Long-term energy storage virtual power plant planning method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of energy storage power plant planning, in particular to a long-term energy storage virtual power plant planning method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly obtaining a representative scene of virtual power plant planning and an energy sharing strategy of a virtual power plant and long-term energy storage; then, according to an energy sharing strategy of long-term energy storage, the installation duration of a virtual power plant system and the total working duration of operation and maintenance in a representative scene, a planning model of the virtual power plant and various limiting conditions of operation and maintenance of the virtual power plant are constructed; solving the planning model of the virtual power plant according to the plurality of limiting conditions by taking the minimum total duration of the virtual power plant as a target to obtain a virtual power plant planning scheme and an optimized operation result; and finally, establishing a plurality of polymers according to the optimal planning scheme of the virtual power plant, and calculating the total apportionment duration of each polymer by adopting a kernel method. According to the invention, the seasonal peak-valley difference can be reduced, and high-proportion new energy consumption can be assisted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage power plant planning, and particularly relates to a long-term energy storage virtual power plant planning method and device, electronic equipment and a storage medium. BACKGROUND

[0002] Under the background of energy saving and emission reduction targets, the penetration rate of renewable energy on the user side is increasing, and the surplus renewable energy is fed into the power grid, which will lead to the "duck curve" phenomenon and increase the burden of the distribution system. The virtual power plant formed by users and renewable energy needs to locally consume renewable energy.

[0003] The existing virtual power plant planning research has few planning problems considering the seasonal variation of renewable energy output. The virtual power plant configures battery energy storage with the maximum seasonal redundancy of renewable energy. Due to the seasonality of renewable energy output, it will lead to resource waste and low utilization rate of energy storage.

[0004] Therefore, it is necessary to develop a long-term energy storage virtual power plant planning method. SUMMARY

[0005] The present application provides a long-term energy storage virtual power plant planning method, device, electronic equipment and storage medium, which is used to solve the problem of waste of renewable energy caused by the seasonal variation of energy output in the prior art.

[0006] In a first aspect, the present application provides a long-term energy storage virtual power plant planning method, comprising: obtaining a representative scenario of virtual power plant planning and an energy sharing strategy of virtual power plant and long-term energy storage; According to the energy sharing strategy of long-term energy storage, the total installation time of the virtual power plant system and the total operation and maintenance time in the representative scenario, a planning model of the virtual power plant and a plurality of constraint conditions of the operation and maintenance of the virtual power plant are constructed; Solving the planning model of the virtual power plant according to the plurality of constraint conditions, obtaining a virtual power plant planning scheme and an optimized operation result, wherein the total time includes the total installation and operation and maintenance time; According to the optimal planning scheme of the virtual power plant, a plurality of aggregates are established, and the total time of each aggregate is calculated by using the kernel method, wherein each aggregate includes a plurality of virtual power plants.

[0007] In one possible implementation, the representative scenario of the virtual power plant planning is generated according to a pre-constructed wind-solar output correlation model by using inverse transform sampling and hierarchical clustering algorithm, comprising: obtaining historical weather data of the region where the virtual power plant is located; Based on the historical weather data and the preset wind and solar power output model, the per-unit value of the historical wind and solar power output data is calculated, and the corresponding photovoltaic and wind power output probability distribution functions are constructed using the non-parametric kernel density estimation method. Based on the probability distribution functions of photovoltaic and wind power output, a probability distribution model of combined wind and solar power output is established using Copula theory. Based on inverse transform sampling, a certain number of sample points are randomly selected to generate random scene samples. Based on the hierarchical clustering algorithm, the data are clustered to reduce the number of representative scenarios for virtual power plant planning.

[0008] One possible approach to achieving this is through energy sharing strategies for long-term energy storage, which includes: Virtual power plant conglomerates prioritize the local consumption of surplus renewable energy. When the net load of the polymer is greater than 0, the polymer prioritizes charging using battery energy storage. When the polymer cannot consume the energy through the battery, it can supply power to other polymers or consume surplus renewable energy through an electrolyzer. The electrolyzer converts electrical energy into hydrogen and heat energy. The hydrogen is stored in a long-term hydrogen storage system. If there is no heat energy deficit in the polymer, the heat energy generated by the electrogenerated hydrogen is stored in a long-term heat storage system. If there is a heat energy deficit in the polymer, the waste heat is directly supplied to the heat load of each polymer. If the heat load still cannot be met, heat is supplied through a long-term heat storage system. When the polymer's electrical energy is insufficient, the polymer's battery discharges. If the polymer has surplus electrical energy when the battery discharges, it supplies electrical energy to other polymers. If the polymer still has a power shortage when the battery discharges, it is powered by other polymers and fuel cells, and finally, it purchases electricity from the upstream power grid. If the polymer's heat load can be met, the waste heat generated by the fuel cell is stored through long-term heat storage. If the heat load cannot be met, the heat is directly supplied to the heat load. If the heat load still cannot be met, long-term heat storage and heat release are carried out.

[0009] In one possible implementation, the planning model for the virtual power plant is as follows:

[0010] In the formula, The total working time for equipment installation, operation and maintenance in the virtual power plant. The total working time for the installation, operation and maintenance of long-term energy storage equipment. For the losses of the distribution network, For scene collection Corresponding probability, The total number of scenes, and These are the aggregate's electricity and gas purchases, as well as the duration of operation and maintenance. This refers to the total operating and maintenance time for long-term energy storage. is a number of time periods in a representative scenario, is a loss penalty coefficient, is a total amount of power loss of the power distribution network, and are a unit capacity of the equipment and a unit power equipment installation work duration, respectively, is a unit capacity of the equipment, is a unit power of the equipment, is an installation number of the equipment in the aggregate, is a number of days in a year, is an actual power purchase of the aggregate, is a gas purchase amount of the aggregate, is a unit operation and maintenance work duration of the equipment.

[0011] In a possible implementation manner, the long-term energy storage virtual power plant planning model related constraints include a power flow balance equation, a node voltage limit range, a power balance equation, a battery operation condition, a long-term hydrogen storage operation condition, and the long-term heat storage operation condition, wherein the power flow balance equation is:

[0012] In the formula, , are active and reactive power injected by the node, respectively; is a voltage of the node i at t; the node voltage limit range is:

[0013] In the formula, , are upper and lower limits of the node voltage, respectively; the power balance equation is specifically:

[0014]

[0015]

[0016]

[0017]

[0018] In the formula, is exchanged power between the aggregates, is an electric power supplied by the long-term energy storage to the aggregate, is an electric power supplied by the aggregate to the long-term energy storage, The thermal power supplied to the polymer for long-term energy storage The thermal power supplied by the polymer to long-term energy storage. , and These are the electrical, thermal, and cooling loads of the polymer, respectively. The battery operating conditions are as follows:

[0019] In the formula, For storing energy in batteries, This represents the battery's self-discharge coefficient. and These are the battery's charging efficiency and power, respectively. and These are the battery's discharge efficiency and power, respectively. and Energy is stored at the beginning and end of daytime operation, respectively. and These represent the maximum charge and discharge power of the battery. and These are the upper and lower limits of the stored energy; The long-term hydrogen storage operation conditions are as follows:

[0020] in, This represents the initial hydrogen storage capacity for the k-th representative scenario of seasonal hydrogen storage throughout the year. and This indicates the inflation and deflation volumes for the scene. and These represent the initial hydrogen storage capacity of the first representative scenario of the year and the hydrogen storage capacity at the end of the last scenario, respectively. and These are the maximum values ​​for inflation and deflation, respectively. and These represent the upper and lower limits of the permissible hydrogen storage capacity for seasonal hydrogen storage. As a representative scene The number of days it lasts; The long-term thermal storage operation conditions are as follows:

[0021] In the formula, This represents the initial heat storage capacity for the k-th representative scenario of seasonal thermal storage throughout the year. This indicates the amount of heat stored in the thermal storage. This indicates the amount of heat released from the thermal storage. and These represent the initial heat storage for the first representative scenario of the year and the heat storage at the end of the last scenario, respectively. and These represent the maximum allowable heat charge and release values ​​for the thermal storage. and These represent the upper and lower limits of the permissible heat storage capacity for seasonal thermal storage.

[0022] In one possible implementation, the construction process of the multiple aggregates includes: Acquire multiple virtual power plants; Based on the multiple virtual power plants, the states of the virtual power plants are divided into participating state and non-participating state, where the participating state is the state of participating in energy sharing and investing in long-term energy storage; The states of the multiple virtual power plants are arranged and combined to obtain the multiple aggregates.

[0023] In one possible implementation, the calculation of the allocated total time for each polymer, including installation and operation / maintenance work time, using the nucleolus method includes: The total allocated time for each polymer, as well as the installation and operation / maintenance time, are calculated according to the first formula, whereby:

[0024] In the formula, As an auxiliary variable, for The minimum value, Marked as a large cooperative alliance composed of aggregates, any subset of the large alliance ,vector Indicating a large cooperative alliance The results of the time allocation for installation and operation maintenance work, among which The characteristic function represents the installation and operation / maintenance time allocated to aggregate n. This indicates the total installation, operation, and maintenance time of the alliance. Allocate installation and operation / maintenance time for any sub-alliance to a given large alliance. Dissatisfaction.

[0025] Secondly, embodiments of the present invention provide a long-term energy storage virtual power plant planning device for implementing the long-term energy storage virtual power plant planning method as described in the first aspect or any possible implementation thereof, the long-term energy storage virtual power plant planning device comprising: The scenario and strategy module is used to obtain representative scenarios for virtual power plant planning and energy sharing strategies between virtual power plants and long-term energy storage. The planning model construction module is used to construct a planning model for the virtual power plant and various constraints on the operation and maintenance of the virtual power plant based on the energy sharing strategy of long-term energy storage, the installation time of the virtual power plant system, and the total operation and maintenance time under the representative scenario. The planning scheme optimization module is used to solve the planning model of the virtual power plant with the goal of minimizing the total duration of the virtual power plant, based on the various constraints, to obtain the planning scheme and optimized operation results of the virtual power plant. The total duration includes the total working time for equipment installation, operation and maintenance. as well as, The aggregate allocation duration determination module is used to establish multiple aggregates based on the optimal planning scheme of virtual power plants, and to calculate the total allocation duration of each aggregate using the nucleolus method. Each aggregate includes multiple virtual power plants.

[0026] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0027] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0028] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention discloses a method for planning a long-term energy storage virtual power plant. First, it obtains a representative scenario for virtual power plant planning and an energy-sharing strategy between the virtual power plant and long-term energy storage. Then, based on the energy-sharing strategy, the installation time of the virtual power plant system, and the total operation and maintenance time under the representative scenario, it constructs a planning model for the virtual power plant and various constraints on its operation and maintenance. Next, with the goal of minimizing the total time of the virtual power plant, it solves the planning model according to the various constraints to obtain a virtual power plant planning scheme and optimized operation results. The total time includes the total operation and maintenance time of equipment installation. Finally, it establishes multiple aggregates based on the optimal planning scheme of the virtual power plant and uses the nucleolus method to calculate the allocated total time for each aggregate. Each aggregate includes multiple virtual power plants. This invention can reduce the total operation and maintenance time of virtual power plant planning, reduce seasonal peak-valley differences, and facilitate the high-proportion consumption of new energy sources. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of the long-term energy storage virtual power plant planning method provided by the embodiments of the present invention; Figure 2 This is a flowchart of a representative scenario for planning a virtual power plant to acquire long-term energy storage, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram representing the daily photovoltaic and wind power output curves in the optimal planning scheme provided by the embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of a virtual power plant system for long-term energy storage provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the planning model solution process provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of the daily operation results of each aggregate in the optimal planning scheme provided by the embodiments of the present invention; Figure 7 This is a schematic diagram representing the daily long-term energy storage operation results in the optimal planning scheme provided by the embodiments of the present invention; Figure 8 This is a functional block diagram of a long-term energy storage virtual power plant planning device provided in an embodiment of the present invention; Figure 9 This is a functional block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0031] In the following description, specific details such as particular system structures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0033] The embodiments of the present invention will be described in detail below. This example is implemented based on the technical solution of the present invention, and provides detailed implementation methods and specific operation processes. However, the protection scope of the present invention is not limited to the following embodiments.

[0034] Figure 1 A flowchart of a long-term energy storage virtual power plant planning method provided for an embodiment of the present invention.

[0035] like Figure 1 As shown, a flowchart illustrating the implementation of the long-term energy storage virtual power plant planning method provided by an embodiment of the present invention is illustrated below: In step 101, representative scenarios for virtual power plant planning and energy sharing strategies between virtual power plants and long-term energy storage are obtained.

[0036] In some implementations, the representative scenarios of the virtual power plant planning are generated using inverse transform sampling and hierarchical clustering algorithms based on a pre-built wind and solar power output correlation model, including: Obtain historical weather data for the area where the virtual power plant is located; Based on the historical weather data and the preset wind and solar power output model, the per-unit value of the historical wind and solar power output data is calculated, and the corresponding photovoltaic and wind power output probability distribution functions are constructed using the non-parametric kernel density estimation method. Based on the probability distribution functions of photovoltaic and wind power output, a probability distribution model of combined wind and solar power output is established using Copula theory. Based on inverse transform sampling, a certain number of sample points are randomly selected to generate random scene samples. Based on the hierarchical clustering algorithm, the data are clustered to reduce the number of representative scenarios for virtual power plant planning.

[0037] In some implementations, energy-sharing strategies for long-term energy storage include: Virtual power plant conglomerates prioritize the local consumption of surplus renewable energy. When the net load of the polymer is greater than 0, the polymer prioritizes charging using battery energy storage. When the polymer cannot consume the energy through the battery, it can supply power to other polymers or consume surplus renewable energy through an electrolyzer. The electrolyzer converts electrical energy into hydrogen and heat energy. The hydrogen is stored in a long-term hydrogen storage system. If there is no heat energy deficit in the polymer, the heat energy generated by the electrogenerated hydrogen is stored in a long-term heat storage system. If there is a heat energy deficit in the polymer, the waste heat is directly supplied to the heat load of each polymer. If the heat load still cannot be met, heat is supplied through a long-term heat storage system. When the polymer's electrical energy is insufficient, the polymer's battery discharges. If the polymer has surplus electrical energy when the battery discharges, it supplies electrical energy to other polymers. If the polymer still has a power shortage when the battery discharges, it is powered by other polymers and fuel cells, and finally, it purchases electricity from the upstream power grid. If the polymer's heat load can be met, the waste heat generated by the fuel cell is stored through long-term heat storage. If the heat load cannot be met, the heat is directly supplied to the heat load. If the heat load still cannot be met, long-term heat storage and heat release are carried out.

[0038] For example, as described in related technologies, under the background of energy conservation and emission reduction goals, the penetration rate of renewable energy on the user side is constantly increasing. The surplus renewable energy fed into the grid can lead to a "duck curve" phenomenon and increase the burden on the distribution network system. Virtual power plants formed by users and renewable energy require local consumption of renewable energy. Existing virtual power plant planning studies have rarely considered the seasonal variations in renewable energy output. Virtual power plants are configured with battery energy storage based on the maximum seasonal redundancy of renewable energy. Due to the seasonality of renewable energy output, this leads to resource waste and low energy storage utilization.

[0039] To address the problems of existing technologies, this invention provides a virtual power plant planning method for long-term energy storage. The method includes: generating representative scenarios for virtual power plant planning using inverse transform sampling and clustering algorithms based on a pre-constructed wind and solar power output correlation model; establishing an energy-sharing strategy between the virtual power plant and long-term energy storage based on the operational characteristics of long-term energy storage and battery energy storage; establishing a virtual power plant planning model for long-term energy storage based on the virtual power plant system's energy-sharing strategy, a preset working time function, and related constraints; solving the virtual power plant planning model to obtain virtual power plant planning schemes and optimized operation results; establishing and solving planning models for long-term energy storage under different aggregate alliances based on the optimal planning scheme of the virtual power plant to obtain planning results for various possible alliances; and calculating the total working time for installation and operation maintenance of each virtual power plant aggregate using the nucleolus method based on the planning schemes and operation results of different alliances. This invention can reduce seasonal peak-valley differences and facilitate the high-proportion consumption of new energy sources.

[0040] To address the problems of existing technologies, embodiments of the present invention provide a method, apparatus, and storage medium for planning virtual power plants for long-term energy storage. The method for planning virtual power plants for long-term energy storage provided by embodiments of the present invention will be described first below.

[0041] like Figure 1 As shown, the virtual power plant planning method for long-term energy storage provided in this embodiment of the invention includes the following steps: 1) Obtain representative scenarios for virtual power plant planning and energy sharing strategies between virtual power plants and long-term energy storage; 2) Based on the energy sharing strategy of long-term energy storage, the installation time of the virtual power plant system, and the total operation and maintenance time under the representative scenario, construct the planning model of the virtual power plant and various constraints on the operation and maintenance of the virtual power plant. 3) With the goal of minimizing the total duration of the virtual power plant, the planning model of the virtual power plant is solved according to the various constraints to obtain the planning scheme and optimized operation results of the virtual power plant. The total duration includes the total working time for equipment installation, operation and maintenance. 4) Establish multiple aggregates based on the optimal planning scheme of the virtual power plant, and use the nucleolus method to calculate the total allocated time of each aggregate, wherein each aggregate includes multiple virtual power plants.

[0042] This involves generating a representative scenario for virtual power plant planning, including the following steps: 1.1 Obtain pre-collected historical weather data for the area to be planned; 1.2 Based on historical weather data, and using kernel density theory and copula theory, a probability distribution model for wind and solar power output is established; 1.3 Based on the probability distribution model of wind and solar power output, a representative scenario for virtual power plant planning is obtained by inverse transformation sampling and hierarchical clustering.

[0043] Specifically, a probability distribution model for wind and solar power output is established based on kernel density theory and copula theory.

[0044] In some embodiments, based on pre-collected historical weather data of the area to be planned, photovoltaic output data is calculated using wind power and photovoltaic output models. A probability distribution model for wind power and photovoltaic output is obtained based on kernel density theory, and then substituted into the copula function to obtain the probability distribution model for wind power and photovoltaic output.

[0045]

[0046]

[0047]

[0048] in: It is the rated capacity of the photovoltaic panel under standard testing conditions; The intensity of solar radiation; It is the intensity of solar radiation under standard test conditions; The temperature of the photovoltaic panel; The temperature of the photovoltaic panel under standard test conditions. This refers to the output power of the wind turbine. This refers to the rated output power of the wind turbine. For wind speed, Rated wind speed, and These are the entry and exit wind speeds, respectively. The kernel function uses a Gaussian distribution function. Bandwidth is estimated using a univariate kernel.

[0049] Referring to the kernel density estimation formula above, wind and solar power output is treated as random variables. Substituting the sample space of wind and solar power output into the kernel density estimation formula yields the probability density models for wind and solar power. and Then, by performing CDF calculation on the wind and solar power output probability density model, the cumulative distribution model of wind and solar power output can be obtained, as follows:

[0050] Specifically, representative scenarios for virtual power plant planning are obtained based on inverse transform sampling and hierarchical clustering, such as... Figure 2 As shown.

[0051] In some embodiments, an inverse transform sampling is performed on the probability distribution function of combined wind and solar power output to obtain the original scene of wind and solar power output in the area to be planned. Based on the original scene of the area to be planned, a hierarchical clustering algorithm is used to cluster and reduce the original scene to obtain a representative scene for virtual power plant planning. The specific steps of the hierarchical clustering algorithm include: Each sample is initialized as a cluster, and each wind and solar power output sample (usually the power output curve for a whole day, as a multi-dimensional vector) is treated as an independent cluster. If there are N samples, there are initially N clusters; Calculate the inter-cluster distance matrix, and then calculate the distance between any two clusters using the Euclidean distance formula. For a single-sample cluster, this is the distance between samples. Based on the selected linking criteria, this patent selects cluster pairs that minimize the variance within the merged group. Merge and update the distance matrix by merging the two selected clusters into a new cluster, then recalculate the distances between the new cluster and all other clusters, and update the distance matrix. Repeat the process of "selecting clusters with minimum variance → merging → updating distance" until the preset number of clusters is reached. K .

[0052] In establishing an energy-sharing strategy for virtual power plants and long-term energy storage, the method employed in this invention includes: 2.1 The Virtual Power Plant Energy Management Center coordinates energy exchange between multiple aggregates and between multiple aggregates within the Virtual Power Plant and shared long-term energy storage. Virtual power plant aggregates prioritize local consumption of surplus renewable energy. When an aggregate's net load is greater than zero, it prioritizes charging using battery storage. When an aggregate cannot consume the energy through batteries, it can supply power to other aggregates or consume surplus renewable energy through an electrolyzer, which converts electrical energy into hydrogen and heat. The hydrogen is stored in a long-term hydrogen storage system. If there is no heat deficit among the multiple aggregates, the heat generated from electrogenerated hydrogen is stored in a long-term thermal storage system. If there is a heat deficit among the multiple aggregates, the waste heat is directly supplied to the heat load of each aggregate. If the heat load still cannot be met, heat is supplied through long-term thermal storage.

[0053] 2.2 When the polymer's electrical energy is insufficient, the polymer's battery discharges. If the polymer has surplus electrical energy during battery discharge, it supplies electrical energy to other polymers; if the polymer still has a power shortage during battery discharge, it is powered by other polymers, fuel cells, and finally purchased from the upstream power grid. If the polymer's heat load is sufficient, the waste heat generated by the fuel cell is stored through long-term heat storage. If the heat load cannot be met, the heat is directly supplied to the heat load. If the heat load still cannot be met, long-term heat storage is used for heat release.

[0054] In step 102, a planning model for the virtual power plant and various constraints on its operation and maintenance are constructed based on the energy sharing strategy for long-term energy storage, the installation time of the virtual power plant system, and the total operation and maintenance time under the representative scenario.

[0055] In some implementations, the planning model for the virtual power plant is as follows:

[0056] In the formula, The total working time for equipment installation, operation and maintenance in the virtual power plant. The total working time for the installation, operation and maintenance of long-term energy storage equipment. For the losses of the distribution network, For scene collection Corresponding probability, The total number of scenes, and These are the aggregate's electricity and gas purchases, as well as the duration of operation and maintenance. This refers to the total operating and maintenance time for long-term energy storage. To represent the number of time periods within the scene, This is the loss penalty coefficient. This represents the total power loss of the distribution network. and These represent the installation and working time per unit capacity and per unit power of the equipment, respectively. For the unit capacity of the equipment, The unit power of the equipment, This refers to the number of devices installed in the polymer. The number of days in a year. This refers to the actual power purchased by the polymer. For the amount of gas purchased by the polymer, The unit operation and maintenance time of the equipment.

[0057] In some implementations, the constraints of the long-term energy storage virtual power plant planning model include: power flow balance equations, node voltage limits, power balance equations, battery operating conditions, long-term hydrogen storage operating conditions, and long-term thermal storage operating conditions, wherein... The current balance equation is:

[0058] In the formula, , These represent the active and reactive power injected into the nodes, respectively. Let be the voltage at node i at time t; The node voltage limit range is as follows:

[0059] In the formula, , These are the upper and lower limits of the node voltage, respectively; The power balance equation is specifically as follows:

[0060]

[0061]

[0062]

[0063]

[0064] In the formula, This refers to the exchange power between polymers. The electrical power supplied to the polymer for long-term energy storage. The electrical power supplied by the polymer to long-term energy storage. The thermal power supplied to the polymer for long-term energy storage The thermal power supplied by the polymer to long-term energy storage. , and These are the electrical, thermal, and cooling loads of the polymer, respectively. The battery operating conditions are as follows:

[0065] In the formula, For storing energy in batteries, This represents the battery's self-discharge coefficient. and These are the battery's charging efficiency and power, respectively. and These are the battery's discharge efficiency and power, respectively. and Energy is stored at the beginning and end of daytime operation, respectively. and These represent the maximum charge and discharge power of the battery. and These are the upper and lower limits of the stored energy; The long-term hydrogen storage operation conditions are as follows:

[0066] in, This represents the initial hydrogen storage capacity for the k-th representative scenario of seasonal hydrogen storage throughout the year. and This indicates the inflation and deflation volumes for the scene. and These represent the initial hydrogen storage capacity of the first representative scenario of the year and the hydrogen storage capacity at the end of the last scenario, respectively. and These are the maximum values ​​for inflation and deflation, respectively. and These represent the upper and lower limits of the permissible hydrogen storage capacity for seasonal hydrogen storage. As a representative scene The number of days it lasts; The long-term thermal storage operation conditions are as follows:

[0067] In the formula, This represents the initial heat storage capacity for the k-th representative scenario of seasonal thermal storage throughout the year. This indicates the amount of heat stored in the thermal storage. This indicates the amount of heat released from the thermal storage. and These represent the initial heat storage for the first representative scenario of the year and the heat storage at the end of the last scenario, respectively. and These represent the maximum allowable heat charge and release values ​​for the thermal storage. and These represent the upper and lower limits of the permissible heat storage capacity for seasonal thermal storage.

[0068] For example, the working time function of the virtual power plant planning model for long-term energy storage is constructed to minimize the total working time of system installation, operation and maintenance.

[0069] The planning model for the virtual power plant is as follows:

[0070] In the formula, The total working time for equipment installation, operation and maintenance in the virtual power plant. The total working time for the installation, operation and maintenance of long-term energy storage equipment. For the losses of the distribution network, For scene collection Corresponding probability, The total number of scenes, and These are the aggregate's electricity and gas purchases, as well as the duration of operation and maintenance. This refers to the total operating and maintenance time for long-term energy storage. To represent the number of time periods within the scene, This is the loss penalty coefficient. This represents the total power loss of the distribution network. and These represent the installation and working time per unit capacity and per unit power of the equipment, respectively. For the unit capacity of the equipment, The unit power of the equipment, This refers to the number of devices installed in the polymer. The number of days in a year. This refers to the actual power purchased by the polymer. For the amount of gas purchased by the polymer, The unit operation and maintenance time of the equipment.

[0071] It should be noted that the constraints of the long-term energy storage virtual power plant planning model include: power flow balance equation, node voltage limit range, power balance equation, battery operating conditions, long-term hydrogen storage operating conditions, and the aforementioned long-term thermal storage operating conditions. The current balance equation is:

[0072] In the formula, , These represent the active and reactive power injected into the nodes, respectively. Let be the voltage at node i at time t; The node voltage limit range is as follows:

[0073] In the formula, , These are the upper and lower limits of the node voltage, respectively; The power balance equation is specifically as follows:

[0074]

[0075]

[0076]

[0077]

[0078] In the formula, This refers to the exchange power between polymers. The electrical power supplied to the polymer for long-term energy storage. The electrical power supplied by the polymer to long-term energy storage. The thermal power supplied to the polymer for long-term energy storage The thermal power supplied by the polymer to long-term energy storage. , and These are the electrical, thermal, and cooling loads of the polymer, respectively. The battery operating conditions are as follows:

[0079] In the formula, For storing energy in batteries, This represents the battery's self-discharge coefficient. and These are the battery's charging efficiency and power, respectively. and These are the battery's discharge efficiency and power, respectively. and Energy is stored at the beginning and end of daytime operation, respectively. and These represent the maximum charge and discharge power of the battery. and These are the upper and lower limits of the stored energy; The long-term hydrogen storage operation conditions are as follows:

[0080] in, This represents the initial hydrogen storage capacity for the k-th representative scenario of seasonal hydrogen storage throughout the year. and This indicates the inflation and deflation volumes for the scene. and These represent the initial hydrogen storage capacity of the first representative scenario of the year and the hydrogen storage capacity at the end of the last scenario, respectively. and These are the maximum values ​​for inflation and deflation, respectively. and These represent the upper and lower limits of the permissible hydrogen storage capacity for seasonal hydrogen storage. As a representative scene The number of days it lasts; The long-term thermal storage operation conditions are as follows:

[0081] In the formula, This represents the initial heat storage capacity for the k-th representative scenario of seasonal thermal storage throughout the year. This indicates the amount of heat stored in the thermal storage. This indicates the amount of heat released from the thermal storage. and These represent the initial heat storage for the first representative scenario of the year and the heat storage at the end of the last scenario, respectively. and These represent the maximum allowable heat charge and release values ​​for the thermal storage. and These represent the upper and lower limits of the permissible heat storage capacity for seasonal thermal storage.

[0082] The specific requirements for renewable energy consumption are as follows: Given the increasingly heavy burden of renewable energy absorption on the distribution network, the aggregate should achieve its own renewable energy absorption.

[0083]

[0084] in: The output power of the polymer photovoltaic; The output power of the aggregated distributed power supply; The energy storage charging and discharging power of the polymer; Power purchased for the polymer; This represents the load power of the polymer.

[0085] The specific requirements for the proportion of renewable energy are as follows: .

[0086] In step 103, with the goal of minimizing the total duration of the virtual power plant, the planning model of the virtual power plant is solved according to the various constraints to obtain the virtual power plant planning scheme and optimized operation results. The total duration includes the total working time for equipment installation, operation and maintenance.

[0087] For example, the virtual power plant planning model is solved to obtain the virtual power plant planning scheme and optimized operation results, including: The virtual power plant planning scheme and optimized operation results are obtained based on a two-stage stochastic optimization method. The first stage is the investment stage, which optimizes the system's installation decisions. The investment decision optimization aims to minimize the total installation time of the system. In this stage, the virtual power plant system makes investment decisions before the uncertainties of wind, solar, and load conditions materialize. Decision variables include the equipment installation type and capacity, and are constrained by the scale of equipment installation. The second stage is the operation stage, which optimizes the system's equipment operation decisions under the existing equipment installation conditions. Based on the investment decisions in the investment stage and the representative scenario of the virtual power plant planning, the expected optimal decision is obtained empirically. This stage yields the system scheduling result that minimizes the total operation and maintenance time under normal operating conditions. The above solution process uses the particle swarm optimization algorithm and the commercial optimization software CPLEX. The per-unit values ​​of wind and solar output in step 4) representing the scenario are as follows: Figure 3 As shown, the structure of a virtual power plant system for long-term energy storage is as follows: Figure 4 As shown, the solution process for the virtual power plant planning model for long-term energy storage is as follows: Figure 5 As shown.

[0088] In some embodiments, the solution results of the virtual power plant model for long-term energy storage are shown in the table below:

[0089] The results of the optimized dispatch of the virtual power plant on the day are as follows: Figure 6 As shown in the diagram, the analysis is based on the perspective of Aggregator 1. When the virtual power plant is equipped with long-term energy storage, there is energy sharing among the aggregates, and indirect energy sharing through long-term energy storage. Since the renewable energy consumption needs of each aggregate offset each other, long-term energy storage provides reserve capacity for the virtual power plant's renewable energy consumption. The configuration of long-term energy storage in the virtual power plant eliminates the need for battery storage in Aggregator 1; surplus renewable energy is consumed by other aggregates or converted into hydrogen through an electrolyzer. Due to the lack of batteries, Aggregator 1 is equipped with more gas turbines to ensure the reliability of its power supply. From 18:00 to 24:00 on each representative day, gas turbine power generation meets the load demand of Aggregator 1. The hydrogen-thermal optimization scheduling results of long-term energy storage on the representative day are shown below. Figure 7 As shown, the electrolyzer operation is mainly concentrated between 10:00 and 14:00 on each representative day, with renewable energy being converted into hydrogen and stored in short-term and seasonal hydrogen storage. Fuel cell output of electricity is mainly between 18:00 and 24:00, as the virtual power plant lacks renewable energy supply during this time. Seasonal hydrogen storage is implemented by storing on representative days in spring and autumn and releasing on representative days in summer and winter, achieving seasonal energy transfer.

[0090] In step 104, multiple aggregates are established based on the optimal planning scheme of the virtual power plant, and the nucleolus method is used to calculate the total allocated time of each aggregate, wherein each aggregate includes multiple virtual power plants.

[0091] In some embodiments, the construction process of the multiple polymers includes: Acquire multiple virtual power plants; Based on the multiple virtual power plants, the states of the virtual power plants are divided into participating state and non-participating state, where the participating state is the state of participating in energy sharing and investing in long-term energy storage; The states of the multiple virtual power plants are arranged and combined to obtain the multiple aggregates.

[0092] In some implementations, the calculation of the allocated total time for each polymer, and the installation and operation / maintenance work time using the nucleolus method, includes: The total allocated time for each polymer, as well as the installation and operation / maintenance time, are calculated according to the first formula, whereby:

[0093] In the formula, As an auxiliary variable, for The minimum value, Marked as a large cooperative alliance composed of aggregates, any subset of the large alliance ,vector Indicating a large cooperative alliance The results of the time allocation for installation and operation maintenance work, among which The characteristic function represents the installation and operation / maintenance time allocated to aggregate n. This indicates the total installation, operation, and maintenance time of the alliance. Allocate installation and operation / maintenance time for any sub-alliance to a given large alliance. Dissatisfaction.

[0094] For example, in establishing a planning model for long-term energy storage in virtual power plants under different consortium alliances, we take a virtual power plant of three consortiums as an example: Single-polymer alliance: {polymer 1}, {polymer 2}, {polymer 3}; Dual polymer alliance: {polymer 1, polymer 2}, {polymer 1, polymer 3}, {polymer 2, polymer 3}; Tri-polymer Alliance: {polymer 1, polymer 2, polymer 3}; There are five virtual power plant alliance planning models for long-term energy storage under different alliance methods: Aggregates 1, 2, and 3 do not participate in energy sharing to form a virtual power plant and each invests in long-term energy storage independently.

[0095] Aggregator 1 does not participate in energy sharing or invest in long-term energy storage, while Aggregators 2 and 3 participate in energy sharing to form a virtual power plant and invest in long-term energy storage.

[0096] Aggregator 2 does not participate in energy sharing or the construction of long-term energy storage, while Aggregators 1 and 3 participate in energy sharing to form a virtual power plant and construct long-term energy storage.

[0097] Aggregator 3 does not participate in energy sharing or the construction of long-term energy storage, while Aggregators 1 and 2 participate in energy sharing to form a virtual power plant and construct long-term energy storage.

[0098] Aggregates 1, 2, and 3 participate in energy sharing to form a virtual power plant and invest in long-term energy storage.

[0099] Based on the planning schemes and operational results of different alliances, the nucleolus method was used to calculate the allocation of the total installation and operation maintenance time for each polymer, including: Initialization: Given an initial feasible allocation vector Set the number of iterations k = 0, sets the convergence tolerance. .

[0100] Calculate the excess of all sub-alliances: based on the value function of different sub-alliances (total working time for sub-alliance installation and operation maintenance) calculated in the previous steps; based on the maximum dissatisfaction of the sub-alliances with the allocation.

[0101]

[0102] Determine if the nucleolus condition is satisfied: for the association vector of the coalition The current allocation is considered to be in effect if the following conditions are met. If it is the nucleus, then continue updating the allocation vector; otherwise, continue updating it.

[0103]

[0104] Update the work duration allocation variable: Adjust the allocation to reduce sub-alliances' dissatisfaction with the current allocation.

[0105]

[0106] Update iteration count: Let k = k +1, and continue iterating until the convergence condition is met or the maximum number of iterations is reached.

[0107] This invention discloses a method for planning long-term energy storage virtual power plants. First, it obtains representative scenarios for virtual power plant planning and energy-sharing strategies between the virtual power plant and long-term energy storage. Then, based on the energy-sharing strategy, the installation time of the virtual power plant system, and the total operation and maintenance time under the representative scenarios, it constructs a planning model for the virtual power plant and various constraints on its operation and maintenance. Next, with the goal of minimizing the total time of the virtual power plant, it solves the planning model according to the various constraints to obtain a virtual power plant planning scheme and optimized operation results. The total time includes the total operation and maintenance time for equipment installation. Finally, based on the optimal planning scheme for the virtual power plant, it establishes multiple aggregates and uses the nucleolus method to calculate the allocated total time for each aggregate. Each aggregate includes multiple virtual power plants. This invention can reduce the total operation and maintenance time of virtual power plant planning, reduce seasonal peak-valley differences, and facilitate high-proportion renewable energy consumption.

[0108] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0109] The following are embodiments of the apparatus of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0110] Figure 8 This is a functional block diagram of a long-term energy storage virtual power plant planning device provided in an embodiment of the present invention, with reference to... Figure 8 The long-term energy storage virtual power plant planning device includes: a scenario and strategy module 801, a planning model construction module 802, a planning scheme optimization module 803, and an aggregate allocation duration determination module 804, wherein: The scenario and strategy module 801 is used to obtain representative scenarios for virtual power plant planning and energy sharing strategies between virtual power plants and long-term energy storage. The planning model construction module 802 is used to construct a planning model for the virtual power plant and various constraints on the operation and maintenance of the virtual power plant based on the energy sharing strategy of long-term energy storage, the installation time of the virtual power plant system, and the total operation and maintenance time under the representative scenario. The planning scheme optimization module 803 is used to solve the planning model of the virtual power plant with the goal of minimizing the total duration of the virtual power plant, based on the various constraints, to obtain the planning scheme and optimized operation results of the virtual power plant. The total duration includes the total working time for equipment installation, operation and maintenance. The aggregate allocation duration determination module 804 is used to establish multiple aggregates based on the optimal planning scheme of virtual power plants, and to calculate the total allocation duration of each aggregate using the nucleolus method. Each aggregate includes multiple virtual power plants.

[0111] Figure 9 This is a functional block diagram of the electronic device provided in an embodiment of the present invention. For example... Figure 9 As shown, the electronic device 9 of this embodiment includes a processor 900 and a memory 901, wherein the memory 901 stores a computer program 902 that can run on the processor 900. When the processor 900 executes the computer program 902, it implements the steps of the various long-term energy storage virtual power plant planning methods and embodiments described above, for example... Figure 1 Steps 101 to 104 are shown.

[0112] For example, the computer program 902 may be divided into one or more modules / units, which are stored in the memory 901 and executed by the processor 900 to complete the present invention.

[0113] The electronic device 9 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device 9 may include, but is not limited to, a processor 900 and a memory 901. Those skilled in the art will understand that... Figure 9 This is merely an example of electronic device 9 and does not constitute a limitation on electronic device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 9 may also include input / output devices, network access devices, buses, etc.

[0114] The processor 900 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0115] The memory 901 can be an internal storage unit of the electronic device 9, such as a hard disk or memory. The memory 901 can also be an external storage device of the electronic device 9, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 901 can include both internal and external storage units of the electronic device 9. The memory 901 is used to store the computer program 902 and other programs and data required by the electronic device 9. The memory 901 can also be used to temporarily store data that has been output or will be output.

[0116] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0117] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0119] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0121] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0122] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various methods and apparatus embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0123] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A long-term energy storage virtual power plant planning method, characterized in that, The method comprises the following steps: obtaining a representative scenario of virtual power plant planning and an energy sharing strategy of virtual power plant and long-term energy storage; constructing a planning model of virtual power plant and multiple constraint conditions of virtual power plant operation and maintenance according to the energy sharing strategy of long-term energy storage, the installation time of virtual power plant system and the total operation and maintenance time under the representative scenario; solving the planning model of virtual power plant according to the multiple constraint conditions, obtaining a virtual power plant planning scheme and an optimized operation result, and taking the total time of virtual power plant as the target, wherein the total time includes the total installation and operation and maintenance time; establishing multiple aggregations according to the optimal planning scheme of virtual power plant, and calculating the total time of each aggregation by using the kernel method, wherein each aggregation includes multiple virtual power plants. 2.The long-term energy storage virtual power plant planning method of claim 1, wherein, The representative scenario of the virtual power plant planning is generated by using inverse transform sampling and hierarchical clustering algorithm according to a pre-constructed wind-solar power output correlation model, and comprises the following steps: obtaining historical weather data of the region where the virtual power plant is located; calculating the per-unit value of wind-solar historical power output data according to the historical weather data and a preset wind-solar power output model, and constructing corresponding photovoltaic and wind power output probability distribution functions by using a non-parametric kernel density estimation method; establishing a wind-solar joint power output probability distribution model by using Copula theory according to the photovoltaic and wind power output probability distribution functions; randomly extracting a certain number of sample points based on inverse transform sampling to generate random scenario samples; performing clustering on the random scenario samples based on a hierarchical clustering algorithm to obtain the representative scenario of virtual power plant planning. 3.The long-term energy storage virtual power plant planning method of claim 1, wherein, The energy sharing strategy of long-term energy storage comprises the following steps: the virtual power plant aggregation gives priority to local consumption of surplus renewable energy; when the net load of the aggregation is greater than 0, the aggregation preferentially uses the battery energy storage to charge, and when the aggregation cannot be consumed through the battery, the aggregation can supply power to other aggregations or consume surplus renewable energy through an electrolytic tank, the electrolytic tank converts electrical energy into hydrogen and heat energy, the hydrogen is stored in long-term hydrogen storage, if there is no heat energy shortage in multiple aggregations, the heat energy generated by the hydrogen production is stored in long-term heat storage; if there is a heat energy shortage in multiple aggregations, the waste heat is directly supplied to the heat load of each aggregation, and if the heat load still cannot be satisfied, the heat is supplied through long-term heat storage; when the electrical energy of the aggregation is insufficient, the battery of the aggregation is discharged, if there is surplus electrical energy when the battery is discharged, the electrical energy is supplied to other aggregations; if there is still an electrical energy shortage in the aggregation when the battery is discharged, the electrical energy is supplied by other aggregations, fuel cells, and finally the power is purchased from the upper power grid; if the heat load of the aggregation can be satisfied, the waste heat generated by the fuel cells is stored in long-term heat storage; if the heat load cannot be satisfied, the heat is directly supplied to the heat load; if the heat load still cannot be satisfied, the heat is released from the long-term heat storage. 4.The long-term energy storage virtual power plant planning method of claim 1, wherein, The planning model of the virtual power plant comprises the following steps: wherein, is the total work time length for installation and operation and maintenance of the equipment of the virtual power plant, is the total work time length for installation and operation and maintenance of the long-term energy storage equipment, is the loss of the distribution network, is the set of scenarios is the corresponding probability, is the total number of scenarios, and are respectively the electricity and gas purchase quantity and the operation and maintenance work time length of the aggregator, is the total work time length for operation and maintenance of the long-term energy storage, is the number of time periods in the representative scenario, is the loss penalty coefficient, is the total power loss of the distribution network, and are respectively the installation work time length of the unit capacity and unit power equipment of the device, is the unit capacity of the device, is the unit power of the device, is the installation number of the equipment in the aggregator, is the number of days in a year, is the actual electricity purchase power of the aggregator, is the gas purchase quantity of the aggregator, is the unit operation and maintenance work time length of the device. 5.The long-term energy storage virtual power plant planning method of claim 1, wherein, the constraint conditions of the long-term energy storage virtual power plant planning model comprise a power flow balance equation, a node voltage limit range, a power balance equation, a battery operation condition, a long-term hydrogen storage operation condition and a long-term heat storage operation condition, wherein the power flow balance equation is: wherein, , are the active and reactive power injected by the node, respectively; is the voltage at node i at time t. the node voltage limit range is: wherein , are the upper and lower limits of the node voltage, respectively. the power balance equation is specifically: wherein P is the power exchanged between the aggregates, P is the electric power supplied by the long-term storage to the aggregates, P is the electric power supplied by the aggregates to the long-term storage, P is the thermal power supplied by the long-term storage to the aggregates, P is the thermal power supplied by the aggregates to the long-term storage, , and are the electric, thermal and cold loads of the aggregates, respectively; The battery operation condition is: wherein, S is the stored energy of the battery, C is the self-discharge coefficient of the battery, and respectively the charge efficiency and the power of the battery, and respectively the discharge efficiency and the power of the battery, and respectively the stored energy at the beginning and at the end of the day operation, and respectively the maximum charge and discharge power of the battery, and respectively the upper and lower limits of the stored energy; The long-term hydrogen storage operation condition is: wherein, is the initial hydrogen storage amount of the kth representative scenario of the whole year, and denote the charging amount and discharging amount of the representative scenario, and are the initial hydrogen storage amount of the first representative scenario of the whole year and the hydrogen storage amount at the end of the last scenario, respectively, and are the maximum values of the charging and discharging, respectively, and are the upper and lower limits of the allowable hydrogen storage amount of the seasonal hydrogen storage, respectively, is the duration of the representative scenario in days. The long-term heat storage operation condition is: In the formula, This represents the initial heat storage capacity for the k-th representative scenario of seasonal thermal storage throughout the year. This indicates the amount of heat stored in the thermal storage. This indicates the amount of heat released from the thermal storage. and These represent the initial heat storage for the first representative scenario of the year and the heat storage at the end of the last scenario, respectively. and These represent the maximum allowable heat charge and release values ​​for the thermal storage. and These represent the upper and lower limits of the permissible heat storage capacity for seasonal thermal storage. 6.The long-term energy storage virtual power plant planning method of claim 1, wherein, The construction process of the plurality of aggregates includes: Obtain a plurality of virtual power plants; According to the plurality of virtual power plants, the state of the virtual power plant is divided into a participation state and a non-participation state, wherein the participation state is a state of participating in energy sharing and building long-term energy storage; The states of the plurality of virtual power plants are arranged and combined to obtain the plurality of aggregates.

7. The long-term energy storage virtual power plant planning method according to any one of claims 1-6, characterized in that, The total time length, installation and operation and maintenance work time length of each aggregate are calculated by using the kernel method, including: The total time length, installation and operation and maintenance work time length of each aggregate are calculated according to the first formula, wherein the first formula is: wherein is an auxiliary variable, is the minimum of is marked as a large coalition consisting of the aggregate, any large coalition subset is a vector denotes the large coalition installation and operational maintenance effort length allocation result, wherein denotes the installation and operational maintenance effort length allocated by the aggregate n, the characteristic function denotes the total installation and operational maintenance effort length of the coalition, is the dissatisfaction of any coalition pair for the given large coalition installation and operational maintenance effort length allocation .

8. A long-term energy storage virtual power plant planning apparatus characterized by comprising: The long-term energy storage virtual power plant planning method according to any one of claims 1-7, the long-term energy storage virtual power plant planning device includes: A scene and strategy module for obtaining a representative scene of virtual power plant planning and an energy sharing strategy of virtual power plant and long-term energy storage; A planning model construction module for constructing a planning model of virtual power plant and a plurality of restriction conditions of virtual power plant operation and maintenance according to the energy sharing strategy of long-term energy storage, the installation time length of virtual power plant system and the total work time length of operation and maintenance under the representative scene; A planning scheme optimization module for solving the planning model of virtual power plant according to the plurality of restriction conditions with the minimum total time length of virtual power plant as the target, obtaining a virtual power plant planning scheme and an optimized operation result, wherein the total time length includes the total work time length of equipment installation and operation and maintenance; And, An aggregate apportioned time length determination module for establishing a plurality of aggregates according to the optimal planning scheme of virtual power plant and calculating the total time length of each aggregate by using the kernel method, wherein each aggregate includes a plurality of virtual power plants.

9. An electronic device comprising a memory and a processor, said memory having stored therein a computer program operable on said processor, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the method of any one of claims 1-7.

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