A method for collaborative configuration of shared energy storage and transmission lines

Abstract

The present invention provides a method for collaborative configuration of shared energy storage and transmission lines, belonging to the field of smart grid technology. The method collects and preprocesses power system data, establishes an uncertainty model for renewable energy output and load, and generates multiple operating scenarios. A collaborative optimization configuration model with the minimization of the system's annual comprehensive cost as the objective function is constructed, taking into account the constraints related to energy storage and transmission lines. An improved particle swarm optimization algorithm is used to solve the model to obtain an energy storage configuration scheme (installation location, capacity configuration, and charging and discharging strategy) and a transmission line planning scheme (expansion path, capacity planning, and upgrade and transformation measures). A multi-index comprehensive evaluation system is used to comprehensively evaluate the scheme. The present invention adopts the above-mentioned method for collaborative configuration of shared energy storage and transmission lines, which can effectively improve the new energy absorption capacity, alleviate transmission line congestion, improve the system economy and reliability, and is suitable for the planning and operation of power systems with large-scale renewable energy access.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a method for collaboratively configuring shared energy storage and transmission lines. Background Art

[0002] As the global energy transition progresses, the penetration of renewable energy sources (such as wind power and photovoltaics) in power systems continues to increase. However, renewable energy generation is intermittent and uncertain, and its output fluctuations can cause grid power imbalances, voltage instability, and transmission line congestion. Traditional power systems, which primarily rely on controllable power sources such as thermal power, are unable to adapt to the needs of large-scale renewable energy integration.

[0003] Existing approaches to energy storage deployment and transmission line planning often operate independently, failing to fully tap the potential for synergy between the two. On the one hand, if energy storage deployment is separated from the transmission line layout, it may not effectively smooth out grid power fluctuations, leading to increased investment costs and operating losses. On the other hand, if transmission line planning lacks energy storage support, it may require excessive expansion to meet the demand for renewable energy access, resulting in a waste of resources.

[0004] Furthermore, the complexity of electricity market mechanisms poses challenges for collaborative optimization. Existing approaches often lack effective game models for navigating the distribution of benefits and strategic interactions among multiple stakeholders (such as energy storage operators, grid companies, and power generation companies), making it difficult to achieve optimal overall system operation. Summary of the Invention

[0005] The purpose of this invention is to provide a method for the coordinated configuration of shared energy storage and transmission lines, which can effectively enhance the capacity to absorb new energy, alleviate transmission line congestion, and improve the economy and reliability of the system. It is suitable for the planning and operation of power systems with large-scale new energy access.

[0006] To achieve the above objectives, the present invention provides a method for collaboratively configuring shared energy storage and transmission lines, comprising the following steps:

[0007] Step S1, collecting relevant data of the power system;

[0008] Step S2: Establish an uncertainty model for renewable energy output and load, and generate multiple operating scenarios using probability distribution fitting and scenario generation methods;

[0009] Step S3: Constructing a collaborative optimization configuration model for shared energy storage and transmission lines, with minimization of the system's annual comprehensive cost as the objective function. The objective function includes the investment cost, operation and maintenance cost, and energy loss cost of the energy storage equipment and transmission lines. Constraints in the collaborative optimization configuration model include energy storage charge and discharge power limits, capacity limits, transmission line power flow constraints, and node voltage constraints.

[0010] Step S4: using an improved particle swarm optimization algorithm to solve the collaborative optimization configuration model to obtain an energy storage configuration scheme and a transmission line planning scheme;

[0011] Step S5: Evaluate the energy storage configuration plan and the transmission line planning plan. The evaluation indicators include the new energy absorption capacity, the transmission line congestion relief effect, the energy storage equipment utilization rate, and the system economy.

[0012] Preferably, in step S1, the relevant data includes output data of the new energy generator set, load data, transmission line parameters and energy storage equipment characteristics, and the collected data is cleaned and normalized preprocessed.

[0013] Preferably, in step S2, the scene generation method adopts Latin hypercube sampling technology.

[0014] Preferably, in step S2, the probability density function of the new energy power generation power is:

[0015] ;

[0016] in, represents the probability density function of renewable energy power generation, Indicates the value of the power generated by renewable energy. represents the variance of renewable energy power generation, represents the base of natural logarithms, Represents the average power of renewable energy generation;

[0017] The probability density function of load power is:

[0018] ;

[0019] in, represents the probability density function of load power, Indicates the value of load power, represents the mean value of load power, Represents the variance of load power.

[0020] Preferably, in step S3, the objective function is:

[0021] ;

[0022] in, Indicates the annual comprehensive cost of the system, represents the investment cost of energy storage equipment, represents the transmission line investment cost, represents the operation and maintenance cost of energy storage equipment, represents the operation and maintenance cost of the transmission line, represents the energy loss cost;

[0023] The charging and discharging power limits of energy storage are as follows:

[0024] ;

[0025] ;

[0026] in, Indicates the energy storage charging power, 、 Represent the minimum and maximum values ​​of energy storage charging power, Indicates the energy storage discharge power, 、 Respectively represent the minimum and maximum values ​​of the energy storage discharge power;

[0027] Capacity limits are as follows:

[0028] ;

[0029] in, Indicates the current capacity of the energy storage device. 、 Respectively represent the minimum and maximum capacity of the energy storage device;

[0030] The power flow constraints of the transmission line are as follows:

[0031] ;

[0032] in, represents the power flow of the transmission line, 、 They represent the minimum and maximum values ​​of the power flow of the transmission line respectively;

[0033] The node voltage constraints are as follows:

[0034] ;

[0035] in, represents the node voltage, 、 Represent the minimum and maximum values ​​of the node voltage respectively.

[0036] Preferably, in step S3, the collaborative optimization configuration model includes the following four-layer structure:

[0037] The strategic planning layer is used to determine the overall strategic direction of the coordinated configuration of shared energy storage and transmission lines, and analyze the cooperative and competitive relationships among stakeholders through a cooperative game model;

[0038] The market transaction layer is used to formulate strategies for shared energy storage to participate in power market transactions and optimize market transaction strategies through a non-cooperative game model;

[0039] The operation scheduling layer is used to optimize the scheduling of shared energy storage and transmission lines based on real-time operating status, and achieve coordinated scheduling through the Stackelberg game model;

[0040] The device control layer is used to achieve precise control of the device and optimize the device control parameters through the evolutionary game model.

[0041] Preferably, in step S4, the improved particle swarm algorithm is as follows:

[0042] Initialize the particle swarm, where each particle represents a combination of an energy storage configuration scheme and a transmission line planning scheme, and the position and velocity of each particle are randomly initialized;

[0043] Calculate the fitness of each particle. The fitness function takes the inverse of the system's annual comprehensive cost:

[0044] ;

[0045] in, represents the adjusted fitness value, represents the original fitness value, represents the dynamic adjustment coefficient, Indicates the current iteration number, Indicates the maximum number of iterations;

[0046] Update the individual extreme value and global extreme value of each particle, and use the improved strategy to adjust the particle speed as follows:

[0047] ;

[0048] in, Indicates the adjusted speed, Represents particles In the The speed of dimension, represents the inertia weight, 、 represents the learning factor, 、 represents a random number, Represents particles The individual extreme value of The location of the dimension, Indicates that the global extreme value is The location of the dimension, Represents particles In the Dimensional location;

[0049] In view of the complex constraints in the coordinated configuration of shared energy storage and transmission lines, an improved penalty function method is used to handle the constraints. For particles that violate the constraints, a penalty term is calculated based on the degree of violation and incorporated into the fitness function. The formula is expressed as follows:

[0050] ;

[0051] in, Indicates the fitness value after adding penalty, represents the penalty coefficient, Indicates the The degree of violation of the constraint condition, Indicates the maximum number of constraints;

[0052] Update the particle position and ensure that the updated particle position meets the constraints of the energy storage and transmission lines. The specific method is: if the updated position causes the energy storage charging and discharging power to exceed the limit or the transmission line power flow to exceed the limit, then adjust the particle position to within the allowable range of the constraints. The adjustment formula is:

[0053] ;

[0054] in, Indicates the updated position. 、 Represents the particle position in The minimum and maximum allowed values ​​for the dimension;

[0055] Repeat the above steps until the maximum number of iterations is reached or the fitness meets the predetermined threshold, and output the energy storage configuration scheme and transmission line planning scheme corresponding to the optimal particle position.

[0056] Preferably, the energy storage configuration plan includes the installation location, capacity configuration and charging and discharging strategy of the energy storage equipment; the transmission line planning plan includes the expansion path, capacity planning and upgrading and transformation measures.

[0057] Preferably, in step S5, the evaluation method adopts a multi-index comprehensive evaluation system, and the comprehensive evaluation formula is:

[0058] ;

[0059] in, represents the comprehensive evaluation index, 、 、 、 、 They are the weight coefficients of new energy consumption rate, transmission line congestion rate, energy storage equipment utilization rate, system annual comprehensive cost and reliability index, represents the evaluation value of new energy consumption rate, represents the transmission line blocking rate evaluation value, represents the utilization evaluation value of energy storage equipment, Indicates the annual comprehensive cost assessment value of the system, Represents the reliability index evaluation value.

[0060] Therefore, the present invention adopts the above-mentioned method for collaborative configuration of shared energy storage and transmission lines, and the beneficial technical effects are as follows:

[0061] (1) This invention organically combines energy storage configuration and transmission line planning by constructing a collaborative optimization configuration model, overcoming the poor coordination problem caused by the independent planning of the two in traditional methods. Based on a multi-layer game model (cooperative game, non-cooperative game, Stackelberg game, evolutionary game), it achieves full-level optimization from strategic planning to equipment control, accurately balances the demands of various stakeholders, and improves the overall efficiency of the system.

[0062] (2) To address the uncertainty of renewable energy output and load, Latin hypercube sampling technology is used to generate multiple operating scenarios, which are then solved using an improved particle swarm optimization algorithm. This algorithm introduces a dynamic adjustment coefficient and an improved penalty function method to effectively handle complex constraints, ensuring the feasibility and robustness of the solution under different operating conditions, significantly outperforming conventional optimization algorithms.

[0063] (3) The evaluation system uses a multi-index comprehensive assessment to comprehensively quantify the scheme's comprehensive benefits in terms of new energy consumption, congestion relief, energy storage utilization, and economic efficiency. Through the precise layout and reasonable configuration of energy storage, combined with the scientific planning of transmission lines, the annual overall system cost can be effectively reduced, equipment utilization efficiency and grid stability can be improved, and the power system can be promoted to be flexible, economical, and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of a method for collaborative configuration of shared energy storage and transmission lines according to the present invention;

[0065] Figure 2 A four-layer structure of the configuration model is configured for collaborative optimization;

[0066] Figure 3 Flowchart of the improved particle swarm optimization algorithm. DETAILED DESCRIPTION

[0067] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0068] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0069] Example 1

[0070] like Figure 1 As shown, the present invention provides a method for collaboratively configuring shared energy storage and transmission lines, comprising the following steps:

[0071] Step S1, collecting relevant data of the power system;

[0072] The relevant data includes output data of new energy generators, load data, transmission line parameters and energy storage equipment characteristics, and the collected data is cleaned and normalized before processing.

[0073] Step S2: Establish an uncertainty model for renewable energy output and load, and use probability distribution fitting and scenario generation methods to generate multiple operating scenarios; the scenario generation method uses Latin hypercube sampling technology.

[0074] The probability density function of renewable energy power generation is:

[0075] ;

[0076] in, represents the probability density function of renewable energy power generation, Indicates the value of the power generated by renewable energy. represents the variance of renewable energy power generation, represents the base of natural logarithms, Represents the average power of renewable energy generation;

[0077] The probability density function of load power is:

[0078] ;

[0079] in, represents the probability density function of load power, Indicates the value of load power, represents the mean value of load power, Represents the variance of load power.

[0080] Step S3: Constructing a collaborative optimization configuration model for shared energy storage and transmission lines, with minimization of the system's annual comprehensive cost as the objective function. The objective function includes the investment cost, operation and maintenance cost, and energy loss cost of the energy storage equipment and transmission lines. Constraints in the collaborative optimization configuration model include energy storage charge and discharge power limits, capacity limits, transmission line power flow constraints, and node voltage constraints.

[0081] like Figure 2 As shown in the figure, the collaborative optimization configuration model includes the following four layers:

[0082] The strategic planning layer is used to determine the overall strategic direction of the coordinated configuration of shared energy storage and transmission lines, and analyze the cooperative and competitive relationships among stakeholders through a cooperative game model;

[0083] The cooperative game model at the strategic planning level adopts the Shapley value method, and its formula is:

[0084] ;

[0085] in, Representing stakeholders The Shapley value of is used to measure its marginal contribution to the overall cooperation. represents the characteristic function of alliance S, represents the set of all stakeholders, represents the size of the alliance S, Represents the total number of stakeholders. By calculating the Shapley value of each stakeholder, we can determine their reasonable benefit distribution in the collaborative configuration.

[0086] The market transaction layer is used to formulate strategies for shared energy storage to participate in power market transactions and optimize market transaction strategies through a non-cooperative game model;

[0087] The non-cooperative game model at the market transaction layer adopts the Nash equilibrium method, and its formula is:

[0088] ;

[0089] in, represents the Nash equilibrium point, that is, under this strategy combination, no single stakeholder can obtain higher returns by changing its own strategy. Representing stakeholders strategy, Representing stakeholders The utility function of , which represents its benefits in market transactions, Represents the strategy combination of other stakeholders. By solving the Nash equilibrium, we can determine the optimal strategy of each market player in the shared energy storage market transaction, and achieve fairness and efficiency in market transactions.

[0090] The operation scheduling layer is used to optimize the scheduling of shared energy storage and transmission lines based on real-time operating status, and achieve coordinated scheduling through the Stackelberg game model;

[0091] In the Stackelberg game model of the operation scheduling layer, the upper-level scheduling center acts as the leader and the lower-level scheduling entities act as followers. The formula is:

[0092] ;

[0093] in, Indicates the strategy of the superior dispatching center, including the power flow distribution of transmission lines and the dispatch instructions of energy storage equipment, Indicates the strategy of the lower-level dispatching entity, including the output adjustment of each generator set and the charging and discharging response of the energy storage equipment. The Stackelberg game model represents the profit function of the higher-level dispatch center, taking into account factors such as system operating costs, renewable energy consumption benefits, and transmission line loss costs. This model can be used to optimize coordination between higher- and lower-level dispatch entities, improving the overall operational efficiency of the system.

[0094] The device control layer is used to achieve precise control of the device and optimize the device control parameters through the evolutionary game model:

[0095] ;

[0096] in, Indicates the manufacturer of the device The probability of adopting strategy A (e.g., improving the performance of energy storage equipment); represents the probability of adopting strategy B (such as reducing the cost of energy storage equipment), 、 、 、 These are the profit coefficients for different strategy combinations, taking into account factors such as market share, cost input, and technological innovation. The evolutionary game model can simulate the strategic evolution trends of equipment manufacturers, operators, and grid operators in the long-term decision-making process, providing a basis for optimizing the equipment control layer.

[0097] The objective function is:

[0098] ;

[0099] in, Indicates the annual comprehensive cost of the system, represents the investment cost of energy storage equipment, represents the transmission line investment cost, represents the operation and maintenance cost of energy storage equipment, represents the operation and maintenance cost of the transmission line, represents the energy loss cost;

[0100] The charging and discharging power limits of energy storage are as follows:

[0101] ;

[0102] ;

[0103] in, Indicates the energy storage charging power, 、 Represent the minimum and maximum values ​​of energy storage charging power, Indicates the energy storage discharge power, 、 Respectively represent the minimum and maximum values ​​of the energy storage discharge power;

[0104] Capacity limits are as follows:

[0105] ;

[0106] in, Indicates the current capacity of the energy storage device. 、 Respectively represent the minimum and maximum capacity of the energy storage device;

[0107] The power flow constraints of the transmission line are as follows:

[0108] ;

[0109] in, represents the power flow of the transmission line, 、 They represent the minimum and maximum values ​​of the power flow of the transmission line respectively;

[0110] The node voltage constraints are as follows:

[0111] ;

[0112] in, represents the node voltage, 、 Represent the minimum and maximum values ​​of the node voltage respectively.

[0113] Step S4: using an improved particle swarm optimization algorithm to solve the collaborative optimization configuration model to obtain an energy storage configuration scheme and a transmission line planning scheme;

[0114] like Figure 3 As shown, the improved particle swarm optimization algorithm is as follows:

[0115] Initialize the particle swarm, where each particle represents a combination of an energy storage configuration scheme and a transmission line planning scheme, and the position and velocity of each particle are randomly initialized;

[0116] Calculate the fitness of each particle. The fitness function takes the inverse of the system's annual comprehensive cost:

[0117] ;

[0118] in, represents the adjusted fitness value, represents the original fitness value, represents the dynamic adjustment coefficient, Indicates the current iteration number, Indicates the maximum number of iterations;

[0119] Update the individual extreme value and global extreme value of each particle, and use the improved strategy to adjust the particle speed as follows:

[0120] ;

[0121] in, Indicates the adjusted speed, Represents particles In the The speed of dimension, represents the inertia weight, 、 represents the learning factor, 、 represents a random number, Represents particles The individual extreme value of The location of the dimension, Indicates that the global extreme value is The location of the dimension, Represents particles In the Dimensional location;

[0122] In view of the complex constraints in the coordinated configuration of shared energy storage and transmission lines, an improved penalty function method is used to handle the constraints. For particles that violate the constraints, a penalty term is calculated based on the degree of violation and incorporated into the fitness function. The formula is expressed as follows:

[0123] ;

[0124] in, Indicates the fitness value after adding penalty, represents the penalty coefficient, Indicates the The degree of violation of the constraint condition, Indicates the maximum number of constraints;

[0125] Update the particle position and ensure that the updated particle position meets the constraints of the energy storage and transmission lines. The specific method is: if the updated position causes the energy storage charging and discharging power to exceed the limit or the transmission line power flow to exceed the limit, then adjust the particle position to within the allowable range of the constraints. The adjustment formula is:

[0126] ;

[0127] in, Indicates the updated position. 、 Represents the particle position in the minimum and maximum allowed values ​​for the dimension, Represents the clmap function;

[0128] Repeat the above steps until the maximum number of iterations is reached or the fitness meets the predetermined threshold, and output the energy storage configuration scheme and transmission line planning scheme corresponding to the optimal particle position.

[0129] Energy storage configuration plan:

[0130] Installation location selection: By analyzing the grid topology, renewable energy generation distribution, and load center locations, the optimal installation node for energy storage equipment is determined to maximize its role in regulating power fluctuations and improving power quality.

[0131] Capacity configuration: Based on the uncertainty of renewable energy generation, load demand characteristics, and system reliability requirements, the reasonable capacity of energy storage equipment is calculated. The formula can be expressed as:

[0132] ;

[0133] in, Indicates the capacity of the energy storage device, Represents the peak power of renewable energy generation, Indicates the discharge time, represents the discharge efficiency, represents the average power, Indicates charging time, Indicates charging efficiency.

[0134] Charging and discharging strategies: Based on the real-time status and forecast information of the power grid, a charging and discharging plan for energy storage equipment is formulated to achieve goals such as peak shaving, tracking planned output, and providing ancillary services.

[0135] Transmission line planning scheme:

[0136] Expansion path selection: Combine geographic information, grid layout, and load growth trends to evaluate the feasibility and economic feasibility of different transmission line expansion paths, and select paths with minimal environmental impact, low construction costs, and high transmission efficiency.

[0137] Capacity planning: Based on the system's future load growth forecast and the scale of renewable energy power generation access, the expansion capacity of the transmission line is determined. The formula can be expressed as:

[0138] ;

[0139] in, represents the expansion capacity of the transmission line, Indicates the total system load, Indicates the maximum transmission distance, represents the transmission efficiency, Representation node Standby power, represents the average transmission distance of each node, Indicates the total number of nodes.

[0140] Upgrading and renovation measures: Upgrading and renovating existing transmission lines, including increasing the cross-sectional area of ​​conductors, optimizing tower structures, and adopting new insulation materials, to improve the transmission capacity and reliability of the lines.

[0141] Step S5: Evaluate the energy storage configuration plan and the transmission line planning plan. The evaluation indicators include the new energy absorption capacity, the transmission line congestion relief effect, the energy storage equipment utilization rate, and the system economy.

[0142] The evaluation method adopts a multi-index comprehensive evaluation system, and the comprehensive evaluation formula is:

[0143] ;

[0144] in, represents the comprehensive evaluation index, 、 、 、 、 They are the weight coefficients of new energy consumption rate, transmission line congestion rate, energy storage equipment utilization rate, system annual comprehensive cost and reliability index, represents the evaluation value of new energy consumption rate, represents the transmission line blocking rate evaluation value, represents the utilization evaluation value of energy storage equipment, Indicates the annual comprehensive cost assessment value of the system, Represents the reliability index evaluation value.

[0145] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0146] Therefore, the present invention adopts the above-mentioned method of collaborative configuration of shared energy storage and transmission lines, which can effectively improve the new energy absorption capacity, alleviate transmission line congestion, improve system economy and reliability, and is suitable for the planning and operation of power systems with large-scale new energy access.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for collaborative configuration of shared energy storage and transmission lines, characterized in that: The following steps are involved: Step S1, collecting relevant data of the power system; Step S2: Establish an uncertainty model for renewable energy output and load, and generate multiple operating scenarios using probability distribution fitting and scenario generation methods; Step S3: Constructing a collaborative optimization configuration model for shared energy storage and transmission lines, with minimizing the system's annual comprehensive cost as the objective function. The objective function includes the investment cost, operation and maintenance cost, and energy loss cost of the energy storage equipment and transmission lines. The constraints of the collaborative optimization configuration model include energy storage charging and discharging power limits, capacity limits, transmission line power flow constraints, and node voltage constraints. Step S4: using an improved particle swarm optimization algorithm to solve the collaborative optimization configuration model to obtain an energy storage configuration solution and a transmission line planning solution; Step S5: Evaluate the energy storage configuration scheme and the transmission line planning scheme, with evaluation indicators including new energy absorption capacity, transmission line congestion relief effect, energy storage equipment utilization rate, and system economy; In step S3, the collaborative optimization configuration model includes the following four layers: The strategic planning layer is used to determine the overall strategic direction of the coordinated configuration of shared energy storage and transmission lines, and analyze the cooperative and competitive relationships among stakeholders through a cooperative game model; The market transaction layer is used to formulate strategies for shared energy storage to participate in power market transactions and optimize market transaction strategies through a non-cooperative game model; The operation scheduling layer is used to optimize the scheduling of shared energy storage and transmission lines based on real-time operating status, and achieve coordinated scheduling through the Stackelberg game model; The device control layer is used to achieve precise control of the device and optimize the device control parameters through the evolutionary game model; In step S4, the improved particle swarm algorithm is as follows: Initialize the particle swarm, where each particle represents a combination of an energy storage configuration scheme and a transmission line planning scheme, and the position and velocity of each particle are randomly initialized; Calculate the fitness of each particle. The fitness function takes the inverse of the system's annual comprehensive cost: ； in, represents the adjusted fitness value, represents the original fitness value, represents the dynamic adjustment coefficient, Indicates the current iteration number, Indicates the maximum number of iterations; Update the individual extreme value and global extreme value of each particle, and use the improved strategy to adjust the particle speed as follows: ； in, Indicates the adjusted speed, Represents particles In the The speed of dimension, represents the inertia weight, 、 represents the learning factor, 、 represents a random number, Represents particles The individual extreme value of The location of the dimension, Indicates that the global extreme value is The location of the dimension, Represents particles In the Dimensional location; In view of the complex constraints in the coordinated configuration of shared energy storage and transmission lines, an improved penalty function method is used to handle the constraints. For particles that violate the constraints, a penalty term is calculated based on the degree of violation and incorporated into the fitness function. The formula is expressed as follows: ； in, Indicates the fitness value after adding penalty, represents the penalty coefficient, Indicates the The degree of violation of a constraint condition, Indicates the maximum number of constraints; Update the particle position and ensure that the updated particle position meets the constraints of the energy storage and transmission lines. The specific method is: if the updated position causes the energy storage charging and discharging power to exceed the limit or the transmission line power flow to exceed the limit, then adjust the particle position to within the allowable range of the constraints. The adjustment formula is: ； in, Indicates the updated position. 、 Represents the particle position in The minimum and maximum allowed values ​​for the dimension; Repeat the above steps until the maximum number of iterations is reached or the fitness meets the predetermined threshold, and output the energy storage configuration scheme and transmission line planning scheme corresponding to the optimal particle position.

2. A method for collaborative configuration of shared energy storage and transmission lines according to claim 1, characterized in that: In step S1, the relevant data includes the output data of the new energy generator set, load data, transmission line parameters and energy storage equipment characteristics, and the collected data is cleaned and normalized preprocessed.

3. A method for collaborative configuration of shared energy storage and transmission lines according to claim 1, characterized in that: In step S2, the scene generation method adopts Latin hypercube sampling technology.

4. A method for collaborative configuration of shared energy storage and transmission lines according to claim 1, characterized in that: In step S2, the probability density function of the renewable energy power generation is: ； in, represents the probability density function of renewable energy power generation, Indicates the value of the power generated by renewable energy. represents the variance of renewable energy power generation, Represents the average power of renewable energy generation; The probability density function of load power is: ； in, represents the probability density function of load power, Indicates the value of load power, represents the mean value of load power, Represents the variance of load power.

5. The method for collaborative configuration of shared energy storage and transmission lines according to claim 1, characterized in that: In step S3, the objective function is: ； in, Indicates the annual comprehensive cost of the system, represents the investment cost of energy storage equipment, represents the transmission line investment cost, represents the operation and maintenance cost of energy storage equipment, represents the operation and maintenance cost of the transmission line, represents the energy loss cost; The charging and discharging power limits of energy storage are as follows: ； ； in, Indicates the energy storage charging power, 、 Represent the minimum and maximum values ​​of energy storage charging power, Indicates the energy storage discharge power, 、 Respectively represent the minimum and maximum values ​​of the energy storage discharge power; Capacity limits are as follows: ； in, Indicates the current capacity of the energy storage device. 、 Respectively represent the minimum and maximum capacity of the energy storage device; The power flow constraints of the transmission line are as follows: ； in, represents the power flow of the transmission line, 、 They represent the minimum and maximum values ​​of the power flow of the transmission line respectively; The node voltage constraints are as follows: ； in, represents the node voltage, 、 Represent the minimum and maximum values ​​of the node voltage respectively.

6. A method for collaborative configuration of shared energy storage and transmission lines according to claim 1, characterized in that: The energy storage configuration plan includes the installation location, capacity configuration and charging and discharging strategies of the energy storage equipment; the transmission line planning plan includes expansion paths, capacity planning and upgrade and transformation measures.

7. A method for collaborative configuration of shared energy storage and transmission lines according to claim 1, characterized in that: In step S5, the evaluation method adopts a multi-index comprehensive evaluation system, and the comprehensive evaluation formula is: ； in, represents the comprehensive evaluation index, 、 、 、 、 They are the weight coefficients of new energy consumption rate, transmission line congestion rate, energy storage equipment utilization rate, system annual comprehensive cost and reliability index, represents the evaluation value of new energy consumption rate, represents the transmission line blocking rate evaluation value, represents the utilization evaluation value of energy storage equipment, Indicates the annual comprehensive cost assessment value of the system, Represents the reliability index evaluation value.

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