A method and system for determining the location of multi-type coupled energy storage systems

Abstract

This invention belongs to the field of power system planning and optimization technology, specifically relating to a method and system for determining the capacity and location of multi-type coupled energy storage systems. The method first generates typical scenarios of new energy output and load demand, then establishes a two-layer planning model for the capacity and location of multi-type coupled energy storage systems. In this model, the upper-layer model is an energy storage capacity and location planning model aiming to minimize the economic cost of the multi-type coupled energy storage system, while the lower-layer model is an operation optimization scheduling model aiming to minimize the penalty cost of the multi-type coupled energy storage system. Finally, based on the typical scenarios of new energy output and load demand, the two-layer planning model is solved to obtain the optimal capacity and location scheme for the multi-type energy storage system. This invention achieves the dual objectives of optimizing system economic cost and grid regulation flexibility by constructing a two-layer optimization model for energy storage capacity and location and operation scheduling, utilizing iterative optimization between the upper and lower layers.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and optimization technology, specifically relating to a method and system for determining the capacity and location of multi-type coupled energy storage systems. Background Technology

[0002] In new power systems with a high proportion of new energy sources, the output of renewable energy sources such as wind power and photovoltaics is random and fluctuating. This disrupts the traditional power system's balance mechanism of source following load, leading to a surge in the demand for system regulation capabilities. Energy storage is a key technology for improving system flexibility and achieving peak shaving and valley filling. Among them, energy-type energy storage such as pumped hydro storage has the advantages of large capacity and long life, but its response speed is relatively slow. On the other hand, power-type energy storage such as electrochemical energy storage has the advantages of fast response and accurate regulation, but its cost is high and its capacity is relatively limited. Therefore, it is necessary to build multi-type coupled energy storage systems to achieve complementary advantages.

[0003] Traditional energy storage planning studies are often limited to single-type energy storage, making it difficult to meet diversified regulation needs. Furthermore, when dealing with multi-type coupled energy storage systems, few planning methods can balance the system's economy and flexibility. In addition, existing planning methods often treat energy storage capacity determination and site selection as two independent steps in calculation, neglecting the inherent correlation and mutual influence between them, resulting in planning results that are difficult to achieve global optimization. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method and system for determining the capacity and location of multi-type coupled energy storage systems. This method involves constructing a two-layer optimization model for energy storage capacity selection and operation scheduling, and utilizing iterative optimization between the upper and lower layers to achieve the dual objectives of minimizing system economic cost and optimizing grid regulation flexibility.

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

[0006] In a first aspect, the present invention provides a method for determining the capacity and location of a multi-type coupled energy storage system, characterized in that:

[0007] The volumetric site selection method includes:

[0008] S1. Typical scenarios for generating new energy output and load demand;

[0009] S2. Establish a two-layer planning model for the fixed-capacity and location selection of multi-type coupled energy storage systems; in the two-layer planning model for fixed-capacity and location selection, the upper-layer model is an energy storage fixed-capacity and location selection planning model with the goal of minimizing the economic cost of multi-type coupled energy storage systems, and the lower-layer model is an operation optimization scheduling model with the goal of minimizing the penalty cost of multi-type coupled energy storage systems.

[0010] S3. Based on typical scenarios of new energy output and load demand, solve the dual-level planning model for fixed capacity and location to obtain the optimal fixed capacity and location scheme for multiple types of energy storage systems.

[0011] The objective function of the upper-level model includes:

[0012] ;

[0013] ;

[0014] In the above formula, Economic cost of multi-type coupled energy storage systems; , These represent the annualized total investment cost and annualized total operation and maintenance cost of a multi-type coupled energy storage system, respectively. , The first type of multi-type coupled energy storage system Annualized total investment cost and annualized total operation and maintenance cost for energy storage systems; The number of energy storage types in a multi-type coupled energy storage system; This is the capital recovery factor; The annual interest rate; For the first The service life of energy storage devices; , The first Rated capacity and rated power of energy storage devices; , The first Unit capacity cost coefficient and unit power cost coefficient for energy storage systems; For the first Annual operation and maintenance cost coefficient for energy storage systems;

[0015] The constraints of the upper-level model include: upper and lower limits of rated power of energy storage, upper and lower limits of rated capacity of energy storage, access node constraints, and maximum continuous charge and discharge hours of energy storage.

[0016] ;

[0017] In the above formula, , The first Rated power and rated capacity of energy storage devices; , The first Upper and lower limits of rated power for energy storage systems; , The first The upper and lower limits of the rated capacity of energy storage systems. For the first Maximum continuous charge / discharge hours for this type of energy storage; It serves as a centralized access node for multi-type coupled energy storage systems; This is a set of alternative nodes for energy storage installations.

[0018] The objective function of the lower-level model includes:

[0019] ;

[0020] ;

[0021] In the above formula, The grid penalty cost refers to the grid to which multiple types of coupled energy storage systems are connected. branch roads and A power grid with one energy storage alternative access node; Net electricity purchase cost to the grid Penalty costs for shedding loads from the power grid; Insufficient flexibility incurs penalties; The number of days included in the scheduling period is set to 365 days in this embodiment; This refers to the number of time periods included in a day; in this embodiment, it is set to 24 hours. , These represent the total daily electricity purchase cost and total daily electricity sales revenue of the power grid, respectively. , The power grid is respectively The electricity purchase price and electricity sales price at any given time; , The power grid is respectively The power purchased and the power sold at any given time; This is the load shedding penalty factor; For the power grid The load shedding power at any given moment; The cost of penalizing the abandonment of wind and solar power; This refers to the penalty coefficient for curtailment of renewable energy. , They are respectively The amount of wind power and solar power that are abandoned at any given moment; , Penalty coefficient for insufficient upward and downward flexibility; , These represent insufficient margins for upward flexibility and insufficient margins for downward flexibility, respectively.

[0022] The constraints of the lower-level model include: grid active power balance constraints, line power flow constraints, energy storage status constraints, renewable energy curtailment constraints, grid flexibility constraints, grid load shedding constraints, and grid power purchase and sale constraints; the grid active power balance constraints include:

[0023] ;

[0024] In the above formula, For the power grid Total load demand at any given time; , The power grid is respectively The actual absorption capacity of wind and solar power at any given time; , The first Energy storage Discharge power and charging power at any given time; For the network The load shedding power at any given moment; , The power grid is respectively The power purchased and the power sold at any given time; For multi-type coupled energy storage systems in Net discharge power at any given time;

[0025] The power flow constraints of the line include:

[0026] ;

[0027] In the above formula, For power grid lines exist Active power transmitted at all times; For nodes in the power grid exist Net active power injected at any given moment; For power grid lines Maximum allowed active power transmission; Represents nodes in the power grid Net injected unit power to line The sensitivity of power flow is that the line power flow is the sum of the products of the net injected power at each node and the corresponding elements of the PTDF matrix. The PTDF matrix (Power Transfer Distribution Factor) is an introduced power transfer distribution factor matrix used to linearize the line power flow constraints. , Nodes in the power grid exist The power purchased and the power sold at any given time; , Nodes in the power grid exist The actual absorption capacity of wind and solar power at any given time; For nodes in the power grid exist Net discharge power at any given time; For nodes in the power grid exist Total load demand at any given time; For nodes in the power grid exist The load shedding power at any given moment;

[0028] The energy storage state constraints include:

[0029] ;

[0030] ;

[0031] ;

[0032] In the above formula, , They are respectively Energy storage at all times The charging power and discharging power at any given time; , The first Energy storage The charging and discharging state variables at any given moment; For the first Rated power of energy storage devices; , The first Energy storage time, Energy storage at all times; For the first Energy storage-like energy storage at the initial moment; , The first type of multi-type coupled energy storage system Energy storage Discharge power and charging power at any given time; , The first Energy storage Discharge power and charging power at any given time; , The first The charging and discharging power of energy storage devices; , The first Initial and final values ​​of the state of charge (SOC) of energy storage systems; , These are the minimum and maximum values ​​of the state of charge, respectively.

[0033] The constraints on the curtailment of renewable energy include:

[0034] ;

[0035] In the above formula, for Total predicted output of new energy sources at any given time; This represents the maximum allowable value for the curtailment rate of renewable energy. , They are respectively The actual absorption capacity of wind and solar power at any given time; , They are respectively The amount of wind power and solar power that are abandoned at any given time;

[0036] The system flexibility constraints include:

[0037] ;

[0038] ;

[0039] ;

[0040] In the above formula, , They are respectively time, Net load on the power grid at any given time; , The power grid is respectively The need for upward and downward flexibility at all times; , These refer to the upward and downward flexibility of power supply from the main grid; , These represent the maximum values ​​for electricity purchased and sold per unit time period, respectively. , These represent the maximum and minimum power consumption for purchasing electricity from the main grid, respectively. , The first Energy storage The ability to provide flexible supply both upwards and downwards at all times; , The first Minimum and maximum energy storage capacity for this type of energy storage; , The first The upward and downward flexibility response efficiency coefficients of different types of energy storage are used to describe the differences in response time scale and regulation accuracy of different types of energy storage as flexibility resources, so as to quantify the actual contribution of different types of energy storage to system flexibility. Power-type energy storage has a faster response speed than energy-type energy storage, thus corresponding to a higher flexibility response efficiency coefficient. , The power grid is respectively The upward and downward flexibility margins at any given time represent the difference between the supply and demand of flexibility in the same time period. A non-negative flexibility margin indicates that the system has sufficient flexibility, while a negative margin indicates that there is a shortage of grid flexibility. When the upward flexibility is insufficient, the grid will face the risk of load shedding; when the downward flexibility is insufficient, the grid will be forced to curtail wind and solar power. , The power grid is respectively Flexibility in adjusting upward and downward deficit values ​​at all times; For the power grid Total predicted output of new energy sources at any given time; For the first Rated power of energy storage devices; , The power grid is respectively Power purchased and power sold at any given time; For the power grid The load shedding power at any given moment; , The first Energy storage Discharge power and charging power at any given time; For the first Energy storage Storing energy at all times; , The first The charging and discharging power of energy storage devices; For the power grid Total load demand at any given time;

[0041] The power grid load shedding constraints include:

[0042] ;

[0043] In the above formula, This represents the maximum upper limit of the power grid load shedding rate. For the power grid The load shedding power at any given moment; For the power grid Total load demand at any given time;

[0044] The constraints on the power grid's purchase and sale of electricity to the main grid include:

[0045] ;

[0046] In the above formula, , These are the state variables for electricity purchase and electricity sale, respectively; , These represent the upper limits for electricity purchase and sale by the power grid within a given time period; , The power grid is respectively The power purchased and the power sold at any given time.

[0047] In S3, an improved Osprey optimization algorithm is used to solve the upper-level model. The obtained initial scheme for site selection and sizing is passed to the lower-level model, and the obtained system operation results are fed back to the upper-level planning model. Iterative optimization is performed until the bi-level planning model for sizing and site selection converges. The improved Osprey optimization algorithm is as follows: a Tent chaotic mapping initialization strategy is introduced in the population initialization stage of the standard Osprey optimization algorithm, and a Levy flight mutation mechanism is introduced in the local development stage.

[0048] Secondly, the present invention provides a capacity-based addressing system for multi-type coupled energy storage systems, the capacity-based addressing system comprising:

[0049] The typical scenario generation module is used to generate typical scenarios of new energy output and load demand.

[0050] The capacity and location model construction module is used to establish a two-layer planning model for capacity and location of multi-type coupled energy storage systems. In the two-layer planning model, the upper-layer model is an energy storage capacity and location planning model with the goal of minimizing the economic cost of multi-type coupled energy storage systems, and the lower-layer model is an operation optimization scheduling model with the goal of minimizing the penalty cost of multi-type coupled energy storage systems.

[0051] The optimization solution module is used to solve the two-level planning model of fixed capacity and location based on typical scenarios of new energy output and load demand, so as to obtain the optimal fixed capacity and location scheme for multiple types of energy storage systems.

[0052] The objective function of the upper-level model includes:

[0053] ;

[0054] ;

[0055] In the above formula, Economic cost of multi-type coupled energy storage systems; , These represent the annualized total investment cost and annualized total operation and maintenance cost of a multi-type coupled energy storage system, respectively. , The first type of multi-type coupled energy storage system Annualized total investment cost and annualized total operation and maintenance cost for energy storage systems; The number of energy storage types in a multi-type coupled energy storage system; This is the capital recovery factor; The annual interest rate; For the first The service life of energy storage devices; , The first Rated capacity and rated power of energy storage devices; , The first Unit capacity cost coefficient and unit power cost coefficient for energy storage systems; For the first Annual operation and maintenance cost coefficient for energy storage systems;

[0056] The constraints of the upper-level model include: upper and lower limits of rated power of energy storage, upper and lower limits of rated capacity of energy storage, access node constraints, and maximum continuous charge and discharge hours of energy storage.

[0057] ;

[0058] In the above formula, , The first Rated power and rated capacity of energy storage devices; , The first Upper and lower limits of rated power for energy storage systems; , The first The upper and lower limits of the rated capacity of energy storage systems. For the first Maximum continuous charge / discharge hours for this type of energy storage; It serves as a centralized access node for multi-type coupled energy storage systems; This is a set of alternative nodes for energy storage installations.

[0059] The objective function of the lower-level model includes:

[0060] ;

[0061] ;

[0062] In the above formula, The grid penalty cost refers to the grid to which multiple types of coupled energy storage systems are connected. branch roads and A power grid with one energy storage alternative access node; Net electricity purchase cost to the grid Penalty costs for shedding loads from the power grid; Insufficient flexibility incurs penalties; The number of days included in the scheduling period is set to 365 days in this embodiment; This refers to the number of time periods included in a day; in this embodiment, it is set to 24 hours. , These represent the total daily electricity purchase cost and total daily electricity sales revenue of the power grid, respectively. , The power grid is respectively The electricity purchase price and electricity sales price at any given time; , The power grid is respectively The power purchased and the power sold at any given time; This is the load shedding penalty factor; For the power grid The load shedding power at any given moment; The cost of penalizing the abandonment of wind and solar power; This refers to the penalty coefficient for curtailment of renewable energy. , They are respectively The amount of wind power and solar power that are abandoned at any given moment; , Penalty coefficient for insufficient upward and downward flexibility; , These represent insufficient margins for upward flexibility and insufficient margins for downward flexibility, respectively.

[0063] The constraints of the lower-level model include: power grid active power balance constraints, line power flow constraints, energy storage status constraints, renewable energy curtailment constraints, power grid flexibility constraints, power grid load shedding constraints, and power grid power purchase and sale to the main grid constraints.

[0064] The active power balance constraints of the power grid include:

[0065] ;

[0066] In the above formula, For the power grid Total load demand at any given time; , The power grid is respectively The actual absorption capacity of wind and solar power at any given time; , The first Energy storage Discharge power and charging power at any given time; For the network The load shedding power at any given moment; , The power grid is respectively The power purchased and the power sold at any given time; For multi-type coupled energy storage systems in Net discharge power at any given time;

[0067] The power flow constraints of the line include:

[0068] ;

[0069] In the above formula, For power grid lines exist Active power transmitted at all times; For nodes in the power grid exist Net active power injected at any given moment; For power grid lines Maximum allowed active power transmission; Represents nodes in the power grid Net injected unit power to line The sensitivity of power flow is that the line power flow is the sum of the products of the net injected power at each node and the corresponding elements of the PTDF matrix. The PTDF matrix (Power Transfer Distribution Factor) is an introduced power transfer distribution factor matrix used to linearize the line power flow constraints. , Nodes in the power grid exist The power purchased and the power sold at any given time; , Nodes in the power grid exist The actual absorption capacity of wind and solar power at any given time; For nodes in the power grid exist Net discharge power at any given time; For nodes in the power grid exist Total load demand at any given time; For nodes in the power grid exist The load shedding power at any given moment;

[0070] The energy storage state constraints include:

[0071] ;

[0072] ;

[0073] ;

[0074] In the above formula, , They are respectively Energy storage at all times The charging power and discharging power at any given time; , The first Energy storage The charging and discharging state variables at any given moment; For the first Rated power of energy storage devices; , The first Energy storage time, Energy storage at all times; For the first Energy storage-like energy storage at the initial moment; , The first type of multi-type coupled energy storage system Energy storage Discharge power and charging power at any given time; , The first Energy storage Discharge power and charging power at any given time; , The first The charging and discharging power of energy storage devices; , The first Initial and final values ​​of the state of charge (SOC) of energy storage systems; , These are the minimum and maximum values ​​of the state of charge, respectively.

[0075] The constraints on the curtailment of renewable energy include:

[0076] ;

[0077] In the above formula, for Total predicted output of new energy sources at any given time; This represents the maximum allowable value for the curtailment rate of renewable energy. , They are respectively The actual absorption capacity of wind and solar power at any given time; , They are respectively The amount of wind power and solar power that are abandoned at any given time;

[0078] The system flexibility constraints include:

[0079] ;

[0080] ;

[0081] ;

[0082] In the above formula, , They are respectively time, Net load on the power grid at any given time; , The power grid is respectively The need for upward and downward flexibility at all times; , These refer to the upward and downward flexibility of power supply from the main grid; , These represent the maximum values ​​for electricity purchased and sold per unit time period, respectively. , These represent the maximum and minimum power consumption for purchasing electricity from the main grid, respectively. , The first Energy storage The ability to provide flexible supply both upwards and downwards at all times; , The first Minimum and maximum energy storage capacity for this type of energy storage; , The first The upward and downward flexibility response efficiency coefficients of different types of energy storage are used to describe the differences in response time scale and regulation accuracy of different types of energy storage as flexibility resources, so as to quantify the actual contribution of different types of energy storage to system flexibility. Power-type energy storage has a faster response speed than energy-type energy storage, thus corresponding to a higher flexibility response efficiency coefficient. , The power grid is respectively The upward and downward flexibility margins at any given time represent the difference between the supply and demand of flexibility in the same time period. A non-negative flexibility margin indicates that the system has sufficient flexibility, while a negative margin indicates that there is a shortage of grid flexibility. When the upward flexibility is insufficient, the grid will face the risk of load shedding; when the downward flexibility is insufficient, the grid will be forced to curtail wind and solar power. , The power grid is respectively Flexibility in adjusting upward and downward deficit values ​​at all times; For the power grid Total predicted output of new energy sources at any given time; For the first Rated power of energy storage devices; , The power grid is respectively Power purchased and power sold at any given time; For the power grid The load shedding power at any given moment; , The first Energy storage Discharge power and charging power at any given time; For the first Energy storage Storing energy at all times; , The first The charging and discharging power of energy storage devices; For the power grid Total load demand at any given time;

[0083] The power grid load shedding constraints include:

[0084] ;

[0085] In the above formula, This represents the maximum upper limit of the power grid load shedding rate. For the power grid The load shedding power at any given moment; For the power grid Total load demand at any given time;

[0086] The constraints on the power grid's purchase and sale of electricity to the main grid include:

[0087] ;

[0088] In the above formula, , These are the state variables for electricity purchase and electricity sale, respectively; , These represent the upper limits for electricity purchase and sale by the power grid within a given time period; , The power grid is respectively The power purchased and the power sold at any given time.

[0089] The optimization solution module is used to solve the upper-level model using an improved Osprey optimization algorithm. The initial site selection and capacity determination scheme obtained from the upper-level model is passed to the lower-level model, and the system operation results obtained from the lower-level model are fed back to the upper-level planning model. Iterative optimization is performed until the capacity and site selection bi-level planning model converges. The improved Osprey optimization algorithm is as follows: a Tent chaotic mapping initialization strategy is introduced in the population initialization stage of the standard Osprey optimization algorithm, and a Levy flight mutation mechanism is introduced in the local development stage.

[0090] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0091] 1. In the multi-type coupled energy storage system capacity and location selection method described in this invention, the rated capacity, power, and access nodes of the energy storage system are used as decision variables for collaborative planning in a two-layer model. Specifically, the upper-layer model aims to minimize the economic cost of the multi-type coupled energy storage system, while the lower-layer operation model aims to minimize the penalty cost of the multi-type coupled energy storage system. The optimal energy storage system location and capacity selection scheme, balancing economy and flexibility, is obtained through iterative solutions using both the upper and lower-layer models. Therefore, this invention achieves the dual objectives of minimizing system economic cost and optimizing grid regulation flexibility by constructing a two-layer optimization model for energy storage capacity and location selection and operation scheduling, and collaboratively planning the location and capacity selection schemes of the energy storage system.

[0092] 2. In the multi-type coupled energy storage system capacity selection and site selection method described in this invention, the upper-level model is solved using an improved Osprey optimization algorithm. During the population initialization phase of the algorithm, a chaotic mapping-based initialization method replaces the traditional random initialization method of the Osprey optimization algorithm, making the initial distribution of the Osprey population more uniform and thus avoiding the possibility of the algorithm getting trapped in local optima due to random initial solutions. Furthermore, a Levy flight strategy is incorporated into the local development phase of the algorithm, with half the probability of linearly decreasing the search step size according to the traditional Osprey optimization algorithm and half the probability of searching according to the Levy flight step size, enabling the algorithm to escape local optima. Therefore, this invention not only effectively improves the premature convergence phenomenon of the traditional Osprey optimization algorithm by introducing a chaotic mapping initialization method, but also effectively enhances the global optimization capability by introducing a Levy flight strategy in the local development phase, enabling the algorithm to escape local optima. Attached Figure Description

[0093] Figure 1 This is a flowchart of the method described in this invention.

[0094] Figure 2 This is a schematic diagram illustrating the operation of the method described in this invention.

[0095] Figure 3 The IEEE-33 standard test system is used in the computational example.

[0096] Figure 4 A schematic diagram illustrating typical scenarios of new energy power output and load demand during spring and summer.

[0097] Figure 5 A schematic diagram illustrating typical scenarios of new energy power output and load demand in autumn and winter.

[0098] Figure 6 This is the convergence curve of the CL-OOA algorithm.

[0099] Figure 7 This is a timeline diagram of the source-load balance of the optimal addressing and occupancy scheme in four typical scenarios.

[0100] Figure 8 This is a schematic diagram of the energy storage charging and discharging power of the optimal addressing and capacity scheme in four typical scenarios.

[0101] Figure 9 This is a schematic diagram of the energy storage SOC under four typical scenarios for the obtained optimal addressing and capacity scheme.

[0102] Figure 10 This is a structural block diagram of the system described in this invention. Detailed Implementation

[0103] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0104] Example 1:

[0105] See Figure 1 , Figure 2 A method for determining the location of a multi-type coupled energy storage system, comprising the following steps:

[0106] S1, Typical scenarios for generating new energy output and load demand.

[0107] Specifically, the typical scene generation steps include:

[0108] S11. Based on the historical data of wind power, photovoltaic output and load demand in the region throughout the year, time series segmentation is performed with a 24-hour period. High-dimensional daily feature matrices of wind power, photovoltaic and load are constructed for each day's data. Each matrix consists of 3 variables and 24 hours, resulting in 365 72-dimensional daily feature matrices for the whole year.

[0109] S12. Perform maximum value normalization processing on the aforementioned daily characteristic high-dimensional matrix data of wind power, photovoltaic and load, and uniformly map physical quantities of different dimensions to the [0,1] interval to eliminate the influence of numerical magnitude differences on subsequent clustering distance calculation.

[0110] S13. In view of the seasonal differences in source load characteristics, the above-mentioned high-dimensional daily feature matrix is ​​divided into four seasonal subsets according to the month: spring, summer, autumn and winter. Each subset contains 92, 92, 91 and 90 high-dimensional daily feature matrices, respectively.

[0111] S14. The K-Medoids clustering algorithm is used to cluster the high-dimensional daily feature matrices of the four seasonal subsets respectively. The real samples that minimize the Euclidean distance in each season are extracted as typical daily curves. The corresponding scenario probability weights are calculated based on the actual number of days in each season. Thus, typical scenarios of new energy output and load demand are obtained.

[0112] Considering the seasonal differences in source and load characteristics, the method described in this invention uses the K-Medoids clustering algorithm to cluster the historical data of wind power, photovoltaic power output and load demand throughout the year into four typical scenarios: spring, summer, autumn and winter. Compared with the common K-means algorithm, the K-Medoids clustering algorithm avoids outlier interference by selecting real samples as cluster centers.

[0113] S2. Establish a two-level planning model for the fixed-capacity and location selection of multi-type coupled energy storage systems.

[0114] In the aforementioned two-layer planning model for fixed-capacity and location selection, the upper-layer model is an energy storage fixed-capacity and location selection planning model with the objective of minimizing the economic cost of multi-type coupled energy storage systems. Using the rated power, rated capacity, and access nodes of the energy storage system as decision variables, it solves for the fixed-capacity and location selection scheme that minimizes the upper-layer objective function and then transmits this scheme to the lower-layer model. The lower-layer model is an operation optimization and scheduling model with the objective of minimizing the penalty cost of multi-type coupled energy storage systems. Using the actual operating parameters of the system under the fixed-capacity and location selection scheme given by the upper-layer model as decision variables, it solves for the operation scheme (including conventional unit operation and energy storage charging and discharging schemes, etc.) that minimizes the lower-layer objective function and then transmits the obtained operation scheme and corresponding penalty cost to the upper-layer model. By iteratively optimizing the upper and lower-layer models, the dual objectives of minimizing system economic cost and optimizing grid regulation flexibility are achieved.

[0115] Specifically, the objective function of the upper-level model includes:

[0116] ;

[0117] ;

[0118] In the above formula, Economic cost of multi-type coupled energy storage systems; , These represent the annualized total investment cost and annualized total operation and maintenance cost of a multi-type coupled energy storage system, respectively. , The first type of multi-type coupled energy storage system Annualized total investment cost and annualized total operation and maintenance cost for energy storage systems; The number of energy storage types in a multi-type coupled energy storage system; This is the capital recovery factor; The annual interest rate; For the first The service life of energy storage devices; , The first Rated capacity and rated power of energy storage devices; , The first Unit capacity cost coefficient and unit power cost coefficient for energy storage systems; For the first Annual operation and maintenance cost coefficient for energy storage systems.

[0119] The constraints of the upper-level model include: upper and lower limits of rated power of energy storage, upper and lower limits of rated capacity of energy storage, access node constraints, and maximum continuous charge and discharge hours of energy storage.

[0120] ;

[0121] In the above formula, , The first Rated power and rated capacity of energy storage devices; , The first Upper and lower limits of rated power for energy storage systems; , The first The upper and lower limits of the rated capacity of energy storage systems. For the first Maximum continuous charge / discharge hours for this type of energy storage; It serves as a centralized access node for multi-type coupled energy storage systems; A set of alternative nodes for energy storage installation;

[0122] The objective function of the lower-level model includes:

[0123] ;

[0124] ;

[0125] In the above formula, The grid penalty cost refers to the grid to which multiple types of coupled energy storage systems are connected. branch roads and A power grid with one energy storage alternative access node; Net electricity purchase cost to the grid Penalty costs for shedding loads from the power grid; Insufficient flexibility incurs penalties; The number of days included in the scheduling period is set to 365 days in this embodiment; This refers to the number of time periods included in a day; in this embodiment, it is set to 24 hours. , These represent the total daily electricity purchase cost and total daily electricity sales revenue of the power grid, respectively. , The power grid is respectively The electricity purchase price and electricity sales price at any given time; , The power grid is respectively The power purchased and the power sold at any given time; This is the load shedding penalty factor; For the power grid The load shedding power at any given moment; The cost of penalizing the abandonment of wind and solar power; This refers to the penalty coefficient for curtailment of renewable energy. , They are respectively The amount of wind power and solar power that are abandoned at any given moment; , Penalty coefficient for insufficient upward and downward flexibility; , These represent insufficient margins for upward flexibility and insufficient margins for downward flexibility, respectively.

[0126] The constraints of the lower-level model include: power grid active power balance constraints, line power flow constraints, energy storage status constraints, renewable energy curtailment constraints, power grid flexibility constraints, power grid load shedding constraints, and power grid power purchase and sale to the main grid constraints.

[0127] The active power balance constraints of the power grid include:

[0128] ;

[0129] In the above formula, For the power grid Total load demand at any given time; , The power grid is respectively The actual absorption capacity of wind and solar power at any given time; , The first Energy storage Discharge power and charging power at any given time; For the network The load shedding power at any given moment; , The power grid is respectively The power purchased and the power sold at any given time; For multi-type coupled energy storage systems in Net discharge power at any given time;

[0130] The power flow constraints of the line include:

[0131] ;

[0132] In the above formula, For power grid lines exist Active power transmitted at all times; For nodes in the power grid exist Net active power injected at any given moment; For power grid lines Maximum allowed active power transmission; Represents nodes in the power grid Net injected unit power to line The sensitivity of power flow is that the line power flow is the sum of the products of the net injected power at each node and the corresponding elements of the PTDF matrix. The PTDF matrix (Power Transfer Distribution Factor) is an introduced power transfer distribution factor matrix used to linearize the line power flow constraints. , Nodes in the power grid exist The power purchased and the power sold at any given time; , Nodes in the power grid exist The actual absorption capacity of wind and solar power at any given time; For nodes in the power grid exist Net discharge power at any given time; For nodes in the power grid exist Total load demand at any given time; For nodes in the power grid exist The load shedding power at any given moment;

[0133] To prevent overcharging or over-discharging of energy storage, state-of-charge (SOC) constraints are used to limit the SOC of energy storage at any given time from exceeding its upper and lower limits. Furthermore, to ensure the sustainability of energy storage operation, the initial and final values ​​of the SOC are guaranteed to be equal throughout a complete scheduling cycle, ensuring that the net charge and discharge of the energy storage system is zero. These state constraints include:

[0134] ;

[0135] ;

[0136] ;

[0137] In the above formula, , They are respectively Energy storage at all times The charging power and discharging power at any given time; , The first Energy storage The charging and discharging state variables at any given time are used to characterize the charging and discharging states of energy storage systems, in order to meet the physical operating characteristics of the energy storage system. This forces the restriction that various types of energy storage cannot simultaneously charge and discharge at the same time. A value of 1 indicates the first... Energy storage devices are in a charging state; when A value of 1 indicates the first... This type of energy storage is in a discharging state; For the first Rated power of energy storage devices; , The first Energy storage time, Energy storage at all times; For the first Energy storage-like energy storage at the initial moment; , The first Energy storage Discharge power and charging power at any given time; , The first The charging and discharging power of energy storage devices; , The first Initial and final values ​​of the state of charge (SOC) of energy storage systems; , These are the minimum and maximum values ​​of the state of charge, respectively.

[0138] The constraints on the curtailment of renewable energy include:

[0139] ;

[0140] In the above formula, for Total predicted output of new energy sources at any given time; This represents the maximum allowable value for the curtailment rate of renewable energy. , They are respectively The actual absorption capacity of wind and solar power at any given time; , They are respectively The amount of wind power and solar power that are abandoned at any given time;

[0141] This invention characterizes system flexibility requirements by utilizing net load changes. Flexibility requirements include upward and downward flexibility requirements. Upward flexibility requirements refer to the upward adjustment capacity of the system's flexible resources to cope with the increase in net load at the next moment. Downward flexibility requirements refer to the downward adjustment capacity of the system's flexible resources to cope with the decrease in net load at the next moment. Both flexibility requirements are non-negative. The system flexibility supply capacity is also characterized by considering grid-purchased electricity and various energy storage resources. Flexibility supply also includes upward and downward flexibility supply. Upward flexibility supply is the adjustable power capacity that flexible resources can provide in response to an increase in net load, and downward flexibility supply is the adjustable power capacity that flexible resources can provide in response to a decrease in net load. The system flexibility constraints include:

[0142] ;

[0143] ;

[0144] ;

[0145] In the above formula, , They are respectively time, Net load on the power grid at any given time; , The power grid is respectively The need for upward and downward flexibility at all times; , These refer to the upward and downward flexibility of power supply from the main grid; , These represent the maximum values ​​for electricity purchased and sold per unit time period, respectively. , These represent the maximum and minimum power consumption for purchasing electricity from the main grid, respectively. , The first Energy storage The ability to provide flexible supply both upwards and downwards at all times; , The first Minimum and maximum energy storage capacity for this type of energy storage; , The first The upward and downward flexibility response efficiency coefficients of different types of energy storage are used to describe the differences in response time scale and regulation accuracy of different types of energy storage as flexibility resources, so as to quantify the actual contribution of different types of energy storage to system flexibility. Power-type energy storage has a faster response speed than energy-type energy storage, thus corresponding to a higher flexibility response efficiency coefficient. , The power grid is respectively The upward and downward flexibility margins at any given time represent the difference between the supply and demand of flexibility in the same time period. A non-negative flexibility margin indicates that the system has sufficient flexibility, while a negative margin indicates that there is a shortage of grid flexibility. When the upward flexibility is insufficient, the grid will face the risk of load shedding; when the downward flexibility is insufficient, the grid will be forced to curtail wind and solar power. , The power grid is respectively Flexibility in adjusting upward and downward deficit values ​​at all times; For the power grid Total predicted output of new energy sources at any given time; For the first Rated power of energy storage devices; , The power grid is respectively Power purchased and power sold at any given time; For the power grid The load shedding power at any given moment; , The first Energy storage Discharge power and charging power at any given time; For the first Energy storage Storing energy at all times; , The first The charging and discharging power of energy storage devices; For the power grid Total load demand at any given time;

[0146] This invention ensures power supply reliability by limiting the system load shedding to a fixed proportion of its load demand. The grid load shedding constraint includes:

[0147] ;

[0148] In the above formula, This represents the maximum upper limit of the power grid load shedding rate. For the power grid The load shedding power at any given moment; For the power grid Total load demand at any given time;

[0149] The constraints on the power grid's purchase and sale of electricity to the main grid include:

[0150] ;

[0151] In the above formula, , These are the state variables for electricity purchase and electricity sale, respectively; , These represent the upper limits for electricity purchase and sale by the power grid within a given time period; , The power grid is respectively The power purchased and the power sold at any given time.

[0152] S3. Based on typical scenarios of new energy output and load demand, solve the dual-level planning model for fixed capacity and location to obtain the optimal fixed capacity and location scheme for multiple types of energy storage systems.

[0153] Specifically, the solution steps include:

[0154] S31. The improved Osprey optimization algorithm (CL-OOA) is used to solve the upper-level model, and the obtained energy storage system location and capacity determination scheme is passed to the lower-level model.

[0155] S32. Based on the given location and capacity scheme, the objective function of the lower-level model is weighted and summed, taking into account the four typical scenarios of spring, summer, autumn and winter. The CPLEX optimization solver is called to solve the lower-level running model in different scenarios, and the obtained running results and penalty costs are fed back to the upper-level planning model.

[0156] S33. Based on the operation results fed back from the lower-level model, the upper-level planning model is re-solved using the CL-OOA algorithm to obtain a site selection and capacity determination scheme for the energy storage system that makes the system more economical and flexible.

[0157] S34. Determine if the maximum number of iterations has been reached. If it has, output the current energy storage system location and capacity scheme. Otherwise, repeat S32-S34 to continue exploring and updating the energy storage system configuration scheme.

[0158] The improved Osprey optimization algorithm is as follows: a Tent chaotic mapping initialization strategy is introduced in the population initialization stage of the standard Osprey optimization algorithm to make the population initialization distribution more uniform; and a Levy flight mutation mechanism is introduced in the local development stage to make the algorithm more likely to escape local optima during fine-grained search.

[0159] The specific process of the CL-OOA algorithm is as follows:

[0160] Step 1: Set the osprey population size N and the maximum number of iterations T, and use the Tent chaotic mapping strategy to generate the initial population position matrix.

[0161] Step 2: Calculate the fitness of individual ospreys in the population. Each osprey will consider other individuals with better fitness as underwater fish and update the underwater fish set for each osprey.

[0162] Step 3: During the global exploration phase, the osprey randomly selects a fish from its underwater fish collection as its hunting target and moves towards it, updating the osprey's position and checking the boundary conditions of the new position.

[0163] Step 4: In the local development phase, individual ospreys search a small area near their current location to find a suitable feeding spot. A random probability p∈[0,1] is generated. When p≤0.5, the osprey, after catching a fish, uses a standard small-step fine-grained search for the optimal solution; when p>0.5, the osprey uses a random step size following a Levy distribution to escape the local optimum.

[0164] Step 5: Adopt a greedy strategy. If the new position has better fitness, update the position of the osprey individual and perform boundary checks on the new position; otherwise, keep the original position.

[0165] Step 6: Determine if the maximum number of iterations has been reached. If it has, proceed to Step 7; otherwise, return to Step 2 to start a new round of iterations.

[0166] Step 7: Output the individual with the best fitness in the osprey population at this time as the final iteration result.

[0167] In step 1, the osprey population size N and the decision variable dimension D are set, and a chaotic sequence of length N×D is generated using the Tent chaotic mapping. The formula for calculating the sequence value is as follows:

[0168] ;

[0169] In the above formula, , They represent the first Only the osprey corresponds to the first Chaotic mapping of initial values ​​of decision variables; Here is the chaos parameter, with a value of The larger the value of the chaos parameter, the better the chaos.

[0170] The initial position of each individual osprey is obtained by performing an inverse mapping on the chaotic sequence generated above. The specific inverse mapping calculation formula is as follows:

[0171] ;

[0172] In the above formula, For the first Only the osprey corresponds to the first Initial values ​​of the decision variables. , The first The upper and lower bounds of the decision variables are used to initialize the uniform distribution of the population in the solution space through the above chaotic mapping and inverse mapping.

[0173] In step 4, since the Levy random movement step size follows a probability distribution with heavy-tailed characteristics, namely the Levy distribution, this distribution can be approximated as a power function. Levy flight stride The calculation formula is: ,in The Levy index is typically taken as 1.5. and All are random numbers that follow a normal distribution with a mean of 0. ,in , All values ​​represent standard deviations, and their calculation formulas are as follows:

[0174] In the above formula, This is the Gamma function.

[0175] The Levy flight strategy allows for alternating movement between short-distance exploration and long-distance development. It utilizes long-distance exploration to escape local optima while continuously searching near the optimum in short-distance exploration, making the algorithm more likely to converge to the global optimum.

[0176] Performance verification:

[0177] To verify the effectiveness of the method proposed in this invention, this example is based on the following... Figure 3 The IEEE-33 standard test system shown was used for simulation testing. This test system comprises 33 nodes and 32 branches, with a reference voltage of 12.66 kV. Wind turbines are connected to nodes 25 and 32, and photovoltaic (PV) generators are connected to nodes 7 and 8, respectively. The total installed wind power capacity is 2 MW, and the total installed PV capacity is 1 MW. The multi-type coupled energy storage system includes two types of energy storage: Type 1 is vanadium redox flow battery energy storage, which has strong flexibility but higher unit capacity cost; Type 2 is pumped hydro storage, which has advantages of large capacity and low cost, but relatively lower flexibility. Energy storage parameter settings are shown in Table 2. The system time-of-use electricity price is shown in Table 1.

[0178] Table 1 System Time-of-Use Electricity Price

[0179]

[0180] Table 2 Energy Storage Parameter Settings

[0181]

[0182] Typical scenarios of renewable energy output and load demand generated in spring, summer, autumn and winter, such as Figure 4 , Figure 5 As shown, the CL-OOA algorithm and CPLEX solver are used to solve the two-layer optimization configuration model, resulting in multiple energy storage location and capacity determination schemes. The CL-OOA algorithm parameter settings are shown in Table 3. The CL-OOA algorithm convergence curve is shown in... Figure 6 As shown in Table 4, the optimal addressing and capacity balancing scheme obtained by the method described in this invention is shown in Table 4. The source-load balance timing, energy storage charge-discharge power, and energy storage SOC schematic diagrams of the optimal addressing and capacity balancing scheme under four typical scenarios are shown in Table 4. Figures 7 to 9 As shown.

[0183] Table 3 CL-OOA Algorithm Parameter Settings

[0184]

[0185] Table 4 Optimal Site and Capacity Scheme

[0186]

[0187] Example 2:

[0188] See Figure 10 A capacity-based location system for multi-type coupled energy storage systems includes a typical scenario generation module, a capacity-based location model construction module, and an optimization solution module. The typical scenario generation module generates typical scenarios of new energy output and load demand. The capacity-based location model construction module establishes a two-level planning model for capacity-based location of the multi-type coupled energy storage system. In the two-level planning model, the upper-level model is a capacity-based location planning model for energy storage aiming to minimize the economic cost of the multi-type coupled energy storage system, and the lower-level model is an operation optimization scheduling model aiming to minimize the penalty cost of the multi-type coupled energy storage system. Specifically, the objective function and constraints of the upper-level model, and the objective function and constraints of the lower-level model are all... As shown in Example 1, it will not be repeated here; the optimization solution module is used to solve the two-layer planning model of fixed capacity and location based on typical scenarios of new energy output and load demand, and obtain the optimal fixed capacity and location scheme of multiple types of energy storage systems; specifically, the optimization solution module is used to solve the upper-layer model with the improved Osprey optimization algorithm, pass the initial location and capacity scheme obtained by the upper-layer model to the lower-layer model, and feed back the system operation results obtained by the lower-layer model to the upper-layer planning model, and iteratively optimize until the fixed capacity and location two-layer planning model converges; the improved Osprey optimization algorithm is: the Tent chaotic mapping initialization strategy is introduced in the population initialization stage of the standard Osprey optimization algorithm, and the Levy flight mutation mechanism is introduced in the local development stage.

[0189] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program goods. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0190] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0191] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-type coupled energy storage system fixed-capacity site selection method, characterized in that: the fixed-capacity site selection method comprises: S1, generating a typical scenario of new energy output and load demand; S2, establishing a fixed-capacity site selection bi-level programming model of a multi-type coupled energy storage system; in the fixed-capacity site selection bi-level programming model, an upper model is an energy storage fixed-capacity site selection programming model with the objective of minimizing the economic cost of the multi-type coupled energy storage system, and a lower model is an operation optimization scheduling model with the objective of minimizing the penalty cost of the multi-type coupled energy storage system; S3, based on the typical scenario of new energy output and load demand, solving the fixed-capacity site selection bi-level programming model to obtain an optimal fixed-capacity site selection scheme of the multi-type energy storage system.

2. The multi-type coupled energy storage system fixed-capacity site selection method according to claim 1, characterized in that: the objective function of the upper model comprises: the constraint conditions of the upper model comprise: upper and lower limit constraints of energy storage rated power, upper and lower limit constraints of energy storage rated capacity, access node constraints, and maximum continuous charging and discharging hour number constraints of energy storage.

3. The multi-type coupled energy storage system fixed-capacity site selection method according to claim 1, characterized in that: the objective function of the lower model comprises:

4. The multi-type coupled energy storage system fixed-capacity site selection method according to claim 1, characterized in that: the constraint conditions of the lower model comprise: grid active power balance constraints, line power flow constraints, energy storage state constraints, new energy curtailment constraints, grid flexibility constraints, grid load shedding constraints, and grid purchase and sale of electricity to the main grid constraints; the grid active power balance constraints comprise: the line power flow constraints comprise: the energy storage state constraints comprise: the new energy curtailment constraints comprise: the system flexibility constraints comprise: the grid load shedding constraints comprise: the grid purchase and sale of electricity to the main grid constraints comprise:

5. The multi-type coupled energy storage system fixed-capacity site selection method according to claim 1, characterized in that: in S3, an improved fish-eagle optimization algorithm is used to solve the upper model, the initial scheme of site selection and fixed capacity obtained by the upper model is transmitted to the lower model, the system operation result obtained by the lower model is fed back to the upper programming model, and iterative optimization is performed until the fixed-capacity site selection bi-level programming model converges; the improved fish-eagle optimization algorithm is that a Tent chaotic mapping initialization strategy is introduced in the population initialization stage of the standard fish-eagle optimization algorithm, and a Levy flight mutation mechanism is introduced in the local development stage.

6. A multi-type coupled energy storage system fixed-capacity site selection system, characterized in that: the fixed-capacity site selection system comprises: a typical scenario generation module for generating a typical scenario of new energy output and load demand; a fixed-capacity site selection model construction module for establishing a fixed-capacity site selection bi-level programming model of a multi-type coupled energy storage system; in the fixed-capacity site selection bi-level programming model, an upper model is an energy storage fixed-capacity site selection programming model with the objective of minimizing the economic cost of the multi-type coupled energy storage system, and a lower model is an operation optimization scheduling model with the objective of minimizing the penalty cost of the multi-type coupled energy storage system. ​ ； ； In the above formula, Economic cost of multi-type coupled energy storage systems; , These represent the annualized total investment cost and annualized total operation and maintenance cost of a multi-type coupled energy storage system, respectively. , The first type of multi-type coupled energy storage system Annualized total investment cost and annualized total operation and maintenance cost for energy storage systems; The number of energy storage types in a multi-type coupled energy storage system; This is the capital recovery factor; The annual interest rate; For the first The service life of energy storage devices; , The first Rated capacity and rated power of energy storage devices; , The first Unit capacity cost coefficient and unit power cost coefficient for energy storage systems; For the first Annual operation and maintenance cost coefficient for energy storage systems; ​ ； In the above formula, , are the rated power and rated capacity of the energy storage of the first class, respectively; , are the upper and lower limits of the rated power of the energy storage of the first class, respectively; , are the upper and lower limits of the rated capacity of the energy storage of the first class, respectively, is the maximum continuous charging and discharging hours of the energy storage of the first class; is a centralized access node of the multi-type coupled energy storage system; is a set of energy storage installation alternative nodes. ​ ​ ； ； In the above formula, The grid is a grid connected to the multi-type coupled energy storage system, which has Branches and Grids with The grid net purchase cost is The grid load shedding penalty cost is The flexibility penalty cost is The dispatch cycle contains the number of days, which is set to 365 days in this embodiment. The number of time periods contained in a day is 24h in this embodiment. , The total daily purchase cost and the total daily sale revenue of the grid are , The purchase price and the sale price of the grid at , The purchase power and the sale power of the grid at The load shedding penalty coefficient is The load shedding power of the grid at The wind and light curtailment penalty cost is The new energy curtailment penalty coefficient is , The wind and light curtailment power at , The upward and downward flexibility penalty coefficients are , The upward and downward flexibility shortage margins are​​​​ ​ ​ ​ ； In the above formula, is the total load demand of the power grid at ; , are the actual consumption power of wind power and photovoltaic at , respectively; , are the discharge power and charging power of the first type of energy storage at , respectively; is the cut load power of the grid at ; , are the power purchase and power sale of the power grid at , respectively; is the net discharge power of the multi-type coupled energy storage system at ; ​ ； In the above formula, is the active power allowed to be transmitted in line In active power transmitted at time is the net active power injected in node In active power injected at time is the active power allowed to be transmitted in line upper limit represents the net active power injected in node to line sensitivity of power flow, the line power flow is the sum of the product of each node net active power and the corresponding element of PTDF matrix, and the PTDF matrix (Power Transfer Distribution Factor) is the introduced power transmission distribution factor matrix for linearization of line power flow constraints; , respectively represents the purchased power and sold power of node In at time , respectively represents the actual consumption power of wind power and photovoltaic in node In at time is the net discharge power of node In at time is the total load demand of node In at time is the load shedding power of node In at time ​ ； ； ； In the above formula, , are respectively the charging power and discharging power of the energy storage at time ; , are respectively the charging and discharging state variables of the energy storage of the first type at time ; is the rated power of the energy storage of the first type; , are respectively the energy storage amount of the energy storage of the first type at time , ; is the energy storage amount of the energy storage of the first type at the initial time; , are respectively the discharging power and charging power of the energy storage of the first type at time in a multi-type coupled energy storage system; , are respectively the discharging power and charging power of the energy storage of the first type at time ; , are respectively the charging and discharging power of the energy storage of the first type; , are respectively the initial value and final value of the state of charge of the energy storage of the first type; , are respectively the minimum value and maximum value of the state of charge; ​ ； In the above formula, is the total predicted output of new energy at the moment; is the maximum allowable value of new energy curtailment rate; , respectively the actual consumption power of wind power and photovoltaic power at the moment; , respectively the wind power curtailment power and the photovoltaic power curtailment power at the moment; ​ ； ； ； In the above formula, , are respectively , the net load of the power grid at ; , are respectively the upward flexibility demand and the downward flexibility demand of the power grid at ; , are respectively the upward and downward flexibility supply capacity of the purchase of electricity from the main grid; , are respectively the maximum value of the purchase of electricity and the maximum value of the sale of electricity per unit period; , are respectively the maximum and minimum power purchased from the main grid; , are respectively the upward and downward flexibility supply capacity of the energy storage of the first at ; , are respectively the minimum and maximum storage capacity of the energy storage of the first ; , are respectively the upward and downward flexibility response efficiency coefficients of the energy storage of the first , which are used to describe the difference in response time scale and adjustment accuracy of the heterogeneous energy storage as a flexible resource, so as to quantify the actual contribution of the heterogeneous energy storage to the system flexibility. The power-type energy storage has a faster response speed than the energy-type energy storage, and thus corresponds to a higher flexibility response efficiency coefficient; , are respectively the upward and downward flexibility margins of the power grid at , which are represented as the difference between the flexibility supply and demand in the same period. The non-negative flexibility margin indicates that the system flexibility is sufficient, otherwise the power grid flexibility is insufficient. When the upward flexibility is insufficient, the power grid will face the risk of load shedding; when the downward flexibility is insufficient, the power grid will be forced to abandon wind and solar power; , are respectively the upward and downward flexibility shortage values of the power grid at ; is the total predicted output of new energy of the power grid at ; is the rated power of the energy storage of the first ; , are respectively the purchase power and the sale power of the power grid at ; is the load shedding power of the power grid at ; , respectively, the discharge power of the energy storage of the first class at the time t; respectively, the discharge power of the energy storage of the first class at the time t; respectively, the discharge power of the energy storage of the first class at the time t; , respectively, the discharge power of the energy storage of the first class at the time t; respectively, the discharge power of the energy storage of the first class at the time t; ​ ； In the above formula, is the maximum upper limit value of the grid load shedding rate; is the load shedding power of the grid at time; is the total load demand of the grid at time; ​ ； In the above formula, , are respectively the state variables of power purchase and power sale; , are respectively the upper limits of power purchase and power sale of the power grid in a unit time period; , are respectively the power purchase and power sale of the power grid at the moment of . ​ ​ ​ ​ ​ ​ An optimization solving module is configured to solve the constant-capacity site selection bi-level programming model based on new energy output and load demand typical scenarios to obtain an optimal constant-capacity site selection scheme of the multi-type energy storage system.

7. The constant-capacity site selection method of the multi-type coupled energy storage system according to claim 6, characterized in that: The objective function of the upper model comprises: ； ； In the above formula, Economic cost of multi-type coupled energy storage systems; , These represent the annualized total investment cost and annualized total operation and maintenance cost of a multi-type coupled energy storage system, respectively. , The first type of multi-type coupled energy storage system Annualized total investment cost and annualized total operation and maintenance cost for energy storage systems; The number of energy storage types in a multi-type coupled energy storage system; This is the capital recovery factor; The annual interest rate; For the first The service life of energy storage devices; , The first Rated capacity and rated power of energy storage devices; , The first Unit capacity cost coefficient and unit power cost coefficient for energy storage systems; For the first Annual operation and maintenance cost coefficient for energy storage systems; The constraint conditions of the upper model comprise: upper and lower limits of energy storage rated power, upper and lower limits of energy storage rated capacity, access node constraint, maximum continuous charging and discharging hours of energy storage constraint; ； In the above formula, , are respectively the rated power and rated capacity of the energy storage of the first class; , are respectively the upper and lower limits of the rated power of the energy storage of the first class; , are respectively the upper and lower limits of the rated capacity of the energy storage of the first class, is the maximum continuous charging and discharging hours of the energy storage of the first class; is a centralized access node of the multi-type coupled energy storage system; is a set of energy storage installation alternative nodes.

8. The constant-capacity site selection method of the multi-type coupled energy storage system according to claim 6, characterized in that: The objective function of the lower model comprises: ； ； In the above formula, The grid penalty cost refers to the grid to which multiple types of coupled energy storage systems are connected. branch roads and A power grid with one energy storage alternative access node; Net electricity purchase cost to the grid Penalty costs for shedding loads from the power grid; Insufficient flexibility incurs penalties; The number of days included in the scheduling period is set to 365 days in this embodiment; This refers to the number of time periods included in a day; in this embodiment, it is set to 24 hours. , These represent the total daily electricity purchase cost and total daily electricity sales revenue of the power grid, respectively. , The power grid is respectively The electricity purchase price and electricity sales price at any given time; , The power grid is respectively The power purchased and the power sold at any given time; This is the load shedding penalty factor; For the power grid The load shedding power at any given moment; The cost of penalizing the abandonment of wind and solar power; This refers to the penalty coefficient for curtailment of renewable energy. , They are respectively The amount of wind power and solar power that are abandoned at any given moment; , Penalty coefficient for insufficient upward and downward flexibility; , These represent insufficient margins for upward flexibility and insufficient margins for downward flexibility, respectively.

9. The constant-capacity site selection method of the multi-type coupled energy storage system according to claim 6, characterized in that: The constraint conditions of the lower model comprise: grid active power balance constraint, line power flow constraint, energy storage state constraint, new energy curtailment constraint, grid flexibility constraint, grid load shedding constraint, and grid purchase and sale of electricity constraint to the main grid; The grid active power balance constraint comprises: ； In the above formula, is the total load demand of the power grid at ; , are the actual consumption power of wind power and photovoltaic power of the power grid at ; , are the discharge power and charging power of the first type of energy storage at ; is the cut load power of the grid at ; , are the power purchase and power sale of the power grid at ; is the net discharge power of the multi-type coupled energy storage system at ; The line power flow constraint comprises: ； In the above formula, is the active power allowed to be transmitted in line In the active power transmitted at time is the net active power injected in node In the net active power injected at time is the active power allowed to be transmitted in line In represents the net active power injected in node to line The sensitivity of power flow to the net active power injected in each node, where the power flow is the sum of the product of the net active power injected in each node and the corresponding element of the PTDF matrix, and the PTDF matrix (Power Transfer Distribution Factor) is introduced to linearize the representation of the power flow constraint; , are the purchased power and the sold power of node In at time , are the actual consumption of wind power and photovoltaic power of node In at time is the net discharge power of node In at time is the total load demand of node In at time is the load shedding power of node In at time The energy storage state constraint comprises: ； ； ； In the above formula, , are respectively the charging power and discharging power of the energy storage at time ; , are respectively the charging and discharging state variables of the energy storage of the first class at time ; is the rated power of the energy storage of the first class; , are respectively the energy storage amount of the energy storage of the first class at time , ; is the energy storage amount of the energy storage of the first class at the initial time; , are respectively the discharging power and charging power of the energy storage of the first class at time in the multi-type coupled energy storage system; , are respectively the discharging power and charging power of the energy storage of the first class at time ; , are respectively the charging and discharging power of the energy storage of the first class; , are respectively the initial value and final value of the state of charge of the energy storage of the first class; , are respectively the minimum value and maximum value of the state of charge; The new energy curtailment constraint comprises: ； In the above formula, is the total predicted output of new energy at the moment; is the maximum allowable value of new energy curtailment rate; , respectively the actual consumption power of wind power and photovoltaic power at the moment; , respectively the wind power curtailment power and the photovoltaic power curtailment power at the moment; The system flexibility constraint comprises: ； ； ； In the above formula, , They are respectively time, Net load on the power grid at any given time; , The power grid is respectively The need for upward and downward flexibility at all times; , These refer to the upward and downward flexibility of power supply from the main grid; , These represent the maximum values ​​for electricity purchased and sold per unit time period, respectively. , These represent the maximum and minimum power consumption for purchasing electricity from the main grid, respectively. , The first Energy storage The ability to provide flexible supply both upwards and downwards at all times; , The first Minimum and maximum energy storage capacity for this type of energy storage; , The first The upward and downward flexibility response efficiency coefficients of different types of energy storage are used to describe the differences in response time scale and regulation accuracy of different types of energy storage as flexibility resources, so as to quantify the actual contribution of different types of energy storage to system flexibility. Power-type energy storage has a faster response speed than energy-type energy storage, thus corresponding to a higher flexibility response efficiency coefficient. , The power grid is respectively The upward and downward flexibility margins at any given time represent the difference between the supply and demand of flexibility in the same time period. A non-negative flexibility margin indicates that the system has sufficient flexibility, while a negative margin indicates that there is a shortage of grid flexibility. When the upward flexibility is insufficient, the grid will face the risk of load shedding; when the downward flexibility is insufficient, the grid will be forced to curtail wind and solar power. , The power grid is respectively Flexibility in adjusting upward and downward deficit values ​​at all times; For the power grid Total predicted output of new energy sources at any given time; For the first Rated power of energy storage devices; , The power grid is respectively Power purchased and power sold at any given time; For the power grid The load shedding power at any given moment; , The first Energy storage Discharge power and charging power at any given time; For the first Energy storage Storing energy at all times; , The first The charging and discharging power of energy storage devices; For the power grid Total load demand at any given time; The grid load shedding constraint comprises: ； In the above formula, is the maximum upper limit value of the grid load shedding rate; is the load shedding power of the grid at time; is the total load demand of the grid at time; The grid purchase and sale of electricity constraint to the main grid comprises: ； In the above formula, , are the state variables of power purchase and power sale, respectively; , are the upper limits of power purchase and power sale of the power grid in a unit time period, respectively; , are the power purchase and power sale of the power grid at the moment of , respectively.

10. The constant-capacity site selection method of the multi-type coupled energy storage system according to claim 6, characterized in that: The optimization solving module is configured to solve the upper model by using an improved fish eagle optimization algorithm, to pass the site selection and capacity selection initial scheme obtained by the upper model to the lower model, to feed back the system operation result obtained by the lower model to the upper programming model, and to iteratively optimize until the constant-capacity site selection bi-level programming model converges; the improved fish eagle optimization algorithm is: introducing a Tent chaotic mapping initialization strategy in the population initialization stage of the standard fish eagle optimization algorithm, and introducing a Levy flight mutation mechanism in the local development stage.

Images