An optimal scheduling method for park micro-grid cluster shared energy storage

Abstract

An optimized scheduling method for shared energy storage in a park microgrid cluster, relating to the field of new energy technology, includes the following steps: collecting output and load data of each microgrid in the park; introducing a truncated multiplicative Gaussian noise method to characterize the fluctuation of microgrid output, obtaining simulated actual microgrid output data; employing a master-slave game theory approach to obtain microgrid leasing demand based on the output and load data of each microgrid in the park, and solving for the optimal leasing price; constructing an optimized scheduling objective function for shared energy storage with the goal of maximizing annual revenue. This invention, by establishing a shared energy storage scheduling objective function with the goal of maximizing annual revenue, considers two revenue-generating modes—leasing and arbitrage—as well as the construction costs incurred based on microgrid demand, thereby improving energy storage utilization and achieving long-term economic viability of the scheduling method.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to an optimized scheduling method for shared energy storage in a cluster of microgrids in a park. Background Technology

[0002] With the rapid development of global industrialization and the accelerated energy transition, the development of new energy sources faces both new opportunities and challenges against the backdrop of promoting new power systems. As the construction of new power systems continues, the high proportion of new energy sources integrated into the grid reduces the environmental impact of traditional energy generation, enriches power generation methods, and ensures the full utilization of new energy. However, while optimizing the power structure, this also brings problems such as reduced power system inertia and a shortage of grid regulation resources. Energy storage, with its flexible regulation capabilities, can effectively buffer fluctuations in new energy output and provide crucial support for the high proportion of renewable energy consumption. However, current energy storage facilities face the dual constraints of high investment costs and low operational utilization rates, making it difficult to form a virtuous cycle of large-scale development and significantly reducing the enthusiasm for configuring energy storage in microgrids. Against this backdrop, shared energy storage, which combines energy storage technology with the sharing economy, has emerged. From traditional one-to-one service to one-to-N service, the operation mode of shared energy storage has become more complex.

[0003] The shared energy storage (SES) model in industrial parks allows commercial and industrial users within the same park to jointly invest in and operate an SES power station. Each user does not need to install energy storage separately to enjoy the same energy storage regulation services. This model can fully leverage the time-complementary characteristics of different users' regulation needs, improve the utilization rate of energy storage equipment and investment efficiency through overall optimization, and provide auxiliary power services for the power grid and microgrid, thereby maximizing the consumption of renewable energy and improving grid stability.

[0004] Chinese patent application CN118281918A, entitled "An Optimal Scheduling Method for Shared Energy Storage in Multi-Microgrids Considering Master-Slave Game Theory," discloses an optimal scheduling method for shared energy storage in multi-microgrids considering master-slave game theory. This method models a shared energy storage system framework containing multiple microgrids, where each microgrid prioritizes absorbing renewable energy to meet its own load demand, with the remaining net power generation participating in shared energy storage. Secondly, the method establishes a master-slave game model with the shared energy storage operator as the main entity. The operator formulates charging and discharging strategies based on time-of-use pricing and participates in peak-shaving scheduling of the distribution network based on low storage and high discharge. Finally, it solves for the charging and discharging decisions of the shared energy storage and the power output of equipment within each microgrid. However, this method does not consider the randomness of renewable energy output in the microgrid, which may affect the actual scheduling effect. Furthermore, the method of setting leasing prices may influence the willingness of microgrids to cooperate, and it does not consider the construction cost of shared energy storage, potentially leading to situations where the method does not meet the needs of the microgrid. Summary of the Invention

[0005] In view of the above-mentioned shortcomings and deficiencies of the existing technology, the present invention provides an optimized scheduling method for shared energy storage in a park microgrid cluster. It establishes a shared energy storage scheduling objective function with the goal of maximizing annual revenue, considers two revenue-generating modes, namely leasing and arbitrage, as well as the construction costs generated according to the microgrid demand, improves the energy storage utilization rate, and realizes the long-term economic efficiency of the scheduling method.

[0006] To achieve the above objectives, the main technical solutions adopted by the present invention include: An optimized scheduling method for shared energy storage in a park microgrid cluster includes the following steps: collecting power output and load data of each microgrid in the park; Based on the collected power output data of each microgrid in the park, a truncated multiplicative Gaussian noise method is introduced to characterize the fluctuation of microgrid power output, resulting in simulated actual power output data of the microgrid: ; In the formula, The noise added during time period t; is the given noise standard deviation coefficient; Z is a random number from the standard normal distribution N(0,1); The actual output power of the microgrid during time period t. The predicted output power of the microgrid during time period t; Based on the simulated microgrid actual output data, load data of each microgrid in the park, microgrid leasing demand, and optimal leasing price obtained by introducing a truncated multiplicative Gaussian noise method, a shared energy storage optimal scheduling objective function is constructed with the goal of maximizing annual revenue. This includes combining the shared energy storage optimal scheduling objective function with constraints to obtain the shared energy storage optimal scheduling model's objective function for maximizing annual revenue: ; In the formula, maxObj represents the maximum annual return. It is the arbitrage profit of shared energy storage operators. It is the leasing revenue of shared energy storage operators. It is the cost of shared energy storage operators acquiring wind and solar power curtailment from microgrids. It is the aging cost of shared energy storage operators. It refers to the surplus energy acquired by shared energy storage operators from microgrid leasing. It is the investment cost of shared energy storage operators; ; In the formula, H is the total number of days in a year, and T represents the corresponding time period. It is the price of arbitrage discharge power during time period t. It is the arbitrage discharge power during time period t. It is a time-period arbitrage of charging power price. It is the charging power for arbitrage during time period t; ; In the formula, It is the price for renting shared energy storage capacity during time period t; It refers to the shared energy storage capacity leased during time period t; The price for shared energy storage capacity during time period t; It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t; ; In the formula, It is the purchase price of wind and solar power curtailment during period t; It refers to the amount of wind and solar power curtailed during time period t; ; In the formula, It is the aging cost coefficient; ; In the formula, It is the price of surplus energy acquired by shared energy storage operators from microgrid leasing during time period t; It is the surplus energy leased by the microgrid during time period t; ; In the formula, d is the total construction cost of shared energy storage, y is the discount rate, and y is the usage period of shared energy storage. Using a master-slave game theory approach, the microgrid leasing price is divided into leased capacity price and leased power price. Based on the output and load data of each microgrid in the park, the microgrid leasing demand is obtained, and the optimal leasing price is solved. ; Where G is the total set of game frameworks, and L SES It is a leader-shared energy storage strategy, F MG For follower microgrid strategies, R SES It is the revenue from shared energy storage, R MG The microgrid revenue is represented by N, and the set of participants is N. The game model described above includes three elements: participants, strategies, and payoffs. Here, G is the total set of game frames, L is the leader's strategy, F is the follower's strategy, and R represents the payoff. SES It is a leader-shared energy storage strategy, F MG For follower microgrid strategies, R SES It is the revenue from shared energy storage, R MG This refers to the revenue generated by the microgrid.

[0007] Participants: Shared Energy Storage (SES), Microgrid 1 (MG1), Microgrid 2 (MG2), and Microgrid 3 (MG3) are the participants in this game. The set of participants is represented as follows: .

[0008] Trading Strategy: The leader SES's strategy consists of 24-hour rental prices (including rental capacity price and rental power price) and the allocation of rental capacity and arbitrage capacity, which can be represented in vector form as follows: The strategy for microgrids is to represent the rental demand (including rental capacity demand and rental power demand) at each time point as a vector. .

[0009] Benefits: The benefits for each participant are defined by the shared energy storage optimal scheduling objective function, which aims to maximize annual benefits. The optimal configuration scheme is obtained by solving the problem using a Cplex solver.

[0010] Furthermore, the output and load data of each microgrid in the park include data of each microgrid at each time of day, divided into 24 time periods, with each time period spaced one hour apart.

[0011] Furthermore, the constraints include power balance constraints, state of charge constraints, power constraints, capacity constraints, and charge / discharge mutual exclusion constraints.

[0012] Furthermore, power balance constraints include: ; in, It is the charging power arbitrage during time period t. It is the arbitrage discharge power during time period t. It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t. It represents the amount of wind and solar power curtailed during time period t.

[0013] Furthermore, the state of charge constraints include: ; ; ; In the formula, SOC int SOC(T) is the initial state of charge of the energy storage during a scheduling cycle, SOC(t) is the state of charge of the energy storage at the end of the scheduling cycle, and SOC(t) is the state of charge of the energy storage at time t. It is an interval period; It refers to the shared energy storage charging and discharging efficiency; It is the lower limit of SOC. It is the upper limit of SOC. It is the charging power arbitrage during time period t. It is the arbitrage discharge power during time period t. It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t. t represents the amount of wind and solar power curtailed during the time period, and E represents the shared energy storage capacity.

[0014] Furthermore, the power constraints include: ; In the formula, It is the charging power arbitrage during time period t. and These are the upper and lower limits of the arbitrage charging power during time period t, respectively. It is the arbitrage discharge power during time period t. and These are the upper and lower limits of the arbitrage discharge power during time period t. ; In the formula, It is the charging power of shared energy storage during the t-period rental period. and These are the upper and lower limits of the charging power for shared energy storage rental during time period t. It is the discharge power of the shared energy storage leased during time period t. and These are the upper and lower limits of the discharge power for shared energy storage leased during time period t; ; In the formula, It is the power of wind and solar power curtailed during period t. It is the rated power of shared energy storage.

[0015] Furthermore, capacity constraints include: ; Where E represents the shared energy storage capacity, E tl For arbitrage capacity, E zl For leased capacity, It is the arbitrage capacity for time period t. and These are the upper and lower limits of arbitrage capacity, respectively. It is the rental capacity for time period t. and These are the upper and lower limits of the rental capacity, respectively.

[0016] Furthermore, the charge-discharge mutual exclusion constraint includes: ; ; In the formula, It is the arbitrage discharge power during time period t. This is the upper limit of the discharge power of shared energy storage leased during time period t. It is the charging power of shared energy storage during the t-period rental period. This is the upper limit of charging power for shared energy storage rental during time period t. It is the arbitrage discharge power during time period t. It is the charging power arbitrage during time period t. This is the upper limit of arbitrage discharge power during time period t. These are the upper limits of the arbitrage charging power during time period t. , , , It is a binary variable.

[0017] The beneficial effects of this invention are: 1. This invention proposes an optimized scheduling method for shared energy storage in a park microgrid cluster. By establishing a shared energy storage scheduling objective function with the goal of maximizing annual revenue, it considers two revenue-generating modes: leasing and arbitrage, as well as the construction costs generated based on microgrid demand, thereby improving energy storage utilization and achieving long-term economic efficiency of the scheduling method.

[0018] 2. In order to maximize the consumption of new energy sources and increase the revenue of shared energy storage, the present invention can acquire the abandoned energy of microgrids and convert it into arbitrage capacity. At the same time, microgrids may have multiple leased energy storage capacities. Shared energy storage can acquire the surplus leased capacity of microgrids, thereby reducing the cost of microgrids and improving the utilization rate of shared energy storage.

[0019] 3. This invention adopts a master-slave game approach, which considers the mixed pricing of leased capacity and leased power factors. By iteratively matching the specified lease price with the microgrid lease demand, a reasonable lease price is obtained. This ensures the fairness of pricing for users with different electricity consumption habits and encourages users to interact with shared energy storage.

[0020] 4. This invention takes into account the fluctuation of new energy output in actual situations, and introduces a truncated multiplicative Gaussian noise method to characterize the fluctuation of microgrid output, thereby realizing the rationality of scheduling of shared energy storage system in response to the fluctuation of new energy output and improving the stability and economy of the system. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the framework of the present invention; Figure 2 This is the original output diagram in the microgrid cluster renewable energy output curve diagram of the present invention; Figure 3 To simulate the actual fluctuations of the microgrid cluster renewable energy output curve in this invention, a multiplicative Gaussian noise-truncated plot is introduced. Figure 4 This is a microgrid cluster load curve diagram of the present invention; Figure 5 This is a schematic diagram of the process of the present invention; Figure 6 This is a schematic diagram of the solution architecture of the present invention; Figure 7 This is a diagram illustrating the energy curtailment of a shared energy storage microgrid according to the present invention. Figure 8 This is a diagram illustrating the shared energy storage acquisition of excess leased capacity from a microgrid according to the present invention. Detailed Implementation

[0022] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] This invention provides an optimized scheduling method for shared energy storage in a park microgrid cluster, such as... Figure 1 As shown, the power grid interacts with the shared energy storage microgrid through the grid dispatch and trading center. The shared energy storage operator, as the leader in a master-slave game, sets the rental price and allocates shared energy storage capacity through the shared energy storage dispatch center based on the microgrid cluster's leasing demand. The microgrid cluster, as a follower, reduces the performance costs caused by energy curtailment and prediction errors by leasing shared energy storage. Based on the shared energy storage leasing price determined by a genetic algorithm, the microgrid leasing demand, and fluctuations in new energy output, a solution is developed to maximize annual revenue, considering factors such as leasing revenue, arbitrage revenue, the cost of acquiring curtailed microgrid energy, and the aging cost of shared energy storage.

[0024] The method of the present invention includes the following steps: Step 1: Collect output and load data for each microgrid in the park, i.e., hourly data for each microgrid within a 24-hour period, such as... Figure 2 As shown.

[0025] Step 2: Based on the collected power output data of each microgrid in the park, a truncated multiplicative Gaussian noise method is introduced to characterize the fluctuation of microgrid power output, resulting in simulated actual microgrid power output data. Specifically, based on the collected power output data of each microgrid, to address the fluctuation of new energy power output in real-world scenarios, a truncated multiplicative Gaussian noise method is introduced, such as... Figure 3 , 4 As shown.

[0026] This embodiment includes three regional microgrids simulating microgrid clusters. Microgrid 1 is a wind-solar hybrid microgrid with an installed capacity of 750kW photovoltaic and 1000kW wind turbine. Microgrid 2 has an installed capacity of 1000kW photovoltaic. Microgrid 3 has an installed capacity of 800kW wind turbine. The assessment cost per unit power prediction deviation is 0.60 yuan / (kW·h). The assessment cost per unit of wind and solar curtailment power adopts a tiered electricity price assessment, which is 0.8 yuan / (kW·h) at peak times, 0.4 yuan / (kW·h) at average times, and 0.2 yuan / (kW·h) at valley times. The electricity purchase and sale prices between microgrids and the grid are as follows: Purchase price: RMB 1.81 / (kW·h) during peak hours, RMB 1.43 / (kW·h) during off-peak hours, and RMB 1.02 / (kW·h) during valley hours; Sale price: RMB 0.96 / (kW·h) during peak hours, RMB 0.51 / (kW·h) during off-peak hours, and RMB 0.33 / (kW·h) during valley hours. The price for selling electricity to the grid is equal to the price for selling electricity to loads within the microgrid. The electricity purchase and sale prices between microgrids are also equal: RMB 0.9 / (kW·h) during peak hours, RMB 0.5 / (kW·h) during off-peak hours, and RMB 0.35 / (kW·h) during valley hours. According to... Figure 5 Process and Figure 6 Simulation calculations are performed using the solution architecture diagram. First, the genetic algorithm parameters are initialized. Based on the microgrid's rental demand in different time periods, the genetic algorithm randomly generates rental prices and rental capacities. Next, shared energy storage parameters are set. A Cplex solver is used to solve the objective function that maximizes the annual revenue of shared energy storage. The optimal scheduling scheme for shared energy storage is output. If the output conditions are met, the operation ends; otherwise, the calculation is repeated. The calculation aims to maximize the annual revenue of shared energy storage. Based on the microgrid's rental demand in different time periods and the microgrid's energy curtailment, factors such as arbitrage revenue with the grid, rental revenue, construction costs, and operation and maintenance costs are considered to obtain the optimal scheduling scheme for shared energy storage.

[0027] Step 3: Based on the simulated microgrid actual output data, load data of each microgrid in the park, microgrid leasing demand and optimal leasing price obtained by introducing the truncated multiplicative Gaussian noise method, construct the shared energy storage optimal scheduling objective function with the goal of maximizing annual revenue. This includes combining the shared energy storage optimal scheduling objective function with the constraint conditions to obtain the shared energy storage optimal scheduling model objective function that maximizes the annual revenue of shared energy storage.

[0028] The rental price includes the rental capacity price and the rental power price, expressed as follows: ; in, The time period during which shared energy storage provides leasing services. It is the total rental price of shared energy storage at time t. The price of shared energy storage leasing capacity at time t. It is the shared energy storage leasing power price at time t.

[0029] Based on the above rental price formula, the objective function set according to the rental price, and the objective function for optimized scheduling of shared energy storage capacity, are as follows: ; In the formula, maxObj is the objective function for optimized scheduling of shared energy storage, aiming to maximize annual revenue. It considers six factors, among which... It is the arbitrage profit of shared energy storage operators; It is the leasing revenue of shared energy storage operators; It is the cost of shared energy storage operators acquiring wind and solar power curtailment from microgrids; It is the aging cost of shared energy storage operators; It refers to the surplus energy acquired by shared energy storage operators from microgrid leasing; This refers to the investment costs of shared energy storage operators.

[0030] ; In the formula, This refers to the arbitrage profits of shared energy storage operators, and the power interaction of shared energy storage based on the grid's time-of-use pricing to smooth out peak-valley fluctuations in the grid. H is the total number of days in a year; T corresponds to each time period. It is the price of arbitrage discharge power during time period t; It is the arbitrage discharge power during time period t; It is the arbitrage of charging power price during the t-period; It is the charging power for arbitrage during time period t.

[0031] ; In the formula, It refers to the leasing revenue of shared energy storage operators, which is the revenue generated by each microgrid in the park leasing shared energy storage capacity according to its own needs. It is the price for renting shared energy storage capacity during time period t; It refers to the shared energy storage capacity leased during time period t; The price for shared energy storage capacity during time period t; It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t.

[0032] ; In the formula, It refers to the cost of shared energy storage operators acquiring the wind and solar power curtailment costs of microgrids. It is the purchase price of wind and solar power curtailment during period t; It represents the amount of wind and solar power curtailed during time period t.

[0033] ; In the formula, It is the aging cost of shared energy storage operators. It is the aging cost coefficient, which is defined by the total charging and discharging power of the shared energy storage.

[0034] ; In the formula, It refers to the surplus energy acquired by shared energy storage operators from microgrid leasing. It is the price of surplus energy acquired by shared energy storage operators from microgrid leasing during time period t; It is the surplus energy leased by the microgrid during time period t.

[0035] ; ; ; In the formula, This is the total construction cost of shared energy storage; , These are the construction costs for shared energy storage power and shared energy storage capacity, respectively. It refers to the construction price of shared energy storage capacity. It is the rated power of the shared energy storage plan and construction; It refers to the construction price of shared energy storage capacity. Plan and construct the rated capacity for shared energy storage.

[0036] ; In the formula, This refers to the investment costs of shared energy storage operators. It is the discount rate. It refers to the shared energy storage usage cycle.

[0037] To ensure the overall power balance of the system, making the total charge equal to the total discharge, the power balance constraint is set as follows: ; To ensure that the shared energy storage capacity operates within the specified range, the SOC constraint is as follows: ; ; ; In the formula, It is the initial SOC of a scheduling cycle, and the initial SOC is constrained to be equal to the SOC at the end of the scheduling cycle; It is an interval period; It refers to the shared energy storage charging and discharging efficiency; It is the lower limit of SOC. It is the upper limit of SOC.

[0038] To ensure that arbitrage charging and discharging of shared energy storage and leasing charging and discharging meet the rated power of shared energy storage, the power constraint is set as follows: ; In the formula, and These are the upper and lower limits of the arbitrage charging power during time period t; and These are the upper and lower limits of the arbitrage discharge power during time period t. ; In the formula, and These are the upper and lower limits of the charging power for shared energy storage rental during time period t; and These are the upper and lower limits of the discharge power for shared energy storage leased during time period t.

[0039] ; In the formula, It is the shared energy storage rated power, ensuring that the total charging and discharging power is less than the shared energy storage rated power.

[0040] To ensure that the shared energy storage capacity used does not exceed the total capacity, the capacity constraint is as follows: ; E represents the shared energy storage capacity, including arbitrage capacity and leased capacity, where, It is the arbitrage capacity for time period t. and These are the upper and lower limits of arbitrage capacity, respectively. It is the rental capacity for time period t. and These are the upper and lower limits of the rental capacity, respectively.

[0041] To ensure that shared energy storage rental power and arbitrage power cannot be charged and discharged simultaneously, a mutual exclusion constraint for charging and discharging is set: ; ; In the formula, , , , The variables are binary, meaning they can only take the value 0 or 1. The formula constrains the sum of the binary variables to not exceed 1, meaning they cannot both be 1 at the same time, thus ensuring that the shared storage cannot be charged and discharged simultaneously.

[0042] With shared energy storage as the leader and microgrids in the park as followers, the optimal leasing price is determined through dynamic game theory based on the shared energy storage price and the microgrid demand. The expression for the master-slave game theory method is as follows: ;

[0043] The above game theory model contains three elements: participants, strategies, and payoffs, specifically represented as follows: Participants: Shared Energy Storage (SES), Microgrid 1 (MG1), Microgrid 2 (MG2), and Microgrid 3 (MG3) are the participants in this game. The set of participants is represented as follows: .

[0044] Trading Strategy: The leader SES's strategy consists of 24-hour rental prices (including rental capacity price and rental power price) and the allocation of rental capacity and arbitrage capacity, which can be represented in vector form as follows: The strategy for microgrids is to represent the rental demand (including rental capacity demand and rental power demand) at each time point as a vector. .

[0045] Benefits: The benefits for each participant are defined by the shared energy storage optimization scheduling objective function aimed at maximizing annual benefits.

[0046] Step 4: Using a master-slave game theory approach, the microgrid leasing price is divided into leased capacity price and leased power price. Based on the output and load data of each microgrid in the park, the microgrid leasing demand is obtained, and the optimal leasing price is calculated. ; Where G is the total set of game frameworks, and L SES It is a leader-shared energy storage strategy, F MG For follower microgrid strategies, R SES It is the revenue from shared energy storage, R MG The value is the microgrid revenue, and N is the set of participants; the optimal configuration scheme is obtained by solving the problem using the Cplex solver.

[0047] This example uses the output and load curves of a microgrid cluster to optimize and solve for the energy demand of each microgrid and the shared energy storage, ultimately calculating the optimal scheduling scheme for the shared energy storage. Two scenarios are compared in this example, as shown in Table 1.

[0048] Table 1: .

[0049] Shared energy storage is divided into leasing and arbitrage models, which coexist. Leasing allows for the time-shifting of renewable energy output from microgrids, while arbitrage smooths out peak-valley fluctuations in the power grid, ensuring stable grid operation. This greatly enriches the operational modes of shared energy storage and improves economic efficiency and system stability. Scenario 1 only considers the price arbitrage model of shared energy storage in the spot market; Scenario 2 is the method proposed in this invention.

[0050] The final optimization results are shown in Table 2: Table 2: .

[0051] Case 1, or Scenario 1, only considers the arbitrage model of shared energy storage in the spot market; Case 2, or Scenario 2, is the method proposed in this invention. It can be seen that the revenue from shared energy storage is lowest when arbitrage is only involved with the leasing market in Case 1, while Case 2, which considers the scheduling method of this invention, shows very good economic efficiency in terms of both the annual revenue from shared energy storage and the annual cost of the microgrid cluster.

[0052] This embodiment uses shared energy storage to acquire abandoned energy from various microgrids and to acquire excess leased capacity from microgrids, such as... Figure 7 , Figure 8 As shown, this can maximize the promotion of new energy consumption, reduce microgrid leasing costs, and improve energy storage utilization efficiency.

[0053] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any modifications, alterations, substitutions, and variations made by those skilled in the art to the above embodiments are within the scope of the present invention.

Claims

1. An optimized scheduling method for shared energy storage in a park microgrid cluster, characterized in that, The steps include: collecting power output and load data for each microgrid in the park; Based on the collected power output data of each microgrid in the park, a truncated multiplicative Gaussian noise method is introduced to characterize the fluctuation of microgrid power output, resulting in simulated actual power output data of the microgrid: ； In the formula, The noise added during time period t; is the given noise standard deviation coefficient; Z is a random number from the standard normal distribution N(0,1); The actual output power of the microgrid during time period t. The predicted output power of the microgrid during time period t; Based on the simulated microgrid actual output data, load data of each microgrid in the park, microgrid leasing demand, and optimal leasing price obtained by introducing a truncated multiplicative Gaussian noise method, a shared energy storage optimal scheduling objective function is constructed with the goal of maximizing annual revenue. This includes combining the shared energy storage optimal scheduling objective function with constraints to obtain the shared energy storage optimal scheduling model's objective function for maximizing annual revenue: ； In the formula, maxObj represents the maximum annual return. It is the arbitrage profit of shared energy storage operators. It is the leasing revenue of shared energy storage operators. It is the cost of shared energy storage operators acquiring wind and solar power curtailment from microgrids. It is the aging cost of shared energy storage operators. It refers to the surplus energy acquired by shared energy storage operators from microgrid leasing. It is the investment cost of shared energy storage operators; ； In the formula, H is the total number of days in a year, and T represents the corresponding time period. It is the price of arbitrage discharge power during time period t. It is the arbitrage discharge power during time period t. It is a time-period arbitrage of charging power price. It is the charging power for arbitrage during time period t; ； In the formula, It is the price for renting shared energy storage capacity during time period t; It refers to the shared energy storage capacity leased during time period t; The price for shared energy storage capacity during time period t; It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t; ； In the formula, It is the purchase price of wind and solar power curtailment during period t; It refers to the amount of wind and solar power curtailed during time period t; ； In the formula, It is the aging cost coefficient; ； In the formula, It is the price of surplus energy acquired by shared energy storage operators from microgrid leasing during time period t; It is the surplus energy leased by the microgrid during time period t; ； In the formula, d is the total construction cost of shared energy storage, y is the discount rate, and y is the usage period of shared energy storage. Using a master-slave game theory approach, the microgrid leasing price is divided into leased capacity price and leased power price. Based on the output and load data of each microgrid in the park, the microgrid leasing demand is obtained, and the optimal leasing price is solved. ； Where G is the total set of game frameworks, and L SES It is a leader-shared energy storage strategy, F MG For follower microgrid strategies, R SES It is the revenue from shared energy storage, R MG It represents the microgrid revenue, where N is the set of participants; The optimal configuration scheme is obtained by solving the Cplex solver.

2. The optimized scheduling method for shared energy storage in a park microgrid cluster according to claim 1, characterized in that: The output and load data of each microgrid in the park include data of each microgrid at various times throughout the day, divided into 24 time periods, with each time period spaced one hour apart.

3. The optimized scheduling method for shared energy storage in a park microgrid cluster according to claim 1, characterized in that: The constraints include power balance constraints, state of charge constraints, power constraints, capacity constraints, and charge / discharge mutual exclusion constraints.

4. The optimized scheduling method for shared energy storage in a park microgrid cluster according to claim 3, characterized in that, Power balance constraints include: ； in, It is the charging power arbitrage during time period t. It is the arbitrage discharge power during time period t. It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t. It represents the amount of wind and solar power curtailed during time period t.

5. The optimized scheduling method for shared energy storage in a park microgrid cluster according to claim 3, characterized in that, The state of charge constraints include: ； ； ； In the formula, SOC int SOC(T) is the initial state of charge of the energy storage during a scheduling cycle, SOC(t) is the state of charge of the energy storage at the end of the scheduling cycle, and SOC(t) is the state of charge of the energy storage at time t. It is an interval period; It refers to the shared energy storage charging and discharging efficiency; It is the lower limit of SOC. It is the upper limit of SOC. It is the charging power arbitrage during time period t. It is the arbitrage discharge power during time period t. It is the charging power of shared energy storage leased during time period t; It is the discharge power of the shared energy storage leased during time period t. t represents the amount of wind and solar power curtailed during the time period, and E represents the shared energy storage capacity.

6. The optimized scheduling method for shared energy storage in a park microgrid cluster according to claim 3, characterized in that, Power constraints include: ； In the formula, It is the charging power arbitrage during time period t. and These are the upper and lower limits of the arbitrage charging power during time period t, respectively. It is the arbitrage discharge power during time period t. and These are the upper and lower limits of the arbitrage discharge power during time period t. ； In the formula, It is the charging power of shared energy storage during the t-period rental period. and These are the upper and lower limits of the charging power for shared energy storage rental during time period t. It is the discharge power of the shared energy storage leased during time period t. and These are the upper and lower limits of the discharge power for shared energy storage leased during time period t; ； In the formula, It is the power of wind and solar power curtailed during period t. It is the rated power of shared energy storage.

7. The optimized scheduling method for shared energy storage in a park microgrid cluster according to claim 3, characterized in that, capacity constraints... include: ； Where E represents the shared energy storage capacity, E tl For arbitrage capacity, E zl For leased capacity, It is the arbitrage capacity for time period t. and These are the upper and lower limits of arbitrage capacity, respectively. It is the rental capacity for time period t. and These are the upper and lower limits of the rental capacity, respectively.

8. The optimized scheduling method for shared energy storage in a park microgrid cluster according to claim 3, characterized in that, Charge-discharge mutual exclusion constraints include: ； ； In the formula, It is the arbitrage discharge power during time period t. This is the upper limit of the discharge power of shared energy storage leased during time period t. It is the charging power of shared energy storage during the t-period rental period. This is the upper limit of charging power for shared energy storage rental during time period t. It is the arbitrage discharge power during time period t. It is the charging power arbitrage during time period t. This is the upper limit of arbitrage discharge power during time period t. These are the upper limits of the arbitrage charging power during time period t. , , , It is a binary variable.

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