Centralized shared energy storage capacity configuration and pricing method based on master-slave game

Abstract

The application relates to a centralized shared energy storage capacity configuration and pricing method based on a principal-agent game, and comprises the following steps: respectively establishing a shared energy storage operator side capacity configuration optimization submodel and a power grid side capacity configuration optimization submodel; calculating a shared energy storage capacity optimal value by using an alternating direction multiplier method; establishing an upper layer model; establishing a lower layer model; converting the double-layer optimization model into a single-layer optimization model by using KKT optimality conditions and a linear programming duality theorem; based on the shared energy storage capacity optimal value obtained in the step (2), solving the single-layer optimization model by using a commercial solver to obtain a principal-agent game equilibrium point, that is, an energy storage operator optimal price. The application conforms to the characteristics of multi-agent decision of the power grid and the shared energy storage operator in reality; can protect the data privacy of different interest agents; the application improves the cost efficiency of the energy storage through cost sharing and scale economy, is suitable for not only small and medium-sized power users but also city area large power systems, and has universality.

Description

Technical Field

[0001] This invention relates to the field of shared energy storage for absorbing new energy power generation technology, and in particular to a centralized shared energy storage capacity configuration and pricing method based on master-slave game theory. Background Technology

[0002] As the application value and commercial development of grid energy storage have gradually gained market attention and recognition, how to absorb new energy power generation has become an urgent problem to be solved, and energy storage is an important means to smooth out the fluctuations of new energy.

[0003] In recent years, research on the business models of energy storage power stations has yielded some results. Traditional distributed energy storage is limited by safety performance, electricity pricing policies, spatial location, and business models. The concept of "cloud energy storage" has emerged in recent years, but this model relies on existing power grid infrastructure. Grid failures may lead to common-mode failures in the energy storage system, and it is only suitable for small and medium-sized users. Therefore, the concepts of "energy storage leasing" and "electricity sharing market" have been proposed. With the rise of the energy internet and the sharing economy, the business model of shared energy storage has emerged and has become an important research direction for the application of energy storage in the energy internet. Existing energy storage business models suffer from limited participation of stakeholders, resulting in a single source of revenue for energy storage power stations. Shared energy storage, due to its flexible application, can integrate regional energy storage resources to provide auxiliary power services, effectively solving problems such as low construction quality and inconvenient dispatching and operation of distributed energy storage. It improves the cost efficiency of energy storage through cost sharing and economies of scale. However, there are currently few domestic investment cases, and foreign research focuses on residential areas and the user side, indicating that the participants in shared energy storage are still not diverse enough.

[0004] Currently, research on the optimal configuration and pricing methods for shared energy storage is still in its early stages. Most studies apply shared energy storage to small and medium-sized power systems and treat its parameters as constant values. Few studies consider the optimal configuration of shared storage capacity under conditions of high-proportion renewable energy integration, and data privacy among different stakeholders is not protected when configuring parameters. Furthermore, pricing methods for shared energy storage also require further research. Therefore, how to set reasonable parameters to ensure that the constructed capacity is not wasted while simultaneously setting reasonable shared electricity prices to maximize benefits is a pressing issue for shared energy storage. Summary of the Invention

[0005] The purpose of this invention is to provide a centralized optimization model for power grid and shared energy storage operators that does not rely on a single decision-making entity for data collection and power generation scheduling. The shared energy storage capacity configuration process has low communication volume, high autonomy, high flexibility, and good information confidentiality. It is applicable not only to small and medium-sized power users, but also to large-scale urban power systems. It is a centralized shared energy storage capacity configuration and pricing method based on master-slave game theory.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a centralized shared energy storage capacity configuration and pricing method based on master-slave game theory, the method comprising the following sequential steps:

[0007] (1) Establish a capacity configuration optimization sub-model on the operator side and a capacity configuration optimization sub-model on the grid side for shared energy storage respectively: The capacity configuration optimization sub-model on the operator side of shared energy storage aims to optimize the investment and construction cost of centralized shared energy storage and the charging and discharging power cost of shared energy storage, and calculates the shared energy storage capacity supply on the operator side. Shared energy storage charging power supply and shared energy storage discharge power supply In the grid-side capacity configuration optimization sub-model, the shared energy storage capacity demand on the grid side is calculated with the objectives of optimizing grid renewable energy absorption, peak-shaving capacity, and cost. Shared energy storage charging power demand and shared energy storage discharge power demand ;

[0008] (2) Calculate the optimal value of shared energy storage capacity using the Alternating Direction Multiplier Method (ADMM): The shared energy storage operator-side capacity configuration optimization sub-model and the grid-side capacity configuration optimization sub-model mutually transfer shared variables. When the convergence criterion is met, the calculation of shared variables stops and the optimal value of shared energy storage capacity is output. Otherwise, the dual variables are updated and iteratively calculated until the shared variables converge. The shared variables include the supply of shared energy storage capacity. Shared energy storage charging power supply and shared energy storage discharge power supply ;

[0009] (3) Establish an upper-level model with shared energy storage investment operators as the main players: The objective function of the upper-level shared energy storage investment operators is the current shared energy storage time period. The goal is to maximize the shared net revenue, which includes shared energy storage service revenue, shared energy storage investment and construction costs, and energy storage charging and discharging power costs. The decision variable is the shared electricity price. ;

[0010] (4) Establishing a lower-level model with multiple new energy power stations as followers: New energy power stations obtain shared energy storage services by paying service fees to shared energy storage operators. The objective function is to minimize net cost, which includes wind and solar curtailment penalties, shared energy storage service fees, and grid connection deviation penalties. The decision variable is the renewable energy power station. exist Shared power during time period Shared energy storage charging power Shared energy storage discharge power New energy power stations exist Wind and solar power curtailment during certain periods and new energy power stations exist Internet usage deviation during different time periods ;

[0011] (5) The upper-level model and the lower-level model form a two-level optimization model. The two-level optimization model is transformed into a single-level optimization model by using the KKT optimality condition and the duality theorem of linear programming.

[0012] (6) Based on the optimal value of shared energy storage capacity obtained in step (2), the single-layer optimization model is solved using a commercial solver to obtain the game equilibrium point of the master-slave game, which is the optimal pricing of the energy storage operator.

[0013] Step (1) specifically includes the following steps:

[0014] (1a) The objective function of the shared energy storage operator-side capacity configuration optimization sub-model is the average daily investment construction cost of centralized shared energy storage, the charging power cost of shared energy storage, the discharging power cost of shared energy storage, and a penalty term:

[0015]

[0016] In the formula, Total cost for shared energy storage operators; The average daily investment and construction cost for centralized shared energy storage; Cost of charging power for shared energy storage; To reduce the cost of shared energy storage discharge power; This is a penalty item;

[0017] The formula for calculating the average daily investment and construction cost of centralized shared energy storage is as follows:

[0018]

[0019] In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage;

[0020] The formulas for calculating the cost of shared energy storage charging power and the cost of shared energy storage discharging power are as follows:

[0021]

[0022]

[0023] In the formula: , All are charging power cost coefficients; , All are discharge power cost coefficients;

[0024] By coupling the decision variables of the grid side and the energy storage supplier side through a penalty term, the formula for calculating the penalty term is as follows:

[0025]

[0026] In the formula: Indicates the number of iterations; , All are Lagrange multipliers; , , All are penalty factors; For the power grid side The shared energy storage capacity demand obtained after the next iteration; For the power grid side The shared energy storage charging power demand obtained after the next iteration. For the power grid side The shared energy storage discharge power demand obtained after the next iteration;

[0027] The objective function shown in formula (1) needs to satisfy the following constraints: power balance constraint, shared energy storage capacity constraint, shared energy storage charging and discharging power constraint, and shared energy storage capacity and charging and discharging power relationship constraint; these constraints and the objective function shown in formula (1) together form the shared energy storage operator-side capacity configuration optimization sub-model;

[0028] The power balance constraint is as follows:

[0029]

[0030] In the formula: for Time-optimized load power; for Generator set power during specific time periods; In order to be in Wind curtailment power during the period; In order to be in The amount of light discarded during a given period; For wind power in Actual output during the time period; For photovoltaic power stations Actual output during the time period;

[0031] Shared energy storage capacity constraints are:

[0032]

[0033]

[0034]

[0035] In the formula: For shared energy storage Real-time capacity for a given time period; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage; and These represent the real-time capacity of the shared energy storage at the beginning and end of the process, respectively.

[0036] The power constraints for shared energy storage charging and discharging are:

[0037]

[0038]

[0039] In the formula: and These represent the upper and lower limits for shared energy storage charging, respectively. and These represent the upper and lower limits of shared energy storage discharge, respectively. It is a Boolean variable;

[0040] The constraint on the relationship between shared energy storage capacity and charging / discharging power is:

[0041]

[0042] In the formula: This represents the ratio of the rated capacity of shared energy storage to its power limit. The charging and discharging power limit for shared energy storage;

[0043] (1b) The objective function of the grid-side capacity configuration optimization sub-model is to optimize the sum of wind curtailment penalty cost, solar curtailment penalty cost, grid-connected power fluctuation penalty, unit operating cost, and penalty terms:

[0044]

[0045] in, This represents the total cost on the grid side. The cost of curtailing wind power; The cost of penalties for abandoning light; Penalty for grid-connected power fluctuations; For unit operating costs; This is a penalty item;

[0046] The formulas for calculating the cost of wind curtailment penalty and the cost of solar curtailment penalty are as follows:

[0047]

[0048]

[0049]

[0050]

[0051] In the formula: , All are cost coefficients for wind curtailment power; , All are cost coefficients for curtailment power penalty; To measure the power output of wind power in Predicted power generation output for the specified time period; For photovoltaic power stations Predicted power generation output for the specified time period;

[0052] The formula for calculating the grid-connected power fluctuation penalty is as follows:

[0053]

[0054] In the formula: This is the penalty coefficient for the power deviation between the optimized load power and the target load power;

[0055] The formula for calculating the unit operating cost is as follows:

[0056]

[0057] In the formula: for Cost of generator unit output per time period; for Power output of generator units during specific time periods; , , All are fitting coefficients for unit output cost;

[0058] By coupling the decision variables on the grid side and the shared energy storage operator side through a penalty term, the calculation formula for the penalty term is as follows:

[0059] In the formula: For energy storage supplier side The energy storage capacity supply obtained after the next iteration , They are respectively the first on the energy storage supplier side The shared energy storage charging and discharging power supply obtained after the next iteration;

[0060] The objective function shown in formula (13) needs to satisfy the following constraints: power balance constraint, shared energy storage capacity constraint, shared energy storage charging and discharging power constraint, shared energy storage capacity and charging and discharging power relationship constraint, node power balance constraint, line transmission capacity constraint, generator output constraint, generator ramp rate constraint, wind curtailment constraint and solar curtailment constraint; these constraints and the objective function shown in formula (13) together form the grid-side capacity configuration optimization sub-model;

[0061] The power balance constraint is as follows:

[0062]

[0063] Shared energy storage capacity constraints are:

[0064]

[0065]

[0066] The power constraints for shared energy storage charging and discharging are:

[0067]

[0068]

[0069] The constraint on the relationship between shared energy storage capacity and charging / discharging power is:

[0070]

[0071] The node power balance constraint is:

[0072]

[0073] In the formula: This represents the active power matrix injected by the nodes; This represents the susceptance matrix of a network node during normal operation. Represents the node voltage phase angle matrix;

[0074] The line transmission capacity constraint is:

[0075]

[0076] In the formula: and These represent the upper and lower limits of the line transmission capacity, respectively. and Representing nodes respectively and nodes The voltage phase angle; Indicates the DC susceptance of the line;

[0077] The generator set output constraint is:

[0078]

[0079] In the formula: and These are the upper and lower limits of the generator set power, respectively.

[0080] The generator set's gradeability constraint is:

[0081]

[0082] In the formula: and These are the upper and lower limits of the generator set's gradeability, respectively.

[0083] The constraints on wind curtailment and solar curtailment are as follows:

[0084]

[0085]

[0086] Step (2) specifically includes the following steps:

[0087] (2a) Initialization: Set the number of iterations Assigning a value of 1 to give the upper limit of the original residual tolerance. Punishment factor Initial values ​​of Lagrange multiplier coefficients Set the initial value of the shared energy storage capacity demand on the grid side. Initial value of grid-side charging and discharging power demand ;

[0088] (2b) Using the YALMIP toolbox and the GUROBI commercial solver, the initial value of the shared energy storage capacity supply on the shared energy storage operator side is calculated and solved in the shared energy storage operator side capacity configuration optimization sub-model. Initial value of shared energy storage charging power supply Initial value of shared energy storage discharge power supply ;

[0089] (2c) Perform iterative calculations until the shared variables converge. At this point, the value of the shared variables is: the shared energy storage capacity supply. Shared energy storage charging power supply Shared energy storage discharge power supply ;

[0090] (2d) Grid-side received shared energy storage capacity supply Shared energy storage charging power supply and shared energy storage discharge power supply ;

[0091] (2e) Using the YALMIP toolbox and the GUROBI commercial solver, calculate the grid-side capacity configuration optimization sub-model to obtain the shared energy storage capacity demand. Shared energy storage charging power demand Shared energy storage discharge power demand ;

[0092] (2f) Determine convergence based on the following formula:

[0093]

[0094] In the formula: For the power grid side The demand for shared energy storage capacity in the next iteration; For the first The shared energy storage capacity supply in the next iteration; For the power grid side The shared energy storage charging power demand obtained after the next iteration; For the first The grid-side charging power supply in the next iteration; For the power grid side The shared energy storage discharge power demand obtained after the next iteration; For the first The grid-side discharge power supply in the next iteration;

[0095] If the convergence criterion is met, the calculation is stopped and the optimal value of the shared energy storage capacity is output; otherwise, the Lagrange multiplier coefficients are updated according to equation (33). Update iteration coefficients Return to step (2c) to perform the next round of iterative optimization calculations until equation (32) converges;

[0096]

[0097] In the formula, As a penalty factor; , All are Lagrange multipliers.

[0098] Step (3) specifically includes the following steps:

[0099] (3a) The objective function of the upper-level shared energy storage investment operator is the current shared energy storage period. The goal is to maximize the shared net revenue, which includes the revenue from shared energy storage services, the average investment and construction cost of shared energy storage, the charging power cost of shared energy storage, and the discharging power cost of shared energy storage.

[0100]

[0101] In the formula: In order to be in Shared net revenue during the time period; In order to be in Revenue from shared energy storage services during specific time periods; In order to be in The average investment and construction cost of shared energy storage during different time periods; They are respectively in Cost of shared energy storage charging power and cost of shared energy storage discharging power during different time periods;

[0102] The formula for calculating revenue from shared energy storage services is as follows:

[0103]

[0104]

[0105] In the formula; This represents the dual variable corresponding to the shared electricity constraint, namely the shared electricity price; For shared energy storage power stations Shared power consumption during different time periods;

[0106] The formula for calculating the average investment and construction cost of shared energy storage is:

[0107]

[0108] In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage;

[0109] The formulas for calculating the cost of shared energy storage charging power and the cost of shared energy storage discharging power are as follows:

[0110]

[0111]

[0112] In the formula: , All are charging power cost coefficients; , All are discharge power cost coefficients;

[0113] (3b) The objective function shown in formula (34) needs to satisfy the following constraints: shared energy storage capacity constraint, shared power constraint, and shared charging and discharging power constraint; these constraints and the objective function shown in formula (34) together form the upper-level model;

[0114] Shared energy storage capacity constraints are:

[0115]

[0116]

[0117] In the formula: For shared energy storage Real-time capacity for a given time period; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage;

[0118] Shared power constraints are:

[0119]

[0120] In the formula, For shared energy storage stations exist Shared power consumption during different time periods;

[0121] The power constraints for shared energy storage charging and discharging are:

[0122]

[0123]

[0124]

[0125]

[0126] Step (4) specifically includes the following steps:

[0127] (4a) The lower layer consists of new energy power station operators, who optimize their own operation strategies based on the pricing of shared energy storage operators;

[0128] New energy power stations The objective function is to minimize net cost, which includes shared energy storage service fees, wind and solar curtailment penalty costs, and grid-connected power deviation penalty costs.

[0129]

[0130] In the formula: Net cost for lower-level new energy power station operators; This refers to the number of new energy power stations; For new energy power stations exist Costs for shared energy storage services during specific time periods; For new energy power stations exist The penalty costs of wind and solar power curtailment during certain periods; For new energy power stations exist Penalty cost for deviations in internet usage during different time periods;

[0131] The formula for calculating the cost of shared energy storage services is as follows:

[0132]

[0133]

[0134] In the formula: In order to be in Shared electricity pricing for specific time periods; For shared duration;

[0135] The formula for calculating the penalty cost of wind and solar power curtailment is as follows:

[0136]

[0137] In the formula: , All are cost coefficients for wind curtailment power;

[0138] The formula for calculating the penalty cost for deviation in internet usage is as follows:

[0139]

[0140] In the formula: , The cost coefficient for penalizing deviations in internet usage;

[0141] (4b) The objective function shown in formula (47) needs to satisfy the following constraints: shared energy storage charging and discharging power constraints, wind and solar curtailment constraints, grid-connected power deviation constraints, and power balance constraints. These constraints and the objective function shown in formula (47) together form the lower-level model.

[0142] The power constraints for shared energy storage charging and discharging are:

[0143]

[0144]

[0145] In the formula: It is a Boolean variable, representing The status of the energy storage device during a given time period ensures that energy storage charging and discharging do not occur simultaneously; The charging and discharging power limit for shared energy storage;

[0146] The constraints on wind and solar curtailment are:

[0147]

[0148] In the formula: For new energy power stations exist Actual wind and solar power output during the period;

[0149] The power consumption deviation limit for internet access is:

[0150]

[0151] In the formula: For new energy power stations exist Electricity consumption during internet usage during a given time period;

[0152] The power balance constraint is:

[0153]

[0154] Step (5) specifically includes the following steps:

[0155] (5a) The upper-level model and the lower-level model constitute a master-slave game. Under the condition that the energy storage charging and discharging efficiency is not 100%, the Boolean variable part is removed in the two-level model. At this time, the lower-level model is transformed into a linear convex problem, which can be solved by KKT conditions.

[0156] (5b) The master-slave game problem of energy storage operators and new energy power plants sharing energy storage leasing is written in a two-layer optimization form, targeting new energy power plants. Its two-layer model is shown in equation (57) below. For simplification, the dual variables corresponding to the constraints are represented by the symbols after the colon:

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172] in, The shared net income during period t; , All are charging power cost coefficients; , All are discharge power cost coefficients; The charging and discharging power limit for shared energy storage; The real-time capacity of shared energy storage during time period t; , All are cost coefficients for wind curtailment power; The power cost of shared energy storage; The capacity cost of shared energy storage; For shared energy storage power stations Shared power consumption during different time periods; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage; This represents the dual variable corresponding to the shared electricity constraint, namely the shared electricity price; For shared duration; For new energy power stations exist Electricity consumption during internet usage during a given time period; For new energy power stations exist Shared power consumption during different time periods; For shared energy storage stations exist Shared power consumption during different time periods; For lower-level new energy power stations Operators Net cost for the period; For new energy power stations exist Actual wind and solar power output during the period; For shared duration; , All are cost coefficients for curtailment power penalty; For new energy power stations exist The amount of light wasted during a given time period; As dual variables;

[0173] (5c) Let the complementary relaxation dual variables corresponding to constraint (57) be respectively , , , , Further details on lower-level new energy power stations The Lagrangian function for the operating cost problem is as follows (58):

[0174]

[0175] In the formula: The Lagrangian function for the operating cost of new energy power plants; For new energy power stations exist Costs for shared energy storage services during specific time periods; For new energy power stations exist The penalty costs of wind and solar power curtailment during certain periods; For new energy power stations exist Actual wind and solar power output during the period; In order to be in Shared electricity pricing for specific time periods; For the grid connection revenue of new energy power station i during time period t;

[0176] (5d) Take the partial derivatives of the Lagrange function with respect to the lower-level variables to find the KKT optimality conditions:

[0177]

[0178]

[0179]

[0180] Equality constraints include:

[0181]

[0182]

[0183] (5e) Write down the KKT directions and complementary conditions corresponding to the inequality constraints, as shown in equations (64)-(71) below:

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192] In the formula: express and Complementary, meaning that there is one and only one of them that is 0;

[0193] (5f) Introducing a series of Boolean variables transforms the complementary relaxation constraint conditions (64)-(71) into cutting plane constraints using the Big-M method. The cutting plane constraints are shown in equations (72)-(87) below:

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209]

[0210] In the formula: It is a sufficiently large positive number that can cover the range of the variable being scaled; It is a Boolean variable;

[0211] The KKT system representation of the operating cost issue of lower-level renewable energy power stations is shown below:

[0212] In the formula: The KKT system represents the operating costs of lower-level renewable energy power stations;

[0213] (5g) The nonlinearity of the objective functions (1) and (13) of the upper and lower models originates from the product of the shared electricity price and the shared electricity volume. However, according to the strong duality theorem of linear programming, the objective function values ​​corresponding to the optimal solutions determined by the original problem and the dual problem are equal. That is, the objective function of the new energy power station operation cost problem has the following equivalent form. By linearizing the nonlinear terms in the objective function of the optimization problem, the following equation is obtained:

[0214]

[0215] The combined formula (88) can be derived The equivalent expression is shown below:

[0216]

[0217] Step (6) specifically includes the following steps:

[0218] (6a) Substituting equation (89) into the upper-level optimization problem equation (57), the pricing game problem of the upper-level shared energy storage operator can then be transformed into the following equilibrium constraint programming problem:

[0219]

[0220] In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage; , All are charging power cost coefficients; , All are discharge power cost coefficients; , All are cost coefficients for wind curtailment power; For new energy power stations exist Electricity consumption during internet usage during a given time period; For new energy power stations exist Actual wind and solar power output during the period;

[0221] (6b) Substitute the optimal energy storage capacity calculated in step (2) into equation (90), and use the YALMIP toolbox and GUROBI commercial solver to solve directly to obtain the shared electricity price at each time point. This refers to the optimal pricing for energy storage operators.

[0222] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, in the configuration of shared energy storage capacity, the present invention does not require a single decision-making body to collect data and schedule power generation for the centralized optimization model of the power grid and the shared energy storage operator, which conforms to the characteristics of multi-entity decision-making in reality between the power grid and the shared energy storage operator; Second, the shared energy storage capacity configuration process has low communication volume, strong autonomy, and high flexibility. The power grid entity and the shared energy storage operator entity can make autonomous decisions and coordinate optimization, and the information confidentiality is good. Compared with centralized solutions, it can protect the data privacy of different stakeholders; Third, the present invention improves the cost efficiency of energy storage through cost sharing and economies of scale. Reasonable parameters ensure that the constructed capacity is not wasted, while reasonable setting of shared electricity prices maximizes the benefits for participants; Fourth, the present invention is not only applicable to small and medium-sized power users, but also applicable to large-scale urban power systems, and has universality. Attached Figure Description

[0223] Figure 1 This is a flowchart of the method of the present invention;

[0224] Figure 2 This is a schematic diagram illustrating the master-slave game relationship between shared energy storage operators and new energy power stations in this invention. Detailed Implementation

[0225] like Figure 1 As shown, a centralized shared energy storage capacity allocation and pricing method based on master-slave game theory is presented, which includes the following sequential steps:

[0226] (1) Establish a capacity configuration optimization sub-model on the operator side and a capacity configuration optimization sub-model on the grid side for shared energy storage respectively: The capacity configuration optimization sub-model on the operator side of shared energy storage aims to optimize the investment and construction cost of centralized shared energy storage and the charging and discharging power cost of shared energy storage, and calculates the shared energy storage capacity supply on the operator side. Shared energy storage charging power supply and shared energy storage discharge power supply In the grid-side capacity configuration optimization sub-model, the shared energy storage capacity demand on the grid side is calculated with the objectives of optimizing grid renewable energy absorption, peak-shaving capacity, and cost. Shared energy storage charging power demand and shared energy storage discharge power demand ;

[0227] (2) Calculate the optimal value of shared energy storage capacity using the Alternating Direction Multiplier Method (ADMM): The shared energy storage operator-side capacity configuration optimization sub-model and the grid-side capacity configuration optimization sub-model mutually transfer shared variables. When the convergence criterion is met, the calculation of shared variables stops and the optimal value of shared energy storage capacity is output. Otherwise, the dual variables are updated and iteratively calculated until the shared variables converge. The shared variables include the supply of shared energy storage capacity. Shared energy storage charging power supply and shared energy storage discharge power supply ;

[0228] (3) Establish an upper-level model with shared energy storage investment operators as the main players: The objective function of the upper-level shared energy storage investment operators is the current shared energy storage time period. The goal is to maximize the shared net revenue, which includes shared energy storage service revenue, shared energy storage investment and construction costs, and energy storage charging and discharging power costs. The decision variable is the shared electricity price. ;

[0229] (4) Establishing a lower-level model with multiple new energy power stations as followers: New energy power stations obtain shared energy storage services by paying service fees to shared energy storage operators. The objective function is to minimize net cost, which includes wind and solar curtailment penalties, shared energy storage service fees, and grid connection deviation penalties. The decision variable is the renewable energy power station. exist Shared power during time period Shared energy storage charging power Shared energy storage discharge power New energy power stations exist Wind and solar power curtailment during certain periods and new energy power stations exist Internet usage deviation during different time periods ;

[0230] (5) The upper-level model and the lower-level model form a two-level optimization model. The two-level optimization model is transformed into a single-level optimization model by using the KKT optimality condition and the duality theorem of linear programming.

[0231] (6) Based on the optimal value of shared energy storage capacity obtained in step (2), the single-layer optimization model is solved using a commercial solver to obtain the game equilibrium point of the master-slave game, which is the optimal pricing of the energy storage operator.

[0232] Step (1) specifically includes the following steps:

[0233] (1a) The objective function of the shared energy storage operator-side capacity configuration optimization sub-model is the average daily investment construction cost of centralized shared energy storage, the charging power cost of shared energy storage, the discharging power cost of shared energy storage, and a penalty term:

[0234]

[0235] In the formula, Total cost for shared energy storage operators; The average daily investment and construction cost for centralized shared energy storage; Cost of charging power for shared energy storage; To reduce the cost of shared energy storage discharge power; This is a penalty item;

[0236] The formula for calculating the average daily investment and construction cost of centralized shared energy storage is as follows:

[0237]

[0238] In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage;

[0239] The formulas for calculating the cost of shared energy storage charging power and the cost of shared energy storage discharging power are as follows:

[0240]

[0241]

[0242] In the formula: , All are charging power cost coefficients; , All are discharge power cost coefficients;

[0243] By coupling the decision variables of the grid side and the energy storage supplier side through a penalty term, the formula for calculating the penalty term is as follows:

[0244]

[0245] In the formula: Indicates the number of iterations; , All are Lagrange multipliers; , , All are penalty factors; For the power grid side The shared energy storage capacity demand obtained after the next iteration; For the power grid side The shared energy storage charging power demand obtained after the next iteration. For the power grid side The shared energy storage discharge power demand obtained after the next iteration;

[0246] The objective function shown in formula (1) needs to satisfy the following constraints: power balance constraint, shared energy storage capacity constraint, shared energy storage charging and discharging power constraint, and shared energy storage capacity and charging and discharging power relationship constraint; these constraints and the objective function shown in formula (1) together form the shared energy storage operator-side capacity configuration optimization sub-model;

[0247] The power balance constraint is as follows:

[0248]

[0249] In the formula: for Time-optimized load power; for Generator set power during specific time periods; In order to be in Wind curtailment power during the period; In order to be in The amount of light discarded during a given period; For wind power in Actual output during the time period; For photovoltaic power stations Actual output during the time period;

[0250] Shared energy storage capacity constraints are:

[0251]

[0252]

[0253]

[0254] In the formula: For shared energy storage Real-time capacity for a given time period; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage; and These represent the real-time capacity of the shared energy storage at the beginning and end of the process, respectively.

[0255] The power constraints for shared energy storage charging and discharging are:

[0256]

[0257]

[0258] In the formula: and These represent the upper and lower limits for shared energy storage charging, respectively. and These represent the upper and lower limits of shared energy storage discharge, respectively. It is a Boolean variable;

[0259] The constraint on the relationship between shared energy storage capacity and charging / discharging power is:

[0260]

[0261] In the formula: A ratio coefficient representing the rated capacity of shared energy storage to its power limit; The charging and discharging power limit for shared energy storage;

[0262] (1b) The objective function of the grid-side capacity configuration optimization sub-model is to optimize the sum of wind curtailment penalty cost, solar curtailment penalty cost, grid-connected power fluctuation penalty, unit operating cost, and penalty terms:

[0263]

[0264] in, This represents the total cost on the grid side. The cost of curtailing wind power; The cost of penalties for abandoning light; Penalty for grid-connected power fluctuations; For unit operating costs; This is a penalty item;

[0265] The formulas for calculating the cost of wind curtailment penalty and the cost of solar curtailment penalty are as follows:

[0266]

[0267]

[0268]

[0269]

[0270] In the formula: , All are cost coefficients for wind curtailment power; , All are cost coefficients for curtailment power penalty; To measure the power output of wind power in Predicted power generation output for the specified time period; For photovoltaic power stations Predicted power generation output for the specified time period;

[0271] The formula for calculating the grid-connected power fluctuation penalty is as follows:

[0272]

[0273] In the formula: The penalty coefficient for the power deviation between the optimized load power and the target load power;

[0274] The formula for calculating the unit operating cost is as follows:

[0275]

[0276] In the formula: for Cost of generator unit output per time period; for Power output of generator units during specific time periods; , , All are fitting coefficients for unit output cost;

[0277] By coupling the decision variables on the grid side and the shared energy storage operator side through a penalty term, the calculation formula for the penalty term is as follows:

[0278]

[0279] In the formula: For the energy storage supplier side The energy storage capacity supply obtained after the next iteration , They are respectively the first on the energy storage supplier side The shared energy storage charging and discharging power supply obtained after the next iteration;

[0280] The objective function shown in formula (13) needs to satisfy the following constraints: power balance constraint, shared energy storage capacity constraint, shared energy storage charging and discharging power constraint, shared energy storage capacity and charging and discharging power relationship constraint, node power balance constraint, line transmission capacity constraint, generator output constraint, generator ramp rate constraint, wind curtailment constraint and solar curtailment constraint; these constraints and the objective function shown in formula (13) together form the grid-side capacity configuration optimization sub-model;

[0281] The power balance constraint is as follows:

[0282]

[0283] Shared energy storage capacity constraints are:

[0284]

[0285]

[0286] The power constraints for shared energy storage charging and discharging are:

[0287]

[0288]

[0289] The constraint on the relationship between shared energy storage capacity and charging / discharging power is:

[0290]

[0291] The node power balance constraint is:

[0292]

[0293] In the formula: This represents the active power matrix injected by the nodes; This represents the susceptance matrix of a network node during normal operation. Represents the node voltage phase angle matrix;

[0294] The line transmission capacity constraint is:

[0295]

[0296] In the formula: and These represent the upper and lower limits of the line transmission capacity, respectively. and Representing nodes respectively and nodes The voltage phase angle; Indicates the DC susceptance of the line;

[0297] The generator set output constraint is:

[0298]

[0299] In the formula: and These are the upper and lower limits of the generator set power, respectively.

[0300] The generator set's gradeability constraint is:

[0301]

[0302] In the formula: and These are the upper and lower limits of the generator set's gradeability, respectively.

[0303] The constraints on wind curtailment and solar curtailment are as follows:

[0304]

[0305]

[0306] Step (2) specifically includes the following steps:

[0307] (2a) Initialization: Set the number of iterations Assigning a value of 1 to give the upper limit of the original residual tolerance. Punishment factor Initial values ​​of Lagrange multiplier coefficients Set the initial value of the shared energy storage capacity demand on the grid side. Initial value of grid-side charging and discharging power demand ;

[0308] (2b) Using the YALMIP toolbox and the GUROBI commercial solver, the initial value of the shared energy storage capacity supply on the shared energy storage operator side is calculated and solved in the shared energy storage operator side capacity configuration optimization sub-model. Initial value of shared energy storage charging power supply Initial value of shared energy storage discharge power supply ;

[0309] (2c) Perform iterative calculations until the shared variables converge. At this point, the value of the shared variables is: the shared energy storage capacity supply. Shared energy storage charging power supply Shared energy storage discharge power supply ;

[0310] (2d) Grid-side received shared energy storage capacity supply Shared energy storage charging power supply and shared energy storage discharge power supply ;

[0311] (2e) Using the YALMIP toolbox and the GUROBI commercial solver, calculate the grid-side capacity configuration optimization sub-model to obtain the shared energy storage capacity demand. Shared energy storage charging power demand Shared energy storage discharge power demand ;

[0312] (2f) Determine convergence based on the following formula:

[0313]

[0314] In the formula: For the power grid side The demand for shared energy storage capacity in the next iteration; For the first The shared energy storage capacity supply in the next iteration; For the power grid side The shared energy storage charging power demand obtained after the next iteration; For the first The grid-side charging power supply in the next iteration; For the power grid side The shared energy storage discharge power demand obtained after the next iteration; For the first The grid-side discharge power supply in the next iteration;

[0315] If the convergence criterion is met, the calculation is stopped and the optimal value of the shared energy storage capacity is output; otherwise, the Lagrange multiplier coefficients are updated according to equation (33). Update iteration coefficients Return to step (2c) to perform the next round of iterative optimization calculations until equation (32) converges;

[0316]

[0317] In the formula, As a penalty factor; , All are Lagrange multipliers.

[0318] Step (3) specifically includes the following steps:

[0319] (3a) The objective function of the upper-level shared energy storage investment operator is the current shared energy storage period. The goal is to maximize the shared net revenue, which includes the revenue from shared energy storage services, the average investment and construction cost of shared energy storage, the charging power cost of shared energy storage, and the discharging power cost of shared energy storage.

[0320]

[0321] In the formula: In order to be in Shared net revenue during the time period; In order to be in Revenue from shared energy storage services during specific time periods; In order to be in The average investment and construction cost of shared energy storage during different time periods; They are respectively in Cost of shared energy storage charging power and cost of shared energy storage discharging power during different time periods;

[0322] The formula for calculating revenue from shared energy storage services is as follows:

[0323]

[0324]

[0325] In the formula; This represents the dual variable corresponding to the shared electricity constraint, namely the shared electricity price; For shared energy storage power stations Shared power consumption during different time periods;

[0326] The formula for calculating the average investment and construction cost of shared energy storage is:

[0327]

[0328] In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage;

[0329] The formulas for calculating the cost of shared energy storage charging power and the cost of shared energy storage discharging power are as follows:

[0330]

[0331]

[0332] In the formula: , All are charging power cost coefficients; , All are discharge power cost coefficients;

[0333] (3b) The objective function shown in formula (34) needs to satisfy the following constraints: shared energy storage capacity constraint, shared power constraint, and shared charging and discharging power constraint; these constraints and the objective function shown in formula (34) together form the upper-level model;

[0334] Shared energy storage capacity constraints are:

[0335]

[0336]

[0337] In the formula: For shared energy storage Real-time capacity for a given time period; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage;

[0338] Shared power constraints are:

[0339]

[0340] In the formula, For shared energy storage stations exist Shared power consumption during different time periods;

[0341] The power constraints for shared energy storage charging and discharging are:

[0342]

[0343]

[0344]

[0345]

[0346] Step (4) specifically includes the following steps:

[0347] (4a) The lower layer consists of new energy power station operators, who optimize their own operation strategies based on the pricing of shared energy storage operators;

[0348] New energy power stations The objective function is to minimize net cost, which includes shared energy storage service fees, wind and solar curtailment penalty costs, and grid-connected power deviation penalty costs.

[0349]

[0350] In the formula: Net cost for lower-level new energy power station operators; This refers to the number of new energy power stations; For new energy power stations exist Costs for shared energy storage services during specific time periods; For new energy power stations exist The penalty costs of wind and solar power curtailment during certain periods; For new energy power stations exist Penalty cost for deviations in internet usage during different time periods;

[0351] The formula for calculating the cost of shared energy storage services is as follows:

[0352]

[0353]

[0354] In the formula: In order to be in Shared electricity pricing for specific time periods; For shared duration;

[0355] The formula for calculating the penalty cost of wind and solar power curtailment is as follows:

[0356]

[0357] In the formula: , All are cost coefficients for wind curtailment power;

[0358] The formula for calculating the penalty cost for deviation in internet usage is as follows:

[0359]

[0360] In the formula: , The cost coefficient for penalizing deviations in internet usage;

[0361] (4b) The objective function shown in formula (47) needs to satisfy the following constraints: shared energy storage charging and discharging power constraints, wind and solar curtailment constraints, grid-connected power deviation constraints, and power balance constraints. These constraints and the objective function shown in formula (47) together form the lower-level model.

[0362] The power constraints for shared energy storage charging and discharging are:

[0363]

[0364]

[0365] In the formula: It is a Boolean variable, representing The status of the energy storage device during a given time period ensures that energy storage charging and discharging do not occur simultaneously; The charging and discharging power limit for shared energy storage;

[0366] The constraints on wind and solar curtailment are:

[0367]

[0368] In the formula: For new energy power stations exist Actual wind and solar power output during the period;

[0369] The power consumption deviation limit for internet access is:

[0370]

[0371] In the formula: For new energy power stations exist Electricity consumption during internet usage during a given time period;

[0372] The power balance constraint is:

[0373]

[0374] Step (5) specifically includes the following steps:

[0375] (5a) The upper-level model and the lower-level model constitute a master-slave game. Under the condition that the energy storage charging and discharging efficiency is not 100%, the Boolean variable part is removed in the two-level model. At this time, the lower-level model is transformed into a linear convex problem, which can be solved by KKT conditions.

[0376] (5b) The master-slave game problem of energy storage operators and new energy power plants sharing energy storage leasing is written in a two-layer optimization form, targeting new energy power plants. Its two-layer model is shown in equation (57) below. For simplification, the dual variables corresponding to the constraints are represented by the symbols after the colon.

[0377]

[0378]

[0379]

[0380]

[0381]

[0382]

[0383]

[0384]

[0385]

[0386]

[0387]

[0388]

[0389]

[0390]

[0391]

[0392] in, The shared net income during period t; , All are charging power cost coefficients; , All are discharge power cost coefficients; The charging and discharging power limit for shared energy storage; The real-time capacity of shared energy storage during time period t; , All are cost coefficients for wind curtailment power; The power cost of shared energy storage; The capacity cost of shared energy storage; For shared energy storage power stations Shared power consumption during different time periods; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage; This represents the dual variable corresponding to the shared electricity constraint, namely the shared electricity price; For shared duration; For new energy power stations exist Electricity consumption during internet usage during a given time period; For new energy power stations exist Shared power consumption during different time periods; For shared energy storage stations exist Shared power consumption during different time periods; For lower-level new energy power stations Operators Net cost for the period; For new energy power stations exist Actual wind and solar power output during the period; For shared duration; , All are cost coefficients for curtailment power penalty; For new energy power stations exist The amount of light wasted during a given time period; As dual variables;

[0393] (5c) Let the complementary relaxation dual variables corresponding to constraint (57) be respectively , , , , Further details on lower-level new energy power stations The Lagrangian function for the operating cost problem is as follows (58):

[0394]

[0395] In the formula: The Lagrangian function for the operating cost of new energy power plants; For new energy power stations exist Costs for shared energy storage services during specific time periods; For new energy power stations exist The penalty costs of wind and solar power curtailment during certain periods; For new energy power stations exist Actual wind and solar power output during the period; In order to be in Shared electricity pricing for specific time periods; For the grid connection revenue of new energy power station i during time period t;

[0396] (5d) Take the partial derivatives of the Lagrange function with respect to the lower-level variables to find the KKT optimality conditions:

[0397]

[0398]

[0399]

[0400] Equality constraints include:

[0401]

[0402]

[0403] (5e) Write down the KKT directions and complementary conditions corresponding to the inequality constraints, as shown in equations (64)-(71) below:

[0404]

[0405]

[0406]

[0407]

[0408]

[0409]

[0410]

[0411]

[0412] In the formula: express and Complementary, meaning that there is one and only one of them that is 0;

[0413] (5f) Introducing a series of Boolean variables transforms the complementary relaxation constraint conditions (64)-(71) into cutting plane constraints using the Big-M method. The cutting plane constraints are shown in equations (72)-(87) below:

[0414]

[0415]

[0416]

[0417]

[0418]

[0419]

[0420]

[0421]

[0422]

[0423]

[0424]

[0425]

[0426]

[0427]

[0428]

[0429]

[0430] In the formula: It is a sufficiently large positive number that can cover the range of the scaled variable; It is a Boolean variable;

[0431] The KKT system representation of the operating cost issue of lower-level renewable energy power stations is shown below:

[0432] In the formula: The KKT system represents the operating costs of lower-level renewable energy power stations;

[0433] (5g) The nonlinearity of the objective functions (1) and (13) of the upper and lower models originates from the product of the shared electricity price and the shared electricity volume. However, according to the strong duality theorem of linear programming, the objective function values ​​corresponding to the optimal solutions determined by the original problem and the dual problem are equal. That is, the objective function of the new energy power station operation cost problem has the following equivalent form. By linearizing the nonlinear terms in the objective function of the optimization problem, the following equation is obtained:

[0434]

[0435] The combined formula (88) can be derived The equivalent expression is shown below:

[0436]

[0437] Step (6) specifically includes the following steps:

[0438] (6a) Substituting equation (89) into the upper-level optimization problem equation (57), the pricing game problem of the upper-level shared energy storage operator can then be transformed into the following equilibrium constraint programming problem:

[0439]

[0440] In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage; , All are charging power cost coefficients; , All are discharge power cost coefficients; , All are cost coefficients for wind curtailment power; For new energy power stations exist Electricity consumption during internet usage during a given time period; For new energy power stations exist Actual wind and solar power output during the period;

[0441] (6b) Substitute the optimal energy storage capacity calculated in step (2) into equation (90), and use the YALMIP toolbox and GUROBI commercial solver to solve directly to obtain the shared electricity price at each time point. This refers to the optimal pricing for energy storage operators.

[0442] like Figure 2 As shown, during the decision-making process, the lease price proposed by energy storage operators conflicts with the shared electricity volume of renewable energy power plants. If the lease price is too high, the shared electricity volume agreed upon by energy storage operators will increase, thus improving their revenue. However, an excessively high price will reduce the economic benefits of renewable energy power plant operators, leading them to adopt a more conservative investment strategy, reducing the shared electricity volume and opting for curtailment of wind and solar power. If the transaction price is too low, renewable energy power plants will increase the shared electricity volume to improve their rate of return, but the charging and discharging volume agreed upon by energy storage operators will decrease, reducing the amount of investment and consequently lowering economic benefits. In this game, energy storage operators and renewable energy power plant operators reach equilibrium through sequential decision-making, forming a master-slave game relationship.

[0443] In summary, in the configuration of shared energy storage capacity, this invention does not rely on a single decision-making entity for data collection and power generation scheduling of the centralized optimization model of the power grid and shared energy storage operators, which is consistent with the characteristics of multi-entity decision-making in reality. Compared with centralized solutions, it can protect the data privacy of different stakeholders. This invention improves the cost efficiency of energy storage through cost sharing and economies of scale. Reasonable parameters ensure that the constructed capacity is not wasted, while reasonable shared electricity prices maximize the benefits for participants. This invention is applicable not only to small and medium-sized power users, but also to large-scale urban power systems, and has universality.

Claims

1. A centralized shared energy storage capacity configuration and pricing method based on master-slave game theory, characterized in that: The method includes the following steps in sequence: (1) Establish a capacity configuration optimization sub-model on the operator side and a capacity configuration optimization sub-model on the grid side for shared energy storage respectively: The capacity configuration optimization sub-model on the operator side of shared energy storage aims to optimize the investment and construction cost of centralized shared energy storage and the charging and discharging power cost of shared energy storage, and calculates the shared energy storage capacity supply on the operator side. Shared energy storage charging power supply and shared energy storage discharge power supply In the grid-side capacity configuration optimization sub-model, the shared energy storage capacity demand on the grid side is calculated with the objectives of optimizing grid renewable energy absorption, peak-shaving capacity, and cost. Shared energy storage charging power demand and the demand for shared energy storage discharge power ; (2) Calculate the optimal value of shared energy storage capacity using the Alternating Direction Multiplier Method (ADMM): The shared energy storage operator-side capacity configuration optimization sub-model and the grid-side capacity configuration optimization sub-model mutually transfer shared variables. When the convergence criterion is met, the calculation of shared variables stops and the optimal value of shared energy storage capacity is output. Otherwise, the dual variables are updated and iteratively calculated until the shared variables converge. The shared variables include the supply of shared energy storage capacity. Shared energy storage charging power supply and shared energy storage discharge power supply ; (3) Establish an upper-level model with shared energy storage investment operators as the main players: The objective function of the upper-level shared energy storage investment operators is the current shared energy storage time period. The goal is to maximize the shared net revenue, which includes shared energy storage service revenue, shared energy storage investment and construction costs, and energy storage charging and discharging power costs. The decision variable is the shared electricity price. ; (4) Establishing a lower-level model with multiple new energy power stations as followers: New energy power stations obtain shared energy storage services by paying service fees to shared energy storage operators. The objective function is to minimize net cost, which includes wind and solar curtailment penalties, shared energy storage service fees, and grid connection deviation penalties. The decision variable is the renewable energy power station. exist Shared power during time period Shared energy storage charging power Shared energy storage discharge power New energy power stations exist Wind and solar power curtailment during certain periods and new energy power stations exist Internet usage deviation during different time periods ; (5) The upper-level model and the lower-level model form a two-level optimization model. The two-level optimization model is transformed into a single-level optimization model by using the KKT optimality condition and the duality theorem of linear programming. (6) Based on the optimal value of shared energy storage capacity obtained in step (2), the single-layer optimization model is solved using a commercial solver to obtain the game equilibrium point of the master-slave game, which is the optimal pricing of the energy storage operator.

2. The centralized shared energy storage capacity configuration and pricing method based on master-slave game theory as described in claim 1, characterized in that: Step (1) specifically includes the following steps: (1a) The objective function of the shared energy storage operator-side capacity configuration optimization sub-model is the average daily investment construction cost of centralized shared energy storage, the charging power cost of shared energy storage, the discharging power cost of shared energy storage, and a penalty term: ； In the formula, Total cost for shared energy storage operators; The average daily investment and construction cost for centralized shared energy storage; Cost of charging power for shared energy storage; To reduce the cost of shared energy storage discharge power; This is a penalty item; The formula for calculating the average daily investment and construction cost of centralized shared energy storage is as follows: ； In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage; The formulas for calculating the cost of shared energy storage charging power and the cost of shared energy storage discharging power are as follows: ； ； In the formula: , All are charging power cost coefficients; , All are discharge power cost coefficients; By coupling the decision variables of the grid side and the energy storage supplier side through a penalty term, the formula for calculating the penalty term is as follows: ； In the formula: Indicates the number of iterations; , All are Lagrange multipliers; , , All are penalty factors; For the power grid side The shared energy storage capacity demand obtained after the next iteration; For the power grid side The shared energy storage charging power demand obtained after the next iteration. For the power grid side The shared energy storage discharge power demand obtained after the next iteration; The objective function shown in formula (1) needs to satisfy the following constraints: power balance constraint, shared energy storage capacity constraint, shared energy storage charging and discharging power constraint, and shared energy storage capacity and charging and discharging power relationship constraint; these constraints and the objective function shown in formula (1) together form the shared energy storage operator-side capacity configuration optimization sub-model; The power balance constraint is as follows: ； In the formula: for Time-optimized load power; for Generator set power during specific time periods; In order to be in Wind curtailment power during the period; In order to be in The amount of light discarded during a given period; For wind power in Actual output during the time period; For photovoltaic power stations Actual output during the time period; Shared energy storage capacity constraints are: ； ； ； In the formula: For shared energy storage Real-time capacity for a given time period; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage; and These represent the real-time capacity of the shared energy storage at the beginning and end of the process, respectively. The power constraints for shared energy storage charging and discharging are: ； ； In the formula: and These represent the upper and lower limits for shared energy storage charging, respectively. and These represent the upper and lower limits of shared energy storage discharge, respectively. It is a Boolean variable; The constraint on the relationship between shared energy storage capacity and charging / discharging power is: ； In the formula: A ratio coefficient representing the rated capacity of shared energy storage to its power limit; The charging and discharging power limit for shared energy storage; (1b) The objective function of the grid-side capacity configuration optimization sub-model is to optimize the sum of wind curtailment penalty cost, solar curtailment penalty cost, grid-connected power fluctuation penalty, unit operating cost, and penalty terms: ； in, This represents the total cost on the grid side. The cost of curtailing wind power; The cost of penalties for abandoning light; Penalty for grid-connected power fluctuations; For unit operating costs; This is a penalty item; The formulas for calculating the cost of wind curtailment penalty and the cost of solar curtailment penalty are as follows: ； ； ； ； In the formula: , All are cost coefficients for wind curtailment power; , All are cost coefficients for curtailment power penalty; To measure the power output of wind power in Predicted power generation output for the specified time period; For photovoltaic power stations Predicted power generation output for the specified time period; The formula for calculating the grid-connected power fluctuation penalty is as follows: ； In the formula: The penalty coefficient for the power deviation between the optimized load power and the target load power; The formula for calculating the unit operating cost is as follows: ； In the formula: for Cost of generator unit output per time period; for Power output of generator units during specific time periods; , , All are fitting coefficients for unit output cost; By coupling the decision variables on the grid side and the shared energy storage operator side through a penalty term, the calculation formula for the penalty term is as follows: ； In the formula: For energy storage supplier side The energy storage capacity supply obtained after the next iteration , They are respectively the first on the energy storage supplier side The shared energy storage charging and discharging power supply obtained after the next iteration; The objective function shown in formula (13) needs to satisfy the following constraints: power balance constraint, shared energy storage capacity constraint, shared energy storage charging and discharging power constraint, shared energy storage capacity and charging and discharging power relationship constraint, node power balance constraint, line transmission capacity constraint, generator output constraint, generator ramp rate constraint, wind curtailment constraint and solar curtailment constraint; these constraints and the objective function shown in formula (13) together form the grid-side capacity configuration optimization sub-model; The power balance constraint is as follows: ； Shared energy storage capacity constraints are: ; ； ； The power constraints for shared energy storage charging and discharging are: ； ； The constraint on the relationship between shared energy storage capacity and charging / discharging power is: ； The node power balance constraint is: ； In the formula: This represents the active power matrix injected by the nodes; This represents the susceptance matrix of a network node during normal operation. Represents the node voltage phase angle matrix; The line transmission capacity constraint is: ； In the formula: and These represent the upper and lower limits of the line transmission capacity, respectively. and Representing nodes respectively and nodes The voltage phase angle; Indicates the DC susceptance of the line; The generator set output constraint is: ； In the formula: and These are the upper and lower limits of the generator set power, respectively. The generator set's gradeability constraint is: ； In the formula: and These are the upper and lower limits of the generator set's gradeability, respectively. The constraints on wind curtailment and solar curtailment are as follows: ； 3. The centralized shared energy storage capacity configuration and pricing method based on master-slave game theory as described in claim 2, characterized in that: Step (2) specifically includes the following steps: (2a) Initialization: Set the number of iterations Assigning a value of 1, giving the upper limit of the original residual tolerance. Punishment factor Initial values ​​of Lagrange multiplier coefficients Set the initial value of the shared energy storage capacity demand on the grid side. Initial value of grid-side charging and discharging power demand ; (2b) Using the YALMIP toolbox and the GUROBI commercial solver, the initial value of the shared energy storage capacity supply on the shared energy storage operator side is calculated and solved in the shared energy storage operator side capacity configuration optimization sub-model. Initial value of shared energy storage charging power supply Initial value of shared energy storage discharge power supply ; (2c) Perform iterative calculations until the shared variables converge. At this point, the value of the shared variables is: the shared energy storage capacity supply. Shared energy storage charging power supply Shared energy storage discharge power supply ; (2d) Grid-side received shared energy storage capacity supply Shared energy storage charging power supply and shared energy storage discharge power supply ; (2e) Using the YALMIP toolbox and the GUROBI commercial solver, calculate the grid-side capacity configuration optimization sub-model to obtain the shared energy storage capacity demand. Shared energy storage charging power demand Shared energy storage discharge power demand ; (2f) Determine convergence based on the following formula: ； In the formula: For the power grid side The demand for shared energy storage capacity in the next iteration; For the first The shared energy storage capacity supply in the next iteration; For the power grid side The shared energy storage charging power demand obtained after the next iteration; For the first The grid-side charging power supply in the next iteration; For the power grid side The shared energy storage discharge power demand obtained after the next iteration; For the first The grid-side discharge power supply in the next iteration; If the convergence criterion is met, the calculation is stopped and the optimal value of the shared energy storage capacity is output; otherwise, the Lagrange multiplier coefficients are updated according to equation (33). Update iteration coefficients Return to step (2c) to perform the next round of iterative optimization calculations until equation (32) converges; ； In the formula, As a penalty factor; , All are Lagrange multipliers.

4. The centralized shared energy storage capacity configuration and pricing method based on master-slave game theory as described in claim 3, characterized in that: Step (3) specifically includes the following steps: (3a) The objective function of the upper-level shared energy storage investment operator is the current shared energy storage period. The goal is to maximize the shared net revenue, which includes the revenue from shared energy storage services, the average investment and construction cost of shared energy storage, the charging power cost of shared energy storage, and the discharging power cost of shared energy storage. ； In the formula: In order to be in Shared net revenue during the time period; In order to be in Revenue from shared energy storage services during specific time periods; In order to be in The average investment and construction cost of shared energy storage during different time periods; They are respectively in Cost of shared energy storage charging power and cost of shared energy storage discharging power during different time periods; The formula for calculating revenue from shared energy storage services is as follows: ； ； In the formula; This represents the dual variable corresponding to the shared electricity constraint, namely the shared electricity price; For shared energy storage power stations Shared power consumption during different time periods; The formula for calculating the average investment and construction cost of shared energy storage is: ； In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage; The formulas for calculating the cost of shared energy storage charging power and the cost of shared energy storage discharging power are as follows: ； ； In the formula: , All are charging power cost coefficients; , All are discharge power cost coefficients; (3b) The objective function shown in formula (34) needs to satisfy the following constraints: shared energy storage capacity constraint, shared power constraint, and shared charging and discharging power constraint; these constraints and the objective function shown in formula (34) together form the upper-level model; Shared energy storage capacity constraints are: ； ； In the formula: For shared energy storage Real-time capacity for a given time period; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage; Shared power constraints are: ； In the formula, For shared energy storage stations exist Shared power consumption during different time periods; The power constraints for shared energy storage charging and discharging are: ； ； ； 5. The centralized shared energy storage capacity configuration and pricing method based on master-slave game theory as described in claim 4, characterized in that: Step (4) specifically includes the following steps: (4a) The lower layer consists of new energy power station operators, who optimize their own operation strategies based on the pricing of shared energy storage operators; New energy power stations The objective function is to minimize net cost, which includes shared energy storage service fees, wind and solar curtailment penalty costs, and grid-connected power deviation penalty costs. ； In the formula: Net cost for lower-level new energy power station operators; This refers to the number of new energy power stations; For new energy power stations exist Costs for shared energy storage services during specific time periods; For new energy power stations exist The penalty costs for wind and solar power curtailment during certain periods; For new energy power stations exist Penalty cost for deviations in internet usage during different time periods; The formula for calculating the cost of shared energy storage services is as follows: ； ； In the formula: In order to be in Shared electricity pricing for specific time periods; For shared duration; The formula for calculating the penalty cost of wind and solar power curtailment is as follows: ； In the formula: , All are cost coefficients for wind curtailment power; The formula for calculating the penalty cost for deviation in internet usage is as follows: ； In the formula: , The cost coefficient for penalizing deviations in internet usage; (4b) The objective function shown in formula (47) needs to satisfy the following constraints: shared energy storage charging and discharging power constraints, wind and solar curtailment constraints, grid-connected power deviation constraints, and power balance constraints. These constraints and the objective function shown in formula (47) together form the lower-level model. The power constraints for shared energy storage charging and discharging are: ； ； In the formula: It is a Boolean variable, representing The status of the energy storage device during a given time period ensures that energy storage charging and discharging do not occur simultaneously; The charging and discharging power limit for shared energy storage; The constraints on wind and solar curtailment are: ； In the formula: For new energy power stations exist Actual wind and solar power output during the period; The power consumption deviation limit for internet access is: ； In the formula: For new energy power stations exist Electricity consumption during internet usage during a given time period; The power balance constraint is:

6. The centralized shared energy storage capacity configuration and pricing method based on master-slave game theory as described in claim 5, characterized in that: Step (5) specifically includes the following steps: (5a) The upper-level model and the lower-level model constitute a master-slave game. Under the condition that the energy storage charging and discharging efficiency is not 100%, the Boolean variable part is removed in the two-level model. At this time, the lower-level model is transformed into a linear convex problem, which can be solved by KKT conditions. (5b) The master-slave game problem of energy storage operators and new energy power plants sharing energy storage leasing is written in a two-layer optimization form, targeting new energy power plants. Its two-layer model is shown in equation (57) below. For simplification, the dual variables corresponding to the constraints are represented by the symbols after the colon: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； in, , All are charging power cost coefficients; , All are discharge power cost coefficients; The charging and discharging power limit for shared energy storage; For shared energy storage Real-time capacity for a given time period; , All are cost coefficients for wind curtailment power; The power cost of shared energy storage; The capacity cost of shared energy storage; For shared energy storage power stations Shared power consumption during different time periods; Charging efficiency for shared energy storage; For the discharge efficiency of shared energy storage; This represents the dual variable corresponding to the shared electricity constraint, namely the shared electricity price; For shared duration; For new energy power stations exist Electricity consumption during internet usage during a given time period; For new energy power stations exist Shared power consumption during different time periods; For shared energy storage stations exist Shared power consumption during different time periods; For lower-level new energy power stations Operators Net cost for the period; For new energy power stations exist Actual wind and solar power output during the period; For shared duration; , All are cost coefficients for curtailment power penalty; For new energy power stations exist The amount of light wasted during a given time period; As dual variables; (5c) Let the complementary relaxation dual variables corresponding to constraint (57) be respectively , , , , Further details on lower-level new energy power stations The Lagrangian function for the operating cost problem is as follows (58): ； In the formula: The Lagrangian function for the operating cost of new energy power plants; For new energy power stations exist Costs for shared energy storage services during specific time periods; For new energy power stations exist The penalty costs for wind and solar power curtailment during certain periods; For new energy power stations exist Actual wind and solar power output during the period; In order to be in Shared electricity pricing for specific time periods; For the grid connection revenue of the new energy power station i during time period t; (5d) Take the partial derivatives of the Lagrange function with respect to the lower-level variables to find the KKT optimality conditions: ； ； ； Equality constraints include: ； ； (5e) Write down the KKT directions and complementary conditions corresponding to the inequality constraints, as shown in equations (64)-(71) below: ； ； ； ； ； ； ； ； In the formula: express and Complementary, meaning that there is one and only one of them that is 0; (5f) Introducing a series of Boolean variables transforms the complementary relaxation constraint conditions (64)-(71) into cutting plane constraints using the Big-M method. The cutting plane constraints are shown in equations (72)-(87) below: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the formula: It is a sufficiently large positive number that can cover the range of the scaled variable; It is a Boolean variable; The KKT system representation of the operating cost issue of lower-level renewable energy power stations is shown below: ; In the formula: The KKT system represents the operating costs of lower-level renewable energy power stations; (5g) The nonlinearity of the objective functions (1) and (13) of the upper and lower models originates from the product of the shared electricity price and the shared electricity volume. However, according to the strong duality theorem of linear programming, the objective function values ​​corresponding to the optimal solutions determined by the original problem and the dual problem are equal. That is, the objective function of the new energy power station operation cost problem has the following equivalent form. By linearizing the nonlinear terms in the objective function of the optimization problem, the following equation is obtained: ； The combined formula (88) can be derived The equivalent expression is shown below: ； Step (6) specifically includes the following steps: (6a) Substituting equation (89) into the upper-level optimization problem equation (57), the pricing game problem of the upper-level shared energy storage operator can then be transformed into the following equilibrium constraint programming problem: ； In the formula: The power cost of shared energy storage; The capacity cost of shared energy storage; The charging and discharging power limit for shared energy storage; The expected number of days of use for shared energy storage; , All are charging power cost coefficients; , All are discharge power cost coefficients; , All are cost coefficients for wind curtailment power; For new energy power stations exist Electricity consumption during internet usage during a given time period; For new energy power stations exist Actual wind and solar power output during the period; (6b) Substitute the optimal energy storage capacity calculated in step (2) into equation (90), and use the YALMIP toolbox and GUROBI commercial solver to solve directly to obtain the shared electricity price at each time point. This refers to the optimal pricing for energy storage operators.

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