Method and system for optimizing reactive voltage of power grid

Abstract

The invention relates to the technical field of new energy power grid system optimization operation, in particular to a power grid reactive voltage optimization method and system. The method comprises the steps that the shared energy storage and micro-grid alliance collaboratively establishes a mathematical model of a power grid and each device in the micro-grid; on the basis of a mathematical model, a game result is determined through day-ahead multi-round games, in the intra-day stage, the micro-grid alliance optimizes charging and discharging behaviors in a rolling mode according to a charging and discharging strategy, shared energy storage is based on the game result, and an improved Shapley value method is adopted to share the shared energy storage cost; the power grid combines the interaction power, constructs day-ahead and intra-day reactive power optimization models and solves the day-ahead and intra-day reactive power optimization models to obtain an optimization result of network loss and voltage deviation minimization; and based on an optimization result, realizing the control of the node voltage of the power grid by utilizing a photovoltaic inverter. The method can solve the problems that in the prior art, the power grid voltage fluctuation is large, the network loss is increased, the micro-grid energy consumption characteristic difference is large, the allocation is not fair, and the multi-main-body cooperation capability is poor.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy power grid system optimal operation, and in particular relates to a power grid reactive power voltage optimization method and system. BACKGROUND

[0002] The global energy transformation process continues to accelerate, and the installed capacity of renewable energy such as photovoltaic and wind power and the proportion of power systems are increasing year by year, driving the transformation of energy structure to clean and low carbon. Under this trend, how to realize large-scale and efficient consumption of renewable energy has become one of the core issues to ensure the stable operation of the power system.

[0003] Currently, microgrids are widely deployed to adapt to renewable energy consumption needs due to their flexible energy management and control capabilities. Most microgrids are interconnected with the main grid, integrating power sources, grids, loads, and energy storage resources to form a source-grid-load-storage integrated operation mode, which has become an important part of the new power system. At the same time, shared energy storage, as a means of optimizing energy allocation, is gradually applied in multi-microgrid scenarios to help improve the efficiency of energy storage resources.

[0004] However, the high proportion of new energy access significantly increases the instability and volatility of the power grid operation. In addition, the energy consumption characteristics of different microgrids differ greatly, and the existing dispatching mechanism lacks effective multi-agent coordination capabilities. Shared energy storage also faces the dilemma of uneven benefit distribution, system safety, and economic benefits in actual dispatching. SUMMARY

[0005] The embodiments of the present application provide a power grid reactive power voltage optimization method and system to solve the problems of large voltage fluctuation of the power grid, increased network loss, large differences in microgrid energy consumption characteristics, unfair allocation, and poor multi-agent coordination capabilities in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a power grid reactive power voltage optimization method, comprising: The shared energy storage and the microgrid alliance cooperatively establish mathematical models of devices in the power grid and the microgrid; Based on the mathematical models, the shared energy storage and the microgrid alliance determine a game result through day-ahead multi-round game, the game result including shared energy storage rental capacity, charging and discharging strategies, and interactive power; In the intra-day stage, the microgrid alliance optimizes charging and discharging behavior according to the charging and discharging strategies, and the shared energy storage adopts an improved Shapley value method to allocate shared energy storage costs based on the game result; The power grid constructs a day-ahead and intra-day reactive power optimization model respectively based on the interactive power and solves the optimization model to obtain an optimization result of minimizing network loss and voltage deviation; Based on the optimization result of minimizing the loss and voltage deviation of the power grid, the photovoltaic inverter is used to control the voltage of the power grid node.

[0007] In a second aspect, the embodiment of the present application provides an optimization system for reactive power and voltage of a power grid, comprising: shared energy storage, a micro-grid alliance and a power grid. The shared energy storage and the micro-grid alliance cooperatively establish mathematical models of devices in the power grid and the micro-grid. Based on the mathematical models, the shared energy storage and the micro-grid alliance determine a game result through day-ahead multi-round game, the game result comprising shared energy storage rental capacity, charging and discharging strategy and interactive power. In an intra-day stage, the micro-grid alliance rolls optimizes charging and discharging behavior according to the charging and discharging strategy, and the shared energy storage adopts an improved Shapley value method to apportion shared energy storage cost based on the game result. The power grid combines the interactive power to respectively construct day-ahead and intra-day reactive power optimization models and solve them, to obtain an optimization result of minimizing the loss and voltage deviation of the power grid. Based on the optimization result of minimizing the loss and voltage deviation of the power grid, the photovoltaic inverter is used to control the voltage of the power grid node.

[0008] The embodiment of the present application provides an optimization method and system for reactive power and voltage of a power grid, which cooperatively establishes mathematical models of devices in the power grid and the micro-grid by the shared energy storage and the micro-grid alliance, determines a game result through day-ahead multi-round game based on the mathematical models, the game result comprising shared energy storage rental capacity, charging and discharging strategy and interactive power, rolls optimizes charging and discharging behavior according to the charging and discharging strategy in an intra-day stage, adopts an improved Shapley value method to apportion shared energy storage cost based on the game result, combines the interactive power to respectively construct day-ahead and intra-day reactive power optimization models and solve them, to obtain an optimization result of minimizing the loss and voltage deviation of the power grid, and uses the photovoltaic inverter to control the voltage of the power grid node based on the optimization result of minimizing the loss and voltage deviation of the power grid.

[0009] The embodiment of the present application realizes a win-win effect of reasonable income of the shared energy storage operator and cost reduction of the micro-grid alliance through the game of the shared energy storage and the micro-grid alliance in the first stage, constructs a power grid reactive power optimization model by introducing the interactive power result in the second stage, realizes collaborative minimization of the loss and voltage deviation, improves the economy and safety of the power grid, enhances the collaborative ability of multiple subjects, and apportions the shared energy storage cost by using the improved Shapley value method, so that the cost apportionment is more fair according to the energy consumption characteristics of the micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0011] Figure 1 is the implementation flowchart of the power grid reactive voltage optimization method provided by the embodiments of the present application; Figure 2 is the implementation flowchart of the method for determining the game result provided by the embodiments of the present application; Figure 3 is the implementation flowchart of the method for sharing the cost of shared energy storage by using the improved Shapley value method provided by the embodiments of the present application; Figure 4 is the IEEE 33 system schematic diagram provided by the embodiments of the present application; Fig. 5(a) is the PV active power output curve provided by the embodiments of the present application; Fig. 5(b) is the PV load variation coefficient curve provided by the embodiments of the present application; Figure 6 is the proposed ladder type charging and discharging price schematic diagram of the shared energy storage operator and the microgrid alliance provided by the embodiments of the present application; Figure 7 is the Pareto optimal solution set schematic diagram under the reactive power optimization strategy provided by the embodiments of the present application; Figure 8 is the schematic diagram of the number of switched groups and the compensation capacity of the capacitor bank changing with time provided by the embodiments of the present application, wherein, Figure 8 (a) in Fig. 5(c) is the schematic diagram of the number of switched groups of CB1 capacitor changing with time provided by the embodiments of the present application; (b) is the schematic diagram of the compensation capacity of CB1 capacitor changing with time provided by the embodiments of the present application; (c) is the schematic diagram of the number of switched groups of CB2 capacitor changing with time provided by the embodiments of the present application; (d) is the schematic diagram of the compensation capacity of CB2 capacitor changing with time provided by the embodiments of the present application; Figure 9 is the schematic diagram of the power factor and the reactive power compensation power of the inverter group changing with time in the day stage provided by the embodiments of the present application, wherein, Figure 9 (a) in Fig. 5(c) is the schematic diagram of the number of switched groups of CB1 capacitor changing with time provided by the embodiments of the present application; (b) is the schematic diagram of the compensation capacity of CB1 capacitor changing with time provided by the embodiments of the present application; (c) is the schematic diagram of the number of switched groups of CB2 capacitor changing with time provided by the embodiments of the present application; (d) is the schematic diagram of the compensation capacity of CB2 capacitor changing with time provided by the embodiments of the present application; Figure 10 is a schematic diagram of the power grid reactive voltage optimization system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0013] In order to make the objects, technical solutions and advantages of the present application clearer, the following will be described by specific embodiments in conjunction with the accompanying drawings.

[0014] Figure 1 The implementation flowchart of the power grid reactive voltage optimization method provided by the embodiment of the present application is described in detail as follows. Step 101, the shared energy storage and micro-grid alliance cooperatively establish mathematical models of various devices in the power grid and micro-grid.

[0015] The constructed mathematical models can include main power generation and regulation devices such as photovoltaic power generation units, wind turbine generators, capacitor banks, and gas turbines, and set basic parameters such as rated capacity, operation constraints, control characteristics, and energy conversion efficiency of various devices, to provide modeling basis for subsequent system optimization scheduling and simulation calculation.

[0016] Step 102, based on the mathematical models, the shared energy storage and micro-grid alliance determine the game results through day-ahead multi-round game, and the game results include shared energy storage rental capacity, charging and discharging strategy, and interactive power.

[0017] In an embodiment, as shown in FIG. 2, based on the mathematical models, the shared energy storage and micro-grid alliance determine the game results through day-ahead multi-round game, which can include the following steps. Figure 2

[0018] Step 201, each micro-grid in the micro-grid alliance calculates the net load power in a scheduling period according to the mathematical models, the micro-grid alliance aggregates the net load powers of various micro-grids to obtain the maximum net load power, and sends the aggregated total net load power and the maximum net load power to the shared energy storage.

[0019] ​In a scheduling period, when the demand of the micro-grid alliance for energy storage is high, the shared energy storage appropriately increases the charging and discharging price of the energy storage to obtain higher income; when the demand of the micro-grid alliance for energy storage is low, the shared energy storage appropriately reduces the charging and discharging price of the energy storage to encourage the micro-grid alliance to lease more energy storage capacity, thereby obtaining higher energy storage leasing income. Therefore, before the shared energy storage is priced, the total net load power of the micro-grid alliance needs to be determined.

[0020] The micro-grid alliance includes a plurality of micro-grids, each of which is equipped with new energy such as photovoltaic and wind turbine, and preferentially consumes its own new energy to obtain net load power. In an embodiment, the net load power in a scheduling period can be calculated, which can include: According to calculating the net load power in a scheduling period; wherein, Pn(t) represents the net load power of the t-th micro-grid in the t-th time period, Pn(t) represents the net load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period; Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period; Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period, Pn(t) represents the predicted load power of the t-th micro-grid in the t-th time period; The micro-grid alliance aggregates the net load power of each micro-grid to obtain the maximum net load power, which can include: According to determining the total net load power and the maximum net load power; wherein, Pn represents the total net load power of the micro-grid alliance, N represents the number of micro-grids in the micro-grid alliance, Pmax represents the maximum net load power.

[0021] In step 202, the shared energy storage sets a step-type pricing strategy including an energy storage leasing price and a charging and discharging service price according to the total net load power and the maximum net load power, and sends the step-type pricing strategy to the micro-grid alliance.

[0022] In an embodiment, the shared energy storage sets a step-type pricing strategy including an energy storage leasing price and a charging and discharging service price according to the total net load power and the maximum net load power, which can include: When the total net load power is less than the maximum net load power multiplied by the first preset multiple, a valley time electricity price is set; When the total net load power is greater than the maximum net load power multiplied by the second preset multiple, a peak time electricity price is set; When the total net load power is greater than or equal to the maximum net load power multiplied by the first preset multiple and less than or equal to the maximum net load power multiplied by the second preset multiple, a flat time electricity price is set; The first preset multiple is less than the second preset multiple.

[0023] Optionally, the first preset multiple and the second preset multiple can be set according to requirements, and in the embodiment, the values of the first preset multiple and the second preset multiple are not limited.

[0024] For example, in a dispatching period, when the net load power of the microgrid alliance is less than 20% of the maximum net load power, the shared energy storage operator appropriately reduces the charging and discharging price; when the net load power is higher than 80% of the maximum net load power, the energy storage charging and discharging price is appropriately increased, so as to obtain higher leasing benefits.

[0025] The step-by-step pricing strategy can be:

[0026] Wherein, represents the microgrid alliance the step-by-step electricity price of the time period, represents the valley time electricity price, represents the flat time electricity price, represents the peak time electricity price.

[0027] In step 203, the microgrid alliance determines the use strategy with the minimum cost according to the step-by-step pricing strategy, and feeds back the use strategy to the shared energy storage. The use strategy includes the energy storage leasing capacity and the time period charging and discharging plan.

[0028] The microgrid alliance measures the total cost corresponding to different energy storage leasing capacities and different charging and discharging time periods according to the total net load power of each dispatching period and in combination with the step-by-step pricing strategy, determines the use strategy with the minimum cost, and feeds back the use strategy to the shared energy storage. The use strategy includes the total capacity of the planned leasing shared energy storage, the charging and discharging demand of each time period, for example, the new energy output is high from 10 o'clock to 12 o'clock in the morning, and the energy storage is planned to be charged; the load is high from 18 o'clock to 20 o'clock in the evening, and the energy storage is planned to be discharged, so as to make the shared energy storage clear about “how much energy storage the microgrid needs and how to use the energy storage”.

[0029] At step 204, the shared energy storage determines the operator revenue according to the use strategy. If the operator revenue cannot meet the preset threshold, the step of adjusting the step-type pricing strategy is performed, and the step of issuing the step-type pricing strategy to the microgrid alliance and subsequent steps are executed until the operator revenue meets the preset threshold, and a game result is obtained.

[0030] In an embodiment, the shared energy storage operator adopts a step-type pricing strategy to trade with the grid and the microgrid alliance. In a dispatch cycle, the shared energy storage determines the operator revenue according to the use strategy, including: According to determining the operator revenue; wherein, represents the operator revenue, represents the transaction revenue of the microgrid, represents the transaction revenue of the grid, represents the operation and maintenance cost of the shared energy storage, represents the transaction power of the shared energy storage and the grid, represents the time-of-use electricity price, represents the charging and discharging power of the shared energy storage during the time period, represents the charging and discharging price of the shared energy storage, represents the total number of time periods in the dispatch period, represents the proportionality coefficient, represents the charging power of the shared energy storage during the time period, represents the discharging power of the shared energy storage during the time period.

[0031] For example, the peak-time charging and discharging price of the step-type pricing strategy of the shared energy storage operator is too high, resulting in too little rental capacity feedback from the microgrid alliance, which leads the operator to find that the operator revenue is insufficient after calculation, and then the step-type pricing strategy is adjusted, such as appropriately reducing the "peak-time charging and discharging price" or reducing the "valley-time rental price", and reissued to the microgrid alliance. The microgrid alliance recalculates: based on the adjusted price, the cost is recalculated, and the rental capacity may be increased, such as from 500 kWh to 800 kWh, and the use strategy is updated and fed back to the shared energy storage operator. Through multiple games, until the balance: both sides repeatedly adjust the pricing details in the step-type pricing strategy and the shared energy storage rental and / or charging and discharging demand until the operator revenue calculated by the shared energy storage operator meets the expectation and the energy cost calculated by the microgrid alliance is controlled within the target range. At this time, the determined shared energy storage rental capacity + charging and discharging strategy is the optimal combination.

[0032] The "optimal combination" is not only a balance of interests of both parties, but also a core basis for subsequent "daily stage refinement scheduling" and "shared energy cost allocation". For example, the leasing capacity determines the total cost base, and the charging and discharging strategy determines the actual energy storage usage of each microgrid.

[0033] Optionally, the essence of step 102 is to lock the core framework of energy storage use in the day-ahead stage in advance, avoid confusion caused by ambiguous rules or demand conflicts in subsequent daily scheduling, and provide the key data of energy storage interaction power for the reactive power optimization of the entire power grid.

[0034] Step 103, in the daily stage, the microgrid alliance rolls up the charging and discharging behavior according to the charging and discharging strategy, and the shared energy storage allocates the shared energy storage cost based on the game results by using the improved Shapley value method.

[0035] In the daily stage, that is, on the same day, on the basis of the part of the scheduling results determined in the day-ahead stage, due to the prediction error of new energy output and load, the daily stage continues to optimize on the basis of the part of the scheduling results determined in the day-ahead stage, so as to obtain more accurate scheduling results.

[0036] In an embodiment, the charging and discharging strategy includes charging and discharging prices; In the daily stage, the microgrid alliance rolls up the charging and discharging behavior according to the charging and discharging strategy, including: The shared energy storage dynamically adjusts the charging and discharging price according to the actual total net load power of the daily microgrid alliance, and sends the adjusted charging and discharging price to the microgrid alliance; The microgrid alliance establishes a target function according to the minimum energy cost in the period according to the adjusted charging and discharging price, and rolls up the charging and discharging behavior according to the solution of the target function.

[0037] The shared energy storage re-determines more accurate operator benefits according to the actual total net load power of the daily microgrid alliance, and establishes a target function as follows: ; Among them, represents the daily income of the shared energy storage operator under the new pricing method, represents the daily trading income of the power grid, represents the daily trading income of the microgrid, represents the daily operation and maintenance cost of the shared energy storage, represents the current time period, represents the number of scheduling periods; represents the time step.

[0038] The system compares the intraday revenue with a preset threshold and dynamically adjusts the charging and discharging prices based on the comparison results. It should be noted that the intraday adjustment only adjusts the charging and discharging prices and does not adjust the shared energy storage rental capacity.

[0039] The microgrid consortium aims to minimize energy costs within a single dispatch cycle, including costs associated with leasing energy storage, transactions with the upper-level grid, and the operating costs of its internal gas turbines. The objective function for constructing the microgrid consortium is: ; in, This represents the energy cost of the microgrid consortium. This indicates the cost of using shared energy storage through leasing. This represents the power interaction cost between the microgrid consortium and the grid. This indicates the cost of generating electricity from a gas turbine. Represents Class A The cost of calling, This indicates the penalty cost for power fluctuations in the tie line. , This indicates the rental price per unit energy capacity and per unit power capacity of shared energy storage. , Indicates the first The energy storage capacity and energy capacity leased by a microgrid.

[0040] The objective function is solved to minimize the energy cost of the microgrid consortium, and the charging and discharging behavior is refined based on the solution results.

[0041] Through multiple rounds of negotiation, the microgrid alliance and shared energy storage have determined the energy storage rental fee and charging / discharging price. The microgrid alliance's energy storage rental capacity and charging / discharging plan will be adjusted daily based on actual electricity consumption. The shared energy storage price will be fine-tuned, and the microgrid will further optimize its own electricity consumption plan to minimize the electricity cost and energy storage rental price within the cycle.

[0042] When allocating costs, the money is no longer shared equally, but rather allocated based on the contribution of each microgrid. This shared energy storage cost-sharing is fair, preventing any microgrid from bearing an excessive share, ensuring fairness for all participating parties.

[0043] In one embodiment, the shared energy storage cost is allocated based on game theory results using an improved Shapley value method, including the following steps: Figure 3 As shown.

[0044] Step 301: Determine the equivalent and actual power output of the new energy sources in each microgrid in the microgrid alliance, and determine the cost sharing coefficient based on the equivalent and actual power output.

[0045] The equivalent renewable energy output of each microgrid is obtained by using the equal power output-load-following method. That is, the equivalent output is equal to the total renewable energy output and the actual renewable energy output during the dispatching period, and the equivalent output changes with the total load of the microgrid alliance. Equivalent output is ; in, Indicates the first A new energy source Equivalent output over a period of time Indicates actual new energy output. Indicates the first New energy sources in microgrids Equivalent output over a period of time Microgrid Alliance Total load during the period Microgrid Alliance Total load for the time period.

[0046] The cost allocation coefficient is determined based on the equivalent output and the actual output, including: according to Determine the cost allocation coefficient; in, This indicates the waveform similarity between the equivalent output and the actual output of the microgrid. This represents the cost allocation coefficient.

[0047] Step 302: Determine the net power generation of each microgrid, and determine the net power generation allocation factor based on the net power generation and the energy cost of the microgrid consortium.

[0048] By calculating the net power generation of each microgrid and redistributing the energy costs of the microgrid consortium, a more reasonable cost allocation result can be obtained.

[0049] In one embodiment, the net power generation allocation factor is determined based on the net power generation and the energy cost of the microgrid consortium, including: according to Determine the net power generation allocation factor; in, Indicates the first Net power generation of a microgrid Indicates the first In a microgrid Total load during the period This represents the net power generation allocation factor; Step 303: Determine the redistribution coefficient based on the correlation coefficients between the microgrids.

[0050] Correlation coefficients between microgrids Take the average value and calculate the redistribution factor.

[0051] In one embodiment, the redistribution factor is determined based on the correlation coefficient between the microgrids, including: according to Determine the redistribution coefficient; in, This represents the correlation coefficient between the various microgrids. Indicates the first The average net power generation of each microgrid. Indicates the first Net power generation of a microgrid express Indicates the first The average net power generation of each microgrid. Indicates the re-allocation coefficient; Step 304: Determine the total allocation coefficient based on the cost allocation coefficient, net power generation allocation coefficient, and re-allocation coefficient.

[0052] In one embodiment, the total allocation factor is determined based on the cost allocation factor, the net power generation allocation factor, and the re-allocation factor, including: according to Determine the total allocation coefficient; in, This represents the total allocation coefficient. , , These represent the weights of the apportionment coefficients, which can be determined using the entropy weighting method.

[0053] Step 305: Determine the improved cost allocation result based on the total allocation coefficient, the Shapley value of the alliance cost allocation result, and the total cost of the microgrid alliance.

[0054] In one embodiment, the improved cost allocation result is determined based on the total allocation factor, the Shapley value of the alliance cost allocation result, and the total cost of the microgrid alliance, including: according to Determine the cost allocation results for the improvement; in, Indicates the first The difference between the total allocation factor and the average allocation factor of a microgrid. This represents the cost-sharing outcome of the alliance improvement, as indicated by the Shapley value. This represents the cost-sharing result of the Shapley value in the alliance. Indicates the control coefficient. This represents the total cost of the microgrid consortium.

[0055] In this embodiment, the optimization method for reactive power and voltage in the power grid is divided into two stages. In the first stage, the shared energy storage operator designs a tiered pricing method, engages in game-theoretic scheduling with the microgrid consortium, and uses an improved Shapley value method to fairly and reasonably allocate the consortium's costs. In the day-ahead game-theoretic scheduling, the shared energy storage operator formulates energy storage leasing and charging / discharging service prices, distributes them to the microgrid consortium, determines its own energy storage leasing and usage strategies, and uploads them back to the shared energy storage operator. The two engage in game-theoretic scheduling to determine their respective optimal strategies. In the intraday rolling game-theoretic scheduling, the shared energy storage determines the energy storage capacity leasing price, only distributing the charging / discharging service price. Based on the day-ahead optimization results, a rolling game is conducted with the goal of minimizing energy costs within a cycle, refining the scheduling plans of each entity.

[0056] Step 104: The power grid, in conjunction with the interactive power, constructs and solves reactive power optimization models for the day-ahead and intraday periods to obtain the optimization results that minimize network losses and voltage deviations.

[0057] Optionally, a day-ahead reactive power optimization model for the power grid is constructed, with the objective of minimizing grid losses and voltage deviation. Based on day-ahead interactive power, predicted load, and photovoltaic output, an operational plan is developed in advance. The first objective function is to minimize the voltage deviation of the power grid. ; in, Indicates the voltage deviation of the power grid. Represents a power grid node exist Voltage at time, Indicates the grid reference voltage. Indicates the current power grid node. Indicates the total number of nodes in the power grid. Indicates the total time.

[0058] The second objective function is to minimize the grid loss: ; in, This represents the grid loss. , Indicates power grid The side road is Active power and reactive power at any given time This indicates the voltage at the beginning of the power grid. Indicates the resistance of the power grid. Indicates the current branch of the power grid. This indicates the main branch of the power grid.

[0059] The two objective functions constructed above are solved using the multi-objective beluga optimization algorithm. Based on the efficient search strategy of the beluga optimization algorithm, non-dominated sorting and crowding distance calculation from multi-objective optimization are introduced to simultaneously optimize multiple conflicting objective functions.

[0060] A rolling reactive power optimization model for the power grid is constructed, considering prediction deviation and interactive power changes. The constructed intraday objective function is to minimize the voltage deviation of the power grid. ; in, Indicates intraday voltage deviation. This indicates the intraday rolling phase of the power grid at the node. exist Voltage at time, Indicates the current power grid dispatch period. This indicates the time step corresponding to the intraday phase; The constructed intraday objective function 2 is to minimize the grid loss: ; in, This indicates the grid loss during the day. Indicates the intraday rolling phase of the power grid The side road is Active power at any given time Indicates the intraday rolling phase of the power grid The side road is Reactive power at any given moment This indicates the voltage at the beginning of the power grid during the day.

[0061] Step 105: Based on the optimization results of minimizing grid loss and voltage deviation, the grid node voltage is controlled using a photovoltaic inverter.

[0062] The intraday power exchange is transferred to the second stage to obtain the operating status indicators of the power grid system. By utilizing the real-time dynamic response capability of the photovoltaic inverter, the operating status of the power grid is optimized to ensure that the node voltage is within a reasonable range.

[0063] In this embodiment, the second stage of the power grid reactive power and voltage optimization method involves establishing a multi-time-scale optimization model for the power grid. Day-ahead optimization scheduling is performed based on the day-ahead load, distributed photovoltaic forecast information, and the day-ahead interactive power from the first stage. The day-ahead stage aims to minimize grid losses and voltage deviations, and scheduling plans for each device are formulated one day in advance with a 1-hour time scale. Intra-day optimization scheduling, as the time scale shortens, deviations occur in the intra-day load, distributed photovoltaic forecast information, and interactive power. Intra-day rolling optimization is performed with the goal of minimizing grid losses and voltage deviations, using a multi-objective white whale optimization algorithm. The intra-day stage fully utilizes the dynamic reactive power response capability of photovoltaic inverters to achieve safe and stable grid operation. This invention can effectively reduce grid losses and voltage fluctuations, ensuring the safe and stable operation of the new power grid.

[0064] The following is a detailed description using specific embodiments.

[0065] The first phase uses a power grid in a region of North my country as a case study for simulation analysis. This region includes one shared energy storage system and three microgrids. The centralized energy storage capacity of the shared energy storage system is... With a power capacity of 40,000 kWh With a power output of 2500kW, the energy storage charging and discharging efficiency is [missing information]. The unit operation and maintenance cost is 95%. The unit compensation cost is 0.1 yuan / (kWh); for Class A and Class B IDRs. The fees are 0.15 yuan / (kWh) and 0.5 yuan / (kWh); the maximum interconnection power between the microgrid, shared energy storage, and the grid is 1000kW; power fluctuation penalty factor. The gas turbine operating parameters configured for microgrid 1 are set at 0.0025 yuan / (kWh). , and The values ​​are 0.0015, 0.3312, and 5.25 respectively; the proportional parameters of the trapezoidal membership function. The confidence levels are 0.6, 0.8, 1, and 1.2. Set to 0.95; the maximum call capacity for PDR, Class A, and Class B IDR is 15%, 5%, and 3% of the total load, respectively. The second phase involved MATLAB simulation analysis based on a modified IEEE 33 bus system model, with a base capacity of 10 MVA and a base voltage of 12.66 kV. The CB capacitor banks are connected to nodes 17 and 31, each with 5 capacitor banks installed, and a maximum allowed switching frequency of 20 times per day. Photovoltaics (PV) are installed on nodes 12 and 19, with capacities of 0.8 MVA and 0.5 MVA respectively. The microgrid consortium system is connected to node 24, and the shared energy storage station is connected to node 18, interacting with the grid. A schematic diagram of the IEEE 33 system is shown below. Figure 4 As shown in Figure 5. The active power output and load variation coefficient curves of PV are shown in Figure 5(a) and Figure 5(b).

[0066] Analysis considering day-ahead tiered electricity pricing dispatch and cost allocation results: To demonstrate the advantages of the proposed two-stage optimization strategy, the following three scenarios are analyzed in the first stage: Scenario 1: Game strategy without considering microgrid alliances and tiered pricing; Scenario 2: Game strategy only considering tiered pricing; Scenario 3: Game strategy considering microgrid alliances and tiered pricing.

[0067] The economic benefits of shared energy storage operators and microgrid alliances are shown in Table 1, and the proposed tiered charging and discharging tariffs are as follows: Figure 6 As shown.

[0068] Table 1. Multi-agent economic benefit results in various scenarios.

[0069] Considering factors such as the waveform similarity between microgrid output and load, renewable energy output and correlation, the cost allocation coefficients of the improved Shapley method are obtained, as shown in Table 2.

[0070] Table 2 Cost Allocation Coefficients for Each Microgrid

[0071] Through calculation and The cost redistribution results for each microgrid are shown in Table 3.

[0072] Table 3 Cost Allocation Results for Each Microgrid

[0073] In summary, the analysis shows that when redistributing the costs of the microgrid consortium based on the improved Shapley value method, each microgrid can still save on energy costs, and the energy costs of each microgrid can be reasonably allocated according to its contribution to the microgrid consortium.

[0074] Analysis of intraday simulation results: To reduce the prediction error of new energy units and user load, intraday optimization is further carried out on the basis of the day-ahead game model. The economic benefits of shared energy storage operators and microgrid alliances are shown in Table 4, and the cost of microgrid alliances is allocated based on the improved Shapley value method.

[0075] Based on the previous optimization results, the time scale was refined to obtain a more precise pricing model for energy storage charging and discharging. As shown in Table 4, the cost of the microgrid was further reduced, ensuring the economic viability of the system.

[0076] Table 4 Comparison of Economic Benefits of Multiple Entities

[0077] Analysis of Reactive Power Optimization Simulation Results (Date): The second stage considers the impact of microgrid alliances and shared energy storage access on the power grid, and proposes a system reactive power optimization strategy that takes into account interactive power. The Pareto optimal solution set under this strategy is as follows: Figure 7 As shown in Table 5, an optimal solution is selected from the optimal Pareto optimal solution set as the optimization result.

[0078] Table 5 Comparison of Multi-Objective Optimization Results

[0079] According to Table 6, when microgrid consortia and shared energy storage are integrated, the grid loss and deviation of the power grid system reach 3.9309 and 0.8604, respectively, and the power grid system will experience severe voltage over-limit situations. The output of the CB capacitor bank and photovoltaic inverter is as follows: Figure 8 and Figure 9 As shown, it can exert its reactive power regulation capability. Figure 8 This is a schematic diagram illustrating the change in the number of capacitor banks switched on and off and the compensation capacity over time. Figure 8 (a) and Figure 8 (b) are schematic diagrams showing the changes in the number of switching groups and compensation capacity of capacitor CB1 over time. Figure 8 (c) and Figure 8 (d) are schematic diagrams showing the changes in the number of switching groups and the compensation capacity of the CB2 capacitor over time. Figure 9 This is a schematic diagram showing the changes in power factor and reactive power compensation power of the inverter group over time during the day. Figure 9 (a) and Figure 9 (b) Schematic diagrams showing the changes in power factor and reactive power compensation power of capacitor PV1 over time. Figure 9 (c) and Figure 9 (d) are schematic diagrams showing the changes in power factor and reactive power compensation power of PV2 capacitor over time.

[0080] After reactive power optimization, the grid loss of the power grid system was reduced by 8.1%, the voltage deviation was reduced by 19%, and the voltage no longer exceeded the limit, ensuring the safe and stable operation of the power grid system.

[0081] Analysis of intraday simulation results: Table 6 Comparison of Reactive Power Optimization Results During the Day

[0082] The intraday power transfer to the second stage yielded the operating status indicators of the power grid system, as shown in Table 6. This indicates a severe voltage exceedance issue and significant grid losses. Therefore, the real-time dynamic response capability of the photovoltaic inverter was utilized to optimize the power grid's operating status. Under this optimization strategy, the voltage deviation and grid losses of the power grid system were reduced by 27% and 18%, respectively, while ensuring that the node voltage remained within a reasonable range. This effectively suppressed voltage exceedances, reduced active power losses, and ensured the economical and stable operation of the power grid system.

[0083] This invention provides a method for optimizing reactive power and voltage in a power grid. It establishes mathematical models of the power grid and various devices within the microgrid through collaboration between shared energy storage and a microgrid alliance. Based on these models, the shared energy storage and microgrid alliance engage in multi-round day-ahead game negotiations to determine the game outcome, which includes the shared energy storage's leased capacity, charging and discharging strategies, and interactive power. During the intraday phase, the microgrid alliance continuously optimizes its charging and discharging behavior according to the charging and discharging strategies. Based on the game outcome, the shared energy storage uses an improved Shapley value method to allocate shared energy storage costs. The power grid, combined with the interactive power, constructs and solves day-ahead and intraday reactive power optimization models to obtain optimization results that minimize network losses and voltage deviations. Based on these optimization results, photovoltaic inverters are used to control the voltage at power grid nodes. This invention employs a phased modeling approach. In the first phase, a multi-timescale game scheduling model between the shared energy storage operator and the microgrid alliance is constructed, proposing a tiered electricity pricing strategy and a revenue incentive mechanism. In the second phase, interactive power results are introduced to construct a power grid reactive power optimization model, achieving the synergistic minimization of network losses and voltage deviations, thus improving the power grid's economy and security.

[0084] The embodiments of this invention fully consider the renewable energy output, load response, and resource complementarity among microgrids. Based on the improved Shapley value method, multiple dimensions such as output similarity, net power generation capacity, and power complementarity are introduced to achieve a fair and reasonable allocation of the cost of shared energy storage. This promotes the joint operation and resource sharing of multiple microgrids and provides a reliable cost coordination mechanism and operation optimization support for the commercialization of shared energy storage.

[0085] This invention, while balancing the benefits of all stakeholders with the safe operation of the power grid, achieves coordinated control of shared energy storage leasing services and microgrid alliance load regulation. By establishing an interactive power mechanism between shared energy storage and microgrids, the system can meet demand response requirements while reducing operating costs and improving scheduling flexibility.

[0086] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0087] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0088] Figure 10 A schematic diagram of the power grid reactive power optimization system provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 10 As shown, the power grid reactive power and voltage optimization system 10 includes: shared energy storage 101, microgrid alliance 102, and power grid 103.

[0089] Shared Energy Storage 101 and Microgrid Alliance 102 collaborate to establish mathematical models of the power grid and various devices in the microgrid; Based on a mathematical model, Shared Energy Storage 101 and Microgrid Alliance 102 have determined the outcome of a multi-round game, which includes the shared energy storage lease capacity, charging and discharging strategies, and interactive power. During the daytime phase, the microgrid consortium 102 continuously optimizes the charging and discharging behavior based on the charging and discharging strategy, while the shared energy storage 101 uses the improved Shapley value method to allocate the shared energy storage cost based on the game results. By combining the interactive power, the reactive power optimization models for the day-ahead and intraday periods were constructed and solved to obtain the optimization results that minimized network loss and voltage deviation. Based on the optimization results of minimizing grid loss and voltage deviation, Grid103 uses photovoltaic inverters to control the voltage of grid nodes.

[0090] In one possible implementation, based on a mathematical model, when the shared energy storage 101 and the microgrid alliance 102 determine the game result through multiple rounds of day-ahead game, each microgrid in the microgrid alliance 102 calculates the net load power within a scheduling cycle according to the mathematical model. The microgrid alliance 102 summarizes the net load power of each microgrid to obtain the maximum net load power, and sends the total net load power and the maximum net load power obtained by summarizing to the shared energy storage 101. Shared Energy Storage 101 sets up a tiered pricing strategy, including energy storage leasing prices and charging and discharging service prices, based on the total net load power and the maximum net load power, and distributes the tiered pricing strategy to the Microgrid Alliance 102. Microgrid Consortium 102 determines the minimum cost usage strategy based on a tiered pricing strategy and feeds the usage strategy back to Shared Energy Storage 101. The usage strategy includes energy storage leasing capacity and time-based charging and discharging plans. Shared Energy Storage 101 determines the operator's revenue based on the usage strategy. If the operator's revenue cannot meet the preset threshold, the tiered pricing strategy is adjusted, and the process jumps to the step of "issuing the tiered pricing strategy to the microgrid alliance" and subsequent steps until the operator's revenue meets the preset threshold, thus obtaining the game result.

[0091] In one possible implementation, the Microgrid Consortium 102, according to Calculate the net load power within a scheduling cycle; in, Indicates the first microgrid Net load power during the period Indicates the first microgrid Forecasted load power for the time period Indicates the first microgrid The amount of resources called for price-based demand response during a given time period. Indicates the first microgrid Forecast power of wind turbines for a given period of time. Indicates the first microgrid Forecasted photovoltaic power for the specified time period; Microgrid Consortium 102 Determine the total net load power and the maximum net load power; in, This represents the total net load power of the microgrid consortium. This indicates the number of microgrids in the microgrid consortium. This indicates the maximum net load power.

[0092] In one possible implementation, when shared energy storage 101 sets a tiered pricing strategy based on total net load power and maximum net load power, including energy storage leasing prices and charging / discharging service prices, it is used for: When the total net load power is less than the maximum net load power of the first preset multiple, an off-peak electricity price is set. When the total net load power is greater than the maximum net load power of the second preset multiple, a peak-hour electricity price is set. When the total net load power is greater than or equal to the maximum net load power of the first preset multiple, and less than or equal to the maximum net load power of the second preset multiple, the normal electricity price is set. The first preset multiple is less than the second preset multiple.

[0093] In one possible implementation, when the shared energy storage 101 determines the operator's revenue based on the usage strategy, it is used for: according to Determine operator revenue; in, Indicates operator revenue. This represents the trading revenue of the microgrid. This represents the trading revenue of the power grid. This represents the operation and maintenance cost of shared energy storage. This indicates the power exchanged between shared energy storage and the grid. Indicates time-of-use electricity pricing. Indicates the charging and discharging power during the shared energy storage period. This indicates the price of shared energy storage charging and discharging. This indicates the total number of time slots in the scheduling period. This represents the proportionality coefficient. Indicates shared energy storage Charging power during the period Indicates shared energy storage Discharge power during a given period.

[0094] In one possible implementation, the charge / discharge strategy includes a charge / discharge price; During the intraday phase, when the Microgrid Consortium 102 is continuously optimizing charge and discharge behavior based on its charge and discharge strategy, it is used for: Shared energy storage dynamically adjusts charging and discharging prices based on the actual total net load power of the microgrid alliance during the day, and sends the adjusted charging and discharging prices to the microgrid alliance. The microgrid consortium establishes an objective function based on the adjusted charging and discharging prices, aiming to minimize energy costs within a cycle, and continuously optimizes charging and discharging behavior based on the solution of the objective function.

[0095] In one possible implementation, when allocating shared energy storage 101 based on game theory results and using an improved Shapley value method to distribute the shared energy storage cost, it is used for: Determine the equivalent and actual power output of new energy sources in each microgrid within the microgrid alliance, and determine the cost allocation coefficient based on the equivalent and actual power output; Determine the net power generation of each microgrid, and determine the net power generation allocation factor based on the net power generation and the energy cost of the microgrid consortium; The redistribution coefficient is determined based on the correlation coefficients between the microgrids. The total allocation coefficient is determined based on the cost allocation coefficient, the net power generation allocation coefficient, and the re-allocation coefficient. The improved cost allocation result is determined based on the total allocation factor, the Shapley value of the alliance cost allocation result, and the total cost of the microgrid alliance.

[0096] In one possible implementation, the equivalent output is equal to the total output of new energy sources and the actual output of new energy sources within the scheduling cycle, and the equivalent output changes with the total load of the microgrid alliance. Equivalent output is ; in, Indicates the first A new energy source Equivalent output over a period of time Indicates actual new energy output. Indicates the first New energy sources in microgrids Equivalent output over a period of time Microgrid Alliance Total load during the period Microgrid Alliance Total load for the time period.

[0097] In one possible implementation, when the shared energy storage 101 determines the cost-sharing coefficient based on the equivalent output and the actual output, it is used for: according to Determine the cost allocation coefficient; in, This indicates the waveform similarity between the equivalent output and the actual output of the microgrid. Indicates the cost allocation coefficient; When determining the net power generation allocation factor based on net power generation and the energy cost of the microgrid consortium, the shared energy storage 101 is used for: according to Determine the net power generation allocation factor; in, Indicates the first Net power generation of a microgrid Indicates the first In a microgrid Total load during the period This represents the net power generation allocation factor; When determining the redistribution coefficient based on the correlation coefficients between microgrids, the shared energy storage 101 is used for: according to Determine the redistribution coefficient; in, This represents the correlation coefficient between the various microgrids. Indicates the first The average net power generation of each microgrid. Indicates the first Net power generation of a microgrid express Indicates the first The average net power generation of each microgrid. Indicates the re-allocation coefficient; When determining the total allocation coefficient based on the cost allocation coefficient, net power generation allocation coefficient, and re-allocation coefficient, the shared energy storage 101 is used for: according to Determine the total allocation coefficient; in, This represents the total allocation coefficient. , , These represent the weights of the apportionment coefficients; When determining the improved cost allocation result based on the total allocation factor, the Shapley value, and the total cost of the microgrid alliance, the shared energy storage 101 is used for: according to Determine the cost allocation results for the improvement; in, Indicates the first The difference between the total allocation factor and the average allocation factor of a microgrid. This represents the cost-sharing outcome of the alliance improvement, as indicated by the Shapley value. This represents the cost-sharing result of the Shapley value in the alliance. Indicates the control coefficient. This represents the total cost of the microgrid consortium.

[0098] The aforementioned power grid reactive power and voltage optimization system establishes mathematical models of the power grid and various devices within the microgrid through collaboration between shared energy storage and a microgrid alliance. Based on these models, the shared energy storage and microgrid alliance determine the game outcome through multiple rounds of day-ahead game theory, including shared energy storage leased capacity, charging and discharging strategies, and interactive power. During the intraday phase, the microgrid alliance continuously optimizes charging and discharging behavior according to the charging and discharging strategies. Based on the game outcome, the shared energy storage uses an improved Shapley value method to allocate shared energy storage costs. The power grid, combined with the interactive power, constructs and solves day-ahead and intraday reactive power optimization models to obtain optimization results that minimize network losses and voltage deviations. Based on these optimization results, photovoltaic inverters are used to control the voltage at power grid nodes. This embodiment of the invention employs a phased modeling method. In the first phase, a multi-timescale game scheduling model between the shared energy storage operator and the microgrid alliance is constructed, proposing a tiered electricity pricing strategy and a revenue incentive mechanism. In the second phase, interactive power results are introduced to construct a power grid reactive power optimization model, achieving the synergistic minimization of network losses and voltage deviations, thus improving the economic efficiency and security of the power grid.

[0099] The embodiments of this invention fully consider the renewable energy output, load response, and resource complementarity among microgrids. Based on the improved Shapley value method, multiple dimensions such as output similarity, net power generation capacity, and power complementarity are introduced to achieve a fair and reasonable allocation of the cost of shared energy storage. This promotes the joint operation and resource sharing of multiple microgrids and provides a reliable cost coordination mechanism and operation optimization support for the commercialization of shared energy storage.

[0100] This invention, while balancing the benefits of all stakeholders with the safe operation of the power grid, achieves coordinated control of shared energy storage leasing services and microgrid alliance load regulation. By establishing an interactive power mechanism between shared energy storage and microgrids, the system can meet demand response requirements while reducing operating costs and improving scheduling flexibility.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for optimizing reactive power and voltage in a power grid, characterized in that, include: The shared energy storage and microgrid alliance collaborates to establish mathematical models of various devices in the power grid and microgrid; Based on the mathematical model, the shared energy storage and the microgrid alliance determined the game result through multiple rounds of game discussion. The game result includes the shared energy storage lease capacity, charging and discharging strategy, and interactive power. During the daytime phase, the microgrid consortium continuously optimizes the charging and discharging behavior according to the charging and discharging strategy, and the shared energy storage uses an improved Shapley value method to allocate the shared energy storage cost based on the game result. The power grid, in conjunction with the aforementioned interactive power, constructs and solves reactive power optimization models for both day-ahead and intraday periods to obtain optimization results that minimize network losses and voltage deviations. Based on the optimization results of minimizing grid loss and voltage deviation, photovoltaic inverters are used to control the voltage of grid nodes.

2. The method for optimizing reactive power and voltage in a power grid according to claim 1, characterized in that, Based on the mathematical model, the shared energy storage and the microgrid alliance determined the game outcome through multiple rounds of recent game discussions, including: Each microgrid in the microgrid alliance calculates the net load power within a scheduling cycle according to the mathematical model. The microgrid alliance aggregates the net load power of each microgrid to obtain the maximum net load power, and sends the aggregated total net load power and the maximum net load power to the shared energy storage. The shared energy storage sets a tiered pricing strategy, including energy storage leasing price and charging and discharging service price, based on the total net load power and the maximum net load power, and distributes the tiered pricing strategy to the microgrid alliance. The microgrid alliance determines the minimum cost usage strategy based on the tiered pricing strategy and feeds back the usage strategy to the shared energy storage. The usage strategy includes energy storage leasing capacity and time-based charging and discharging plans. The shared energy storage determines the operator's revenue based on the usage strategy. If the operator's revenue cannot meet the preset threshold, the tiered pricing strategy is adjusted, and the process jumps to the step of "issuing the tiered pricing strategy to the microgrid alliance" and subsequent steps until the operator's revenue meets the preset threshold, thus obtaining the game result.

3. The method for optimizing reactive power and voltage in a power grid according to claim 2, characterized in that, Calculating the net load power within a scheduling cycle includes: according to Calculate the net load power within a scheduling cycle; in, Indicates the first microgrid Net load power during the period Indicates the first microgrid Forecasted load power for the time period Indicates the first microgrid The amount of resources called for price-based demand response during a given time period. Indicates the first microgrid Forecast power of wind turbines for a given period of time. Indicates the first microgrid Forecasted photovoltaic power for the specified time period; The microgrid consortium aggregates the net load power of each microgrid to obtain the maximum net load power, including: according to Determine the total net load power and the maximum net load power; in, This represents the total net load power of the microgrid consortium. This indicates the number of microgrids in the microgrid consortium. This indicates the maximum net load power.

4. The method for optimizing reactive power and voltage in a power grid according to claim 3, characterized in that, The shared energy storage system establishes a tiered pricing strategy based on the total net load power and the maximum net load power, including energy storage leasing prices and charging / discharging service prices, comprising: When the total net load power is less than the maximum net load power by a first preset multiple, an off-peak electricity price is set; When the total net load power is greater than the maximum net load power by a second preset multiple, a peak-hour electricity price is set. When the total net load power is greater than or equal to the maximum net load power of a first preset multiple, and less than or equal to the maximum net load power of a second preset multiple, the normal electricity price is set. The first preset multiple is less than the second preset multiple.

5. The method for optimizing reactive power and voltage in a power grid according to claim 4, characterized in that, The shared energy storage determines the operator's revenue based on the usage strategy, including: according to Determine operator revenue; in, Indicates operator revenue. This represents the trading revenue of the microgrid. This represents the trading revenue of the power grid. This represents the operation and maintenance cost of shared energy storage. This indicates the power exchanged between shared energy storage and the grid. Indicates time-of-use electricity pricing. Indicates the charging and discharging power during the shared energy storage period. This indicates the price of shared energy storage charging and discharging. This indicates the total number of time slots in the scheduling period. This represents the proportionality coefficient. Indicates shared energy storage Charging power during the period Indicates shared energy storage Discharge power during a given period.

6. The method for optimizing reactive power voltage in a power grid according to any one of claims 1-5, characterized in that, The charging and discharging strategy includes charging and discharging prices; During the daytime phase, the microgrid consortium continuously optimizes the charging and discharging behavior based on the charging and discharging strategy, including: The shared energy storage dynamically adjusts the charging and discharging price based on the actual total net load power of the microgrid alliance during the day, and sends the adjusted charging and discharging price to the microgrid alliance. The microgrid alliance establishes an objective function based on the adjusted charging and discharging prices, aiming to minimize energy costs within a cycle, and continuously optimizes charging and discharging behavior based on the solution of the objective function.

7. The method for optimizing reactive power and voltage in a power grid according to claim 6, characterized in that, The shared energy storage, based on the game outcome, uses an improved Shapley value method to allocate the shared energy storage cost, including: Determine the equivalent and actual output of new energy sources for each microgrid in the microgrid alliance, and determine the cost sharing coefficient based on the equivalent and actual output. Determine the net power generation of each microgrid, and determine the net power generation allocation coefficient based on the net power generation and the energy cost of the microgrid alliance; The redistribution coefficient is determined based on the correlation coefficients between the microgrids. The total allocation coefficient is determined based on the cost allocation coefficient, the net power generation allocation coefficient, and the re-allocation coefficient. The improved cost allocation result is determined based on the total allocation coefficient, the Shapley value of the alliance cost allocation result, and the total cost of the microgrid alliance.

8. The method for optimizing reactive power and voltage in a power grid according to claim 7, characterized in that, The equivalent output is the sum of the total output of new energy sources and the actual output of new energy sources within the scheduling cycle, and the equivalent output varies with the total load of the microgrid alliance. The equivalent output force is ； in, Indicates the first A new energy source Equivalent output over a period of time Indicates actual new energy output. Indicates the first New energy sources in microgrids Equivalent output over a period of time Microgrid Alliance Total load during the period Microgrid Alliance Total load for the time period.

9. The method for optimizing reactive power and voltage in a power grid according to claim 8, characterized in that, The cost allocation coefficient is determined based on the equivalent output and the actual output, including: according to Determine the cost allocation coefficient; in, This indicates the waveform similarity between the equivalent output and the actual output of the microgrid. Indicates the cost allocation coefficient; The net power generation allocation factor is determined based on the net power generation and the energy cost of the microgrid consortium, including: according to Determine the net power generation allocation factor; in, Indicates the first Net power generation of a microgrid Indicates the first In a microgrid Total load during the period This represents the net power generation allocation factor; Based on the correlation coefficients between the microgrids, the redistribution coefficients are determined, including: according to Determine the redistribution coefficient; in, This represents the correlation coefficient between the various microgrids. Indicates the first The average net power generation of each microgrid Indicates the first Net power generation of a microgrid express Indicates the first The average net power generation of each microgrid Indicates the re-allocation coefficient; The total allocation coefficient is determined based on the cost allocation coefficient, the net power generation allocation coefficient, and the re-allocation coefficient, including: according to Determine the total allocation coefficient; in, This represents the total allocation coefficient. , , These represent the weights of the apportionment coefficients; Based on the total allocation factor, the Shapley value, and the total cost of the microgrid alliance, an improved cost allocation result is determined, including: according to Determine the cost allocation results for the improvement; in, Indicates the first The difference between the total allocation factor and the average allocation factor of a microgrid. This indicates the cost-sharing outcome of the Shapley value consortium improvement. This represents the cost-sharing result of the Shapley value in the alliance. Indicates the control coefficient. This represents the total cost of the microgrid consortium.

10. A power grid reactive power and voltage optimization system, characterized in that, include: Shared energy storage, microgrid alliances, and the power grid; The shared energy storage and the microgrid alliance work together to establish mathematical models of the power grid and various devices in the microgrid; Based on the mathematical model, the shared energy storage and the microgrid alliance determined the game result through multiple rounds of game discussion. The game result includes the shared energy storage lease capacity, charging and discharging strategy, and interactive power. During the daytime phase, the microgrid consortium continuously optimizes the charging and discharging behavior according to the charging and discharging strategy, and the shared energy storage uses an improved Shapley value method to allocate the shared energy storage cost based on the game result. The power grid, in conjunction with the interactive power, constructs and solves reactive power optimization models for the day-ahead and intraday periods, respectively, to obtain optimization results that minimize network losses and voltage deviations. Based on the optimization results of minimizing grid loss and voltage deviation, photovoltaic inverters are used to control the voltage of grid nodes.

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