] With the development of renewable energy technology, wind and solar power generation systems are becoming increasingly mature. In order to solve the intermittent and volatile problems of renewable energy, wind, solar and storage combined systems have gradually become an important system. How to efficiently configure the capacity of wind, solar and storage combined systems has become an important research direction.

[0003] Traditional technology usually relies on manual experience to determine the capacity configuration plan of the wind, solar and storage combined system; however, determining the capacity configuration plan of the wind, solar and storage combined system in this way requires a lot of manual processing time, resulting in low efficiency in determining the capacity configuration plan of the wind, solar and storage combined system. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for capacity configuration of a wind, solar and storage combined system taking into account time-of-use electricity prices, which can improve the efficiency of determining the capacity configuration plan of the wind, solar and storage combined system and address the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for configuring the capacity of a wind-solar-storage combined system taking into account time-of-use electricity prices. The method comprises:

[0006] Establish the structural model of the wind-solar-storage combined system;

[0007] According to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity ratio as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system;

[0008] Using the capacity configuration model, outputting evaluation index information of the wind-solar-storage combined system; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system;

[0009] According to the evaluation index information and the preset energy information based on the time-of-use electricity price, the capacity configuration model is iteratively analyzed to obtain the capacity configuration plan of the wind-solar-storage combined system.

[0010] In one embodiment, establishing a structural model of a wind-solar-storage combined system includes:

[0011] Construct the structural model of wind power generation system, photovoltaic power generation system, gravity energy storage system, battery system and supercapacitor system;

[0012] The structural model of the wind power generation system, the structural model of the photovoltaic power generation system, the structural model of the gravity energy storage system, the structural model of the battery system and the structural model of the supercapacitor system are combined to obtain the structural model of the wind-solar-storage combined system.

[0013] In one embodiment, according to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity rate as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system, including:

[0014] According to the structural model of the wind-solar-storage combined system, the capacity constraint conditions of the wind-solar-storage combined system are determined, and the minimum consumption of the resource and the minimum wind-solar complementarity rate are set as the optimization objectives;

[0015] According to the capacity constraint conditions and the optimization objectives, a capacity configuration model of the wind-solar-storage combined system is constructed.

[0016] In one embodiment, the outputting of evaluation index information of the wind-solar-storage combined system by using the capacity configuration model includes:

[0017] According to the capacity configuration model, identifying the operating status of the wind-solar-storage combined system;

[0018] According to the operating status of the wind-solar-storage combined system, the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system are calculated, and the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system are used as the evaluation index information.

[0019] In one embodiment, the capacity configuration model is iteratively analyzed according to the evaluation index information and the preset energy information based on the time-of-use electricity price to obtain the capacity configuration scheme of the wind-solar-storage combined system, including:

[0020] Using the preset energy information, the capacity configuration model is updated to obtain a target capacity configuration model of the wind-solar-storage combined system;

[0021] According to the evaluation index information, the target capacity configuration model is iteratively analyzed to obtain a capacity configuration plan for the wind-solar-storage combined system.

[0022] In one embodiment, the target capacity configuration model is iteratively analyzed according to the evaluation index information to obtain a capacity configuration scheme for the wind-solar-storage combined system, including:

[0023] According to the evaluation index information, the target capacity configuration model is iteratively analyzed to obtain a capacity configuration sub-scheme for a wind power generation system, a capacity configuration sub-scheme for a photovoltaic power generation system, a capacity configuration sub-scheme for a gravity energy storage system, a capacity configuration sub-scheme for a battery system, and a capacity configuration sub-scheme for a supercapacitor system;

[0024] The capacity configuration sub-scheme of the wind power generation system, the capacity configuration sub-scheme of the photovoltaic power generation system, the capacity configuration sub-scheme of the gravity energy storage system, the capacity configuration sub-scheme of the battery system and the capacity configuration sub-scheme of the supercapacitor system are combined to obtain the capacity configuration scheme of the wind-solar-storage combined system.

[0025] In a second aspect, the present application also provides a capacity configuration device for a wind-solar-storage combined system taking into account time-of-use electricity prices. The device comprises:

[0026] The first building module is used to build a structural model of the wind-solar-storage combined system;

[0027] A second establishment module is used to establish a capacity configuration model with resources and wind-solar complementarity rate as optimization targets according to the structural model of the wind-solar-storage combined system, as the capacity configuration model of the wind-solar-storage combined system;

[0028] An indicator output module is used to output evaluation indicator information of the wind-solar-storage combined system using the capacity configuration model; the evaluation indicator information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system;

[0029] The model analysis module is used to iteratively analyze the capacity configuration model according to the evaluation index information and the preset energy information based on the time-of-use electricity price, so as to obtain the capacity configuration plan of the wind-solar-storage combined system.

[0030] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0031] Establish the structural model of the wind-solar-storage combined system;

[0032] According to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity ratio as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system;

[0033] Using the capacity configuration model, outputting evaluation index information of the wind-solar-storage combined system; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system;

[0034] According to the evaluation index information and the preset energy information based on the time-of-use electricity price, the capacity configuration model is iteratively analyzed to obtain the capacity configuration plan of the wind-solar-storage combined system.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0036] Establish the structural model of the wind-solar-storage combined system;

[0037] According to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity ratio as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system;

[0038] Using the capacity configuration model, outputting evaluation index information of the wind-solar-storage combined system; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system;

[0039] According to the evaluation index information and the preset energy information based on the time-of-use electricity price, the capacity configuration model is iteratively analyzed to obtain the capacity configuration plan of the wind-solar-storage combined system.

[0040] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Establish the structural model of the wind-solar-storage combined system;

[0042] According to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity ratio as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system;

[0043] Using the capacity configuration model, outputting evaluation index information of the wind-solar-storage combined system; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system;

[0044] According to the evaluation index information and the preset energy information based on the time-of-use electricity price, the capacity configuration model is iteratively analyzed to obtain the capacity configuration plan of the wind-solar-storage combined system.

[0045] The above-mentioned wind-solar-storage combined system capacity configuration method, device, computer equipment, computer-readable storage medium and computer program product taking into account the time-of-use electricity price establish a structural model of the wind-solar-storage combined system; according to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity rate as the optimization target is established as the capacity configuration model of the wind-solar-storage combined system; using the capacity configuration model, the evaluation index information of the wind-solar-storage combined system is output; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system; according to the evaluation index information and the preset energy information based on the time-of-use electricity price, the capacity configuration model is iteratively analyzed to obtain the capacity configuration scheme of the wind-solar-storage combined system. The scheme is conducive to comprehensively evaluating the performance of the wind-solar-storage combined system by establishing the wind-solar-storage combined system structure model and capacity configuration model, combining the evaluation index information such as power supply loss rate, energy waste rate and system contribution rate, and considering the time-of-use electricity price, so as to improve the efficiency and accuracy of determining the capacity configuration scheme of the wind-solar-storage combined system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are 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.

[0047] Figure 1 It is a flow chart of a method for configuring the capacity of a wind-solar-storage combined system taking into account time-of-use electricity prices in one embodiment;

[0048] Figure 2 A schematic diagram of the structure of a hybrid energy storage system in one embodiment;

[0049] Figure 3 A schematic diagram of the principle of a gravity energy storage system in one embodiment;

[0050] Figure 4 A schematic diagram of a flow chart of a system energy management strategy in one embodiment;

[0051] Figure 5 A schematic diagram of a flow chart of a model solving algorithm in one embodiment;

[0052] Figure 6 It is a structural block diagram of a capacity configuration device for a wind-solar-storage combined system taking into account time-of-use electricity prices in one embodiment;

[0053] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0056] In an exemplary embodiment, Figure 1 As shown, a method for configuring the capacity of a wind-solar-storage combined system taking into account time-of-use electricity prices is provided. This embodiment uses the method applied to a terminal as an example for illustration; it can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones, tablet computers, etc.; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the method includes the following steps:

[0057] Step S101, establishing a structural model of a wind-solar-storage combined system.

[0058] Step S102, based on the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity ratio as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system.

[0059] Step S103, using the capacity configuration model, outputs evaluation index information of the wind-solar-storage combined system; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system.

[0060] Step S104, iteratively analyzing the capacity configuration model according to the evaluation index information and the preset energy information based on the time-of-use electricity price, and obtaining a capacity configuration plan for the wind-solar-storage combined system.

[0061] Among them, the wind, solar and storage combined system can be a comprehensive energy system composed of wind power generation, photovoltaic power generation and energy storage devices. For example, the wind, solar and storage combined system can be a system composed of wind power generation devices, photovoltaic power generation devices, batteries, supercapacitors, gravity energy storage devices, converters and corresponding loads.

[0062] Among them, the structural model can be a model that describes the components of the system and their interrelationships. For example, the structural model can be a model that describes the connection and interaction relationships between wind power generation devices, photovoltaic power generation devices, batteries, supercapacitors, gravity energy storage devices, converters and loads.

[0063] Among them, the capacity configuration model can be a mathematical model used to determine the optimal capacity of each component of the system. For example, the capacity configuration model can be a mathematical model with the optimization goals of minimizing the annual system total cost and minimizing the annual system wind-solar complementarity rate.

[0064] The evaluation index information may be a quantitative index for measuring system performance, for example, the evaluation index information may include power supply loss rate, energy waste rate and system contribution rate.

[0065] The power supply loss rate may be an indicator reflecting the reliability of the power supply of the system. For example, the power supply loss rate may be the ratio of the difference between the load demand and the actual power supply of the system to the load demand.

[0066] The energy waste rate may be an indicator reflecting the energy utilization efficiency of the system. For example, the energy waste rate may be the ratio of the amount of power abandoned by the system to the total power generation of the system.

[0067] The system contribution rate may be an indicator reflecting the system's ability to supply loads. For example, the system contribution rate may be the ratio of the power provided by the system to the load to the power required by the load.

[0068] Among them, the time-of-use electricity price can be a differential electricity price set according to different time periods. For example, the time-of-use electricity price can be to divide a day into three time periods: peak time, normal time and off-peak time, and set different electricity prices accordingly.

[0069] The preset energy information may be an energy management strategy, and the energy management strategy may be an energy distribution and scheduling scheme among various components of the system. For example, the energy management strategy may be a strategy for guiding the charging and discharging of the system based on time-of-use electricity prices.

[0070] Optionally, the terminal establishes a structural model of the wind-solar-storage combined system, which includes a wind power generation device, a photovoltaic power generation device, a battery, a supercapacitor, a gravity energy storage device, a converter and corresponding loads. Based on the structural model of the wind-solar-storage combined system, the terminal establishes a capacity configuration model of the wind-solar-storage combined system with the optimization objectives of minimizing the annual system total cost and minimizing the annual system wind-solar complementarity rate. The terminal uses the capacity configuration model to output the power loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system. The terminal iteratively analyzes the capacity configuration model based on the power loss rate, energy waste rate, system contribution rate and the preset energy management strategy based on the time-of-use electricity price to obtain the capacity configuration plan of the wind-solar-storage combined system.

[0071] For example, the terminal establishes a wind-solar-storage combined system structure model that includes wind power generation devices, photovoltaic power generation devices, batteries, supercapacitors, and gravity energy storage devices. The terminal establishes a capacity configuration model with the annual system total cost and wind-solar complementarity rate as the optimization target, where the annual system total cost includes the initial investment and installation cost, operation and maintenance cost, replacement cost, power purchase cost, power sales income, and environmental protection income. The terminal calculates the power supply loss rate, energy waste rate, and system contribution rate, where the power supply loss rate reflects the system power supply reliability, the energy waste rate reflects the system energy utilization efficiency, and the system contribution rate reflects the system load supply capacity. The terminal adopts an energy management strategy based on peak-valley time-of-use electricity prices, giving priority to energy storage discharge during peak hours and purchasing electricity from the power grid during valley hours. The terminal uses an improved second-order particle swarm algorithm to iteratively analyze the capacity configuration model, and finally obtains the optimal capacity configuration plan for the wind-solar-storage combined system.

[0072] In the above-mentioned capacity configuration method of the wind-solar-storage combined system taking into account the time-of-use electricity price, a structural model of the wind-solar-storage combined system is established; based on the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity as the optimization target is established as the capacity configuration model of the wind-solar-storage combined system; using the capacity configuration model, the evaluation index information of the wind-solar-storage combined system is output; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system; according to the evaluation index information and the preset energy information based on the time-of-use electricity price, the capacity configuration model is iteratively analyzed to obtain the capacity configuration scheme of the wind-solar-storage combined system. This scheme is conducive to comprehensively evaluating the performance of the wind-solar-storage combined system by establishing the wind-solar-storage combined system structural model and capacity configuration model, combining the evaluation index information such as power supply loss rate, energy waste rate and system contribution rate, and considering the time-of-use electricity price, so as to improve the efficiency and accuracy of determining the capacity configuration scheme of the wind-solar-storage combined system.

[0073] In an exemplary embodiment, a structural model of a wind-solar-storage combined system is established, which specifically includes the following contents: constructing a structural model of a wind power generation system, a structural model of a photovoltaic power generation system, a structural model of a gravity energy storage system, a structural model of a battery system, and a structural model of a supercapacitor system; combining the structural model of the wind power generation system, the structural model of the photovoltaic power generation system, the structural model of the gravity energy storage system, the structural model of the battery system, and the structural model of the supercapacitor system to obtain the structural model of the wind-solar-storage combined system.

[0074] The structural model of the wind power generation system may be a mathematical model that describes the working principle and output characteristics of the wind power generation device. For example, the structural model of the wind power generation system may be a functional model that describes the relationship between wind speed and output power of the wind generator.

[0075] The structural model of the photovoltaic power generation system may be a mathematical model that describes the working principle and output characteristics of the photovoltaic power generation device. For example, the structural model of the photovoltaic power generation system may be a functional model that describes the relationship between light intensity, ambient temperature and photovoltaic power generation output power.

[0076] Among them, the structural model of the gravity energy storage system can be a mathematical model that describes the working principle and energy storage characteristics of the gravity energy storage device. For example, the structural model of the gravity energy storage system can be a function model that describes the relationship between the mass of the heavy object, the effective energy storage height and the energy storage capacity.

[0077] The structural model of the battery system may be a mathematical model that describes the working principle and charging and discharging characteristics of the battery. For example, the structural model of the battery system may be a functional model that describes the relationship between the remaining power of the battery, the charging and discharging power, and the battery capacity.

[0078] The structural model of the supercapacitor system may be a mathematical model that describes the working principle and charging and discharging characteristics of the supercapacitor. For example, the structural model of the supercapacitor system may be a functional model that describes the relationship between the capacitance value, working voltage and energy storage capacity of the supercapacitor.

[0079] Among them, the combination processing can be the process of integrating various subsystem models into a complete system model. For example, the combination processing can be the process of integrating the structural models of wind power generation system, photovoltaic power generation system, gravity energy storage system, battery system and supercapacitor system through energy balance equations and power system constraints.

[0080] Optionally, the terminal constructs a structural model of a wind power generation system, a structural model of a photovoltaic power generation system, a structural model of a gravity energy storage system, a structural model of a battery system, and a structural model of a supercapacitor system. The terminal combines the structural models of the wind power generation system, the photovoltaic power generation system, the gravity energy storage system, the battery system, and the supercapacitor system, and obtains the overall structural model of the wind-solar-storage combined system by establishing an energy balance equation and considering the interactions between the subsystems.

[0081] The technical solution provided in this embodiment, by separately constructing structural models of wind power generation, photovoltaic power generation, gravity energy storage, battery and supercapacitor systems and combining them, is conducive to comprehensively considering the characteristics and interactions of each subsystem, thereby facilitating the establishment of a more accurate and complete structural model of the wind, photovoltaic and storage combined system, providing a reliable foundation for subsequent capacity optimization configuration.

[0082] In an exemplary embodiment, according to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity rate as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system, which specifically includes the following contents: according to the structural model of the wind-solar-storage combined system, the capacity constraints of the wind-solar-storage combined system are determined, and the minimum resource consumption and the minimum wind-solar complementarity rate are set as optimization targets; according to the capacity constraints and optimization targets, the capacity configuration model of the wind-solar-storage combined system is constructed.

[0083] Among them, the capacity constraint conditions may be conditions that limit the capacity of each component of the wind, solar and storage combined system. For example, the capacity constraint conditions may include the upper limit of the installation quantity of wind turbines, the upper limit of the installation quantity of photovoltaics, the upper limit of the installation quantity of battery groups, the upper limit of the installation quantity of supercapacitors and the upper limit of the energy storage capacity of gravity energy storage devices.

[0084] Here, resources may refer to various inputs required for system operation, for example, resources may include economic resources such as initial investment, operation and maintenance costs, and replacement costs.

[0085] Among them, the lowest resource consumption can mean using the least resource input while meeting the system operation requirements. For example, the lowest resource consumption can be expressed as the minimum annual system total cost, including minimizing the initial investment and installation cost, operation and maintenance cost, replacement cost, and electricity purchase cost.

[0086] Among them, the wind-solar complementarity rate can refer to the degree of fluctuation of the sum of the output power of wind power generation and photovoltaic power generation relative to the load power. For example, the wind-solar complementarity rate can be expressed by the standard deviation of the difference between the sum of wind power generation power and photovoltaic power generation power and the load power.

[0087] Among them, the lowest wind-solar complementarity rate can refer to the highest matching degree between the sum of the output power of wind power generation and photovoltaic power generation and the load power. For example, the lowest wind-solar complementarity rate can be expressed as the smallest standard deviation of the difference between the sum of wind power generation power and photovoltaic power generation power and the load power.

[0088] Optionally, the terminal determines the capacity constraints of the wind-solar-storage combined system according to the structural model of the wind-solar-storage combined system, including the upper limit of the installation amount of wind turbines, photovoltaic power generation devices, battery groups, supercapacitors and gravity energy storage devices. The terminal sets the lowest resource consumption and the lowest wind-solar complementarity rate as optimization goals, where the lowest resource consumption is expressed as the minimum annual average system total cost, and the lowest wind-solar complementarity rate is expressed as the minimum standard deviation of the difference between the sum of wind power generation power and photovoltaic power generation power and the load power. The terminal constructs a capacity configuration model of the wind-solar-storage combined system based on the determined capacity constraints and the set optimization goals.

[0089] The technical solution provided in this embodiment is conducive to balancing economy and stability by determining the capacity constraints of the wind-solar-storage combined system and setting the minimum resource consumption and the minimum wind-solar complementarity rate as optimization goals, thereby facilitating the construction of a capacity configuration model for the wind-solar-storage combined system that takes into account both cost-effectiveness and power supply reliability, providing a reliable foundation for subsequent capacity optimization configuration.

[0090] In an exemplary embodiment, a capacity configuration model is used to output evaluation index information of a wind-solar-storage combined system, specifically including the following contents: according to the capacity configuration model, the operating status of the wind-solar-storage combined system is identified; according to the operating status of the wind-solar-storage combined system, the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system are calculated, and the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system are used as evaluation index information.

[0091] Among them, the operating status may refer to the working conditions of each component of the wind, photovoltaic and storage combined system at a preset time or time period. For example, the operating status may include the real-time values ​​of parameters such as wind power generation power, photovoltaic power generation power, charging and discharging power of each energy storage device, and load power.

[0092] Optionally, the terminal identifies the operating status of the wind-solar-storage combined system according to the capacity configuration model of the wind-solar-storage combined system, including wind power generation power, photovoltaic power generation power, gravity energy storage system power, battery pack power, supercapacitor power and load power. The terminal calculates the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system according to the identified operating status of the wind-solar-storage combined system. The terminal uses the calculated power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system as evaluation index information.

[0093] The technical solution provided in this embodiment, by identifying the operating status of the wind, solar and storage combined system and calculating the power supply loss rate, energy waste rate and system contribution rate as evaluation index information, is conducive to comprehensively evaluating the system's power supply reliability, energy utilization efficiency and overall performance, thereby facilitating accurate reflection of the system's operating status and providing a reliable quantitative basis for capacity optimization configuration.

[0094] In an exemplary embodiment, the capacity configuration model is iteratively analyzed according to the evaluation index information and the preset energy information based on the time-of-use electricity price to obtain the capacity configuration plan of the wind, solar and storage combined system, which specifically includes the following contents: using the preset energy information, the capacity configuration model is updated to obtain the target capacity configuration model of the wind, solar and storage combined system; according to the evaluation index information, the target capacity configuration model is iteratively analyzed to obtain the capacity configuration plan of the wind, solar and storage combined system.

[0095] The preset energy information may refer to a system energy management strategy pre-set based on a time-of-use electricity price strategy. For example, the preset energy information may include a strategy of giving priority to charging during periods with lower electricity prices and giving priority to discharging during periods with higher electricity prices.

[0096] Among them, the target capacity configuration model may refer to the capacity configuration model of the wind, solar and storage combined system updated according to the preset energy information. For example, the target capacity configuration model may be an optimization model based on the original capacity configuration model and adding energy management strategy constraints based on time-of-use electricity prices.

[0097] Among them, iterative analysis can refer to the process of repeatedly optimizing and adjusting model parameters. For example, iterative analysis can be the use of an improved second-order particle swarm optimization algorithm to continuously adjust the number of wind turbines, the number of photovoltaic generators, the number of gravity energy storage devices, the number of battery packs, and the number of supercapacitors to achieve the optimal value of the evaluation index information.

[0098] Among them, the capacity configuration plan may refer to the optimal capacity combination of the various components of the wind, solar and storage combined system. For example, the capacity configuration plan may include the optimal capacity of the wind turbine, the optimal capacity of the photovoltaic generator, the optimal capacity of the gravity energy storage device, the optimal capacity of the battery pack and the optimal capacity of the supercapacitor.

[0099] Optionally, the terminal uses preset energy information based on time-of-use electricity prices to update the capacity configuration model of the wind, solar and storage combined system. The terminal adds energy management strategy constraints based on time-of-use electricity prices to the constraints of the original capacity configuration model to form a target capacity configuration model. The terminal iteratively analyzes the target capacity configuration model based on the three evaluation index information of power supply loss rate, energy waste rate and system contribution rate. The terminal uses an improved second-order particle swarm optimization algorithm to continuously adjust the number of wind turbines, photovoltaic generators, gravity energy storage devices, battery packs and supercapacitors until the optimal value of the evaluation index information is reached, thereby obtaining the capacity configuration plan of the wind, solar and storage combined system.

[0100] The technical solution provided in this embodiment, by updating the capacity configuration model using preset energy information based on time-of-use electricity prices and performing iterative analysis based on evaluation index information, is conducive to optimizing the system configuration while taking electricity price fluctuations into consideration, thereby facilitating improving the accuracy of determining the capacity configuration plan of the wind, solar and storage combined system.

[0101] In an exemplary embodiment, the target capacity configuration model is iteratively analyzed according to the evaluation index information to obtain the capacity configuration scheme of the wind-solar-storage combined system, which specifically includes the following contents: according to the evaluation index information, the target capacity configuration model is iteratively analyzed to obtain the capacity configuration sub-scheme of the wind power generation system, the capacity configuration sub-scheme of the photovoltaic power generation system, the capacity configuration sub-scheme of the gravity energy storage system, the capacity configuration sub-scheme of the battery system and the capacity configuration sub-scheme of the supercapacitor system; the capacity configuration sub-scheme of the wind power generation system, the capacity configuration sub-scheme of the photovoltaic power generation system, the capacity configuration sub-scheme of the gravity energy storage system, the capacity configuration sub-scheme of the battery system and the capacity configuration sub-scheme of the supercapacitor system are combined to obtain the capacity configuration scheme of the wind-solar-storage combined system.

[0102] Among them, the capacity configuration sub-plan may refer to the optimal capacity configuration of each subsystem in the wind, solar and storage combined system. For example, the capacity configuration sub-plan may include the optimal number of wind turbines in the wind power generation system, the optimal photovoltaic panel area in the photovoltaic power generation system, the optimal energy storage capacity in the gravity energy storage system, the optimal number of batteries in the battery system and the optimal number of capacitors in the supercapacitor system.

[0103] The capacity configuration sub-scheme of the wind power generation system may refer to the optimal capacity configuration of the wind power generation system. For example, the capacity configuration sub-scheme of the wind power generation system may be the optimal number and single-machine capacity of wind turbines.

[0104] The capacity configuration sub-scheme of the photovoltaic power generation system may refer to the optimal capacity configuration of the photovoltaic power generation system. For example, the capacity configuration sub-scheme of the photovoltaic power generation system may be the optimal area and optimal inclination angle of the photovoltaic panel.

[0105] Among them, the capacity configuration sub-scheme of the gravity energy storage system may refer to the optimal capacity configuration of the gravity energy storage system. For example, the capacity configuration sub-scheme of the gravity energy storage system may be the optimal energy storage capacity and the optimal effective height.

[0106] The capacity configuration sub-scheme of the battery system may refer to an optimal capacity configuration of the battery system. For example, the capacity configuration sub-scheme of the battery system may be an optimal number of batteries and an optimal capacity of a single battery.

[0107] The capacity configuration sub-scheme of the supercapacitor system may refer to the optimal capacity configuration of the supercapacitor system. For example, the capacity configuration sub-scheme of the supercapacitor system may be the optimal number of supercapacitors and the optimal capacity of a single supercapacitor.

[0108] The combination processing may refer to integrating the capacity configuration sub-plans of each subsystem into a complete system configuration plan. For example, the combination processing may be summarizing the optimal capacity parameters of each subsystem to form an overall plan that includes the optimal configuration of all subsystems.

[0109] Optionally, the terminal iteratively analyzes the target capacity configuration model based on the three evaluation index information of power supply loss rate, energy waste rate and system contribution rate. The terminal uses the improved second-order particle swarm optimization algorithm to optimize the wind power generation system, photovoltaic power generation system, gravity energy storage system, battery system and supercapacitor system respectively to obtain the capacity configuration sub-scheme of each subsystem. The terminal combines the capacity configuration sub-schemes of each subsystem and integrates them into a complete capacity configuration scheme for the wind, photovoltaic and storage combined system.

[0110] The technical solution provided in this embodiment optimizes the capacity configuration of each subsystem separately and then combines the sub-schemes, which is conducive to fully considering the characteristics and mutual influence of each subsystem, thereby facilitating the optimal capacity configuration of the entire wind-solar-storage combined system while ensuring the coordinated operation of each subsystem.

[0111] The following is an application example to illustrate the capacity configuration method of the wind-solar-storage combined system taking into account time-of-use electricity prices provided in this application. This application example uses the method applied to a terminal as an example.

[0112] With the continuous utilization of renewable energy and the continuous development of energy storage technology, how to reasonably deploy energy storage systems has a significant effect on improving the operational flexibility of microgrids and enhancing the stability of power supply, and has become a research hotspot. This application example focuses on the operation and planning of gravity energy storage-battery-supercapacitor hybrid energy storage system in the microgrid structure. By modeling each component in the system, a system capacity configuration model with the minimum annual system total cost and the minimum system wind-solar complementarity rate as the objective function is established. Combined with constraint conditions and evaluation index information, a capacity configuration method for wind-solar-storage combined system taking into account time-of-use electricity prices is proposed.

[0113] Faced with the gradual depletion of fossil fuel resources and environmental problems such as the greenhouse effect caused by their combustion, actively promoting the use of clean energy has become a key strategy to solve energy shortages and environmental challenges. Existing energy storage technologies, such as pumped storage, compressed air storage, and flywheel storage, are limited in their application due to site selection and scale restrictions. Therefore, it is particularly urgent to develop new and efficient energy storage technologies.

[0114] In microgrids, due to the inherent characteristics of renewable energy, such as its randomness, intermittency and volatility, the reasonable deployment of energy storage systems plays a significant role in improving the operational flexibility of microgrids and enhancing the stability of power supply. When a microgrid faces a shortage of renewable energy supply during a specific period of time, the energy storage device can respond quickly and release its stored electric energy to meet the load demand and ensure the continuous and stable operation of the microgrid. If the amount of electricity generated by renewable energy exceeds the immediate load demand, the energy storage device can intervene and store this additional electricity, thereby avoiding waste of resources and preparing for possible future demand peaks. By carefully evaluating the performance characteristics of different energy storage devices and selecting the most suitable energy storage solution and corresponding capacity scale accordingly, the overall efficiency of the microgrid system and the optimization of energy management can be effectively enhanced.

[0115] The innovations of this application example in optimizing the capacity configuration of the wind-solar-storage combined system can be divided into the following aspects: First, establish a structural model of the wind-solar-storage combined system, establish mathematical models of each link in the system, and analyze the principles and power generation and energy storage characteristics of each part. Second, establish a capacity configuration model of the wind-solar-storage combined system with the minimum annual system total cost and the minimum annual system wind-solar complementarity rate as the optimization goals, and propose three evaluation index information: power supply loss rate, energy waste rate, and system contribution rate to measure the capacity configuration planning results. Third, adopt the energy management strategy of time-of-use electricity price, and analyze the power supply reliability and economy of the strategy. Finally, use the improved second-order particle swarm optimization algorithm to solve the model, and analyze the superiority of the algorithm compared with the traditional particle swarm algorithm. In summary, this technical solution improves the economy and power supply reliability of the wind-solar-storage combined system, and provides a reference for the capacity planning of microgrids with new energy storage technologies.

[0116] 1. System structure model:

[0117] The hybrid energy storage system (wind, photovoltaic and storage combined system) is composed of wind power generation devices, photovoltaic power generation devices, batteries, supercapacitors, gravity energy storage devices, converters and corresponding loads, and is connected to the main power grid. The structural diagram of the hybrid energy storage system is shown in Figure 2, including: photovoltaic power generation system, DC / AC, wind power generation system, AC / DC, DC / AC, gravity energy storage, AC / DC, DC / AC, battery, DC / DC, DC / AC, supercapacitor, DC / DC, DC / AC, AC bus, power grid, load. In this system, wind power generation and photovoltaic power generation are the main sources of power generation. When the power generation of these two cannot meet the load demand, the battery and supercapacitor are discharged first. If the demand is still not met, the gravity energy storage device is discharged to supply the remaining demand. On the contrary, when the wind and photovoltaic power generation are in excess, the entire hybrid energy storage system is in a charging state, and the gravity energy storage device is charged first. If there is still excess power, the battery and supercapacitor device are charged through the current converter to store energy.

[0118] 1.2 Basic model of wind power generation system:

[0119] Since wind energy has a certain degree of randomness and uncontrollability, wind turbines also have this randomness and uncertainty, and their output power is related to many factors. Therefore, the functional relationship between the output power of wind turbines and wind speed is shown in formula (1):

[0120] (1)

[0121] In the formula, is the output power of the wind turbine; is the rated power of the wind turbine; is the actual wind speed; is the cut-in wind speed; To cut out the wind speed; is the rated wind speed.

[0122] 1.3 Basic model of photovoltaic power generation system:

[0123] The output power of the photovoltaic power generation system is determined by the ambient temperature, light intensity and the rated power of the photovoltaic system. Its actual output can be simplified as:

[0124] (2)

[0125] In the formula, for The actual output power of photovoltaic power generation at any moment; is the rated output power of photovoltaic power generation under standard conditions; for Light intensity at the time; is the light intensity under standard conditions; is the power temperature coefficient; for Working temperature at all times; is the operating temperature under standard conditions.

[0126] 1.4 Basic model of gravity energy storage system:

[0127] Gravity energy storage uses the height difference of the mountain to store energy. Its construction can adapt to the changes of various complex terrains. Its energy storage carriers are mostly heavy objects, which can be recycled and will not pollute the surrounding environment. It has certain environmental protection value. Its principle is similar to pumped storage, but the construction conditions are not as strict as pumped storage power stations and are less affected by the geographical environment. Here, the high-altitude area is used as the background, and the surrounding terrain conditions are used to establish a gravity energy storage model. The principle diagram of the gravity energy storage system is referenced Figure 3 : Electric motor, electrical energy → gravitational potential energy, at high altitude, heavy objects fall, generator, electrical energy ← gravitational potential energy, at low altitude, heavy objects rise.

[0128] The basic principle is as follows: when the power load is at a low point, the wind and solar power output is greater than the load demand, and the motor drives the heavy objects from the low altitude area to the high altitude area, completing the energy conversion from electrical energy to kinetic energy and then to gravitational potential energy, and finally the electricity is stored in the form of gravitational potential energy; when the power load is at a peak state, the heavy objects in the high altitude area are transported to the low altitude area through the conveyor belt, driving the generator to work, converting the gravitational potential energy into kinetic energy and then into electrical energy. Among them, the heavy objects can be stones, sand or other objects with high density and easy to obtain locally.

[0129] The output characteristics analysis of the gravity energy storage device includes its charging process and discharging process.

[0130] Charging process:

[0131] (3)

[0132] (4)

[0133] (5)

[0134] In the formula, is the power of the motor during the ascent; is the pulling force of the motor during the ascent; is the friction force during the ascent; is the mass of the heavy object; is the acceleration due to gravity; is the angle between the conveyor belt and the horizontal direction; is the friction coefficient; is the speed of the weight during its ascent.

[0135] Discharge process:

[0136] (6)

[0137] (7)

[0138] In the formula, is the power of the generator during descent; is the pulling force of the motor during the descent; is the pulling force of the generator during the descent; is the speed during descent.

[0139] The charge and discharge efficiency of gravity energy storage is 90% when friction loss is considered during the energy conversion process. Since the slope and effective height are fixed in most cases, it is assumed that the slope, weight mass and other conditions are known. Only the effective energy storage height in the uniform speed stage is considered here, and the effective height between the high and low areas is used as the only variable to explore the optimal capacity of gravity energy storage.

[0140] (8)

[0141] In the formula, The capacity for gravitational energy storage; is the effective energy storage height.

[0142] 1.5Battery system basic model:

[0143] As a high-energy-density chemical energy storage, the energy density of batteries is much higher than that of general physical energy storage. The charging and discharging conditions are quickly converted, but the service life is short and the cost is high. In an independent microgrid, the battery group can smooth the fluctuations caused by changes in load demand. When the system is in a power shortage state, it discharges, and vice versa. Its output model is as follows.

[0144] When the system is charging:

[0145] (9)

[0146] When the system is in discharge state:

[0147] (10)

[0148] In the formula, for The remaining capacity of the battery pack after the time; is the self-discharge rate of the battery; is the interval time; , They are The charging and discharging power of the battery at all times; , are the charging and discharging efficiency of the battery respectively; is the rated capacity of the battery.

[0149] 1.6 Basic model of supercapacitor system:

[0150] Supercapacitors are different from traditional chemical power sources. They are a type of power source that is between traditional capacitors and batteries and has special properties. They mainly rely on double-layer and redox pseudocapacitor charges to store electrical energy. Their advantages are high power density, short charge and discharge time, long cycle life, and wide operating temperature range. Assume that the capacitance of each supercapacitor is ,Voltage exist and The energy formula is as follows:

[0151] (11)

[0152] (12)

[0153] (13)

[0154] In the formula, , are the maximum capacity and minimum capacity of the supercapacitor respectively; is the maximum output power of the supercapacitor; is the maximum operating current of the supercapacitor; is the number of supercapacitors.

[0155] 2. Capacity configuration optimization model of wind, solar and storage system:

[0156] 2.1 Objective function:

[0157] The goal of optimizing the capacity configuration of wind, solar and storage systems is to reasonably allocate the capacity of photovoltaic power generation, wind power generation and energy storage devices under the conditions of meeting load demand and external constraints, so that the total cost of the wind, solar and storage system can be minimized and the wind-solar complementarity rate can be minimized. It mainly consists of the following two goals.

[0158] Goal 1: Minimize total system cost.

[0159] The annual average system cost of the wind-solar-storage hybrid power generation system includes the annual average initial investment and installation cost, operation and maintenance cost, replacement cost, power purchase cost, power sales income and environmental protection income of each power component. Objective function 1 is expressed as:

[0160] (14)

[0161] In the formula, is the total system cost; The annual initial investment and installation cost of the system; System operation and maintenance costs; is the system replacement cost; The cost of purchasing electricity for the system; Profit from electricity sales to the system; Environmental benefits for the system.

[0162] Goal 2: The system’s wind-solar complementarity rate is the lowest.

[0163] When the regional power grid intervenes in the low load period or the wind power fluctuates greatly, the wind farm will face a certain amount of power abandonment. The wind-solar complementarity ratio is the sum of the output power of wind power generation and photovoltaic power generation relative to the fluctuation of load power, and its function expression is:

[0164] (15)

[0165] In the formula, is the wind-solar complementarity ratio; is the average power of the load; for The power of wind power at any moment; for The power of photovoltaic power generation at all times; for The power of the load at any moment.

[0166] 2.2 Evaluation index information:

[0167] The performance of the microgrid can be reflected by the evaluation index information, which mainly includes the power supply loss rate, energy waste rate and system contribution rate. Among them, the power supply loss rate is used to reflect the reliability of the wind, solar and storage combined power generation system, the energy waste rate can effectively reflect the energy waste of the system, and the system contribution rate can effectively reflect the power supply of the system to the load, and can also reflect the comprehensive economic benefits of the system.

[0168] (1) Power supply loss rate:

[0169] (16)

[0170] In the formula, is the power supply loss rate; for The power of the gravity energy storage system at all times; for The power of the battery pack at all times.

[0171] (2) Energy waste rate:

[0172] (17)

[0173] In the formula, is the energy waste rate; for The amount of power wasted at any given moment; for The system power generation at the moment.

[0174] (3) System contribution rate:

[0175] (18)

[0176] In the formula, Contribution rate to the system; for The power supplied by the system to the load at any moment.

[0177] 2.3 Constraints:

[0178] (1) Distributed power quantity constraints:

[0179] (19)

[0180] In the formula, , , , They respectively represent the upper limit of wind turbine installation capacity, the upper limit of photovoltaic installation capacity, the upper limit of battery installation capacity, and the upper limit of supercapacitor installation capacity.

[0181] (2) Gravity energy storage capacity constraints:

[0182] (20)

[0183] In the formula, Indicates the energy storage capacity of the gravity energy storage device; Indicates the mass of a heavy object; represents the acceleration due to gravity; Indicates the maximum height.

[0184] (3) Battery charge and discharge capacity constraints:

[0185] (twenty one)

[0186] In the formula, is the rated capacity of the battery; and are the upper and lower limits of the remaining power of the battery system respectively; and They are the upper and lower limits of the battery charging and discharging power constraints respectively.

[0187] (4) Evaluation index information constraints

[0188] (twenty two)

[0189] In the formula, is the maximum power loss rate of the system; is the maximum energy waste rate of the system; is the minimum contribution rate of the system.

[0190] 3. Energy management strategy and model solving algorithm of hybrid energy storage system:

[0191] 3.1 Energy management strategy of hybrid energy storage system:

[0192] The energy management and allocation strategy of the hybrid energy storage system will have a certain impact on the optimization configuration results. Therefore, how to choose a reasonable energy management strategy is also extremely important. At the same time, the charging and discharging sequence of each energy storage unit in the hybrid energy storage system is mainly controlled by the energy management strategy, which will affect the working conditions of the energy storage unit and the operating status of the entire system, thereby having a certain impact on the final configuration plan. Based on the research and analysis of the characteristics of the above gravity energy storage device, battery pack and supercapacitor, this paper proposes an energy management strategy based on time-of-use electricity prices to guide the charging and discharging of the system.

[0193] Flowchart reference for system energy management strategy (charging and discharging strategy of hybrid energy storage system) Figure 4 :Start, read the output power such as wind speed, light intensity, load data, calculate the unbalanced power ∆P, determine whether ∆P>0, if so, supply the load demand, determine whether there is surplus power, if there is no surplus power, jump to the battery and super capacitor charging, if there is surplus power, charge the gravity energy storage and determine whether there is surplus power again, if so, charge the battery and super capacitor and feed it into the grid; if ∆P>0 is no, enter the supply load demand, determine the peak electricity price, discharge the gravity energy storage, determine whether there is insufficient power, discharge the battery and super capacitor, purchase power from the grid, and end. For example, refer to Figure 4, calculate the difference between the sum of wind power generation and photovoltaic power generation and load power as unbalanced power. If the sum of real-time wind power generation and photovoltaic power generation is greater than the load power, first supply the load demand. If there is still surplus power, charge the gravity energy storage, and store the surplus energy through batteries and supercapacitors. If there is still surplus power at this time, feed it into the power grid. If the sum of real-time wind power generation and photovoltaic power generation is less than the load power, first supply the load demand, and then make a choice based on the time-of-use electricity price. If it is a peak electricity price at this time, first choose to discharge the gravity energy storage. If it still cannot meet the load demand after discharge, choose to discharge by batteries and supercapacitors. If it still cannot meet the demand, choose to purchase electricity from the power grid to supply the load; if it is a valley electricity price at this time, directly purchase electricity from the power grid, and keep the electricity stored by gravity energy storage as backup power for the next moment.

[0194] 3.2 Model solving algorithm:

[0195] The standard particle swarm algorithm is derived from complex adaptive systems and is a model that simulates the behavior of bird flocks. Its main purpose is to enable particles to find the solution space and find the optimal solution. The key to the particle swarm algorithm is the update of speed and position. The update formula is as follows:

[0196] (twenty three)

[0197] In the formula, , Respectively represent The particle in The speed and position during the iteration; , Respectively represent The particle in The speed and position during the iteration; is the coefficient of inertia; , is the learning factor; , yes A random number uniformly distributed within; is the historical optimal position of the particle swarm; It is The historical optimal position of a particle.

[0198] From formula (23), we can see that the search space and convergence of the standard particle swarm algorithm in the later stage are affected by the initial population position, and its inertia coefficient and learning factor are almost unchanged, which makes it easy to fall into the local optimal value, thus affecting the performance of the algorithm. When the distance between the particle and the optimal particle is far, that is, When the value is large, the particle has a stronger global search ability; when the distance between the particle and the optimal particle is close, that is, When the value is small, the particles have stronger local search ability.

[0199] In order to increase the diversity of particles and improve the search and convergence capabilities of the particle swarm algorithm, one of the common ways is to introduce a second-order oscillation link into the speed update formula, which can be derived into a second-order particle swarm algorithm, and its speed update formula is as follows:

[0200] (twenty four)

[0201] In the formula, , , , are the oscillation factors for the second-order particle velocity update.

[0202] Substitute the second-order particle swarm algorithm into the above model for solution calculation, taking the number of wind turbines, photovoltaic generators, gravity energy storage devices, battery groups and supercapacitors as variables, and under the above constraints, calculate the optimal capacity configuration of the system with the minimum fitness function value as the goal. The specific steps and algorithm flow are as follows:

[0203] (1) Obtain wind power, photovoltaic, load data and parameters of each energy storage unit, and at the same time obtain the basic parameters, population size, number of iterations, etc. of the second-order particle swarm algorithm.

[0204] (2) Initialize the velocity and position of each particle in the population.

[0205] (3) Calculate the fitness value of each particle according to the fitness function, and update the individual optimal value and global optimal value of each particle and population.

[0206] (4) Update the current velocity and position of the particle, and calculate the corresponding inertia coefficient, learning factor, and oscillation factor for the second-order particle velocity update according to formula (24).

[0207] (5) Determine the number of iterations. When the maximum number of iterations is reached, the operation ends; otherwise, jump to step (3).

[0208] Model Solving Algorithm Reference Figure 5 :Start, initialize the population, calculate the fitness value of each particle, calculate the individual optimal value and the global optimal value of the population, update the speed and position of the particle, calculate the inertia coefficient, learning factor and oscillation factor of the particle, and determine whether the number of iterations is ≥ 500. If not, return to the time before calculating the fitness value of each particle. If so, the optimal configuration solution is obtained and the process ends.

[0209] The technical solution provided in this application example, by establishing a wind-solar-storage combined system structure model and capacity configuration model, combining evaluation indicator information such as power supply loss rate, energy waste rate and system contribution rate, and taking into account time-of-use electricity prices, is conducive to comprehensively evaluating the performance of the wind-solar-storage combined system, thereby helping to improve the efficiency and accuracy of determining the capacity configuration plan of the wind-solar-storage combined system.

[0210] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments 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 explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these 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 part of the steps or stages in other steps.

[0211] Based on the same inventive concept, the embodiment of the present application also provides a wind-solar-storage combined system capacity configuration device taking into account the time-of-use electricity price for implementing the wind-solar-storage combined system capacity configuration method taking into account the time-of-use electricity price involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more wind-solar-storage combined system capacity configuration devices taking into account the time-of-use electricity price provided below can refer to the limitations of the wind-solar-storage combined system capacity configuration method taking into account the time-of-use electricity price above, and will not be repeated here.

[0212] In an exemplary embodiment, Figure 6 As shown, a wind-solar-storage combined system capacity configuration device taking into account the time-of-use electricity price is provided. The wind-solar-storage combined system capacity configuration device 600 taking into account the time-of-use electricity price may include:

[0213] The first building module 601 is used to build a structural model of the wind-solar-storage combined system;

[0214] The second establishing module 602 is used to establish a capacity configuration model with resources and wind-solar complementarity rate as optimization targets according to the structural model of the wind-solar-storage combined system, as a capacity configuration model of the wind-solar-storage combined system;

[0215] The index output module 603 is used to output the evaluation index information of the wind-solar-storage combined system by using the capacity configuration model; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system;

[0216] The model analysis module 604 is used to iteratively analyze the capacity configuration model according to the evaluation index information and the preset energy information based on the time-of-use electricity price, so as to obtain the capacity configuration plan of the wind-solar-storage combined system.

[0217] In an exemplary embodiment, the first establishing module 601 is also used to construct a structural model of a wind power generation system, a structural model of a photovoltaic power generation system, a structural model of a gravity energy storage system, a structural model of a battery system, and a structural model of a supercapacitor system; the structural model of the wind power generation system, the structural model of the photovoltaic power generation system, the structural model of the gravity energy storage system, the structural model of the battery system, and the structural model of the supercapacitor system are combined to obtain a structural model of the wind-solar-storage combined system.

[0218] In an exemplary embodiment, the second establishment module 602 is also used to determine the capacity constraints of the wind, solar and storage combined system based on the structural model of the wind, solar and storage combined system, and set the minimum resource consumption and the minimum wind-solar complementarity rate as optimization goals; based on the capacity constraints and optimization goals, construct a capacity configuration model of the wind, solar and storage combined system.

[0219] In an exemplary embodiment, the indicator output module 603 is also used to identify the operating status of the wind-solar-storage combined system based on the capacity configuration model; calculate the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system based on the operating status of the wind-solar-storage combined system, and use the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system as evaluation indicator information.

[0220] In an exemplary embodiment, the model analysis module 604 is also used to use preset energy information to update the capacity configuration model to obtain the target capacity configuration model of the wind, solar and storage combined system; according to the evaluation index information, the target capacity configuration model is iteratively analyzed to obtain the capacity configuration plan of the wind, solar and storage combined system.

[0221] In an exemplary embodiment, the model analysis module 604 is also used to iteratively analyze the target capacity configuration model according to the evaluation index information to obtain the capacity configuration sub-scheme of the wind power generation system, the capacity configuration sub-scheme of the photovoltaic power generation system, the capacity configuration sub-scheme of the gravity energy storage system, the capacity configuration sub-scheme of the battery system and the capacity configuration sub-scheme of the supercapacitor system; the capacity configuration sub-scheme of the wind power generation system, the capacity configuration sub-scheme of the photovoltaic power generation system, the capacity configuration sub-scheme of the gravity energy storage system, the capacity configuration sub-scheme of the battery system and the capacity configuration sub-scheme of the supercapacitor system are combined to obtain the capacity configuration scheme of the wind-solar-storage combined system.

[0222] Each module in the above-mentioned wind, solar and storage combined system capacity configuration device taking into account time-of-use electricity prices can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0223] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for configuring the capacity of a wind, light and storage combined system taking into account the time-of-use electricity price is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0224] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application 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.

[0225] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0226] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0227] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0228] 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 the memory, database or other medium used in the embodiments provided in this application 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, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. 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). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0229] 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.

[0230] The above-described 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 method for configuring the capacity of a wind, solar and energy storage combined system taking into account time-of-use electricity prices, characterized in that: The method comprises: Establish the structural model of the wind-solar-storage combined system; According to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity ratio as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system; Using the capacity configuration model, outputting evaluation index information of the wind-solar-storage combined system; the evaluation index information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system; According to the evaluation index information and the preset energy information based on the time-of-use electricity price, the capacity configuration model is iteratively analyzed to obtain the capacity configuration plan of the wind-solar-storage combined system.

2. The method according to claim 1, characterized in that The structural model of the wind-solar-storage combined system is established, including: Construct the structural model of wind power generation system, photovoltaic power generation system, gravity energy storage system, battery system and supercapacitor system; The structural model of the wind power generation system, the structural model of the photovoltaic power generation system, the structural model of the gravity energy storage system, the structural model of the battery system and the structural model of the supercapacitor system are combined to obtain the structural model of the wind-solar-storage combined system.

3. The method according to claim 1, characterized in that According to the structural model of the wind-solar-storage combined system, a capacity configuration model with resources and wind-solar complementarity rate as optimization targets is established as the capacity configuration model of the wind-solar-storage combined system, including: According to the structural model of the wind-solar-storage combined system, the capacity constraint conditions of the wind-solar-storage combined system are determined, and the minimum consumption of the resource and the minimum wind-solar complementarity rate are set as the optimization objectives; According to the capacity constraint conditions and the optimization objectives, a capacity configuration model of the wind-solar-storage combined system is constructed.

4. The method according to claim 1, characterized in that: The method of using the capacity configuration model to output evaluation index information of the wind-solar-storage combined system includes: According to the capacity configuration model, identifying the operating status of the wind-solar-storage combined system; According to the operating status of the wind-solar-storage combined system, the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system are calculated, and the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system are used as the evaluation index information.

5. The method according to claim 1, characterized in that The method of iteratively analyzing the capacity configuration model according to the evaluation index information and the preset energy information based on the time-of-use electricity price to obtain the capacity configuration scheme of the wind-solar-storage combined system includes: Using the preset energy information, the capacity configuration model is updated to obtain a target capacity configuration model of the wind-solar-storage combined system; According to the evaluation index information, the target capacity configuration model is iteratively analyzed to obtain a capacity configuration plan for the wind-solar-storage combined system.

6. The method according to claim 5, characterized in that The target capacity configuration model is iteratively analyzed according to the evaluation index information to obtain a capacity configuration scheme for the wind-solar-storage combined system, including: According to the evaluation index information, the target capacity configuration model is iteratively analyzed to obtain a capacity configuration sub-scheme for a wind power generation system, a capacity configuration sub-scheme for a photovoltaic power generation system, a capacity configuration sub-scheme for a gravity energy storage system, a capacity configuration sub-scheme for a battery system, and a capacity configuration sub-scheme for a supercapacitor system; The capacity configuration sub-scheme of the wind power generation system, the capacity configuration sub-scheme of the photovoltaic power generation system, the capacity configuration sub-scheme of the gravity energy storage system, the capacity configuration sub-scheme of the battery system and the capacity configuration sub-scheme of the supercapacitor system are combined to obtain the capacity configuration scheme of the wind-solar-storage combined system.

7. A capacity configuration device for a wind, solar and energy storage combined system taking into account time-of-use electricity prices, characterized in that: The device comprises: The first building module is used to build a structural model of the wind-solar-storage combined system; A second establishment module is used to establish a capacity configuration model with resources and wind-solar complementarity rate as optimization targets according to the structural model of the wind-solar-storage combined system, as the capacity configuration model of the wind-solar-storage combined system; An indicator output module is used to output evaluation indicator information of the wind-solar-storage combined system using the capacity configuration model; the evaluation indicator information includes the power supply loss rate, energy waste rate and system contribution rate of the wind-solar-storage combined system; The model analysis module is used to iteratively analyze the capacity configuration model according to the evaluation index information and the preset energy information based on the time-of-use electricity price, so as to obtain the capacity configuration plan of the wind-solar-storage combined system.

8. 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 6 are implemented.

9. 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 6 are implemented.

10. A computer program product, comprising a computer program, 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 6 are implemented.

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