Shared energy storage and micro-grid coordinated optimization method based on Nash negotiation

Abstract

The invention discloses a shared energy storage and micro-grid coordinated optimization method based on Nash negotiation, and the method comprises the steps: constructing a micro-grid group and shared energy storage planning model considering energy sharing according to the collaborative operation of a micro-grid and shared energy storage; constructing a Nash negotiation coordination optimization model based on the ADMM; based on the constructed Nash negotiation coordinated optimization model based on the ADMM, constructing a cooperative operation negotiation model between the multiple microgrids and the shared energy storage; a cooperative operation negotiation model between a plurality of microgrids and shared energy storage is converted into a linear model, and the linear model comprises two sub-problems: a sub-problem 1 and a sub-problem 2; and calling a solver to solve the sub-problem 1, and adopting an ADMM algorithm to solve the sub-problem 2 in a distributed manner. According to the method, consumption and utilization of new energy can be realized, and the operation cost of the two parties is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimization dispatching, and in particular to a method for coordinating and optimizing shared energy storage and microgrid based on Nash negotiation. Background Art

[0002] By introducing a sharing model, energy sharing among multiple microgrids becomes an effective means to give full play to the synergistic advantages of microgrid clusters and improve the energy efficiency of energy storage systems. Microgrid clusters usually have two dimensions of sharing: one is energy sharing among multiple microgrids. The other is energy sharing between the Micro-Grid Cluster Operator (MGCO) and the Shared Energy Storage Operator (SESO).

[0003] For the former, reference [1]: Wang Zaichuang, Chen Laijun, Li Xiaozhu, Mei Shengwei. Optimization of distributed photovoltaic and shared energy storage capacity in producer-seller communities based on cooperative game [J]. Transactions of the Chinese Society of Electrotechnical Engineering, 2022, 37(23): 5922-5932. A method for optimizing the energy purchase strategy of each microgrid based on cooperative game theory was proposed, and a model for energy interaction between microgrids was established. The economic dispatch between microgrids was realized and the operating cost of the system was effectively reduced. It was verified that energy sharing between multiple microgrids can effectively improve the distributed new energy absorption capacity of the microgrid group.

[0004] For the latter, the interaction between MGCO and SESO can realize the coordinated optimization among multiple subjects. Considering the sharing of energy storage capacity between MGCO and SESO, the energy trading mode can realize the flexible power interaction between microgrid group and shared energy storage system, such as reference [2]: Xie Yulong, Luo Yiyang, Li Zhiwei, Xu Zhenyu, Li Li, Yang Kai. Optimal configuration of shared energy storage considering the economic absorption of new energy in microgrid [J]. High Voltage Technology, 2022, 48(11): 4403-4413. An operation method considering the investment benefits of microgrid operators and shared energy storage operators in the sharing scenario is proposed, and the configuration and allocation of shared energy storage capacity is realized. It is verified that energy sharing with SESO can effectively reduce the investment cost of MGCO's energy storage system and is conducive to promoting the absorption and utilization of distributed new energy. Reference [2] verifies that energy sharing between MGCO and SESO can effectively reduce the investment cost of MGCO's energy storage system through the configuration of shared energy storage capacity, but does not optimize the interests between MGCO and SESO to maximize the overall interests of both parties. Summary of the invention

[0005] In order to solve the above technical problems, the present invention takes microgrid groups as the research object, and proposes a shared energy storage and microgrid coordination optimization method based on Nash negotiation. By analyzing the interaction characteristics between MGCO and SESO, the method constructs a multi-agent system framework of microgrid groups considering energy sharing; at the same time, a multi-agent planning model of MGCO and SESO based on negotiation game is established, and it is proposed to use Nash negotiation to solve the problem of profit distribution among the subjects, and the alternating direction multiplier algorithm is used to perform distributed solution of the profit distribution of each subject; the analysis shows that through the cooperation between each microgrid and shared energy storage, the consumption and utilization of new energy can be realized and the operating costs of both parties can be effectively reduced.

[0006] The technical solution adopted by the present invention is:

[0007] The coordinated optimization method of shared energy storage and microgrid based on Nash negotiation includes the following steps:

[0008] Step 1: Based on the coordinated operation of microgrids and shared energy storage, a microgrid cluster and shared energy storage planning model considering energy sharing is constructed;

[0009] Step 2: Construct a Nash negotiation coordination optimization model based on ADMM;

[0010] Step 3: Based on the ADMM-based Nash negotiation coordination optimization model constructed in step 2, the Nash negotiation theory is used to construct a cooperative operation negotiation model between multi-microgrids and shared energy storage;

[0011] Step 4: Convert the cooperative operation negotiation model between multiple microgrids and shared energy storage into a linear model, which includes two sub-problems: Sub-problem 1: Minimum cost problem between MGCO and SESO; Sub-problem 2: Negotiation problem of electricity transaction price between each microgrid and shared energy storage;

[0012] Step 5: Call the solver to solve sub-problem 1, and use the ADMM algorithm to solve sub-problem 2 in a distributed manner.

[0013] In step 1, the planning goal of the microgrid group and the shared energy storage multi-agent is to minimize the total cost, including investment cost and operation cost. The operation cost mainly includes: the cost of the microgrid to purchase electricity from the distribution network, the power generation cost of the controllable distributed power source, the cost of the microgrid to purchase fuel, and the power interaction cost with the shared energy storage; the investment cost of the shared energy storage includes the investment and construction cost and the cost of purchasing electricity from the microgrid; the specific expression is as follows:

[0014] minC=C M +C S

[0015] C S =C ESS,buy -U ESS,sell

[0016] C M =C DG +C PG +C flue +C MG,buy -U MG,sell

[0017] Where: C DG is the investment cost of distributed power generation in microgrid; C PG C is the cost of purchasing electricity from the distribution network for the microgrid; fule Cost of purchasing fuel for the microgrid; C MG,buy The cost of purchasing electricity from the shared energy storage power station for the microgrid; U MG,sell The income from selling electricity to the microgrid; C S Shared energy storage power station investment and operation and maintenance costs; C ESS,buy The cost of purchasing electricity for shared energy storage; U ESS,sell Profit from selling electricity to microgrids for shared energy storage; C M represents the operating cost; minC represents the lowest total cost of the microgrid group and shared energy storage.

[0018] The investment cost of distributed power generation in microgrid is:

[0019]

[0020] Where: is the return on investment; b n is the construction cost coefficient of distributed generation in microgrid n; Q n is the capacity of distributed generation in microgrid n.

[0021] The operating cost of a micro gas turbine is:

[0022]

[0023] In the formula, is the cost of purchasing fuel for microgrid n in period t; c is the natural gas cost matrix per unit volume; is the power matrix of the micro-turbine in microgrid No. n during period t; η MT is the efficiency of the micro-turbine; Q 0 is the calorific value of gas; Γ and N are the dispatching period and the number of microgrids respectively; C flue Represents the cost of purchasing fuel for the microgrid.

[0024] The cost of purchasing electricity from the distribution network for the microgrid group is:

[0025]

[0026] In the formula, is the cost of purchasing electricity from the distribution network for microgrid n during period t; PG,tis the unit electricity price matrix of the distribution network in period t; It is the power matrix of microgrid n purchasing electricity from the distribution network in period t.

[0027] The cost of purchasing electricity from the shared energy storage power station by the microgrid group is:

[0028]

[0029] In the formula, is the cost of purchasing electricity from the energy storage power station for microgrid n during period t; n,t is the unit electricity price matrix of the energy storage power station during period t; is the power matrix of microgrid n purchasing electricity from energy storage power station in period t; C MG,buy is the total electricity purchase cost of the microgrid.

[0030] The revenue from electricity sales of the microgrid group is:

[0031]

[0032] Where U MG,sell Profit from electricity sales for microgrids; Selling power to the distribution grid for the microgrid; Sell ​​power to the energy storage power station for the microgrid; n,t It is the unit electricity price matrix of the energy storage power station in period t.

[0033] The average daily investment and maintenance cost of shared energy storage equipment is:

[0034]

[0035] In the formula, C S Investment and operation and maintenance costs of shared energy storage power stations; η p , η e are the power and capacity costs of the equipment respectively; T E is the service life of the equipment; m Maintaining costs for shared energy storage systems; Optimal power for dynamically leasing capacity for shared energy storage systems; The optimal capacity for dynamic leasing of capacity for shared energy storage systems.

[0036] The cost of purchasing electricity for shared energy storage is:

[0037]

[0038] In the formula, The cost of electricity purchased by the shared energy storage power station from microgrid n during period t; is the power matrix of the energy storage power station purchasing electricity from microgrid n during period t; C EES,buy represents the electricity purchasing cost of shared energy storage; T represents the scheduling period.

[0039] The revenue from selling electricity to microgrids through shared energy storage is:

[0040]

[0041] In the formula, The revenue from electricity sales from the shared energy storage power station to microgrid n during period t; is the power matrix of the energy storage power station selling electricity to microgrid n during period t; U ESS,sell The total electricity sales revenue of shared energy storage.

[0042] In step 2, the solution that maximizes the Nash product is the equilibrium solution to the Nash negotiation game problem. This method can reasonably distribute the profits of SESO and MGCO. Taking the cost of SESO and MGCO operating independently as the breakdown point of the negotiation, the Nash negotiation model is:

[0043]

[0044] Where: I is the set of entities participating in the negotiation; U i and are the benefits of subject i after participating in the negotiation and the solution of the minimization cost model, i.e., the negotiation breakdown point; i represents a subject participating in the negotiation; Represents the maximum total benefit after participating in the negotiation.

[0045] In step 3, the above Nash negotiation model is applied to the present invention, and the Nash negotiation theory is used to construct a cooperative operation negotiation model between multiple microgrids and shared energy storage;

[0046]

[0047] Where: U ESS The optimal benefit of shared energy storage after participating in energy sharing; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; U MG,n is the optimal benefit of the microgrid after participating in energy sharing; max(U ESS -U ESS,0 ) represents the revenue of energy storage operators after microgrids and shared energy storage participate in the negotiation;

[0048] represents the revenue of the microgrid operator after the microgrid and shared energy storage participate in the negotiation; n represents one of the multiple microgrids; N represents the total number of microgrids.

[0049] In step 4, since the above equation is a nonlinear optimization problem, the model cannot be solved directly and needs to be converted into a linear model to solve it. It can be converted into two sub-problems for solution.

[0050] Sub-problem 1: Minimum cost problem for both MGCO and SESO:

[0051]

[0052] In the formula, C MG,n is the cost of microgrid n; C ESS is the cost of shared energy storage; U MG,sell is the electricity sales revenue of the microgrid; C DG is the investment cost of distributed power generation in microgrid; C PG C is the cost of purchasing electricity from the distribution network for the microgrid; fule Cost of purchasing fuel for the microgrid; C MG,buy The cost of purchasing electricity from the shared energy storage power station for the microgrid; C S To share the investment and operation and maintenance costs of energy storage power stations.

[0053] Sub-problem 2: Negotiation of electricity trading prices between microgrids and shared energy storage:

[0054]

[0055] Where: is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; U MG,sell The revenue from electricity sales of the microgrid; Selling power to the distribution grid for the microgrid; Sell ​​power to the energy storage power station for the microgrid.

[0056] In step 5, subproblem 1 can be solved by calling the CPLEX solver in Matlab2018b; subproblem 2 can be solved in a distributed manner using the ADMM algorithm. In order to decouple the transaction electricity price between each microgrid and the shared energy storage, auxiliary variables need to be introduced as follows:

[0057]

[0058] Where: n,t is the unit electricity price matrix of the energy storage power station during period t; is the price that the microgrid expects to pay for electricity from the shared energy storage at time t.

[0059] The augmented Lagrangian function of subproblem 2 is:

[0060]

[0061] Where: is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; n,t is the Lagrange multiplier of microgrid n; δ n,t is the unit electricity price matrix of the energy storage power station during period t; is the price that the microgrid expects to pay for electricity from the shared energy storage at time t; n is the penalty factor.

[0062] Solving the above equation according to the ADMM decomposition idea, we get the distributed optimization model of the electricity transaction price between the microgrid and the shared energy storage power station:

[0063]

[0064] In the formula, is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 To obtain the optimal benefit of shared energy storage before participating in energy sharing; n,t is the Lagrange multiplier of microgrid n; δ n,t is the unit electricity price matrix of the energy storage power station during period t; is the electricity purchase and sale price that the microgrid expects to pay to the shared energy storage at time t; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 The optimal benefit of the microgrid before participating in energy sharing.

[0065] The present invention provides a method for coordinating and optimizing shared energy storage and microgrids based on Nash negotiation, and the technical effects are as follows:

[0066] 1) In step 1 of the present invention, the total cost of the microgrid group and shared energy storage is minimized as the objective function, the microgrid and shared energy storage are operated in coordination, and a microgrid group and shared energy storage planning model including energy sharing is constructed. This model is the basis for analyzing the following problems.

[0067] 2) In step 2 of the present invention, a Nash negotiation coordination optimization model based on ADMM is constructed; the alternating direction multiplier method (ADMM) combines the decomposability of the dual ascent method and the faster convergence speed of the multiplier method, and does not require the objective function to be in a strictly convex form. It converges faster than other distributed algorithms and is currently widely used in Nash negotiation theory.

[0068] 3) Step 3 of the present invention further concretizes the model based on the Nash negotiation coordination optimization model based on ADMM constructed in step 2. Step 3 converts the generalized mathematical model in step 2 into the cooperative operation negotiation model between multi-microgrids and shared energy storage required in this patent.

[0069] 4) Since the model in step 3 is a nonlinear optimization problem, it is impossible to solve the model directly. The present invention converts it into two linear sub-problems for solution. It includes two sub-problems: Sub-problem 1: the cost minimization problem between MGCO and SESO; Sub-problem 2: the negotiation problem of the electricity transaction price between each microgrid and the shared energy storage; these two sub-problems are the problems that need to be solved by this patent.

[0070] 5) In step 5 of the present invention, sub-problem 1 can be solved by directly calling the solver without the need for negotiation and game between the cooperating parties. Sub-problem 2 requires Nash negotiation to negotiate the electricity transaction price between each microgrid and the shared energy storage. Both parties expect to maximize their own benefits. When the negotiation between the two parties reaches equilibrium, the negotiation is completed, that is, neither party can unilaterally change its own strategy to obtain greater benefits. The ADMM algorithm can perform distributed solution to the Nash negotiation problem of sub-problem 2. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0072] Figure 1 The figure is a flow chart of the optimization method of the present invention.

[0073] Figure 2 The following is a comparison chart of the power purchased from the distribution network by the microgrids in the three models at different time periods.

[0074] Figure 3 The comparison diagram of the interactive power between the microgrid and SESS of the three models in each time period.

[0075] Figure 4 It is the iterative process of ADMM in model 2.

[0076] Figure 5 It is the iterative process of ADMM in model 3. DETAILED DESCRIPTION

[0077] The cooperative operation of energy storage and microgrids can greatly improve the economy of system operation. The present invention provides a method for coordinating and optimizing shared energy storage and microgrids based on Nash negotiation. The method considers the collaborative interaction mechanism between microgrid groups and shared energy storage, establishes a microgrid group model based on shared energy storage, and considers energy sharing between microgrids to reduce the cost of microgrids purchasing electricity from distribution networks and shared energy storage. In order to minimize the operating cost, the Nash negotiation theory is used to establish a cooperative operation negotiation model between multiple microgrids and shared energy storage, and a distributed solution algorithm based on the alternating direction multiplier method (ADMM) is used to solve the problem. Finally, a case analysis is performed based on MATLAB. The results show that through the cooperation between microgrids and shared energy storage, the utilization of new energy can be realized and the operating costs of both parties can be effectively reduced.

[0078] The coordinated optimization method of shared energy storage and microgrid based on Nash negotiation includes the following steps:

[0079] (1): According to the coordinated operation of microgrids and shared energy storage, a microgrid group and shared energy storage planning model considering energy sharing is constructed, as follows:

[0080] The planning goal of the microgrid group and shared energy storage multi-agent is to minimize the total cost, including the investment cost and operation cost of the microgrid group. The operation cost mainly includes the cost of purchasing electricity from the distribution network, the power generation cost of the controllable distributed power source, and the power interaction cost with the shared energy storage. The investment cost of shared energy storage includes the investment and construction cost and the cost of purchasing electricity from the microgrid, which is specifically expressed as follows:

[0081] minC=C M +C S

[0082] C S =C ESS,buy -U ESS,sell

[0083] C M =C DG +C PG +C flue +C MG,buy -U MG,sell

[0084] Where: C DG is the investment cost of distributed power generation in microgrid; C PG C is the cost of purchasing electricity from the distribution network for the microgrid; fule Cost of purchasing fuel for the microgrid; C MG,buy The cost of purchasing electricity from the shared energy storage power station for the microgrid; U MG,sell The income from selling electricity to the microgrid; C S Shared energy storage power station investment and operation and maintenance costs; CESS,buy The cost of purchasing electricity for shared energy storage; U ESS,sell Proceeds from selling electricity to microgrids for shared energy storage.

[0085] The investment cost of distributed power generation in microgrid is:

[0086]

[0087] Where: is the return on investment; b n is the construction cost coefficient of distributed generation in microgrid n; Q n is the capacity of distributed generation in microgrid n.

[0088] The operating cost of a micro gas turbine is:

[0089]

[0090] In the formula, is the cost of purchasing fuel for microgrid n in period t; c is the natural gas cost matrix per unit volume; is the power matrix of the micro-turbine in microgrid No. n during period t; η MT is the efficiency of the micro-turbine; Q 0 is the calorific value of gas. Γ and N are the dispatching period and the number of microgrids respectively.

[0091] The cost of purchasing electricity from the distribution network for the microgrid group is:

[0092]

[0093] In the formula, is the cost of purchasing electricity from the distribution network for microgrid n during period t; PG,t is the unit electricity price matrix of the distribution network in period t; is the power matrix of microgrid n purchasing electricity from the distribution network in period t; Γ and N are the scheduling period and the number of microgrids respectively.

[0094] The cost of purchasing electricity from the shared energy storage power station by the microgrid group is:

[0095]

[0096] In the formula, is the cost of purchasing electricity from the energy storage power station for microgrid n during period t; n,t is the unit electricity price matrix of the energy storage power station during period t; is the power matrix of microgrid n purchasing electricity from energy storage power station in period t. MG,buy is the total electricity purchase cost of the microgrid; Γ and N are the dispatching period and the number of microgrids respectively.

[0097] The revenue from electricity sales of the microgrid group is:

[0098]

[0099] In the formula, U MG,sell Profit from electricity sales for microgrids; Selling power to the distribution grid for the microgrid; Sell ​​power to the energy storage power station for the microgrid; PG,t is the unit electricity price matrix of the distribution network in period t; n,t is the unit electricity price matrix of the energy storage power station in period t; Γ and N are the scheduling period and the number of microgrids respectively.

[0100] The average daily investment and maintenance cost of shared energy storage equipment is:

[0101]

[0102] In the formula, C S Investment and operation and maintenance costs of shared energy storage power stations; η p , η e are the power and capacity costs of the equipment respectively; T E is the service life of the equipment; m Maintaining costs for shared energy storage systems. Optimal power for dynamically leasing capacity for shared energy storage systems; The optimal capacity for dynamic leasing of capacity for shared energy storage systems.

[0103] The cost of purchasing electricity for shared energy storage is:

[0104]

[0105] In the formula, is the electricity purchase cost of the shared energy storage power station from microgrid n during period t; n,t is the unit electricity price matrix of the energy storage power station during period t; is the power matrix of the energy storage power station purchasing electricity from microgrid n during period t; Γ and N are the scheduling period and the number of microgrids respectively.

[0106] The revenue from selling electricity to microgrids through shared energy storage is:

[0107]

[0108] In the formula, is the revenue from electricity sales from the shared energy storage power station to microgrid n during period t; n,t is the unit electricity price matrix of the energy storage power station during period t; is the power matrix of the energy storage power station selling electricity to microgrid n during period t; U ESS,sell is the total electricity sales revenue of shared energy storage; Γ and N are the scheduling period and the number of microgrids, respectively.

[0109] (2): Construct a Nash negotiation coordination optimization model based on ADMM applicable to the present invention, as follows:

[0110] In the formula, the solution that maximizes the Nash product is the equilibrium solution to the Nash negotiation game problem. This method can reasonably distribute the profits of SESO and MGCO. The cost of SESO and MGCO operating independently is taken as the breakdown point of the negotiation. The generalized Nash negotiation model is

[0111]

[0112] Where: I is the set of entities participating in the negotiation; U i and They are respectively the benefit of subject i after participating in the negotiation and the solution of the minimization cost model, i.e., the negotiation breakdown point.

[0113] The above generalized Nash negotiation model is extended to this patent, and the Nash negotiation theory is used to construct a cooperative operation negotiation model between multi-microgrids and shared energy storage:

[0114]

[0115] Where: U ESS The optimal benefit of shared energy storage after participating in energy sharing; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; U MG,n The optimal benefit of the microgrid after participating in energy sharing.

[0116] (3): The equivalent transformation of the Nash negotiation model for cooperative operation between multiple microgrids and shared energy storage is as follows:

[0117] Since the above equation is a nonlinear optimization problem, the model cannot be solved directly and needs to be converted into a linear model to solve it. It can be converted into two sub-problems for solution.

[0118] Sub-problem 1: Minimum cost problem for both MGCO and SESO

[0119]

[0120] In the formula, C MG,n is the cost of microgrid n; C ESS is the cost of shared energy storage; U MG,sell is the electricity sales revenue of the microgrid; C DG is the investment cost of distributed power generation in microgrid; C PG C is the cost of purchasing electricity from the distribution network for the microgrid; fule Cost of purchasing fuel for the microgrid; C MG,buy The cost of purchasing electricity from the shared energy storage power station for the microgrid; CS To share the investment and operation and maintenance costs of energy storage power stations.

[0121] Sub-problem 2: Negotiation of electricity trading prices between microgrids and shared energy storage

[0122]

[0123] Where: is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; U MG,sell The revenue from electricity sales of the microgrid; Selling power to the distribution grid for the microgrid; Sell ​​power to the energy storage power station for the microgrid; PG,t is the unit electricity price matrix of the distribution network in period t; n,t is the unit electricity price matrix of the energy storage power station in period t; Γ and N are the scheduling period and the number of microgrids respectively.

[0124] (4): The Nash negotiation model of cooperative operation between multiple microgrids and shared energy storage is solved as follows: Subproblem 1 can be solved by calling the CPLEX solver in Matlab2018b, and subproblem 2 can be solved in a distributed manner using the ADMM algorithm. In order to decouple the electricity price of the transaction power between each microgrid and the shared energy storage, auxiliary variables need to be introduced as follows:

[0125]

[0126] Where: n,t is the unit electricity price matrix of the energy storage power station during period t; is the price that the microgrid expects to pay for electricity from the shared energy storage at time t.

[0127] Introducing the penalty factor ω n = 1 and the Lagrange multiplier ξ n,t = 0, solve the augmented Lagrangian function of subproblem 2;

[0128] The augmented Lagrangian function of subproblem 2 is:

[0129]

[0130] Where: is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage;ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; n,t is the Lagrange multiplier of microgrid n; δ n,t is the unit electricity price matrix of the energy storage power station during period t; is the price that the microgrid expects to pay for electricity from the shared energy storage at time t; n is the penalty factor; Γ and N are the scheduling period and the number of microgrids respectively.

[0131] Set the initial number of iterations k = 1, the maximum number of iterations k = 100, and the iteration convergence accuracy ε = 10 -3 , the initial microgrid-shared energy storage transaction power is 0

[0132] Update the Lagrange multiplier:

[0133]

[0134] In the formula is the transaction power between microgrid i and shared energy storage j at time t

[0135] (5): Rolling shift to k=k+1, updating the Lagrange multiplier, judging the convergence of the algorithm, terminating the algorithm if the convergence accuracy is met, and obtaining the distributed optimization model of the electricity transaction price between the microgrid and the shared energy storage power station, otherwise returning to step (4) and repeating step (4).

[0136] Update the Lagrange multiplier:

[0137]

[0138] Determine the convergence of the algorithm. If the convergence accuracy is met, terminate the algorithm and output the interactive power between the microgrid and the shared energy storage. Otherwise, repeat the above steps. In the distributed optimization process, the microgrid and other shared energy storage realize iterative solution through the interaction of power information and the update of the augmented Lagrangian function. When the original residual and the dual residual reach the convergence condition, the iteration ends. At this time, the electric energy transaction price negotiation model between each microgrid and the shared energy storage realizes the distributed optimization solution.

[0139] (6): Solving the above equation according to the ADMM decomposition idea, we get the distributed optimization model of the electricity transaction price between the microgrid and the shared energy storage power station:

[0140]

[0141]

[0142] In the formula, is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 To obtain the optimal benefit of shared energy storage before participating in energy sharing; n,t is the Lagrange multiplier of microgrid n; δ n,t is the unit electricity price matrix of the energy storage power station during period t; is the electricity purchase and sale price that the microgrid expects to pay to the shared energy storage at time t; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 The optimal benefit of the microgrid before participating in energy sharing.

[0143] Figure 2 , Figure 3 The microgrid is shown to charge from the distribution grid and interact with the SESS in different modes at different times of the day. Figure 2 It can be seen that the time when the microgrids in Model 1 and Model 2 charge from the distribution network is concentrated between 10:00-20:00 and 11:00-15:00, but the microgrid in Model 3 does not charge from the distribution network. It is reasonable that the power load from 10:00-20:00 is higher than other times of the day. However, Model 2 considers the connection between SESS and MG, and Model 3 further considers the connection of each MG, which reduces the pressure on the power for charging from the distribution network. This reduces the cost of purchasing electricity for the microgrid from the distribution network. According to Figure 3 As shown in Figure 2, the microgrids in Model 2 interact more frequently with the SESS than those in Model 3. The following two aspects contribute to this situation:

[0144] 1) Using the energy storage services provided by SESS can minimize the total cost;

[0145] 2) The microgrid in Model 3 shares energy with other microgrids, which reduces the dependence on SESS.

[0146] Among them: Model 1 is an independently operated microgrid, with no interconnection between SESS and MG, and no interconnection between different MGs;

[0147] Model 2 is that SESS and MG are interconnected, but different MGs are not interconnected;

[0148] Model 3 is a coordinated optimal scheduling model with Nash negotiation, which considers the interconnection between SESS and MG and between different MGs.

[0149] Depend on Figure 4 , Figure 5 It can be seen that the algorithm converges to an accuracy of 10 after multiple iterations.-3 Therefore, it is shown that the shared energy storage-multi-microgrid system power transaction payment negotiation solution algorithm proposed by the present invention based on ADMM has good convergence characteristics.

[0150] The present invention takes into account the negotiation transaction model between MGCO and SESO, and the electricity transaction model between microgrid operators and energy storage operators, so as to maximize the overall interests of both parties.

Claims

1. A coordinated optimization method of shared energy storage and microgrid based on Nash negotiation, characterized by The following steps are involved: Step 1: Based on the coordinated operation of microgrids and shared energy storage, a microgrid cluster and shared energy storage planning model considering energy sharing is constructed; Step 2: Construct a Nash negotiation coordination optimization model based on ADMM; Step 3: Based on the ADMM-based Nash negotiation coordination optimization model constructed in step 2, a cooperative operation negotiation model between multi-microgrids and shared energy storage is constructed; Step 4: Convert the cooperative operation negotiation model between multiple microgrids and shared energy storage into a linear model, which includes two sub-problems: Sub-problem 1: Minimum cost problem between MGCO and SESO; Sub-problem 2: Negotiation problem of electricity transaction price between each microgrid and shared energy storage; Step 5: Call the solver to solve sub-problem 1, and use the ADMM algorithm to solve sub-problem 2 in a distributed manner.

2. The method for coordinated optimization of shared energy storage and microgrid based on Nash negotiation according to claim 1 is characterized in that: In step 1, the planning goal of the microgrid group and the shared energy storage multi-agent is to minimize the total cost, including investment cost and operation cost. The operation cost includes: the cost of the microgrid to purchase electricity from the distribution network, the power generation cost of the controllable distributed power source, the cost of the microgrid to purchase fuel, and the power interaction cost with the shared energy storage; the investment cost of the shared energy storage includes the investment and construction cost and the cost of purchasing electricity from the microgrid; the specific expression is as follows: minC=C M +C S C S =C ESS,buy -U ESS,sell C M =C DG +C PG +C flue +C MG,buy -U MG,sell Where: C DG is the investment cost of distributed power generation in microgrid; C PG C is the cost of purchasing electricity from the distribution network for the microgrid; fule Cost of purchasing fuel for the microgrid; C MG,buy The cost of purchasing electricity from the shared energy storage power station for the microgrid; U MG,sell The income from selling electricity to the microgrid; C S Shared energy storage power station investment and operation and maintenance costs; C ESS,buy The cost of purchasing electricity for shared energy storage; U ESS,sell Profit from selling electricity to microgrids for shared energy storage; C M represents the operating cost; minC represents the lowest total cost of the microgrid group and shared energy storage; The investment cost of distributed power generation in microgrid is: Where: is the return on investment; b n is the construction cost coefficient of distributed generation in microgrid n; Q n is the capacity of distributed generation in microgrid n; The operating cost of a micro gas turbine is: In the formula, is the cost of purchasing fuel for microgrid n in period t; c is the natural gas cost matrix per unit volume; is the power matrix of the micro-turbine in microgrid No. n during period t; η MT is the efficiency of the micro-turbine; Q0 is the calorific value of the gas; Γ and N are the dispatching period and the number of microgrids respectively; C flue represents the fuel purchase cost of the microgrid; The cost of purchasing electricity from the distribution network for the microgrid group is: In the formula, is the cost of purchasing electricity from the distribution network for microgrid n during period t; PG,t is the unit electricity price matrix of the distribution network in period t; The power matrix of microgrid n purchasing electricity from the distribution network in time period t; The cost of purchasing electricity from the shared energy storage power station by the microgrid group is: In the formula, is the cost of purchasing electricity from the energy storage power station for microgrid n during period t; n,t is the unit electricity price matrix of the energy storage power station during period t; is the power matrix of microgrid n purchasing electricity from energy storage power station in period t; C MG,buy is the total electricity purchase cost of the microgrid; The revenue from electricity sales of the microgrid group is: Where U MG,sell Profit from electricity sales for microgrids; Selling power to the distribution grid for the microgrid; Sell ​​power to the energy storage power station for the microgrid; n,t is the unit electricity price matrix of the energy storage power station during period t; The average daily investment and maintenance cost of shared energy storage equipment is: In the formula, C S Investment and operation and maintenance costs of shared energy storage power stations; η p , η e are the power and capacity costs of the equipment respectively; T E is the service life of the equipment; m Maintaining costs for shared energy storage systems; Optimal power for dynamically leasing capacity for shared energy storage systems; Optimal capacity for dynamically leasing capacity for shared energy storage systems; The cost of purchasing electricity for shared energy storage is: In the formula, The cost of electricity purchased by the shared energy storage power station from microgrid n during period t; is the power matrix of the energy storage power station purchasing electricity from microgrid n during period t; C EES,buy represents the electricity purchase cost of shared energy storage; T represents the dispatch period; The revenue from selling electricity to microgrids through shared energy storage is: In the formula, The revenue from electricity sales from the shared energy storage power station to microgrid n during period t; is the power matrix of the energy storage power station selling electricity to microgrid n during period t; U ESS,sell The total electricity sales revenue of shared energy storage.

3. The method for coordinated optimization of shared energy storage and microgrid based on Nash negotiation according to claim 1 is characterized in that: In step 2, the cost of SESO and MGCO operating independently is taken as the breakdown point of the negotiation, and the Nash negotiation model is: Where: I is the set of entities participating in the negotiation; U i and are the benefits of subject i after participating in the negotiation and the solution of the minimization cost model, i.e., the negotiation breakdown point; i represents a subject participating in the negotiation; Represents the maximum total benefit after participating in the negotiation.

4. The method for coordinated optimization of shared energy storage and microgrid based on Nash negotiation according to claim 3 is characterized by: In step 3, Nash negotiation theory is used to construct a cooperative operation negotiation model between multiple microgrids and shared energy storage; Where: U ESS The optimal benefit of shared energy storage after participating in energy sharing; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; U MG,n is the optimal benefit of the microgrid after participating in energy sharing; max(U ESS -U ESS,0 ) represents the revenue of energy storage operators after microgrids and shared energy storage participate in the negotiation; represents the revenue of the microgrid operator after the microgrid and shared energy storage participate in the negotiation; n represents one of the multiple microgrids; N represents the total number of microgrids.

5. The method for coordinated optimization of shared energy storage and microgrid based on Nash negotiation according to claim 1 is characterized in that: In step 4, the cooperative operation negotiation model between the multi-microgrid and the shared energy storage is converted into a linear model to solve it, specifically, it is converted into two sub-problems to solve: Sub-problem 1: Minimum cost problem for both MGCO and SESO: In the formula, C MG,n is the cost of microgrid n; C ESS is the cost of shared energy storage; U MG,sell is the electricity sales revenue of the microgrid; C DG is the investment cost of distributed power generation in microgrid; C PG C is the cost of purchasing electricity from the distribution network for the microgrid; fule Cost of purchasing fuel for the microgrid; C MG,buy The cost of purchasing electricity from the shared energy storage power station for the microgrid; C S To share the investment and operation and maintenance costs of energy storage power stations; Sub-problem 2: Negotiation of electricity trading prices between microgrids and shared energy storage: Where: is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; U MG,sell The revenue from electricity sales of the microgrid; Selling power to the distribution grid for the microgrid; Sell ​​power to the energy storage power station for the microgrid.

6. The method for coordinated optimization of shared energy storage and microgrid based on Nash negotiation according to claim 5 is characterized in that: Subproblem 1 is solved by calling the CPLEX solver in Matlab2018b; the ADMM algorithm is used to solve subproblem 2 in a distributed manner. In order to decouple the transaction electricity price between each microgrid and the shared energy storage, the auxiliary variables are introduced as follows: Where: n,t is the unit electricity price matrix of the energy storage power station during period t; is the price that the microgrid expects to pay for electricity from the shared energy storage at time t; The augmented Lagrangian function of subproblem 2 is: Where: is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 The optimal benefit of shared energy storage before participating in energy sharing; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 is the optimal benefit of the microgrid before participating in energy sharing; n,t is the Lagrange multiplier of microgrid n; δ n,t is the unit electricity price matrix of the energy storage power station during period t; is the price that the microgrid expects to pay for electricity from the shared energy storage at time t; n is the penalty factor; Solving the above equation according to the ADMM decomposition idea, we get the distributed optimization model of the electricity transaction price between the microgrid and the shared energy storage power station: In the formula, is the optimal solution to subproblem 1; U ESS,sell U is the revenue from electricity sales of shared energy storage; ESS,buy Profit from electricity purchase for shared energy storage; U ESS,0 To obtain the optimal benefit of shared energy storage before participating in energy sharing; n,t is the Lagrange multiplier of microgrid n; δ n,t is the unit electricity price matrix of the energy storage power station during period t; is the electricity purchase and sale price that the microgrid expects to pay to the shared energy storage at time t; U MG,sell is the electricity sales revenue of the microgrid; U MG,n,0 The optimal benefit of the microgrid before participating in energy sharing.

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