Novel power system energy storage structure optimization method and device and storage medium

Abstract

The invention relates to a novel power system energy storage structure optimization method and device and a storage medium, and is applied to the technical field of energy storage network structure optimization, and the method comprises the steps: constructing a multi-objective optimization model for energy storage configuration optimization, and setting the constraint condition of the multi-objective optimization model; an improved multi-objective particle swarm optimization algorithm is adopted to solve a multi-objective optimization model, the improved multi-objective particle swarm optimization algorithm is used for improving the global exploration ability of a particle swarm to a solution space and overcoming the problem that a traditional particle swarm algorithm is prone to falling into local optimum, a random mutation operator is added into a basic framework of the algorithm, and the algorithm is optimized. Through the random variation of the intergenerational global optimal position, the capability of jumping out of the local optimal solution of the particle swarm algorithm is improved, so that the algorithm can enter the solution space when premature convergence occurs, and search is continued in other areas of the solution space until the global optimal solution is found.

Description

Technical Field

[0001] This invention relates to the field of energy storage network structure optimization technology, specifically to a novel method, device, and storage medium for optimizing the energy storage structure of a power system. Background Technology

[0002] With the acceleration of global industrialization and economic development, the overuse of fossil fuels has triggered serious environmental problems and an energy crisis. Against this backdrop, developing renewable energy has become an important energy strategy for my country. Although clean energy sources such as solar and wind power have enormous development potential, their inherent intermittency and instability limit their large-scale application.

[0003] Modern power systems require real-time balance between power generation and consumption, and energy storage technology has effectively solved this problem. By combining energy storage devices with power electronic devices, energy storage systems can smooth out spatiotemporal differences in power supply and play multiple key roles in grid operation: First, large-capacity energy storage devices significantly improve the utilization rate of power generation equipment through "peak shaving and valley filling," that is, storing excess energy during off-peak hours and releasing it during peak hours, which optimizes resource allocation and reduces grid investment costs; second, energy storage technology greatly improves power supply reliability and power quality. It is particularly noteworthy that with the increasing proportion of fluctuating power sources such as wind and solar power, energy storage systems demonstrate irreplaceable value in smoothing output fluctuations and ensuring stable grid operation. Therefore, developing new energy storage systems with high energy density and long cycle life has become a key research focus.

[0004] The existing energy storage configuration optimization of distribution network systems usually adopts the standard MOPSO algorithm. However, when facing multidimensional problems, this algorithm has problems such as uneven distribution of Pareto optimal solutions, tendency of particles to aggregate too quickly due to social cognition of particles, resulting in loss of population diversity and getting trapped in local optima, and insufficient exploration of the solution space by particles. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a novel method, device and storage medium for optimizing the energy storage structure of a power system, which aims to solve the problems of uneven distribution of Pareto optimal solutions when facing multidimensional problems in existing energy storage configuration optimization methods, the tendency of social cognition of particles to cause particles to aggregate too quickly, thereby losing the diversity of the population and getting trapped in local optima, and the insufficient exploration of the solution space by particles.

[0006] According to a first aspect of the present invention, a novel method for optimizing energy storage structures in power systems is provided, the method comprising: A multi-objective optimization model is constructed with the configuration parameters of the energy storage system as decision variables. The multi-objective optimization model includes economic objectives, stability objectives, and reliability objectives. The constraints of the multi-objective optimization model are set, including: rated power constraint, rated capacity constraint, charge / discharge power constraint, and charge state constraint. An improved multi-objective particle swarm optimization algorithm is used to iteratively solve the multi-objective optimization model to obtain the optimal energy storage configuration scheme. This includes: initializing a particle swarm, where each particle represents an energy storage configuration scheme; determining whether mutation is triggered by calculating the population fitness variance of the current particle swarm; if mutation is not triggered, selecting the optimal particle in the current particle swarm, i.e., the optimal energy storage configuration scheme; if mutation is triggered, performing random mutation operations on the locally optimal particles in the particle swarm using a method that adds random perturbation, according to a preset mutation probability. Update the grid topology based on the optimal energy storage configuration.

[0007] Preferably, The economic objectives include: Obtain the total investment cost, operation and maintenance cost, and disposal cost after the end of the energy storage system's lifespan for the entire energy storage lifecycle, and construct an objective function to minimize the energy storage cost; The stability objectives include: The average frequency of power outages in the power generation system, the average duration of outages, and the average stable continuous power supply rate are obtained, and an objective function for minimizing the comprehensive stability index is constructed. The reliability objectives include: We obtain the expected value of supply shortage, the time probability of insufficient power supply, the expected number of power supply shortage events, and the expected time of power supply shortage, and construct an objective function that minimizes the comprehensive reliability index.

[0008] Preferably, The initialization of the particle swarm includes: Based on the grid topology including wind power, photovoltaic and diesel engines, the location of the energy storage access node is randomly selected from the specified candidate sensitive nodes, and a 24-hour charge and discharge power curve is randomly generated to form an initial energy storage configuration scheme set; Based on the initial charge and discharge power curves, calculate the rated energy storage capacity and rated power of each initial energy storage configuration scheme, and remove the initial energy storage configuration schemes whose rated energy storage capacity and rated power do not meet the rated power constraint and rated capacity constraint from the initial energy storage configuration scheme set. Calculate the real-time SOC curve of the remaining initial energy storage configuration schemes based on the charge state constraints, and remove the initial energy storage configuration schemes whose real-time SOC curves exceed the minimum and maximum charging states of the energy storage device from the initial energy storage configuration scheme set. The remaining initial energy storage configuration schemes are used as the initial particle swarm for the improved multi-objective particle swarm optimization algorithm.

[0009] Preferably, The step of determining whether mutation is triggered by calculating the population fitness variance of the current particle swarm includes: Calculate the fitness value of each particle in the particle swarm; The average fitness value of the particle swarm is calculated based on the fitness value of each particle. The particle swarm normalization calibration factor is calculated based on the average fitness value of the current particle swarm. The population fitness variance of the current particle swarm is calculated based on the fitness value of each particle, the average fitness value of the particle swarm, and the normalized calibration factor. The population fitness variance of the current particle swarm is compared with a preset variance threshold. If the population fitness variance of the current particle swarm is less than the preset variance threshold, mutation is triggered; if the population fitness variance of the current particle swarm is greater than or equal to the preset variance threshold, mutation is not triggered.

[0010] Preferably, The fitness value of each particle in the particle swarm is calculated as follows: The function values ​​of each particle on the economic, stability, and reliability objectives are obtained through power grid power flow calculation. The function values ​​on the economic, stability, and reliability objectives are then weighted and summed according to preset weights to obtain a comprehensive objective function value. This comprehensive objective function value is used as the fitness value of the particle.

[0011] Preferably, The locally optimal particles in the particle swarm include: Set the fitness threshold; The fitness value of each particle in the particle swarm is compared with the fitness threshold. If the fitness value of a particle is greater than the fitness threshold, the particle is regarded as a local optimal particle and subjected to random mutation operation.

[0012] Preferably, The decision variables include: Location of energy storage access node, rated power of energy storage, rated capacity of energy storage, and charging and discharging power.

[0013] According to a second aspect of the present invention, a novel power system energy storage structure optimization device is provided, the device comprising: Multi-objective optimization model construction module: used to construct a multi-objective optimization model with the configuration parameters of the energy storage system as decision variables. The multi-objective optimization model includes economic objectives, stability objectives, and reliability objectives. Constraint setting module: used to set the constraints of the multi-objective optimization model, including: rated power constraint, rated capacity constraint, charge and discharge power constraint and charge state constraint; The solution module is used to iteratively solve the multi-objective optimization model using an improved multi-objective particle swarm optimization algorithm to obtain the optimal energy storage configuration scheme. This includes: initializing the particle swarm, where each particle represents an energy storage configuration scheme; determining whether mutation is triggered by calculating the population fitness variance of the current particle swarm; if mutation is not triggered, selecting the optimal particle in the current particle swarm, i.e., the optimal energy storage configuration scheme; if mutation is triggered, performing random mutation operations on the locally optimal particles in the particle swarm using a method that adds random perturbation, according to a preset mutation probability. Update module: Used to update the grid topology based on the optimal energy storage configuration.

[0014] According to a third aspect of the present invention, a storage medium is provided, the storage medium storing a computer program, which, when executed by a host controller, implements the steps of the above-described method.

[0015] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This application constructs a multi-objective optimization model for energy storage configuration optimization and sets constraints for the model. An improved multi-objective particle swarm optimization algorithm is used to solve the model. To enhance the global exploration capability of the particle swarm in the solution space and overcome the problem of traditional particle swarm algorithms easily getting trapped in local optima, a random mutation operator is added to the basic framework of the algorithm. By randomly mutating the global optimum position across generations, the algorithm's ability to escape local optima is improved. This allows the algorithm to enter the solution space when premature convergence occurs and continue searching in other regions until the global optimum is found. This addresses the problems of existing energy storage configuration optimization methods when facing multi-dimensional problems, such as uneven distribution of Pareto optimal solutions, a tendency for particles to aggregate too quickly due to social cognition leading to loss of population diversity and trapping in local optima, and insufficient exploration of the solution space by particles.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] Figure 1This is a flowchart illustrating a novel power system energy storage structure optimization method according to an exemplary embodiment; Figure 2 This is a system schematic diagram illustrating a novel power system energy storage structure optimization device according to another exemplary embodiment; In the attached diagram: 1-Multi-objective optimization model construction module, 2-Constraint setting module, 3-Solve module, 4-Update module. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0020] Example 1 Figure 1 This is a flowchart illustrating a novel power system energy storage structure optimization method according to an exemplary embodiment, such as... Figure 1 As shown, the method includes: S1, Construct a multi-objective optimization model with the configuration parameters of the energy storage system as decision variables. The multi-objective optimization model includes economic objectives, stability objectives, and reliability objectives. S2, Set the constraints of the multi-objective optimization model, the constraints include: rated power constraint, rated capacity constraint, charge and discharge power constraint and charge state constraint; S3, an improved multi-objective particle swarm optimization algorithm is used to iteratively solve the multi-objective optimization model to obtain the optimal energy storage configuration scheme; including: initializing the particle swarm, where each particle represents an energy storage configuration scheme; determining whether mutation is triggered by calculating the population fitness variance of the current particle swarm; if mutation is not triggered, selecting the optimal particle in the current particle swarm, i.e., the optimal energy storage configuration scheme; if mutation is triggered, performing random mutation operation on the local optimal particles in the particle swarm by adding random perturbation according to the preset mutation probability; S4, update the grid topology according to the optimal energy storage configuration scheme; It is understood that this embodiment specifically includes: A multi-objective optimization model is constructed, which includes individual objective functions for economy, stability, and reliability. Specifically, three objective functions are constructed, including: (1) Objective function for minimizing energy storage costs: Economic efficiency is a key indicator for the optimal allocation of energy storage. By constructing a mathematical model of energy storage investment and operating costs, minimizing investment and operating costs and maximizing operational efficiency can be taken as the optimization objectives. Through scientific calculation and analysis, a reasonable allocation of energy storage systems can be achieved. The total life-cycle cost of energy storage is:

[0021] In the formula, C inv This represents the total investment cost throughout the entire lifecycle of energy storage. C OM Indicates the cost of energy storage operation and maintenance. C sd This indicates the disposal cost after the energy storage system reaches the end of its lifespan. Among them, the total investment cost of energy storage throughout its entire life cycle C inv The expression is:

[0022] In the formula, C c Indicates the initial investment cost over the entire life cycle. C z Indicates the cost of replacing investment; Rated power and capacity are key parameters that determine the scale and performance of a system, directly affecting the initial investment and its total life-cycle initial investment cost. C c The calculation formula is:

[0023] In the formula, C PCS The unit power cost of the power conversion system P rated This refers to the rated power of the energy storage system. Replacement investment costs C z The calculation formula is:

[0024] In the formula, Cost per unit capacity The discount rate is... For the life cycle of energy storage systems, Indicates the number of replacements (total input energy storage) frequency), , This refers to the equivalent cycle life of energy storage. Energy storage operation and maintenance costs C OM The expression is:

[0025] In the formula, C POM This represents the maintenance cost per unit of power. C EOM The cost of operation and maintenance per unit capacity; This indicates the annual charge and discharge capacity of the energy storage system (the annual charge and discharge cycle is calculated as 300 days). Disposal costs after the end of the lifespan of an energy storage system C sd The expression is:

[0026] In the formula, C Psd This represents the cost of disposal per unit of power. C Esd This represents the cost of scrapping per unit of production capacity; E rated Indicates the rated capacity of the energy storage system; (2) Objective function for minimizing the stable comprehensive index: Energy generation is inherently unpredictable. In the event of power system failures or power shortages, it is necessary to equip the system with appropriately sized energy storage systems to continuously provide users with the necessary power to meet load demands for a certain period, while also satisfying power fluctuation limits and ensuring the stability of the power grid system. Energy storage devices can store electricity during periods of low demand and release it during peak periods, ensuring users have a stable power supply at all times.

[0027] This process may be affected by two key indicators: outage time and outage frequency. Outage time refers to the total length of power interruption experienced by customers, while outage frequency refers to the number of outage events. Efficient energy storage systems can not only significantly reduce the duration and frequency of outages, but also improve the overall resilience and stability of the power grid, providing users with a more stable power experience. In summary, the objective function for minimizing the stability comprehensive index is as follows:

[0028] In the formula, E a This indicates the average frequency of power outages. E b This indicates the average duration of any outage. E c Indicates the average stable continuous power supply rate; , , The sub-objective weights satisfy the following conditions: ; in,

[0029]

[0030]

[0031] In the above formula, N d It is the number of nodes in the power system. N i It is a node i The number of users on the platform b i,y It is one year y Middle node i The number of service interruptions that occurred on the platform. It is the total number of years used to calculate statistical information. U i,y For the year y node i The time of the power outage is 8760, which is the total number of hours in a year. (3) Objective function for minimizing the comprehensive reliability index: The reliability goal of energy storage configuration optimization is to ensure that the power system can stably and continuously provide sufficient and reliable power under any operating scenario, fully meet the electricity demand of users, and effectively avoid the risk of insufficient or interrupted power supply. Power shortage is also a key indicator, indicating that the energy storage system cannot meet the load demand. The corresponding power shortage time probability and power shortage time expectation help to evaluate the system performance and its ability to cope with sudden load growth. The adequacy of power security can effectively reduce power supply risks, improve the resilience of the overall power system, and provide users with a reliable power supply environment. In summary, the objective function for minimizing the overall reliability index is as follows:

[0032] In the formula, E d This represents the expected value for power supply shortages. L The probability of insufficient power supply in time. E f The expected number of power supply shortage events. E p This represents the expected duration of a power supply shortage. , , , ,satisfy ; in,

[0033]

[0034]

[0035]

[0036] In the above formula, For nodes Year Power outage load, The probability of a load outage occurring. This refers to the duration during which the load is not in use. It is the number of time periods in a year. In the th time period The number of days, It is the first time period. Peak load of the day Is the system in The installed capacity for the first time period. for The first period No. A power outage at a certain point on [date] resulted in a load greater than [amount]. probability, For the first Within the time period day The load during the time.

[0037] Set constraints for the multi-objective optimization model, including: rated power constraints, rated capacity constraints, charging / discharging power constraints, and charge state constraints; in, The expression for the rated power constraint is:

[0038] The expression for the rated capacity constraint is:

[0039] The charging / discharging power constraint expression is:

[0040]

[0041] The expression for charge state constraint is:

[0042]

[0043]

[0044] In the above formula, as well as These represent the charging and discharging efficiency of the energy storage device. It is an energy storage device at all times 'output power' The rated capacity of the energy storage device, This refers to the initial charging state of the energy storage device. as well as These are the minimum and maximum states of charge for energy storage, respectively. It is the charging / discharging time for stored energy. It is energy storage The charging status, as well as These represent the charging power and discharging power at that moment, respectively. as well as These represent the maximum charging power and maximum discharging power at that moment. as well as These represent the high and low values ​​of the stored charge state, respectively.

[0045] Solving the multi-objective optimization model: Based on the three optimization objectives of "economy, stability, and reliability" and the four constraints established above, the improved multi-objective particle swarm optimization (MOPSO) algorithm is used to solve for the optimal energy storage configuration, as detailed below: Identify decision variables: Define the key parameters to be optimized—location of energy storage access nodes and rated power of energy storage. Rated capacity and charge / discharge power curves, For charging power, This refers to the discharge power. Initialize algorithm parameters: Set the initial particle swarm size, external file size, maximum number of iterations, and define parameters such as inertia weight; Initial particle swarm generation: Based on the grid topology including wind power, photovoltaics, and diesel engines, the location of the energy storage access node is randomly selected from the designated candidate sensitive nodes, and a 24-hour charging and discharging power plan is randomly generated to form an initial energy storage configuration scheme set. Based on the initial charge and discharge power curves, calculate the rated energy storage capacity for each scheme. With rated power Schemes that do not meet the above-mentioned rated power and rated capacity constraints will be eliminated. Calculate the real-time SOC curve for each initial scheme based on the above charge state constraint formula, and discard those with SOC exceeding the limit. to The effective schemes within the range are used as the initial particle swarm for algorithm iteration; Algorithm iteration: For each particle in the initial particle swarm, three objective function values ​​are obtained through power grid flow calculations. These three objective function values ​​are then weighted and summed using custom weights to obtain the comprehensive objective function value for each particle. This comprehensive objective function value is then used as the composite objective function value for each particle. i fitness value Based on the fitness value of each particle Get the current particle swarm Average fitness The specific calculation formula is as follows:

[0046] Then, determine the particle swarm normalization calibration factor. The calculation formula is:

[0047] Finally, the population fitness variance of the entire population is obtained. The calculation formula is:

[0048] Through population fitness variance To determine whether mutation has been triggered, population diversity is insufficient, i.e., population fitness variance is low. Less than the preset variance threshold When mutation occurs, the mutation rule is: for the local optimal particle in the current particle swarm... Locally optimal particles The fitness value of the particle Greater than the theoretical optimal value of the set objective function Locally optimal particles With a certain probability When random mutations occur, the expression for the above process is:

[0049] in: It is a random number between 0.1 and 0.3. The value is usually much smaller than . The maximum value, for The variational operations are performed by adding random perturbations. Set as of The number to be taken in the $th dimension To conform to the distribution The random variable, that is:

[0050] When the number of iterations reaches the upper limit, or the distribution of the Pareto front solution set of consecutive generations of schemes tends to stabilize, that is, when the population fitness variance of the particle swarm for consecutive generations reaches a certain level. Greater than or equal to the preset variance threshold Stop iteration; The optimal solution with the best overall performance is selected from the results of the last iteration, i.e., the fitness value in the particle swarm. The smallest particle is taken as the globally optimal solution; For the globally optimal solution, the selected energy storage access node and rated power are... Rated capacity The charging and discharging power curves are integrated into the grid topology, and the system structure is updated in real time.

[0051] Example 2 Figure 2 This is a system schematic diagram illustrating a novel power system energy storage structure optimization device according to another exemplary embodiment, the device comprising: Multi-objective optimization model construction module 1: used to construct a multi-objective optimization model with the configuration parameters of the energy storage system as decision variables. The multi-objective optimization model includes economic objectives, stability objectives, and reliability objectives. Constraint setting module 2: used to set the constraints of the multi-objective optimization model, including: rated power constraint, rated capacity constraint, charge and discharge power constraint and charge state constraint; Solving module 3: Used to iteratively solve the multi-objective optimization model using an improved multi-objective particle swarm optimization algorithm to obtain the optimal energy storage configuration scheme; including: initializing the particle swarm, where each particle represents an energy storage configuration scheme; determining whether mutation is triggered by calculating the population fitness variance of the current particle swarm; if mutation is not triggered, selecting the optimal particle in the current particle swarm, i.e., the optimal energy storage configuration scheme; if mutation is triggered, performing random mutation operation on the local optimal particles in the particle swarm by adding random perturbation according to the preset mutation probability; Update Module 4: Used to update the grid topology based on the optimal energy storage configuration.

[0052] Example 3 This embodiment provides a storage medium storing a computer program, which, when executed by a host controller, implements the various steps in the above method. It is understood that the storage medium mentioned above can be a read-only memory, a hard disk, or an optical disk, etc.

[0053] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0054] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0055] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0056] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0057] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0058] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0059] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0060] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0061] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A novel power system energy storage structure optimization method, characterized in that, The method comprises: constructing a multi-objective optimization model with configuration parameters of the energy storage system as decision variables, the multi-objective optimization model comprising an economy target, a stability target and a reliability target; setting constraint conditions of the multi-objective optimization model, the constraint conditions comprising: a rated power constraint, a rated capacity constraint, a charging and discharging power constraint and a state of charge constraint; iteratively solving the multi-objective optimization model by using an improved multi-objective particle swarm optimization algorithm to obtain an optimal energy storage configuration scheme; comprising: initializing a particle swarm, wherein each particle represents an energy storage configuration scheme; judging whether to trigger mutation by calculating a population fitness variance of the current particle swarm, if not, selecting an optimal particle in the current particle swarm, i.e. the optimal energy storage configuration scheme; if triggering mutation, performing a random mutation operation on the local optimal particle in the particle swarm by using a method of adding random disturbance according to a preset mutation probability; updating the power grid topology structure according to the optimal energy storage configuration scheme.

2. The method according to claim 1, wherein the economy target comprises: obtaining total life cycle investment cost of the energy storage, operation and maintenance cost of the energy storage and disposal cost after the energy storage system reaches the end of life, and constructing a minimum energy storage cost minimization target function; the stability target comprises: obtaining average frequency of power generation system outage, average duration of outage and average stable continuous power supply rate, and constructing a stability comprehensive index minimization target function; the reliability target comprises: obtaining supply shortage expectation value, time probability of power supply shortage, expected number of power supply shortage events and expected value of power supply shortage time, and constructing a reliability comprehensive index minimization target function.

3. The method according to claim 2, wherein the initializing of the particle swarm comprises: randomly selecting energy storage access node positions from specified alternative sensitive nodes based on a power grid topology containing wind power, photovoltaic and diesel engine, and randomly generating 24-hour charging and discharging power curves to form an initial energy storage configuration scheme set; calculating rated capacity and rated power of the energy storage of each initial energy storage configuration scheme according to the initial charging and discharging power curves, and removing the initial energy storage configuration scheme that does not meet the rated power constraint and rated capacity constraint from the initial energy storage configuration scheme set; calculating real-time SOC curves of the remaining initial energy storage configuration schemes according to the state of charge constraint, and removing the initial energy storage configuration scheme whose real-time SOC curve exceeds the minimum and maximum charging states of the energy storage device from the initial energy storage configuration scheme set; taking the remaining initial energy storage configuration schemes as the initial particle swarm of the improved multi-objective particle swarm optimization algorithm.

4. The method according to claim 3, wherein the judging whether to trigger mutation by calculating the population fitness variance of the current particle swarm comprises: calculating fitness values of each particle in the particle swarm; calculating an average fitness value of the particle swarm according to the fitness value of each particle; calculating a particle swarm normalization calibration factor by the average fitness value of the current particle swarm; calculate a population fitness variance of the current particle swarm according to the fitness value of each particle, the average fitness value of the particle swarm, and a normalized calibration factor; compare the population fitness variance of the current particle swarm with a preset variance threshold, if the population fitness variance of the current particle swarm is less than the preset variance threshold, trigger mutation, if the population fitness variance of the current particle swarm is greater than or equal to the preset variance threshold, do not trigger mutation.

5. The method of claim 4, wherein, the calculation of the fitness value of each particle in the particle swarm comprises: obtaining the function value of each particle on the economy target, the stability target and the reliability target through power flow calculation, performing weighted summation on the function value on the economy target, the stability target and the reliability target according to a preset weight, and obtaining a comprehensive target function value, and taking the comprehensive target function value as the fitness value of the particle.

6. The method of claim 5, wherein, the local optimal particle in the particle swarm comprises: setting a fitness threshold; comparing the fitness value of each particle in the particle swarm with the fitness threshold, if the fitness value of the particle is greater than the fitness threshold, taking the particle as a local optimal particle for random mutation operation.

7. The method of claim 6, wherein, the decision variable comprises: the energy storage access node position, the energy storage rated power, the energy storage rated capacity and the charging and discharging power.

8. A novel power system energy storage structure optimization device, characterized by, The device comprises: a multi-objective optimization model construction module for constructing a multi-objective optimization model taking the configuration parameters of the energy storage system as decision variables, the multi-objective optimization model comprising an economy target, a stability target and a reliability target; a constraint setting module for setting the constraint conditions of the multi-objective optimization model, the constraint conditions comprising: rated power constraint, rated capacity constraint, charging and discharging power constraint and state of charge constraint; a solving module for iteratively solving the multi-objective optimization model by using an improved multi-objective particle swarm optimization algorithm to obtain an optimal energy storage configuration scheme; comprising: initializing a particle swarm, wherein each particle represents an energy storage configuration scheme; determining whether to trigger mutation by calculating the population fitness variance of the current particle swarm, if mutation is not triggered, selecting the optimal particle in the current particle swarm, i.e. the optimal energy storage configuration scheme; if mutation is triggered, performing random mutation operation on the local optimal particle in the particle swarm by using the method of adding random disturbance according to a preset mutation probability; an updating module for updating the power grid topology structure according to the optimal energy storage configuration scheme.

9. A storage medium, characterized by The storage medium stores a computer program, and the computer program is executed by the host controller to realize each step in the novel power system energy storage structure optimization method of any one of claims 1-7.

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