Multi-set meal game pricing method for coordinated operation of microgrid alliance and shared energy storage

Abstract

The application provides a multi-set meal game pricing method for coordinated operation of micro-grid alliances and shared energy storage, and belongs to the technical field of power energy storage. Firstly, a top shared energy storage operator model considering constraints such as power interaction with micro-grids and distribution networks is established with the maximum operation income of the shared energy storage system of the shared energy storage operator as a target. Secondly, a lower micro-grid optimization operation model considering constraints such as power balance is established with the minimum operation cost of each micro-grid alliance as a target. Then, based on the above model, a master-slave game basic framework and model are established with the shared energy storage operator as a leader and multiple wind farms as followers according to the actual shared energy storage system pricing field. Finally, the above model is solved to obtain a pricing scheme. The application can propose different energy storage set meals according to the power consumption characteristics of different micro-grid alliances, thereby effectively improving the economy of the operation of the micro-grid alliance, being also beneficial to improving the energy storage utilization rate and realizing mutual benefits of both parties.

Description

Technical Field

[0001] This invention relates to the field of microgrid alliance optimization operation and energy storage pricing technology in power systems, specifically a multi-package game pricing method for coordinated operation of microgrid alliances and shared energy storage. Background Technology

[0002] With increasing global attention to environmental pollution and the energy crisis, traditional thermal power generation is facing severe challenges due to its low efficiency, high carbon emissions, and serious pollution. With the advancement of "dual-carbon goals" and "new power systems," the structure and operation of power systems have undergone fundamental changes, becoming cleaner and more digital. To address the increasingly complex and diverse power systems, experts from various countries have proposed the concept of microgrids. Microgrids enable multi-energy complementarity, optimized management, and coordinated control of regional power resources, thereby transforming the energy consumption structure. Microgrids offer advantages such as high energy utilization, strong power supply reliability, flexible operation, and environmental friendliness, and are gradually becoming the future direction of power grid development. Microgrids incorporating distributed power sources such as photovoltaics and wind power are widely used in power grids. In recent years, to further improve energy efficiency, microgrids are gradually developing into an energy internet centered on integrated energy systems. The energy internet has become the latest direction in global energy development and a strategic focus for China's future energy development. The energy internet, centered on integrated energy systems, can effectively improve energy efficiency through the complementarity and cascade utilization of multiple energy sources, while also reducing emissions of greenhouse gases such as carbon dioxide, thus achieving the "dual carbon" goal. Against the backdrop of continuously increasing installed capacity of new energy sources such as wind power and photovoltaics, developing energy storage technology is a necessary way to solve the supply and demand matching problem and reduce the impact of uncertain wind and solar power output on the power grid. Utilizing energy storage to coordinate differences in wind and solar resources across different regions can improve the economic efficiency of system operation. Introducing the sharing economy into the power system and leveraging the economies of scale of shared energy storage can further enhance the user experience, releasing benefits to users while improving system performance.

[0003] The development of shared energy storage depends on the potential profits and the benefits it provides to users. Pricing of energy storage services is particularly important for both investors and users. Therefore, a shared energy storage pricing model based on a master-slave game, considering both investors and users, is of significant research value. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a multi-package game-theoretic pricing method for the coordinated operation of microgrid consortia and shared energy storage. This method comprehensively considers the interests of both shared energy storage operators and individual microgrid consortia. Under the premise of satisfying basic constraints, it not only maximizes the utilization of energy storage resources and allows shared energy storage operators to earn higher profits, but also ensures more stable operation for each microgrid using the service, thus possessing high application value. The technical solution is as follows:

[0005] A multi-package game-theoretic pricing method for the coordinated operation of microgrid consortia and shared energy storage, where multiple independent microgrid consortia share the same shared energy storage system, includes the following steps:

[0006] Step 1: With the goal of maximizing the operating benefits of the shared energy storage system for shared energy storage operators, establish an upper-level shared energy storage operator model that considers the power interaction constraints with microgrids and distribution networks, as well as its own capacity constraints.

[0007] Step 2: With the goal of minimizing the operating cost of each microgrid alliance, establish an optimized operation model for the lower-level microgrid alliance that considers power balance constraints, gas turbine-related constraints, wind power output constraints, power purchase constraints from the distribution network, and shared energy storage package constraints.

[0008] Step 3: Based on the upper-level shared energy storage operator model and the lower-level microgrid alliance optimization operation model, and according to the actual pricing field of shared energy storage systems, establish a basic framework and model of master-slave game with shared energy storage operators as leaders and multiple wind farms as followers;

[0009] Step 4: Using a combination of the Gurobi solver and particle swarm optimization algorithm, the upper-layer shared energy storage operator model, the lower-layer microgrid alliance optimization operation model, and the master-slave game basic framework and model are solved to obtain the pricing scheme.

[0010] Furthermore, the objective function of the upper-level shared energy storage operator model in step 1 is to maximize the operating revenue of the shared energy storage system. Without considering equipment investment and construction costs, the operator's objective function is as follows:

[0011]

[0012] In the formula, t is the time index, and n is the microgrid consortium index; F stg For revenue from selling electricity to the distribution network, F bfg To calculate the cost of purchasing electricity from the distribution network, F loss For energy storage operation losses and costs, This represents the revenue generated by providing shared energy storage services to the microgrid consortium n. To penalize the auxiliary variable's penalty unit price, and These are auxiliary variables representing penalties for charging, discharging, and exceeding the battery limit during time period t.

[0013] 1) Benefits from power exchange with the distribution network

[0014] The revenue from power exchange with the distribution network is equal to the revenue F from selling electricity from shared energy storage to the distribution network. stg Subtract the cost of purchasing electricity from the distribution network F bfg Revenue F from selling electricity to the distribution network stg and the cost of purchasing electricity from the distribution network F bfg As shown in the following formula:

[0015]

[0016]

[0017] In the formula, s is the index of the shared energy storage device; The power sold to the distribution network by the shared energy storage system during time period t. The price at which shared energy storage is sold to the distribution network during time period t; For time period t, the power that the shared energy storage s purchases from the distribution network. The price at which shared energy storage purchases electricity from the distribution network during time period t;

[0018] 2) Energy storage operation loss cost

[0019]

[0020] In the formula, π loss This is the discounted price based on the lifespan loss per unit charge / discharge power of the energy storage system. and These represent the discharge power and charging power of the shared energy storage system during time period t, respectively.

[0021] 3) Revenue from providing energy storage services to microgrid consortia

[0022]

[0023] In the formula, and The revenue generated by shared energy storage for providing services under Package A, Package B, Package C, and Package D to the microgrid consortium n is respectively.

[0024] Furthermore, the constraints of the upper-level shared energy storage operator model in step 1 include power interaction constraints with the microgrid, power interaction constraints with the distribution network, and its own capacity constraints, as detailed below:

[0025] a) Power interaction constraints with microgrids and distribution networks

[0026] The following formula indicates that the power range constraint must be met for both the purchase and sale of electricity and the charging and discharging of shared energy storage s:

[0027]

[0028]

[0029] The following formula represents the constraint that shared energy storage s cannot simultaneously engage in electricity sales and purchase operations, nor can it simultaneously engage in discharging and charging operations:

[0030]

[0031] The following formula represents the constraint that shared energy storage s cannot simultaneously perform discharge and electricity purchase operations, nor can it simultaneously perform charging and electricity sales operations:

[0032]

[0033] In the formula, P s stg,max and P s stg,min These represent the maximum and minimum power output of the shared energy storage s to the distribution network, respectively. Let s be the state variable for the shared energy storage to sell electricity to the distribution network during time period t. A value of 1 indicates that an electricity selling operation is performed, and a value of 0 indicates that no electricity selling operation is performed. P represents the power purchased from the distribution network by the shared energy storage system during time period t; s bfg,max and P s bfg，min These represent the maximum and minimum power values ​​that the shared energy storage s purchases from the distribution network, respectively. Let P be the state variable for the shared energy storage s to purchase electricity from the distribution network during time period t. A value of 1 indicates that an electricity purchase operation is performed, and a value of 0 indicates that no electricity purchase operation is performed. s dis,max and P s dis,min These represent the maximum and minimum power values ​​of the shared energy storage s discharging to the microgrid consortium, respectively. Let t be the state variable for the shared energy storage s to discharge to the alliance during time period t. A value of 1 indicates that a discharge operation is performed, and a value of 0 indicates that a discharge operation is not performed. The power of shared energy storage s charged from the microgrid consortium during time period t; P s cha,max and P s cha,min These represent the maximum and minimum power values ​​for the shared energy storage s to charge from the microgrid consortium, respectively. The state variable for charging shared energy storage s from the microgrid alliance during time period t is 1, which indicates that a charging operation is performed, and 0 indicates that a charging operation is not performed.

[0034] b) Shared energy storage capacity constraints:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] In the formula, η represents the total amount of electricity stored in the shared energy storage system during time period t. in and η out These refer to the shared energy storage charging efficiency and discharging efficiency, respectively. and These represent the discharge power and charging power of the microgrid consortium during time period t, respectively; P cha,min and P cha,max These represent the minimum and maximum values ​​of the shared energy storage charging power, respectively; P dis,min and P dis,max These represent the minimum and maximum values ​​of the shared energy storage discharge power, respectively; E ses,min and E ses,max These are the minimum and maximum values ​​of the shared energy storage capacity, respectively.

[0043] Furthermore, the objective function of the lower-level microgrid alliance optimization operation model established in step 2 is to minimize the operating cost of each microgrid alliance; specifically as follows:

[0044] The objective function is as follows:

[0045]

[0046] In the formula, n∈{1,2,3,4} represents the microgrid consortium number; the operating cost F of microgrid consortium n is... n Including gas turbine fuel costs wind abandonment punishment Electricity purchase cost from distribution network and the cost of using shared energy storage Specifically:

[0047] 1) Gas turbine fuel cost for:

[0048]

[0049] In the formula, E(n) is the set of devices in the microgrid consortium n; Let W be the price of fuel during time period t. g,t Let g be the fuel consumption of the gas turbine during time period t;

[0050] 2) Cost of wind curtailment penalty for:

[0051]

[0052] In the formula, π w,t The price for wind curtailment during time period t; For the predicted power output of the wind farm w during time period t, P w,t The power output of the wind farm is scheduled for time period t;

[0053] 3) Cost of purchasing electricity from the distribution network for:

[0054]

[0055] In the formula, The distribution network electricity price for time period t. Let n be the power purchased by the microgrid consortium from the distribution network during time period t;

[0056] 4) Cost of using shared energy storage for:

[0057]

[0058] In the formula, and These represent the revenue generated by the microgrid consortium using shared energy storage packages A, B, C, and D.

[0059] Furthermore, the constraints of the lower-level microgrid alliance optimized operation model include microgrid alliance power balance constraints, gas turbine-related constraints, wind power output constraints, power purchase constraints from the distribution network, and shared energy storage package constraints; specifically:

[0060] a) Power balance constraints of microgrid consortia

[0061]

[0062] In the formula, D represents the set of loads in the microgrid group, and P... g,t P represents the active power output of the gas turbine g during time period t; d,t Let d be the active power load during time period t.

[0063] b) Gas turbine related constraints

[0064] Gas turbine output constraints:

[0065]

[0066] Gas turbine minimum start-stop time constraints:

[0067]

[0068]

[0069] Fuel consumption constraints:

[0070] K g,t ≥k g ·(I g,t -I g,t-1 ), K g,t ≥0

[0071] H g,t ≥h g ·(I g,t-1 -I g,t ), H g,t ≥0

[0072] Gas turbine ramp rate constraint:

[0073]

[0074]

[0075] In the formula, I g,t Let I be the operating state variable of gas turbine g during time period t. g,t When I is 1, it indicates that the gas turbine g is in the on state during time period t. g,t When the value is 0, it indicates that the gas turbine g is in a shutdown state during time period t; and These represent the minimum and maximum output values ​​of the gas turbine, g, respectively. and These are the start-up and shutdown time counters for gas turbine g, respectively. and These represent the minimum start-up and shutdown times for the gas turbine, respectively; k g and h g These represent the fuel consumption during gas turbine start-up and shutdown, respectively; R u,g and R d,g These represent the uphill and downhill ramp rates of the gas turbine, respectively; K g,t and H g,t These represent the fuel consumption of the gas turbine during time period t, specifically the fuel consumption during startup and shutdown of gas turbine g.

[0076] c) Wind power output constraints:

[0077]

[0078] d) Constraints on purchasing electricity from the distribution network

[0079]

[0080] In the formula, and These represent the minimum and maximum power that the microgrid consortium n purchases from the distribution network, respectively.

[0081] e) Constraints on shared energy storage packages:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] In the formula, and These are the state variables for selecting Package A, Package B, Package C, and Package D for the microgrid alliance n. A value of 1 indicates that the package has been selected, and a value of 0 indicates that the package has not been selected. and These represent the charging and discharging state variables of the microgrid consortium during time period t, respectively; P n cha,min and P n cha,max P represents the minimum and maximum charging power of the microgrid consortium n, respectively. n dis,min and P n dis,max These represent the minimum and maximum discharge power of the microgrid consortium n, respectively.

[0088] Package A constraints:

[0089] By choosing Package A, the Microgrid Consortium can use shared energy storage for charging and discharging at any time, with low usage costs. It equals the discharge capacity multiplied by the discharge price minus the charging capacity multiplied by the charging price; as shown in the following formula:

[0090]

[0091] In the formula, and These are the unit price for discharging and the unit price for charging of Package A during time period t, respectively, with M being an auxiliary parameter;

[0092] Package B constraints:

[0093] Choosing Package B allows the shared energy storage to provide capacity leasing services to the microgrid consortium n, while the consortium receives half of the capacity's electricity, with no time restrictions and no usage costs. It equals the leased capacity multiplied by the unit price of the capacity; as shown in the following formula:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] In the formula, This refers to the unit price per unit capacity for Package B. For the leased capacity of microgrid consortium n, The actual electricity consumption of the leased capacity of microgrid alliance n for time period package B during time period t. To share the initial energy storage capacity;

[0101] Package C constraints:

[0102] Choosing Package C allows shared energy storage to provide time-based leasing services to the microgrid consortium n, with no power limit and low usage costs. It equals the rental period multiplied by the unit price per rental period; as shown in the following formula:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] In the formula, For the microgrid consortium n, select package C for the state variables. The unit price for package C during time period t. and These are the new binary auxiliary variables introduced in Package C;

[0113] Package D constraints:

[0114] Choosing Package D allows shared energy storage to provide electricity storage services for the microgrid consortium n, with usage costs... The value is equal to the stored energy multiplied by the storage unit price; and within the specified storage period TC, the microgrid consortium n stores energy into the shared energy storage, during which the shared energy storage cannot discharge to the microgrid consortium n; within the specified release period TD, the shared energy storage needs to release the previously stored energy to the microgrid consortium n, during which the shared energy storage cannot charge the microgrid consortium n. The specific formula is as follows:

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] In the formula, The amount of electricity stored in microgrid n The unit price of stored electricity during time period t.

[0121] Furthermore, in step 3, the game process of the master-slave game basic framework and model is described as follows: First, the shared energy storage operator sets the package prices for different microgrid alliances within a day; then, the microgrid alliance selects a suitable package and formulates an electricity consumption strategy based on the pricing and feeds it back to the shared energy storage operator; subsequently, the shared energy storage operator adjusts the package price according to the electricity consumption strategy of the microgrid alliance to maximize its own revenue and feeds this price back to the microgrid alliance; then, the microgrid alliance formulates an electricity consumption strategy based on the pricing and feeds it back to the shared energy storage operator; this cycle continues until the optimal pricing strategy that satisfies both is found.

[0122] Furthermore, the model solution method in step 4 is as follows: the model established in step 4 is solved using MATLAB 2022a by combining the Gurobi solver with the particle swarm optimization algorithm. The specific solution process is as follows:

[0123] Step 4.1: Input the predicted power output and load values ​​of the microgrid consortium, as well as other system parameters;

[0124] Step 4.2: Set the number of particles m and the number of iterations H, and use the particle swarm optimization algorithm to generate the package price. At this time, the number of iterations h = 1.

[0125] Step 4.3: The shared energy storage transmits the price to the microgrid alliance. Each microgrid alliance uses this price to solve the optimization operation model of the lower-level microgrid alliance, formulates its own charging and discharging strategy, and feeds it back to the shared energy storage.

[0126] Step 4.4: Based on the charging and discharging strategy of the microgrid alliance, the shared energy storage solves the upper-level shared energy storage operator model, formulates its own charging and discharging strategy, compares each particle and updates the current iterative optimal solution, and saves and updates the current optimal price strategy.

[0127] Step 4.5: If h > H and the convergence criterion is satisfied, then end the program; otherwise, h = h + 1, update the package price and return to step 4.3.

[0128] The beneficial effects of this invention are:

[0129] (1) The present invention provides a master-slave game pricing model with shared energy storage operators as the main body and each microgrid alliance as the slave body. Compared with the prior art, this scheme comprehensively considers the interests of both the shared energy storage operators and each microgrid alliance. Under the condition of satisfying the basic constraints, it can make both parties benefit to reach equilibrium. The optimized solution algorithm is combined with specific calculation examples for verification and analysis.

[0130] (2) The pricing method proposed in this invention enables shared energy storage operators to formulate different personalized price curves for different microgrids with different energy storage needs. This not only allows shared energy storage operators to maximize the use of their energy storage resources and earn higher profits, but also makes each microgrid using the service operate more stably, which has high application value. Attached Figure Description

[0131] Figure 1 This is a flowchart of the multi-package game pricing method for the coordinated operation of microgrid alliances and shared energy storage in this invention.

[0132] Figure 2 This is the system operating framework of the present invention.

[0133] Figure 3 This is a flowchart of the solution process for the pricing model of this invention.

[0134] Figure 4 This is a predicted load curve diagram of four microgrid alliances according to an embodiment of the present invention.

[0135] Figure 5 This is a graph showing the predicted power output of wind power generation equipment in four microgrid alliances according to an embodiment of the present invention.

[0136] Figure 6 This diagram illustrates the power interaction between shared energy storage and other microgrid alliances under the optimal pricing strategy, as well as the price of the selected packages, in an embodiment of the present invention.

[0137] Figure 7 This diagram illustrates the shared energy storage and power interaction between the two microgrid consortia under the optimal pricing strategy, as well as the price of the selected packages, according to an embodiment of the present invention.

[0138] Figure 8 This diagram illustrates the shared energy storage and power interaction between the three microgrid consortia under the optimal pricing strategy, as well as the price of the selected packages, according to an embodiment of the present invention.

[0139] Figure 9 This diagram illustrates the shared energy storage and power interaction between the four microgrid consortia under the optimal pricing strategy, as well as the price of the selected packages, according to an embodiment of the present invention.

[0140] Figure 10(a) shows the situation where shared energy storage s1 is used by the microgrid consortium in the time dimension under the optimal pricing strategy.

[0141] Figure 10(b) shows the scenario where shared energy storage s2 is used by the microgrid consortium in the time dimension under the optimal pricing strategy. Detailed Implementation

[0142] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0143] The flowchart of the multi-package game pricing method for coordinated operation of microgrid alliances and shared energy storage in this invention is as follows: Figure 1 As shown, this invention employs a microgrid alliance-shared energy storage pricing model based on master-slave game theory. Unlike the traditional scenario where multiple microgrids use their own energy storage independently, this invention allows multiple independent microgrid alliances to share the same shared energy storage system. The model's operational framework is shown in the attached figure. Figure 2 As shown, the specific construction method includes the following steps:

[0144] Step 1: With the goal of maximizing the operating benefits of the shared energy storage system for shared energy storage operators, establish an upper-level shared energy storage operator model that considers power interaction constraints with microgrids and distribution networks, as well as its own capacity constraints.

[0145] The upper-layer shared energy storage operator model is shown below:

[0146] 1. Objective function

[0147] The objective function of a shared energy storage operator is to maximize the operating revenue of shared energy storage. Without considering equipment investment costs, the operator's objective function is as follows:

[0148]

[0149] In the formula, t is the time index, and n is the microgrid consortium index; F stg For revenue from selling electricity to the distribution network, F bfg To calculate the cost of purchasing electricity from the distribution network, F loss For energy storage operation losses and costs, To provide revenue from shared energy storage services to the microgrid consortium n To penalize the auxiliary variable's penalty unit price, These are auxiliary variables representing penalties for charging, discharging, and exceeding the power limit during time period t.

[0150] (1) Benefits from power exchange with the distribution network

[0151] The revenue from power exchange with the distribution network is equal to the revenue F from the shared energy storage system selling electricity to the distribution network. stg Subtract the cost of purchasing electricity from the distribution network F bfg Revenue F from selling electricity to the distribution network stg and the cost of purchasing electricity from the distribution network F bfg As shown in the following formula:

[0152]

[0153]

[0154] In the formula, s is the index of the shared energy storage device; For time period t, the shared energy storage power s is the power sold to the distribution network. The price at which shared energy storage is sold to the distribution network during time period t; For time period t, the shared energy storage power s is the power purchased from the distribution network. The price at which shared energy storage purchases electricity from the distribution network during time period t.

[0155] (2) Energy storage operation loss cost:

[0156]

[0157] In the formula, π loss This is the discounted price based on the lifespan loss per unit charge / discharge power of the energy storage system. These represent the discharge power and charging power of the shared energy storage system during time period t, respectively.

[0158] (3) Benefits of providing energy storage services to microgrid consortia:

[0159]

[0160] In the formula, and The revenue generated by shared energy storage for providing services under Package A, Package B, Package C, and Package D to the microgrid alliance n is respectively.

[0161] 2. Constraints

[0162] The constraints include power interaction constraints with the microgrid, power interaction constraints with the distribution network, and self-capacity constraints.

[0163] a) Power interaction constraints with microgrids and distribution networks

[0164] The following formula indicates that the power range constraint must be met for both the purchase and sale of electricity and the charging and discharging of shared energy storage s:

[0165]

[0166]

[0167] The following formula represents the constraint that shared energy storage s cannot simultaneously engage in electricity sales and purchase operations, nor can it simultaneously engage in discharging and charging operations:

[0168]

[0169] The following formula represents the constraint that shared energy storage s cannot simultaneously perform discharge and electricity purchase operations, nor can it simultaneously perform charging and electricity sales operations:

[0170]

[0171] In the formula, P s stg,max P s stg,min These represent the maximum and minimum power output of the shared energy storage s to the distribution network, respectively. Let s be the state variable for the shared energy storage to sell electricity to the distribution network during time period t. A value of 1 indicates that an electricity sales operation is performed, and a value of 0 indicates that no electricity sales operation is performed. P represents the power purchased from the distribution network by the shared energy storage system during time period t; s bfg,max P s bfg,min These represent the maximum and minimum power consumption of the shared energy storage s from the distribution network, respectively. Let P be the state variable for the shared energy storage s to purchase electricity from the distribution network during time period t. A value of 1 indicates that an electricity purchase operation is performed, and a value of 0 indicates that no electricity purchase operation is performed. s dis,max P s dis,min These represent the maximum and minimum discharge power of the shared energy storage s to the microgrid consortium, respectively. Let t be the state variable for the shared energy storage s to discharge to the microgrid alliance during time period t. A value of 1 indicates that a discharge operation is performed, and a value of 0 indicates that a discharge operation is not performed. The power of shared energy storage s charged from the microgrid consortium during time period t; P s cha ,max P s cha,min These represent the maximum and minimum charging power of the shared energy storage s from the microgrid consortium, respectively. The state variable for charging the shared energy storage s from the microgrid alliance during time period t is 1, which indicates that a charging operation is performed, and 0 indicates that a charging operation is not performed.

[0172] b) Shared energy storage capacity constraints:

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180] In the formula, η represents the total amount of electricity stored in the shared energy storage system during time period t. in η out These represent the shared energy storage charging efficiency and discharging efficiency, respectively; P cha,min P cha,max These represent the minimum and maximum values ​​of the shared energy storage charging power, respectively; P dis,min P dis,max These are the minimum and maximum values ​​of the shared energy storage discharge power, respectively; E ses,min E ses,max These are the minimum and maximum values ​​of the shared energy storage capacity, respectively.

[0181] Step 2: With the goal of minimizing the operating costs of each microgrid alliance, establish an optimized operation model for the lower-level microgrids that considers power balance constraints, gas turbine-related constraints, wind power output constraints, power purchase constraints from the distribution network, and shared energy storage package constraints.

[0182] The lower-level microgrid alliance model is shown below:

[0183] 1. Objective function:

[0184] The lower-level objective function is to minimize the operating cost of each microgrid consortium, as shown below:

[0185]

[0186] In the formula, n∈{1,2,3,4}, the operating cost of the microgrid consortium n includes the gas turbine fuel cost. wind abandonment punishment Electricity purchase cost from distribution network Shared energy storage usage costs

[0187] (1) Gas turbine fuel cost:

[0188]

[0189] In the formula, E(n) is the index of the device in the microgrid consortium n; Let W be the price of fuel during time period t. g,t Let g be the fuel consumption of the gas turbine during time period t.

[0190] (2) Cost of wind curtailment penalty:

[0191]

[0192] In the formula, π w,t The price for wind curtailment during time period t; For the predicted power output of the wind farm w during time period t, P w,t The power output of the wind farm is scheduled for time period t.

[0193] (3) Cost of purchasing electricity from the distribution network:

[0194]

[0195] In the formula, The distribution network electricity price for time period t. Let n be the power purchased by the microgrid from the distribution network during time period t.

[0196] (4) Cost of using shared energy storage:

[0197]

[0198] In the formula, and These represent the costs for the microgrid consortium n to use shared energy storage packages A, B, C, and D.

[0199] 2. Constraints

[0200] The constraints of the lower-level microgrid alliance optimization model include microgrid alliance power balance constraints, gas turbine-related constraints, wind power output constraints, power purchase constraints from the distribution network, and shared energy storage package constraints.

[0201] a) Microgrid power balance constraints

[0202]

[0203] In the formula, D represents the set of loads in the microgrid group, and P... g,t P represents the active power output of the gas turbine g during time period t; d,t Let d be the active load during time period t.

[0204] b) Gas turbine related constraints

[0205] Gas turbine output constraints:

[0206]

[0207] Gas turbine minimum start-stop time constraints:

[0208]

[0209]

[0210] Fuel consumption constraints:

[0211] Kg,t≥kg·(Ig,t-Ig,t-1),K g,t ≥0

[0212] Hg,t≥hg·(Ig,t-1-Ig,t),H g,t ≥0

[0213] Gas turbine ramp rate constraint:

[0214]

[0215]

[0216] In the formula, I g,t Let I be the operating state variable of gas turbine g during time period t. g,t When I is 1, it indicates that the gas turbine g is in the on state during time period t. g,t When the value is 0, it indicates that the gas turbine g is in a shutdown state during time period t; These represent the minimum and maximum output values ​​of the gas turbine, respectively. These are the start-up and shutdown time counters for gas turbine g, respectively. These represent the minimum start-up and shutdown times for the gas turbine, respectively. g h g R represents the fuel consumption during gas turbine start-up and shutdown, respectively. u,g R d,g These represent the uphill and downhill ramp rates of the gas turbine, respectively.

[0217] c) Wind power output constraints:

[0218]

[0219] d) Constraints on purchasing electricity from the distribution network

[0220]

[0221] In the formula, These represent the minimum and maximum power that the microgrid consortium n purchases from the distribution network, respectively.

[0222] e) Constraints on shared energy storage packages:

[0223]

[0224]

[0225]

[0226]

[0227]

[0228] In the formula, The state variable is used to select package A, package B, package C, and package D for microgrid consortium n. A value of 1 indicates that the package has been selected, and a value of 0 indicates that the package has not been selected. These represent the charging and discharging state variables of the microgrid consortium during time period t; P n cha,min P n cha,max P represents the minimum and maximum charging power of the microgrid consortium n, respectively. n dis ,min P n dis,max These represent the minimum and maximum discharge power of the microgrid consortium n, respectively.

[0229] Package A constraints:

[0230]

[0231]

[0232]

[0233] By choosing Package A, the Microgrid Consortium can use shared energy storage for charging and discharging at any time, with low usage costs. It equals the discharge capacity multiplied by the discharge price minus the charging capacity multiplied by the charging price. Where, These represent the unit price for discharging and the unit price for charging in Package A during time period t, respectively, with M being an auxiliary parameter.

[0234] Package B constraints:

[0235]

[0236]

[0237]

[0238]

[0239]

[0240]

[0241] Shared energy storage can provide capacity leasing services to microgrid consortia (while the microgrid consortium receives half of the capacity's electricity), with no time restrictions and low usage costs. It equals the leased capacity multiplied by the unit price per unit capacity. Where, This refers to the unit price per unit capacity for Package B. For the leased capacity of microgrid consortium n, The actual electricity consumption of the leased capacity of microgrid alliance n for time period package B during time period t. To share the initial amount of energy stored.

[0242] Package C constraints:

[0243]

[0244]

[0245]

[0246]

[0247]

[0248]

[0249]

[0250]

[0251]

[0252] Shared energy storage can provide time-based leasing services for microgrid consortia, with no power limit and low usage costs. It equals the rental period multiplied by the unit price per rental period. Where, For the microgrid consortium n, select package C for the state variables. The unit price for package C during time period t. These are the new binary auxiliary variables introduced in Package C.

[0253] Package D constraints:

[0254]

[0255]

[0256]

[0257]

[0258]

[0259] Shared energy storage can provide electricity storage services for microgrid consortia, with lower usage costs. This equals the stored energy multiplied by the storage unit price. During the specified storage period TC, microgrid consortium n can store energy into the shared energy storage system; during this period, the shared energy storage system cannot discharge energy into microgrid consortium n. During the specified release period TD, the shared energy storage system must release previously stored energy to microgrid consortium n; during this period, the shared energy storage system cannot charge energy into microgrid consortium n. Where, The amount of electricity stored in microgrid n The unit price of stored electricity during time period t.

[0260] Step 3: Based on the upper-level shared energy storage operator model and the lower-level microgrid optimization operation model established in Steps 1 and 2, the proposed method is applied to the actual pricing field of shared energy storage systems to establish a basic framework and model of master-slave game with shared energy storage operators as leaders and various microgrid alliances as followers.

[0261] The upper-level shared energy storage operator model, which aims to maximize the operating revenue of shared energy storage, and the lower-level microgrid optimization operation model, which aims to minimize the operating costs of each microgrid alliance, proposed in steps 1 and 2 respectively, are applied to the actual shared energy storage pricing field. The basic framework of the master-slave game in step 3 is described as follows:

[0262] Considering shared energy storage as an independent operator, a master-slave game model, also known as the Stackelberg model, is established. The participants in the game include the shared energy storage operator and each microgrid. First, the shared energy storage operator sets the daily package prices for different microgrid alliances. Based on the pricing, the microgrids select the appropriate package and formulate their electricity consumption strategies, thus affecting their own interests. Then, the shared energy storage operator adjusts the package prices based on the microgrid alliance's strategy to maximize its own profit. This cycle continues until an optimal pricing strategy that satisfies both parties is found. Clearly, the profits of the two parties conflict, and their decisions are influenced by each other and have a sequential order. Due to policy requirements, the microgrid alliance needs to utilize energy storage to achieve its goals; therefore, the shared energy storage operator holds a dominant position in the game, i.e., the leader is the shared energy storage operator, and the followers are the microgrid alliances.

[0263] Step 4: Using a combination of the Gurobi solver and particle swarm optimization algorithm, follow the steps outlined in the appendix. Figure 3 The solution process shown solves the upper and lower layer models established in steps 1, 2, and 3.

[0264] The solution was obtained using a combination of the Gurobi solver and particle swarm optimization algorithm in MATLAB 2022a. The specific solution steps are as follows:

[0265] First, input the known parameters of each microgrid consortium and the shared energy storage system, and set the relevant parameters and iteration number of the particle swarm optimization algorithm. Second, initialize the operator's price data using the particle swarm optimization algorithm, and use Gurobi to solve for the demand of the lower-level microgrid and the upper-level operation optimization strategy, calculate the revenue of both parties, and obtain the initial optimal strategy. Third, set the iteration number t=1, enter the iterative optimization part, generate a new pricing strategy using the particle swarm optimization algorithm based on the current charging and discharging service usage strategy of the lower-level microgrid, use Gurobi to solve for the charging and discharging service usage of the lower-level microgrid, calculate the revenue of the shared energy storage operator in this segment, and save the current iterative optimal solution. Finally, determine whether the current iteration number is greater than the maximum iteration number T. If the maximum iteration number has not been reached, repeat the above iterative process until the maximum iteration number T is reached, and output the current optimal solution.

[0266] Simulation analysis is conducted using the example scenario shown in Table 1. Assume a day is divided into 24 time periods, i.e., t=24. The shared energy storage operator owns two shared energy storage units, and four microgrid consortia require energy storage services. The four microgrid consortia are N1, N2, N3, and N4, as follows: Figure 4 The figure shows the projected load curves for four microgrid consortia, as follows: Figure 5 The figure shows the predicted power output curves of the wind power generation equipment of the four microgrid consortia.

[0267] Table 1 shows the parameter settings for a shared energy storage system. In this example, we consider that the shared energy storage operator has two shared energy storage systems.

[0268] Table 1. Parameter Settings for Shared Energy Storage Systems

[0269]

[0270] Figures 6 to 9 The figures show the power interaction between shared energy storage and various microgrid alliances under the optimal pricing strategy, along with the prices of the selected packages. Figures 6 to 9 It can be seen that Alliance 1 chose Package D, where, during the early morning when load is low and wind power output is relatively high, Alliance 1 deposits electricity with the shared energy storage. During the midday and afternoon peak electricity consumption periods, the shared energy storage releases the corresponding electricity to Alliance 1. Alliance 2 chose Package B, leasing 38.45 kWh of energy storage. During periods of relatively high power output, Alliance 2 charges the leased energy storage, and during periods of relatively heavy load, the energy storage discharges. During periods 13-14, Alliance 3's net load increases sharply. Alliance 3 chose Package C, which is billed by time period, saving 8.32 yuan in energy storage costs and 32.04 yuan in total operating cost savings. Alliance 4 chose Package A, which has a low energy storage power requirement but a high demand during many time periods.

[0271] Figures 10(a) and 10(b) illustrate the use of shared energy storage by microgrid consortia over time under the optimal pricing strategy. Of the 24 scheduling periods, shared energy storage is used in 23 periods: 10 periods are used by only one consortium, 10 periods are used by two consortia, and 3 periods are used by three consortia simultaneously. In period 13, shared energy storage s1 charges from consortium 4, while shared energy storage s2 discharges to consortia 1 and 3. In periods 17 and 20, shared energy storage s2 charges from consortium 4, while shared energy storage s1 discharges to consortia 1 and 2. Consortia can use shared energy storage together during the same period or individually at different times.

[0272] Table 2 shows the calculation setup, and Table 3 shows the comparison of the operating costs of the microgrid alliance and the revenue of the shared energy storage operator under different pricing strategies for different calculations.

[0273] Table 2. Comparison of Scheme Settings

[0274]

[0275] Table 3. Operating costs and shared energy storage revenues for each scheme alliance

[0276]

[0277] Table 3 shows that the operating costs of Alliances 1-4 in Scheme 1 are RMB 206.27, RMB 1340.83, RMB 546.88, and RMB 185.43, respectively, while those in Scheme 2 are RMB 185.36, RMB 1315.21, RMB 531.32, and RMB 162.81, respectively. After introducing shared energy storage, the operating costs of Alliances 1-4 decreased by RMB 20.19, RMB 25.62, RMB 15.56, and RMB 22.62, respectively. In Scheme 1, the energy storage is self-built by the microgrid alliance, requiring consideration of its investment costs. In Scheme 2, the energy storage is built by a shared energy storage operator, eliminating the need for the microgrid alliance to build its own energy storage equipment, thus saving construction costs. After introducing shared energy storage, the total amount of electricity charged by the shared energy storage from the microgrid alliance increases compared to the total amount charged by the alliance's self-built distributed energy storage from the microgrid alliance. The microgrid alliance's sale of electricity to shared energy storage further reduces its operating costs.

[0278] Compared to Scheme 2, which introduces shared energy storage, Scheme 3, based on the concept of shared energy storage, introduces a game-theoretic pricing mechanism between the shared energy storage provider and the microgrid consortium, with the shared energy storage provider as the dominant party and the microgrid consortium as the follower. In Scheme 3, the operating costs of each microgrid consortium increase by 1.73%, 0.56%, 1.02%, and 2.09% respectively compared to Scheme 2, while the revenue from shared energy storage increases by 9.46%.

[0279] Compared to Option 3, Option 4 introduces shared energy storage packages. These diverse packages provide microgrid consortia with different billing methods. The operating costs of microgrid consortia 1-3 decreased by 3.21%, 4.57%, and 5.97%, respectively. This is because shared energy storage offers different packages tailored to the load and power output characteristics of different microgrid consortia, allowing them to choose a suitable package and negotiate pricing with shared energy storage providers. Notably, the operating costs of Consortium 4 remain essentially unchanged because the billing method for Package A chosen by Consortium 4 in Option 4 is the same as the traditional billing method for shared energy storage in Option 3. The introduction of diversified packages increased shared energy storage revenue by 2.62%. These diversified packages both reduce the operating costs of microgrid consortia and increase shared energy storage revenue, achieving a win-win situation.

Claims

1. A multi-package game pricing method for coordinated operation of microgrid alliances and shared energy storage, characterized in that, Multiple independent microgrid consortia share the same shared energy storage system, and the pricing method includes the following steps: Step 1: With the goal of maximizing the operating benefits of the shared energy storage system for shared energy storage operators, establish an upper-level shared energy storage operator model that considers the power interaction constraints with microgrids and distribution networks, as well as its own capacity constraints. Step 2: With the goal of minimizing the operating cost of each microgrid alliance, establish an optimized operation model for the lower-level microgrid alliance that considers power balance constraints, gas turbine-related constraints, wind power output constraints, power purchase constraints from the distribution network, and shared energy storage package constraints. Step 3: Based on the upper-level shared energy storage operator model and the lower-level microgrid alliance optimization operation model, and according to the actual pricing field of shared energy storage systems, establish a basic framework and model of master-slave game with shared energy storage operators as leaders and multiple wind farms as followers; Step 4: Using a combination of the Gurobi solver and particle swarm optimization algorithm, the upper-layer shared energy storage operator model, the lower-layer microgrid alliance optimization operation model, and the master-slave game basic framework and model are solved to obtain the pricing scheme; In step 1, the objective function of the upper-layer shared energy storage operator model is to maximize the operating revenue of the shared energy storage system. Without considering equipment investment and construction costs, the operator's objective function is as follows: ； In the formula, t is the time index and n is the microgrid consortium index; To generate revenue from selling electricity to the distribution network, To determine the cost of purchasing electricity from the distribution network, For energy storage operation losses and costs, This represents the revenue generated by providing shared energy storage services to the microgrid consortium n. To penalize the auxiliary variable's penalty unit price, , and These are auxiliary variables representing penalties for charging, discharging, and exceeding the power limit during time period t; 1) Benefits of power interaction with the distribution network The revenue from power exchange with the distribution network is equal to the revenue from selling electricity to the distribution network through shared energy storage. Subtract the cost of purchasing electricity from the distribution network Revenue from selling electricity to the distribution network and the cost of purchasing electricity from the distribution network As shown in the following formula: ； ； In the formula, s is the index of the shared energy storage device; The power sold to the distribution network by the shared energy storage system during time period t. The price at which shared energy storage is sold to the distribution network during time period t; For time period t, the power that the shared energy storage s purchases from the distribution network. The price at which shared energy storage purchases electricity from the distribution network during time period t; 2) Energy storage operation loss cost ； In the formula, This is the discounted price based on the lifespan loss per unit charge / discharge power of the energy storage system. and These represent the discharge power and charging power of the shared energy storage system during time period t, respectively. 3) Revenue from providing energy storage services to microgrid consortia ； In the formula, , , and The revenue from shared energy storage for providing services under Package A, Package B, Package C, and Package D to the microgrid consortium n is respectively. The objective function of the lower-level microgrid alliance optimization operation model established in step 2 is to minimize the operating cost of each microgrid alliance; specifically as follows: The objective function is as follows: ； In the formula, the operating cost of the microgrid consortium n is... Including gas turbine fuel costs Punishment for abandoning wind Cost of purchasing electricity from the distribution network and the cost of using shared energy storage Specifically: 1) Gas turbine fuel cost for: ； In the formula, E(n) is the set of devices in the microgrid consortium n; Let t be the price of fuel during time period t. Let g be the fuel consumption of the gas turbine during time period t; 2) Cost of wind curtailment penalty for: ； In the formula, The price for wind curtailment during time period t; The predicted power output of the wind farm at time t is given. The power output of the wind farm is scheduled for time period t; 3) Cost of purchasing electricity from the distribution network for: ； In the formula, The distribution network electricity price for time period t. Let n be the power purchased by the microgrid consortium from the distribution network during time period t; 4) Cost of using shared energy storage for: ； In the formula, , , and These represent the costs for the microgrid consortium n to use shared energy storage packages A, B, C, and D. Package A constraints: By choosing Package A, the Microgrid Consortium can use shared energy storage for charging and discharging at any time, with low usage costs. It equals the discharge capacity multiplied by the discharge price minus the charging capacity multiplied by the charging price; as shown in the following formula: ； ； ； In the formula, and These are the unit price for discharging and the unit price for charging of Package A during time period t, respectively, with M being an auxiliary parameter; Package B constraints: Choosing Package B allows the shared energy storage to provide capacity leasing services to the microgrid consortium n, while the consortium receives half of the capacity's electricity, with no time restrictions and no usage costs. It equals the leased capacity multiplied by the unit price of the capacity; as shown in the following formula: ； ； ； ； ； ； In the formula, This refers to the unit price per unit capacity for Package B. For the leased capacity of microgrid consortium n, The actual electricity consumption of the leased capacity of microgrid alliance n for time period package B during period t. To share the initial energy storage capacity; Package C constraints: Choosing Package C allows shared energy storage to provide time-based leasing services to the microgrid consortium n, with no power limit and low usage costs. It equals the rental period multiplied by the unit price per rental period; as shown in the following formula: ； ； ； ； ； ； ； ； ； In the formula, For the microgrid consortium n, select package C for the state variables. The unit price per hour for package C during time period t. and These are the new binary auxiliary variables introduced in Package C; Package D constraints: Choosing Package D allows shared energy storage to provide electricity storage services for the microgrid consortium n, with usage costs... The value is equal to the stored energy multiplied by the storage unit price; and within the specified storage period TC, the microgrid consortium n stores energy into the shared energy storage, during which the shared energy storage cannot discharge to the microgrid consortium n; within the specified release period TD, the shared energy storage needs to release the previously stored energy to the microgrid consortium n, during which the shared energy storage cannot charge the microgrid consortium n. The specific formula is as follows: ； ； ； ； ； In the formula, The amount of electricity stored in microgrid n The unit price of stored electricity during time period t.

2. The multi-package game pricing method for coordinated operation of microgrid alliances and shared energy storage as described in claim 1, characterized in that, The constraints of the upper-level shared energy storage operator model in step 1 include power interaction constraints with the microgrid, power interaction constraints with the distribution network, and its own capacity constraints, as detailed below: a) Power interaction constraints with microgrids and distribution networks The following formula indicates that the power range constraint must be met for both the purchase and sale of electricity and the charging and discharging of shared energy storage: ； ； The following formula represents the constraint that shared energy storage s cannot simultaneously engage in electricity sales and purchase operations, nor can it simultaneously engage in discharging and charging operations: ； The following formula represents the constraint that shared energy storage s cannot simultaneously perform discharge and electricity purchase operations, nor can it simultaneously perform charging and electricity sales operations: ； In the formula, and These represent the maximum and minimum power output of the shared energy storage s to the distribution network, respectively. Let s be the state variable for the shared energy storage to sell electricity to the distribution network during time period t. A value of 1 indicates that an electricity selling operation is performed, and a value of 0 indicates that no electricity selling operation is performed. The power purchased by the shared energy storage system from the distribution network during time period t; and These represent the maximum and minimum power that the shared energy storage s purchases from the distribution network, respectively. Let t be the state variable for the shared energy storage s to purchase electricity from the distribution network during time period t. A value of 1 indicates that an electricity purchase operation is performed, and a value of 0 indicates that no electricity purchase operation is performed. and These represent the maximum and minimum power values ​​of the shared energy storage s discharging to the microgrid consortium, respectively. Let t be the state variable for the shared energy storage s to discharge to the alliance during time period t. A value of 1 indicates that a discharge operation is performed, and a value of 0 indicates that a discharge operation is not performed. The power of shared energy storage s charged from the microgrid consortium during time period t; and These represent the maximum and minimum power values ​​for the shared energy storage s to charge from the microgrid consortium, respectively. The state variable for charging shared energy storage s from the microgrid alliance during time period t is 1, which indicates that a charging operation is performed, and 0 indicates that a charging operation is not performed. b) Shared energy storage capacity constraints: ； ； ； ； ； ； ； In the formula, The total amount of electricity stored in the shared energy storage system during time period t is s. and These refer to the shared energy storage charging efficiency and discharging efficiency, respectively. and These represent the discharge power and charging power of the microgrid alliance during time period t, respectively. and These are the minimum and maximum values ​​of the shared energy storage charging power, respectively. and These are the minimum and maximum values ​​of the shared energy storage discharge power, respectively. and These are the minimum and maximum values ​​of the shared energy storage capacity, respectively.

3. The multi-package game pricing method for coordinated operation of microgrid alliances and shared energy storage as described in claim 1, characterized in that, The constraints of the lower-level microgrid alliance optimization operation model include microgrid alliance power balance constraints, gas turbine-related constraints, wind power output constraints, power purchase from the distribution network constraints, and shared energy storage package constraints; specifically: a) Power balance constraints of microgrid alliances ； In the formula, D represents the set of loads in the microgrid group. Let g be the active power output of the gas turbine during time period t; Let d be the active load during time period t. b) Gas turbine-related constraints Gas turbine output constraints: ； Gas turbine minimum start-stop time constraints: ； ； Fuel consumption constraints: ， ； ， ； Gas turbine ramp rate constraint: ； ； In the formula, Let g be the operating state variable of the gas turbine during time period t. A value of 1 indicates that the gas turbine g is in the on state during time period t. When the value is 0, it indicates that the gas turbine g is in a shutdown state during time period t; and These represent the minimum and maximum output values ​​of the gas turbine, g, respectively. and These are the start-up and shutdown time counters for gas turbine g, respectively. and These represent the minimum start-up and shutdown times for the gas turbine, respectively. and These represent the fuel consumption during the start-up and shutdown of the gas turbine g, respectively. and These are the uphill and downhill ramp rates of the gas turbine, respectively. and These represent the fuel consumption of the gas turbine during time period t, specifically the fuel consumption during startup and shutdown of gas turbine g. c) Wind power output constraints: ； d) Constraints on purchasing electricity from the distribution network ； In the formula, and These represent the minimum and maximum power that the microgrid consortium n purchases from the distribution network, respectively. e) Constraints on shared energy storage packages: ； ， ， ， ； ； ； ； In the formula, , , and These are the state variables for selecting Package A, Package B, Package C, and Package D for the microgrid alliance n. A value of 1 indicates that the package has been selected, and a value of 0 indicates that the package has not been selected. and These represent the charging and discharging state variables of the microgrid consortium during time period t, respectively. and These represent the minimum and maximum charging power of the microgrid consortium n, respectively. and These represent the minimum and maximum discharge power of the microgrid consortium n, respectively.

4. The multi-package game pricing method for coordinated operation of microgrid alliances and shared energy storage as described in claim 1, characterized in that, In step 3, the game process of the master-slave game basic framework and model is described as follows: First, the shared energy storage operator sets the package prices for different microgrid alliances within a day; then, the microgrid alliance selects a suitable package and formulates an electricity consumption strategy based on the pricing and feeds it back to the shared energy storage operator; then, the shared energy storage operator adjusts the package price according to the electricity consumption strategy of the microgrid alliance to maximize its own profit and feeds this price back to the microgrid alliance; then, the microgrid alliance formulates an electricity consumption strategy based on the pricing and feeds it back to the shared energy storage operator; this cycle continues until the optimal pricing strategy that satisfies both is found.

5. The multi-package game pricing method for coordinated operation of microgrid alliances and shared energy storage as described in claim 1, characterized in that, The model solution method in step 4 is as follows: the model established in step 4 is solved using MATLAB 2022a by combining the Gurobi solver with the particle swarm optimization algorithm. The specific solution process is as follows: Step 4.1: Input the predicted power output and load values ​​of the microgrid consortium, as well as other system parameters; Step 4.2: Set the number of particles m and the number of iterations H, and use the particle swarm optimization algorithm to generate the package price. At this time, the number of iterations h=1. Step 4.3: The shared energy storage transmits the price to the microgrid alliance. Each microgrid alliance uses this price to solve the optimization operation model of the lower-level microgrid alliance, formulates its own charging and discharging strategy, and feeds it back to the shared energy storage. Step 4.4: Based on the charging and discharging strategy of the microgrid alliance, the shared energy storage solves the upper-level shared energy storage operator model, formulates its own charging and discharging strategy, compares each particle and updates the current iterative optimal solution, and saves and updates the current optimal price strategy. Step 4.5: If h If H is satisfied and the convergence criterion is met, then the program ends; otherwise, h = h + 1, update the package price and return to step 4.3.

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