Shared energy storage optimal configuration method and device considering power grid bearing capacity

Abstract

The invention belongs to the technical field of energy storage optimal configuration, and particularly relates to a shared energy storage optimal configuration method and device considering the power grid bearing capacity, and the method comprises the steps: constructing a shared energy storage structure facing a micro-energy grid cluster; considering energy transmission capability and operation bearing capability of the power grid, and constructing an upper-layer model; setting constraint conditions of the upper-layer model; constructing a lower-layer model to optimize and decide the operation problem of each micro-energy grid; setting constraint conditions of the lower-layer model; based on a Lagrange function and a KKT condition, a double-layer optimization model composed of the upper layer model and the lower layer model is converted into a single-layer mixed integer linear programming model easy to solve; and solving the single-layer mixed integer linear programming model to obtain a shared energy storage optimal configuration scheme. According to the method, the power grid operation bearing capacity can be reasonably considered, and the position and capacity of shared energy storage are optimally configured.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage optimization configuration, and specifically relates to a shared energy storage optimization configuration method and device taking into account the carrying capacity of a power grid. Background Art

[0002] In recent years, energy and environmental issues have become increasingly prominent, and exploring new energy paths and promoting clean energy transformation has become an inevitable choice. Micro energy networks integrate distributed power sources, energy storage and multiple types of integrated energy equipment to achieve cascade utilization of energy and complementary conversion of heterogeneous energy such as cold, heat and electricity, promote the consumption of new energy and improve energy quality and efficiency, and become an important boost to promote clean energy transformation.

[0003] Within the micro-energy grid, energy storage devices can absorb and release energy in different time periods, achieving energy complementarity at the time level, and becoming an important means to smooth out fluctuations in new energy output and load. The reasonable configuration of energy storage devices is an important basis for micro-energy grids to promote the consumption of new energy and improve energy quality and efficiency. The document "Capacity Optimization Configuration of Multi-Microgrid Systems with Electric-Hydrogen Hybrid Energy Storage Considering Power Interaction Constraints" introduces a power interaction constraint model into the electric-hydrogen hybrid energy storage optimization configuration model, and proposes a gray wolf-sine cosine algorithm to solve the model. The document "Optimal Configuration of Hybrid Energy Storage Microgrid Considering Battery Life" relies on the demand degree Shapley value to achieve economic cost sharing, and proposes a method for optimizing the configuration of microgrid energy storage devices considering equipment life. The document "Optimal Configuration of Multi-Microgrid Energy Storage Capacity Based on Master-Slave Game" proposes an energy storage configuration strategy suitable for multiple microgrids based on the master-slave game strategy to give full play to the leverage effect of electricity prices in the process of microgrid energy storage configuration, and realizes the optimal configuration of independent energy storage in multiple microgrids. However, when multiple micro-energy grids form a cluster, due to the high investment cost and long payback period of self-built independent energy storage, if independent energy storage is configured in each micro-energy grid, its capacity is usually small, resulting in limited development of energy storage devices in the micro-energy grid cluster.

[0004] Shared energy storage decouples the ownership and use rights of energy storage, providing a new solution to reduce the investment cost of energy storage in microgrid clusters. Compared with independent energy storage, shared energy storage can achieve energy complementarity at the time level, and can also use the complementary characteristics of different micro-energy grids for energy storage needs to achieve energy complementarity at the spatial level. The paper "Game Pricing Method for User-side Shared Energy Storage with Multiple Billing Methods Considering the Coordinated Operation of Microgrid Alliances" proposes a user-side shared energy storage game pricing model in view of the current situation that traditional independent energy storage is difficult to meet the requirements of microgrid cluster operation. The paper "Optimal Configuration of Shared Energy Storage in Multiple Microgrids Considering the Ladder Utilization of Retired Batteries" uses retired batteries of new energy vehicles to deal with the problem of excessive initial capital of microgrid energy storage systems, and constructs a multi-microgrid shared energy storage optimization configuration model based on the idea of ​​cascade utilization of retired batteries. The paper "Optimal Configuration of Shared Energy Storage Considering the Economic Consumption of New Energy in Microgrids" focuses on the problem of economic consumption of new energy in microgrid clusters, proposes a two-layer configuration model of shared energy storage considering the economic consumption rate, and uses the Karush Kuhn Tucker (KKT) method to achieve the transformation and solution of the model. The document "Optimal Configuration Strategy for Multi-agent Shared Energy Storage Capacity in Microgrid Clusters Based on Negotiation Game" fully considers the collaborative interaction mechanism between microgrid operators and shared energy storage operators, and establishes a shared energy storage capacity configuration model based on the negotiation game model. However, the micro-energy grid cluster models established in the above documents are all busbar structures, that is, each micro-energy grid and shared energy storage are connected to the same busbar, and energy interaction and transmission are all carried out through the busbar.

[0005] However, most of the existing micro-energy grids are connected to different nodes of the large power grid. The energy interaction between micro-energy grids and between micro-energy grids and shared energy storage requires the energy channel formed by the large power grid lines. With the access of micro-energy grid clusters and shared energy storage, the power flow of the large power grid has become more complex and changeable, and the problems of insufficient line capacity and node voltage exceeding the limit have been further aggravated, and the grid carrying capacity has been limited. In this context, if the busbar structure model is continued to be used and the energy transmission path of the large power grid is simplified to the busbar structure, two problems will arise: first, only the energy interaction demand between shared energy storage and microgrid clusters is considered, and the energy transmission capacity constraints of the large power grid are not taken into account, resulting in the inability to fully utilize the shared energy storage after configuration; second, after simplifying the large power grid to a busbar model, the grid node information is ignored, and the shared energy storage optimization configuration model can only "determine the capacity" but not "select the site". Therefore, it is necessary to consider the transmission capacity and carrying capacity of the grid in the shared energy storage optimization configuration model of the micro-energy grid cluster. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a method and device for optimizing the configuration of shared energy storage that takes into account the carrying capacity of the power grid, which can reasonably consider the operating carrying capacity of the power grid and optimize the location and capacity of the shared energy storage.

[0007] To achieve the above objectives, the present invention provides a shared energy storage optimization configuration method considering the carrying capacity of the power grid, comprising the following steps: S1. Construct a shared energy storage structure for micro energy grid clusters. The shared energy storage structure includes shared energy storage and several micro energy grids. The shared energy storage and micro energy grids are connected to the power grid respectively. S2. Considering the energy transmission capacity and operation carrying capacity of the power grid, an upper-level model is constructed to optimize the location (access location) and capacity of shared energy storage; the objective function of the upper-level model is established with the goal of minimizing the power grid operation cost and the shared energy storage planning operation cost; S3. Setting constraints of the upper model, including shared energy storage constraints and grid operation constraints; S4. Construct a lower-level model to optimize the decision-making of the operation of each micro-energy network; establish the objective function of the lower-level model with the goal of minimizing the operation cost of each micro-energy network; S5. Setting constraints of the lower model, including energy balance constraints and equipment operation constraints in the micro energy grid; S6. Based on the Lagrangian function and KKT conditions, the two-level optimization model consisting of the upper model and the lower model is converted into a single-level mixed integer linear programming model that is easy to solve; S7. Solve the single-layer mixed integer linear programming model (using a known solver) to obtain an optimal configuration scheme for shared energy storage.

[0008] As a preferred solution of the present invention, in S2, the objective function f of the upper model is expressed as: (1); In the formula, Investing in operation and maintenance costs for shared energy storage; the cost of purchasing electricity for the grid; is the gas purchase cost of each micro-energy network; the calculation methods are: (2); (3); (4); In the formula, i represents the node number of the power grid; A collection of candidate grid nodes for building shared energy storage; The construction cost per unit power of shared energy storage; The construction cost of shared energy storage unit capacity; The annual maintenance cost per unit power of shared energy storage; is the rated power of the shared energy storage connected to node i; is the rated capacity of the shared energy storage connected to node i; d represents the serial number of a typical day; is a set of typical days; h represents the time sequence number within a typical day; It is a collection of typical moments in a typical day; The set of connection points between the current level power grid and the upper level power grid (i.e., the set of nodes connecting the current level power grid and the upper level power grid); is the unit electricity cost at a typical time h in a typical day d; is the active power of the substation on node i at typical time h in typical day d; It is a collection of grid nodes connected to the micro energy grid; is the unit gas cost; is the electric power of the gas unit on node i at typical time h in a typical day d; is the thermal power of the gas boiler on node i at typical time h in typical day d; is the calorific value of gas; is the efficiency of the gas unit; is the calorific value of the gas boiler, is the annuity present value coefficient, expressed as: (5); Where r is the interest rate and T is the planning period.

[0009] As a preferred solution of the present invention, in the above S3, among the constraints of the upper model, the shared energy storage constraints include energy storage operation constraints, energy and power upper and lower limit constraints, and energy storage construction capacity constraints, which are respectively expressed as: Energy storage operation constraints: (6); (7); (8); (9); In the formula, , , , are the electric energy stored in the shared energy storage running on node i at typical times h+1, h, 0 and 24 in a typical day d; , are the charging power and discharging power of the shared energy storage running on node i at typical time h in a typical day d, respectively; , They are the charging efficiency and discharging efficiency of shared energy storage respectively; is the power of the micro energy grid at node i that obtains electricity from the shared energy storage at typical time h in a typical day d; is the power delivered by the micro energy grid at node i to the shared energy storage at typical time h in a typical day d; is the ratio of the rated capacity to the rated power of the shared energy storage; is the rated capacity of the shared energy storage in the planning scheme; Energy and power upper and lower limit constraints: (10); (11); (12); Energy storage construction capacity constraints: (13); (14); In the formula, is a Boolean variable, representing whether node i builds shared energy storage; , They are the maximum and minimum capacities of shared energy storage allowed for construction.

[0010] As a preferred solution of the present invention, in S3, the power grid operation constraints include Dist-Flow constraints, power balance constraints, line capacity constraints, substation capacity constraints and node voltage constraints, which are respectively expressed as: Dist-Flow Constraints: (15); In the formula, j represents the node number of the power grid, and j≠i; ij represents a line in the power grid; , are the squares of node voltages of nodes i and j at typical time h in typical day d, respectively; , are the resistance and reactance of line ij respectively; , are respectively the active power and reactive power flowing through line ij at typical time h in typical day d; Power balance constraints: (16); (17); In the formula, is the set of nodes that have branch connections with node i; is the active power purchased by the micro energy grid at node i from the grid at typical time h in a typical day d; is the active power of the grid load on node i at typical time h in typical day d; is the reactive power of the substation on node i at typical time h in typical day d; is the reactive power of the grid load on node i at typical time h in typical day d; Line capacity constraints: (18); In the formula, is the capacity of line ij; Substation capacity constraints: (19); In the formula, is the substation capacity at node i; Node voltage constraints: (20); (twenty one); (twenty two); In the formula, is the upper limit of reverse load rate; , are the upper and lower limits of node voltage respectively; is the rated voltage; It is the short-circuit current of the power grid system under the maximum operation mode before the micro energy grid and the shared energy storage device are connected; is the short-circuit current proportionality coefficient; is the short-circuit current limit.

[0011] As a preferred embodiment of the present invention, The value is 0.8; , The values ​​are 0.95 and 1.05 respectively; The value is 1.5.

[0012] As a preferred solution of the present invention, in the above S4, for the micro energy grid at node i, the objective function of the lower model is It is expressed as: (twenty three); In the formula, is the gas purchase cost of the micro energy grid at node i; is the cost of purchasing electricity from the power grid for the micro energy grid at node i; The cost of obtaining electricity from shared energy storage for the micro energy grid at node i; is the cost of delivering electricity from the micro energy grid at node i to the shared energy storage; is the shared energy storage service fee of the micro energy network at node i; they are respectively expressed as: (twenty four); (25); (26); (27); (28); In the formula, is the unit price of electricity purchased from the grid at typical time h on a typical day d; is the cost of obtaining unit electricity from shared energy storage at typical time h on a typical day d; is the cost of delivering a unit of electricity to the shared energy storage at a typical time h on a typical day d; It is the electricity service fee for shared energy storage units.

[0013] As a preferred solution of the present invention, in S5, the energy balance constraint includes an electric power balance constraint, a thermal power balance constraint, a waste heat balance constraint and a cold power balance constraint, which are respectively expressed as: Electric power balance constraints: (29); In the formula, is the electric power of the wind turbine on node i at typical time h in typical day d; is the electric power of the PV unit on node i at typical time h in a typical day d; is the electric power consumed by the electric cooling on node i at typical time h in a typical day d; is the electrical load of the micro energy grid at node i at typical time h in a typical day d; Thermal power balance constraints: (30); In the formula, is the thermal power of the heat exchanger on node i at typical time h in typical day d; is the heat load of the micro energy grid at node i at typical time h in a typical day d; Waste heat balance constraints: (31); In the formula, is the efficiency of the heat exchange device; is the cooling power of the absorption refrigeration device on node i at typical time h in typical day d; is the efficiency of the absorption refrigeration unit; is the heat-to-electricity ratio of the gas unit; is the waste heat boiler efficiency; Cold power balance constraints: (32); In the formula, is the cooling load of the micro energy grid at node i at typical time h in a typical day d; is the efficiency of electric cooling.

[0014] As a preferred solution of the present invention, in S5, the operation constraints of the equipment in the micro energy network include the upper and lower limits of the power exchange between the micro energy network and the shared energy storage, the upper and lower limits of the power purchased by the micro energy network from the power grid, the upper and lower limits of the power of each energy conversion device, and the upper and lower limits of the power of photovoltaic and wind turbines, which are respectively expressed as: The upper and lower limits of power exchange between micro energy grid and shared energy storage: (33); (34); In the formula, The power limit of the micro energy grid at node i that obtains electric energy from the shared energy storage; is the upper limit of the power that the micro energy grid at node i can transmit to the shared energy storage; The upper and lower limits of the power purchased by the micro energy grid from the power grid are: (35); In the formula, is the upper limit of the power that the micro energy grid at node i can purchase from the grid; The upper and lower power limits of each energy conversion device are as follows: (36); (37); (38); (39); (40); In the formula, , are the upper and lower limits of the gas generator power at node i respectively; , are the upper and lower limits of the electric cooling power on node i respectively; , are the upper and lower limits of the heat exchanger power at node i, respectively; , are the upper and lower limits of the cooling power of the absorption refrigeration device on node i respectively; , are the upper and lower limits of the thermal power of the gas boiler at node i respectively; Photovoltaic and wind turbine power upper and lower limits: (41); (42); In the formula, is the upper limit of the power of the PV unit on node i at typical time h in a typical day d; is the upper limit of the electric power of the wind turbine on node i at typical time h in a typical day d.

[0015] As a preferred solution of the present invention, in S6, the process of converting the double-layer optimization model composed of the upper layer model and the lower layer model into a single-layer mixed integer linear programming model that is easy to solve is: S61. Express the lower model in the following form: (43); In the formula, , , They are column vectors consisting of equation constraints, inequality constraints, and decision variables in the lower model; S62. Based on formula (43), the Lagrangian function is constructed and expressed as: (44); In the formula, is the Lagrangian function; , are the Lagrange multiplier column vectors corresponding to the equality constraints and inequality constraints respectively; the superscript T indicates transposition; S63. Partially differentiate the decision variables in the Lagrangian function and combine the KKT conditions to convert the lower model into additional conditions for the upper model, expressed as: (45); (46); (47); (48); (49); Where x is the decision variable (the access location and capacity of the shared energy storage power station); Represents the partial derivative of the vector matrix with respect to x; S64. Use the Big M method to linearize equation (47) and express it as: (50); (51); Where M is the constant in the Big M method (a very large constant); Column vector of auxiliary Boolean variables.

[0016] The shared energy storage optimization configuration device considering the grid carrying capacity includes: a memory having a computer processing program stored thereon; The processor is used to execute the computer processing program in the memory to implement the above-mentioned shared energy storage optimization configuration method considering the carrying capacity of the power grid.

[0017] The beneficial effects of the present invention are: The present invention establishes a two-layer optimization allocation model for shared energy storage in a micro-energy grid cluster that takes into account the grid carrying capacity. The two-layer model is converted into a single-layer mixed integer linear programming model using Lagrangian functions and KKT conditions, realizing the site selection and capacity determination of shared energy storage equipment in the grid. It can reasonably consider the grid operation carrying capacity and optimize the location and capacity of shared energy storage. It has been verified that from an overall perspective, the total cost of the scheme of the present invention is only 0.03% different from that of the traditional model planning scheme, but the shared energy storage configuration capacity is reduced by 35.97%; from the perspective of the shared energy storage investor, the one-time investment cost of the scheme of the present invention is reduced, and the financial indicators are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow diagram of the method of the present invention; Figure 2 is a schematic diagram of a shared energy storage structure in Example 1 of the present invention; Figure 3 is a schematic diagram of the structure of the micro energy network in Example 1 of the present invention; Figure 4 It is a schematic diagram of the conversion and solution of the double-layer optimization model in Example 1 of the present invention; Figure 5 It is a structural schematic diagram of a test example system in the verification process of the present invention; Figure 6 It is a schematic diagram of energy exchange between shared energy storage and micro energy grid in a typical summer day during the verification process of the present invention; Figure 6 (a) is the energy exchange diagram of Example 1; Figure 6 (b) is the energy exchange diagram of Example 2; Figure 7 It is a comparison chart of the cumulative net cash flows of the shared energy storage investment scheme during the verification process of the present invention; Figure 8 This is a schematic diagram of a typical solar balance in summer within a micro energy grid during the verification process of the present invention; Figure 8 (a) is a schematic diagram of the electrical balance of micro energy grid No. 1; Figure 8 (b) is a schematic diagram of the power balance of the No. 2 micro energy grid; Figure 8 (c) is the schematic diagram of the power balance of Micro Energy Grid No. 3; Fig. 9 This is a schematic diagram of a typical summer day heat balance inside the micro energy grid during the verification process of the present invention; Fig. 9 (a) is a schematic diagram of the thermal balance of micro energy grid No. 1; Fig. 9(b) is a schematic diagram of the thermal balance of micro energy grid No. 2; Fig. 9 (c) is a schematic diagram of the thermal balance of the No. 3 micro energy grid; Fig.10 It is a schematic diagram of a typical daily cooling balance in summer inside the micro energy grid during the verification process of the present invention; Fig.10 (a) is a schematic diagram of the cold balance of micro energy grid No. 1; Fig.10 (b) is a schematic diagram of the cold balance of micro energy grid No. 2; Fig.10 (c) in the figure is a schematic diagram of the cold balance of micro energy grid No. 3. DETAILED DESCRIPTION

[0019] The embodiments of the present invention are further described below in conjunction with the accompanying drawings: Example 1 like Figure 1 As shown, the shared energy storage optimization configuration method considering the grid carrying capacity includes the following steps: S1. Construct a shared energy storage structure for micro energy grid clusters. The shared energy storage structure includes shared energy storage and several micro energy grids. The shared energy storage and micro energy grids are connected to the power grid respectively. S2. Considering the energy transmission capacity and operation carrying capacity of the power grid, an upper-level model is constructed to optimize the location and capacity of shared energy storage; the objective function of the upper-level model is established with the goal of minimizing the power grid operation cost and the shared energy storage planning operation cost; S3. Setting constraints of the upper model, including shared energy storage constraints and grid operation constraints; S4. Construct a lower-level model to optimize the decision-making of the operation of each micro-energy network; establish the objective function of the lower-level model with the goal of minimizing the operation cost of each micro-energy network; S5. Setting constraints of the lower model, including energy balance constraints and equipment operation constraints in the micro energy grid; S6. Based on the Lagrangian function and KKT conditions, the two-level optimization model consisting of the upper model and the lower model is converted into a single-level mixed integer linear programming model that is easy to solve; S7. Solve the single-layer mixed integer linear programming model to obtain the shared energy storage optimization configuration plan.

[0020] A simplified shared energy storage structure is as follows Figure 2 As shown in Figure 1, it contains n micro energy grids. The specific structure of the micro energy grid is as follows: Figure 3 As shown, it includes wind turbines, photovoltaic units, electric refrigeration, absorption refrigeration, waste heat boilers, heat exchange devices, gas units, gas boilers, as well as external electricity, external gas, electric load, gas load, and thermal load.

[0021] In S2, the objective function f of the upper model is expressed as: (1); In the formula, Investing in operation and maintenance costs for shared energy storage; the cost of purchasing electricity for the grid; is the gas purchase cost of each micro-energy network; the calculation methods are: (2); (3); (4); In the formula, i represents the node number of the power grid; A collection of candidate grid nodes for building shared energy storage; The construction cost per unit power of shared energy storage; The construction cost of shared energy storage unit capacity; The annual maintenance cost per unit power of shared energy storage; is the rated power of the shared energy storage connected to node i; is the rated capacity of the shared energy storage connected to node i; d represents the serial number of a typical day; is a set of typical days; h represents the time sequence number within a typical day; It is a collection of typical moments in a typical day; It is the set of connection points between the power grid at this level and the power grid at the upper level; is the unit electricity cost at a typical time h in a typical day d; is the active power of the substation on node i at typical time h in typical day d; It is a collection of grid nodes connected to the micro energy grid; is the unit gas cost; is the electric power of the gas unit on node i at typical time h in a typical day d; is the thermal power of the gas boiler on node i at typical time h in typical day d; is the calorific value of gas; is the efficiency of the gas unit; is the calorific value of the gas boiler, is the annuity present value coefficient, expressed as: (5); Where r is the interest rate and T is the planning period.

[0022] In S3, among the constraints of the upper model, shared energy storage constraints include energy storage operation constraints, energy and power upper and lower limit constraints, and energy storage construction capacity constraints, which are expressed as: Energy storage operation constraints: (6); (7); (8); (9); In the formula, , , , are the electric energy stored in the shared energy storage running on node i at typical times h+1, h, 0 and 24 in a typical day d; , are the charging power and discharging power of the shared energy storage running on node i at typical time h in a typical day d, respectively; , They are the charging efficiency and discharging efficiency of shared energy storage respectively; is the power of the micro energy grid at node i that obtains electricity from the shared energy storage at typical time h in a typical day d; is the power delivered by the micro energy grid at node i to the shared energy storage at typical time h in a typical day d; is the ratio of the rated capacity to the rated power of the shared energy storage; is the rated capacity of the shared energy storage in the planning scheme; Energy and power upper and lower limit constraints: (10); (11); (12); Energy storage construction capacity constraints: (13); (14); In the formula, is a Boolean variable, representing whether node i builds shared energy storage; , They are the maximum and minimum capacities of shared energy storage allowed for construction.

[0023] In S3, the grid operation constraints include Dist-Flow constraints, power balance constraints, line capacity constraints, substation capacity constraints, and node voltage constraints, which are expressed as: Dist-Flow Constraints: (15); In the formula, j represents the node number of the power grid, and j≠i; ij represents a line in the power grid; , are the squares of node voltages of nodes i and j at typical time h in typical day d, respectively; , are the resistance and reactance of line ij respectively; , are respectively the active power and reactive power flowing through line ij at typical time h in typical day d; Power balance constraints: (16); (17); In the formula, is the set of nodes that have branch connections with node i; is the active power purchased by the micro energy grid at node i from the grid at typical time h in a typical day d; is the active power of the grid load on node i at typical time h in typical day d; is the reactive power of the substation on node i at typical time h in typical day d; is the reactive power of the grid load on node i at typical time h in typical day d; Line capacity constraints: (18); In the formula, is the capacity of line ij; Substation capacity constraints: (19); In the formula, is the substation capacity at node i; Node voltage constraints: (20); (twenty one); (twenty two); In the formula, is the upper limit of reverse load rate, the value is 0.8; , are the upper and lower limits of node voltage, with values ​​of 0.95 and 1.05 respectively; is the rated voltage; It is the short-circuit current of the power grid system under the maximum operation mode before the micro energy grid and the shared energy storage device are connected; is the short-circuit current proportionality coefficient, which is 1.5; is the short-circuit current limit.

[0024] In S4, for the micro energy grid at node i, the objective function of the lower model is It is expressed as: (twenty three); In the formula, is the gas purchase cost of the micro energy grid at node i; is the cost of purchasing electricity from the power grid for the micro energy grid at node i; The cost of obtaining electricity from shared energy storage for the micro energy grid at node i; is the cost of delivering electricity from the micro energy grid at node i to the shared energy storage; is the shared energy storage service fee of the micro energy network at node i; they are respectively expressed as: (twenty four); (25); (26); (27); (28); In the formula, is the unit price of electricity purchased from the grid at typical time h on a typical day d; is the cost of obtaining unit electricity from shared energy storage at typical time h on a typical day d; is the cost of delivering a unit of electricity to the shared energy storage at a typical time h on a typical day d; It is the electricity service fee for shared energy storage units.

[0025] In S5, the energy balance constraints include electric power balance constraints, thermal power balance constraints, waste heat balance constraints, and cold power balance constraints, which are expressed as: Electric power balance constraints: (29); In the formula, is the electric power of the wind turbine on node i at typical time h in typical day d; is the electric power of the PV unit on node i at typical time h in a typical day d; is the electric power consumed by the electric cooling on node i at typical time h in a typical day d; is the electrical load of the micro energy grid at node i at typical time h in a typical day d; Thermal power balance constraints: (30); In the formula, is the thermal power of the heat exchanger on node i at typical time h in typical day d; is the heat load of the micro energy grid at node i at typical time h in a typical day d; Waste heat balance constraints: (31); In the formula, is the efficiency of the heat exchange device; is the cooling power of the absorption refrigeration device on node i at typical time h in typical day d; is the efficiency of the absorption refrigeration unit; is the heat-to-electricity ratio of the gas unit; is the waste heat boiler efficiency; Cold power balance constraints: (32); In the formula, is the cooling load of the micro energy grid at node i at typical time h in a typical day d; is the efficiency of electric cooling.

[0026] In S5, the equipment operation constraints in the micro energy grid include the upper and lower limits of the power exchange between the micro energy grid and the shared energy storage, the upper and lower limits of the power purchased by the micro energy grid from the power grid, the upper and lower limits of the power of each energy conversion device, and the upper and lower limits of the power of photovoltaic and wind turbines, which are expressed as: The upper and lower limits of power exchange between micro energy grid and shared energy storage: (33); (34); In the formula, The power limit of the micro energy grid at node i that obtains electric energy from the shared energy storage; is the upper limit of the power that the micro energy grid at node i can transmit to the shared energy storage; The upper and lower limits of the power purchased by the micro energy grid from the power grid are: (35); In the formula, is the upper limit of the power that the micro energy grid at node i can purchase from the grid; The upper and lower power limits of each energy conversion device are as follows: (36); (37); (38); (39); (40); In the formula, , are the upper and lower limits of the gas generator power at node i respectively; , are the upper and lower limits of the electric cooling power on node i respectively; , are the upper and lower limits of the heat exchanger power at node i, respectively; , are the upper and lower limits of the cooling power of the absorption refrigeration device on node i respectively; , are the upper and lower limits of the thermal power of the gas boiler at node i respectively; Photovoltaic and wind turbine power upper and lower limits: (41); (42); In the formula, is the upper limit of the power of the PV unit on node i at typical time h in a typical day d; is the upper limit of the electric power of the wind turbine on node i at typical time h in a typical day d.

[0027] The shared energy storage two-layer optimization model contains two levels, upper and lower. The objective functions and constraints of the two-layer problems are coupled with each other, making it difficult to solve directly. This embodiment converts the two-layer optimization model into a single-layer mixed integer linear programming model that is easy to solve based on the Lagrangian function and KKT conditions. Its essence is to solve the optimal solution of the lower model based on the prior decision of the upper problem. The process of converting the two-layer configuration model into a single-layer mixed integer linear programming problem is as follows: Figure 4 As shown, the upper layer optimization model and the lower layer optimization model are the upper layer model and the lower layer model.

[0028] In S6, the process of converting the two-layer optimization model consisting of the upper model and the lower model into a single-layer mixed integer linear programming model that is easy to solve is: S61. Express the lower model in the following form: (43); In the formula, , , They are column vectors consisting of equation constraints, inequality constraints, and decision variables in the lower model; S62. Based on formula (43), the Lagrangian function is constructed and expressed as: (44); In the formula, is the Lagrangian function; , are the Lagrange multiplier column vectors corresponding to the equality constraints and inequality constraints respectively; the superscript T indicates transposition; S63. Partially differentiate the decision variables in the Lagrangian function and combine the KKT conditions to convert the lower model into additional conditions for the upper model, expressed as: (45); (46); (47); (48); (49); In the formula, x is the decision variable; Represents the partial derivative of the vector matrix with respect to x; S64. Use the Big M method to linearize equation (47) and express it as: (50); (51); Where M is the constant in the Big M method; Column vector of auxiliary Boolean variables.

[0029] The verification process is: The test case system is as follows Figure 5 As shown in the figure, the IEEE33-node typical example system is used for verification. During the verification, the simulation model is built on the MATLAB R2020a platform and CPLEX 12.10.0 is called for solution.

[0030] Three micro energy grids are set up, connected to the grid nodes 9, 19, and 31 respectively. The internal equipment parameters are shown in Table 1. The shared energy storage parameters are shown in Table 2. The time-of-use electricity price parameters are shown in Table 3. The system cost coefficient is shown in Table 4. Four typical days of spring, summer, autumn, and winter are set, and each typical day contains 24 scheduling periods. Other parameters are as follows: interest rate r=8%, planning period T=8.

[0031] Table 1 Micro energy network equipment parameters

[0032] Table 2 Shared energy storage power station parameters

[0033] Table 3 Time-of-use electricity prices

[0034] Table 4 Cost parameters

[0035] In order to verify the effectiveness of the proposed two-level optimization model, the following two examples are compared and analyzed: Example 1: Considering the impact of grid carrying capacity on the location and capacity of shared energy storage, the grid adopts the Dist-Flow model, the objective function is equation (1), and the constraints are equations (6)-(14), (45)-(48), and (50)-(51).

[0036] Calculation example 2: Example 2: Without considering the impact of grid carrying capacity on shared energy storage site selection and capacity determination, the grid is simplified to a busbar model with the objective (1) and constraints as (6)-(14), (45)-(48), and (50)-(51).

[0037] Result analysis: Table 5 compares the shared energy storage configuration schemes of Example 1 and Example 2. Compared with Example 2 which does not consider the grid carrying capacity, the power and capacity of the shared energy storage in the configuration scheme of Example 1 are reduced by 35.97%.

[0038] Table 5 Comparison of shared energy storage site selection and capacity planning

[0039] Table 6 compares the overall investment and operating costs of the system in Example 1 and Example 2. The total costs of Example 1 and Example 2 are basically the same, with a difference of only 0.03%. Comparing the shared energy storage configuration capacity with the overall system cost, it can be found that the shared energy storage capacity in Example 2 increased by 35.97%, but its investment and operating costs only decreased by 0.03%. This is because Example 2 failed to optimize the equipment installation nodes and failed to take into account the limiting factors such as flow changes, line capacity, and voltage limits in the actual operation of the power grid. Therefore, the shared energy storage capacity in the configuration plan is too large, resulting in a waste of capacity and cost.

[0040] Table 6 Comparison of overall investment and operating costs

[0041] Figure 6 The energy exchange between shared energy storage and micro-energy grid on a typical day in summer is further compared. The information in the figure shows that the charging and discharging of shared energy storage in Example 1 and Example 2 are basically the same, which once again verifies that the energy storage capacity in Example 2 is over-configured, resulting in part of the capacity being restricted by the grid's carrying capacity.

[0042] Figure 7 From the perspective of shared energy storage investors, the comparison of the cumulative net cash flows of shared energy storage investment plans is shown. Compared with Example 2, the shared energy storage construction capacity in Example 1 is small, the initial one-time investment cost is low, and the cash flow is fast, and positive cash flow is achieved in the second to third year.

[0043] Table 7 shows the comparison of financial indicators of shared energy storage investment schemes from the perspective of shared energy storage investors. Compared with Example 2, the net present value of Example 1 increased by 3.326×106 yuan, the internal rate of return increased by 29.67%, and the payback period decreased from 5.788 years to 2.344 years. This is because the one-time investment cost of shared energy storage in Example 1 is low, and the subsequent operating profit is basically the same as that in Example 2. It proves that the profitability of the shared energy storage configuration scheme in Example 1 is far better than that in Example 2, which is conducive to attracting independent operators to invest in the construction and operation of shared energy storage business, and promoting the sustainable and healthy development of micro energy grid clusters and shared energy storage.

[0044] Table 7 Comparison of financial indicators of shared energy storage investment solutions

[0045] Figure 8 , Fig. 9 , Fig.10 The balance of electric energy, heat energy and cooling energy of the micro energy grid in a typical summer day in the configuration scheme of Example 1 is shown respectively. Through the combined effect of various internal integrated energy equipment, external power grid and shared energy storage, the micro energy grid realizes the demand and supply of electric load, heat load and cooling load.

[0046] This embodiment uses Lagrangian functions and KKT conditions to transform the double-layer model into a single-layer mixed integer linear programming model, realizing the location and capacity determination of shared energy storage equipment in the power grid. The results of the example analysis show that: (1) the proposed model can reasonably consider the power grid operation carrying capacity and optimize the location and capacity of shared energy storage; (2) from the perspective of the overall system, the total cost of the proposed model and the traditional model planning scheme is only 0.03% different, but the shared energy storage configuration capacity is reduced by 35.97%. (3) from the perspective of the shared energy storage investor, the one-time investment cost of the proposed model planning scheme is reduced, and the financial indicators are greatly improved.

[0047] Example 2 The shared energy storage optimization configuration device considering the grid carrying capacity includes: a memory having a computer processing program stored thereon; The processor is used to execute the computer processing program in the memory to implement the shared energy storage optimization configuration method considering the grid carrying capacity as described in Example 1.

Claims

1. A method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid, characterized in that The following steps are involved: S1. Construct a shared energy storage structure for micro energy grid clusters. The shared energy storage structure includes shared energy storage and several micro energy grids. The shared energy storage and micro energy grids are connected to the power grid respectively. S2. Considering the energy transmission capacity and operation carrying capacity of the power grid, an upper-level model is constructed to optimize the location and capacity of shared energy storage; The objective function of the upper model is established with the goal of minimizing the operating cost of the power grid and the operating cost of the shared energy storage planning; S3. Setting constraints of the upper model, including shared energy storage constraints and grid operation constraints; S4. Construct a lower-level model to optimize the decision-making of the operation of each micro-energy network; establish the objective function of the lower-level model with the goal of minimizing the operation cost of each micro-energy network; S5. Setting constraints of the lower model, including energy balance constraints and equipment operation constraints in the micro energy grid; S6. Based on the Lagrangian function and KKT conditions, the two-level optimization model consisting of the upper model and the lower model is converted into a single-level mixed integer linear programming model that is easy to solve; S7. Solve the single-layer mixed integer linear programming model to obtain the shared energy storage optimization configuration plan.

2. The method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid according to claim 1, characterized in that: In S2, the objective function f of the upper model is expressed as: （1）； In the formula, Investing in O&M costs for shared energy storage; the cost of purchasing electricity for the grid; is the gas purchase cost of each micro-energy network; the calculation methods are: （2）； （3）； （4）； In the formula, i represents the node number of the power grid; A collection of candidate grid nodes for building shared energy storage; The construction cost per unit power of shared energy storage; The construction cost of shared energy storage unit capacity; The annual maintenance cost per unit power of shared energy storage; is the rated power of the shared energy storage connected to node i; is the rated capacity of the shared energy storage connected to node i; d represents the serial number of the typical day; is a set of typical days; h represents the time sequence number within a typical day; It is a collection of typical moments in a typical day; It is the set of connection points between the power grid at this level and the power grid at the upper level; is the unit electricity cost at a typical time h in a typical day d; is the active power of the substation on node i at typical time h in typical day d; It is a collection of grid nodes connected to the micro energy grid; is the unit gas cost; is the electric power of the gas unit on node i at typical time h in a typical day d; is the thermal power of the gas boiler on node i at typical time h in typical day d; is the calorific value of gas; is the efficiency of the gas unit; is the calorific value of the gas boiler, is the annuity present value coefficient, expressed as: （5）； Where r is the interest rate and T is the planning period.

3. The method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid according to claim 2, characterized in that: In S3, among the constraints of the upper model, the shared energy storage constraints include energy storage operation constraints, energy and power upper and lower limit constraints, and energy storage construction capacity constraints, which are respectively expressed as: Energy storage operation constraints: （6）； （7）； （8）； （9）； In the formula, , , , are the electric energy stored in the shared energy storage running on node i at typical times h+1, h, 0 and 24 in a typical day d; , are the charging power and discharging power of the shared energy storage running on node i at typical time h in a typical day d, respectively; , They are the charging efficiency and discharging efficiency of shared energy storage respectively; is the power of the micro energy grid at node i that obtains electricity from the shared energy storage at typical time h in a typical day d; is the power delivered by the micro energy grid at node i to the shared energy storage at typical time h in a typical day d; is the ratio of the rated capacity to the rated power of the shared energy storage; is the rated capacity of the shared energy storage in the planning scheme; Energy and power upper and lower limits: （10）； （11）； （12）； Energy storage construction capacity constraints: （13）； （14）； In the formula, is a Boolean variable, representing whether node i builds shared energy storage; , They are the maximum and minimum capacities of shared energy storage allowed for construction.

4. The method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid according to claim 3 is characterized in that: In S3, the power grid operation constraints include Dist-Flow constraints, power balance constraints, line capacity constraints, substation capacity constraints, and node voltage constraints, which are respectively expressed as: Dist-Flow Constraints: （15）； In the formula, j represents the node number of the power grid, and j≠i; ij represents a line in the power grid; , are the squares of node voltages at nodes i and j at typical time h in typical day d, respectively; , are the resistance and reactance of line ij respectively; , are respectively the active power and reactive power flowing through line ij at typical time h in typical day d; Power balance constraints: （16）； （17）； In the formula, is the set of nodes that have branch connections with node i; is the active power purchased by the micro energy grid at node i from the grid at typical time h in a typical day d; is the active power of the grid load on node i at typical time h in typical day d; is the reactive power of the substation on node i at typical time h in typical day d; is the reactive power of the grid load on node i at typical time h in typical day d; Line capacity constraints: （18）； In the formula, is the capacity of line ij; Substation capacity constraints: （19）； In the formula, is the substation capacity at node i; Node voltage constraints: （20）； （21）； （22）； In the formula, is the upper limit of reverse load rate; , are the upper and lower limits of node voltage respectively; is the rated voltage; It is the short-circuit current of the power grid system under the maximum operation mode before the micro energy grid and the shared energy storage device are connected; is the short-circuit current proportionality coefficient; is the short-circuit current limit.

5. The method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid according to claim 4, characterized in that: The value is 0.8; , The values ​​are 0.95 and 1.05 respectively; The value is 1.

5.

6. The method for optimizing shared energy storage configuration considering grid carrying capacity according to claim 4, characterized in that: In S4, for the micro energy grid at node i, the objective function of the lower model is It is expressed as: （23）； In the formula, is the gas purchase cost of the micro energy grid at node i; is the cost of purchasing electricity from the power grid for the micro energy grid at node i; The cost of obtaining electricity from shared energy storage for the micro energy grid at node i; The cost of delivering electricity from the micro energy grid at node i to the shared energy storage; is the shared energy storage service fee of the micro energy network at node i; they are respectively expressed as: （24）； （25）； （26）； （27）； （28）； In the formula, is the unit price of electricity purchased from the grid at typical time h on a typical day d; is the cost of obtaining unit electricity from shared energy storage at typical time h on a typical day d; is the cost of delivering a unit of electricity to the shared energy storage at a typical time h on a typical day d; It is the electricity service fee for shared energy storage units.

7. The method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid according to claim 6, characterized in that: In S5, the energy balance constraint includes an electric power balance constraint, a thermal power balance constraint, a waste heat balance constraint, and a cooling power balance constraint, which are respectively expressed as: Electric power balance constraints: （29）； In the formula, is the electric power of the wind turbine on node i at typical time h in typical day d; is the electric power of the PV unit on node i at typical time h in a typical day d; is the electric power consumed by the electric cooling on node i at typical time h in a typical day d; is the electrical load of the micro energy grid at node i at typical time h in a typical day d; Thermal power balance constraints: （30）； In the formula, is the thermal power of the heat exchanger on node i at typical time h in typical day d; is the heat load of the micro energy grid at node i at typical time h in a typical day d; Waste heat balance constraints: （31）； In the formula, is the efficiency of the heat exchange device; is the cooling power of the absorption refrigeration device on node i at typical time h in typical day d; is the efficiency of the absorption refrigeration unit; is the heat-to-electricity ratio of the gas unit; is the waste heat boiler efficiency; Cold power balance constraints: （32）； In the formula, is the cooling load of the micro energy grid at node i at typical time h in a typical day d; is the efficiency of electric cooling.

8. The method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid according to claim 7, characterized in that: In S5, the operation constraints of the equipment in the micro energy grid include the upper and lower limits of the power exchanged between the micro energy grid and the shared energy storage, the upper and lower limits of the power purchased from the power grid by the micro energy grid, the upper and lower limits of the power of each energy conversion device, and the upper and lower limits of the power of photovoltaic and wind turbines, which are respectively expressed as: The upper and lower limits of power exchange between micro energy grid and shared energy storage: （33）； （34）； In the formula, The power limit of the micro energy grid at node i that obtains electric energy from the shared energy storage; is the upper limit of the power that the micro energy grid at node i can transmit to the shared energy storage; The upper and lower limits of the power purchased by the micro energy grid from the power grid are: （35）； In the formula, is the upper limit of the power that the micro energy grid at node i can purchase from the grid; The upper and lower power limits of each energy conversion device are as follows: （36）； （37）； （38）； （39）； （40）； In the formula, , are the upper and lower limits of the gas generator power at node i respectively; , are the upper and lower limits of the electric cooling power on node i respectively; , are the upper and lower limits of the heat exchanger power at node i, respectively; , are the upper and lower limits of the cooling power of the absorption refrigeration device on node i respectively; , are the upper and lower limits of the thermal power of the gas boiler at node i respectively; Photovoltaic and wind turbine power upper and lower limits: （41）； （42）； In the formula, is the upper limit of the power of the PV unit on node i at typical time h in a typical day d; is the upper limit of the electric power of the wind turbine on node i at typical time h in a typical day d.

9. The method for optimizing the configuration of shared energy storage considering the carrying capacity of the power grid according to claim 8, characterized in that: In the above S6, the process of converting the double-layer optimization model composed of the upper layer model and the lower layer model into a single-layer mixed integer linear programming model that is easy to solve is: S61. Express the lower model in the following form: （43）； In the formula, , , They are column vectors consisting of equation constraints, inequality constraints, and decision variables in the lower model; S62. Based on formula (43), the Lagrangian function is constructed and expressed as: （44）； In the formula, is the Lagrangian function; , are the Lagrange multiplier column vectors corresponding to the equality constraints and inequality constraints respectively; the superscript T indicates transposition; S63. Partially differentiate the decision variables in the Lagrangian function and combine the KKT conditions to convert the lower model into additional conditions for the upper model, expressed as: （45）； （46）； （47）； （48）； （49）； In the formula, x is the decision variable; Represents the partial derivative of the vector matrix with respect to x; S64. Use the Big M method to linearize equation (47) and express it as: （50）； （51）； Where M is the constant in the Big M method; Column vector of auxiliary Boolean variables.

10. A shared energy storage optimization configuration device considering the carrying capacity of the power grid, characterized in that: include: a memory having a computer processing program stored thereon; A processor is used to execute a computer processing program in a memory to implement the shared energy storage optimization configuration method considering the grid carrying capacity as described in any one of claims 1-9.

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