Networking type energy storage locating and sizing method and system based on intraday short-circuit ratio

Abstract

The invention discloses a network construction type energy storage locating and sizing method and system based on the intraday short circuit ratio, and belongs to the technical field of power systems. The method comprises the following steps: acquiring intra-day scheduling time mode data of a regional power grid, and establishing a power grid load flow calculation and stability calculation model according to the intra-day scheduling time mode data; on the basis of the power grid load flow calculation and stability calculation model, performing initial configuration on the network construction type energy storage of the regional power grid, and after the configuration is completed, establishing a locating and sizing optimization model of the network construction type energy storage; and solving the optimal value of the addressing and sizing optimization model, and determining the addressing and sizing result of the network construction type energy storage according to the optimal value. According to the method, the short-circuit ratio level of the regional power grid can be remarkably improved, the voltage supporting capacity of the new energy station is improved, and the access capacity of the new energy station is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to a method and system for selecting a site and determining the capacity of a grid-connected energy storage system based on an intra-day short-circuit ratio. Background Art

[0002] With the large-scale global transition to renewable energy, the power system is undergoing profound changes, but this also brings new challenges to the stability of the power grid. The output power of these renewable energy sources is highly dependent on environmental conditions (such as wind speed and solar radiation), so it is volatile and uncertain, which puts significant pressure on the operation and dispatch of the power grid. At the same time, with the continuous expansion of the scale of new energy grid connection, the power grid faces new challenges in terms of new energy absorption capacity, grid operation safety, and peak load regulation and mutual assistance capabilities across transmission sections. These capabilities are limited by the characteristics of conventional power sources and the structure of the power grid, and it is difficult to meet the needs of a high proportion of new energy access.

[0003] Grid-connected energy storage technology has been recognized as one of the key means to meet the above challenges. By configuring grid-connected energy storage, the power grid can significantly improve its voltage support capability and dynamic stability. This is particularly important in power grids with a high proportion of renewable energy access, which helps maintain the reliability of the system. The control technology of grid-connected energy storage not only supports voltage regulation and frequency stability, but also improves power quality by enhancing fault ride-through capability and dynamic stability. Especially when the output power of renewable energy changes rapidly, grid-connected energy storage can effectively prevent grid collapse and ensure the reliability of power supply. In addition, these converter-based technologies provide greater operational flexibility, enabling seamless grid synchronization and precise power control.

[0004] At present, in the research on energy storage optimization configuration, the power supply side mainly focuses on comprehensive power performance and volatility indicators, such as minimizing the wind and solar power abandonment rate, smoothing output power, improving power generation efficiency, optimizing grid-connected voltage quality, and reducing prediction errors. On the grid side, it focuses on comprehensive technical and economic indicators, including peak and frequency regulation capabilities, grid upgrade and transformation progress, environmental benefits, network congestion, network loss optimization, and new energy consumption. Current energy storage planning research mainly focuses on improving economic performance and some technical performance, while the role of the key indicator of short-circuit ratio is generally lacking. Short-circuit ratio is an important indicator for evaluating the voltage support capacity of new energy stations and is closely related to the stability and fault ride-through capability of the system. In regional power grids with a high proportion of new energy access, insufficient short-circuit ratio will lead to a decrease in the voltage support capacity of new energy stations, thereby causing the risk of grid instability. Therefore, the existing energy storage planning methods fail to fully combine the optimization of short-circuit ratio and cannot fully reflect the potential of energy storage systems in improving the voltage support capacity of regional power grids. Summary of the invention

[0005] In view of the above problems, the present invention proposes a method for selecting a site and determining the capacity of a grid-type energy storage system based on the intra-day short-circuit ratio, comprising:

[0006] Acquire the intraday dispatching time mode data of the regional power grid, and establish a power grid flow calculation and stability calculation model based on the intraday dispatching time mode data;

[0007] Based on the power grid flow calculation and stability calculation model, the grid-forming energy storage of the regional power grid is initialized and configured. After the configuration is completed, a site selection and capacity optimization model for the grid-forming energy storage is established;

[0008] The optimal value of the site selection and capacity determination optimization model is solved, and the site selection and capacity determination result of the grid-type energy storage is determined according to the optimal value.

[0009] Optional, intraday scheduling time mode data, including:

[0010] Renewable energy and conventional hydropower data, thermal power installed capacity and output data, generator and its excitation system data, speed regulator data, power system stabilizer data, AC transmission line parameter data, transformer parameter data, load model data, network interconnection topology data and DC transmission system control method and controller parameter data.

[0011] Optionally, the grid-connected energy storage is initialized and configured according to 20% of the capacity of the new energy station devices in the regional power grid.

[0012] Optionally, establish a site selection and capacity optimization model for grid-connected energy storage, including:

[0013] Determine the optimization goal of address sizing of grid-type energy storage, establish an objective function based on the optimization goal, and determine constraints for the objective function, and use the objective function and constraints as an address sizing optimization model.

[0014] Optional, optimization goals, including:

[0015] Grid-type energy storage maximizes the daily short-circuit ratio capability evaluation index and minimizes system network losses;

[0016] The grid-type energy storage maximizes the evaluation index of the intraday short-circuit ratio, and the corresponding objective function is as follows:

[0017]

[0018] Among them, η is the evaluation index of the ability of network-type energy storage to improve the short-circuit ratio within a day, N is the number of new energy power stations in the regional power grid, and SCR GFti To configure the short-circuit ratio of multiple stations of the grid-connected energy storage power station for the new energy source i in period t, SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured;

[0019] The system network loss is minimized, and the corresponding objective function is as follows:

[0020]

[0021] Among them, P loss is the system network loss, P losst is the network loss of the system at time t.

[0022] Optional constraints, including:

[0023] Power flow constraints, transient time domain solutions, voltage safety constraints, and short-circuit ratio constraints for new energy stations;

[0024] The power flow constraints are as follows:

[0025]

[0026] Where n is the number of nodes in the sending-end power grid, P i and Q i are the active and reactive power of node i, U i and U j are the voltages of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the node admittance matrix (i, j) respectively, θ ij is the phase difference between nodes i and j;

[0027] The transient time domain solution and voltage safety constraints are as follows:

[0028] U(t)≤U max (t)

[0029] Among them, U(t) is the voltage of a new energy machine at time t, U max (t) is the maximum allowable voltage on the bus;

[0030] The short-circuit ratio constraints of the new energy stations are as follows:

[0031] SCR GFti ≥SCR min

[0032] Among them, SCR GFti is the short-circuit ratio of the new energy station i, SCR min is the minimum short-circuit ratio requirement for new energy station i.

[0033] Optionally, the whale optimization algorithm and the Pareto sorting method are used to solve the optimal value of the site selection and capacity optimization model, including:

[0034] Based on the whale optimization algorithm, the site selection and sizing optimization model is solved to obtain the optimal solution of the whale population, and the optimal solution is updated through the Pareto sorting method. The updated optimal solution is scored, and the updated optimal solution is sorted according to the score. Based on the sorting, the optimal value of the site selection and sizing optimization model is obtained.

[0035] Optional, site selection and capacity determination results, including: installation location and capacity of grid-type energy storage equipment.

[0036] On the other hand, the present invention also proposes a grid-type energy storage site selection and capacity determination system based on intra-day short-circuit ratio, comprising:

[0037] A first modeling unit is used to obtain the intraday dispatching time mode data of the regional power grid, and establish a power grid flow calculation and stability calculation model according to the intraday dispatching time mode data;

[0038] The second modeling unit is used to initialize the configuration of the grid-forming energy storage of the regional power grid based on the power grid flow calculation and stability calculation model, and after the configuration is completed, establish a site selection and capacity optimization model for the grid-forming energy storage;

[0039] A solving unit is used to solve the optimal value of the site selection and capacity determination optimization model, and determine the site selection and capacity determination result of the grid-type energy storage according to the optimal value.

[0040] Optional, intraday scheduling time mode data, including:

[0041] Renewable energy and conventional hydropower data, thermal power installed capacity and output data, generator and its excitation system data, speed regulator data, power system stabilizer data, AC transmission line parameter data, transformer parameter data, load model data, network interconnection topology data and DC transmission system control method and controller parameter data.

[0042] Optionally, the grid-connected energy storage is initialized and configured according to 20% of the capacity of the new energy station devices in the regional power grid.

[0043] Optionally, establish a site selection and capacity optimization model for grid-connected energy storage, including:

[0044] Determine the optimization goal of address sizing of grid-type energy storage, establish an objective function based on the optimization goal, and determine constraints for the objective function, and use the objective function and constraints as an address sizing optimization model.

[0045] Optional, optimization goals, including:

[0046] Grid-type energy storage maximizes the daily short-circuit ratio capability evaluation index and minimizes system network losses;

[0047] The grid-type energy storage maximizes the evaluation index of the intraday short-circuit ratio, and the corresponding objective function is as follows:

[0048]

[0049] Among them, η is the evaluation index of the ability of network-type energy storage to improve the short-circuit ratio within a day, N is the number of new energy power stations in the regional power grid, and SCR GFti To configure the short-circuit ratio of multiple stations of the grid-connected energy storage power station for the new energy source i in period t, SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured;

[0050] The system network loss is minimized, and the corresponding objective function is as follows:

[0051]

[0052] Among them, P loss is the system network loss, P losst is the network loss of the system at time t.

[0053] Optional constraints, including:

[0054] Power flow constraints, transient time domain solutions, voltage safety constraints, and short-circuit ratio constraints for new energy stations;

[0055] The power flow constraints are as follows:

[0056]

[0057] Where n is the number of nodes in the sending-end power grid, P i and Q i are the active and reactive power of node i, U i and U j are the voltages of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the node admittance matrix (i, j) respectively, θ ij is the phase difference between nodes i and j;

[0058] The transient time domain solution and voltage safety constraints are as follows:

[0059] U(t)≤U max (t)

[0060] Among them, U(t) is the voltage of a new energy machine at time t, U max (t) is the maximum allowable voltage on the bus;

[0061] The short-circuit ratio constraints of the new energy stations are as follows:

[0062] SCRGFti ≥SCR min

[0063] Among them, SCR GFti is the short-circuit ratio of the new energy station i, SCR min is the minimum short-circuit ratio requirement for new energy station i.

[0064] Optionally, the optimal value of the site selection and capacity optimization model is solved by using a whale optimization algorithm and a Pareto sorting method, including:

[0065] Based on the whale optimization algorithm, the site selection and sizing optimization model is solved to obtain the optimal solution of the whale population, and the optimal solution is updated through the Pareto sorting method. The updated optimal solution is scored, and the updated optimal solution is sorted according to the score. Based on the sorting, the optimal value of the site selection and sizing optimization model is obtained.

[0066] Optional, site selection and capacity determination results, including: installation location and capacity of grid-type energy storage equipment.

[0067] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0068] a processor for executing one or more programs;

[0069] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0070] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention provides a method for site selection and capacity determination of grid-type energy storage based on intraday short-circuit ratio, including: obtaining intraday dispatching time mode data of a regional power grid, and establishing a power grid flow calculation and stability calculation model based on the intraday dispatching time mode data; initializing and configuring the grid-type energy storage of the regional power grid based on the power grid flow calculation and stability calculation model, and after the configuration is completed, establishing a site selection and capacity determination optimization model for the grid-type energy storage; solving the optimal value of the site selection and capacity determination optimization model, and determining the site selection and capacity determination result of the grid-type energy storage based on the optimal value. The present invention can significantly improve the short-circuit ratio level of the regional power grid, enhance the voltage support capacity of the new energy station, and improve the access capacity of the new energy station. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of the method of the present invention;

[0074] Figure 2 A diagram of the power grid structure of a calculation example system of an embodiment of the method of the present invention;

[0075] Figure 3 It is a schematic diagram of the short-circuit ratio index of a new energy station within a day when no grid-connected energy storage is added according to an embodiment of the method of the present invention;

[0076] Figure 4 This is a schematic diagram of the short-circuit ratio index of a new energy station within a day after adding grid-type energy storage according to an embodiment of the method of the present invention;

[0077] Figure 5 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0078] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0079] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0080] Embodiment 1:

[0081] The present invention proposes a method for selecting a site and determining the capacity of a grid-type energy storage system based on the intra-day short-circuit ratio. Figure 1 As shown, including:

[0082] Step 1: Obtain the intraday dispatching time mode data of the regional power grid, and establish a power grid flow calculation and stability calculation model based on the intraday dispatching time mode data;

[0083] Step 2: Based on the power grid flow calculation and stability calculation model, the grid-forming energy storage of the regional power grid is initialized and configured. After the configuration is completed, a site selection and capacity optimization model for the grid-forming energy storage is established;

[0084] Step 3: Solve the optimal value of the site selection and capacity determination optimization model, and determine the site selection and capacity determination result of the grid-type energy storage according to the optimal value.

[0085] Among them, the intraday scheduling time mode data includes:

[0086] Renewable energy and conventional hydropower data, thermal power installed capacity and output data, generator and its excitation system data, speed regulator data, power system stabilizer data, AC transmission line parameter data, transformer parameter data, load model data, network interconnection topology data and DC transmission system control method and controller parameter data.

[0087] Among them, the grid-type energy storage is initialized and configured according to 20% of the capacity of the new energy station equipment in the regional power grid.

[0088] Among them, the site selection and capacity optimization model of grid-type energy storage is established, including:

[0089] Determine the optimization goal of address sizing of grid-type energy storage, establish an objective function based on the optimization goal, and determine constraints for the objective function, and use the objective function and constraints as an address sizing optimization model.

[0090] Among them, the optimization objectives include:

[0091] Grid-type energy storage maximizes the daily short-circuit ratio capability evaluation index and minimizes system network losses;

[0092] The grid-type energy storage maximizes the evaluation index of the intraday short-circuit ratio, and the corresponding objective function is as follows:

[0093]

[0094] Among them, η is the evaluation index of the ability of network-type energy storage to improve the short-circuit ratio within a day, N is the number of new energy power stations in the regional power grid, and SCR GFti To configure the short-circuit ratio of multiple stations of the grid-connected energy storage power station for the new energy source i in period t, SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured;

[0095] The system network loss is minimized, and the corresponding objective function is as follows:

[0096]

[0097] Among them, P loss is the system network loss, P losst is the network loss of the system at time t.

[0098] The constraints include:

[0099] Power flow constraints, transient time domain solutions, voltage safety constraints, and short-circuit ratio constraints for new energy stations;

[0100] The power flow constraints are as follows:

[0101]

[0102] Where n is the number of nodes in the sending-end power grid, P i and Q i are the active and reactive power of node i, U i and U j are the voltages of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the node admittance matrix (i, j) respectively, θ ij is the phase difference between nodes i and j;

[0103] The transient time domain solution and voltage safety constraints are as follows:

[0104] U(t)≤U max (t)

[0105] Among them, U(t) is the voltage of a new energy machine at time t, U max (t) is the maximum allowable voltage on the bus;

[0106] The short-circuit ratio constraints of the new energy stations are as follows:

[0107] SCR GFti ≥SCR min

[0108] Among them, SCR GFti is the short-circuit ratio of the new energy station i, SCR min is the minimum short-circuit ratio requirement for new energy station i.

[0109] Among them, the whale optimization algorithm and the Pareto sorting method are used to solve the optimal value of the site selection and capacity optimization model, including:

[0110] Based on the whale optimization algorithm, the site selection and sizing optimization model is solved to obtain the optimal solution of the whale population, and the optimal solution is updated through the Pareto sorting method. The updated optimal solution is scored, and the updated optimal solution is sorted according to the score. Based on the sorting, the optimal value of the site selection and sizing optimization model is obtained.

[0111] Among them, the site selection and capacity determination results include: the installation location and capacity of the grid-type energy storage equipment.

[0112] The present invention will be further described below in conjunction with specific embodiments:

[0113] The embodiment steps include:

[0114] Step 1: Obtain the daily dispatching time mode data of the power grid, and establish the power grid flow calculation and stability calculation model based on the obtained daily dispatching time mode data.

[0115] The grid operation mode data include: installed capacity and output of new energy and conventional hydropower and thermal power, generators and their excitation systems, speed regulators, power system stabilizer data, AC transmission line parameters, transformer parameters, load models, network interconnection topology data, and DC transmission system control mode and controller parameters.

[0116] Step 2: Configure grid-connected energy storage according to 20% of the installed capacity of new energy stations.

[0117] Step 3: Establish a grid-type energy storage site selection and capacity optimization model.

[0118] 3.1 Establish the objective function of the grid-type energy storage site selection and capacity optimization model, including two objective functions: maximizing the daily short-circuit ratio evaluation index of the grid-type energy storage and minimizing the system network loss.

[0119] The evaluation index η of the ability of grid-type energy storage to improve the short-circuit ratio within a day can quantify the comprehensive improvement ability of the grid-type energy storage power station on the short-circuit ratio of new energy sources in the regional power grid during the dispatch day. The objective function considering the evaluation index of the ability of grid-type energy storage to improve the short-circuit ratio within a day is:

[0120]

[0121] Where: SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured; SCR GFti is the short-circuit ratio of multiple stations of renewable energy i in the period t of the grid-connected energy storage power station; N is the number of renewable energy power stations in the regional power grid. The larger the value of η, the stronger the ability of the grid-connected energy storage power station to improve the short-circuit ratio of the regional power grid.

[0122] The objective function considering the network loss index is:

[0123]

[0124] Among them, P losst is the network loss of the system at time t.

[0125] 3.2 The constraints of the grid-type energy storage optimization configuration model include:

[0126] (1) Power flow constraints:

[0127]

[0128] Where n is the number of nodes in the sending grid; Pi and Qi represent the active and reactive power of node i, respectively; Ui and Uj represent the voltages of the i-th and j-th nodes; Gij and Bij are the real and imaginary parts of the node admittance matrix (i, j) position; θij represents the phase difference between nodes i and j.

[0129] (2) Transient time domain solution and voltage safety constraints:

[0130] Transient time domain constraints are expressed through differential algebraic equations that model the dynamic behavior of grid components such as renewable energy units and grid-connected energy storage. Differential equations describe the dynamic response of these components, while algebraic equations describe the instantaneous power balance of network nodes during transient events. The mathematical representation usually includes

[0131]

[0132] In the formula: x represents the system state variable; y represents the algebraic variable; t represents time.

[0133] The overvoltage safety constraint ensures that the bus voltage level at the renewable energy generator end remains within a safe range during and after transient events. The constraint can be expressed as:

[0134] U(t)≤U max (t) (5)

[0135] Where: U(t) is the voltage of a new energy machine at time t; U max (t) is the maximum allowable voltage on the busbar to avoid high voltage disconnection of the new energy unit. When different new energy fields meet the transient overvoltage safety requirements under all AC and DC fault scenarios, η is, otherwise, it is a positive large number.

[0136] (3) Short-circuit ratio constraints of new energy station i:

[0137] SCR GFti ≥SCR min (6)

[0138] In the formula, SCR min It is the minimum short-circuit ratio requirement for new energy station i.

[0139] 4. Run the whale optimization algorithm to find the optimal value, including the following steps:

[0140] 4.1 Set the parameters of the whale optimization algorithm, including the population size and the maximum number of iterations.

[0141] 4.2 Initialize the position of the whale population, the calculation formula is as follows:

[0142] X i =lb+rand(ub-lb) (7)

[0143] Where Xi is the position of individual i, lb and ub are the lower and upper bounds of the search space, and rand is a random number between 0 and 1.

[0144] 4.3 Each whale will explore the space according to certain rules. Simulate the process of whales surrounding, chasing and attacking prey.

[0145] Surrounding the prey: As each whale determines the location of the prey (optimal solution) and surrounds it, the mathematics is modeled as:

[0146] X(t+1)=X * -A·|C·X * -X| (8)

[0147] Where X* represents the location of the optimal solution, and A and C are coefficient vectors that are dynamically calculated according to the situation to ensure convergence to the prey or around the prey.

[0148] Attack Mechanism: This behavior simulates the spiral motion used by humpback whales during hunting, imitating the spiral path towards prey. The mechanism is represented by:

[0149] X(t+1)=X * -D′·e b·l ·cos(2πl) (9)

[0150] Where D' is the distance to the prey, b is the constant for forming the spiral, and l is a random number in [-1,1].

[0151] Finding prey: In order to avoid local optimality and improve exploration ability, the whale optimization algorithm randomly selects search populations to modify their positions. The formula is:

[0152] X new =X rand -A·|C·X rand -X| (10)

[0153] Among them, X rand is the random position of the whale in the current population.

[0154] 4.4 Whenever a whale moves, it is determined whether the current whale population meets the constraints and the corresponding multi-objective function is calculated.

[0155] 4.5 Update the optimal solution of the population through the Pareto sorting method, use the whale optimization algorithm to update the position of each individual, and adjust according to the current optimal solution.

[0156] Step 5: Use the Pareto sorting method to specifically include:

[0157] 5.1 Domination relationship determination, determine whether each solution is dominated by other solutions. The condition for solution x1 to dominate solution x2 is: x1 is not inferior to x2 in all objectives and x1 is better than x2 in at least one objective.

[0158] 5.2 Pareto layer construction, recording the Pareto front solution set and its suboptimal solutions in layers; the Pareto front refers to the solution with a dominated count of 0, and the second and subsequent layers refer to recalculating the domination counts of the remaining solutions after eliminating the solutions of the previous layer until all solutions are allocated.

[0159] 5.3 Non-dominated sorting, further sorting the priority of the solutions in the Pareto layer according to the comprehensive score, so as to make a choice between multiple solutions. The comprehensive score is usually based on the objective function value and weight, and is obtained through some normalization method and weighted calculation.

[0160] 5.4 Determine whether the maximum number of iterations has been reached. If not, return to step 5.3; otherwise, complete the calculation.

[0161] Step 6: Output the results of site selection and capacity determination for the grid-type energy storage, including the installation location and capacity of the grid-type energy storage equipment.

[0162] The following is an example of a simulation system of a real renewable energy transmission end power grid to verify the present invention:

[0163] 1. Obtain the daily dispatching time data of the power grid and establish the power grid flow calculation and stability calculation model.

[0164] Taking a certain actual renewable energy transmission grid as an example, the grid dispatching time data is collected every 15 minutes, including AC transmission line and transformer parameters, network topology interconnection data, generator output and load power data, generator and its excitation and speed regulation system data. A grid power flow steady-state simulation model and an electromechanical transient simulation model are established, including 17 renewable energy stations concentrated in a specific area. The system geographic wiring diagram is shown in the figure below. Figure 2 As shown in Figure 1, these new energy sites are connected to six 330kV substations. The power will be transmitted through a 750kV AC system with a total installed capacity of approximately 4,600MW. Figure 3 shown.

[0165] 2. Configure grid-type energy storage according to 20% of the installed capacity of new energy stations, totaling 920MW.

[0166] 3. Establish an optimization model for site selection and capacity determination of grid-type energy storage.

[0167] 3.1 Establish the objective function of the grid-type energy storage site selection and capacity optimization model, including two objective functions: maximizing the daily short-circuit ratio evaluation index of the grid-type energy storage and minimizing the system network loss.

[0168] The evaluation index η of the ability of grid-type energy storage to improve the short-circuit ratio within a day can quantify the comprehensive improvement ability of the grid-type energy storage power station on the short-circuit ratio of new energy sources in the regional power grid during the dispatch day. The objective function considering the evaluation index of the ability of grid-type energy storage to improve the short-circuit ratio within a day is:

[0169]

[0170] Where: SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured; SCR GFti is the short-circuit ratio of multiple stations of renewable energy i in the period t of the grid-connected energy storage power station; N is the number of renewable energy power stations in the regional power grid. The larger the value of η, the stronger the ability of the grid-connected energy storage power station to improve the short-circuit ratio of the regional power grid.

[0171] The objective function considering the network loss index is:

[0172]

[0173] Among them, P losst is the network loss of the system at time t.

[0174] 4. The constraints of the grid-type energy storage optimization configuration model include:

[0175] (1) Power flow constraints:

[0176]

[0177] Where n is the number of nodes in the sending grid; Pi and Qi represent the active and reactive power of node i, respectively; Ui and Uj represent the voltages of the i-th and j-th nodes; Gij and Bij are the real and imaginary parts of the node admittance matrix (i, j) position; θij represents the phase difference between nodes i and j.

[0178] (2) Transient time domain solution and voltage safety constraints:

[0179] Transient time domain constraints are expressed through differential algebraic equations that model the dynamic behavior of grid components such as renewable energy units and grid-connected energy storage. Differential equations describe the dynamic response of these components, while algebraic equations describe the instantaneous power balance of network nodes during transient events. The mathematical representation usually includes

[0180]

[0181] In the formula: x represents the system state variable; y represents the algebraic variable; t represents time.

[0182] The overvoltage safety constraint ensures that the bus voltage level at the renewable energy generator end remains within a safe range during and after transient events. The constraint can be expressed as

[0183] U(t)≤U max (t)

[0184] Where: Maximum allowable voltage on busbar U max (t) is 1.3pu.

[0185] (3) Short-circuit ratio constraints of new energy station i:

[0186] SCR GFti ≥SCR min

[0187] In the formula, the minimum short-circuit ratio requirement of the new energy station is SCR min is 1.5.

[0188] 4. Run the whale optimization algorithm to solve the optimal value. The algorithm is configured with the following parameters: the population size is set to 50, the maximum number of iterations is 20, and 36-bit binary encoding is used each time. The spiral coefficient is set to 1 and the convergence factor is 0.1. In the whale optimization algorithm, each generation selects the Pareto frontier solution as the basis for population update. The configuration results show that 920MW of grid-connected energy storage is configured at 6 different sites. The grid-connected energy storage configuration results are shown in Table 1.

[0189] Table 1

[0190] Station Grid-type energy storage capacity Station Grid-type energy storage capacity GF1 120 GF4 160 GF2 200 GF5 170 GF3 140 GF6 130

[0191] 5. Output the optimization configuration results of grid-type energy storage. After configuring the grid-type energy storage, the daily short-circuit ratio index of the new energy station is as follows: Figure 4 After configuring the grid-type energy storage, the short-circuit ratio of renewable energy is improved, which can meet the safety requirement of not less than 1.5pu, ensuring the safety of the transient voltage of the transmission network after the failure of the renewable energy unit, and the reasonable configuration of energy storage capacity reduces the system network loss.

[0192] Embodiment 2:

[0193] The present invention also proposes a grid-type energy storage site selection and capacity determination system 200 based on intra-day short-circuit ratio, such as Figure 5 As shown, including:

[0194] The first modeling unit 201 is used to obtain the intraday dispatching time mode data of the regional power grid, and establish a power grid flow calculation and stability calculation model according to the intraday dispatching time mode data;

[0195] The second modeling unit 202 is used to initialize the configuration of the grid-forming energy storage of the regional power grid based on the power grid flow calculation and stability calculation model, and after the configuration is completed, establish a site selection and capacity optimization model for the grid-forming energy storage;

[0196] The solving unit 203 is used to solve the optimal value of the site selection and capacity determination optimization model, and determine the site selection and capacity determination result of the grid-type energy storage according to the optimal value.

[0197] Among them, the intraday scheduling time mode data includes:

[0198] Renewable energy and conventional hydropower data, thermal power installed capacity and output data, generator and its excitation system data, speed regulator data, power system stabilizer data, AC transmission line parameter data, transformer parameter data, load model data, network interconnection topology data and DC transmission system control method and controller parameter data.

[0199] Among them, the grid-type energy storage is initialized and configured according to 20% of the capacity of the new energy station equipment in the regional power grid.

[0200] Among them, the site selection and capacity optimization model of grid-type energy storage is established, including:

[0201] Determine the optimization goal of address sizing of grid-type energy storage, establish an objective function based on the optimization goal, and determine constraints for the objective function, and use the objective function and constraints as an address sizing optimization model.

[0202] Among them, the optimization objectives include:

[0203] Grid-type energy storage maximizes the daily short-circuit ratio capability evaluation index and minimizes system network losses;

[0204] The grid-type energy storage maximizes the evaluation index of the intraday short-circuit ratio, and the corresponding objective function is as follows:

[0205]

[0206] Among them, η is the evaluation index of the ability of network-type energy storage to improve the short-circuit ratio within a day, N is the number of new energy power stations in the regional power grid, and SCR GFti To configure the short-circuit ratio of multiple stations of the grid-connected energy storage power station for the new energy source i in period t, SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured;

[0207] The system network loss is minimized, and the corresponding objective function is as follows:

[0208]

[0209] Among them, P loss is the system network loss, P losst is the network loss of the system at time t.

[0210] The constraints include:

[0211] Power flow constraints, transient time domain solutions, voltage safety constraints, and short-circuit ratio constraints for new energy stations;

[0212] The power flow constraints are as follows:

[0213]

[0214] Where n is the number of nodes in the sending-end power grid, P i and Q i are the active and reactive power of node i, U i and U j are the voltages of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the node admittance matrix (i, j) respectively, θ ij is the phase difference between nodes i and j;

[0215] The transient time domain solution and voltage safety constraints are as follows:

[0216] U(t)≤U max (t)

[0217] Among them, U(t) is the voltage of a new energy machine at time t, U max (t) is the maximum allowable voltage on the bus;

[0218] The short-circuit ratio constraints of the new energy stations are as follows:

[0219] SCR GFti ≥SCR min

[0220] Among them, SCR GFti is the short-circuit ratio of the new energy station i, SCR min is the minimum short-circuit ratio requirement for new energy station i.

[0221] The optimal value of the site selection and capacity optimization model is solved by using the whale optimization algorithm and the Pareto sorting method, including:

[0222] Based on the whale optimization algorithm, the site selection and sizing optimization model is solved to obtain the optimal solution of the whale population, and the optimal solution is updated through the Pareto sorting method. The updated optimal solution is scored, and the updated optimal solution is sorted according to the score. Based on the sorting, the optimal value of the site selection and sizing optimization model is obtained.

[0223] Among them, the site selection and capacity determination results include: the installation location and capacity of the grid-type energy storage equipment.

[0224] The present invention can significantly improve the short-circuit ratio level of the regional power grid, enhance the voltage support capacity of the new energy stations, and improve the access capacity of the new energy stations.

[0225] Embodiment 3:

[0226] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0227] Embodiment 4:

[0228] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0229] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0230] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0231] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0232] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0233] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0234] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for selecting a site and determining the capacity of a grid-type energy storage system based on the intra-day short-circuit ratio, characterized in that: include: Acquire the intraday dispatching time mode data of the regional power grid, and establish a power grid flow calculation and stability calculation model based on the intraday dispatching time mode data; Based on the power grid flow calculation and stability calculation model, the grid-forming energy storage of the regional power grid is initialized and configured. After the configuration is completed, a site selection and capacity optimization model for the grid-forming energy storage is established; The optimal value of the site selection and capacity determination optimization model is solved, and the site selection and capacity determination result of the grid-type energy storage is determined according to the optimal value.

2. The method for site selection and capacity determination of grid-type energy storage according to claim 1, characterized in that: The intraday scheduling time mode data includes: Renewable energy and conventional hydropower data, thermal power installed capacity and output data, generator and its excitation system data, speed regulator data, power system stabilizer data, AC transmission line parameter data, transformer parameter data, load model data, network interconnection topology data and DC transmission system control method and controller parameter data.

3. The method for site selection and capacity determination of grid-type energy storage according to claim 1, characterized in that: The grid-connected energy storage is initialized and configured according to 20% of the capacity of the new energy station devices in the regional power grid.

4. The method for site selection and capacity determination of grid-type energy storage according to claim 1, characterized in that: The establishment of a site selection and capacity optimization model for grid-type energy storage includes: Determine the optimization goal of address sizing of grid-type energy storage, establish an objective function based on the optimization goal, and determine constraints for the objective function, and use the objective function and constraints as an address sizing optimization model.

5. The method for determining the address capacity of a network-type energy storage according to claim 4, characterized in that: The optimization objectives include: Grid-type energy storage maximizes the daily short-circuit ratio capability evaluation index and minimizes system network losses; The grid-type energy storage maximizes the evaluation index of the intraday short-circuit ratio, and the corresponding objective function is as follows: Among them, η is the evaluation index of the ability of network-type energy storage to improve the short-circuit ratio within a day, N is the number of new energy power stations in the regional power grid, and SCR GFti To configure the short-circuit ratio of multiple stations of the grid-connected energy storage power station for the new energy source i in period t, SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured; The system network loss is minimized, and the corresponding objective function is as follows: Among them, P loss is the system network loss, P losst is the network loss of the system at time t.

6. The method for determining the address capacity of a grid-type energy storage according to claim 4, characterized in that: The constraints include: Power flow constraints, transient time domain solutions, voltage safety constraints, and short-circuit ratio constraints for new energy stations; The power flow constraints are as follows: Where n is the number of nodes in the sending-end power grid, P i and Q i are the active and reactive power of node i, U i and U j are the voltages of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the node admittance matrix (i, j) respectively, θ ij is the phase difference between nodes i and j; The transient time domain solution and voltage safety constraints are as follows: U(t)≤U max (t) Among them, U(t) is the voltage of a new energy machine at time t, U max (t) is the maximum allowable voltage on the bus; The short-circuit ratio constraints of the new energy stations are as follows: SCR GFti ≥SCR min Among them, SCR GFti is the short-circuit ratio of the new energy station i, SCR min is the minimum short-circuit ratio requirement for new energy station i.

7. The method for determining the address capacity of a grid-type energy storage according to claim 1, characterized in that: The optimal value of the site selection and capacity optimization model is solved by the whale optimization algorithm and the Pareto sorting method, including: Based on the whale optimization algorithm, the site selection and sizing optimization model is solved to obtain the optimal solution of the whale population, and the optimal solution is updated through the Pareto sorting method. The updated optimal solution is scored, and the updated optimal solution is sorted according to the score. Based on the sorting, the optimal value of the site selection and sizing optimization model is obtained.

8. The method for determining the address capacity of a grid-type energy storage according to claim 1, characterized in that: The site selection and capacity determination results include: the installation location and capacity of the grid-type energy storage equipment.

9. A grid-type energy storage site selection and capacity determination system based on intra-day short-circuit ratio, characterized in that: include: A first modeling unit is used to obtain the intraday dispatching time mode data of the regional power grid, and establish a power grid flow calculation and stability calculation model according to the intraday dispatching time mode data; The second modeling unit is used to initialize the configuration of the grid-forming energy storage of the regional power grid based on the power grid flow calculation and stability calculation model, and after the configuration is completed, establish a site selection and capacity optimization model for the grid-forming energy storage; A solving unit is used to solve the optimal value of the site selection and capacity determination optimization model, and determine the site selection and capacity determination result of the grid-type energy storage according to the optimal value.

10. The grid-type energy storage site selection and capacity determination system according to claim 9, characterized in that: The intraday scheduling time mode data includes: Renewable energy and conventional hydropower data, thermal power installed capacity and output data, generator and its excitation system data, speed regulator data, power system stabilizer data, AC transmission line parameter data, transformer parameter data, load model data, network interconnection topology data and DC transmission system control method and controller parameter data.

11. The grid-type energy storage site selection and capacity determination system according to claim 8, characterized in that: The grid-connected energy storage is initialized and configured according to 20% of the capacity of the new energy station devices in the regional power grid.

12. The grid-type energy storage site selection and capacity determination system according to claim 8, characterized in that: The establishment of a site selection and capacity optimization model for grid-type energy storage includes: Determine the optimization goal of address sizing of grid-type energy storage, establish an objective function based on the optimization goal, and determine constraints for the objective function, and use the objective function and constraints as an address sizing optimization model.

13. The grid-type energy storage address constant capacity system according to claim 8, characterized in that: The optimization objectives include: Grid-type energy storage maximizes the daily short-circuit ratio capability evaluation index and minimizes system network losses; The grid-type energy storage maximizes the evaluation index of the intraday short-circuit ratio, and the corresponding objective function is as follows: Among them, η is the evaluation index of the ability of network-type energy storage to improve the short-circuit ratio within a day, N is the number of new energy power stations in the regional power grid, and SCR GFti To configure the short-circuit ratio of multiple stations of the grid-connected energy storage power station for the new energy source i in period t, SCR NOti is the short-circuit ratio of multiple stations of new energy power station i in period t when no grid-connected energy storage power station is configured; The system network loss is minimized, and the corresponding objective function is as follows: Among them, P loss is the system network loss, P losst is the network loss of the system at time t.

14. The grid-type energy storage address constant capacity system according to claim 12, characterized in that: The constraints include: Power flow constraints, transient time domain solutions, voltage safety constraints, and short-circuit ratio constraints for new energy stations; The power flow constraints are as follows: Where n is the number of nodes in the sending-end power grid, P i and Q i are the active and reactive power of node i, U i and U j are the voltages of the i-th and j-th nodes, G ij and B ij are the real and imaginary parts of the node admittance matrix (i, j) respectively, θ ij is the phase difference between nodes i and j; The transient time domain solution and voltage safety constraints are as follows: U(t)≤U max (t) Among them, U(t) is the voltage of a new energy machine at time t, U max (t) is the maximum allowable voltage on the bus; The short-circuit ratio constraints of the new energy stations are as follows: SCR GFti ≥SCR min Among them, SCR GFti is the short-circuit ratio of the new energy station i, SCR min is the minimum short-circuit ratio requirement for new energy station i.

15. The grid-type energy storage address constant capacity system according to claim 9, characterized in that: The optimal value of the site selection and capacity optimization model is solved by the whale optimization algorithm and the Pareto sorting method, including: Based on the whale optimization algorithm, the site selection and sizing optimization model is solved to obtain the optimal solution of the whale population, and the optimal solution is updated through the Pareto sorting method. The updated optimal solution is scored, and the updated optimal solution is sorted according to the score. Based on the sorting, the optimal value of the site selection and sizing optimization model is obtained.

16. The grid-type energy storage address constant capacity system according to claim 9, characterized in that: The site selection and capacity determination results include: the installation location and capacity of the grid-type energy storage equipment.

17. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 8 is implemented.

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