A method for coordinated control of public grid voltage and reactive power

Abstract

This invention discloses a method for coordinated voltage and reactive power control in a public power grid, comprising the following steps: S1, constructing a multi-objective optimization model based on weight allocation or Pareto optimal solution prioritization, simultaneously achieving three types of objectives: public power grid interface compliance, internal node voltage security, and control economy through reactive power autonomous control; S2, adjusting the reactive power autonomous control strategy implemented in step S1 according to different types of source-grid-load-storage projects; This invention balances grid interface requirements, internal voltage security, and economy through multi-objective optimization, and proposes a hierarchical optimization framework (upper-level centralized decision-making + lower-level distributed execution), combined with an improved PSO algorithm and local feedback control, enabling adaptive adjustment under different scenarios, significantly improving internal voltage security and reducing costs while meeting public power grid requirements.

Description

Technical Field

[0001] This invention belongs to the field of voltage optimization control technology, specifically relating to a method for coordinated control of voltage and reactive power in public power grids. Background Technology

[0002] With the accelerated advancement of energy transition, a large number of distributed power sources (such as photovoltaic and wind power) and energy storage systems are being connected to the public power grid, forming a complex integrated operation pattern of power generation, grid, load, and storage. While this transformation improves energy utilization efficiency and promotes the consumption of clean energy, it also brings many challenges to the voltage security of the public power grid. Voltage, as one of the important indicators of power quality, is directly related to the safe and reliable operation of the power system and the normal operation of various electrical equipment. In grid-connected operation scenarios, in-depth analysis of the voltage security status of the public power grid is of great significance for ensuring the stable operation of the power system and improving power supply quality.

[0003] After integrated power generation, grid, load, and energy storage projects are connected to the public power grid, the intermittency and volatility of their distributed power sources (such as photovoltaic and wind power), the randomness of their loads, and the dynamic response characteristics of their energy storage systems pose unprecedented challenges to the safe and stable operation of the public power grid. Traditional power grid dispatching and control models are ill-equipped to effectively address this complex situation of multi-faceted interaction and high uncertainty among power generation, grid, load, and energy storage, particularly in areas such as voltage and reactive power control, power balance, and fault response, where significant technical bottlenecks exist. To solve these problems, it is essential to develop a coordinated voltage and reactive power control method for the public power grid. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a public power grid voltage and reactive power coordination control method that can achieve adaptive adjustment under different scenarios, and can significantly improve internal voltage security and reduce costs while meeting the requirements of the public power grid.

[0005] The objective of this invention is achieved as follows: a method for coordinated control of voltage and reactive power in a public power grid, comprising the following steps:

[0006] S1, based on weight allocation or Pareto optimal solution to weigh priorities, constructs a multi-objective optimization model to simultaneously achieve three types of objectives, including public grid interface compliance, internal node voltage security, and reactive power autonomous control with control economy;

[0007] S2, adjust the reactive power autonomous control strategy implemented in step S1 according to different types of source-grid-load-storage projects.

[0008] Preferably, step S1 includes the following steps:

[0009] S11, Objective function construction:

[0010] S111, Public Grid Interface Target :

[0011] The public power grid requires that voltage deviation and reactive power fluctuation at the point of contact of the integrated power generation, grid, load and energy storage project be controlled within the allowable range. The objective function is defined as:

[0012] ;

[0013] In the formula, The cut-off voltage, Rated voltage, For reactive power at the threshold, To maximize the reactive power allowed at the checkpoint Weighting coefficients (usually taken as...) , (Prioritize ensuring voltage stability).

[0014] S112, Internal node voltage safety target :

[0015] The voltage at each node of the internal power grid must be maintained within a safe range. The objective function is defined as follows:

[0016] ;

[0017] In the formula, Let N be the voltage at internal node i, and N be the total number of nodes. The node weights are set as follows: 1.2 for load center nodes and 1.0 for ordinary nodes. The allowable voltage deviation threshold;

[0018] S113, Controlling economic objectives :

[0019] Reactive power regulation needs to minimize equipment losses and operating costs. The objective function is defined as follows:

[0020] ;

[0021] In the formula, For energy storage charging and discharging power, These are the reactive power outputs of photovoltaic and wind power, respectively. For the reactive power output of SVG, This is the corresponding loss coefficient;

[0022] S114, Multi-objective Integrated Optimization Model:

[0023] By summing the above objectives with weights, a single-objective optimization problem is formed: In the formula, The weights are dynamically adjusted based on the scenario.

[0024] Alternatively, Pareto optimality can be used to find the solution;

[0025] S12, Constraint Setting:

[0026] S121, Equipment capacity constraint:

[0027] Photovoltaic inverter: In the formula, , This refers to the rated capacity of the inverter.

[0028] Wind power converter: Doubly fed wind turbines are typically selected , This refers to the rated capacity of the fan.

[0029] Energy storage PCS: ; In the formula Rated capacity of PCS;

[0030] SVG: ; Usually take ;

[0031] S122, Network Flow Constraints:

[0032] The reactive power of the internal power grid must satisfy the node balance equations:

[0033] ;

[0034] In the formula, The reactive power output of node i. For reactive power compensation equipment output, For the reactive power of the load at node i, Let i be the set of nodes connected to node i. The reactive power loss of line ij: In the formula For line current, For line reactance;

[0035] S123, Voltage safety constraint:

[0036] All internal node voltages must be within safe limits:

[0037] ;

[0038] In the formula, , (For 10kV and below power grids);

[0039] S124, Control rate constraint:

[0040] To avoid frequent equipment operation, the reactive power regulation rate needs to be limited:

[0041] ;

[0042] In the formula, The maximum adjustment rate;

[0043] S13, Hierarchical optimization solution:

[0044] A hierarchical algorithm of "centralized decision-making - distributed execution" is adopted, as follows:

[0045] S131, Upper Level:

[0046] Centralized optimization (rolling time domain control) is adopted, with an optimization cycle of 15 minutes and a rolling update every 5 minutes. Based on the prediction data, a multi-objective optimization model is solved to determine the reference value of reactive power output of each device.

[0047] S132, lower layer:

[0048] Distributed tracking (local feedback control) is adopted, and each device performs closed-loop adjustment based on the optimized reference value from the upper layer and local real-time measurement data to compensate for prediction errors.

[0049] Preferably, in step S113, the equipment loss and operating cost include energy storage charging and discharging losses, efficiency losses from distributed power generation reactive power, and operating losses of reactive power compensation equipment.

[0050] Preferably, in step S114, the rules for dynamically adjusting the weights according to the scenario are as follows:

[0051] During normal operation, Balanced control;

[0052] When the voltage of the public power grid exceeds the limit, Prioritize meeting the requirements of the power grid;

[0053] When the internal node voltage exceeds the limit, Prioritize internal security.

[0054] Preferably, in step S131, the multi-objective optimization model is solved using an improved particle swarm optimization (PSO) algorithm, as follows:

[0055] Initialization: Generate M particles, each particle representing a set of reactive power output schemes. ;

[0056] Fitness calculation: For each particle, calculate the objective function Q and apply a penalty according to the constraints. The penalty method is to multiply the over-limit term by the penalty coefficient K=100.

[0057] Particle Update: Particle positions are updated using individual best (pbest) and global best (gbest) values.

[0058] ;

[0059] ;

[0060] In the formula, The inertial weight decreases linearly from 0.9 to 0.4. As a learning factor, for Random numbers;

[0061] Convergence criterion: When the number of iterations reaches the upper limit or the objective function changes by a certain amount. When the optimal solution is found, output the solution.

[0062] Preferably, step S2 includes the following steps:

[0063] S21, Industrial Enterprises Projects:

[0064] SVG should be prioritized to handle high-frequency fluctuation components.

[0065] The energy storage system is responsible for medium-frequency regulation and optimizes the charging and discharging plan through MPC to avoid exceeding the SOC limit;

[0066] Photovoltaic inverters handle the reactive power at the base frequency, utilizing their redundant capacity, typically reserving 20% ​​of the reactive power capacity.

[0067] S22, Incremental Distribution Network Projects:

[0068] Zonal control is adopted: the distribution network is divided into 3 to 5 zones, and each zone is equipped with a "virtual power plant" to coordinate local photovoltaic, energy storage and load; reactive power support is provided between zones through tie lines. When the voltage in a certain zone is low, the adjacent zone transmits reactive power through SVG or energy storage, and the transmitted power is supplied through zonal control. , For tie line capacity; the public power gateway interface adopts "constant power factor" control;

[0069] S23, Whole Village Development Projects:

[0070] Residential photovoltaic inverters have enabled "voltage-reactive power droop control". In the formula Dynamically adjusts with node position, end node Near-end node ;

[0071] Centralized energy storage generates additional reactive power during peak load periods to mitigate voltage drops.

[0072] Simplified optimization algorithm: Rule-based control is adopted to reduce computational costs.

[0073] Due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows: The present invention balances the requirements of the power grid interface, internal voltage safety and economy through multi-objective optimization, and proposes a hierarchical optimization framework (upper-level centralized decision-making + lower-level distributed execution). Combined with the improved PSO algorithm and local feedback control, it can achieve adaptive adjustment under different scenarios, and can significantly improve internal voltage safety and reduce costs while meeting the requirements of the public power grid. Detailed Implementation

[0074] The technical solution of the present invention will be further described in detail below through embodiments.

[0075] Reactive power autonomous control in integrated power generation, grid, load, and energy storage projects is a core technology for achieving coordinated operation of the "power generation, grid, load, and energy storage" system. Its core objective is to ensure voltage safety at each node of the internal power grid while meeting the voltage and reactive power regulation requirements of the public power grid. Since the voltage and reactive power characteristics of different units (photovoltaics, wind power, energy storage, loads, etc.) differ significantly, and there are potential conflicts between the objectives of the public power grid and the internal power grid (e.g., the public power grid requires minimal reactive power fluctuations at the control points, while internal nodes may require local reactive power support), a control model needs to be constructed based on multi-objective optimization theory. The optimal control strategy is then determined through hierarchical coordination and intelligent solution.

[0076] Based on this, the present invention provides a method for coordinated control of voltage and reactive power in a public power grid, comprising the following steps:

[0077] S1, a multi-objective optimization framework for reactive power autonomous control:

[0078] Reactive power autonomous control needs to achieve three objectives simultaneously: compliance of the public grid interface, voltage security of internal nodes, and control economy. These three objectives constitute a multi-objective optimization problem, which requires prioritization through weight allocation or Pareto optimal solution.

[0079] S11, Objective function construction:

[0080] S111, Public Grid Interface Target :

[0081] The public power grid requires that voltage deviation and reactive power fluctuation at the point of contact of the integrated power generation, grid, load, and energy storage project be controlled within allowable ranges (e.g., voltage deviation ≤ ±5%, reactive power fluctuation rate ≤ 10% / min). The objective function is defined as:

[0082] ;

[0083] In the formula, The cut-off voltage, The rated voltage (e.g., 10kV). For reactive power at the threshold, To maximize the reactive power allowed at the checkpoint Weighting coefficients (usually taken as...) , (Prioritize ensuring voltage stability).

[0084] S112, Internal node voltage safety target :

[0085] The voltage at each node of the internal power grid must be maintained within a safe range (e.g., 0.95–1.05 pu). The objective function is defined as:

[0086] ;

[0087] In the formula, Let N be the voltage at internal node i, and N be the total number of nodes. The node weights are set as follows: 1.2 for load center nodes and 1.0 for ordinary nodes. The allowable voltage deviation threshold is set (e.g., 0.05). This function penalizes voltage deviations that exceed the limit; the larger the deviation, the higher the penalty.

[0088] S113, Controlling economic objectives :

[0089] Reactive power regulation needs to minimize equipment losses and operating costs, including: energy storage charging and discharging losses (proportional to the square of the charging and discharging power), efficiency losses from reactive power generation by distributed power sources (reactive power generation by photovoltaics / wind turbines reduces active power output), and operating losses of reactive power compensation equipment (such as SVG). The objective function is defined as:

[0090] ;

[0091] In the formula, For energy storage charging and discharging power, These are the reactive power outputs of photovoltaic and wind power, respectively. The reactive power output of the SVG (Static Var Generator). For the corresponding loss coefficient (e.g.) (Determined based on actual measurements of equipment parameters).

[0092] S114, Multi-objective Integrated Optimization Model:

[0093] By summing the above objectives with weights, a single-objective optimization problem is formed (or solved using Pareto optimality):

[0094] ;

[0095] In the formula, The weights are dynamically adjusted based on the scenario:

[0096] During normal operation, Balanced control;

[0097] When the voltage of the public power grid exceeds the limit, Prioritize meeting the requirements of the power grid;

[0098] When the internal node voltage exceeds the limit, Prioritize internal security.

[0099] S12, Constraint Setting:

[0100] S121, Equipment capacity constraint:

[0101] Reactive power autonomous control needs to meet equipment physical constraints, network topology constraints, and safe operation constraints, mainly including:

[0102] Photovoltaic inverter: In the formula, , This refers to the rated capacity of the inverter.

[0103] Wind power converter: Doubly fed wind turbines are typically selected , This refers to the rated capacity of the fan.

[0104] Energy storage PCS: ; In the formula Rated capacity of PCS;

[0105] SVG: ; Usually take .

[0106] S122, Network Flow Constraints:

[0107] The reactive power of the internal power grid must satisfy the node balance equations:

[0108] ;

[0109] In the formula, The reactive power output of node i. For reactive power compensation equipment output, For the reactive power of the load at node i, Let i be the set of nodes connected to node i. The reactive power loss of line ij: In the formula For line current, This refers to the line reactance.

[0110] S123, Voltage safety constraint:

[0111] All internal node voltages must be within safe limits:

[0112] ;

[0113] In the formula, , (For 10kV and below power grids).

[0114] S124, Control rate constraint:

[0115] To avoid frequent equipment operation, the reactive power regulation rate needs to be limited:

[0116] ;

[0117] In the formula, For maximum regulation rate (e.g., photovoltaic) Energy storage ).

[0118] S13, Hierarchical optimization solution:

[0119] Since the source-grid-load-storage system contains a large number of distributed devices, directly solving the global optimization problem has high computational complexity (especially when the number of nodes N>50). Therefore, a hierarchical algorithm of "centralized decision-making-distributed execution" is required, as follows:

[0120] S131, Upper layer: Centralized optimization (rolling time domain control)

[0121] The optimization cycle is 15 minutes, and the data is updated every 5 minutes. Based on the predicted data (photovoltaic output and load demand), the multi-objective optimization model is solved to determine the reference value of reactive power output of each device.

[0122] The improved Particle Swarm Optimization (PSO) algorithm is used to solve the problem, and the steps are as follows:

[0123] Initialization: Generate M particles (e.g., M=100), each particle representing a set of reactive power output schemes. ;

[0124] Fitness calculation: For each particle, calculate the objective function Q and penalize it according to the constraints (the out-of-limit term is multiplied by the penalty coefficient K=100).

[0125] Particle Update: Particle positions are updated using individual best (pbest) and global best (gbest) values.

[0126] ;

[0127] ;

[0128] In the formula, The inertial weight decreases linearly from 0.9 to 0.4. As a learning factor, for Random numbers;

[0129] Convergence criterion: When the number of iterations reaches the upper limit (e.g., 200 times) or the objective function changes... When the optimal solution is found, output the solution.

[0130] S132, Lower layer: Distributed tracing (local feedback control)

[0131] Each device performs closed-loop adjustment based on the optimized reference values ​​from the upper layer, combined with local real-time measurement data (voltage, current), to compensate for prediction errors. For example:

[0132] Photovoltaic inverter: Employs a proportional-resonant (PR) controller to track reactive power commands.

[0133] ;

[0134] In the formula, , It can quickly eliminate voltage deviation.

[0135] Energy storage PCS: Employs model predictive control (MPC) to track commands while meeting SOC constraints.

[0136] ;

[0137] In the formula, To predict the step size, λ=0.1 is used as the SOC weight to ensure that the energy storage maintains a reasonable SOC (e.g., 30-70%) during the adjustment process.

[0138] S2, Control Strategy Optimization in Different Scenarios:

[0139] Based on the differences in the types of source-grid-load-storage projects, control strategies need to be adjusted accordingly:

[0140] S21, Industrial Enterprises Projects:

[0141] Characteristics: The internal load is mainly inductive (such as electric motors), the reactive power demand is large and fluctuates drastically (electric arc furnaces cause fluctuations of ±30%), and the public power grid has strict requirements for reactive power fluctuation rate at the threshold (≤5% / min).

[0142] Strategy: Prioritize the use of SVG (fast response speed, no active power loss) to handle high-frequency fluctuation components (such as below 10Hz); the energy storage system handles medium-frequency regulation (0.1~10Hz), and optimizes the charging and discharging plan through MPC to avoid SOC exceeding the limit; the photovoltaic inverter handles the base frequency reactive power (<0.1Hz), utilizing its redundant capacity (usually reserving 20% ​​reactive power capacity).

[0143] Example: In a steel plant project, the reactive power fluctuation caused by the electric arc furnace is ±2Mvar, the real-time compensation of SVG is ±1.5Mvar, the energy storage compensation is ±0.5Mvar, the reactive power fluctuation rate at the cut-off point is reduced to 3% / min, and the voltage deviation of the internal nodes is controlled within ±3%.

[0144] S22, Incremental Distribution Network Projects:

[0145] Features: High penetration rate of distributed power sources (photovoltaic + wind power account for more than 50% of the load), many internal nodes (>30), and need to take into account the voltage balance of each area.

[0146] Strategy: Zonal control is adopted: the distribution network is divided into 3-5 zones, and each zone is equipped with a "virtual power plant" to coordinate local photovoltaic, energy storage, and loads; reactive power support is provided between zones through tie lines. When the voltage in a certain zone is low, the adjacent zone transmits reactive power through SVG or energy storage, and the transmitted power is supplied. , For tie line capacity; the public power gateway interface adopts "constant power factor" control (such as 0.95 lag) to reduce the impact on the main network.

[0147] Example: The power distribution network of an industrial park is divided into two zones, A and B. The photovoltaic output of zone A suddenly drops, causing the voltage to drop to 0.94 pu. The SVG of zone B transmits 0.8 Mvar of reactive power to zone A through the tie line. The voltage of zone A recovers to 0.97 pu, and the power factor at the threshold is maintained at 0.95 ± 0.02.

[0148] S23, Whole Village Development Projects:

[0149] Features: Dispersed load (farmers + small-scale agricultural loads), large voltage fluctuations (peak deviation can reach ±8%), and sensitive to equipment costs (prioritize the use of redundant photovoltaic capacity).

[0150] Strategy: Enable "voltage-reactive power droop control" for residential photovoltaic inverters. In the formula Dynamically adjusts according to node position (end node) Near-end node Centralized energy storage (e.g., 500kWh) generates additional reactive power during peak load periods (18:00–22:00) to mitigate voltage drops; simplified optimization algorithms and rule-based control (e.g., when voltage < 0.95pu, energy storage generates additional reactive power to 50% of rated capacity) reduce computational costs.

[0151] Example: In a village-wide project, the increased evening peak load caused the terminal voltage to drop to 0.92 pu. The household photovoltaic system generated an additional 0.3 Mvar of reactive power, and the centralized energy storage system generated an additional 0.2 Mvar. The voltage rebounded to 0.96 pu, and the control cost was reduced by 40% compared to the SVG solution.

[0152] The following is a verification of the effectiveness of the strategy provided by this invention.

[0153] A simulation platform was built using MATLAB / Simulink to compare the performance of traditional "local independent control" with the "multi-objective autonomous control" proposed in this paper:

[0154]

[0155] The results show that multi-objective autonomous control can significantly improve internal voltage security and reduce costs while meeting the requirements of the public power grid, thus verifying the effectiveness of the strategy.

[0156] In summary, this invention achieves adaptive adjustment in different scenarios by optimizing the balance between grid interface requirements, internal voltage security, and economy through multi-objective optimization. Its proposed hierarchical optimization framework (upper-level centralized decision-making + lower-level distributed execution), combined with an improved PSO algorithm and local feedback control, enables adaptive adjustment: industrial enterprises need to strengthen the coordination of fast-response equipment (SVG + energy storage); incremental distribution networks need to optimize reactive power flow through zoned control; and village-wide development needs to utilize low-cost solutions (photovoltaics + centralized energy storage) to meet basic requirements. Furthermore, the strategy provided by this invention offers a localized solution for subsequent global voltage and reactive power coordination control and lays the algorithmic foundation for software module development.

[0157] Finally, 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 the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated control of voltage and reactive power in a public power grid, characterized in that, Includes the following steps: S1, based on weight allocation or Pareto optimal solution to weigh priorities, constructs a multi-objective optimization model to simultaneously achieve three types of objectives, including public grid interface compliance, internal node voltage security, and reactive power autonomous control with control economy; S2, adjust the reactive power autonomous control strategy implemented in step S1 according to different types of source-grid-load-storage projects.

2. The public power grid voltage and reactive power coordinated control method according to claim 1, characterized in that, Step S1 includes the following steps: S11, Objective function construction: S111, Public Grid Interface Target : The public power grid requires that voltage deviation and reactive power fluctuation at the point of contact of the integrated power generation, grid, load and energy storage project be controlled within the allowable range. The objective function is defined as: ； In the formula, The cut-off voltage, Rated voltage, For reactive power at the threshold, To maximize the reactive power allowed at the checkpoint Weighting coefficients (usually taken as...) , (Prioritize ensuring voltage stability). S112, Internal node voltage safety target : The voltage at each node of the internal power grid must be maintained within a safe range. The objective function is defined as follows: ； In the formula, Let N be the voltage at internal node i, and N be the total number of nodes. The node weights are set as follows: 1.2 for load center nodes and 1.0 for ordinary nodes. The allowable voltage deviation threshold; S113, Controlling economic objectives : Reactive power regulation needs to minimize equipment losses and operating costs. The objective function is defined as follows: ； In the formula, For energy storage charging and discharging power, These are the reactive power outputs of photovoltaic and wind power, respectively. For the reactive power output of SVG, This is the corresponding loss coefficient; S114, Multi-objective Integrated Optimization Model: By summing the above objectives with weights, a single-objective optimization problem is formed: In the formula, The weights are dynamically adjusted based on the scenario. Alternatively, Pareto optimality can be used to find the solution; S12, Constraint Setting: S121, Equipment capacity constraint: Photovoltaic inverter: In the formula, , This refers to the rated capacity of the inverter. Wind power converter: Doubly fed wind turbines are typically selected , This refers to the rated capacity of the fan. Energy storage PCS: ; In the formula Rated capacity of PCS; SVG: ; Usually take ; S122, Network Flow Constraints: The reactive power of the internal power grid must satisfy the node balance equations: ； In the formula, The reactive power output of node i. For reactive power compensation equipment output, The reactive power of the load at node i, Let i be the set of nodes connected to node i. The reactive power loss of line ij: In the formula For line current, For line reactance; S123, Voltage safety constraint: All internal node voltages must be within safe limits: ； In the formula, , (For 10kV and below power grids); S124, Control rate constraint: To avoid frequent equipment operation, the reactive power regulation rate needs to be limited: ； In the formula, The maximum adjustment rate; S13, Hierarchical optimization solution: A hierarchical algorithm of "centralized decision-making and distributed execution" is adopted, as follows: S131, Upper Level: Centralized optimization (rolling time domain control) is adopted, with an optimization cycle of 15 minutes and a rolling update every 5 minutes. Based on the prediction data, a multi-objective optimization model is solved to determine the reference value of reactive power output of each device. S132, lower layer: Distributed tracking (local feedback control) is adopted, and each device performs closed-loop adjustment based on the optimized reference value from the upper layer and local real-time measurement data to compensate for prediction errors.

3. The public power grid voltage and reactive power coordinated control method according to claim 2, characterized in that, In step S113, equipment losses and operating costs include energy storage charging and discharging losses, efficiency losses from distributed power generation reactive power generation, and operating losses of reactive power compensation equipment.

4. The public power grid voltage and reactive power coordinated control method according to claim 2, characterized in that, In step S114, the rules for dynamically adjusting the weights according to the scenario are as follows: During normal operation, Balanced control; When the voltage of the public power grid exceeds the limit, Prioritize meeting the requirements of the power grid; When the internal node voltage exceeds the limit, Prioritize internal security.

5. The public power grid voltage and reactive power coordinated control method according to claim 2, characterized in that, In step S131, the multi-objective optimization model is solved using an improved particle swarm optimization (PSO) algorithm, and the steps are as follows: Initialization: Generate M particles, each particle representing a set of reactive power output schemes. ; Fitness calculation: For each particle, calculate the objective function Q and apply a penalty according to the constraints. The penalty method is to multiply the over-limit term by the penalty coefficient K=100. Particle Update: Particle positions are updated using individual best (pbest) and global best (gbest) values. ； ； In the formula, The inertial weight decreases linearly from 0.9 to 0.

4. As a learning factor, for Random numbers; Convergence criterion: When the number of iterations reaches the upper limit or the objective function changes by a certain amount. When the optimal solution is found, output the solution.

6. The public power grid voltage and reactive power coordinated control method according to claim 1, characterized in that, Step S2 includes the following steps: S21, Industrial Enterprises Projects: SVG should be prioritized to handle high-frequency fluctuation components. The energy storage system is responsible for medium-frequency regulation and optimizes the charging and discharging plan through MPC to avoid exceeding the SOC limit; Photovoltaic inverters handle the reactive power at the base frequency, utilizing their redundant capacity, typically reserving 20% ​​of the reactive power capacity. S22, Incremental Distribution Network Projects: Zonal control is adopted: the distribution network is divided into 3 to 5 zones, and each zone is equipped with a "virtual power plant" to coordinate local photovoltaic, energy storage and load; reactive power support is provided between zones through tie lines. When the voltage in a certain zone is low, the adjacent zone transmits reactive power through SVG or energy storage, and the transmitted power is supplied through tie lines. , For tie line capacity; the public power gateway interface adopts "constant power factor" control; S23, Whole Village Development Projects: The residential photovoltaic inverter has enabled "voltage-reactive droop control". In the formula Dynamically adjusts with node position, end node Near-end node ; Centralized energy storage generates additional reactive power during peak load periods to mitigate voltage drops. Simplified optimization algorithm: Rule-based control is adopted to reduce computational costs.

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