Energy storage charging pile locating and sizing optimization method considering battery degradation and V2G

Abstract

The invention discloses an energy storage charging pile locating and sizing optimization method considering battery degradation and V2G, and the method comprises the steps: firstly, building a refined battery degradation cost quantification model, and converting the capacity attenuation of a V2G electric vehicle power battery and an energy storage charging pile battery caused by charging and discharging circulation into dominant economic cost based on an equivalent full cycle life theory; secondly, the battery degradation cost serves as a key decision variable and is embedded into an energy storage charging pile locating and sizing multi-target optimization planning model with the minimum total cost in the whole life cycle as the target; and finally, solving the multi-objective optimization planning model by adopting a non-dominated sorting genetic algorithm (NSGA-II). According to the method, the investment cost, the operation and maintenance cost, the environment cost and the battery degradation cost can be optimized at the same time, and collaborative optimization of the economy, the environmental protection property and the battery health degree of the planning scheme in the whole life cycle is ensured. The problem of long-term economy distortion caused by neglecting battery loss in the existing planning method is effectively solved.

Description

Technical Field

[0001] This invention relates to a method for optimizing the site selection and capacity determination of energy storage charging piles, taking into account battery degradation and V2G, and belongs to the field of energy storage charging pile site selection and capacity determination technology. Background Technology

[0002] Energy security and climate change are major challenges facing the world today. As a major source of carbon emissions, the green and low-carbon transformation of the power system is crucial to achieving this goal. Against this backdrop, the proportion of renewable energy generation, represented by wind and solar power, continues to increase, while low-carbon transportation modes, represented by electric vehicles, are also developing rapidly. However, the development of these two technologies has brought unprecedented challenges to the planning and operation of traditional power systems, especially distribution networks. On the one hand, the intermittency and randomness of renewable energy output greatly increase system power fluctuations, threatening the stability and reliability of the power grid. On the other hand, the large-scale, unregulated charging of electric vehicles into the grid, with its spatiotemporal uncertainty, easily overlaps with the existing peak load of the grid, creating a "peak-on-peak" effect, exacerbating the peak-valley difference in the system, leading to problems such as line overload and voltage exceeding limits, further increasing the difficulty of peak regulation and operational risks of the power grid.

[0003] To mitigate renewable energy fluctuations and alleviate grid peak-shaving pressure, energy storage systems are widely recognized as an effective technological solution. Distributed energy storage systems, in particular, demonstrate broad application prospects in distribution networks due to their advantages such as rapid response, flexible configuration, and ability to absorb renewable energy locally. Meanwhile, with the continuous growth of electric vehicle ownership, their onboard power batteries, as a potential large-scale distributed energy storage resource, can achieve bidirectional energy interaction with the grid through V2G (Vehicle-to-Grid) technology. This not only allows them to supply power to the grid during peak hours to provide peak-shaving services but also effectively improves the local absorption capacity of renewable energy. Furthermore, energy storage charging piles integrating built-in energy storage units can better coordinate the contradiction between charging demand and distribution capacity, achieving orderly charging and energy buffering.

[0004] Currently, scholars both domestically and internationally have conducted extensive research in this field. Existing research mainly focuses on three directions: first, the scheduling strategies and benefit analysis of electric vehicles participating in V2G, aiming to explore their potential as a flexible resource; second, the site selection and capacity planning of charging piles to optimize their service capacity and economics; and third, power planning considering high proportions of renewable energy and carbon emission reduction targets, aiming to optimize system structure. However, existing research still has significant shortcomings: First, most studies treat V2G and physical energy storage systems as independent regulation methods, failing to fully consider their synergistic and complementary effects at the distribution network planning level and their impact on power structure optimization; second, in the planning modeling process, the charging and discharging behavior of V2G and the battery degradation caused by frequent operation of energy storage systems are generally ignored. Battery life loss is a core factor affecting users' willingness to participate in V2G and the economics of energy storage systems. Excluding it from the planning model will lead to a distortion in the economic assessment of the planning scheme throughout its entire life cycle, failing to reflect the true cost, and potentially resulting in overly optimistic or even infeasible planning conclusions.

[0005] Therefore, there is an urgent need for an innovative approach that can comprehensively consider the coordinated planning of V2G, energy storage charging piles, and distributed renewable energy. This approach requires deeply integrating the impact of large-scale V2G and energy storage systems on top of traditional power planning models, and incorporating battery degradation costs as a key economic cost into the optimization objective. This will allow for the comprehensive optimization of the planning scheme's economic efficiency, environmental friendliness, and asset sustainability throughout its entire lifecycle, while ensuring the safe and reliable operation of the system. This will provide a scientific and precise decision-making basis for the green and low-carbon transformation of the power distribution network. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method for optimizing the site selection and capacity of energy storage charging piles that takes into account battery degradation and V2G, thereby solving the problem of synergistic optimization of economic efficiency, environmental protection and battery life in power distribution network planning under the access of high proportion of renewable energy and electric vehicles, and improving the planning scientificity and operational sustainability of vehicle-charging pile-grid collaborative system.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for optimizing the site selection and capacity of energy storage charging piles, taking into account battery degradation and V2G, includes the following steps: Step 1: Obtain basic data for the planning period of the energy storage charging pile planning area. The basic data includes distribution network parameters, load demand data, wind and solar renewable energy output data, electric vehicle forecast data participating in V2G, and battery technical and economic parameters. Step 2: Based on the battery techno-economic parameters, construct a quantitative model for battery degradation costs to calculate the economic cost of life loss of V2G electric vehicle power batteries and energy storage charging pile batteries due to participation in grid dispatch activities. Step 3: Establish a multi-objective optimization planning model for the site selection and capacity determination of energy storage charging piles with the goal of minimizing the total cost throughout the entire life cycle. The total cost throughout the entire life cycle includes investment and construction costs, operation and maintenance costs, environmental compensation costs, and battery degradation costs output by the battery degradation cost quantification model. Step 4: Construct the constraints of the model, and use a non-dominated sorting genetic algorithm to solve the multi-objective optimization programming model based on the constraints to obtain the optimal planning scheme for the address and capacity of energy storage charging piles.

[0008] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: 1. This invention constructs a multi-objective optimization planning model that integrates the full life cycle cost of battery degradation and solves it using the NSGA-II algorithm. This achieves synergistic optimization of economic efficiency, environmental protection, and battery asset sustainability in distribution network planning, effectively solving the planning challenges under the high proportion of renewable energy and electric vehicle access, and significantly improving the accuracy, scientificity, and practicality of the planning scheme.

[0009] 2. This invention is the first to accurately quantify the battery life loss during the scheduling process of V2G and energy storage systems into economic costs and incorporate it into the optimization objectives. This overcomes the major defect of traditional planning that ignores the battery health status, resulting in distorted economic assessment of the entire life cycle. It also avoids misjudgments in investment decisions caused by underestimating long-term operating costs, and greatly improves the accuracy and reliability of investment benefit assessment.

[0010] 3. By outputting a uniformly distributed Pareto optimal solution set, this invention clearly and quantitatively reveals the complex trade-offs between investment costs, operation and maintenance costs, environmental costs, and battery degradation costs. It provides decision-makers with a comprehensive and intuitive decision-making space, enabling them to flexibly select the most suitable technical path according to actual policy requirements and preferences, thereby enhancing the flexibility and scientific nature of planning decisions.

[0011] 4. This invention embeds strict power grid safety operation constraints (such as power flow, voltage, and SOC constraints) and innovative daily battery degradation cost ceiling constraints into the planning model, ensuring the technical feasibility of the planning scheme from the source and effectively preventing excessive damage to battery assets in pursuit of power grid economy, thus protecting the interests of users and operators and stimulating the enthusiasm of market players to participate in V2G and configure energy storage.

[0012] 5. The optimized planning scheme provided by this invention can effectively guide the rational layout of distributed power sources and energy storage charging piles and the orderly scheduling of V2G resources, significantly improving the distribution network's ability to absorb high proportions of renewable energy and its friendly access capability for large-scale electric vehicles. It provides core technical support for building a safe, economical, and low-carbon new power distribution system and powerfully promotes the green and low-carbon transformation of the energy system. Attached Figure Description

[0013] Figure 1 This is an overall flowchart of the present invention, which is a method for optimizing the location and capacity of energy storage charging piles that takes into account battery degradation and V2G. Figure 2 This is a flowchart of the NSGA-II algorithm, which aims to minimize the total cost of the distribution network system involving V2G and energy storage charging piles. Detailed Implementation

[0014] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0015] This invention proposes a site selection and capacity optimization method for energy storage charging piles that considers battery degradation and V2G. First, a quantitative model of battery degradation costs is constructed, accurately converting the battery life loss caused by V2G and energy storage charging / discharging into economic costs. Then, a multi-objective optimization planning model covering the entire lifecycle, including investment costs, operation and maintenance costs, environmental costs, and battery degradation costs, is established. Finally, a non-dominated sorting genetic algorithm (NSGA-II) is used to solve the model, outputting the Pareto optimal solution set. This method innovatively integrates battery health management into power grid planning. Through the coordinated scheduling of V2G and energy storage, it achieves a multi-objective balance of power structure optimization, carbon emission reduction, and battery loss control while ensuring system safety, providing a scientific decision-making tool for the low-carbon transformation of distribution networks. It significantly improves the economy and reliability of planning schemes and effectively promotes the consumption of renewable energy and the large-scale, friendly integration of electric vehicles.

[0016] like Figure 1 As shown, the specific steps include the following: Step 1: Obtain basic data for the site selection and capacity planning area of ​​energy storage charging piles, including grid parameters, load demand data, wind and solar renewable energy output data, electric vehicle forecast data, and battery technical and economic parameters; Power Grid Topology and Electrical Parameters: Obtain a single-line diagram of the distribution network in the planning area, including the connections of all nodes and branches. Specific data should include: voltage levels, reference voltages, and upper and lower limits for safe voltage operation at each node (typically 0.95 and 1.05 per unit); resistance, reactance, and admittance of each branch; and capacity and location information of existing distributed generation sources and substation main transformers. This data forms the basis for constructing power flow calculation constraints and voltage constraints.

[0017] Load demand data: Collect historical hourly active and reactive load data for all load nodes within the planning area in recent years, and analyze this data to obtain typical daily load curves. In addition, based on factors such as regional economic development planning and population growth, determine the annual load growth rate for the planning period to predict future load levels.

[0018] Wind and solar renewable energy output data: Obtain historical daily sunshine intensity and wind speed data for at least one year in the planned area to generate power characteristic curves for wind turbines and photovoltaic units. Using professional software such as Homer and PVsyst, or probabilistic models such as the two-parameter Weibull distribution and beta distribution, generate typical scenarios for wind and solar power output and their probabilities of occurrence. , , The total number of scenes is used to characterize the uncertainty and correlation of wind and solar power output.

[0019] Electric Vehicle and Charging Infrastructure Data: The survey will cover the number of electric vehicles participating in V2G within the planned area, their type composition (private cars, taxis, buses), growth trends, and user travel habits. Key data includes: the probability distribution of daily mileage for each type of electric vehicle, the probability distribution of initial charging time, battery capacity, charging power, discharging power, and the proportion of willingness or potential to participate in V2G. Simultaneously, the distribution, number, and type (fast charging / slow charging) of existing charging stations need to be investigated.

[0020] Equipment parameters: power characteristic curves of the selected wind turbine (cut-in, rated, and cut-out wind speeds), conversion efficiency of the photovoltaic unit, rated cycle life, charge and discharge efficiency, maximum depth of charge and discharge, and upper and lower limits of state of charge (SOC) of the energy storage battery (including electric vehicle power batteries and energy storage charging pile batteries).

[0021] Economic parameters: unit capacity investment cost and unit power investment cost of wind turbines, photovoltaic units, power storage converters (PCS), and energy storage batteries; fixed installation costs of equipment; system operation and maintenance cost coefficients; time-of-use tariffs for electricity purchased from the grid; discharge compensation tariffs for V2G users; various pollutants ( The unit treatment cost and emission factors of ( ).

[0022] Step Two: Based on battery aging mechanisms and lifecycle cost analysis theory, this invention employs the energy throughput method as the core model for quantifying battery degradation costs, calculating the economic cost of lifespan loss in V2G electric vehicle power batteries and energy storage charging pile batteries due to their participation in grid dispatch activities. The basic principle of this model is that the end of a battery's lifespan is directly related to the total energy throughput accumulated throughout its entire lifecycle. This method avoids the high computational cost of complex electrochemical models while effectively characterizing the cumulative loss effect of charge-discharge cycles on battery lifespan, making it suitable for optimization calculations at the grid planning level.

[0023] The battery degradation cost generated for any charge-discharge cycle activity at any time. Calculated using the following formula: , in, This represents the battery degradation cost incurred during this charge-discharge cycle, expressed in RMB. This value is the core output of the model and will be embedded as a variable in subsequent optimization models. This represents the energy throughput of the cycle during this period, expressed in kilowatt-hours (kWh). Its value is the absolute value of the electrical energy charged or discharged during this period. For the discharge process, , For time period The discharge power (kW). The length of a single time period (h). This refers to the inverter efficiency. The calculation method is similar for the charging process. This represents the total cost of replacing the battery, expressed in RMB. This cost includes the purchase price of the new battery and the replacement installation costs, and reflects the residual value of the battery asset. This indicates the battery's rated capacity, expressed in kilowatt-hours (kWh). This indicates the cycle life of a battery under rated test conditions, which is the number of standard charge-discharge cycles (100% DoD) it can withstand from brand new to the end of its life (usually defined as the capacity retention dropping to 80%). This value must be obtained from the battery manufacturer's technical manual or through experimental data.

[0024] Step 3: Construct a multi-objective optimization planning model with the core objective of minimizing the total cost throughout the system's lifecycle. This model innovatively incorporates battery degradation costs along with traditional power planning costs into the optimization framework, thereby achieving synergistic optimization of the planning scheme's economic efficiency, environmental friendliness, and asset sustainability. Battery degradation costs The output of the quantification model is the expected value of the degradation cost generated by all V2G activities and energy storage charging and discharging activities during the planning period. Its calculation formula is: , in, For the scene The probability of occurrence, This represents the total number of time periods within the planning period. For the number of V2G electric vehicles, This refers to the number of energy storage charging piles.

[0025] Investment and construction costs This includes the capacity investment and power conversion system investment of distributed wind turbines, photovoltaic units, and energy storage charging piles, expressed as follows: , in, Indicates thermal power generating unit The unit capacity investment cost, expressed in RMB 10,000 / MW; Indicates wind power or photovoltaic units The unit capacity investment cost, expressed in RMB 10,000 / MW; Indicates energy storage charging pile The unit capacity investment cost, expressed in RMB 10,000 / MWh; V2G electric vehicles The unit investment cost is expressed in ten thousand yuan per vehicle. Indicates thermal power generating unit The newly installed capacity, in MW; Indicates wind power or photovoltaic units The newly installed capacity, in MW; Indicates energy storage charging pile The new capacity, in MWh; V2G electric vehicles The capacity or quantity decision variable, in units of vehicles. A collection of thermal power generating units. A collection of renewable energy units, For the number of V2G electric vehicles, This refers to the number of energy storage charging piles.

[0026] Operation and maintenance costs This includes the operation and maintenance costs of various generating units, the cost of purchasing electricity from the main grid, the discharge compensation costs paid to V2G users, and the grid loss costs. Its expression is: , in, Indicates thermal power generating unit The operation and maintenance cost coefficient, expressed in yuan / MWh; Indicates wind power or photovoltaic units The operation and maintenance cost coefficient, expressed in yuan / MWh; Indicates energy storage charging pile The operation and maintenance cost coefficient, expressed in yuan / MWh; V2G electric vehicles The operation and maintenance cost coefficient, expressed in yuan / MWh; Indicates thermal power generating unit In the scene Time period The contribution of energy, measured in MW; Indicates wind power or photovoltaic units In the scene Time period Actual output, in MW; and These represent energy storage charging piles. In the scene Time period Charging power and discharging power, in MW; and These represent V2G electric vehicles. In the scene Time period The charging and discharging power, in MW.

[0027] Environmental compensation costs The cost of treating pollutants generated by consuming traditional energy sources for power generation can be quantified by the following expression: , in, Indicates thermal power generating unit The environmental compensation cost coefficient is usually related to carbon emissions or pollutant emission factors, and is expressed in yuan / MWh.

[0028] Step 4: The multi-objective optimization planning model must meet the following constraints to ensure the technical feasibility, safety, and reliability of the planning scheme: System AC power balance constraints: for each node Each time period Active power balance: , Reactive power balance: , in, Represents a node During the period The active power of thermal power generators, Represents a node Renewable energy (wind / solar) during the period The active output, Represents a node On the electric vehicle group during the time period The discharge power, Represents a node During the period of time, the energy storage charging piles The charging power, Represents a node During the period The active power demand is given above, with the active power unit being MW. Represents a node During the period The reactive power of thermal power generators, Represents a node Renewable energy during the period reactive power output, Represents a node During the period The reactive load demand is given above, with the reactive power unit being MVar. Represents a node Voltage amplitude, in kV; Represents a node Voltage amplitude, in kV; The real part of the nodal admittance matrix represents the node's admittance. With nodes Mutual conductance between them, in seconds (s); Represents the imaginary part of the nodal admittance matrix, corresponding to the node. The mutual susceptance between node j and node j, in seconds; Represents a node With nodes The phase angle difference in time interval t; This represents the total number of nodes.

[0029] Node voltage safety operation upper and lower limit constraints, for each node Each time period : , Under the per-unit system =0.95, =1.05. This constraint ensures that the node voltage is within the allowable range.

[0030] Upper and lower limits of output constraints for wind turbines and photovoltaic units, for each wind turbine and photovoltaic unit in the scenario Time period The expression is: , , in, Indicates renewable energy units In the scene Time period The actual active power, in MW; Indicates renewable energy units Rated installed capacity, in MW; Indicates renewable energy units In the scene Time period The availability coefficient (determined by wind speed or lighting conditions, with a value range of 0-1). Indicates renewable energy units In the scene Time period The reactive power is expressed in MVar.

[0031] Charging and discharging power constraints of energy storage charging piles and V2G electric vehicles: The charging power and discharging power are modeled separately for energy storage charging piles. (node (Above) During the time period The expression is: , , To avoid bidirectional losses, the scheduling model requires that neither of the two values ​​be positive simultaneously, as expressed in the following expression: , Electric vehicles participating in V2G At the node : , , If discharge is defined as positive injection into the power grid, then the discharge term is positive in the power flow equations; If V2G electric vehicles During the period If it is not in a schedulable state, then ; in, Indicates energy storage charging pile During the period The charging power (absorbed from the grid); Indicates energy storage charging pile Maximum charging power; Indicates energy storage charging pile During the period The discharge power (feedback to the grid); Indicates energy storage charging pile Maximum discharge power; V2G electric vehicles During the period The charging power; V2G electric vehicles Maximum charging power; V2G electric vehicles During the period The discharge power; V2G electric vehicles The maximum discharge power, all of which are in MW.

[0032] Dynamic evolution constraints on State of Charge (SOC) and upper and lower limits of SOC for energy storage charging piles : , V2G electric vehicles : , Simultaneously apply upper and lower limits of SOC: , , in, State of charge (SOC) is the ratio of the battery's current stored charge to its maximum capacity. Indicates energy storage charging pile During the period The state of charge; Indicates energy storage charging pile In the previous period ( The state of charge of ) Indicates the charging efficiency of the energy storage unit; Indicates the scheduling time interval; Indicates the discharge power of the energy storage unit; Indicates energy storage charging pile Rated energy capacity; V2G electric vehicles Battery during the period The state of charge; V2G electric vehicles The battery in the previous period ( The state of charge of ) Indicates the charging efficiency of electric vehicles; Indicates the discharge efficiency of an electric vehicle; V2G electric vehicles The rated capacity of the battery; Indicates energy storage charging pile The minimum SOC allowed by the battery; Indicates energy storage charging pile The maximum SOC allowed by the battery; This indicates the minimum permissible state of charge (SOC) of an electric vehicle's battery. This indicates the maximum allowable state of charge (SOC) of the electric vehicle's battery.

[0033] The daily degradation cost ceiling constraint for batteries is used to prevent excessive battery use, and its expression is: , in, It is a collection of all time periods within a day. This is the maximum permissible degradation cost per day.

[0034] The constructed multi-objective optimization programming model is solved using an optimization method based on Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This algorithm can achieve global optimization of investment costs, operation and maintenance costs, environmental compensation costs, and battery degradation costs while ensuring constraint feasibility, thus obtaining the Pareto optimal solution set within the planning period. The specific solution process includes the following steps: A hybrid encoding method is used to encode the solution vector. Each chromosome represents a complete planning and execution scheme, consisting of two concatenated parts: Part 1 (Investment Decision Variables, Integer Encoding): The gene bit represents the combination of node-device-capacity. For example, Indicates at node Construction of the Type of equipment (including renewable energy and energy storage charging piles) and capacity level is ( (Indicates no construction); Part Two (Running Scheduler Variables, Real Number Encoding): Gene segments represent power values. For example, Indicates device In the scene Time period The dispatch power (positive for discharging, negative for charging). Equipment This includes V2G electric vehicles and energy storage charging stations.

[0035] Investment variables are encoded with integers to represent capacity decisions for wind power, solar power, and energy storage charging piles; operation and scheduling variables are encoded with real numbers to represent the status of each device in the scenario. Time period The charging and discharging power is measured. During decoding, the chromosome is converted into a power flow equation input to calculate node voltage, line power flow, and battery SOC evolution, ensuring the physical feasibility of the solution.

[0036] Randomly generated The initial population consists of individuals. Each individual's gene value is randomly generated within the upper and lower limits of its variables and must pass a feasibility test to ensure that some hard constraints (such as the capacity limit) are met.

[0037] For each individual in the population Perform the following operations: Decoding and Simulation: The chromosomes are decoded into specific planning schemes and scheduling strategies, which are then substituted into the power flow calculation and operation simulation model of the distribution network to calculate its four objective function values, forming the original fitness vector: , Constraint handling (penalty function method): Calculates the total number of violations of all constraints by an individual. Each candidate solution It is simultaneously constrained by power flow, node voltage, SOC dynamics, and the upper limit of daily battery degradation cost. (Regarding inequality constraints...) The amount of violation is For equality constraints The amount of violation is The total number of violations is the sum of all constraint violations: , Adjusted fitness: Multiply the constraint violation by a large penalty factor. (For example This is then added to all the original objective function values ​​to form a modified fitness vector for algorithm selection. , in, It is a vector of all 1s. This operation ensures that infeasible solutions always perform worse than any feasible solutions.

[0038] Non-dominated ordering: for individuals in a population and ,if and ,but Dominate Based on dominance relationships, the population is divided into multiple non-dominated layers. ,in, It is the set of individuals that are not dominated by any other individual (Pareto optimal frontier).

[0039] Crowding calculation: for each individual in the same non-dominated layer The congestion distance was calculated on four target dimensions: investment cost, operation and maintenance cost, environmental cost, and battery degradation cost. This measures the distribution density of individuals in the target space, preventing the search from getting trapped in a local Pareto front. It also sets the crowding level of boundary individuals. .

[0040] For the intermediate individual, calculate: , An individual's total crowding level is the sum of its crowding levels across all its objectives: , The greater the crowding, the smaller the solution density around the individual, and the better the diversity.

[0041] Selection operation (binary tournament selection): Randomly select two individuals from the population. and .if choose .if and choose ,otherwise .

[0042] Crossover operation: For the selected parent individuals, with probability... Perform crossover. Both investment and scheduling variables use simulated binary crossover (SBX). For both parent generations... offspring Generate using the following formula: , , in, It is a distribution index The random variable that determines the outcome.

[0043] Mutation operation: For offspring individuals, with probability Perform polynomial mutation. For the parent generation... offspring Generate using the following formula: , in, It is a distribution index The small disturbance of the decision, and It represents the upper and lower bounds of a gene locus.

[0044] Elite Preservation and Iteration: Preserving the Parent Population With offspring population merged into (size is) ).right Perform a non-dominated sort and fill the new parent population with the sorted set of individuals in ascending order of non-dominated rank (from best to worst). In the middle. When filling to a non-dominated layer, adding all individuals from that layer would cause the population size to exceed [a certain threshold]. Then only individuals with the largest crowding distance in that layer are selected and added until the layer is full. Individual.

[0045] The above operation will be repeated until the set maximum number of generations is reached. After the algorithm terminates, output the final population containing all populations that satisfy the condition. And it belongs to the first non-dominated layer The individuals constitute the Pareto optimal solution set of the multi-objective optimization problem. Each solution corresponds to a specific power planning scheme (capacity and location of wind turbines, photovoltaics, and energy storage charging piles) and a V2G / energy storage scheduling strategy.

[0046] Using fuzzy membership functions or decision-maker preferences to obtain Pareto optimal solutions Choose a satisfactory solution from the options. The four objective function values ​​for each solution are normalized using the following formula: , For each solution, calculate its standardized membership sum. .choose The largest solution is taken as the final optimal planning scheme and running strategy. Specific algorithm flow. Figure 2 This approach minimizes the total cost over the entire system lifecycle while ensuring technical feasibility and balancing multiple conflicting objectives.

[0047] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for optimizing the location and capacity of energy storage charging piles that takes into account battery degradation and V2G.

[0048] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for optimizing the location and capacity of energy storage charging piles that considers battery degradation and V2G.

[0049] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for optimizing the site selection and capacity allocation of energy storage charging piles considering battery degradation and V2G, characterized in that, Includes the following steps: Step 1: Obtain basic data for the planning period of the energy storage charging pile planning area. The basic data includes distribution network parameters, load demand data, wind and solar renewable energy output data, electric vehicle forecast data participating in V2G, and battery technical and economic parameters. Step 2: Based on the battery techno-economic parameters, construct a quantitative model for battery degradation costs to calculate the economic cost of life loss of V2G electric vehicle power batteries and energy storage charging pile batteries due to participation in grid dispatch activities. Step 3: Establish a multi-objective optimization planning model for the site selection and capacity determination of energy storage charging piles with the goal of minimizing the total cost throughout the entire life cycle. The total cost throughout the entire life cycle includes investment and construction costs, operation and maintenance costs, environmental compensation costs, and battery degradation costs output by the battery degradation cost quantification model. Step 4: Construct the constraints of the model, and use a non-dominated sorting genetic algorithm to solve the multi-objective optimization programming model based on the constraints to obtain the optimal planning scheme for the address and capacity of energy storage charging piles.

2. The method for optimizing the location and capacity of energy storage charging piles considering battery degradation and V2G as described in claim 1, characterized in that, In step 1, the distribution network parameters include: distribution network topology, line impedance parameters, transformer parameters, and upper and lower limits for safe operation of distribution network node voltages. Load demand data includes: annual and hourly active and reactive load data for each distribution network node during the planning period, and the average annual growth rate of active and reactive load for each distribution network node. The power output data of wind and solar renewable energy includes: the power characteristic curves of wind turbines, the power characteristic curves of photovoltaic units, and the various wind and solar power output scenarios and their occurrence probabilities obtained after scenario reduction; The electric vehicle forecasting data involved in V2G includes: the proportion of electric vehicle types, the number of vehicles in use and their growth rate, the battery capacity of each type of vehicle, the charging and discharging power, the daily driving range, and the probability distribution of the starting charging time. Battery technical and economic parameters include: rated cycle life, maximum depth of charge and discharge, charge and discharge efficiency, unit capacity construction cost, unit power construction cost, and unit replacement cost of V2G electric vehicle power batteries and energy storage charging pile batteries.

3. The method for optimizing the location and capacity of energy storage charging piles considering battery degradation and V2G as described in claim 2, characterized in that, In step 2, the calculation formula for the battery degradation cost quantification model is as follows: ， in, This represents the battery degradation cost generated by the charge-discharge cycles at various times during the planning period for V2G electric vehicle power batteries or energy storage charging pile batteries. This indicates the energy throughput of each cycle during the planning period, i.e., the electrical energy charged in or released. This indicates the total replacement cost of the power battery or energy storage charging pile battery for V2G electric vehicles. This indicates the rated capacity of the power battery or energy storage charging pile battery in V2G electric vehicles. This indicates the cycle life of the power battery or energy storage charging pile battery of a V2G electric vehicle under rated test conditions.

4. The method for optimizing the location and capacity of energy storage charging piles considering battery degradation and V2G as described in claim 2, characterized in that, In step 3, the investment and construction costs The investment in capacity and power conversion system for distributed wind turbines, photovoltaic units, and energy storage charging piles is expressed as follows: ， in, Indicates thermal power generating unit The unit capacity investment cost Indicates wind power or photovoltaic units The unit capacity investment cost Indicates energy storage charging pile The unit capacity investment cost V2G electric vehicles The unit investment cost; Indicates thermal power generating unit The newly installed capacity Indicates wind power or photovoltaic units The newly installed capacity Indicates energy storage charging pile The new capacity, V2G electric vehicles Capacity or quantity decision variables; A collection of thermal power generating units. A collection of renewable energy units, The number of energy storage charging piles, For the number of V2G electric vehicles; Operation and maintenance costs This includes the operation and maintenance costs of various generating units, the cost of purchasing electricity from the main grid, the discharge compensation costs paid to V2G electric vehicle users, and the grid loss costs, expressed as follows: ， in, Indicates the scene of wind and light exerting force The probability of occurrence, This represents the total number of landscape output scenes obtained after scene reduction; 𝑇 represents the total number of time periods within the planning period; Indicates thermal power generating unit The operation and maintenance cost coefficient, Indicates wind power or photovoltaic units The operation and maintenance cost coefficient, Indicates energy storage charging pile The operation and maintenance cost coefficient, V2G electric vehicles The operation and maintenance cost coefficient; Indicates thermal power generating unit In the scene Time period Those who have made contributions Indicates wind power or photovoltaic units In the scene Time period Actual output and These represent energy storage charging piles. In the scene Time period The charging power and discharging power, and These represent V2G electric vehicles. In the scene Time period The charging power and discharging power; Indicates the length of a single time period; Environmental compensation costs The expression used to quantify the cost of treating pollutants generated from the consumption of thermal power generation is: ， in, Indicates thermal power generating unit The environmental compensation cost coefficient is related to carbon emissions or pollutant emission factors; Battery degradation cost The expected value of the degradation cost generated by all V2G activities of electric vehicles and charging / discharging activities of energy storage charging piles during the planning period is expressed as: ， in, For V2G electric vehicles In the scene Time period The cost of battery degradation resulting from charging and discharging behavior. For energy storage charging piles In the scene Time period The cost of battery degradation caused by charging and discharging behavior.

5. The method for optimizing the location and capacity of energy storage charging piles considering battery degradation and V2G as described in claim 2, characterized in that, In step 4, the constraints of the model include: Power flow balance constraints exist for each node in the distribution network, including the following active and reactive power balance constraints: ， ， in, Represents a node During the period The active power of thermal power generators, Represents a node Renewable energy during the period The active output, Represents a node V2G electric vehicles during the time period The discharge power, Represents a node During the period of time, the energy storage charging piles The charging power, Represents a node During the period The active power load demand; Represents a node During the period The reactive power of thermal power generators, Represents a node Renewable energy during the period reactive power output, Represents a node During the period The reactive load demand; Represents a node Voltage amplitude, Represents a node Voltage amplitude; The real part of the nodal admittance matrix represents the node's admittance. With nodes Mutual conductance between them; The imaginary part of the nodal admittance matrix represents the node's... Mutual susceptance with node j; Represents a node With nodes During the period The phase angle difference; The total number of nodes; Upper and lower limits of safe operation of node voltage: ， in, These represent the node voltages during the time periods. Minimum and maximum values; Upper and lower limits of output constraints for wind turbines and photovoltaic units: ， ， in, Indicates wind power or photovoltaic units In the scene Time period Actual output; Indicates wind power or photovoltaic units Rated installed capacity, Indicates wind power or photovoltaic units In the scene Time period Availability coefficient; Indicates wind power or photovoltaic units In the scene Time period reactive power, These represent wind power or photovoltaic units, respectively. In the scene Time period The minimum and maximum reactive power that can be generated; Charging and discharging power constraints of energy storage charging piles and V2G electric vehicles: For energy storage charging piles ,have: ， ， ， For V2G electric vehicles ,have: ， ， ， in, Indicates energy storage charging pile During the period The charging power; Indicates energy storage charging pile Maximum charging power; Indicates energy storage charging pile During the period The discharge power; Indicates energy storage charging pile Maximum discharge power; V2G electric vehicles During the period The charging power; V2G electric vehicles Maximum charging power; V2G electric vehicles During the period The discharge power; V2G electric vehicles Maximum discharge power; V2G electric vehicles The set of time periods during which grid access is possible; Upper and lower limits of state of charge constraints for energy storage charging piles and V2G electric vehicles: ， ， ， ， in, State of charge (SOC) is the ratio of the battery's current stored charge to its maximum capacity. These represent energy storage charging piles. During the period The state of charge; Indicates energy storage charging pile Charging efficiency; Indicates the length of a single time period; Indicates energy storage charging pile Discharge power; Indicates energy storage charging pile Rated energy capacity; These represent V2G electric vehicles. Battery during the period The state of charge; V2G electric vehicles Charging efficiency; V2G electric vehicles Discharge efficiency; V2G electric vehicles The rated capacity of the battery; These represent energy storage charging piles. The minimum and maximum SOC allowed by the battery; These represent V2G electric vehicles. The minimum and maximum SOC allowed by the battery; Battery degradation cost ceiling constraint: ， in, It is a collection of all time periods within a day. Indicates in the scene Time period Below, battery equipment The resulting battery degradation costs, battery equipment For V2G electric vehicle power batteries or energy storage charging pile batteries. This is the maximum permissible degradation cost per day.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the energy storage charging pile site selection and capacity optimization method as described in any one of claims 1 to 5, which takes into account battery degradation and V2G.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy storage charging pile site selection and capacity optimization method as described in any one of claims 1 to 5, which takes into account battery degradation and V2G.

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