A distributed optimization scheduling method and system for multi-microgrid energy storage sharing

Abstract

A distributed optimization scheduling method and system for energy storage sharing of multiple microgrids, the method takes the lowest operating cost of the multi-microgrid system as the goal, constructs an optimal scheduling model for energy storage shared by multiple microgrids, and obtains the optimal scheduling decision plan for energy storage shared by multiple microgrids by solving the optimal scheduling model for energy storage shared by multiple microgrids. The present invention adopts shared energy storage in the multi-microgrid system, considers the degradation cost of the energy storage battery shared by multiple microgrids, constrains the charging and discharging power, charging and discharging period and charging and discharging state conversion period during the battery operation, including the peak period conversion, and decomposes the sub-problem model into decomposed sub-problems corresponding to each microgrid when solving the optimal scheduling model, and through the iterative alternation of each sub-problem and the main problem, the transmission of each variable in the iterative solution process is independent of each other, and the model solution efficiency is improved under the premise of protecting the privacy of microgrid user data, and the operation of the multi-microgrid power system is more realistically simulated to achieve the safe and economical operation of the multi-microgrid system.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering, and in particular relates to a distributed optimization scheduling method and system for multi-microgrid energy storage sharing. Background Art

[0002] In recent years, the increasingly severe global energy shortage and environmental pollution problems have greatly promoted the development of renewable energy generation technologies such as photovoltaics and wind power. However, the randomness and intermittency of renewable energy have led to a large number of wind and solar power abandonment phenomena, posing a challenge to the reliability of power supply in microgrids. Configuring energy storage systems in microgrids has become an important means to solve this problem. Energy storage systems can shift energy in time. Configuring energy storage systems in microgrids with a high proportion of renewable energy can absorb energy when wind and solar power generate a lot of electricity, and release energy when wind and solar power generation is insufficient, ensuring the safe and stable operation of the microgrid system.

[0003] In the power grid system, if the energy storage system configured in a single microgrid is only used by the microgrid, the high investment cost of the energy storage system often leads to a small configuration capacity, which cannot significantly improve the utilization rate of renewable energy. At present, multi-microgrid shared energy storage system has received widespread attention as an economically feasible solution. The configuration capacity of shared energy storage is relatively large, which can not only improve the absorption capacity of new energy and reduce the investment payback period, but also improve the utilization rate of energy storage and avoid over-investment. However, the existing research on the optimization and scheduling of shared energy storage in microgrids has the problems of low utilization rate, long investment payback period, and the optimization and scheduling process cannot protect the privacy data of microgrids. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed optimization scheduling method and system for multi-microgrid energy storage sharing in view of the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention proposes a distributed optimization scheduling method for multi-microgrid energy storage sharing, comprising:

[0007] S1. With the goal of minimizing the operating cost of the multi-microgrid system, an optimal scheduling model for multi-microgrid shared energy storage is constructed. The constraints of the optimal scheduling model include shared energy storage charging and discharging state variable constraints, shared energy storage charging and discharging time constraints, and shared energy storage peak time and valley time constraints.

[0008] S2. Solve the optimal scheduling model for multiple microgrids to share energy storage and obtain the optimal scheduling decision plan for multiple microgrids to share energy storage.

[0009] In S1, the objective function of the optimal scheduling model for multi-microgrid shared energy storage includes:

[0010]

[0011] In the above formula, For multiple microgrids, Optimize the time period set for multiple microgrids, μ b，t is the price of electricity purchased by multiple microgrids from the main grid during period t, is the amount of electricity purchased by microgrid m from the main grid during period t, μ s，t is the electricity price sold by multiple microgrids to the main grid during period t, is the amount of electricity sold by microgrid m to the main grid during period t, C deg is the degradation cost of the energy storage battery shared by multiple microgrids; β is the fitting parameter, ξ is the unit energy cost of the shared energy storage, B cap To share energy storage capacity, The state of charge of the shared energy storage during peak hours. It is the charge state of shared energy storage during off-peak hours.

[0012] In S1, the shared energy storage charging and discharging state variable constraints include:

[0013]

[0014] In the above formula, are the maximum charging power and discharging power of shared energy storage, respectively. They are 0-1 identification variables for charging and discharging of shared energy storage during period t, is the operating power of the shared energy storage in period t, ∈ p is a very small positive number, are the charging power and discharging power of the shared energy storage in period t respectively;

[0015] The shared energy storage charging and discharging period constraints include:

[0016]

[0017] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during the t period;

[0018] The shared energy storage peak period and valley period constraints include:

[0019]

[0020] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t. They are the charge states of shared energy storage during peak and valley periods, respectively. is the upper limit of the shared energy storage state of charge, SOC t is the state of charge of the shared energy storage during period t.

[0021] The S2 includes:

[0022] S21, simplifying the optimal scheduling model of multi-microgrid shared energy storage, obtaining the following simplified optimal scheduling model of multi-microgrid shared energy storage, and setting the number of iterations to k, initializing the upper and lower bounds of the simplified optimal scheduling model of multi-microgrid shared energy storage, and obtaining the initial value of the model;

[0023]

[0024] In the above formula, For multiple microgrids, f T , b m ,d m , g1, g2 are all constant matrices, x 1m 、x 2m , z are both variable vectors, y m is a complex variable vector; A 1m , A 2m , A 3m , B m , C, D, G1, G2 are all constant coefficients; are the amount of electricity purchased from the main grid and the amount of electricity sold to the main grid by microgrid m during period t, are the charging power and discharging power of microgrid m to the shared energy storage in period t, respectively, i，t is the square of the voltage amplitude of node i in period t, i is the node number of the microgrid network, is the set of microgrid network nodes, is the set of multi-microgrid optimization time periods, (i, j) is the microgrid network branch number, P (i，j)，t , Q (i，j)，t are respectively the active power and reactive power flowing through branch ij during period t, is the microgrid network branch set, The variables are The repeated introduction of is the operating power of the shared energy storage in period t, are the charging power and discharging power of the shared energy storage in period t, SOC t is the state of charge of the shared energy storage during period t, They are 0-1 identification variables for charging and discharging of shared energy storage during period t, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during period t. are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t, respectively. deg Degradation costs of shared energy storage batteries for multiple microgrids;

[0025] S22, divide the optimal scheduling simplified model of multi-microgrid shared energy storage into a main problem model and a sub-problem model, and solve the main problem model according to the obtained initial value to obtain the complex variable y corresponding to each microgrid m ;

[0026] The main problem model is:

[0027] min(π+f T z);

[0028]

[0029] In the above formula, π is the optimal value variable of the corresponding sub-problem model in the main problem model, are all constant matrices, is a complex variable vector matrix, are the values ​​of the dual variables when solving the dual decomposition subproblem model. is the feasibility benders cut set, is the optimal running benders cut set;

[0030] The sub-problem model is:

[0031]

[0032] In the above formula, is the complex variable vector y obtained when solving the main problem model m The value of

[0033] S23, decomposing the sub-problem model into decomposition sub-problem models corresponding to each microgrid, and converting the decomposition sub-problem model corresponding to each microgrid into a dual decomposition sub-problem model of each microgrid;

[0034] The decomposition sub-problem model corresponding to each microgrid is:

[0035]

[0036] The dual decomposition sub-problem model of each microgrid is:

[0037]

[0038] In the above formula, To solve the complex variable vector matrix The value of u 1m 、u 2m 、u 3mare dual variables, are all constant coefficient matrices, c m is a constant;

[0039] S24, corresponding complex variables y of each microgrid obtained based on S22 m , solve whether the dual decomposition subproblem model of each microgrid in S23 has a solution. If the dual decomposition subproblem model of a microgrid has a solution, the resulting feasibility benders cut set is added to the main problem model as a constraint condition. If the dual decomposition subproblem model of a microgrid has no solution, the resulting optimal operation benders cut set is added to the main problem model as a constraint condition. Solve the main problem model and update the upper and lower bounds of the optimal scheduling simplified model.

[0040] The feasibility benders cut set is:

[0041]

[0042] At this time, the upper bound of the optimal scheduling simplified model is:

[0043] In the above formula, UB (k) is the upper bound of the simplified model for the optimal scheduling of the kth iteration, is the optimal value of the dual decomposition subproblem model corresponding to the microgrid m, is the value of the variable vector z when solving the main problem model;

[0044] The optimal running benders cut set is:

[0045]

[0046] At this time, the upper bound of the optimal scheduling simplified model is: UB (k) =UB (k-1) ;

[0047] The lower bound of the optimal scheduling simplified model is:

[0048] In the above formula, LB (k) is the lower bound of the simplified model for the optimal scheduling of the kth iteration, The value of the optimal value variable π of the corresponding sub-problem model in the main problem model;

[0049] S25. Determine whether the updated optimal scheduling simplified model meets the convergence condition UB (k) -LB (k)<ε or reaches the maximum number of iterations, where ε is the preset error threshold. If neither is satisfied, set k=k+1 and return to step S22 to iterate until the convergence condition is met or the maximum number of iterations is reached. The iteration stops and the complex variables y corresponding to each microgrid are m Value Substitute the decomposed sub-problem model corresponding to each microgrid to obtain the scheduling decision plan for each microgrid and shared energy storage.

[0050] In a second aspect, the present invention proposes a distributed optimization scheduling system for multi-microgrid energy storage sharing, including an optimal scheduling model construction module for multi-microgrid shared energy storage and an optimal scheduling model solution module for multi-microgrid shared energy storage;

[0051] The optimal scheduling model construction module for multi-microgrid shared energy storage is used to construct an optimal scheduling model for multi-microgrid shared energy storage with the goal of minimizing the operating cost of the multi-microgrid system;

[0052] The optimal scheduling model solving module for multiple microgrids sharing energy storage is used to solve the optimal scheduling model for multiple microgrids sharing energy storage and obtain the optimal scheduling decision plan for multiple microgrids sharing energy storage.

[0053] The optimal scheduling model construction module for multi-microgrid shared energy storage includes an objective function construction unit;

[0054] The objective function construction unit is used to construct the following objective function:

[0055]

[0056] In the above formula, For multiple microgrids, Optimize the time period set for multiple microgrids, μ b，t is the price of electricity purchased by multiple microgrids from the main grid during period t, is the amount of electricity purchased by microgrid m from the main grid during period t, μ s，t is the electricity price sold by multiple microgrids to the main grid during period t, is the amount of electricity sold by microgrid m to the main grid during period t, C deg is the degradation cost of the energy storage battery shared by multiple microgrids; β is the fitting parameter, ξ is the unit energy cost of the shared energy storage, B cap To share energy storage capacity, The state of charge of the shared energy storage during peak hours. It is the charge state of shared energy storage during off-peak hours.

[0057] The optimal scheduling model construction module for multi-microgrid shared energy storage also includes a shared energy storage charging and discharging state variable constraint construction unit, a shared energy storage charging and discharging period constraint construction unit, and a shared energy storage peak period and valley period constraint construction unit;

[0058] The shared energy storage charging and discharging state variable constraint construction unit is used to construct the following shared energy storage charging and discharging state variable constraints:

[0059]

[0060] In the above formula, are the maximum charging power and discharging power of shared energy storage, respectively. They are 0-1 identification variables for charging and discharging of shared energy storage during period t, is the operating power of the shared energy storage in period t, ∈ p is a very small positive number, P t ch , P t dis are the charging power and discharging power of the shared energy storage in period t respectively;

[0061] The shared energy storage charging and discharging period constraint construction unit is used to construct the following shared energy storage charging and discharging period constraint:

[0062]

[0063]

[0064] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during the t period;

[0065] The shared energy storage peak period and valley period constraint construction unit is used to construct the following shared energy storage peak period and valley period constraints:

[0066]

[0067] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t. They are the charge states of shared energy storage during peak and valley periods, respectively. is the upper limit of the shared energy storage state of charge, SOC t is the state of charge of the shared energy storage during period t.

[0068] The optimal scheduling model solving module for multi-microgrid shared energy storage includes a model simplification unit, a simplified model division unit, a sub-problem model decomposition conversion unit, an upper and lower bound updating unit, and a judgment iteration unit;

[0069] The model simplification unit is used to simplify the optimal scheduling model of multi-microgrid shared energy storage, obtain the following optimal scheduling simplified model of multi-microgrid shared energy storage, and set the number of iterations to k, initialize the upper and lower bounds of the optimal scheduling simplified model of multi-microgrid shared energy storage, and obtain the initial value of the model;

[0070]

[0071] In the above formula, For multiple microgrids, f T , b m ,d m , g1, g2 are all constant matrices, x 1m 、x 2m , z are both variable vectors, y m is a complex variable vector; A 1m , A 2m , A 3m , B m , C, D, G1, G2 are all constant coefficients; are the amount of electricity purchased from the main grid and the amount of electricity sold to the main grid by microgrid m during period t, are the charging power and discharging power of microgrid m to the shared energy storage in period t, respectively, i，t is the square of the voltage amplitude of node i in period t, i is the node number of the microgrid network, is the set of microgrid network nodes, is the set of multi-microgrid optimization time periods, (i, j) is the microgrid network branch number, P (i，j)，t , Q (i，j)，t are respectively the active power and reactive power flowing through branch ij during period t, is the microgrid network branch set, The variables are The repeated introduction of is the operating power of the shared energy storage in period t, P t ch , P t dis are the charging power and discharging power of the shared energy storage in period t, SOC t is the state of charge of the shared energy storage during period t, They are 0-1 identification variables for charging and discharging of shared energy storage during period t, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during period t. are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t, respectively.deg Degradation costs of shared energy storage batteries for multiple microgrids;

[0072] The simplified model division unit is used to divide the optimal scheduling simplified model of multi-microgrid shared energy storage into a main problem model and a sub-problem model, and solve the main problem model according to the obtained initial value to obtain the complex variable y corresponding to each microgrid m ;

[0073] The main problem model is:

[0074] min(π+f T z);

[0075]

[0076] In the above formula, π is the optimal value variable of the corresponding sub-problem model in the main problem model, are all constant matrices, is a complex variable vector matrix, are the values ​​of the dual variables when solving the dual decomposition subproblem model. is the feasibility benders cut set, is the optimal running benders cut set;

[0077] The sub-problem model is:

[0078]

[0079] In the above formula, is the complex variable vector y obtained when solving the main problem model m The value of

[0080] The sub-problem model decomposition conversion unit is used to decompose the sub-problem model into decomposition sub-problem models corresponding to each microgrid, and convert the decomposition sub-problem model corresponding to each microgrid into a dual decomposition sub-problem model of each microgrid;

[0081] The decomposition sub-problem model corresponding to each microgrid is:

[0082]

[0083]

[0084] The dual decomposition sub-problem model of each microgrid is:

[0085]

[0086] In the above formula, To solve the complex variable vector matrix The value of u 1m 、u2m 、u 3m are dual variables, are all constant coefficient matrices, c m is a constant;

[0087] The upper and lower bounds updating unit is used to obtain the complex variables y corresponding to each microgrid based on the simplified model division unit. m , solve whether the dual decomposition sub-problem model of each microgrid in the decomposition conversion unit has a solution. If the dual decomposition sub-problem model of a microgrid has a solution, the resulting feasibility benders cut set is added as a constraint to the main problem model. If the dual decomposition sub-problem model of a microgrid has no solution, the resulting optimal operation benders cut set is added as a constraint to the main problem model, solve the main problem model and update the upper and lower bounds of the optimal scheduling simplified model;

[0088] The feasibility benders cut set is:

[0089]

[0090] At this time, the upper bound of the optimal scheduling simplified model is:

[0091] In the above formula, UB (k) is the upper bound of the simplified model for the optimal scheduling of the kth iteration, is the optimal value of the dual decomposition subproblem model corresponding to the microgrid m, is the value of the variable vector z when solving the main problem model;

[0092] The optimal running benders cut set is:

[0093]

[0094] At this time, the upper bound of the optimal scheduling simplified model is: UB (k) =UB (k-1) ;

[0095] The lower bound of the optimal scheduling simplified model is:

[0096] In the above formula, LB (k) is the lower bound of the simplified model for the optimal scheduling of the kth iteration, The value of the optimal value variable π of the corresponding sub-problem model in the main problem model;

[0097] The iterative judgment unit is used to judge whether the updated optimal scheduling simplified model meets the convergence condition UB (k) -LB (k)<ε or reaches the maximum number of iterations, where ε is the preset error threshold. If none of them are satisfied, set k = k + 1 and return to the simplified model division unit for iteration until the convergence condition is met or the maximum number of iterations is reached. The iteration stops and the complex variables y corresponding to each microgrid are m Value Substitute the decomposed sub-problem model corresponding to each microgrid to obtain the scheduling decision plan for each microgrid and shared energy storage.

[0098] In a third aspect, the present invention proposes a distributed optimization scheduling device for multi-microgrid energy storage sharing, including a processor and a memory;

[0099] The memory is used to store computer program code and transmit the computer program code to the processor;

[0100] The processor is used to execute the aforementioned distributed optimization scheduling method for multi-microgrid energy storage sharing according to the instructions in the computer program code.

[0101] In a fourth aspect, the present invention provides a computer storage medium having a computer program stored thereon;

[0102] When the computer program is executed by the processor, the steps of the aforementioned distributed optimization scheduling method for multi-microgrid energy storage sharing are implemented.

[0103] Compared with the prior art, the present invention has the following beneficial effects:

[0104] 1. The present invention proposes a distributed optimization scheduling method and system for multi-microgrid energy storage sharing. The method takes the lowest operating cost of the multi-microgrid system as the goal, constructs an optimal scheduling model for multi-microgrid shared energy storage, and the constraints of the optimal scheduling model include shared energy storage charging and discharging state variable constraints, shared energy storage charging and discharging period constraints, shared energy storage peak period and valley period constraints, and obtains the optimal scheduling decision plan for multi-microgrid shared energy storage by solving the optimal scheduling model for multi-microgrid shared energy storage. On the one hand, the method adopts shared energy storage in the multi-microgrid system, and the configuration capacity of shared energy storage is relatively large, which can improve the absorption capacity of new energy, reduce the energy storage investment payback period, and improve the utilization rate of energy storage equipment; on the other hand, the method considers the degradation cost of multi-microgrid shared energy storage batteries when constructing the optimal scheduling model for multi-microgrid shared energy storage, and constrains the charging and discharging power, charging and discharging period, and charging and discharging state conversion period during the battery operation, including peak period conversion, which can better characterize the battery loss in the scheduling process, so that the scheduling decision plan is more in line with the application of actual engineering, and realizes the safe and economical operation of the multi-microgrid system;

[0105] 2. The present invention proposes a distributed optimization scheduling method and system for multi-microgrid energy storage sharing. When solving the optimal scheduling model of multi-microgrid shared energy storage, the method decomposes the sub-problem model into decomposed sub-problems corresponding to each microgrid, and then converts each into a corresponding dual sub-problem, thereby corresponding to the operation scheduling of multiple microgrids. Through the iterative alternation of each sub-problem and the main problem, the transmission of each variable in the iterative solution process is independent of each other, and the variable is only a dual variable, which does not contain sensitive information of each microgrid. The model solving efficiency is improved while protecting the privacy of microgrid user data, and the operation of the multi-microgrid power system is more realistically simulated, ensuring the safe and stable operation of the power system, and improving the operation economy of the shared energy storage multi-microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1 This is a topological diagram of the shared energy storage multi-microgrid system described in Example 1.

[0107] Figure 2 The figure is an overall flow chart of the method of the present invention.

[0108] Figure 3 The structure diagram of the system of the present invention is shown in FIG.

[0109] Figure 4 This is a structural diagram of the device described in Example 3. DETAILED DESCRIPTION

[0110] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0111] The present invention proposes a distributed optimization scheduling method and system for multi-microgrid energy storage sharing. With the goal of minimizing the operating cost of the multi-microgrid system, the objective function and constraint conditions of the optimal scheduling model of multi-microgrid shared energy storage are constructed, and the Benders decomposition method is used to solve the optimal scheduling model of multi-microgrid shared energy storage, and the optimal scheduling decision plan of multi-microgrid shared energy storage is obtained. The operation of the multi-microgrid power system is simulated more realistically, the safe and stable operation of the power system is guaranteed while protecting the privacy of user data, and the operation economy of the shared energy storage multi-microgrid system is improved.

[0112] Embodiment 1:

[0113] This embodiment takes a multi-microgrid system with shared energy storage as the research object, and its topology is as follows: Figure 1As shown in the figure, the multi-microgrid system includes wind turbines, photovoltaic panels, AC lines, etc. The capacity of shared energy storage is 10MWh, the maximum charging and discharging power is 3MW, the system rated voltage is 220kV, and the node voltage fluctuation range is 0.95pu-1.05pu. Before executing the distributed optimization scheduling method for multi-microgrid energy storage sharing, the technical parameters of each component in the system are collected, including: system rated voltage, allowable fluctuation range of node voltage, resistance value and reactance value of each line, installed capacity of new energy generators, capacity of shared energy storage system, charging and discharging power of shared energy storage system, charging and discharging efficiency of shared energy storage system, load curve of multi-microgrid system and theoretical maximum power generation curve of renewable energy, and main grid electricity purchase price.

[0114] like Figure 2 As shown, a distributed optimization scheduling method for multi-microgrid energy storage sharing is performed in the following steps:

[0115] 1. With the goal of minimizing the operating cost of multi-microgrid systems, an optimal scheduling model for multi-microgrid shared energy storage is constructed;

[0116] The objective function of the optimal dispatch model for multi-microgrid shared energy storage includes:

[0117]

[0118]

[0119] In the above formula, For multiple microgrids, Optimize the time period set for multiple microgrids, μ b，t is the price of electricity purchased by multiple microgrids from the main grid during period t, is the amount of electricity purchased by microgrid m from the main grid during period t, μ s，t is the electricity price sold by multiple microgrids to the main grid during period t, is the amount of electricity sold by microgrid m to the main grid during period t, C deg is the degradation cost of the energy storage battery shared by multiple microgrids; β is the fitting parameter, ξ is the unit energy cost of the shared energy storage, B cap To share energy storage capacity, The state of charge of the shared energy storage during peak hours. The state of charge of the shared energy storage during off-peak hours;

[0120] The constraints of the optimal dispatch model of multi-microgrid shared energy storage are divided into two categories: microgrid grid constraints and shared energy storage operation constraints. Among them, microgrid grid constraints include power flow constraints, voltage amplitude constraints and common connection point power constraints, and shared energy storage operation constraints include state of charge balance constraints, state of charge level constraints, charge and discharge state variable constraints, charge and discharge period constraints, peak period and valley period constraints and operation power constraints.

[0121] The power flow constraint is:

[0122]

[0123] In the above formula, P (i，j)，t , Q (i，j)，t are the active power and reactive power flowing through branch ij during period t, respectively. j，t ,q j，t are the active power and reactive power injected by node j in period t, P (j，k)，t , Q (i，j)，t are respectively the active power and reactive power flowing through branch jk in period t, is the microgrid network branch set, v j，t 、v i，t are the squares of the voltage amplitudes at nodes j and i in time period t, r i，j 、x i，j are the resistance and reactance of branch ij respectively, p i，t ,q i，t are the active power and reactive power injected by node i in period t, are the active load power and reactive load power of node i in period t, is a 0-1 variable indicating whether node i is connected to a renewable energy generator, is the renewable energy power generation of node i in period t;

[0124] The voltage amplitude constraint is:

[0125] In the above formula, V , They are the lower and upper limits of the voltage of the multi-microgrid system respectively;

[0126] The power constraint at the common connection point is:

[0127]

[0128] In the above formula, is the active power and reactive power flowing through microgrid m in branch ij during period t, are the amount of electricity purchased from the main grid and the amount of electricity sold to the main grid by microgrid m during period t, They are respectively the charging power and discharging power of microgrid m to the shared energy storage in period t;

[0129] The state of charge balance constraint is:

[0130]

[0131] In the above formula, SOC t is the state of charge of the shared energy storage during period t, B cap is the shared energy storage capacity, η ch , η dis are the shared energy storage charging and discharging efficiency coefficients, are the charging power and discharging power of the shared energy storage in period t, respectively, and Δt is the optimized cycle interval;

[0132] The state of charge level constraint is:

[0133] In the above formula, SOC , They are the upper and lower limits of the shared energy storage state of charge respectively;

[0134] In the shared energy storage operation constraints of charge and discharge state variable constraints, charge and discharge period constraints, peak period and valley period constraints, the charge and discharge power and charge and discharge time are used to characterize the charge and discharge depth of the shared energy storage, that is, in a charge and discharge behavior, how much power is charged and discharged by the shared energy storage in which period of time;

[0135] The charge and discharge state variable constraints are:

[0136]

[0137]

[0138] In the above formula, are the maximum charging power and discharging power of shared energy storage, respectively. They are 0-1 identification variables for charging and discharging of shared energy storage during period t, is the operating power of the shared energy storage in period t, ∈ p is a very small positive number;

[0139] The charge and discharge period constraints are:

[0140]

[0141] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during the t period;

[0142] The peak period and valley period constraints are:

[0143] Among them, the judgment basis of the charge state peak period 0-1 identification variable is: the period is in the charging period, and the next period is in the discharging period, otherwise it does not belong to the peak period. The logical relationship is shown in the following formula:

[0144]

[0145] Linearizing the above logical relationship, we can obtain the following peak period identification variable constraint relationship:

[0146]

[0147] Similarly, the following low period identification variable constraint relationship is obtained:

[0148]

[0149] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t. They are the charge states of shared energy storage during peak and valley periods respectively;

[0150] The operating power constraint is:

[0151] 2. Use the Benders decomposition method to solve the optimal scheduling model of multi-microgrid shared energy storage, obtain the optimal scheduling decision plan for multi-microgrid shared energy storage, simulate the operation of multi-microgrid power system more realistically, ensure the safe and stable operation of the power system while protecting user data privacy, and improve the operation economy of shared energy storage multi-microgrid system;

[0152] The optimal scheduling model of multi-microgrid shared energy storage is simplified to obtain the following simplified optimal scheduling model of multi-microgrid shared energy storage, and the number of iterations is set to k, the initial value of k is set to 1, and the upper and lower bounds of the simplified optimal scheduling model of multi-microgrid shared energy storage are initialized to obtain the initial value of the model;

[0153]

[0154]

[0155] In the above formula, For multiple microgrids, f T , b m ,d m , g1, g2 are all constant matrices, x 1m 、x 2m , z are both variable vectors, y m is a complex variable vector; A 1m , A2m , A 3m , B m , C, D, G1, G2 are all constant coefficients; are the amount of electricity purchased from the main grid and the amount of electricity sold to the main grid by microgrid m during period t, are the charging power and discharging power of microgrid m to the shared energy storage in period t, respectively, i，t is the square of the voltage amplitude of node i in period t, i is the node number of the microgrid network, is the set of microgrid network nodes, is the set of multi-microgrid optimization time periods, (i, j) is the microgrid network branch number, P (i，j)，t , Q (i，j)，t are respectively the active power and reactive power flowing through branch ij during period t, is the microgrid network branch set, The variables are The repeated introduction of is the operating power of the shared energy storage in period t, are the charging power and discharging power of the shared energy storage in period t, SOC t is the state of charge of the shared energy storage during period t, They are 0-1 identification variables for charging and discharging of shared energy storage during period t, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during period t. are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t, respectively. deg Degradation costs of shared energy storage batteries for multiple microgrids;

[0156] The optimal dispatching simplified model of multi-microgrid shared energy storage is divided into a main problem model and a sub-problem model, and the Benders decomposition method is used to solve the main problem model and the sub-problem model alternately.

[0157] Solve the main problem model according to the initial value obtained to obtain the complex variables y corresponding to each microgrid m Value

[0158] The main problem model is:

[0159] min(π+f T z);

[0160]

[0161] In the above formula, π is the optimal value variable of the corresponding sub-problem model in the main problem model, are all constant matrices, is a complex variable vector matrix, are the values ​​of the dual variables when solving the dual decomposition subproblem model. is the feasibility benders cut set, is the optimal running benders cut set;

[0162] The sub-problem model is:

[0163]

[0164] In the above formula, is the complex variable vector y obtained when solving the main problem model m The value of

[0165] It should be noted that when k = 0, the target value of the main problem model, that is, the lower bound LB of the optimal scheduling simplified model (0) =-∞, the target value of the subproblem model, that is, the upper bound LB of the optimal scheduling simplified model (0) =+∞;

[0166] Decompose the subproblem model into decomposition subproblem models corresponding to each microgrid, and convert the decomposition subproblem model corresponding to each microgrid into a dual decomposition subproblem model of each microgrid;

[0167] The decomposition sub-problem model corresponding to each microgrid is:

[0168]

[0169]

[0170] The dual decomposition sub-problem model of each microgrid is:

[0171]

[0172] In the above formula, To solve the complex variable vector matrix The value of u 1m 、u 2m 、u 3m are dual variables, are all constant coefficient matrices, c m is a constant;

[0173] Given the complex variables y corresponding to each microgrid mIn the case of , solve whether the dual decomposition subproblem model of each microgrid has a solution. If the dual decomposition subproblem model of a microgrid has a solution, the resulting feasibility benders cut set is added as a constraint to the main problem model. If the dual decomposition subproblem model of a microgrid has no solution, the resulting optimal operation benders cut set is added as a constraint to the main problem model, solve the main problem model and update the upper and lower bounds of the optimal scheduling simplified model.

[0174] The feasibility benders cut set is:

[0175]

[0176] At this time, the upper bound of the optimal scheduling simplified model is:

[0177] In the above formula, UB (k) is the upper bound of the simplified model for the optimal scheduling of the kth iteration, is the optimal value of the dual decomposition subproblem model corresponding to the microgrid m, The value of the variable vector Z when solving the main problem model;

[0178] The optimal running benders cut set is:

[0179]

[0180] At this time, the upper bound of the optimal scheduling simplified model is: UB (k) =UB (k-1) ;

[0181] The lower bound of the optimal scheduling simplified model is:

[0182] In the above formula, LB (k) is the lower bound of the simplified model for the optimal scheduling of the kth iteration, The value of the optimal value variable π of the corresponding sub-problem model in the main problem model;

[0183] Determine whether the updated optimal scheduling simplified model meets the convergence condition UB (k) -LB (k) <ε or reaches the maximum number of iterations, where ε is the preset error threshold. If none of them are satisfied, set k = k + 1 and return to step 1 to divide the optimal scheduling simplified model of multi-microgrid shared energy storage into the main problem model and the sub-problem model for iteration until the convergence condition is met or the maximum number of iterations is reached. The iteration stops and the complex variables y corresponding to each microgrid are calculated. m Value Substitute the decomposed sub-problem model corresponding to each microgrid to obtain the scheduling decision plan for each microgrid and shared energy storage.

[0184] by Figure 1 Taking the multi-microgrid system shown in as an example, the operating cost of the multi-microgrid system before executing the method described in this solution is compared, as shown in Table 1, and the operating cost of the multi-microgrid system after executing the method described in this solution is shown in Table 2, verifying the effect of the distributed optimization scheduling method for multi-microgrid energy storage sharing in this embodiment;

[0185] Table 1 Operation cost table of multi-microgrid system before executing the method described in this scheme

[0186]

[0187] Table 2 Operation cost table of multi-microgrid system after executing the method described in this scheme

[0188]

[0189] From the comparison between Table 1 and Table 2, it can be seen that after implementing the distributed optimization scheduling method for multi-microgrid energy storage sharing described in this scheme, the operating cost of each microgrid is significantly reduced, with a total reduction of about 12%.

[0190] Embodiment 2:

[0191] like Figure 3 As shown, a distributed optimization scheduling system for multi-microgrid energy storage sharing includes an optimal scheduling model building module for multi-microgrid energy storage sharing and an optimal scheduling model solving module for multi-microgrid energy storage sharing;

[0192] The optimal scheduling model construction module for multi-microgrid shared energy storage is used to construct an optimal scheduling model for multi-microgrid shared energy storage with the goal of minimizing the operating cost of the multi-microgrid system;

[0193] The optimal scheduling model solving module for multiple microgrids sharing energy storage is used to solve the optimal scheduling model for multiple microgrids sharing energy storage and obtain the optimal scheduling decision plan for multiple microgrids sharing energy storage.

[0194] The optimal scheduling model construction module for multi-microgrid shared energy storage includes an objective function construction unit;

[0195] The objective function construction unit is used to construct the following objective function:

[0196]

[0197] In the above formula, For multiple microgrids, Optimize the time period set for multiple microgrids, μ b，t is the price of electricity purchased by multiple microgrids from the main grid during period t, is the amount of electricity purchased by microgrid m from the main grid during period t, μs，t is the electricity price sold by multiple microgrids to the main grid during period t, is the amount of electricity sold by microgrid m to the main grid during period t, C deg is the degradation cost of the energy storage battery shared by multiple microgrids; β is the fitting parameter, ξ is the unit energy cost of the shared energy storage, B cap To share energy storage capacity, The state of charge of the shared energy storage during peak hours. It is the charge state of shared energy storage during off-peak hours.

[0198] The optimal scheduling model construction module for multi-microgrid shared energy storage also includes a shared energy storage charging and discharging state variable constraint construction unit, a shared energy storage charging and discharging period constraint construction unit, and a shared energy storage peak period and valley period constraint construction unit;

[0199] The shared energy storage charging and discharging state variable constraint construction unit is used to construct the following shared energy storage charging and discharging state variable constraints:

[0200]

[0201]

[0202] In the above formula, are the maximum charging power and discharging power of shared energy storage, respectively. They are 0-1 identification variables for charging and discharging of shared energy storage during period t, is the operating power of the shared energy storage in period t, ∈p is a very small positive number, P t ch , P t dis are the charging power and discharging power of the shared energy storage in period t respectively;

[0203] The shared energy storage charging and discharging period constraint construction unit is used to construct the following shared energy storage charging and discharging period constraint:

[0204]

[0205] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during the t period;

[0206] The shared energy storage peak period and valley period constraint construction unit is used to construct the following shared energy storage peak period and valley period constraints:

[0207]

[0208]

[0209] In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t. They are the charge states of shared energy storage during peak and valley periods, respectively. is the upper limit of the shared energy storage state of charge, SOC t is the state of charge of the shared energy storage during period t.

[0210] The optimal scheduling model solving module for multi-microgrid shared energy storage includes a model simplification unit, a simplified model division unit, a sub-problem model decomposition conversion unit, an upper and lower bound updating unit, and a judgment iteration unit;

[0211] The model simplification unit is used to simplify the optimal scheduling model of multi-microgrid shared energy storage, obtain the following optimal scheduling simplified model of multi-microgrid shared energy storage, and set the number of iterations to k, initialize the upper and lower bounds of the optimal scheduling simplified model of multi-microgrid shared energy storage, and obtain the initial value of the model;

[0212]

[0213] In the above formula, For multiple microgrids, f T , b m ,d m , g1, g2 are all constant matrices, x 1m 、x 2m , z are both variable vectors, y m is a complex variable vector; A 1m , A 2m , A 3m , B m , C, D, G1, G2 are all constant coefficients; are the amount of electricity purchased from the main grid and the amount of electricity sold to the main grid by microgrid m during period t, are the charging power and discharging power of microgrid m to the shared energy storage in period t, respectively, i，t is the square of the voltage amplitude of node i in period t, i is the node number of the microgrid network, is the set of microgrid network nodes, is the set of multi-microgrid optimization time periods, (i, j) is the microgrid network branch number, P (i，j)，t , Q (i，j)，t are respectively the active power and reactive power flowing through branch ij during period t, is the microgrid network branch set, The variables are The repeated introduction of is the operating power of the shared energy storage in period t, P tch , P t dis are the charging power and discharging power of the shared energy storage in period t, SOC t is the state of charge of the shared energy storage during period t, They are 0-1 identification variables for charging and discharging of shared energy storage during period t, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during period t. are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t, respectively. deg Degradation costs of shared energy storage batteries for multiple microgrids;

[0214] The simplified model division unit is used to divide the optimal scheduling simplified model of multi-microgrid shared energy storage into a main problem model and a sub-problem model, and solve the main problem model according to the obtained initial value to obtain the complex variable y corresponding to each microgrid m ;

[0215] The main problem model is:

[0216] min(π+f T z);

[0217]

[0218] In the above formula, π is the optimal value variable of the corresponding sub-problem model in the main problem model, are all constant matrices, is a complex variable vector matrix, are the values ​​of the dual variables when solving the dual decomposition subproblem model. is the feasibility benders cut set, is the optimal running benders cut set;

[0219] The sub-problem model is:

[0220]

[0221]

[0222] In the above formula, is the complex variable vector y obtained when solving the main problem model m The value of

[0223] The sub-problem model decomposition conversion unit is used to decompose the sub-problem model into decomposition sub-problem models corresponding to each microgrid, and convert the decomposition sub-problem model corresponding to each microgrid into a dual decomposition sub-problem model of each microgrid;

[0224] The decomposition sub-problem model corresponding to each microgrid is:

[0225]

[0226] The dual decomposition sub-problem model of each microgrid is:

[0227]

[0228] In the above formula, To solve the complex variable vector matrix The value of u 1m 、u 2m 、u 3m are dual variables, are all constant coefficient matrices, c m is a constant;

[0229] The upper and lower bounds updating unit is used to obtain the complex variables y corresponding to each microgrid based on the simplified model division unit. m , solve whether the dual decomposition sub-problem model of each microgrid in the decomposition conversion unit has a solution. If the dual decomposition sub-problem model of a microgrid has a solution, the resulting feasibility benders cut set is added as a constraint to the main problem model. If the dual decomposition sub-problem model of a microgrid has no solution, the resulting optimal operation benders cut set is added as a constraint to the main problem model, solve the main problem model and update the upper and lower bounds of the optimal scheduling simplified model;

[0230] The feasibility benders cut set is:

[0231]

[0232] At this time, the upper bound of the optimal scheduling simplified model is:

[0233] In the above formula, UB (k) is the upper bound of the simplified model for the optimal scheduling of the kth iteration, is the optimal value of the dual decomposition subproblem model corresponding to the microgrid m, is the value of the variable vector z when solving the main problem model;

[0234] The optimal running benders cut set is:

[0235]

[0236] At this time, the upper bound of the optimal scheduling simplified model is: UB (k) =UB (k-1) ;

[0237] The lower bound of the optimal scheduling simplified model is:

[0238] In the above formula, LB (k) is the lower bound of the simplified model for the optimal scheduling of the kth iteration, The value of the optimal value variable π of the corresponding sub-problem model in the main problem model;

[0239] The iterative judgment unit is used to judge whether the updated optimal scheduling simplified model meets the convergence condition UB (k) -LB (k) <ε or reaches the maximum number of iterations, where ε is the preset error threshold. If none of them are satisfied, set k = k + 1 and return to the simplified model division unit for iteration until the convergence condition is met or the maximum number of iterations is reached. The iteration stops and the complex variables y corresponding to each microgrid are m Value Substitute the decomposed sub-problem model corresponding to each microgrid to obtain the scheduling decision plan for each microgrid and shared energy storage.

[0240] Embodiment 3:

[0241] like Figure 4 As shown, a distributed optimization scheduling device for multi-microgrid energy storage sharing includes a processor and a memory;

[0242] The memory is used to store computer program code and transmit the computer program code to the processor;

[0243] The processor is used to execute a distributed optimization scheduling method for multi-microgrid energy storage sharing described in Example 1 according to the instructions in the computer program code.

[0244] Embodiment 4:

[0245] A computer storage medium having a computer program stored thereon;

[0246] When the computer program is executed by the processor, the steps of a distributed optimization scheduling method for multi-microgrid energy storage sharing described in this solution are implemented.

Claims

1. A distributed optimization scheduling method for multi-microgrid energy storage sharing, characterized in that: The method comprises: S1. With the goal of minimizing the operating cost of the multi-microgrid system, an optimal scheduling model for multi-microgrid shared energy storage is constructed. The constraints of the optimal scheduling model include shared energy storage charging and discharging state variable constraints, shared energy storage charging and discharging time constraints, and shared energy storage peak time and valley time constraints. S2. Solve the optimal scheduling model for multiple microgrids to share energy storage and obtain the optimal scheduling decision plan for multiple microgrids to share energy storage.

2. A distributed optimization scheduling method for multi-microgrid energy storage sharing according to claim 1, characterized in that: In S1, the objective function of the optimal scheduling model for multi-microgrid shared energy storage includes: In the above formula, is a set of multiple microgrids, τ is a set of optimized time periods for multiple microgrids, μ b，t is the price of electricity purchased by multiple microgrids from the main grid during period t, is the amount of electricity purchased by microgrid m from the main grid during period t, μ s，t is the electricity price sold by multiple microgrids to the main grid during period t, is the amount of electricity sold by microgrid m to the main grid during period t, C deg is the degradation cost of the energy storage battery shared by multiple microgrids; β is the fitting parameter, ξ is the unit energy cost of the shared energy storage, B cap To share energy storage capacity, The state of charge of the shared energy storage during peak hours. It is the charge state of shared energy storage during off-peak hours.

3. A distributed optimization scheduling method for multi-microgrid energy storage sharing according to claim 1, characterized in that: In S1, the shared energy storage charging and discharging state variable constraints include: In the above formula, are the maximum charging power and discharging power of shared energy storage, respectively. They are 0-1 identification variables for charging and discharging of shared energy storage during period t, is the operating power of the shared energy storage in period t, ∈ p is a very small positive number, P t ch , are the charging power and discharging power of the shared energy storage in period t respectively; The shared energy storage charging and discharging period constraints include: In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during the t period; The shared energy storage peak period and valley period constraints include: In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t. They are the charge states of shared energy storage during peak and valley periods, respectively. is the upper limit of the shared energy storage state of charge, SOC t is the state of charge of the shared energy storage during period t.

4. A distributed optimization scheduling method for multi-microgrid energy storage sharing according to claim 1, characterized in that: The S2 includes: S21, simplifying the optimal scheduling model of multi-microgrid shared energy storage, obtaining the following simplified optimal scheduling model of multi-microgrid shared energy storage, and setting the number of iterations to k, initializing the upper and lower bounds of the simplified optimal scheduling model of multi-microgrid shared energy storage, and obtaining the initial value of the model; In the above formula, For multiple microgrids, f T 、b m ,d m , g1, g2 are all constant matrices, x 1m 、x 2m , z are both variable vectors, y m is a complex variable vector; A 1m , A 2m , A 3m , B m , C, D, G1, and G2 are all constant coefficients; are the amount of electricity purchased from the main grid and the amount of electricity sold to the main grid by microgrid m during period t, are the charging power and discharging power of microgrid m to the shared energy storage in period t, respectively, i，t is the square of the voltage amplitude of node i in period t, i is the node number of the microgrid network, is the set of microgrid network nodes, is the set of multi-microgrid optimization time periods, (i, j) is the microgrid network branch number, P (i，j)，t , Q (i，j)，t are respectively the active power and reactive power flowing through branch ij during period t, is the microgrid network branch set, The variables are The repeated introduction term, P t bat is the operating power of the shared energy storage in period t, P t ch , are the charging power and discharging power of the shared energy storage in period t, SOC t is the state of charge of the shared energy storage during period t, They are 0-1 identification variables for charging and discharging of shared energy storage during period t, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during period t. are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t, respectively. deg Degradation costs of shared energy storage batteries for multiple microgrids; S22, divide the optimal scheduling simplified model of multi-microgrid shared energy storage into a main problem model and a sub-problem model, and solve the main problem model according to the obtained initial value to obtain the complex variable y corresponding to each microgrid m ; The main problem model is: min(π+f T With); In the above formula, π is the optimal value variable of the corresponding sub-problem model in the main problem model, are all constant matrices, is a complex variable vector matrix, are the values ​​of the dual variables when solving the dual decomposition subproblem model. is the feasibility benders cut set, is the optimal running benders cut set; The sub-problem model is: In the above formula, is the complex variable vector y obtained when solving the main problem model m The value of S23, decomposing the sub-problem model into decomposition sub-problem models corresponding to each microgrid, and converting the decomposition sub-problem model corresponding to each microgrid into a dual decomposition sub-problem model of each microgrid; The decomposition sub-problem model corresponding to each microgrid is: The dual decomposition sub-problem model of each microgrid is: In the above formula, To solve the complex variable vector matrix The value of u 1m 、u 2m 、u 3m are dual variables, are all constant coefficient matrices, c m is a constant; S24, corresponding complex variables y of each microgrid obtained based on S22 m , solve whether the dual decomposition subproblem model of each microgrid in S23 has a solution. If the dual decomposition subproblem model of a microgrid has a solution, the resulting feasibility benders cut set is added to the main problem model as a constraint condition. If the dual decomposition subproblem model of a microgrid has no solution, the resulting optimal operation benders cut set is added to the main problem model as a constraint condition. Solve the main problem model and update the upper and lower bounds of the optimal scheduling simplified model. The feasibility benders cut set is: At this time, the upper bound of the optimal scheduling simplified model is: In the above formula, UB (k) is the upper bound of the simplified model for the optimal scheduling of the kth iteration, is the optimal value of the dual decomposition subproblem model corresponding to the microgrid m, The value of the variable vector Z when solving the main problem model; The optimal running benders cut set is: At this time, the upper bound of the optimal scheduling simplified model is: UB (k) =UB (k-1) ; The lower bound of the optimal scheduling simplified model is: In the above formula, LB (k) is the lower bound of the simplified model for the optimal scheduling of the kth iteration, The value of the optimal value variable π of the corresponding sub-problem model in the main problem model; S25. Determine whether the updated optimal scheduling simplified model meets the convergence condition UB (k) -LB (k) <ε or reaches the maximum number of iterations, where ε is the preset error threshold. If neither is satisfied, set k=k+1 and return to step S22 to iterate until the convergence condition is met or the maximum number of iterations is reached. The iteration stops and the complex variables y corresponding to each microgrid are m Value Substitute the decomposed sub-problem model corresponding to each microgrid to obtain the scheduling decision plan for each microgrid and shared energy storage.

5. A distributed optimization dispatching system for multi-microgrid energy storage sharing, characterized in that: The system includes an optimal dispatch model construction module for multiple microgrids to share energy storage and an optimal dispatch model solution module for multiple microgrids to share energy storage; The optimal scheduling model construction module for multi-microgrid shared energy storage is used to construct an optimal scheduling model for multi-microgrid shared energy storage with the goal of minimizing the operating cost of the multi-microgrid system; The optimal scheduling model solving module for multiple microgrids sharing energy storage is used to solve the optimal scheduling model for multiple microgrids sharing energy storage and obtain the optimal scheduling decision plan for multiple microgrids sharing energy storage.

6. A distributed optimization dispatching system for multi-microgrid energy storage sharing according to claim 5, characterized in that: The optimal scheduling model construction module for multi-microgrid shared energy storage includes an objective function construction unit; The objective function construction unit is used to construct the following objective function: In the above formula, For multiple microgrids, Optimize the time period set for multiple microgrids, μ b，t is the price of electricity purchased by multiple microgrids from the main grid during period t, is the amount of electricity purchased by microgrid m from the main grid during period t, μ s，t is the electricity price sold by multiple microgrids to the main grid during period t, is the amount of electricity sold by microgrid m to the main grid during period t, C deg is the degradation cost of the energy storage battery shared by multiple microgrids; β is the fitting parameter, ξ is the unit energy cost of the shared energy storage, B cap To share energy storage capacity, The state of charge of the shared energy storage during peak hours. It is the charge state of shared energy storage during off-peak hours.

7. A distributed optimization dispatching system for multi-microgrid energy storage sharing according to claim 5, characterized in that: The optimal scheduling model construction module for multi-microgrid shared energy storage also includes a shared energy storage charging and discharging state variable constraint construction unit, a shared energy storage charging and discharging period constraint construction unit, and a shared energy storage peak period and valley period constraint construction unit; The shared energy storage charging and discharging state variable constraint construction unit is used to construct the following shared energy storage charging and discharging state variable constraints: In the above formula, are the maximum charging power and discharging power of shared energy storage, respectively. They are 0-1 identification variables for charging and discharging of shared energy storage during period t, is the operating power of the shared energy storage in period t, ∈ p is a very small positive number, P t ch , are the charging power and discharging power of the shared energy storage in period t respectively; The shared energy storage charging and discharging period constraint construction unit is used to construct the following shared energy storage charging and discharging period constraint: In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during the t period; The shared energy storage peak period and valley period constraint construction unit is used to construct the following shared energy storage peak period and valley period constraints: In the above formula, They are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t. They are the charge states of shared energy storage during peak and valley periods, respectively. is the upper limit of the shared energy storage state of charge, SOC t is the state of charge of the shared energy storage during period t.

8. A distributed optimization dispatching system for multi-microgrid energy storage sharing according to claim 5, characterized in that: The optimal scheduling model solving module for multi-microgrid shared energy storage includes a model simplification unit, a simplified model division unit, a sub-problem model decomposition conversion unit, an upper and lower bound updating unit, and a judgment iteration unit; The model simplification unit is used to simplify the optimal scheduling model of multi-microgrid shared energy storage, obtain the following optimal scheduling simplified model of multi-microgrid shared energy storage, and set the number of iterations to k, initialize the upper and lower bounds of the optimal scheduling simplified model of multi-microgrid shared energy storage, and obtain the initial value of the model; In the above formula, For multiple microgrids, f T , b m d m , g1, g2 are all constant matrices, x 1m 、x 2m , z are both variable vectors, y m is a complex variable vector; A 1m , A 2m , A 3m , B m , C, D, G1, and G2 are all constant coefficients; are the amount of electricity purchased from the main grid and the amount of electricity sold to the main grid by microgrid m during period t, are the charging power and discharging power of microgrid m to the shared energy storage in period t, respectively, i，t is the square of the voltage amplitude of node i in period t, i is the node number of the microgrid network, is the set of microgrid network nodes, is the set of multi-microgrid optimization time periods, (i, j) is the microgrid network branch number, P (i，j)，t , Q (i，j)，t are respectively the active power and reactive power flowing through branch ij during period t, is the microgrid network branch set, The variables are The repeated introduction term, P t bat is the operating power of the shared energy storage in period t, P t ch , P t dis are the charging power and discharging power of the shared energy storage in period t, SOC t is the state of charge of the shared energy storage during period t, They are 0-1 identification variables for charging and discharging of shared energy storage during period t, They are 0-1 identification variables for whether the shared energy storage is in the charging period or the discharging period during period t. are 0-1 identification variables for whether the shared energy storage is in the peak period or valley period of the state of charge during period t, respectively. deg Degradation costs of shared energy storage batteries for multiple microgrids; The simplified model division unit is used to divide the optimal scheduling simplified model of multi-microgrid shared energy storage into a main problem model and a sub-problem model, and solve the main problem model according to the obtained initial value to obtain the complex variable y corresponding to each microgrid m ; The main problem model is: min(π+f T With); In the above formula, π is the optimal value variable of the corresponding sub-problem model in the main problem model, are all constant matrices, is a complex variable vector matrix, are the values ​​of the dual variables when solving the dual decomposition subproblem model. is the feasibility benders cut set, is the optimal running benders cut set; The sub-problem model is: In the above formula, is the complex variable vector y obtained when solving the main problem model m The value of The sub-problem model decomposition conversion unit is used to decompose the sub-problem model into decomposition sub-problem models corresponding to each microgrid, and convert the decomposition sub-problem model corresponding to each microgrid into a dual decomposition sub-problem model of each microgrid; The decomposition sub-problem model corresponding to each microgrid is: The dual decomposition sub-problem model of each microgrid is: In the above formula, To solve the complex variable vector matrix The value of u 1m 、u 2m 、u 3m are dual variables, are all constant coefficient matrices, c m is a constant; The upper and lower bounds updating unit is used to obtain the complex variables y corresponding to each microgrid based on the simplified model division unit. m , solve whether the dual decomposition sub-problem model of each microgrid in the decomposition conversion unit has a solution. If the dual decomposition sub-problem model of a microgrid has a solution, the resulting feasibility benders cut set is added as a constraint to the main problem model. If the dual decomposition sub-problem model of a microgrid has no solution, the resulting optimal operation benders cut set is added as a constraint to the main problem model, solve the main problem model and update the upper and lower bounds of the optimal scheduling simplified model; The feasibility benders cut set is: At this time, the upper bound of the optimal scheduling simplified model is: In the above formula, UB (k) is the upper bound of the simplified model for the optimal scheduling of the kth iteration, is the optimal value of the dual decomposition subproblem model corresponding to the microgrid m, is the value of the variable vector z when solving the main problem model; The optimal running benders cut set is: At this time, the upper bound of the optimal scheduling simplified model is: UB (k) =UB (k-1) ; The lower bound of the optimal scheduling simplified model is: In the above formula, LB (k) is the lower bound of the simplified model for the optimal scheduling of the kth iteration, The value of the optimal value variable π of the corresponding sub-problem model in the main problem model; The iterative judgment unit is used to judge whether the updated optimal scheduling simplified model meets the convergence condition UB (k) -LB (k) <ε or reaches the maximum number of iterations, where ε is the preset error threshold. If none of them are satisfied, set k = k + 1 and return to the simplified model division unit for iteration until the convergence condition is met or the maximum number of iterations is reached. The iteration stops and the complex variables y corresponding to each microgrid are m Value Substitute the decomposed sub-problem model corresponding to each microgrid to obtain the scheduling decision plan for each microgrid and shared energy storage.

9. A distributed optimization dispatching device for multi-microgrid energy storage sharing, characterized in that: including a processor and a memory; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute a distributed optimization scheduling method for multi-microgrid energy storage sharing according to any one of claims 1-4 according to the instructions in the computer program code.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a distributed optimization scheduling method for multi-microgrid energy storage sharing described in any one of claims 1-4 are implemented.

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