A power distribution network-optical storage charging station distributed voltage coordination control method, system, device and medium

Abstract

The application discloses a kind of distribution network-optical storage charging station distributed voltage coordination control method, system, equipment and medium, comprising: constructing distribution network dispatching center and the double-layer master-slave game voltage coordination control framework of optical storage charging station, the sum of voltage deviation penalty, reactive power support incentive and line loss cost is minimum as target in upper layer, reactive power output is guided in lower layer by differentiating dynamic incentive price;Each optical storage charging station in lower layer takes the maximum as target reactive power support net income, and independently optimizes the reactive power output and V2G incentive strategy of photovoltaic inverter, energy storage converter and V2G charging pile, and S-type function is embedded to describe the real response behavior of electric vehicle user to incentive price;Rolling game optimization is realized using model predictive control, and a prediction-game-execution-feedback closed loop is formed.The application effectively improves the voltage stability and operation economy of distribution network while taking into account the nonlinear response behavior of electric vehicle users.

Description

Technical Field

[0001] This invention relates to the fields of distribution network voltage control and electricity market technology, and in particular to a distributed voltage coordination control method, system, equipment and medium for distribution network-photovoltaic-storage-charging station. Background Technology

[0002] With the widespread integration of high-proportion distributed photovoltaic (PV) and electric vehicle (EV) charging facilities into power distribution networks, voltage exceeding limits has become increasingly prominent, affecting not only the quality of electricity for users but also threatening the safe operation of power distribution network equipment. PV-storage charging stations, integrating PV, energy storage, and EV charging piles, possess the potential to participate in power distribution network node voltage regulation by injecting or absorbing reactive power, thanks to the rapid power regulation capabilities of their internal equipment, particularly the PV inverters, energy storage converters, and EV chargers based on four-quadrant AC / DC converters.

[0003] However, existing voltage regulation research mostly adopts a centralized optimization framework, which has significant shortcomings: First, it assumes that photovoltaic-storage charging stations unconditionally obey instructions, ignoring their autonomous decision-making rights and economic interests as independent operating entities, making it difficult to implement the optimal solution in practice; Second, the scheduling of vehicle-to-grid (V2G) reactive resources mostly assumes that they are completely controllable ideal reactive sources, ignoring the actual response behavior of electric vehicle users to incentive prices, resulting in a reactive power gap due to insufficient user participation when implementing the optimization solution, and the voltage control effect is far lower than theoretical expectations. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a distributed voltage coordination control method, system, equipment, and medium for distribution networks and photovoltaic-storage charging stations to solve the problem that the existing centralized voltage regulation framework ignores the autonomous economic demands of photovoltaic-storage charging stations and the real user response behavior of vehicle-grid interaction resources, resulting in theoretical optimization schemes being difficult to implement in practical applications and the control effect being greatly reduced.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station system, comprising: The distribution network topology parameters, photovoltaic-storage charging station equipment parameters and operating status data are obtained. Based on the data, a two-layer master-slave game voltage coordination control framework between the distribution network dispatch center and the photovoltaic-storage charging station is constructed. The upper-layer model is the distribution network dispatch center, which acts as the game leader, and the lower-layer model is each photovoltaic-storage charging station, which acts as the game follower. The distribution network dispatch center, as the leader, aims to minimize the overall system cost and makes decisions to issue differentiated voltage regulation incentive prices to each photovoltaic-storage charging station. Each of the photovoltaic and energy storage charging stations acts as a follower, receives the voltage regulation incentive price, and aims to maximize its own net reactive power support revenue. Under the constraint of considering the nonlinear response behavior of electric vehicle users, it autonomously makes a reactive power output plan and feeds it back to the distribution network dispatch center. The distribution network dispatch center and each photovoltaic-storage charging station iterate alternately based on the feedback reactive power output plan and the published incentive price until a game equilibrium is reached. The process is then embedded in the model predictive control framework for rolling optimization and execution to obtain the optimal control scheme for each time period.

[0007] As a preferred embodiment of the distributed voltage coordination control method for distribution network-photovoltaic-storage-charging station described in this invention, the distribution network dispatch center, when modeling with the goal of minimizing the overall system cost, includes: A system comprehensive cost objective function is constructed, which consists of voltage deviation penalty cost, reactive power support incentive cost, and line loss cost. The voltage deviation penalty cost is calculated based on the voltage deviation amount, voltage quality weight, and unit voltage deviation penalty coefficient of each node in the distribution network node set, and the voltage deviation amount is generated when the node voltage amplitude deviates from the allowable lower limit and upper limit range. The reactive power support incentive cost is calculated based on the voltage regulation incentive price issued by the distribution network to each photovoltaic and energy storage charging station and the absolute value of the purchased reactive power. The line loss cost is calculated based on the branch current between nodes, the branch resistance, and the unit line loss price.

[0008] As a preferred embodiment of the distributed voltage coordination control method for distribution network-photovoltaic-storage-charging station described in this invention, wherein: when the photovoltaic-storage-charging station is modeled with the objective of maximizing its own net reactive power support benefit, it includes: Construct a net revenue objective function for reactive power support, which consists of reactive power support revenue minus reactive power incentive expenditure for vehicle-to-grid interaction and reactive power operating costs. The revenue from reactive power support is calculated based on the voltage regulation incentive price received by the photovoltaic-storage charging station and the absolute value of the reactive power supplied to the grid. The vehicle-to-grid (V2G) interactive reactive power incentive expenditure is calculated based on the unit cost of the V2G interactive reactive power incentive set for the photovoltaic-storage charging station and the V2G interactive reactive power provided by the electric vehicle cluster under its jurisdiction. The reactive power operating cost is calculated based on the reactive power output of energy storage and photovoltaics and the corresponding reactive power regulation unit operating cost.

[0009] As a preferred embodiment of the distributed voltage coordination control method for power distribution networks and photovoltaic-storage charging stations described in this invention, the method, under the constraint of considering the nonlinear response behavior of electric vehicle users, includes: A reactive power response model for vehicle-to-grid interaction is constructed, and an sigmoid function is used to describe the nonlinear relationship between reactive power output and incentive price in vehicle-to-grid interaction. The parameters of the S-shaped function are set, including the maximum reactive capacity of the vehicle-to-grid interaction cluster within the jurisdiction of the photovoltaic-storage charging station as the upper limit, the benchmark incentive price as the price inflection point, and the introduction of a price sensitivity coefficient to characterize the user's response to the price incentive. The benchmark incentive price and price sensitivity coefficient are obtained by fitting historical response data of electric vehicle users.

[0010] As a preferred embodiment of the distributed voltage coordination control method for power distribution networks and photovoltaic-storage charging stations described in this invention, wherein: when making autonomous decision-making decisions on reactive power output plans, constraints are established, including: Establish reactive capacity constraints for equipment, limit the reactive output of photovoltaic inverters to meet the power capacity coupling relationship with their predicted active output, and set upper limits for reactive output of energy storage converters and vehicle-to-grid interaction clusters. Establish network operation constraints, construct the reactive power balance equations within the photovoltaic-storage charging station, and the power flow constraints of the distribution network describing the relationship between node voltage and branch power; Establish market price order constraints and limit the incentive price for vehicle-to-network interaction to a preset upper and lower price range.

[0011] As a preferred embodiment of the distributed voltage coordination control method for distribution networks and photovoltaic-storage charging stations described in this invention, the method includes: when the distribution network dispatch center makes decisions and issues differentiated voltage regulation incentive prices to each photovoltaic-storage charging station, it includes: The constraints of the optimization model of the distribution network dispatch center are constructed, and a limit range is set for the voltage regulation incentive price of each photovoltaic-storage charging station, limiting the voltage regulation incentive price between a preset lower limit and an upper limit. The power distribution network dispatch center indirectly guides the reactive power output behavior of each photovoltaic-storage charging station by publishing the voltage regulation incentive price. After receiving the voltage regulation incentive price, each of the photovoltaic-storage charging stations feeds back its determined reactive power output plan to the distribution network dispatch center. The distribution network dispatch center and each of the photovoltaic-storage charging stations interact through a closed-loop mechanism of price, reactive power, and voltage.

[0012] The beneficial effect of this preferred technical solution is that by setting an incentive price range and constructing a price-reactive power-voltage closed-loop interaction mechanism, it is possible to indirectly and accurately guide the reactive power output of photovoltaic-storage charging stations while ensuring the rationality of price signals, thereby effectively improving the stability and response efficiency of voltage coordination control in the distribution network.

[0013] As a preferred embodiment of the distributed voltage coordination control method for distribution networks and photovoltaic-storage-charging stations described in this invention, the process of alternating iterations until a game equilibrium is reached, when embedded in a model predictive control framework for rolling optimization and execution, includes: Set the current decision time and the prediction time domain length, update the system state, and solve the two-layer game equilibrium within the optimization window; An iterative search algorithm is used to solve the problem alternately. The distribution network dispatch center initializes the incentive price sequence and sends it to each of the photovoltaic-storage charging stations. After receiving the sequence, each photovoltaic-storage charging station independently solves its own revenue maximization problem and feeds back the optimal reactive power output plan. The distribution network dispatch center summarizes the feedback results, performs power flow calculation for the entire network, and updates the price strategy using an optimization algorithm until the price change is less than the convergence threshold and outputs the game equilibrium solution. Only the game equilibrium solution at the current decision moment is executed. At the next moment, the system state is updated and the prediction window is scrolled forward to re-solve the game equilibrium, forming a dynamic closed loop of prediction-game-execution-feedback.

[0014] The beneficial effects of this preferred technical solution are that by embedding the game equilibrium solution into the model predictive control framework, it can effectively cope with the uncertainty of the system state through rolling optimization and dynamic closed-loop mechanism, and ensure that the two-layer master-slave game converges in the finite time domain, thereby realizing the real-time performance and robustness of the distribution network voltage coordination control.

[0015] Secondly, this invention provides a distributed voltage coordination control system for a power distribution network-photovoltaic-storage charging station, comprising: The game framework construction module is used to obtain the distribution network topology parameters, photovoltaic energy storage charging station equipment parameters and operating status data, and construct a two-layer master-slave game voltage coordination control framework between the distribution network dispatch center and the photovoltaic energy storage charging station based on the data. The upper-layer model is the distribution network dispatch center, which acts as the game leader, and the lower-layer model is each photovoltaic energy storage charging station, which acts as the game follower. The upper-level incentive price decision module is used by the distribution network dispatch center as the leader to make decisions and issue differentiated voltage regulation incentive prices to each photovoltaic-storage charging station with the goal of minimizing the overall system cost. The lower-level reactive power output autonomous decision-making module is used by each of the photovoltaic and energy storage charging stations as followers to receive the voltage regulation incentive price and, with the goal of maximizing its own net reactive power support revenue, autonomously decide on the reactive power output plan and feed it back to the distribution network dispatch center under the constraint of the nonlinear response behavior of electric vehicle users. The rolling optimization module is used by the distribution network dispatch center and each photovoltaic-storage charging station to iteratively perform reactive power output plans and published incentive prices based on feedback until a game equilibrium is reached. The process is embedded in the model predictive control framework for rolling optimization and execution to obtain the optimal control scheme for each time period.

[0016] Thirdly, the present invention provides a computer device, comprising: Memory, used to store programs; A processor is configured to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the distributed voltage coordination control method for the power distribution network-photovoltaic-storage-charging station.

[0017] Fourthly, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the distributed voltage coordination control method of the power distribution network-photovoltaic-storage charging station.

[0018] The beneficial effects of this invention are as follows: By constructing a two-layer master-slave game framework between the distribution network dispatch center and the photovoltaic-storage charging station, this invention transforms voltage regulation from a centralized command mode to a market incentive mode, achieving both voltage quality assurance and economic benefits for multiple stakeholders, including the distribution network and the photovoltaic-storage charging station. By embedding a V2G reactive power response model based on an S-shaped function into the lower-level optimization model, this invention accurately characterizes the nonlinear response behavior of electric vehicle users to incentive prices, avoiding the optimistic estimation of V2G resource dispatchability in traditional centralized optimization and ensuring the real feasibility of the optimization scheme. By introducing a model predictive control (MPC) rolling optimization mechanism, a dynamic closed loop of prediction-game-execution-feedback is formed, effectively addressing the real-time uncertainty of photovoltaic output and load, and achieving rolling robust voltage control. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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 these drawings without creative effort. Wherein: Figure 1 This is a basic flowchart illustrating a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station according to an embodiment of the present invention. Figure 2 A schematic diagram of a two-layer master-slave game voltage coordination control framework for a distribution network-photovoltaic-storage-charging station distributed voltage coordination control method provided in an embodiment of the present invention; Figure 3A schematic diagram of the rolling optimization framework of a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station according to an embodiment of the present invention; Figure 4 A schematic diagram of an improved IEEE 33-node distribution network for a distributed voltage coordination control method for a distribution network-photovoltaic-storage-charging station according to an embodiment of the present invention; Figure 5 A schematic diagram of node voltage distribution in Scheme 1 of a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station according to an embodiment of the present invention; Figure 6 A schematic diagram of node voltage distribution in Scheme 2 of a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station provided in an embodiment of the present invention; Figure 7 A schematic diagram of the voltage distribution at node 3 of a distributed voltage coordination control method for a power distribution network-photovoltaic-storage charging station provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the voltage regulation incentive price of Scheme 3 of a distributed voltage coordination control method for a power distribution network-photovoltaic-storage charging station provided in one embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail 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 them. 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 protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As an embodiment of the present invention, a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station system is provided, comprising: S100: Obtain the distribution network topology parameters, photovoltaic-storage charging station equipment parameters and operating status data, and construct a two-layer master-slave game voltage coordination control framework between the distribution network dispatch center and the photovoltaic-storage charging station based on the data. The upper-layer model is determined to be the distribution network dispatch center, which acts as the game leader, and the lower-layer model is each photovoltaic-storage charging station, which acts as the game follower. S200: The distribution network dispatch center, as the leader, aims to minimize the overall system cost and makes decisions to issue differentiated voltage regulation incentive prices to each photovoltaic-storage charging station; S300: Each of the photovoltaic and energy storage charging stations acts as a follower, receives the voltage regulation incentive price, and aims to maximize its own net reactive power support revenue. Under the constraint of considering the nonlinear response behavior of electric vehicle users, it autonomously decides on the reactive power output plan and feeds it back to the distribution network dispatch center. S400: The distribution network dispatch center and each photovoltaic-storage charging station alternately iterate based on the feedback reactive power output plan and the published incentive price until a game equilibrium is reached. The process is then embedded in the model predictive control framework for rolling optimization and execution to obtain the optimal control scheme for each time period.

[0022] It should be noted that in distribution networks with a high proportion of distributed photovoltaic (PV) power generation and electric vehicle (EV) integration, peak PV power generation at midday can easily cause voltage to exceed the upper limit, while peak evening charging can easily cause voltage to exceed the lower limit. Traditional centralized voltage control methods often overlook the economic demands of PV-storage-charging stations as independent operators and idealize the reactive power resources of EV V2G as completely controllable, ignoring users' actual response to incentive prices. This leads to reactive power gaps in the actual implementation of optimized solutions due to insufficient user participation, resulting in voltage control effects far below expectations. Therefore, constructing a voltage coordination control method that can balance the interests of all parties and accurately characterize users' real responses is also crucial.

[0023] Therefore, to address the problem that the existing centralized voltage regulation framework neglects the autonomous economic demands of photovoltaic-storage charging stations and the real user response behavior of vehicle-to-grid interaction resources, resulting in the difficulty of implementing theoretical optimization schemes in practical applications and a significant reduction in control effectiveness, a two-layer master-slave game framework is constructed through steps S100-S400 between the distribution network dispatch center and the photovoltaic-storage charging stations. The upper layer guides the lower layer to provide reactive power support through differentiated dynamic incentive prices, while the lower layer autonomously decides reactive power output and V2G incentive strategies with the goal of maximizing its own benefits. The model predictive control rolling optimization is used to cope with the uncertainty of new energy and load, ensuring voltage quality while taking into account the economic benefits of each entity.

[0024] Example 2, refer to Figures 2-3 As one embodiment of the present invention, based on the previous embodiment, a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station is provided, comprising: In this embodiment of the application, when constructing the two-layer master-slave game voltage coordination control framework between the distribution network dispatch center and the photovoltaic-storage charging station in step S100, it specifically includes determining the roles and interaction relationships of the two game parties, the objectives and core decision variables, and the solution and execution mechanisms: In distribution networks with a high proportion of photovoltaic (PV) power and electric vehicles (EVs), the reverse power injection during peak PV power generation at midday can easily cause the voltage at the feeder's end node to exceed its upper limit, while the sudden drop in PV output in the evening, coupled with peak charging load, can easily cause the voltage to exceed its lower limit. The distribution network dispatch center, as the overall coordinator for system voltage security, cannot directly control the internal equipment of each PV-storage-charging station, but it can guide their reactive power output behavior through economic signals. Each charging station, as an independent operating entity, makes autonomous decisions with the goal of maximizing its own profits. Therefore, constructing a system like... Figure 2The Stackelberg master-slave game two-layer framework shown consists of the distribution network dispatch center at the top, which acts as the game leader, and the various photovoltaic, energy storage and charging stations at the bottom, which act as the game followers.

[0025] The roles and interactions of the two parties in the game are defined. The distribution network dispatch center acts as the game leader, and each photovoltaic-storage charging station acts as a follower. The upper layer does not directly control the equipment of each charging station, but guides their reactive power output behavior by issuing differentiated voltage regulation incentive prices. After receiving the price signal, each charging station autonomously decides its reactive power output plan and V2G incentive strategy with the goal of maximizing its own benefits, and feeds back the reactive power output plan to the upper layer. The upper and lower layers interact in a closed loop of "price-reactive power-voltage" to ultimately reach game equilibrium.

[0026] Identify the objectives and core decision variables of both sides in the game. The upper-level distribution network dispatch center aims to minimize the overall system cost, including voltage deviation penalty cost, line loss cost, and reactive power support incentive cost paid to each photovoltaic-storage charging station. Its core decision variable is the voltage regulation incentive price of each charging station. The lower-level photovoltaic-storage charging stations aim to maximize their own operating revenue, which includes reactive power support revenue, and costs include V2G reactive power incentive expenditure, energy storage, and photovoltaic reactive power operating costs. Their core decision variables are photovoltaic and energy storage reactive power output, as well as the V2G reactive power incentive price.

[0027] The solution and execution mechanism of the game is determined. An iterative search algorithm is used to solve the Stackelberg equilibrium and an embedded model predictive control framework is established. The optimization is performed in a rolling manner within a finite time domain and executed time-by-time, forming a dynamic closed loop of "prediction-game-execution-feedback".

[0028] In an optional implementation, the game-solving algorithm in step S100 can also encode the incentive price sequence published by the upper layer into particles, update the particle positions iteratively, and solve the lower-level optimization problem in each iteration to evaluate the upper-level objective function, thereby searching for the optimal incentive price strategy.

[0029] In an optional implementation, the game-theoretic algorithm in step S100 can also transform the optimization problem of the lower-level photovoltaic-storage-charging station into constraints through KKT conditions, and merge it with the upper-level model into a single-level mixed integer programming (MILP / MIQP) problem, thereby replacing the original iterative search process and achieving a one-time direct solution.

[0030] In this embodiment of the application, when the distribution network dispatch center performs modeling with the goal of minimizing the overall system cost in step S200, it includes: A higher-level objective function is constructed. The objective of the higher-level decision is to minimize the overall cost of the system within the optimization window. This cost consists of three parts: voltage deviation penalty cost, reactive power support incentive cost, and line loss cost. Its mathematical expression is: (1) in, This represents the reactive power support incentive cost paid by the distribution network to each photovoltaic-storage charging station. This represents the penalty cost for system voltage deviation. This indicates system line loss.

[0031] Regarding the voltage deviation penalty cost, it reflects the severity of the system voltage exceeding the limit and is a quantitative assessment of voltage quality by the distribution network dispatch center. The calculation formula is as follows: (2) in, Represents the set of nodes in a distribution network; For the current decision-making moment, Indicates the length of the prediction time domain; This is the penalty coefficient per unit voltage deviation; Assign voltage quality weights to nodes, with critical load nodes receiving higher weights; Let be the voltage deviation of the load at node i at time t. The voltage deviation is calculated using a piecewise function, defined as follows: For nodes At any moment voltage amplitude, and These represent the lower and upper limits of the allowable node voltage, respectively. When the node voltage is below the lower limit, the deviation is the difference between the lower limit and the actual voltage; when the node voltage is above the upper limit, the deviation is the difference between the actual voltage and the upper limit; otherwise, the deviation is zero. Specifically: (3) in, This represents the voltage magnitude at node i at time t. and These represent the lower and upper limits of the node voltage, respectively.

[0032] Regarding the aforementioned reactive power support incentive cost, it is the economic cost paid by the distribution network dispatch center to obtain reactive power support from each photovoltaic-storage charging station. The calculation formula is as follows: (4) in, This represents the voltage regulation incentive price issued by the distribution network to the photovoltaic-storage charging station at node i during time period t; This represents the reactive power purchased by the distribution network from the photovoltaic-storage charging station at node i during time period t (this value is 0 if node i is not equipped with a photovoltaic-storage charging station). Positive values ​​represent the injection of inductive reactive power to boost voltage, while negative values ​​represent the absorption of inductive reactive power to lower voltage. The absolute value is used here. The calculation is performed because the distribution network purchases voltage regulation services from the charging station. Whether the charging station injects or absorbs reactive power, it is not providing an effective service, and the distribution network needs to pay positive incentive costs.

[0033] Regarding the aforementioned line loss cost, the distribution of reactive power affects the system's line loss. The upper-level dispatch center incorporates line loss cost into its objectives, ensuring that incentive pricing decisions consider both voltage quality and system operating efficiency. The calculation formula is as follows: (5) in, Price per unit of line loss Let i be the branch current between node i and node j. Let be the branch resistance between node i and node j.

[0034] In this embodiment of the application, when the distribution network dispatch center makes a decision and issues differentiated voltage regulation incentive prices to each photovoltaic-storage charging station in step S200, it includes: The constraints for constructing the upper-level model are as follows: To ensure market stability and prevent price runaway, the voltage regulation incentive price for each photovoltaic-storage charging station is limited to a reasonable range. These constraints are expressed as follows: (6) in, and These represent the lower and upper limits of the voltage regulation incentive price, respectively.

[0035] The power distribution network dispatch center indirectly guides the reactive power output behavior of each photovoltaic-storage charging station by publishing the voltage regulation incentive price. After receiving the voltage regulation incentive price, each of the photovoltaic-storage charging stations feeds back its determined reactive power output plan to the distribution network dispatch center. The distribution network dispatch center and each of the photovoltaic-storage charging stations interact through a closed-loop mechanism of price, reactive power, and voltage.

[0036] In this embodiment of the application, when the photovoltaic-storage charging station models itself with the goal of maximizing its own net reactive power support revenue in step S300, it includes: Construct the lower-level objective function. The optimization objective of the lower-level (photovoltaic-storage-charging station at node i) is to maximize its total operating revenue within the optimization window. This revenue is obtained by subtracting various operating costs from electricity sales revenue, and its mathematical expression is: (7) in, The photovoltaic-storage charging station at node i participates in reactive power support revenue. The reactive power incentive fee paid to electric vehicle users participating in V2G for the photovoltaic-storage charging station at node i; Let $i$ be the reactive power operating cost of the photovoltaic-storage charging station at node $i$. The formulas for calculating the revenue or cost of each component are as follows: 1) Reactive power support revenue: Reactive power support revenue is the income obtained by the lower-level photovoltaic-storage-charging stations in response to the upper-level incentive price and by providing reactive power to the grid. It is the core economic motivation for the lower-level stations to participate in the game. (8) in, This refers to the voltage regulation incentive price for the photovoltaic-storage charging station at node i at time t, as published by the upper layer. This represents the reactive power purchased by the distribution network from the photovoltaic-storage charging station at node i during time period t. Since the reactive power support revenue is the income obtained by the photovoltaic-storage charging station in response to upper-level incentives and by providing voltage regulation services, its service value is positive regardless of whether reactive power is injected or absorbed. Therefore, the absolute value of reactive power is used in the formula. Perform calculations to ensure that the payoff function is always non-negative.

[0037] 2) V2G reactive power incentive cost: When a photovoltaic-storage charging station uses the reactive power resources of electric vehicle users, it needs to pay incentive fees to the users, which is the main expenditure faced by the lower level when deciding on the amount of V2G usage. (9) in, The unit cost of V2G reactive power incentive set at node i of the photovoltaic-storage charging station at time t. V2G reactive power provided to the electric vehicle cluster under the jurisdiction of the photovoltaic-storage-charging station at node i at time t; 3) Reactive power operating costs: While photovoltaic inverters and energy storage converters do not consume active power when providing reactive power, they generate additional switching and conduction losses. (10) in, To adjust the unit operating cost of energy storage reactive power, The reactive power output of the energy storage section of the photovoltaic-storage-charging station at node i at time t; The unit operating cost of photovoltaic reactive power regulation, Let t be the reactive power output of the photovoltaic component of the photovoltaic-storage charging station at node i at time t.

[0038] In this embodiment of the application, step S300, under the constraint of considering the nonlinear response behavior of electric vehicle users, includes: A V2G reactive power response model is constructed. Electric vehicle chargers are typically based on four-quadrant AC / DC converters, capable of independently providing reactive power without consuming battery energy. To accurately characterize the nonlinear response behavior of electric vehicle users to incentive prices, a sigmoid function is used to describe the relationship between V2G reactive power output and incentive prices: (11) in, The maximum reactive power capacity of the V2G cluster within the jurisdiction of the photovoltaic-storage charging station; The V2G incentive price set for the photovoltaic-storage charging station at time t; This serves as the benchmark incentive price and also represents a price inflection point. The price sensitivity coefficient characterizes the degree of linear response of users to price incentives. User participation exhibits an S-shaped growth pattern as the incentive price increases: growth is slow in the low-price range (insufficient user participation), rapid growth occurs near the benchmark price, and saturation occurs in the high-price range (all available capacity has been used). This model is embedded in the lower-level optimization, ensuring the realistic executability of the optimization results from the modeling source, and avoiding the optimistic estimation of V2G resources in traditional centralized optimization.

[0039] In an alternative implementation, the user response behavior modeling in step S300 can also replace the nonlinear S-shaped function describing the V2G reactive response with a piecewise function consisting of multiple linear intervals, and transform it into a mixed integer linear constraint embedded in the lower-level optimization model by introducing 0-1 variables or the Big M method.

[0040] In an optional implementation, the user response behavior modeling in step S300 can also utilize historical incentive prices and V2G response data to train a neural network or support vector regression model, and embed the trained model as a nonlinear constraint into the optimization problem of the lower-level photovoltaic energy storage and charging station to replace the original sigmoid function analytical expression.

[0041] In this embodiment of the application, the constraints satisfied during the autonomous decision-making process for reactive power output in step S300 include: 1) Reactive power capacity constraints: The output of each reactive power source is limited by the rated capacity of its converter. The available reactive power capacity of a photovoltaic inverter is affected by the current predicted active power output—the greater the active power output, the smaller the remaining reactive power capacity, reflecting the physical coupling relationship of the PQ capacity curve; the reactive power output of energy storage is also limited by its rated capacity; while the reactive power output of V2G does not exceed the maximum capacity of the electric vehicle cluster. (12) (13) (14) in, Let be the rated capacity of the photovoltaic inverter at node i, which is the photovoltaic-storage charging station. The maximum predicted output of photovoltaic power in time period t; Let be the rated energy storage capacity of the photovoltaic-storage-charging station at node i.

[0042] 2) Network Operation Constraints: The reactive power output schemes of lower-level decisions must satisfy the physical operating laws of the distribution network. The total reactive power output balance constraint ensures that the reactive power output of each device, after being offset by the substation load, is consistent with the reactive power transmitted to the grid. Power flow constraints describe the nonlinear relationship between node injected power and node voltage, and are the basis for calculating the impact of reactive power output on voltage. (15) (16) Equation (15) is the reactive power balance constraint. The reactive load of the photovoltaic-storage charging station node itself; Equation (16) represents the power flow constraint. and Let t be the node Next node The outflow of active and reactive power; The square of the branch current amplitude between node j and node k; and For nodes 、 The square of the node voltage magnitude, i.e. , ;; 、 These are the resistance and reactance of the branch between node j and node k, respectively.

[0043] 3) Market price order constraints: The V2G incentive price set by the lower layer must be within a reasonable range to prevent price signals from getting out of control and maintain the stable operation of the reactive power ancillary service market. (17) in, and These represent the lower and upper limits of the V2G incentive price, respectively.

[0044] In an optional implementation, the power flow calculation and constraint processing in step S300 can also ignore the reactive power flow and node voltage amplitude changes in the distribution network, and establish a linear power flow equation that only includes active power and voltage phase angle, thereby transforming the originally complex second-order cone power flow constraint into a simple linear equation constraint, so as to greatly reduce the complexity of model solution.

[0045] In an optional implementation, the power flow calculation and constraint processing in step S300 can also be transformed into a linear sensitivity matrix constraint between node injected power and voltage amplitude by ignoring higher-order small quantities such as network loss, while retaining the coupling relationship between voltage amplitude and active and reactive power.

[0046] In this embodiment of the application, the process of alternating iterations until game equilibrium is reached in step S400 is embedded in the model predictive control framework for rolling optimization and execution. Specifically, it includes the iterative solution process of the two-layer game and the rolling execution mechanism of the model predictive control: An iterative solution to a two-layer game is performed. The distribution network dispatch center initializes the incentive price sequence and distributes it to each of the photovoltaic-storage-charging stations. After receiving the price, each photovoltaic-storage-charging station independently solves its own revenue maximization problem, obtains the optimal reactive power output plan, and feeds it back. The distribution network dispatch center summarizes the feedback results, performs power flow calculations across the entire network, evaluates the objective function value, and updates the price strategy using an optimization algorithm. The upper and lower layers iterate alternately until the price change is less than the convergence threshold, and the game equilibrium solution is output.

[0047] Embedding the above game theory solution into the model prediction and control framework, a result is formed as follows: Figure 3 The rolling optimization closed loop is shown. During periods of voltage exceedance risk, the current decision time is set to m, and the prediction time domain length is T. At each decision time, the system state (including voltage amplitude at each node, energy storage SOC, photovoltaic ultra-short-term prediction, and load ultra-short-term prediction) is updated. The two-level game equilibrium is solved within the optimization window [m, m+T-1], but only the equilibrium solution at the current time m is executed. At the next time m+1, the system state is updated, the prediction window is rolled forward, and the game equilibrium is solved again. Through the dynamic closed loop of "prediction-game-execution-feedback", the game strategy continuously adapts to the real-time uncertainty of new energy output and load, achieving rolling robust voltage control.

[0048] In an optional implementation, the dynamic rolling optimization strategy in step S400 can also solve the two-level game equilibrium for the entire next 24 hours at once based on the predicted new energy output and load curves during the day-ahead phase, and directly issue the incentive price and reactive power output plan for each time period, without performing rolling corrections and feedback updates throughout the day.

[0049] In an optional implementation, the dynamic rolling optimization strategy in step S400 can also construct an uncertainty set that includes photovoltaic output and load fluctuations, and set the optimization objective to meet voltage constraints and minimize system operating costs under the worst-case scenario within the uncertainty set.

[0050] Example 3, referring to Figures 4-8 Tables 1-5 illustrate one embodiment of the present invention, which provides a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station. To verify the beneficial effects of the present invention, specific implementation methods and their effects are scientifically demonstrated.

[0051] The specific details of this embodiment are as follows: Taking the improved IEEE 33-bus distribution network system as an example, such as Figure 4As shown, the system reference voltage is 12.66kV and the reference capacity is 1MVA. Four photovoltaic-storage charging stations are configured at nodes 3, 13, 24, and 30, and the configuration parameters of each station are shown in Table 1. The photovoltaic penetration rate is high, with a significant overvoltage risk occurring between 12:00-15:00 (high photovoltaic power generation with low load) and a significant undervoltage risk occurring between 17:00-21:00 (photovoltaic power consumption drops to zero while charging load is concentrated). The allowable range for node voltage is 0.95pu to 1.05pu. Furthermore, the voltage quality weight for critical load nodes (nodes 2, 13, 14, 24, and 31) is set to 10, while the weight for the remaining nodes is 1.

[0052] The proposed method is compared by setting up three voltage regulation schemes for power distribution networks: Option 1: Voltage-free coordination control scheme, each photovoltaic-storage charging station operates at unit power factor, relying solely on traditional reactive power compensation equipment and on-load tap changer for voltage regulation, serving as the baseline scenario for reactive power support without photovoltaic-storage charging stations.

[0053] Option 2: A centralized voltage optimization scheme including V2G, in which the dispatch center uniformly optimizes the reactive power output of photovoltaic, energy storage and V2G, and the V2G incentive price is predetermined by the centralized optimization.

[0054] Option 3: This paper proposes a master-slave game-based voltage coordination control scheme. The distribution network dispatch center and the photovoltaic-storage charging station interact through a master-slave game, with dynamic pricing at the upper level and autonomous optimization of reactive power output and V2G incentive strategies at the lower level.

[0055] It is worth noting that, to reveal the optimistic bias caused by traditional centralized optimization ignoring actual user responses, Scheme 2 is further divided into two scenarios: ideal plan (ignoring actual user responses) and actual results (considering actual user responses). Subsequent data for Scheme 2 will all be results considering actual user responses, serving as a real-world executable solution for comparison with traditional centralized optimization. The ideal plan for Scheme 2 will be analyzed specifically in the V2G call comparison.

[0056] Table 1 Basic Parameter Table

[0057] (1) Comparative analysis of total system cost The total system cost for each scheme is shown in Table 2. From the perspective of system economy, the total system cost is defined as the sum of voltage deviation penalty, line loss cost, equipment reactive power operation cost, and V2G incentive cost. The upper-level reactive power support incentive cost and the lower-level reactive power support revenue are internal transfer payments between systems and are not included in the total system cost.

[0058] Table 2. Cost Comparison of Different Options (RMB)

[0059] As shown in Table 2, Scheme 1 incurs a voltage deviation penalty cost of RMB 2518.63 due to the lack of reactive power support from the photovoltaic-storage charging station, resulting in a total cost of RMB 2857.05. Scheme 2, through centralized optimization of photovoltaic and energy storage reactive power, reduces the voltage deviation penalty cost to RMB 1499.47, lowering the total cost by 18.5% compared to Scheme 1. However, the centralized optimization model does not embed the actual V2G response function, resulting in an incentive price far lower than the benchmark price. The actual V2G usage is significantly lower than expected, limiting further improvement in voltage quality, and the voltage deviation penalty cost remains high.

[0060] Scheme 3 uses a master-slave game to generate time-varying incentive prices, guiding resource collaboration. Within this game framework, the upper layer provides economic incentives for reactive power resources through differentiated pricing, while lower-level charging stations, aiming to maximize profits, proactively increase reactive power output from photovoltaic and energy storage systems and rationally set V2G incentive prices. Therefore, the voltage deviation penalty cost in Scheme 3 drops sharply to 112.18 yuan, a 92.5% reduction compared to Scheme 2. Although the combined V2G incentive cost and reactive power operating cost increase to 1123.22 yuan, the significant reduction in voltage deviation penalty cost lowers the total system cost to 1553.96 yuan, a 33.3% reduction compared to Scheme 2 and a 45.6% reduction compared to Scheme 1. This demonstrates that the master-slave game mechanism in Scheme 3 effectively improves the system's economic efficiency.

[0061] (2) Comparison of voltage quality improvement effects Voltage distribution under different schemes, such as Figure 5-7 As shown in Table 3, the voltage quality indicators are as follows.

[0062] Table 3. Comparison of Voltage Quality under Different Schemes

[0063] As shown in Table 3, the reactive power support of the photovoltaic-storage charging station in Scheme 1 is zero, and the voltage relies entirely on the line's own regulation. During periods of high photovoltaic power generation, reverse power injection causes the voltage at the feeder end to rise, while the peak charging load in the evening, combined with the photovoltaic power returning to zero, causes the voltage at the feeder end to drop. Therefore, the voltage quality is the worst, with the highest overvoltage at noon reaching 1.072 pu and the lowest undervoltage at evening reaching 0.902 pu. The over-limit duration accounts for 58.33%, and the voltage deviation penalty cost reaches 2518.63 yuan.

[0064] After introducing V2G in Scheme 2, the voltage quality improved compared to Scheme 1, with the minimum voltage rising to 0.916 pu and the over-limit duration decreasing to 45.83%. However, traditional centralized optimization typically treats V2G as an ideal, controllable reactive power source, ignoring the actual response behavior of electric vehicle users to incentive prices. The V2G incentive price given is lower than the user participation threshold, resulting in actual usage far below expectations and a significant reactive power deficit. Simultaneously, due to the low incentive price, photovoltaic and energy storage charging stations lack sufficient economic incentive to fully utilize the reactive power resources of photovoltaics and energy storage. Therefore, the voltage deviation penalty cost for Scheme 2 still reaches 1499.47 yuan.

[0065] Scheme 3 generates a more reasonable incentive price through game-theoretic pricing, giving lower-level charging stations a stronger incentive to increase reactive power output. V2G response is also fully activated due to the price exceeding the threshold, reducing the proportion of over-limit time to 12.50%, raising the minimum voltage to 0.945 pu, and lowering the average voltage deviation by 62.5% compared to Scheme 2. The results show that the game-theoretic pricing mechanism effectively incentivizes the full utilization of reactive power resources and significantly improves voltage quality.

[0066] (3) Comparison of the actual availability of V2G reactive resources Further comparison of the ideal plan and actual results of Scheme 2 reveals the optimistic estimation bias of traditional centralized optimization of V2G capabilities, as shown in Table 4.

[0067] Table 4 Comparison of V2G call behavior under different schemes

[0068] As shown in Table 4, the traditional centralized optimization scheme ignores the actual response intentions of electric vehicle users, treating V2G as an ideal and controllable reactive power source, leading to a significant overestimation of its dispatchable capacity. Scheme 2's ideal optimization model does not consider the actual response of EV users to the incentive price, resulting in a V2G planned price series discharge of approximately 4498 kvarh of reactive power. However, electric vehicle users exhibit a real nonlinear response to the incentive price—according to the S-shaped response model, this price is far lower than the benchmark price, and the user participation rate is far lower than expected. The actual cumulative reactive power output of V2G is only 672.56 kvarh, with a planned-to-actual deviation rate as high as -85.05%. This indicates that the traditional centralized optimization scheme, by ignoring the actual user response behavior, has a significant optimistic bias in estimating the dispatchable capacity of V2G, and will face the risk of voltage control failure during actual operation.

[0069] Scheme 3 embeds a V2G response model based on an S-curve of price factors into the lower-level optimization model. Through a two-layer game, it dynamically adjusts the incentive price, achieving better resource coordination under the constraint of real response. After game equilibrium, the average V2G incentive price is 0.37 yuan / kvarh, and the cumulative reactive power output of V2G reaches 1620.12 kvarh. Simultaneously, the photovoltaic-storage charging station also increases the reactive power output of photovoltaic and storage systems, successfully compensating for the reactive power gap caused by insufficient V2G response in Scheme 2. The results show that the proposed scheme, by accurately characterizing EV user response behavior, avoids the optimistic bias of traditional centralized optimization and achieves effective utilization of V2G resources.

[0070] (4) Comparison of price curves and economic benefits of photovoltaic-storage charging stations Depend on Figure 8 It is evident that the voltage regulation incentive prices issued by the distribution network to each photovoltaic-storage charging station in Scheme 3 exhibit significant spatiotemporal differences. In terms of time, the incentive prices are significantly higher during peak photovoltaic power generation periods (12:00-15:00) and peak charging load periods (17:00-21:00), guiding charging stations to absorb and inject reactive power to suppress voltage exceedances. During periods of normal voltage, the price remains low to save incentive costs. Spatially, charging stations at the feeder end (nodes 24 and 30) receive slightly higher incentive prices than those closer to the busbar (nodes 3 and 13) due to the more significant voltage support effect per unit of reactive power, accurately reflecting the marginal support value of reactive power at different spatial locations.

[0071] In addition, from the perspective of reactive power support revenue of photovoltaic-storage charging stations, the net revenue of each photovoltaic-storage charging station under different schemes is shown in Table 5.

[0072] Table 5. Comparison of Economic Benefits of Photovoltaic-Storage Charging Stations under Different Schemes (RMB)

[0073] As shown in Table 5, Scheme 1 provides no reactive power support, resulting in zero revenue and zero cost. Scheme 2, using a pre-determined fixed incentive price through centralized optimization, yields only 1881.56 yuan in reactive power support revenue for the photovoltaic-storage charging station, with a net profit of 1384.88 yuan. Traditional centralized optimization treats V2G as an ideal and controllable reactive power source, resulting in a low V2G incentive price. Furthermore, the incentive price for photovoltaic-storage reactive power also lacks elasticity, leaving the photovoltaic-storage charging station with insufficient economic incentive to fully utilize reactive power resources. This demonstrates that the fixed electricity price model under centralized optimization cannot provide dynamic incentives for charging stations, and the revenue level is limited by rigid price signals.

[0074] Scheme 3 achieves dynamic pricing through a two-layer game. The differentiated incentive price released at the upper layer effectively stimulates the resource utilization enthusiasm of photovoltaic-storage charging stations, resulting in reactive power support revenue of 4416.73 yuan, an increase of 134.8% compared to Scheme 2. The net revenue is 3293.51 yuan, approximately 2.38 times that of Scheme 2. This demonstrates that the game-theoretic pricing mechanism of this invention not only ensures voltage quality but also effectively incentivizes the market participation of photovoltaic-storage charging stations, achieving a synergistic improvement in grid security and the economic benefits of market participants.

[0075] In summary, this solution, based on a thorough consideration of the independent operating status of photovoltaic-storage charging stations and the actual response behavior of electric vehicle users, constructs a two-layer master-slave game framework between the distribution network dispatch center and the photovoltaic-storage charging stations. By using differentiated dynamic incentive prices to guide the optimal allocation of reactive power resources, the solution reduces the proportion of voltage overruns in the distribution network from 58.33% to 12.50%, lowers the total system cost by 33.3% compared to traditional centralized optimization schemes, and increases the net revenue of photovoltaic-storage charging stations by approximately 138%. This solution transforms voltage regulation from a "centralized command" model to a "market incentive" model, ensuring voltage quality while balancing the economic benefits of all parties, achieving a synergistic improvement in grid security and market participant revenue, and possesses significant practical application value.

[0076] Example 3 is an embodiment of the present invention. This embodiment differs from the first embodiment in that it provides a distributed voltage coordination control method system for power distribution network-photovoltaic-storage charging station.

[0077] It should be noted that the technical solution of the distributed voltage coordination control system of the distribution network-photovoltaic-storage charging station is based on the same concept as the technical solution of the distributed voltage coordination control method of the distribution network-photovoltaic-storage charging station described above. For details not described in detail in the technical solution of the distributed voltage coordination control system of the distribution network-photovoltaic-storage charging station described above, please refer to the description of the technical solution of the distributed voltage coordination control method of the distribution network-photovoltaic-storage charging station described above.

[0078] This embodiment of a distributed voltage coordination control system for a power distribution network-photovoltaic-storage charging station includes: The game framework construction module is used to obtain the distribution network topology parameters, photovoltaic energy storage charging station equipment parameters and operating status data, and construct a two-layer master-slave game voltage coordination control framework between the distribution network dispatch center and the photovoltaic energy storage charging station based on the data. The upper-layer model is the distribution network dispatch center, which acts as the game leader, and the lower-layer model is each photovoltaic energy storage charging station, which acts as the game follower. The upper-level incentive price decision module is used by the distribution network dispatch center as the leader to make decisions and issue differentiated voltage regulation incentive prices to each photovoltaic-storage charging station with the goal of minimizing the overall system cost. The lower-level reactive power output autonomous decision-making module is used by each of the photovoltaic and energy storage charging stations as followers to receive the voltage regulation incentive price and, with the goal of maximizing its own net reactive power support revenue, autonomously decide on the reactive power output plan and feed it back to the distribution network dispatch center under the constraint of the nonlinear response behavior of electric vehicle users. The rolling optimization module is used by the distribution network dispatch center and each photovoltaic-storage charging station to iteratively perform reactive power output plans and published incentive prices based on feedback until a game equilibrium is reached. The process is embedded in the model predictive control framework for rolling optimization and execution to obtain the optimal control scheme for each time period.

[0079] This embodiment also provides a computer device applicable to a distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station system, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a distributed voltage coordination control method for a power distribution network-photovoltaic-storage charging station, as proposed in the above embodiments.

[0080] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a distributed voltage coordination control method for a power distribution network-photovoltaic-storage charging station as proposed in the above embodiments.

[0081] The storage medium proposed in this embodiment belongs to the same inventive concept as the distributed voltage coordination control method for a power distribution network-photovoltaic-storage charging station proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0082] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed voltage coordination control method for a power distribution network-photovoltaic-storage-charging station system, characterized in that, include: The distribution network topology parameters, photovoltaic-storage charging station equipment parameters and operating status data are obtained. Based on the data, a two-layer master-slave game voltage coordination control framework between the distribution network dispatch center and the photovoltaic-storage charging station is constructed. The upper-layer model is the distribution network dispatch center, which acts as the game leader, and the lower-layer model is each photovoltaic-storage charging station, which acts as the game follower. The distribution network dispatch center, as the leader, aims to minimize the overall system cost and makes decisions to issue differentiated voltage regulation incentive prices to each photovoltaic-storage charging station. Each of the photovoltaic and energy storage charging stations acts as a follower, receives the voltage regulation incentive price, and aims to maximize its own net reactive power support revenue. Under the constraint of considering the nonlinear response behavior of electric vehicle users, it autonomously makes a reactive power output plan and feeds it back to the distribution network dispatch center. The distribution network dispatch center and each photovoltaic-storage charging station iterate alternately based on the feedback reactive power output plan and the published incentive price until a game equilibrium is reached. The process is then embedded in the model predictive control framework for rolling optimization and execution to obtain the optimal control scheme for each time period.

2. The distributed voltage coordination control method for distribution network-photovoltaic-storage charging station as described in claim 1, characterized in that: When the distribution network dispatch center models its system with the goal of minimizing overall system cost, it includes: A system comprehensive cost objective function is constructed, which consists of voltage deviation penalty cost, reactive power support incentive cost, and line loss cost. The voltage deviation penalty cost is calculated based on the voltage deviation amount, voltage quality weight, and unit voltage deviation penalty coefficient of each node in the distribution network node set, and the voltage deviation amount is generated when the node voltage amplitude deviates from the allowable lower limit and upper limit range. The reactive power support incentive cost is calculated based on the voltage regulation incentive price issued by the distribution network to each photovoltaic and energy storage charging station and the absolute value of the purchased reactive power. The line loss cost is calculated based on the branch current between nodes, the branch resistance, and the unit line loss price.

3. The distributed voltage coordination control method for distribution network-photovoltaic-storage charging station as described in claim 1 or 2, characterized in that: When modeling the photovoltaic-storage charging station with the goal of maximizing its net reactive power support revenue, it includes: Construct a net revenue objective function for reactive power support, which consists of reactive power support revenue minus reactive power incentive expenditure for vehicle-to-grid interaction and reactive power operating costs. The revenue from reactive power support is calculated based on the voltage regulation incentive price received by the photovoltaic-storage charging station and the absolute value of the reactive power supplied to the grid. The vehicle-to-grid (V2G) interactive reactive power incentive expenditure is calculated based on the unit cost of the V2G interactive reactive power incentive set for the photovoltaic-storage charging station and the V2G interactive reactive power provided by the electric vehicle cluster under its jurisdiction. The reactive power operating cost is calculated based on the reactive power output of energy storage and photovoltaics and the corresponding reactive power regulation unit operating cost.

4. The distributed voltage coordination control method for distribution network-photovoltaic-storage charging station as described in claim 3, characterized in that: The constraint considering the nonlinear response behavior of electric vehicle users includes: A reactive power response model for vehicle-to-grid interaction is constructed, and an sigmoid function is used to describe the nonlinear relationship between reactive power output and incentive price in vehicle-to-grid interaction. The parameters of the S-shaped function are set, including the maximum reactive capacity of the vehicle-to-grid interaction cluster within the jurisdiction of the photovoltaic-storage charging station as the upper limit, the benchmark incentive price as the price inflection point, and the introduction of a price sensitivity coefficient to characterize the user's response to the price incentive. The benchmark incentive price and price sensitivity coefficient are obtained by fitting historical response data of electric vehicle users.

5. The distributed voltage coordination control method for distribution network-photovoltaic-storage charging station as described in claim 4, characterized in that: When making autonomous decision-making decisions regarding reactive power output, constraints are established, including: Establish reactive capacity constraints for equipment, limit the reactive output of photovoltaic inverters to meet the power capacity coupling relationship with their predicted active output, and set upper limits for reactive output of energy storage converters and vehicle-to-grid interaction clusters. Establish network operation constraints, construct the reactive power balance equations within the photovoltaic-storage charging station, and the power flow constraints of the distribution network describing the relationship between node voltage and branch power; Establish market price order constraints and limit the incentive price for vehicle-to-network interaction to a preset upper and lower price range.

6. The distributed voltage coordination control method for distribution network-photovoltaic-storage charging station as described in claim 5, characterized in that: When the distribution network dispatch center makes decisions and issues differentiated voltage regulation incentive prices to each photovoltaic-storage charging station, it includes: The constraints of the optimization model of the distribution network dispatch center are constructed, and a limit range is set for the voltage regulation incentive price of each photovoltaic-storage charging station, limiting the voltage regulation incentive price between a preset lower limit and an upper limit. The power distribution network dispatch center indirectly guides the reactive power output behavior of each photovoltaic-storage charging station by publishing the voltage regulation incentive price. After receiving the voltage regulation incentive price, each of the photovoltaic-storage charging stations feeds back its determined reactive power output plan to the distribution network dispatch center. The distribution network dispatch center and each of the photovoltaic-storage charging stations interact through a closed-loop mechanism of price, reactive power, and voltage.

7. The distributed voltage coordination control method for distribution network-photovoltaic-storage charging station as described in claim 6, characterized in that: The process of alternating iterations until game equilibrium is reached is embedded in the model prediction and control framework for rolling optimization and execution, including: Set the current decision time and the prediction time domain length, update the system state, and solve the two-layer game equilibrium within the optimization window; An iterative search algorithm is used to solve the problem alternately. The distribution network dispatch center initializes the incentive price sequence and sends it to each of the photovoltaic-storage charging stations. After receiving the sequence, each photovoltaic-storage charging station independently solves its own revenue maximization problem and feeds back the optimal reactive power output plan. The distribution network dispatch center summarizes the feedback results, performs power flow calculation for the entire network, and updates the price strategy using an optimization algorithm until the price change is less than the convergence threshold and outputs the game equilibrium solution. Only the game equilibrium solution at the current decision moment is executed. At the next moment, the system state is updated and the prediction window is scrolled forward to re-solve the game equilibrium, forming a dynamic closed loop of prediction-game-execution-feedback.

8. A distributed voltage coordination control system for a power distribution network-photovoltaic-storage-charging station, using the method described in any one of claims 1-7, characterized in that, include: The game framework construction module is used to obtain the distribution network topology parameters, photovoltaic energy storage charging station equipment parameters and operating status data, and construct a two-layer master-slave game voltage coordination control framework between the distribution network dispatch center and the photovoltaic energy storage charging station based on the data. The upper-layer model is the distribution network dispatch center, which acts as the game leader, and the lower-layer model is each photovoltaic energy storage charging station, which acts as the game follower. The upper-level incentive price decision module is used by the distribution network dispatch center as the leader to make decisions and issue differentiated voltage regulation incentive prices to each photovoltaic-storage charging station with the goal of minimizing the overall system cost. The lower-level reactive power output autonomous decision-making module is used by each of the photovoltaic and energy storage charging stations as followers to receive the voltage regulation incentive price and, with the goal of maximizing its own net reactive power support revenue, autonomously decide on the reactive power output plan and feed it back to the distribution network dispatch center under the constraint of the nonlinear response behavior of electric vehicle users. The rolling optimization module is used by the distribution network dispatch center and each photovoltaic-storage charging station to iteratively perform reactive power output plans and published incentive prices based on feedback until a game equilibrium is reached. The process is embedded in the model predictive control framework for rolling optimization and execution to obtain the optimal control scheme for each time period.

9. A computer device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the steps of the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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