A method, equipment, and medium for multi-stakeholder coordinated planning of power distribution networks

Abstract

This invention relates to a multi-stakeholder coordinated planning method, equipment, and medium for power distribution networks. The method includes the following steps: acquiring various heterogeneous data involving distributed photovoltaic (PV) power and electric vehicles (EVs) in power distribution network scheduling; setting the planning cycle of the power distribution network and dividing it into multiple stages; constructing a hierarchical evolution scenario; and setting a comprehensive evaluation index for the carrying capacity of V2G-friendly power distribution networks. In each stage, a two-layer optimization model is used to achieve dynamic linkage between planning and operation. The upper layer makes investment and construction decisions, obtaining multiple candidate solutions. The lower layer, based on the operating characteristics of a multi-stakeholder game simulation system, quantitatively evaluates and optimizes the candidate solutions according to the set indicators, obtaining the optimal solution for the stage. This optimal solution is then fed back to the upper layer of the next stage as known conditions and network foundation, until the planning for all stages is completed. Compared with existing technologies, this invention has advantages such as accurate full lifecycle cost accounting, dynamic balance of multi-stakeholder interests, and adaptive optimization of planning schemes.

Description

Technical Field

[0001] This invention relates to the field of power distribution network planning technology, and in particular to a method, equipment and medium for multi-entity coordinated planning of power distribution networks. Background Technology

[0002] With the advancement of dual-carbon goals and the construction of new power systems, the penetration rate of distributed photovoltaic (PV) and electric vehicles (EVs) in distribution networks is growing at an unprecedented rate. However, the random fluctuations in PV output and the spatiotemporal uncertainty of EV charging loads pose severe challenges to the safe, economical, and reliable operation of distribution networks. Traditional distribution network planning methods are ill-suited to this high-proportion, high-uncertainty source-load access pattern. To address these challenges, the research in "Active Distribution Network Planning Technology Considering Demand-Side Response" emphasizes the flexibility of utilizing demand-side resources. The literature "Application Analysis of Distribution Network Power Supply Planning Model under Large-Scale V2G and Energy Storage Charging Pile Complementarity" also points out that vehicle-to-grid (V2G) technology enables EVs to participate in grid regulation as mobile energy storage units, demonstrating enormous potential. Meanwhile, the literature "A Distribution Network Reliability Optimization and Evaluation Method Based on Vehicle-to-Grid Interaction" proposes that V2G can not only smooth load peaks and valleys and promote PV absorption but also improve system reliability.

[0003] However, effectively integrating V2G and PV into distribution network planning still faces many challenges. While the paper "Considering the Coupling of Transportation Network and Distribution Network" uses Data Envelopment Analysis (DEA) for multi-objective evaluation and multi-stage planning by coupling transportation and distribution networks, enabling a comprehensive assessment of scheme advantages and disadvantages and making the planning more dynamic and forward-looking, it only considers the site selection and capacity determination of charging facilities and does not fully consider the reverse discharge function of V2G and the situation of distributed photovoltaic access. Chinese patent CN119944606A constructs a dynamic vehicle-to-grid interaction model, combines Monte Carlo algorithm to generate random samples, and relies on multi-source power databases to obtain data, but it does not link distributed power sources and has weak evaluation capabilities for multi-source collaborative distribution networks. Chinese patent CN120218559A uses a two-layer planning model to link the distribution network, PVG stations, and V2G charging and discharging stations, and combines improved K-means clustering and Monte Carlo algorithm to accurately predict data, but it relies on known assumptions such as future load growth rates, and the deviation in actual data prediction can easily affect the robustness of the scheme.

[0004] While existing research has achieved multi-stakeholder interest coordination and multi-stage planning, it still has core shortcomings: First, it has not transformed reliability indicators (such as line overload rate and voltage deviation rate) into quantifiable economic costs, resulting in planning schemes that "emphasize short-term investment and neglect long-term risks"; second, it lacks a closed-loop mechanism of "reliability assessment - scheme revision", making it impossible to optimize planning parameters in reverse based on reliability changes caused by the dynamic growth of PV / EV; and third, multi-stakeholder coordination only extends to the distribution of benefits during the operation phase and does not extend to cost sharing during the planning revision phase, which can easily lead to resistance to the implementation of the scheme. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a multi-stakeholder coordinated planning method, equipment, and medium for distribution networks. It primarily addresses issues such as conflicts of interest among multiple stakeholders, inaccurate V2G spatiotemporal potential modeling, and the inability of planning schemes to adapt to dynamic development. This method constructs a two-layer, multi-stage planning framework. The upper layer is responsible for planning decisions, while the lower layer simulates operation through multi-stakeholder game theory. Its core is to utilize the flexibility of V2G to absorb photovoltaic power and serve the grid, and to achieve win-win cooperation among stakeholders through a Nash negotiation mechanism, ultimately obtaining a user-friendly distribution network evolution path that balances economic efficiency, safety, reliability, and service friendliness.

[0006] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a multi-stakeholder coordinated planning method for a power distribution network is provided, the method comprising the following steps: S1: Acquire various heterogeneous data on distributed photovoltaic and electric vehicles participating in distribution network scheduling, set the planning cycle of the distribution network and divide it into multiple stages, construct a hierarchical evolution scenario, and set a comprehensive evaluation index for the carrying capacity of V2G-friendly distribution network. S2, based on the acquired data and constructed scenario parameters, achieves dynamic linkage between planning and operation in each stage through a two-layer optimization model. The upper layer makes investment and construction decisions and obtains multiple candidate solutions. The lower layer, based on the operating characteristics of the multi-agent game simulation system, performs quantitative evaluation and selection of candidate solutions according to the set comprehensive evaluation indicators to obtain the optimal solution for the stage. This optimal solution is then fed back to the upper layer of the next stage as known conditions and network foundation, until the planning of all stages is completed.

[0007] The aforementioned setting of the distribution network planning cycle and dividing it into multiple stages to construct a hierarchical evolution scenario specifically includes: A complete planning cycle covering the nonlinear growth cycle of photovoltaics and electric vehicles is set, and it is divided into multiple stages as needed. For each stage, the boundary conditions are determined by a hierarchical forecasting method in combination with regional energy and transportation development policies. A multi-stage hierarchical evolution scenario of PV-EV-load coordinated growth is constructed. The hierarchical forecasting method is based on at least the growth level of photovoltaic penetration rate, electric vehicle ownership, and base load when it is hierarchically defined.

[0008] The comprehensive evaluation index for the carrying capacity of the V2G-friendly distribution network is obtained by combining multiple indicators into a comprehensive score. These multiple indicators include at least the line overload rate, renewable energy absorption rate, V2G dispatchable capacity, and charging waiting time. The line load rate The calculation method is as follows: , Where H(·) is the step function, For the line l exist t Current at any moment The rated current of the line. For peak hours, L For the set of all lines, Total number of lines; The new energy consumption rate The calculation method is as follows: , in, For nodes i exist t The actual PV power consumed at any given time. For available PV power, This represents the number of PV nodes. The total number of time periods in a day; The V2G schedulable capacity The calculation method is as follows: , in, A set of V2G nodes. For nodes i At a specific moment t Scheduled V2G power; the charging wait time The calculation method is as follows: , in, For arrival rate, The service rate is S, and the number of charging stations is S. To improve system utilization, This represents the system's idle probability.

[0009] The decision variables in the upper layer of the two-layer optimization model are the investment and construction required in the current stage, including decisions on line expansion, site selection and capacity configuration of charging stations, and PV expansion.

[0010] The optimization objective of the upper layer of the two-layer optimization model is a multi-objective function, which includes minimizing the total cost and maximizing the comprehensive evaluation index fed back from the lower layer. The minimization of the total cost is the sum of the investment cost and the simulation operation cost.

[0011] The lower layer of the aforementioned two-layer optimization model performs the following steps: Initiate traffic-grid coupling simulation, utilize the user balanced flow distribution traffic model, simulate the spatiotemporal distribution of electric vehicles based on the electric vehicle travel OD matrix, and accurately predict the charging demand of each charging station. A multi-party game is initiated, and a game model is constructed that includes distribution network operators, V2G aggregators, and EV users. Nash negotiation theory is used to solve the optimal charging and discharging equilibrium strategy for V2G that achieves win-win cooperation among all parties. Under the equilibrium strategy, time-series power flow calculations are performed, and a comprehensive evaluation index of the carrying capacity of the V2G-friendly distribution network is calculated. The results are then returned to the upper layer for decision-making.

[0012] The method further includes: executing step S2 multiple times to obtain multiple planning scheme combinations covering all stages, forming multiple planning paths, and using a data envelopment analysis model to perform multi-stage extended path optimization from the multiple planning paths to select the optimal planning scheme.

[0013] The objective of the data envelopment analysis model is to maximize the efficiency of the j0th path. : , in, For path j The r Each output metric value, For path j The i One input cost value, As weight variables, For non-Archimedean infinitesimals, To output the total number of indicators, This represents the total input cost.

[0014] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0015] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) It realizes closed-loop feedback and adaptive optimization of planning and operation, overcoming the defect of "emphasizing short-term investment and neglecting long-term risks". This invention constructs a two-layer optimization model. The upper layer conducts investment planning, and the lower layer simulates the operation of multiple entities and calculates the "V2G-friendly carrying capacity comprehensive evaluation index" which includes reliability indicators such as line load factor (LOLR). The evaluation results of the lower layer are fed back to the upper layer to correct the multi-objective optimization decision of the upper layer (simultaneously minimizing costs and maximizing the comprehensive evaluation index fed back by the lower layer). In this way, the upper layer must balance short-term investment (cost) and long-term risks (reliability evaluation index) when making decisions. This setting forms a closed-loop mechanism of "reliability evaluation-scheme correction" to ensure that the planning scheme takes into account both economy and long-term reliability throughout the entire life cycle.

[0017] (2) This invention achieves dynamic equilibrium of interests among multiple stakeholders at the planning level, solving the problem that coordination mechanisms only remain at the operational stage. In the lower-level operational simulation, this invention uses Nash negotiation theory to construct a multi-stakeholder game model involving distribution network operators, V2G aggregators, and EV users. The simulation results of the game equilibrium strategy (such as V2G charging and discharging strategy) directly determine the comprehensive evaluation score at the lower level, which in turn guides the investment decisions at the upper level (such as charging station site selection and PV capacity expansion). This allows the planning scheme to consider the interests of all parties from the very beginning, extending the distribution of benefits in the operational stage to the planning revision stage, thus improving the feasibility of the scheme.

[0018] (3) It characterizes the spatiotemporal uncertainty of V2G, improving the robustness of the planning scheme. Addressing the problems of inaccurate V2G potential modeling and reliance on simple assumptions in the background technology, this invention couples a traffic-grid model into the lower-level simulation, utilizing a user equalization current distribution (NUE) traffic model to accurately predict the spatiotemporal distribution of EVs and charging demand. Combined with a multi-stage evolution scenario (considering the nonlinear growth of PV and EVs), the planning scheme can adapt to the dynamic development of future source loads, exhibiting stronger robustness and adaptability to uncertainty. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of a distribution network carrying capacity assessment system in one embodiment of the present invention, consisting of a target layer, an object layer, a criterion layer, and an indicator layer. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0022] Example 1 This embodiment addresses the shortcomings of existing distribution network planning in handling core issues such as the dynamic growth of high-proportion distributed photovoltaic (PV) and electric vehicle (EV) systems, coordinating conflicts of interest among multiple stakeholders, accurately assessing the potential of vehicle-to-grid (V2G) interaction, and the lack of systematic and comprehensive evaluation standards. It provides a multi-stakeholder coordinated planning method for distribution networks, such as... Figure 1 As shown, the method includes the following steps: S1 acquires various heterogeneous data on distributed photovoltaic and electric vehicles participating in distribution network scheduling, sets the planning cycle of the distribution network and divides it into multiple stages, constructs a hierarchical evolution scenario, and sets a comprehensive evaluation index for the carrying capacity of V2G-friendly distribution networks.

[0023] S11, Data Acquisition.

[0024] The first step is to lay the foundation for planning, which begins with comprehensive data collection. This involves collecting all kinds of heterogeneous data required for planning, including topology parameters of distribution networks such as the IEEE 69-node network, detailed data of urban transportation networks such as the 12-node network, typical daily power generation curves of distributed photovoltaic (PV) systems, trip origin-destination (OD) matrices of electric vehicles (EVs), vehicle battery parameters, and related economic parameters (equipment investment costs, operation and maintenance costs, peak and off-peak electricity prices, etc.).

[0025] S12, Hierarchical Evolution Scenario Construction.

[0026] Based on the collected data, a hierarchical evolution scenario needs to be constructed to reflect the dynamism and forward-looking nature of the planning. A complete planning cycle covering the nonlinear growth cycle of photovoltaics and electric vehicles is set, and it is divided into K adjustable stages. The planning is not completed all at once, but first, it solves how to optimally reach the milestone of "Stage 1" from the current situation; then, the optimal solution of "Stage 1" is used as the "known conditions and network foundation," and on this basis, it solves how to optimally reach the milestone of "Stage 2," and so on, completing the planning of all stages in a rolling manner. For each stage k, the boundary conditions are determined according to the hierarchical forecasting method, combined with regional energy and transportation development policies. The main indicators considered include: photovoltaic penetration rate α. PV，k (For example, 15% in Phase 1, 30% in Phase 2, and 50% in Phase 3), Electric vehicle ownership N EV，k (Combining population growth and EV promotion policies on an annual basis, and dynamically adjusting the previously collected EV travel OD matrix) and the growth level of the base load (annual growth rate of 8% in commercial areas and 5% in residential areas), thereby constructing a multi-stage evolution scenario of PV-EV-load coordinated growth adapted to future development.

[0027] S13, Comprehensive evaluation index setting for the carrying capacity of V2G-friendly distribution networks.

[0028] After preparing the basic data and scenarios, this invention also establishes a scientific evaluation standard to quantitatively evaluate the merits of the planning scheme. For example... Figure 2 The four-level evaluation system shown, namely "Target Layer - Object Layer - Criterion Layer - Indicator Layer," is called the "V2G-Friendly Distribution Network Carrying Capacity Comprehensive Evaluation System." This system starts from three objects: the distribution network, charging facilities, and users with charging needs. At the criterion layer, it is further subdivided into multiple dimensions, including safety and reliability, response flexibility, operational economy, facility functionality, and service friendliness. Finally, at the indicator layer, a series of calculable key secondary indicators are defined, among which the most representative indicators include: (1) Line overload rate (LOLR): to assess the safety of the power grid.

[0029] , Where H(·) is the step function (takes 1 if greater than 0, otherwise takes 0). For the line l exist t Current at any moment The rated current of the line. For peak hours, L For the set of all lines, This represents the total number of lines.

[0030] (2) New energy consumption rate (PVR): to assess economic efficiency and flexibility.

[0031] , in, For nodes i exist t The actual PV power consumed at any given time. For available PV power, This represents the number of PV nodes. This represents the total number of time periods within a day.

[0032] (3) V2G schedulable capacity ( ): Assessing response flexibility, referring to the V2G power aggregated at a certain moment that can be used to respond to grid dispatch.

[0033] , in, A set of V2G nodes. For nodes i At a specific moment t Dispatchable V2G power.

[0034] (4) Charging wait time (CWT): To assess service friendliness, the average wait time of a specific charging station can be estimated using the M / M / S queuing theory model. : , in, For arrival rate, The service rate is S, and the number of charging stations is S. To improve system utilization, This represents the system's idle probability.

[0035] Based on the indicators in the comprehensive evaluation system for the carrying capacity of V2G-friendly distribution networks, a comprehensive score is formed by combining them using the AHP-TOPSIS method, which is the comprehensive evaluation indicator.

[0036] S2, based on the acquired data and constructed scenario parameters, achieves dynamic linkage between planning and operation in each stage through a two-layer optimization model. The upper layer makes investment and construction decisions and obtains multiple candidate solutions. The lower layer, based on the operating characteristics of the multi-agent game simulation system, performs quantitative evaluation and selection of candidate solutions according to the set comprehensive evaluation indicators to obtain the optimal solution for the stage. This optimal solution is then fed back to the upper layer of the next stage as known conditions and network foundation, until the planning of all stages is completed.

[0037] During implementation, the solution is iteratively solved in each stage. The upper layer generates a planning scheme, the lower layer evaluates it, and the evaluation is fed back to the upper layer for decision-making. Then, the optimal planning scheme obtained in the previous stage is input to the upper layer of the next stage, realizing the iterative solution between stages.

[0038] The upper layer is the PDN strategic planning layer, which plays the role of "decision-maker." The decision variables for this layer are the investments and constructions required in the current stage k, including decisions on line expansion, charging station site selection and capacity configuration, and PV capacity expansion. Its optimization objective is a multi-objective function, including: (1) Minimize the total cost, that is, minimize the sum of the investment cost and the simulation operation cost, as shown in the following formula: , in, This includes investment costs (including line expansion, charging station site selection and capacity configuration, PV expansion, etc.). To simulate operating costs.

[0039] (2) Maximize the comprehensive evaluation indicators fed back from the lower level.

[0040] In this embodiment, the upper layer uses multi-objective evolutionary algorithms such as NSGA-III to generate a set of Pareto non-dominated programming schemes, i.e., candidate schemes.

[0041] The lower layer is the multi-agent simulation operation layer, which acts as the "evaluator." It receives and evaluates each "candidate solution" from the upper layer. The evaluation process includes: Initiate traffic-grid coupling simulation, utilize the User Equalization Distribution (NUE) traffic model, simulate the spatiotemporal distribution of electric vehicles based on the electric vehicle travel OD matrix, and accurately predict the charging demand of each charging station; A multi-party game is initiated, and a game model is constructed that includes distribution network operators, V2G aggregators, and EV users. Nash negotiation theory is used to solve the optimal charging and discharging equilibrium strategy for V2G that achieves win-win cooperation among all parties. Under the equilibrium strategy, time-series power flow calculations are performed, and a comprehensive evaluation index of the carrying capacity of the V2G-friendly distribution network is calculated. The results are then returned to the upper layer for decision-making.

[0042] After receiving feedback evaluation results from all candidate solutions, the upper layer selects the solution with the best overall performance (a solution from the Pareto front that satisfies specific preferences) as the optimal planning solution for the current stage k. This solution (e.g., which lines and charging stations were newly built in stage k=1) is recorded and used as known conditions and network foundation to iteratively proceed to the solution of the next planning stage (k+1). If the current stage k is less than the total number of stages K, then the planning of stage k+1 begins; if k is already equal to K (i.e., the planning of all stages has been completed), it means that a complete planning path has been generated.

[0043] This invention constructs a two-layer optimization model that links upper-level planning and decision-making with lower-level operational simulation, approximating the global optimum through multi-stage rolling iterations. Its core lies in the fact that the planning scheme in each stage k is dynamically corrected based on the optimization results of the previous stage k-1. The upper layer generates a Pareto solution set, while the lower layer uses multi-agent game theory and power flow calculation feedback to achieve a comprehensive score Z.

[0044] S3. Execute step S2 multiple times to obtain multiple planning scheme combinations covering all stages (i.e., a series of scheme combinations from stage 1 to K), forming multiple planning paths. Use the data envelopment analysis model to perform multi-stage extended path optimization from the multiple planning paths and select the optimal planning scheme.

[0045] The objective of the data envelopment analysis model is to maximize the efficiency of the j0th path. : , in, For path j The r Each output metric value, For path j The i One input cost value, As weight variables, For non-Archimedean infinitesimals, To output the total number of indicators, This represents the total input cost.

[0046] Example 2 Building upon Example 1, this embodiment combines a user-balanced traffic distribution model with a fuzzy user response model to accurately characterize the spatiotemporal dynamics of electric vehicles, constructing a traffic-grid coupling simulation. The user-balanced traffic distribution model ensures reasonable traffic flow distribution, while the fuzzy user response model simulates the random behavior of users in response to electricity price signals, jointly determining the dispatchable capacity of the V2G network. , in, The total dispatchable V2G power (in kW or MW) that can be supplied to the grid after all electric vehicles in the region are aggregated at time segment t. The maximum responsive charging / discharging power (in kW) of a single electric vehicle at time t is typically limited by its battery capacity and the power of the charging station. The membership degree, derived from the fuzzy user response model, represents the willingness of electric vehicle users to participate in V2G response at time t. It is a value between 0 (no participation at all) and 1 (full participation).

[0047] Example 3 This embodiment provides a specific implementation of the lower-level game model based on Embodiment 1.

[0048] The lower-level model aims to simulate the interest game and coordination among distribution network operators (DSO), V2G aggregators (V2Ga), and electric vehicle (EV) users in V2G charging and discharging scheduling.

[0049] Step 1: Construct a multi-party game model, with participants including: distribution network operators, V2G aggregators, and EV users.

[0050] Step 2: To achieve a balance of interests among all parties, the model uses the "Nash Negotiation Theory" to solve the problem, with the goal of finding the optimal equilibrium strategy of "win-win cooperation among all parties".

[0051] Step 3: Solve for the output equalization strategy using the solution from Step 2.

[0052] Step 4: Perform time-series power flow and calculate comprehensive evaluation indicators of the carrying capacity of V2G-friendly distribution networks (such as LOLR, PVR, V2GCap, CWT, etc.).

[0053] Step 5: Return the comprehensive evaluation index calculated in Step 4 as feedback to the upper level for the planning layer to make decisions.

[0054] Example 4 To verify the effectiveness of the planning method proposed in this invention, a computational example system was constructed that couples an IEEE 69-node distribution network with a 12-node urban transportation network.

[0055] Step 1: Collect topology and load data of the IEEE 69-node distribution network, OD matrix data of the 12-node transportation network, as well as photovoltaic, electric vehicle and related economic cost parameters.

[0056] Step 2: Construct a case study scenario, setting the planning period to T=10 years and dividing it into K=3 phases. Set the PV penetration rate, EV ownership, and base load growth rate for each phase to construct a phased evolution scenario.

[0057] The key parameters for each stage are shown in the table below: Table 1 Key parameters for each stage Step 3: Apply the V2G-friendly carrying capacity assessment system to calculate the benchmark values ​​of the indicators: Line Load Rate (LOLR) 6.8% (peak hours 10:00-14:00, 18:00-21:00), New Energy Consumption Rate (PVR) 75.2% (available PV 32GWh, actual consumption 24.06GWh), V2G dispatchable capacity (V2GCap) 2.8MW (peak 3.5MW), and charging waiting time (CWT) 8.2 minutes (based on the M / M / S model, λ=12 vehicles / hour, μ=3 vehicles / hour·pile). Set the assessment thresholds as LOLR≤5%, PVR≥90%, V2GCap≥peak-valley difference 30%, and CWT≤6 minutes.

[0058] Step 4: Enter the two-layer multi-stage planning and solution process. Taking Stage 1 as an example, the upper PDN strategic planning layer defines the decision variables for line upgrades, V2G base station construction, and PV capacity expansion. It constructs a multi-objective function of "minimizing cost (investment of 51.1 million + operation of 31.2 million + maximizing overall friendliness (0.78)" and generates 20 candidate schemes through the NSGA-III algorithm. The optimal scheme A is then passed to the lower layer. The lower multi-subject simulation operation layer simulates the peak demand of charging stations (35 / 28 / 22 vehicles / hour) based on the NUE traffic model. The Nash negotiation is used to determine the discharge electricity price (peak price of 1.1 yuan / kWh) and user participation rate (65%). The operation indicators LOLR=4.2%, PVR=91.5%, and CWT=5.8 minutes are calculated. The comprehensive score of 0.82 is fed back to the upper layer. Finally, scheme A is determined as the optimal scheme for Stage 1, and the configuration and benefit distribution are recorded (the average annual charging cost per user is reduced by 180 yuan / vehicle).

[0059] Step 5: Conduct phased verification and dynamic optimization. After Phase 1 verification is successful, Phase 2 is launched based on its topology. To address the issue of CWT=7.3 minutes (exceeding the threshold) for Node 18, an 800kW V2G charging and discharging station is added to reduce CWT to 5.5 minutes. In Phase 3, to address PV curtailment (PVR=88.2%), the capacity of the V2G charging and discharging station is expanded to 800kW, and the PVR is improved to 92.4% after correction.

[0060] Step 6: Perform multi-stage path optimization based on DEA, construct a DEA model, input the cumulative cost of 5 paths (2.86-3.05 billion yuan), output the cumulative PV absorption (86.4-105.6 GWh) and average friendliness (0.72-0.89), solve for the highest DEA efficiency of the path of this invention (dynamic correction of stages 1-3), and visualize the results as shown in Table 2.

[0061] Table 2. DEA Model Results Analysis and Validation Case 1 achieved the highest DEA efficiency of 0.963, indicating that it achieved an optimal balance between economic input, operational reliability, and user service. Through Monte Carlo randomized scenario testing (1000 samples), Case 1 maintained PVR ≥ 90% and CWT ≤ 5.5 min at a 95% confidence level, demonstrating the method's strong robustness and adaptability.

[0062] Example 5 To further verify the adaptability and robustness of the planning method of the present invention under uncertain scenarios, this embodiment constructs a sensitivity analysis scenario that considers the participation intentions of different electric vehicle users. It is also a computational example system based on the coupling of an IEEE 69-node distribution network and a 12-node urban transportation network.

[0063] Step 1: Collect topology and load data of the IEEE 69-node distribution network, OD matrix data of the 12-node transportation network, as well as photovoltaic, electric vehicle and related economic cost parameters.

[0064] Step 2: Construct a case study scenario, setting the planning period to T=10 years and dividing it into K=3 phases. Set the PV penetration rate, EV ownership, and base load growth rate for each phase to construct a phased evolution scenario.

[0065] The key parameters for each stage are shown in the table below: Table 3 Key parameters for each stage Step 3: Apply the V2G-friendly carrying capacity assessment system to calculate the benchmark values ​​of the indicators: Line Load Rate (LOLR) 6.8% (peak hours 10:00-14:00, 18:00-21:00), New Energy Consumption Rate (PVR) 75.2% (available PV 32GWh, actual consumption 24.06GWh), V2G dispatchable capacity (V2GCap) 2.8MW (peak 3.5MW), and charging waiting time (CWT) 8.2 minutes (based on the M / M / S model, λ=12 vehicles / hour, μ=3 vehicles / hour·pile). Set the assessment thresholds as LOLR≤5%, PVR≥90%, V2GCap≥peak-valley difference 30%, and CWT≤6 minutes.

[0066] Step 4: Proceed to the two-layer, multi-stage planning and solving phase. This phase focuses on the sensitivity analysis of V2G user participation rate in Phase 2, with the core objective of exploring the impact of different user participation intentions on the planning scheme. The specific process is as follows: (1) Scenario 1: Low participation rate scenario (V2G user participation rate) = 40%) The lower-level multi-agent simulation operation layer first constructs a game theory model based on Nash negotiation theory to solve for the equilibrium strategy under this participation rate; then, through time-series power flow calculation, it obtains the operation indicators: the V2G schedulable capacity (V2GCap) drops to 1.5MW (below the evaluation threshold requirement), the renewable energy absorption rate (PVR) drops to 88.5% (not reaching the ≥90% threshold), and the comprehensive score drops to 0.75. After receiving the feedback of this low score, the upper-level PDN strategic planning layer makes targeted adjustments to the investment decision: the PV expansion scale is adjusted from the benchmark 500kW to 750kW, while the V2G charging and discharging station capacity is expanded to 1000kW (higher than the benchmark 800kW), in order to compensate for the loss of system flexibility caused by insufficient user participation.

[0067] (2) Scenario 2: High participation rate scenario (V2G user participation rate) During the lower-level simulation (with 80% participation), the high participation rate made the game equilibrium strategy more inclined towards grid regulation demand. Calculated operating indicators showed that V2GCap increased to 4.5MW, PVR increased to 95.5% (both better than the threshold), and the overall score improved to 0.88. The upper-level system then optimized cost control based on this high score: reducing the V2G charging and discharging station capacity to 500kW (lower than the baseline of 800kW) to minimize investment costs while meeting system operating requirements.

[0068] Step 5: Through sensitivity analysis, it is verified that this method can ultimately meet the predetermined threshold requirements of the distribution network operation evaluation indicators by adjusting the planned PV capacity expansion and V2G station construction capacity under different user participation intentions, which reflects the adaptive ability of the method to the uncertainty of user behavior.

[0069] Step 6: Incorporate the planned paths of Scenario 1 and Scenario 2, along with the baseline path (Case 1), into the constructed DEA model for evaluation.

[0070] Table 4. DEA Model Results Analysis and Validation Case 2 and Case 3 represent the modified low-participation and high-participation scenarios, respectively, and Case 1 still demonstrates the highest efficiency. Although the cost increased after the low-participation correction in Case 2, the modified scenario still achieved a benefit score close to the baseline, validating the robustness of this method.

[0071] Example 6 The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0072] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0073] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0074] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0075] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0076] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0077] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-stakeholder coordinated planning method for power distribution networks, characterized in that, The method includes the following steps: S1: Acquire various heterogeneous data on distributed photovoltaic and electric vehicles participating in distribution network scheduling, set the planning cycle of the distribution network and divide it into multiple stages, construct a hierarchical evolution scenario, and set a comprehensive evaluation index for the carrying capacity of V2G-friendly distribution network. S2, based on the acquired data and constructed scenario parameters, achieves dynamic linkage between planning and operation in each stage through a two-layer optimization model. The upper layer makes investment and construction decisions and obtains multiple candidate solutions. The lower layer, based on the operating characteristics of the multi-agent game simulation system, performs quantitative evaluation and selection of candidate solutions according to the set comprehensive evaluation indicators to obtain the optimal solution for the stage. This optimal solution is then fed back to the upper layer of the next stage as known conditions and network foundation, until the planning of all stages is completed.

2. The multi-entity coordinated planning method for power distribution networks according to claim 1, characterized in that, The aforementioned setting of the distribution network planning cycle and dividing it into multiple stages to construct a hierarchical evolution scenario specifically includes: A complete planning cycle covering the nonlinear growth cycle of photovoltaics and electric vehicles is set, and it is divided into multiple stages as needed. For each stage, the boundary conditions are determined by a hierarchical forecasting method in combination with regional energy and transportation development policies. A multi-stage hierarchical evolution scenario of PV-EV-load coordinated growth is constructed. The hierarchical forecasting method is based on at least the growth level of photovoltaic penetration rate, electric vehicle ownership, and base load when it is hierarchically defined.

3. The multi-entity coordinated planning method for power distribution networks according to claim 1, characterized in that, The comprehensive evaluation index for the carrying capacity of the V2G-friendly distribution network is obtained by combining multiple indicators into a comprehensive score. These multiple indicators include at least the line overload rate, renewable energy absorption rate, V2G dispatchable capacity, and charging waiting time. The line load rate The calculation method is as follows: ， Where H(·) is the step function, For the line l exist t Current at any moment The rated current of the line. For peak hours, L For the set of all lines, Total number of lines; The new energy consumption rate The calculation method is as follows: ， in, For nodes i exist t The actual PV power consumed at any given time. For available PV power, This represents the number of PV nodes. The total number of time periods in a day; The V2G schedulable capacity The calculation method is as follows: ， in, A set of V2G nodes. For nodes i At a specific moment t Dispatchable V2G power; The charging waiting time The calculation method is as follows: ， in, For arrival rate, The service rate is S, and the number of charging stations is S. To improve system utilization, This represents the system's idle probability.

4. The multi-entity coordinated planning method for power distribution networks according to claim 1, characterized in that, The decision variables in the upper layer of the two-layer optimization model are the investment and construction required in the current stage, including decisions on line expansion, site selection and capacity configuration of charging stations, and PV expansion.

5. The multi-entity coordinated planning method for power distribution networks according to claim 1, characterized in that, The optimization objective of the upper layer of the two-layer optimization model is a multi-objective function, which includes minimizing the total cost and maximizing the comprehensive evaluation index fed back from the lower layer. The minimization of the total cost is the sum of the investment cost and the simulation operation cost.

6. The multi-entity coordinated planning method for a power distribution network according to claim 1, characterized in that, The lower layer of the aforementioned two-layer optimization model performs the following steps: Initiate traffic-grid coupling simulation, utilize the user balanced flow distribution traffic model, simulate the spatiotemporal distribution of electric vehicles based on the electric vehicle travel OD matrix, and accurately predict the charging demand of each charging station. A multi-party game is initiated, and a game model is constructed that includes distribution network operators, V2G aggregators, and EV users. Nash negotiation theory is used to solve the optimal charging and discharging equilibrium strategy for V2G that achieves win-win cooperation among all parties. Under the equilibrium strategy, time-series power flow calculations are performed, and a comprehensive evaluation index of the carrying capacity of the V2G-friendly distribution network is calculated. The results are then returned to the upper layer for decision-making.

7. The multi-entity coordinated planning method for power distribution networks according to claim 1, characterized in that, The method further includes: executing step S2 multiple times to obtain multiple planning scheme combinations covering all stages, forming multiple planning paths, and using a data envelopment analysis model to perform multi-stage extended path optimization from the multiple planning paths to select the optimal planning scheme.

8. The multi-entity coordinated planning method for a power distribution network according to claim 7, characterized in that, The objective of the data envelopment analysis model is to maximize the efficiency of the j0th path. : ， in, For path j The r Each output metric value, For path j The i One input cost value, As weight variables, For non-Archimedean infinitesimals, To output the total number of indicators, This represents the total input cost.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

Images