Energy storage system optimal configuration method and system for green power direct supply scene

Abstract

The invention relates to an energy storage system optimization configuration method and system oriented to a green power direct supply scene. The method comprises the following steps: obtaining structure information of a hybrid energy storage system; a three-dimensional multi-target optimization model based on capacity, power and site selection is adopted for optimization solution, and an upper optimization model determines the optimal access position of the hybrid energy storage system by taking the capacity investment cost of the energy storage system, the deployment cost of a voltage sag detection device and the electric energy quality loss cost caused by substandard sensitive load voltage as optimization targets; the lower-layer optimization model optimizes and determines energy storage capacity and output power configuration by taking a combined compensation cost and voltage satisfaction index of the distributed energy storage and dynamic voltage restorer as a target; and solving the three-dimensional multi-objective optimization model by adopting a self-adaptive particle swarm optimization algorithm. Compared with the prior art, the method can effectively improve the reasonability of the access decision of the energy storage system, reduce the configuration cost, and enhance the flexibility and operation stability of the system in the green power direct supply mode.

Description

Technical Field

[0001] This invention relates to the field of energy storage system configuration technology, and in particular to an optimized configuration method and system for energy storage systems oriented towards direct green electricity supply scenarios. Background Technology

[0002] In recent years, with the widespread application of various sensitive load devices based on computer and microprocessor control, the power grid's requirements for power quality have been continuously increasing. In the distribution system, the large-scale connection of nonlinear loads, impulsive loads, and distributed power sources has triggered a variety of power quality problems, among which voltage sags have become one of the key factors affecting the safe and stable operation of power equipment.

[0003] Existing voltage sag mitigation methods mainly include solutions based on energy storage technology, constant voltage transformers, solid-state switches, inverters, and combined compensation. Among these, dynamic voltage restorers (DVRs) are an effective compensation method for sensitive loads, exhibiting good response characteristics during sag suppression. However, the compensation performance of DVRs is limited by their internal energy storage capacity, making it difficult to cope with voltage sag events that are prolonged or deep.

[0004] In recent years, with the advancement of battery technology and the gradual reduction in costs, distributed energy storage systems have experienced rapid development. Energy storage systems possess strong active and reactive power regulation capabilities, playing a crucial role in mitigating power fluctuations and improving voltage quality. However, the investment cost of distributed energy storage devices is relatively high, and their location and capacity significantly impact economic efficiency and compensation effectiveness. Therefore, optimizing the site selection and operation strategy of energy storage systems is a key issue in enabling their participation in voltage sag control.

[0005] Existing research has explored optimal configuration and control strategies for energy storage. For example, some literature proposes constructing a two-layer multi-objective optimization model with the goal of minimizing energy storage investment costs and voltage deviation, and solving it using the particle swarm optimization algorithm. Other studies have used time-series sensitivity analysis to optimize the location and capacity allocation of energy storage systems from the perspective of voltage improvement. In addition, a method for configuring energy storage sequences in unbalanced distribution networks based on voltage sensitivity analysis has been proposed to optimize system layout from the perspective of improving voltage support capabilities. However, current research is mostly limited to single energy storage forms or fixed control strategies, and lacks systematic methods for green electricity direct supply scenarios that integrate capacity-power-location coordinated optimization and joint compensation strategies. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an energy storage system optimization configuration method and system for green electricity direct supply scenarios. This method can select a reasonable access location for the energy storage system, reduce capacity configuration requirements, and improve the compensation effect through a joint compensation strategy, thereby reducing the overall investment cost of compensation equipment.

[0007] The objective of this invention can be achieved through the following technical solutions: An optimized configuration method for energy storage systems in green electricity direct supply scenarios includes: The grid topology and spatiotemporal distribution characteristics of the source and load of the hybrid energy storage system are obtained. The hybrid energy storage system includes a hydrogen energy storage unit and an electrochemical energy storage unit that operate in concert. A three-dimensional multi-objective optimization model based on capacity, power, and location is used to optimize the solution according to the power grid topology and spatiotemporal distribution characteristics. The three-dimensional multi-objective optimization model is divided into an upper-level optimization model and a lower-level optimization model. The upper-level optimization model takes the capacity cost of energy storage devices, the deployment cost of voltage sag detection devices, and the power quality loss cost caused by the voltage failure of sensitive loads as optimization objectives to determine the optimal access location of the hybrid energy storage system. The lower-level optimization model takes the joint compensation cost of distributed energy storage and dynamic voltage restorers and the voltage satisfaction index as objectives to optimize and determine the configuration of energy storage capacity and output power. An adaptive particle swarm optimization algorithm is used to solve a three-dimensional multi-objective optimization model and obtain an optimal configuration scheme.

[0008] Furthermore, the upper-level optimization model is used to determine the optimal access location of the distributed energy storage system. Based on the characteristics of the power grid topology and the spatiotemporal distribution of source loads, candidate nodes are initially screened using the overall ranking method. Combined with the clustering analysis results of typical scenarios, the energy storage investment cost and the voltage ride-through performance of sensitive loads are comprehensively considered to determine an energy storage deployment scheme that balances economy and reliability.

[0009] Furthermore, the objective function of the upper-level optimization model is expressed as follows: In the formula, The objective function of the upper-level optimization model is... For the capacity cost of energy storage equipment, The deployment cost of voltage sag detection devices, Costs related to power quality losses caused by substandard voltage at sensitive loads. , , ,..., It is the node number; and They are installed on the corresponding nodes. The energy storage power and capacity.

[0010] Furthermore, in the calculation of the capacity cost of the energy storage device, the capacity cost and energy consumption of the energy storage device are considered, and the calculation is based on the response behavior of the hybrid energy storage system in the j-th voltage sag event. The corresponding calculation expression is: In the formula, and Let be the capacity and energy consumed by energy storage device i during the j-th voltage drop event, respectively. , is the active and reactive power output of energy storage device i during the j-th voltage drop event; n is the installation location of the energy storage system; It is the duration of the voltage sag. Let be the capacity consumed during j voltage sag events. Let be the energy consumed during j voltage drop events.

[0011] Furthermore, the calculation expression for the deployment cost of the voltage sag detection device is as follows: In the formula, The installation cost of voltage sag detection device i; It is a constant for installation costs; It is a set indicator variable to indicate whether a voltage sag detection device is installed at that location.

[0012] Furthermore, the calculation expression for the power quality loss cost caused by the substandard voltage of the sensitive load is as follows: In the formula, This is the lower limit of the compliant voltage. It is the voltage cost of the sensitive load g; It is the quality cost of the g-th sensitive load node during the transient process; To penalize nonlinear exponents.

[0013] Furthermore, the lower-level optimization model is used to determine the capacity configuration ratio of the distributed energy storage system. It uses the energy storage system capacity, voltage satisfaction, and compensation cost as multi-objective optimization functions, and the control variables are energy storage capacity and power ratio. The optimal capacity configuration scheme is obtained through global optimization.

[0014] Furthermore, the objective function of the lower-level optimization model is expressed as follows: In the formula, The objective function of the lower-level optimization model; and These are the BESS and DVR costs for node i, respectively; For sensitive load voltage satisfaction index; , and This is a weighting factor.

[0015] Furthermore, the particle swarm algorithm adopts an adaptive adjustment strategy for the inertia weight during the solution process. When the particle swarm tends to concentrate, the inertia weight is increased to enhance the global search capability, and when the particle swarm tends to disperse, the inertia weight is decreased to improve the local search accuracy. The update expression for the inertia weight is: In the formula, For the first k The th iteration i Inertial weight, To adjust the coefficient, For the first k The baseline inertia weight in the next iteration For the first k Particle swarm aggregation index in the next iteration. This is the lower limit threshold for clustering. This represents the upper limit threshold for clustering.

[0016] The present invention also provides an energy storage system optimization configuration system for green electricity direct supply scenarios, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.

[0017] Compared with the prior art, the present invention has the following advantages: (1) This invention proposes a joint optimization configuration method for energy storage system capacity determination, power, and location selection for green electricity direct supply scenarios. Aiming to balance system economy and voltage support performance, a two-layer optimization model with interconnected upper and lower layers is established. The upper-layer model optimizes the access location of distributed energy storage, while the lower-layer model optimizes energy storage capacity and output power strategies. In the upper-layer optimization, node sorting is performed based on voltage sensitivity analysis to initially screen energy storage deployment areas, thereby effectively reducing the problem dimension and computational complexity. The lower layer combines compensation effect and cost for capacity configuration, ultimately obtaining the optimal energy storage deployment scheme that meets the characteristics of green electricity direct supply and sag suppression requirements, significantly improving power quality control capabilities while reducing investment costs. Attached Figure Description

[0018] Figure 1 This is a structural block diagram of a voltage sag mitigation mode provided in an embodiment of the present invention.

[0019] Figure 2 This is a flowchart of a system using the IEEE 33 bus provided in an embodiment of the present invention.

[0020] Figure 3This is a schematic diagram of an IEEE 33 bus system structure provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0024] Example 1 like Figure 1 and Figure 2 As shown, this embodiment provides an optimized configuration method for energy storage systems in green electricity direct supply scenarios, including: S1: Obtain the grid topology and spatiotemporal distribution characteristics of the source and load of the hybrid energy storage system, which includes hydrogen energy storage units and electrochemical energy storage units operating in tandem. S2: A three-dimensional multi-objective optimization model based on capacity, power, and location is adopted to optimize the solution according to the grid topology and spatiotemporal distribution characteristics. The three-dimensional multi-objective optimization model is divided into an upper-level optimization model and a lower-level optimization model. The upper-level optimization model takes the capacity investment cost of the energy storage system, the deployment cost of the voltage sag detection device, and the power quality loss cost caused by the voltage failure of sensitive loads as optimization objectives to determine the optimal access location of the hybrid energy storage system. The lower-level optimization model takes the joint compensation cost of distributed energy storage and dynamic voltage restorer and the voltage satisfaction index as objectives to optimize and determine the configuration of energy storage capacity and output power. S3: Use the adaptive particle swarm optimization algorithm to solve the three-dimensional multi-objective optimization model and obtain the optimal configuration scheme.

[0025] Furthermore, the upper-level optimization model is used to determine the optimal access location of the distributed energy storage system. Based on the characteristics of the power grid topology and the spatiotemporal distribution of source loads, candidate nodes are initially screened using the overall ranking method. Combined with the clustering analysis results of typical scenarios, the energy storage investment cost and the voltage ride-through performance of sensitive loads are comprehensively considered to determine an energy storage deployment scheme that balances economy and reliability.

[0026] Furthermore, the lower-level optimization model is used to determine the capacity configuration ratio of the distributed energy storage system, and a hybrid mutation differential particle swarm optimization algorithm is used to solve it. This algorithm combines random mutation and adaptive inertial weight adjustment mechanism, takes the energy storage system capacity, voltage satisfaction and compensation cost as multi-objective optimization functions, and the energy storage capacity and power ratio as control variables, and obtains the optimal capacity configuration scheme through global optimization.

[0027] Furthermore, the particle swarm optimization algorithm adopts an adaptive adjustment strategy for the inertia weight during the solution process. When the particle swarm tends to concentrate, the inertia weight is increased to enhance the global search capability, and when the particle swarm tends to disperse, the inertia weight is decreased to improve the local search accuracy.

[0028] In this embodiment, a two-layer optimization model is established to address the location and capacity determination problems of energy storage systems in voltage sag control. This model includes upper-layer location optimization and lower-layer capacity and power configuration. The upper-layer model determines the optimal access point for energy storage with economic efficiency and voltage quality as objectives. The lower-layer model combines distributed energy storage and DVR to construct a joint compensation strategy, which is solved with compensation cost and voltage satisfaction as objectives, using a stochastic particle swarm optimization algorithm with adaptive inertia weights. Finally, the effectiveness of the proposed method in improving compensation performance and reducing configuration costs is verified through IEEE 33-bus system simulation.

[0029] The above scheme is described in detail below: For an energy storage system participating in voltage sag control mode, the energy change of the energy storage system can be expressed as: SOE for energy storage; This is the rated energy storage capacity; It is the change of SOE from time t to time t+1; as well as The charging and discharging efficiency of energy storage systems; This is the loss factor.

[0030] The upper-level optimization model has two objectives: economic and voltage quality. The economic objective considers the capacity cost of the energy storage device. Installation cost of voltage descent detection equipment And voltage quality costs of sensitive equipment It is represented as follows: The capacity cost of the energy storage device, for the entire energy storage system, is as follows: The required capacity and energy consumed during j voltage sag events are: and Let be the capacity and energy consumed by energy storage system i during the j-th voltage sag events, respectively. , is the active and reactive power output of energy storage system i during the j-th voltage drop event; n is the installation location of the energy storage system; It is the duration of the voltage dip.

[0031] In the compensation for j voltage sag events, the investment cost of energy storage is: This refers to the comprehensive unit price on the capacity side. This refers to the comprehensive unit price on the energy side. It is the fixed investment cost of energy storage.

[0032] The allocation cost of energy storage is typically linearly related to its capacity, as shown in the following equation: and These are the capacity and energy conversion factors, respectively.

[0033] During the m voltage dips, the total storage capacity and energy required by the energy storage system are: In many voltage sag events, the cost of the energy storage system is: The installation cost of voltage sag detection equipment is necessary because of the randomness of voltage sags, requiring compensation equipment to respond quickly to voltage sag compensation needs. Therefore, it is essential to install voltage sag detection equipment on energy storage devices. The installation cost can be expressed as: Installation cost of energy storage testing equipment; It is a constant for installation costs; It is a deployment indicator variable that indicates whether a temporary descent detection device is installed at that location.

[0034] The voltage quality cost of sensitive equipment is expressed as follows: Let be the voltage of the g-th sensitive load node; This is the lower limit of the compliant voltage. It is the voltage cost of the sensitive load g; It is the quality cost of the g-th sensitive load node during the transient process; To penalize nonlinear exponents.

[0035] The objective function of the upper-level optimization model is to minimize the voltage sag compensation cost. The installation location and rated capacity of the energy storage device are used as decision variables. Constraints include transient voltage stability constraints, active and reactive power balance constraints of the system, and transmission line power constraints. The optimization model is expressed as: , , ,..., It is the node number; and They are installed on the corresponding nodes. Energy storage power and capacity; These are constraints. To ensure the safe and stable operation of the power grid, a series of equality and inequality constraints need to be satisfied.

[0036] The equality constraints include system power flow constraints and energy storage rated power and capacity constraints. The system power flow constraints can be expressed as: , and These represent the active power output of the energy storage at node i, the active power output of the balancing node, and the active power demand of the load. , These represent the reactive power output of the energy storage at node i, the reactive power output of the balancing node, and the reactive power demand of the load, respectively. It is the voltage at node i; and These represent the conductance and susceptance between nodes i and j, respectively. It is the phase difference between nodes i and j.

[0037] The rated power and capacity constraints of energy storage can be expressed as: ,and These are the lower and upper limits for the active and reactive power output of each energy storage unit, respectively. It is the rated capacity of energy storage.

[0038] The inequality constraints include: state of charge constraints for the energy storage system; node voltage constraints; and transmission line power constraints. The state of charge constraints for the energy storage system can be expressed as: Node voltage constraints can be expressed as: The power constraint of a transmission line can be expressed as: The voltage at the sensitive load; The value is the unit voltage value, and its value is 1. It is k times the active power transmitted by the line.

[0039] The lower-level optimization model optimizes the energy storage system capacity, employing a combination of distributed energy storage and DVR to compensate for voltage sags. The established joint compensation model... Cost compensation through distributed energy storage , The objective function is to satisfy the minimum compensation cost of the DVR and the maximum voltage of the sensitive load, taking into account constraints such as node voltage sag, distributed energy storage, DVR output constraints, and power flow convergence. The objective function can be expressed as: In the formula: Cost compensation for capacity configuration and operation of distributed energy storage systems; and These are the BESS and DVR costs for node i, respectively; For sensitive load voltage satisfaction index; This is a weighting factor.

[0040] The distributed energy storage compensation cost is expressed as follows: and These are the active and reactive power outputs of the i-th energy storage event; It is the unit price coefficient on the capacity side. It is the reactive power penalty coefficient.

[0041] The DVR compensation cost is expressed as: , It is a cost coefficient; It is the upper limit of the DVR's power output.

[0042] The voltage adequacy of sensitive loads is divided into three parts based on the sensitive load's withstand voltage and normal voltage: Let be the voltage of the sensitive load at node i; It is the lower limit of the target compliance voltage; It is the withstand voltage of the sensitive load.

[0043] When the objective function is adopted When used as the objective function, it can be expressed as: This refers to the charging and discharging power of the distributed energy storage system, where DVR=0. It is an equality constraint condition; These are inequality constraints.

[0044] To improve the global search and convergence performance of solving the lower-level model, the particle swarm optimization algorithm adopts an adaptive strategy of individual fitness-driven and population diversity correction.

[0045] 1) Adaptive update of inertia weight Define normalized fitness In the formula It represents the particle fitness. This is the population average. It is the best of our time. It is a very small constant.

[0046] Benchmark weight Diversity Indicators Segmented correction by weight 2) The learning factor is linearly scheduled according to the iteration. Where C1 is the individual learning factor and C2 is the group learning factor.

[0047] 3) Speed-Position Update in , The contraction factor is set at 0.7-0.9.

[0048] like Figure 3 As shown, this invention uses the IEEE 33 system as the distribution network test system for simulation experiments. Nodes 7, 25, 26, and 33, acting as sensitive loads, are generated using the Monte Carlo method, and voltage sag test waveforms at the connection points are measured. Then, phase transition information is calculated based on the distribution network parameters. The distribution network parameters and control variable information are shown in Table 1.

[0049] Table 1 See Figure 3 By sorting the voltage recovery amount of each sensitive load and the total voltage recovery amount, the optimal access point and the optimal integrated access point for compensation of a single sensitive load are obtained. The access location with the best effect on a single sensitive load point and the access location with the best overall effect are selected as effective access locations. The selected nodes are 32, 31, 33, 30, 26, 25, 12, and 2. The location and capacity determination scheme of distributed energy storage is obtained by solving the problem, as shown in Table 2.

[0050] Table 2 See Figure 3 Under optimal configuration, the voltage at the node containing the sensitive load meets the compensation requirements. Furthermore, since the principle of energy storage differs from that of DVR compensation, using energy storage compensation can increase the voltage level of the branch where the access point is located. In the example, although the objective function only includes the voltage parameters of four sensitive loads, after optimization, the voltage of all nodes has increased, and the voltage of two-thirds of the nodes has reached more than 90% of the rated voltage.

[0051] Three locations were randomly selected from the initial eight distributed energy storage access sites, and capacity was optimized only. The results are shown in Table 3.

[0052] Table 3 Referring to Table 3, although groups three and five meet the compensation requirements, they require more capacity than group one; groups two and four do not meet the voltage requirements of sensitive loads. The results show that the proposed optimized configuration scheme can meet the economic and technical requirements.

[0053] This invention addresses the optimization of distributed energy storage configuration, establishing a two-layer optimization model with economic efficiency and voltage safety as objectives. The upper layer optimizes the access location of distributed energy storage, while the lower layer optimizes storage capacity. Results show that determining the initial installation location range based on voltage sensitivity ranking reduces computational complexity, improves optimization speed, and ultimately yields the optimal solution for energy storage device access location and configuration capacity.

[0054] Example 2 This embodiment provides an energy storage system optimization configuration system for green electricity direct supply scenarios, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method in Embodiment 1 above.

[0055] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0056] The computer program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This computer program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the computer program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0057] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0058] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for optimizing the configuration of energy storage systems for direct green electricity supply scenarios, characterized in that, include: The grid topology and spatiotemporal distribution characteristics of the source and load of the hybrid energy storage system are obtained. The hybrid energy storage system includes a hydrogen energy storage unit and an electrochemical energy storage unit that operate in concert. A three-dimensional multi-objective optimization model based on capacity, power, and location is used to optimize the solution according to the power grid topology and spatiotemporal distribution characteristics. The three-dimensional multi-objective optimization model is divided into an upper-level optimization model and a lower-level optimization model. The upper-level optimization model takes the capacity cost of energy storage devices, the deployment cost of voltage sag detection devices, and the power quality loss cost caused by the voltage failure of sensitive loads as optimization objectives to determine the optimal access location of the hybrid energy storage system. The lower-level optimization model takes the joint compensation cost of distributed energy storage and dynamic voltage restorers and the voltage satisfaction index as objectives to optimize and determine the configuration of energy storage capacity and output power. An adaptive particle swarm optimization algorithm is used to solve a three-dimensional multi-objective optimization model and obtain an optimal configuration scheme.

2. The method for optimizing the configuration of an energy storage system for direct green electricity supply scenarios according to claim 1, characterized in that, The upper-level optimization model is used to determine the optimal access location of the distributed energy storage system. Based on the characteristics of the power grid topology and the spatiotemporal distribution of source loads, the model performs preliminary screening of candidate nodes through the overall ranking method. Combined with the clustering analysis results of typical scenarios, the model comprehensively considers the energy storage investment cost and the voltage ride-through performance of sensitive loads to determine an energy storage deployment scheme that balances economy and reliability.

3. The method for optimizing the configuration of an energy storage system for direct green electricity supply scenarios according to claim 1, characterized in that, The objective function of the upper-level optimization model is expressed as follows: In the formula, The objective function of the upper-level optimization model is... For the capacity cost of energy storage equipment, The deployment cost of voltage sag detection devices, Costs related to power quality losses caused by substandard voltage at sensitive loads. , , ,..., It is the node number; and They are installed on the corresponding nodes. The energy storage power and capacity.

4. The energy storage system optimization configuration method for green electricity direct supply scenarios according to claim 3, characterized in that, In the calculation of the capacity cost of the energy storage device, the capacity cost and energy consumption of the energy storage device are considered. The calculation is based on the response behavior of the hybrid energy storage system in the j-th voltage sag event, and the corresponding calculation expression is: In the formula, and Let be the capacity and energy consumed by energy storage device i during the j-th voltage drop event, respectively. , is the active and reactive power output of energy storage device i during the j-th voltage drop event; n is the installation location of the energy storage system; It is the duration of the voltage sag. Let be the capacity consumed during j voltage sag events. Let be the energy consumed during j voltage drop events.

5. The method for optimizing the configuration of an energy storage system for direct green electricity supply scenarios according to claim 3, characterized in that, The formula for calculating the deployment cost of the voltage sag detection device is as follows: In the formula, The installation cost of voltage sag detection device i; It is a constant for installation costs; It is a set indicator variable to indicate whether a voltage sag detection device is installed at that location.

6. The method for optimizing the configuration of an energy storage system for direct green electricity supply scenarios according to claim 3, characterized in that, The formula for calculating the cost of power quality loss caused by the substandard voltage of the sensitive load is as follows: In the formula, This is the lower limit of the compliant voltage. It is the voltage cost of the sensitive load g; It is the quality cost of the g-th sensitive load node during the transient process; To penalize nonlinear exponents.

7. The method for optimizing the configuration of an energy storage system for direct green electricity supply scenarios according to claim 1, characterized in that, The lower-level optimization model is used to determine the capacity configuration ratio of the distributed energy storage system. It uses the energy storage system capacity, voltage satisfaction and compensation cost as multi-objective optimization functions, and the control variables are energy storage capacity and power ratio. The optimal capacity configuration scheme is obtained through global optimization.

8. The method for optimizing the configuration of an energy storage system for direct green electricity supply scenarios according to claim 7, characterized in that, The objective function of the lower-level optimization model is expressed as follows: In the formula, The objective function of the lower-level optimization model; and These are the BESS and DVR costs for node i, respectively; For sensitive load voltage satisfaction index; , and This is a weighting factor.

9. The method for optimizing the configuration of an energy storage system for direct green electricity supply scenarios according to claim 1, characterized in that, The particle swarm optimization algorithm adopts an adaptive adjustment strategy for inertia weight during the solution process. When the particle swarm tends to concentrate, the inertia weight is increased to enhance the global search capability, and when the particle swarm tends to disperse, the inertia weight is decreased to improve the local search accuracy. The update expression for the inertia weight is: In the formula, For the first k The th iteration i Inertial weight, To adjust the coefficient, For the first k The baseline inertia weight in the next iteration For the first k Particle swarm aggregation index in the next iteration. This is the lower limit threshold for clustering. This represents the upper limit threshold for clustering.

10. An energy storage system optimization configuration system for green electricity direct supply scenarios, characterized in that, It includes a memory and a processor, the memory storing a computer program, the processor invoking the computer program to perform the steps of the method as described in any one of claims 1 to 9.

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