A network-constructing source-storage-charging integrated charging station planning method, device and medium

Abstract

This invention relates to the field of charging station planning technology, and particularly to a planning method, equipment, and medium for a grid-based integrated charging station with energy source, storage, and charging capacity. The method includes: setting up a multi-dimensional grid in the target planning area and constructing a data layer; performing a first round of screening of candidate charging station sites based on source constraints to obtain alternative sites; dividing the charging block area into a balanced energy source and demand zone based on an improved K-means clustering algorithm; conducting a comprehensive evaluation of site resource conditions based on fuzzy hierarchical analysis; establishing an equivalent grid-based parameter model for the charging station; generating a net load curve based on flexible load; constructing a three-layer collaborative optimization model using the Aurora optimization algorithm to solve for the charging blocks; and generating a charging station planning scheme based on the solution results. This invention's method achieves collaborative optimization across four dimensions: energy source, demand, storage, and grid. The resulting charging station planning scheme satisfies both energy self-sufficiency and traffic demand, and possesses dynamic stability.

Description

Technical Field

[0001] This invention relates to the field of charging pile planning technology, and in particular to a planning method, equipment and medium for a grid-type integrated power generation, energy storage and charging station. Background Technology

[0002] Currently, transportation and travel are essential basic needs for people's livelihoods and even crucial support for local economies worldwide. However, fuel, as the most important energy source for global transportation, has long been affected by various factors, severely restricting the responsiveness to transportation energy demands across the globe, particularly causing irreversible and profound impacts on remote, impoverished, and underdeveloped regions. In recent years, the continuous development and advancement of electric vehicle manufacturing and charging technologies have propelled global transportation into a new era of electric-powered vehicles. Electricity, with its diverse availability and convenience, has become the best alternative to solving the persistent problem of limited transportation energy resources. However, all current charging infrastructure planning and design is based on systems and conditions supported by the power grid. For underdeveloped regions such as Africa and the Americas, where the demand for electric vehicles is most urgent but where there is no power infrastructure or inadequate power infrastructure, there are currently no usable, scientific, and systematic planning and design methods.

[0003] The existing charging station planning and design schemes have the following problems: First, there's the problem of overly high or insufficient planning boundary conditions rendering the methods unusable. Existing patents and technologies all assume grid support and unrestricted access as the basic conditions for planning, and public grid voltage and frequency as default conditions for planning and design. However, in regions with power shortages, low power availability, or even no power, such as Africa, Latin America, and South Asia, the grid is a scarce resource. Based on existing planning methods, the resulting site selection may completely lack grid access conditions, making power supply impossible and ultimately preventing the planning scheme from being implemented.

[0004] Secondly, it addresses the theoretical gap in planning methods for off-grid (grid-based) self-generating, self-storing, and self-consuming charging station systems. For the construction of universal charging systems, it is essential to break free from the constraints of the public power grid and create a charging service system based on self-sufficiency, achieving "self-generation, self-storage, and self-consumption" in both grid-based and off-grid charging stations. In this context, planning must not only consider traffic demand but also be deeply integrated with the site's renewable energy generation potential (photovoltaic / wind power resource assessment), energy storage deployment conditions (land availability, geological conditions), and system self-balancing capabilities. While current academic and engineering research includes single-point capacity configuration studies for photovoltaic, energy storage, and charging stations, a comprehensive planning method for networked site selection and scale matching that can simultaneously balance "traffic demand," "renewable energy supply capacity," and "energy storage constraints" is lacking, resulting in a lack of clear guidelines for the construction of off-grid (grid-based) charging stations.

[0005] Third, the overall grid-building capability of integrated power generation, energy storage, and charging stations has been overlooked in planning. As an independent microgrid, an integrated power generation, energy storage, and charging station possesses the ability to autonomously construct voltage, stabilize frequency, and balance load fluctuations—a capability known as "autonomous grid-building capability." This capability stems from the synergistic effect of the station's renewable energy generation, energy storage, charging piles, and their control systems. Furthermore, energy storage is not merely the storage and supply of electricity; it is also a crucial system stability support in grid-based charging stations. With the rapid development of V2G technology, the definition and scope of energy storage in charging systems have expanded, leading to new energy storage classifications of fixed and mobile energy storage systems. This includes all electric vehicles involved in transportation activities within the system's energy storage scope, further enhancing the system's regulation capabilities and enriching regulation methods. However, existing plans simply simplify the energy storage system as an energy throughput unit for planning and capacity configuration, neglecting the contribution of vehicle V2G discharge to the system's dynamic energy storage. This results in incomplete planning and unreasonable capacity configuration. This invention will address these issues and strive to improve upon them.

[0006] Fourth, there is the issue of compatibility between charging infrastructure and electrified transportation systems. Existing charging infrastructure planning technologies have failed to accurately identify the proprietary nature of electric vehicle charging methods, such as "non-exclusivity, privatization, and station affiliation." Issues such as how to rationally allocate private and public charging facilities, how to effectively incorporate V2G and other two-way interactive technologies into the planning system, and how to conduct dynamic planning for the gradual and uncertain development of electrification rates are all unresolved in existing technologies.

[0007] Therefore, it is necessary to provide a planning method, equipment, and medium for a grid-type integrated power generation, energy storage, and charging station to solve the above-mentioned technical problems. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention proposes a planning method, equipment, and medium for a grid-type integrated power generation, energy storage, and charging station.

[0009] In a first aspect, the present invention provides a planning method for a grid-type integrated power generation, energy storage, and charging station, comprising: S100: Set up a multi-dimensional grid in the target planning area and build a data layer; S200: The first round of screening of candidate charging station sites is carried out based on source constraints to obtain alternative site locations; S300: Partitioning of source-balanced charging block regions based on improved K-means clustering algorithm; S400: Comprehensive evaluation of site resource conditions based on fuzzy hierarchical analysis; S500: Based on the charging station, establish an equivalent network parameter model for the charging station; S600: Introduces V2G on the load side and distributed photovoltaic and wind power on the power supply side, generating a net load curve based on flexible load; S700: It adopts the Aurora optimization algorithm to construct a three-layer collaborative optimization model that includes upper-layer addressing, lower-layer capacity setting, and lower-layer network parameter tuning, and solves for each source-needed equalization charging block; S800: Generates a planning scheme to guide engineering design based on the optimal solution output by S700.

[0010] Optionally, in S100, a multi-dimensional grid is set up in the target planning area, and a data layer is constructed. The process includes: S101: Divide the target planning area into several basic grids of the same size according to the latitude and longitude coordinate system; S102: Construct a spatial data layer for each basic grid, the spatial data layer including demand-side data, supply-side data and infrastructure constraints.

[0011] Optionally, in S200, the first round of screening for candidate charging station sites is performed based on source constraints. The process includes: S201: Power Generation Potential Assessment: Calculate the photovoltaic development potential index for each basic grid. P pv Wind power development potential index P wind The expression is as follows: ; ; in, H year This represents the total annual solar radiation. T full-load This refers to the annual equivalent full-load hours of wind power. A available The usable land area or Energy conversion efficiency coefficient, subscript pv and wind These represent photovoltaic and wind power, respectively. Set a filter threshold: Filter out P pv > P pv-min or P wind > P wind-min The grid, as a candidate region with source basis, P pv-min and P wind-min These represent the potential thresholds for photovoltaic development and wind power development, respectively. S202: Screening of physical conditions for site construction, combining land type data and terrain slope data to eliminate unsuitable construction areas from the candidate areas; S203: Preliminary assessment of the feasibility of grid-type multi-site parallel connection. For multiple candidate sites that are relatively close to each other, determine whether they have the potential to form a multi-site parallel microgrid and record the possibility of grid-type clustering of candidate sites. S204: Forming a candidate site database: The screened candidate sites are used as candidate sites to construct a candidate site database. P ={p1,p2,...,p m Each alternative site The record contains attribute information, including geographic coordinates and available land area. 、 New energy resource endowment coefficient 、 Traffic Accessibility Index 、 Network cluster identifiers and information on surrounding heavy-duty vehicles.

[0012] Optionally, in S300, the partitioning of the source-to-equilibrium charging block region based on the improved K-means clustering algorithm includes the following process: S301: Use the candidate sites selected in S200 as the initial cluster centers; S302: Define the composite distance metric and define the base mesh. G j To alternative station Comprehensive distance D ij The comprehensive distance incorporates energy supply impedance, demand intensity, and grid coordination impedance on top of the geographic spatial distance. The calculation expression for the comprehensive distance is as follows: ; in: d ij Represents the basic grid G j To alternative station Geographical distance; R res,i Indicate alternative site The resource endowment coefficient, R res,i =α· P pv,i +β· P wind,i α+β=1; D dem,j Represents the basic grid G j The intensity of charging demand; C ij Indicates the network cooperative impedance, if the base grid Gj Located at the alternative site Within the network cluster to which it belongs, C ij Take the smaller value, otherwise take the larger value; oh 1 ,oh 2 ,oh 3 ,oh 4 This represents the adjustable weighting coefficient, and oh 1 +oh 2 +oh 3 +oh 4 =1; S303: Dynamic clustering and supply-demand balance adjustment, allocating all basic grids to the comprehensive distance. D ij The smallest cluster centers form the initial cluster blocks. C k k=1,2,...,K; calculate each block region C k Total charging demand D emk The charging demand for heavy-duty trucks is calculated separately based on the number of trucks, average daily mileage, and unit power consumption, taking into account the distribution of charging times; based on the alternative site selection... Maximum power supply capacity S upi Set the upper limit threshold for the demand of block k. D em max,k The calculation expression is as follows: ; in, c Power supply guarantee factor; r storage The energy storage regulation coefficient represents the percentage increase in power supply capacity after configuring energy storage; if a certain block area D em k > D em max,k If this occurs, the equalization adjustment mechanism is triggered: some grids at the edge of the block are redistributed to neighboring blocks with surplus power supply capacity until all blocks meet the initial supply-demand balance, ultimately resulting in K source-demand equalization charging blocks.

[0013] Optionally, in S400, a comprehensive evaluation of site resource conditions is performed based on fuzzy AHP, the process of which includes: S401: Construct an evaluation index system, which includes an objective layer, a criterion layer, and an index layer; S402: A judgment matrix is ​​constructed using triangular fuzzy numbers, and the weight vectors W = [w1, w2, ..., w] of each indicator are obtained through fuzzy comprehensive operations. n ]; S403: Calculate the site resource condition coefficient: For each candidate site Based on its scores for each indicator x i,j Calculate the resource condition coefficient: ; in, F site,i The value range of is [0,1]. x i,j The value range is also [0,1], and the larger the value, the better the construction conditions of the site.

[0014] Optionally, in S500, based on the charging station, an equivalent network parameter model of the charging station is established, wherein: S501: Define the key parameters for the charging station network construction capability, including the equivalent virtual inertia. H station Equivalent droop coefficient m station Equivalent short-circuit ratio SCR station Black Start-up Ability BS station ; S502: Calculate the contribution of V2G discharge to the charging station network capability based on key parameters, where: 1) Contribution of V2G discharge to equivalent virtual inertia The calculation expression is as follows: ; in, For the first v The vehicle's equivalent inertia time constant. S vehicle,v For its rated capacity, V The number of vehicles that can be dispatched. S total This refers to the total capacity of the charging station; 2) Calculation of the contribution of V2G discharge to the equivalent droop coefficient: The V2G system participates in frequency regulation through droop control. The V2G equivalent droop coefficient... m V2G Determined by the aggregation characteristics, the introduction m V2G The overall equivalent droop coefficient of the post-charging station is: ; in, mESS and m PV These represent the equivalent droop coefficient of the energy storage system and the equivalent droop coefficient of the photovoltaic power generation system for the charging pile, respectively. 3) Contribution of V2G discharge to the equivalent short-circuit ratio The calculation expression: ; in, S SC,V2G The short-circuit capacity provided for the V2G converter For the short-circuit capacity of the energy storage system, Short-circuit capacity provided for photovoltaics; 4) Contribution of V2G discharge to black start: During the black start process, vehicles with V2G function serve as important power sources, providing initial energy to establish voltage for auxiliary power supply; S504: Parametric representation at the planning level, in the equivalent network construction parameter model, the overall network construction capability of each candidate site. Represented as: ; in: H i Indicate alternative site The equivalent virtual inertia; m i Indicate alternative site The equivalent droop coefficient; SCR i Indicate alternative site The equivalent short-circuit ratio; BS i Indicate alternative site The black-start capability is set to 0 or 1.

[0015] Optionally, in the S600, V2G is introduced on the load side, and distributed photovoltaic and wind power are introduced on the power supply side. A net load curve is generated based on the flexible load. The process includes: S601: Basic charging load modeling. Based on the predicted number of electric vehicles of various types, the Monte Carlo simulation method is used to generate a typical daily charging load curve Lbsae(t). The charging load of heavy trucks is modeled separately based on the mining area operation data. Considering the characteristics of concentrated charging during shift change, multiple charging peak periods are formed. S602: Modeling V2G schedulable potential, defining the first... i V2G schedulable potential of the alternative sites: ; in: V i Indicate alternative site Number of V2G vehicles within the service area; P vmax This represents the maximum charging and discharging power of the v-th vehicle; d v (t) : Indicates the vehicle access status at time t, which can be 0 or 1, and is determined according to the operation pattern; or v Indicates charge / discharge efficiency; α v ( t The ) represents the dispatchability coefficient, which indicates the proportion of vehicles willing to participate in V2G discharge, and is set according to electricity price incentives or contractual agreements; S603: Generate net load curve, including photovoltaic output As a negative load, it is superimposed with the base load and V2G load to obtain alternative site selections. i Net load curve: ; in, This represents a positive V2G load. This represents V2G underload. Represents photovoltaic power generation; The maximum value of the net load curve determines the maximum power that the charging station needs to draw from the energy storage system, and its integral energy determines the capacity requirement of the energy storage system.

[0016] Optionally, in the S700, the Aurora optimization algorithm is used to construct a three-layer collaborative optimization model to solve for each source-level charging block that needs to be balanced. The process includes: S701: Construct a three-layer collaborative optimization model, which includes an upper-layer site selection model, a lower-layer capacity configuration optimization model, and a lower-layer network parameter optimization model. S702: The Aurora Optimization Algorithm is used to solve the three-layer collaborative optimization model and output the optimal site selection, capacity configuration, V2G participation, and network parameter tuning values.

[0017] Optionally, the decision variables, objective function, and constraints of the upper-level site selection model are as follows: The decision variable is to select N sites as the first construction sites from M candidate sites within the block; the objective function is to maximize the sum of the comprehensive resource condition coefficients of the selected sites within the block, and the expression of the objective function is as follows: ; The constraints include: Website quantity constraint: ∑ y i ≤ N max ; Service coverage constraint: Ensure that all demand grids within the block are covered by at least one selected site; Network cluster constraints: If multiple sites belong to the same network cluster, it is recommended to retain at least two sites to form a multi-site parallel microgrid; in, y i For 0-1 decision variables, y i =1 indicates that the i-th candidate site has been selected.

[0018] Optionally, the objective function and constraints of the lower-level capacity configuration optimization model are as follows: For the first construction site selected by the upper level, the photovoltaic capacity is optimized with the goal of maximizing the net present value (NPV) over the entire life cycle. P PV Energy storage capacity E ESS Rated power of energy storage P ESS and the number of charging piles N CS And optimize the V2G participation parameters; the objective function of the lower-layer capacity configuration optimization model is expressed as follows: ; in, T Indicates the project's operating lifespan; r Indicates the discount rate; C inv Indicates initial investment; R t Indicates income item, , R charge,t Revenue from charging service fees; R V2G,t Revenue from V2G discharge services; R carbon,t For carbon emission reduction benefits; C t Indicates cost item, ; C { O & M,t} represents the operating and maintenance cost in year t. C { replace,t} represents the equipment replacement cost in year t; The constraints of the lower-level capacity configuration optimization model include: 1) Energy balance constraint: at any time t It must meet the following conditions: ; That is, the sum of photovoltaic output, energy storage discharge, and V2G discharge must always be greater than or equal to the net load demand; 2) Photovoltaic capacity constraints: P PV ≤ Sland, PV × k PV ; 3) Energy storage capacity and power constraints: E ESS ≤ Sland,ESS × k ESS ; P ESS ,cha ≤ PESS,rated , PESS,dis ≤ PESS,rated ; SOCmin ≤ SOC(t) ≤ SOCmax ; 4) Discharge energy constraint: The daily discharge of a single vehicle shall not exceed the set threshold of its battery capacity to ensure the power required for operation the next day; 5) Access time constraints: Set the permitted discharge periods based on operating shifts; 6) Scheduling coefficient constraint: 0 ≤ α v ( t If ≤ 1, optimize according to the incentive policy; Among them, the discharge energy constraint, access time constraint, and schedulable coefficient constraint are all V2G constraints; 7) Constraints on the overall network construction capacity of charging stations: , , ; The threshold for the overall network construction capability constraint of charging stations is set according to the system scale and control requirements, and the contribution of V2G has been taken into account in the constraint. For multiple sites belonging to the same network cluster, the multi-site parallel coordination constraint must also be satisfied: ; ; That is, the equivalent droop coefficient and equivalent inertia of each station should be similar to ensure the power distribution accuracy and dynamic response coordination during parallel operation; 8) Black start capability constraint: If the planning requires the system to have black start capability, then at least one site must reserve sufficient initial energy for energy storage and have corresponding control strategies, while also considering the feasibility of V2G-assisted black start.

[0019] Optionally, the objective function of the underlying network parameter optimization model is as follows: Based on the capacity configuration and V2G participation determined by the lower-level capacity configuration optimization model, the equivalent network parameters of the charging station are further optimized to minimize the dynamic response time or maximize the stability margin. The objective function of the lower-level network parameter optimization model is: ; in, For the maximum frequency deviation, The settling time after the disturbance. α and β These are the weighting coefficients.

[0020] Optionally, the process of solving the three-layer collaborative optimization model using the Aurora Optimization Algorithm is as follows: Initialization: Randomly generate an upper-layer location scheme and a lower-layer capacity configuration scheme. The upper-layer location scheme is set to the particle position, and the lower-layer capacity configuration scheme is set to the particle velocity. Lower-level solution: For each upper-level location scheme, call the Aurora optimization algorithm to optimize capacity configuration and V2G participation, and return the optimal NPV; Bottom-level solution: For each lower-level capacity scheme, the Aurora optimization algorithm is called to optimize the network parameters and return dynamic performance indicators; Upper-layer update: Based on the NPV and dynamic performance metrics returned from the lower layer, update the fitness of the upper-layer particles and update the particle positions; Convergence criterion: If the maximum number of iterations is reached or the fitness converges, then the iteration stops; Output: Outputs the optimal site selection, capacity configuration, V2G participation, and network parameter settings.

[0021] In a second aspect, the present invention also provides a computer device, including a memory and a processor; The memory is used to store computer programs that can run on the processor; When the processor executes the computer program, it implements the steps of the planning method for a grid-type integrated power generation, energy storage, and charging station as described above.

[0022] In a third aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described planning method for a grid-type integrated power generation, energy storage, and charging station.

[0023] The application of the technical solution of the present invention has at least the following beneficial effects: (1) This invention provides a planning method for a grid-type integrated charging station that combines power generation, energy storage and charging. Starting with the available charging resources (new energy power generation endowment, available land), it reversely matches the traffic charging demand and introduces the overall grid-building capacity parameters of the charging station (equivalent virtual inertia, equivalent droop coefficient, equivalent short-circuit ratio, black start capability) as key constraints in the planning model. Among them, the inertia contribution and power support of vehicle V2G discharge are explicitly included in the model. Through intelligent algorithms, the four dimensions of "source" (power generation capacity), "demand" (charging demand), "storage" (energy storage energy and power), and "structure" (overall voltage and frequency stability grid-building requirements of the charging station) are optimized in a coordinated manner. Finally, a network layout and capacity configuration scheme for charging stations that meets the requirements of energy self-sufficiency, meets traffic demand, and has dynamic stability is obtained. This provides a scientific basis and feasible engineering solution for the energy transformation of underdeveloped areas, remote areas without grid coverage, and highway transportation in the world.

[0024] (2) Unlike the traditional one-way planning logic of demand-driven supply, this invention takes resource endowment (source) as the planning starting point, matches traffic demand (demand) in reverse, introduces energy storage regulation (storage) as a key coupling link in supply and demand balance, and incorporates the overall grid construction capacity (structure) of charging stations as a dynamic stability constraint into the planning model, forming a four-dimensional collaborative planning framework with source determining scope, demand determining scale, storage determining reliability, and structure determining stability. The method of this invention solves the problems of "where can charging facilities be built" and "can they operate stably after being built" in areas without power grids.

[0025] (3) The method of this invention is based on a dynamic clustering partitioning method of "energy supply impedance + grid coordination impedance". When partitioning regions, a "comprehensive distance" measurement formula is proposed to quantify the new energy power generation capacity as "energy supply impedance" and the grid cluster relationship as "grid coordination impedance". This makes the service radius of the charging station not only related to its own power generation capacity, but also to the parallel coordination of multiple stations. This method lays the spatial layout foundation for the formation of future grid-type microgrid clusters.

[0026] (4) The method of this invention also includes parameterized modeling of the overall network construction capability of the integrated power generation, energy storage and charging station. The equivalent network construction parameters of the entire charging station (equivalent virtual inertia H, equivalent droop coefficient m, equivalent short-circuit ratio SCR, black start capability BS) are incorporated into the planning model, and a contribution model of V2G discharge to these parameters is established, making them optimizable and constrainable decision variables, thus realizing a deep integration of planning and control. These parameters reflect the overall dynamic characteristics of the collaborative contribution of photovoltaic, energy storage, charging piles and V2G, and a quantitative correlation relationship with equipment configuration and vehicle participation is established.

[0027] (5) The method of the present invention introduces a capacity configuration model with overall network construction capacity constraints of charging stations. In the in-station optimization model, the overall network construction capacity constraints of charging stations are innovatively added. H≥H min , m min ≤m≤m max , SCR≥SCR min In addition to multi-site parallel coordination constraints, discharge energy constraints, access time constraints, and schedulable coefficient constraints have been added for V2G to ensure that the planned sites not only have energy balance, but also have sufficient inertia, adjustment capability, and short-circuit capacity to resist disturbances and operate stably.

[0028] (6) The method of the present invention also includes the planning level integration of V2G bidirectional interactive resources, which elevates V2G technology from the operation and scheduling level to the planning level, establishes a schedulable potential model based on vehicle characteristics, incorporates it as a flexible load into the net load curve calculation, and includes the inertia contribution and short-circuit capacity contribution of V2G into the overall network capacity calculation of the charging station, realizing the planning of bidirectional energy interaction and dynamic support of "vehicle-station-network", laying the planning foundation for the integration of transportation and energy.

[0029] (7) The method of this invention constructs a three-layer optimization architecture of “upper layer site selection, lower layer capacity determination (including V2G participation) and lower layer network parameter setting” based on the three-layer collaborative optimization of “site selection-capacity determination-network parameter setting” of the PLO algorithm. It also introduces the Aurora optimization algorithm for nested solution, realizing the whole chain collaborative optimization from regional network layout to single-site technical configuration to network parameter setting, ensuring the global optimality and dynamic feasibility of the planning results. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the planning steps of a grid-type integrated power generation, energy storage, and charging station in a preferred embodiment of the present invention. Detailed Implementation

[0032] 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 a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] Example: like Figure 1 As shown, this embodiment provides a planning method for a grid-type integrated power generation, energy storage, and charging station, including (S100 to S800): S100: Set up a multi-dimensional grid in the target planning area and build a data layer. The process includes: S101: Divide the target planning area into several basic grids of the same size according to the latitude and longitude coordinate system; S102: Construct a spatial data layer for each basic grid, the spatial data layer including demand-side data, supply-side data and infrastructure constraints.

[0034] It should be noted that the basic grid can be a 5km×5km or 10km×10km grid, and the specific accuracy can be dynamically adjusted according to the area and data accuracy.

[0035] Specifically, the preferred spatial data layer in this embodiment includes three major categories and seven subcategories, as shown in Table 1.

[0036] Table 1 Spatial Data Layer

[0037] S200: The first round of screening of candidate charging station sites is carried out based on source constraints to obtain alternative site locations.

[0038] This embodiment starts with "resource endowment" for the first round of screening to ensure that all subsequent planned sites have the inherent conditions for energy self-sufficiency. The process is as follows: S201: Power Generation Potential Assessment: Calculate the photovoltaic development potential index for each basic grid. P pv Wind power development potential index P wind The expression is as follows: ; ; in, H year This represents the total annual solar radiation. T full-load This refers to the annual equivalent full-load hours of wind power. A available The usable land area or Energy conversion efficiency coefficient, subscript pv and wind These represent photovoltaic and wind power, respectively. Set a filter threshold: Filter out P pv > P pv-min orP wind > P wind-min The grid, as a candidate region with source basis, P pv-min and P wind-min These represent the potential thresholds for photovoltaic development and wind power development, respectively.

[0039] S202: Screening of physical conditions for site construction. Combining land type data and terrain slope data, unsuitable construction areas are eliminated from the candidate areas. The unsuitable construction areas selected in this embodiment are as follows: ① Ecological red line areas, nature reserves, and basic farmland; ② Areas with a slope greater than 15° that are unsuitable for construction; ③ Areas prone to geological disasters; ④ Remote areas more than 10km from existing roads; ⑤ The area surrounding the station site has good public security and safety environment.

[0040] S203: Preliminary assessment of the feasibility of multi-site parallel connection in grid-type microgrid. For multiple candidate sites that are close to each other (e.g., the distance between them is less than 5km), determine whether they have the potential to form a multi-site parallel microgrid and record the possibility of grid-connected clusters of candidate sites for use in subsequent steps.

[0041] S204: Forming a candidate site database: The screened candidate sites are used as candidate sites to construct a candidate site database. P ={p1,p2,...,p m Each alternative site The record contains attribute information, including geographic coordinates and available land area. 、 New energy resource endowment coefficient 、 Traffic Accessibility Index 、 Network cluster identifiers and information on surrounding heavy-duty vehicles, including: Geographic coordinates (x i ,y i ); usable land area S land,i ; New energy resource endowment coefficient, alternative site The calculation expression is as follows R res,i =α·P pv,i +β·P wind,i , α+β =1; Traffic Accessibility Index Tacci,i(Based on distance from main roads, normalized); Network cluster identifier Cluster_ID (If it can form a multi-station parallel system with other stations); Information on heavy-duty vehicles in the surrounding area: including the number of heavy-duty trucks within the service radius, average battery capacity, and typical operating shifts.

[0042] S300: Partitioning of source-needed balanced charging block regions based on improved K-means clustering algorithm.

[0043] Considering that the service range of an off-grid charging station is determined by the power supply capacity of its "source" rather than a fixed geographical radius, this step uses an improved clustering algorithm to dynamically divide the surrounding demand grid by using the candidate site as the cluster center.

[0044] In this embodiment, the process of S300 includes: S301: Use the candidate sites selected in S200 as the initial cluster centers.

[0045] S302: Define the composite distance metric and define the base mesh. G j To alternative station Comprehensive distance D ij The comprehensive distance incorporates energy supply impedance, demand intensity, and grid coordination impedance on top of the geographic spatial distance. The calculation expression for the comprehensive distance is as follows: ; in: d ij Represents the basic grid G j To alternative station Geographical distance; R res,i Indicate alternative site The resource endowment coefficient, R res,i =α· P pv,i +β· P wind,i α+β=1; D dem,j Represents the basic grid G j The intensity of charging demand; C ij Indicates the network cooperative impedance, if the base grid G j Located at the alternative site Within the network cluster to which it belongs, C ij Take the smaller value (e.g., 0.1), otherwise take the larger value (e.g., 1.0). oh 1 ,oh 2 ,oh 3 ,oh 4 This represents the adjustable weighting coefficient, and oh 1 +oh 2 +oh 3 +oh 4 =1; The physical meaning of this formula can be dynamically adjusted according to planning objectives: the better the site has in terms of new energy resources ( R res,i The larger the grid, the smaller its "energy supply impedance"; the stronger its charging demand ( D dem,j Larger sites are more likely to be included in the service area; sites belonging to the same network cluster have lower cooperative impedance with the grid, which is conducive to forming a stable multi-site parallel system.

[0046] S303: Dynamic clustering and supply-demand balance adjustment, allocating all basic grids to the comprehensive distance. D ij The smallest cluster centers form the initial cluster blocks. C k k=1,2,...,K; calculate each block region C k Total charging demand D emk The charging demand for heavy-duty trucks is calculated separately based on the number of trucks, average daily mileage, and unit power consumption, taking into account the distribution of charging times; based on the alternative site selection... Maximum power supply capacity S upi Set the upper limit threshold for the demand of block k. Dem max,k The calculation expression is as follows: ; in, c This is the power supply guarantee factor (considering the uncertainty of photovoltaic output, it is usually taken as 0.7-0.85). r storage The energy storage regulation coefficient represents the percentage increase in power supply capacity after configuring energy storage; if a certain block area D emk > Dem max,k If this occurs, the equalization adjustment mechanism is triggered: some grids at the edge of the block are redistributed to neighboring blocks with surplus power supply capacity until all blocks meet the initial supply-demand balance, ultimately resulting in K source-demand equalization charging blocks.

[0047] S400: Comprehensive evaluation of site resource conditions based on fuzzy hierarchical analysis.

[0048] Within each source-equalization charging block area, there may be multiple alternative sites. To quantify the suitability of each site, a comprehensive resource condition evaluation model based on the fuzzy hierarchical analysis method (Fuzzy-AHP) is established.

[0049] In this embodiment, the S400 process includes: S401: Construct an evaluation index system, which includes a target layer, a criterion layer, and an index layer. The evaluation index system in this embodiment is shown in Table 2.

[0050] Table 2 Evaluation Index System

[0051] S402: A judgment matrix is ​​constructed using triangular fuzzy numbers, and the weight vectors W = [w1, w2, ..., w] of each indicator are obtained through fuzzy comprehensive operations. n ].

[0052] The process of obtaining the weight vectors of each indicator in this embodiment is as follows: 1. Constructing a fuzzy judgment matrix: Experts use triangular fuzzy numbers instead of precise numbers for pairwise comparisons. For example, if "photovoltaic power generation potential" is considered slightly more important than "wind power development potential," it can be represented by (1,2,3).

[0053] 2. Calculate the fuzzy comprehensive degree value: First, calculate the sum of the fuzzy numbers of each index in all comparisons, and then divide it by the sum of all fuzzy numbers in the entire matrix to obtain the fuzzy comprehensive degree value Si of each index.

[0054] 3. Calculate the probability: Compare the merits of each Si. Calculate the probability V (Si≥Sj) of Si≥Sj, and take the minimum value d'(Ai) = min V (Si≥Sj).

[0055] 4. Normalization to obtain weights: The vector composed of d'(Ai) is normalized to obtain the weight vector W = [w1, w2, ..., w...] for each indicator. n ].

[0056] S403: Calculate the site resource condition coefficient: For each candidate site Based on its scores for each indicator x i,j Calculate the resource condition coefficient: ; Among them, the resource condition coefficient Fsite,i The value range of is [0,1]. x i,j The value range is also [0,1], and the larger the value, the better the construction conditions of the site.

[0057] S500: Based on the charging station, establish an equivalent network parameter model for the charging station.

[0058] This embodiment treats the entire charging station as a microgrid node with grid-building capabilities and establishes its equivalent grid-building parameter model. These equivalent parameters are the result of the synergistic effect of photovoltaics, energy storage, charging piles, and their control systems within the station. In particular, the contribution of V2G discharge from heavy-duty trucks is explicitly included, reflecting the charging station's overall support capability for the power grid. The definition of the overall grid-building capability of the charging station is: grid-building capability refers to the ability of the charging station, as an independent microgrid, to autonomously establish stable voltage and frequency and provide reliable power support to the load. It mainly includes the following four dimensions: Voltage support capability: The charging station can establish rated voltage without an external power grid and can withstand load fluctuations to maintain voltage stability.

[0059] Frequency support capability: The charging station has a certain inertia and damping, which can suppress the rate of frequency change and maintain the frequency within the allowable range.

[0060] Short-circuit capacity contribution: The charging station can provide sufficient short-circuit current in the event of a fault, ensuring that the protection device operates reliably.

[0061] Black start capability: The charging station can start automatically from a completely dark state, gradually establish the power grid, and supply power to other stations or loads.

[0062] In this embodiment, the process of establishing the equivalent network parameter model of the charging station includes: S501: Defines the key parameters for the charging station network construction capability, including: Equivalent virtual inertia H station This reflects the charging station's ability to resist frequency changes. It is jointly determined by the virtual inertia of the energy storage system, the virtual inertia contribution of the photovoltaic inverter (if virtual synchronous machine control is used), and the inertia provided by vehicles with V2G capabilities. H station The larger the value, the better the frequency stability.

[0063] Equivalent droop coefficient m station This reflects the static characteristics of the charging station's active power-frequency regulation. It is jointly determined by the droop factor of energy storage, the active power limitation of photovoltaics, and the demand response capability of the charging load (including V2G). m stationThe smaller the value, the higher the frequency adjustment accuracy.

[0064] Equivalent short-circuit ratio SCR station This reflects the ratio of the short-circuit capacity that a charging station can provide to its rated capacity. It depends on the number of parallel energy storage converters and photovoltaic inverters, the control strategy, and the line impedance. V2G converters can also contribute short-circuit capacity. SCR station The larger the value, the stronger the voltage support capability.

[0065] Black Start Capability BS station : A Boolean variable indicating whether the charging station has the ability to start from zero voltage and establish a grid. Stations with black-start capability need to be equipped with sufficient energy storage capacity and a self-synchronization control strategy. Additionally, photovoltaic systems and vehicles with V2G functionality must be able to cooperate in the startup process.

[0066] S502: Calculate the contribution of V2G discharge to the charging station network capability based on key parameters, where: 1) Contribution of V2G discharge to equivalent virtual inertia The calculation expression is as follows: ; in, For the first v The vehicle's equivalent inertia time constant. S vehicle,v For its rated capacity, V The number of vehicles that can be dispatched. S total This refers to the total capacity of the charging station; 2) Calculation of the contribution of V2G discharge to the equivalent droop coefficient: The V2G system participates in frequency regulation through droop control. The V2G equivalent droop coefficient... m V2G Determined by the aggregation characteristics, the introduction m V2G The overall equivalent droop coefficient of the post-charging station is: ; in, m ESS and m PV These represent the equivalent droop coefficient of the energy storage system and the equivalent droop coefficient of the photovoltaic power generation system for the charging pile, respectively. 3) Contribution of V2G discharge to the equivalent short-circuit ratio The calculation expression: ; in, S SC,V2G The short-circuit capacity provided for the V2G converter For the short-circuit capacity of the energy storage system, Short-circuit capacity provided for photovoltaics; 4) Contribution of V2G discharge to black start: During the black start process, vehicles with V2G function serve as important power sources, providing initial energy to establish voltage for auxiliary power supply; S504: Parametric representation at the planning level, in the equivalent network construction parameter model, the overall network construction capability of each candidate site. Represented as: ; in: H i Indicate alternative site The equivalent virtual inertia (unit: seconds) can be indirectly controlled by optimizing energy storage capacity, photovoltaic control mode, V2G participation, etc. m i Indicate alternative site The equivalent droop coefficient (dimensionless) can be indirectly controlled by optimizing the control parameters of energy storage and V2G. SCR i Indicate alternative site The equivalent short-circuit ratio depends on the number and design of the converters connected in parallel, and can be indirectly controlled by equipment selection and the number of V2G connections. BS i Indicate alternative site The black start capability, taken as 0 or 1, needs to be clearly defined during planning, and the potential for V2G-assisted black starts can be considered. These equivalent parameters serve as decision variables or constraints for subsequent optimization models, ensuring that the planned charging stations not only meet energy balance requirements but also possess sufficient dynamic support capabilities.

[0067] S600: Introduces V2G on the load side and distributed photovoltaic and wind power on the power supply side, generating a net load curve based on flexible load.

[0068] In this embodiment, the S600 process includes: S601: Basic charging load modeling. Based on the predicted number of electric vehicles of various types, the Monte Carlo simulation method is used to generate typical daily charging load curves. L bsae,i ( t The charging load of heavy trucks is modeled separately based on the mining area operation data, taking into account the characteristics of concentrated charging during shift change times, forming multiple peak charging periods. S602: Modeling V2G schedulable potential, defining the first... i V2G schedulable potential of the alternative sites: ; in:V i Indicate alternative site Number of V2G vehicles within the service area; P vmax This represents the maximum charging and discharging power of the v-th vehicle; d v (t) : Indicates the vehicle access status at time t, which can be 0 or 1, and is determined according to the operation pattern; or v Indicates charge / discharge efficiency; α v ( t The ) represents the dispatchability coefficient, which indicates the proportion of vehicles willing to participate in V2G discharge, and is set according to electricity price incentives or contractual agreements; S603: Generate net load curve, including photovoltaic output As a negative load, it is superimposed with the base load and V2G load to obtain alternative site selections. i Net load curve: ; in, This represents a positive V2G load (charging). This represents a V2G negative load (discharge). Represents photovoltaic power generation; The maximum value of the net load curve determines the maximum power that the charging station needs to draw from the energy storage system, and its integral energy determines the capacity requirement of the energy storage system.

[0069] It should be noted that the timing characteristics of V2G discharge are as follows: During off-peak hours at night: vehicles park in a concentrated manner, providing stable discharge capacity to support basic loads such as nighttime lighting or to charge energy storage.

[0070] During peak solar PV periods: If solar PV output is excessive, vehicles can absorb the excess electricity; if solar PV output is insufficient, vehicles can discharge electricity to support the charging load.

[0071] Peak shift change times: During this period, charging demand is high while vehicle discharge demand is low, requiring reasonable allocation.

[0072] S700: It adopts the Aurora optimization algorithm to construct a three-layer collaborative optimization model that includes upper-layer addressing, lower-layer capacity setting, and lower-layer network parameter tuning, and solves for each source-needed equalization charging block.

[0073] Using the Aurora optimization algorithm, a three-layer collaborative optimization model is constructed to solve for each source-requiring equalization charging block. The process includes: S701: Construct a three-layer collaborative optimization model, which includes an upper-layer site selection model, a lower-layer capacity configuration optimization model, and a lower-layer network parameter optimization model, wherein: The decision variables, objective function, and constraints of the upper-level site selection model are as follows: The decision variable is to select N sites as the first construction sites from M candidate sites within the block; the objective function is to maximize the sum of the comprehensive resource condition coefficients of the selected sites within the block, and the expression of the objective function is as follows: ; The constraints are as follows: Website quantity constraint: ∑ y i ≤ N max ; Service coverage constraint: Ensure that all demand grids within the block are covered by at least one selected site; Network cluster constraints: If multiple sites belong to the same network cluster, it is recommended to retain at least two sites to form a multi-site parallel microgrid; in, y i For 0-1 decision variables, y i =1 indicates the first i One alternative site was selected; The objective function and constraints of the lower-level capacity configuration optimization model are as follows: For the first construction site selected by the upper level, the photovoltaic capacity is optimized with the goal of maximizing the net present value (NPV) over the entire life cycle. P PV Energy storage capacity E ESS Rated power of energy storage P ESS and the number of charging piles N CS And optimize the V2G participation parameters; the objective function of the lower-layer capacity configuration optimization model is expressed as follows: ; in, T Indicates the project's operating lifespan; r Indicates the discount rate; C inv Indicates initial investment; R t Indicates income item, , R charge,t Revenue from charging service fees; R V2G,t Revenue from V2G discharge services;R carbon,t For carbon emission reduction benefits; C t Indicates cost item, ; C { O & M,t} represents the operating and maintenance cost in year t. C { replace,t} represents the equipment replacement cost in year t; The constraints of the lower-level capacity configuration optimization model include: 1) Energy balance constraint: at any time t It must meet the following conditions: ; That is, the sum of photovoltaic output, energy storage discharge, and V2G discharge must always be greater than or equal to the net load demand; 2) Photovoltaic capacity constraints: P PV ≤ Sland, PV × k PV ; 3) Energy storage capacity and power constraints: E ESS ≤ Sland,ESS × k ESS ; P ESS ,cha ≤ PESS,rated , PESS,dis ≤ PESS,rated ; SOCmin ≤ SOC(t) ≤ SOCmax ; 4) Discharge energy constraint: The daily discharge of a single vehicle shall not exceed the set threshold of its battery capacity to ensure the power required for operation the next day; 5) Access time constraints: Set the permitted discharge periods based on operating shifts; 6) Scheduling coefficient constraint: 0 ≤ α v ( t If ≤ 1, optimize according to the incentive policy; It should be noted that the discharge energy constraint, access time constraint, and schedulable coefficient constraint are all V2G constraints.

[0074] 7) Constraints on the overall network construction capacity of charging stations: , , ; The threshold for the overall network construction capability constraint of charging stations is set according to the system scale and control requirements, and the contribution of V2G has been taken into account in the constraint; it should be noted thatH min , m min 、m max and SCR min They represent H i 、m i 、SCR i The upper or lower limit of the threshold.

[0075] For multiple sites belonging to the same network cluster, the multi-site parallel coordination constraint must also be satisfied: ; ; That is, the equivalent droop coefficient and equivalent inertia of each station should be similar to ensure the power distribution accuracy and dynamic response coordination during parallel operation; 8) Black start capability constraint: If the planning requires the system to have black start capability, then at least one site must reserve sufficient initial energy for energy storage and have corresponding control strategies, while also considering the feasibility of V2G-assisted black start.

[0076] The objective function of the underlying network parameter optimization model is as follows: Based on the capacity configuration and V2G participation determined by the lower-level capacity configuration optimization model, the equivalent network parameters of the charging station are further optimized to minimize the dynamic response time or maximize the stability margin. The objective function of the lower-level network parameter optimization model is: ; in, For the maximum frequency deviation, The settling time after the disturbance. α and β These are the weighting coefficients; S702: The aurora optimization algorithm is used to solve the three-layer collaborative optimization model. The process is as follows: Initialization: Randomly generate an upper-layer location scheme and a lower-layer capacity configuration scheme. The upper-layer location scheme is set to the particle position, and the lower-layer capacity configuration scheme is set to the particle velocity. Lower-level solution: For each upper-level location scheme, call the Aurora optimization algorithm to optimize capacity configuration and V2G participation, and return the optimal NPV; Bottom-level solution: For each lower-level capacity scheme, the Aurora optimization algorithm is called to optimize the network parameters and return dynamic performance indicators; Upper-layer update: Based on the NPV and dynamic performance metrics returned from the lower layer, update the fitness of the upper-layer particles and update the particle positions; Convergence criterion: If the maximum number of iterations is reached or the fitness converges, then the iteration stops; Output: Outputs the optimal site selection, capacity configuration, V2G participation, and network parameter settings.

[0077] S800: Generates a planning scheme to guide engineering design based on the optimal solution output by S700.

[0078] In this embodiment, the planning scheme includes at least a site list, a construction scale table, a V2G participation table, and a charging station overall network parameter table.

[0079] Simulation Case: A mining area-port transportation corridor was selected as a verification case. The power grid coverage rate in this area is 0, and the existing mining area transportation relies entirely on imported diesel. The plan is to construct a mining area transportation highway with a length of about 350 kilometers, along which there are 3 large mining areas. The goal is to provide charging services for electric heavy trucks and logistics vehicles for the next ten years (2026-2035).

[0080] Basic data: Number of heavy trucks in the mining area: Currently 500, with a target of increasing to 1,500 annually; Heavy-duty truck parameters: average battery capacity 500kWh, maximum charging power 300kW, maximum discharging power 250kW, V2G participation rate 70%; Electrification rate target: 80% by 2035; New energy resources: The average annual solar radiation is 2100 kWh / m², indicating huge potential for photovoltaic development; Available land: There are 22 potential station sites along the route, including abandoned mining areas and reserved land for highway service areas.

[0081] The process of planning and designing charging stations using the method described in this embodiment is as follows: Step 1: Divide the study area into 2km×2km grids, totaling approximately 3000 grids, and construct a multi-dimensional data layer, highlighting the location of mining areas and the density of heavy trucks.

[0082] Step 2: Based on the assessment of new energy resources (annual equivalent utilization hours of photovoltaic power > 1500h) and land availability screening, 15 sites were selected from 22 potential sites as candidate sites, of which 10 are close to the mining area.

[0083] Step 3: Use the improved K-means algorithm ( oh 1 =0.35, oh 2 =0.35, oh 3 =0.2, oh4 =0.1), the 15 candidate sites were clustered into 8 charging block areas, and 3 network clusters were identified, each of which covers at least one mining area.

[0084] Step 4: Perform fuzzy AHP evaluation on the candidate sites within each block area and calculate the resource condition coefficient. F site,i The scores range from 0.92 to 0.45, with sites closer to mining areas generally scoring higher.

[0085] Step 5: Construct a V2G dispatchable potential model, assuming that 70% of heavy trucks in the target year have V2G functionality, and set the discharge period as 22:00-06:00 (nighttime parking) and 12:00-14:00 (lunch break), with a dispatchable coefficient α=0.3.

[0086] Step 6: Use the PLO algorithm for three-layer optimization. Algorithm parameter settings: population size 100, maximum number of iterations 500, crossover probability 0.8, mutation probability 0.1. Network constraints: Hmin=3.0s, m∈[2%,5%], SCRmin=2.0.

[0087] The final optimized output results are shown in Table 3.

[0088] Table 3 Optimization Results

[0089] System metrics: Target annual charging demand satisfaction rate: 94.3%; The system's average annual power supply is 42.5 million kWh, of which heavy-duty truck charging accounts for 82%. The proportion of renewable energy power generation is 100% (completely off-grid). Power supply reliability: 99.2% (considering energy storage backup); V2G annual discharge capacity: 6.8 million kWh, accounting for 16% of the total power supply of the system; Dynamic performance indicators: Maximum frequency deviation: ±0.28Hz (meets the frequency requirements of GB / T 33589-2017 microgrids), thanks to the inertia contributed by V2G; Maximum voltage deviation: 4.5%; Black boot time: <30s (S-01 site can utilize V2G to assist in black boot); Economic indicators: Total project investment: approximately US$56 million; Weighted average cost of electricity (LCOE): $0.108 / kWh; The project's internal rate of return (IRR) is 13.2%, of which V2G discharge revenue contributes 1.8 percentage points. Dynamic payback period: 6.0 years; Comparative verification: Compared with the traditional demand-driven approach, the method of this invention: Implementation rate of the plan: 100% vs 20% (only 3 out of 15 requirements are feasible); System LCOE: $0.108 vs $0.185 / kWh (a decrease of 41.6%); Overall investment efficiency: The number of vehicles served per unit of investment increased by 62%; Dynamic stability: If the S-03 site planned by the traditional method is configured with energy storage only according to energy balance and ignores the overall grid construction capacity of the charging station and V2G, the simulation shows that the frequency drops to 49.2Hz (triggering low-frequency load shedding) when the load changes stepwise; the method of the present invention maintains the frequency above 49.8Hz by optimizing the overall grid construction parameters and using V2G discharge support.

[0090] As can be seen from the above simulation process, the planning method proposed in this invention is technically feasible and can effectively solve the core problem of planning charging facilities in mines without power grids; it is significantly superior economically, with a strong return on investment, and V2G discharge contributes considerable revenue and dynamic support; it is stable and reliable in terms of dynamic performance, meeting the microgrid operation standards; and it makes outstanding contributions to social benefits, and has important value in promoting the transformation of transportation and energy in underdeveloped mining areas.

[0091] In addition, this embodiment also provides a computer device, including a memory and a processor; The memory is used to store computer programs that can run on the processor; When the processor executes the computer program, it implements the steps of the planning method for a grid-type integrated power generation, energy storage, and charging station as described above.

[0092] It should be noted that computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Matlab, Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] In addition, this embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described planning method for a grid-type integrated power generation, energy storage, and charging station.

[0095] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., a computer program) for executing the above-described planning method for a grid-type integrated power generation, energy storage, and charging station. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the planning method for a grid-type integrated power generation, energy storage, and charging station provided in the above embodiments, and will not be repeated here.

[0096] The above description is only a preferred embodiment of the present invention and does not limit the scope of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of the present invention.

Claims

1. A planning method for a grid-type integrated power generation, energy storage, and charging station, characterized in that, include: S100: Set up a multi-dimensional grid in the target planning area and build a data layer; S200: The first round of screening of candidate charging station sites is carried out based on source constraints to obtain alternative site locations; S300: Partitioning of source-balanced charging block regions based on improved K-means clustering algorithm; S400: Comprehensive evaluation of site resource conditions based on fuzzy hierarchical analysis; S500: Based on the charging station, establish an equivalent network parameter model for the charging station; S600: Introduces V2G on the load side and distributed photovoltaic and wind power on the power supply side, generating a net load curve based on flexible load; S700: It adopts the Aurora optimization algorithm to construct a three-layer collaborative optimization model that includes upper-layer addressing, lower-layer capacity setting, and lower-layer network parameter tuning, and solves for each source-needed equalization charging block; S800: Generates a planning scheme to guide engineering design based on the optimal solution output by S700.

2. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 1, characterized in that, In S100, a multi-dimensional grid is set up in the target planning area, and a data layer is constructed. The process includes: S101: Divide the target planning area into several basic grids of the same size according to the latitude and longitude coordinate system; S102: Construct a spatial data layer for each basic grid, the spatial data layer including demand-side data, supply-side data and infrastructure constraints.

3. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 2, characterized in that, In S200, the first round of screening for candidate charging station sites based on source constraints includes the following process: S201: Power Generation Potential Assessment: Calculate the photovoltaic development potential index for each basic grid. P pv Wind power development potential index P wind The expression is as follows: ； ； in, H year This represents the total annual solar radiation. T full-load This refers to the annual equivalent full-load hours of wind power. A available The usable land area η Energy conversion efficiency coefficient, subscript pv and wind These represent photovoltaic and wind power, respectively. Set a filter threshold: Filter out P pv > P pv-min or P wind > P wind-min The grid, as a candidate region with source basis, P pv-min and P wind-min These represent the potential thresholds for photovoltaic development and wind power development, respectively. S202: Screening of physical conditions for site construction, combining land type data and terrain slope data to eliminate unsuitable construction areas from the candidate areas; S203: Preliminary assessment of the feasibility of grid-type multi-site parallel connection. For multiple candidate sites that are relatively close to each other, determine whether they have the potential to form a multi-site parallel microgrid and record the possibility of grid-type clustering of candidate sites. S204: Forming a candidate site database: The screened candidate sites are used as candidate sites to construct a candidate site database. P ={p1,p2,...,p m Each alternative site The record contains attribute information, including geographic coordinates and available land area. 、 New energy resource endowment coefficient 、 Traffic Accessibility Index 、 Network cluster identifiers and information on surrounding heavy-duty vehicles.

4. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 3, characterized in that, In S300, the process of partitioning the source-to-equilibrium charging block region based on the improved K-means clustering algorithm includes: S301: Use the candidate sites selected in S200 as the initial cluster centers; S302: Define the composite distance metric and define the base mesh. G j To alternative station Comprehensive distance D ij The comprehensive distance incorporates energy supply impedance, demand intensity, and grid coordination impedance on top of the geographic spatial distance. The calculation expression for the comprehensive distance is as follows: ； in: d ij Represents the basic grid G j To alternative station Geographical distance; R res,i Indicate alternative site The resource endowment coefficient, R res,i =α· P pv,i +β· P wind,i α+β=1; D dem,j Represents the basic grid G j The intensity of charging demand; C ij Indicates the network cooperative impedance, if the base grid G j Located at the alternative site Within the network cluster to which it belongs, C ij Take the smaller value, otherwise take the larger value; ω 1 ,ω 2 ,ω 3 ,ω 4 This represents the adjustable weighting coefficient, and ω 1 +ω 2 +ω 3 +ω 4 =1; S303: Dynamic clustering and supply-demand balance adjustment, allocating all basic grids to the comprehensive distance. D ij The smallest cluster centers form the initial cluster blocks. C k k=1,2,...,K; calculate each block region C k Total charging demand D emk The charging demand for heavy-duty trucks is calculated separately based on the number of trucks, average daily mileage, and unit power consumption, taking into account the distribution of charging times; based on the alternative site selection... Maximum power supply capacity S upi Set the upper limit threshold for the demand of block k. D em max,k The calculation expression is as follows: ； in, γ Power supply guarantee factor; ρ storage The energy storage regulation coefficient represents the percentage increase in power supply capacity after configuring energy storage; if a certain block area D em k > D em max,k If this occurs, the equalization adjustment mechanism is triggered: some grids at the edge of the block are redistributed to neighboring blocks with surplus power supply capacity until all blocks meet the initial supply-demand balance, ultimately resulting in K source-demand equalization charging blocks.

5. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 4, characterized in that, In S400, a comprehensive evaluation of site resource conditions is conducted based on fuzzy AHP. The process includes: S401: Construct an evaluation index system, which includes an objective layer, a criterion layer, and an index layer; S402: A judgment matrix is ​​constructed using triangular fuzzy numbers, and the weight vectors W = [w1, w2, ..., w] of each indicator are obtained through fuzzy comprehensive operations. n ]; S403: Calculate the site resource condition coefficient: For each candidate site Based on its scores for each indicator x i,j Calculate the resource condition coefficient: ； in, F site,i The value range of is [0,1]. x i,j The value range is also [0,1], and the larger the value, the better the construction conditions of the site.

6. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 5, characterized in that, In S500, based on charging stations, an equivalent network parameter model for charging stations is established, where: S501: Define the key parameters for the charging station network construction capability, including the equivalent virtual inertia. H station Equivalent droop coefficient m station Equivalent short-circuit ratio SCR station Black Start-up Ability BS station ; S502: Calculate the contribution of V2G discharge to the charging station network capability based on key parameters, where: 1) Contribution of V2G discharge to equivalent virtual inertia The calculation expression is as follows: ； in, For the first v The vehicle's equivalent inertia time constant. S vehicle,v For its rated capacity, V The number of vehicles that can be dispatched. S total This refers to the total capacity of the charging station; 2) Calculation of the contribution of V2G discharge to the equivalent droop coefficient: The V2G system participates in frequency regulation through droop control. The V2G equivalent droop coefficient... m V2G Determined by the aggregation characteristics, the introduction m V2G The overall equivalent droop coefficient of the post-charging station is: ； in, m ESS and m PV These represent the equivalent droop coefficient of the energy storage system and the equivalent droop coefficient of the photovoltaic power generation system for the charging pile, respectively. 3) Contribution of V2G discharge to the equivalent short-circuit ratio The calculation expression: ； in, S SC,V2G The short-circuit capacity provided for the V2G converter For the short-circuit capacity of the energy storage system, Short-circuit capacity provided for photovoltaics; 4) Contribution of V2G discharge to black start: During the black start process, vehicles with V2G function serve as important power sources, providing initial energy to establish voltage for auxiliary power supply; S504: Parametric representation at the planning level, in the equivalent network construction parameter model, the overall network construction capability of each candidate site. Represented as: ； in: H i Indicate alternative site The equivalent virtual inertia; m i Indicate alternative site The equivalent droop coefficient; SCR i Indicate alternative site The equivalent short-circuit ratio; BS i Indicate alternative site The black-start capability is set to 0 or 1.

7. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 6, characterized in that, In the S600, V2G is introduced on the load side, and distributed photovoltaic and wind power are introduced on the power supply side. A net load curve is generated based on the flexible load. The process includes: S601: Basic charging load modeling. Based on the predicted number of electric vehicles of various types, the Monte Carlo simulation method is used to generate a typical daily charging load curve Lbsae(t). The charging load of heavy trucks is modeled separately based on the mining area operation data. Considering the characteristics of concentrated charging during shift change, multiple charging peak periods are formed. S602: Modeling V2G schedulable potential, defining the first... i V2G schedulable potential of the alternative sites: ； in: V i Indicate alternative site Number of V2G vehicles within the service area; P vmax This represents the maximum charging and discharging power of the v-th vehicle; δ v (t) : Indicates the vehicle access status at time t, which can be 0 or 1, and is determined according to the operation pattern; η v Indicates charge / discharge efficiency; α v ( t The ) represents the dispatchability coefficient, which indicates the proportion of vehicles willing to participate in V2G discharge, and is set according to electricity price incentives or contractual agreements; S603: Generate net load curve, including photovoltaic output As a negative load, it is superimposed with the base load and V2G load to obtain alternative site selections. i Net load curve: ； in, This represents a positive V2G load. This represents V2G underload. Represents photovoltaic power generation; The maximum value of the net load curve determines the maximum power that the charging station needs to draw from the energy storage system, and its integral energy determines the capacity requirement of the energy storage system.

8. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 7, characterized in that, In the S700, the Aurora optimization algorithm is used to construct a three-layer collaborative optimization model to solve for each source-requiring equalization charging block. The process includes: S701: Construct a three-layer collaborative optimization model, which includes an upper-layer site selection model, a lower-layer capacity configuration optimization model, and a lower-layer network parameter optimization model. S702: The Aurora Optimization Algorithm is used to solve the three-layer collaborative optimization model and output the optimal site selection, capacity configuration, V2G participation, and network parameter tuning values.

9. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 8, characterized in that, The decision variables, objective function, and constraints of the upper-level site selection model are as follows: The decision variable is to select N sites as the first construction sites from M candidate sites within the block; the objective function is to maximize the sum of the comprehensive resource condition coefficients of the selected sites within the block, and the expression of the objective function is as follows: ； The constraints include: Website quantity constraint: ∑ y i ≤ N max ; Service coverage constraint: Ensure that all demand grids within the block are covered by at least one selected site; Network cluster constraints: If multiple sites belong to the same network cluster, it is recommended to retain at least two sites to form a multi-site parallel microgrid; in, y i For 0-1 decision variables, y i =1 indicates that the i-th candidate site has been selected.

10. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 9, characterized in that, The objective function and constraints of the lower-level capacity configuration optimization model are as follows: For the first construction site selected by the upper level, the photovoltaic capacity is optimized with the goal of maximizing the net present value (NPV) over the entire life cycle. P PV Energy storage capacity E ESS Rated power of energy storage P ESS and the number of charging piles N CS And optimize the V2G participation parameters; the objective function of the lower-layer capacity configuration optimization model is expressed as follows: ； in, T Indicates the project's operating lifespan; r Indicates the discount rate; C inv Indicates initial investment; R t Indicates income item, , R charge,t Revenue from charging service fees; R V2G,t Revenue from V2G discharge services; R carbon,t For carbon emission reduction benefits; C t Indicates cost item, ; C { O & M,t } represents the operating and maintenance cost in year t. C { replace,t } represents the equipment replacement cost in year t; The constraints of the lower-level capacity configuration optimization model include: 1) Energy balance constraint: at any time t It must meet the following conditions: ； That is, the sum of photovoltaic output, energy storage discharge, and V2G discharge must always be greater than or equal to the net load demand; 2) Photovoltaic capacity constraints: P PV ≤ Sland, PV × k PV ; 3) Energy storage capacity and power constraints: E ESS ≤ Sland,ESS × k ESS ； P ESS ,cha ≤ PESS,rated ， PESS,dis ≤ PESS,rated ； SOCmin ≤ SOC(t) ≤ SOCmax ； 4) Discharge energy constraint: The daily discharge of a single vehicle shall not exceed the set threshold of its battery capacity to ensure the power required for operation the next day; 5) Access time constraints: Set the permitted discharge periods based on operating shifts; 6) Scheduling coefficient constraint: 0 ≤ α v ( t If ≤ 1, optimize according to the incentive policy; Among them, the discharge energy constraint, access time constraint, and schedulable coefficient constraint are all V2G constraints; 7) Constraints on the overall network construction capacity of charging stations: , , ; The threshold for the overall network construction capability constraint of charging stations is set according to the system scale and control requirements, and the contribution of V2G has been taken into account in the constraint. For multiple sites belonging to the same network cluster, the multi-site parallel coordination constraint must also be satisfied: ； ； That is, the equivalent droop coefficient and equivalent inertia of each station should be similar to ensure the power distribution accuracy and dynamic response coordination during parallel operation; 8) Black start capability constraint: If the planning requires the system to have black start capability, then at least one site must reserve sufficient initial energy for energy storage and have corresponding control strategies, while also considering the feasibility of V2G-assisted black start.

11. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 10, characterized in that, The objective function of the underlying network parameter optimization model is as follows: Based on the capacity configuration and V2G participation determined by the lower-level capacity configuration optimization model, the equivalent network parameters of the charging station are further optimized to minimize the dynamic response time or maximize the stability margin. The objective function of the lower-level network parameter optimization model is: ； in, For the maximum frequency deviation, The settling time after the disturbance. α and β These are the weighting coefficients.

12. The planning method for a grid-type integrated power generation, energy storage, and charging station according to claim 11, characterized in that, The process of solving the three-layer collaborative optimization model using the Aurora Optimization Algorithm is as follows: Initialization: Randomly generate an upper-layer location scheme and a lower-layer capacity configuration scheme. The upper-layer location scheme is set to the particle position, and the lower-layer capacity configuration scheme is set to the particle velocity. Lower-level solution: For each upper-level location scheme, call the Aurora optimization algorithm to optimize capacity configuration and V2G participation, and return the optimal NPV; Bottom-level solution: For each lower-level capacity scheme, the Aurora optimization algorithm is called to optimize the network parameters and return dynamic performance indicators; Upper-layer update: Based on the NPV and dynamic performance metrics returned from the lower layer, update the fitness of the upper-layer particles and update the particle positions; Convergence criterion: If the maximum number of iterations is reached or the fitness converges, then the iteration stops; Output: Outputs the optimal site selection, capacity configuration, V2G participation, and network parameter settings.

13. A computer device, characterized in that, Including memory and processor; The memory is used to store computer programs that can run on the processor; When the processor executes the computer program, it implements the steps of the planning method for a grid-type integrated power generation, energy storage, and charging station as described in any one of claims 1 to 12.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the planning method for a grid-type integrated power generation, storage, and charging station as described in any one of claims 1 to 12.

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