A Planning Method and System for Electrochemical Energy Storage Power Station Considering Dynamic Changes of Power Grid

Abstract

The present invention discloses a planning method and system for an electrochemical energy storage power station considering the dynamic changes of the power grid, belonging to the technical field of electrochemical energy storage power station planning. First, considering the change in node network loss and the change in line load rate before and after the access of the energy storage system, a mathematical model of the siting index for the grid-side energy storage power station is established; secondly, according to the mathematical model of the siting index for the grid-side energy storage power station, an evaluation model of the siting index for the grid-side energy storage power station is established using the entropy weight method; finally, considering the new energy consumption capacity and the revenue after configuring the energy storage, a capacity configuration model of the electrochemical energy storage power station is established to provide a method for the planning of the grid-side energy storage power station.

Description

Technical Field

[0001] The present invention relates to the technical field of electrochemical energy storage power station planning, and particularly to an electrochemical energy storage power station planning method and system considering dynamic changes of the power grid. Background Art

[0002] Due to the over-exploitation and utilization of traditional fossil energy, the fossil energy resources are gradually decreasing, and environmental pollution is becoming more and more serious. Therefore, energy conservation and emission reduction, as well as the vigorous development of clean energy, are becoming more and more urgent. However, the output of new energy has characteristics such as randomness, volatility, intermittency, and weak regulation ability, which makes the power grid power flow distribution uneven. The rapid development of large-scale renewable energy such as wind power and photovoltaic has a great impact on the reliable and stable operation of the power grid. The energy storage system has the characteristics of flexibility and fast response speed, and is an effective means to solve the above problems. The key to the large-scale popularization and application of energy storage technology in the power system lies in formulating a reasonable energy storage plan, that is, energy storage site selection and capacity determination planning. Determining the optimal site selection and capacity of energy storage in the power grid can effectively reduce the overall system cost. Reasonably selecting the energy storage construction location and capacity can not only maximize the economic benefits of the entire system, but also improve and enhance the stability and security of the system.

[0003] The electrochemical energy storage power station planning method needs to pay attention to the dynamic changes of the power grid. Due to the power grid load fluctuation and the uncertainty of new energy power generation, its complexity is becoming increasingly significant. The electrochemical energy storage system has become an important means to cope with the dynamic changes of the power grid due to its advantages such as fast response, flexible regulation, and small floor area. The planning method aims to achieve the coordinated operation of energy storage and the power grid through scientific site selection and capacity determination, improve the stability and reliability of the power grid, and promote the consumption of new energy. It is necessary to comprehensively consider the construction cost, operation cost of the energy storage power station, and its benefit contribution to the power grid to achieve the balance between economy and technology. At present, most of the energy storage planning is carried out from the perspectives of energy storage and intermittent power stations, users, etc., and there is less research on the coordinated planning of energy storage considering the dynamic changes of the power grid.

[0004] The invention patent with the publication number of CN119204841A discloses a method, system, device and medium for configuring energy storage capacity based on typical days. It screens typical days by analyzing the effective utilization value of energy storage for the annual power consumption dataset, and constructs a comprehensive evaluation index for configured capacity based on the energy storage charging amount and energy storage discharging amount in each energy storage cycle within the typical day to obtain the optimal capacity configuration strategy. It can effectively balance the capacity configuration rate and the utilization rate of configured capacity on the basis of improving the accuracy of typical day screening, improve the rationality of energy storage capacity configuration, and then comprehensively improve the utilization rate of the energy storage system. It mainly considers capacity configuration by using the power consumption dataset, but does not consider economy and technology, and has low practicability.

[0005] The invention patent with the publication number CN119231601A discloses a method for configuring the energy storage capacity of a wind-solar power generation system, which relates to the technical field of energy storage capacity optimization planning. The method includes the following steps: obtaining the average wind power and photovoltaic output data of the area to be optimized and calculating the target output power of the wind-solar-storage power generation system; calculating the energy storage capacity required for the wind-solar-storage power generation system based on the target output power and the actual output power of the wind-solar-storage power generation system; establishing a minimum-cost objective function model for the wind-solar-storage combined power generation system and setting the index constraint conditions for the output of the wind-solar-storage combined power generation system; obtaining the optimal energy storage capacity scheme for the wind-solar-storage combined power generation system based on the minimum-cost objective function model of the wind-solar-storage combined power generation system and the index constraint conditions for the output of the wind-solar-storage combined power generation system, which solves the problem that the traditional energy storage capacity configuration method cannot configure a more accurate energy storage capacity to better meet the system's requirements, realizes the optimization planning of the energy storage capacity of the wind-solar power generation system, and its principle is: alleviating the contradiction between the inherent intermittency and volatility of wind power and photovoltaic power generation and the requirements of the power system for real-time balance, reducing the negative impact of new energy power generation on the power grid. However, this patent solves the problem of energy storage configuration on the source side and reduces the impact of source-side wind-solar fluctuations on the power grid.

[0006] Therefore, there are currently the following two problems that need further research: ① Considering the new energy consumption capacity and the benefits after configuring energy storage, reasonably select the energy storage construction location and capacity, and configure the capacity of the electrochemical energy storage power station to provide a method for the planning of the power grid-side energy storage power station; ② By studying different optimization indicators selected after the power grid joins energy storage, such as cost, social benefits, and power grid operation benefits, considering the reliability and economy of energy storage access to the power grid, determine the optimal location and capacity of the energy storage. Summary of the Invention

[0007] Object of the Invention: The object of the present invention is to solve the above problems and propose a method and system for planning an electrochemical energy storage power station considering the dynamic changes of the power grid.

[0008] Technical Solution: In the first aspect, the present invention provides a method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid. The method includes the following steps:

[0009] Construct a mathematical model of the change in network loss before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in network loss, and the objective function of each node in the distribution network for the change in network loss;

[0010] Construct a mathematical model of the change in line load rate before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate;

[0011] Establish a constraint condition mathematical model based on the power of each node in the distribution network, the output of power sources and energy storage, and the capacity of inverters. The constraint conditions include line power flow constraints, transmission capacity constraints, and source-storage output constraints;

[0012] Combined with the above constraint conditions, use an optimization algorithm to solve the objective function of each node in the distribution network for the change in network loss and the objective function of each node in the distribution network for the change in line load rate;

[0013] Merge the optimized objective functions corresponding to all nodes and normalize them. Determine the information entropy of the index parameters according to the normalization result, and obtain the weights of each index parameter according to the information entropy, so as to obtain the scores of each index;

[0014] Sort the scores of each index, determine the nodes corresponding to the selected index scores, and perform capacity configuration under these nodes.

[0015] Furthermore, it includes:

[0016] The sorting of the scores of each index and determining the nodes corresponding to the selected index scores and performing capacity configuration under these nodes specifically include:

[0017] Construct relevant capacity configuration mathematical models, including: a capacity configuration mathematical model considering new energy consumption and a configuration mathematical model considering maximizing benefits;

[0018] Use the capacity configuration mathematical model considering new energy consumption and the configuration mathematical model considering maximizing benefits to construct a capacity configuration objective function;

[0019] According to the constructed constraint conditions, use an optimization algorithm to obtain the optimal value of the objective function, so as to obtain the best configuration capacity and the best location of the electrochemical energy storage system.

[0020] Furthermore, it includes:

[0021] The capacity configuration mathematical model considering new energy consumption is expressed as: the sum of the output of new energy and the output of the electrochemical energy storage system after adding the electrochemical energy storage system;

[0022] The configuration mathematical model considering maximizing benefits is expressed as: the sum of the benefits obtained from the increased power fed into the grid after configuring the electrochemical energy storage system, the assessment costs reduced by reducing the output prediction deviation, and the benefits obtained by removing active power reserve, and then subtracting the annual value of the cost of the electrochemical energy storage system.

[0023] Furthermore, it includes:

[0024] The capacity configuration objective function is the maximum value of the configuration mathematical model considering revenue maximization and the capacity configuration mathematical model considering new energy consumption.

[0025] Furthermore, it includes:

[0026] The constraint conditions are expressed as: ;

[0027] Wherein, S OC,min and S OC,max are the minimum SOC and maximum SOC thresholds of the battery of the electrochemical energy storage system respectively; P rate is the rated power of the energy storage system; P ( t ) is the output power of the electrochemical energy storage system t at time P ref is the energy storage power demand; T is the output time for the energy storage power station to provide power within the demand time.

[0028] Furthermore, it includes:

[0029] The network loss model corresponding to before the access of the electrochemical energy storage system is expressed as: ;

[0030] Wherein, N is the number of nodes; z i-1,i is the impedance value between the i -1th node and the i th node; P j is the power injected into node j ; is j the voltage amplitude of the node;

[0031] The network loss model corresponding to after the access of the electrochemical energy storage system is expressed as: ;

[0032] Wherein, M is the number of energy storage access nodes; P B is the total active power output of the energy storage;

[0033] Therefore, the network loss change amount is expressed as: ;

[0034] The objective function of each node in the distribution network for the network loss change amount is expressed as:

[0035] For node i , its objective function based on the network loss change amount is: 。

[0036] Furthermore, it includes:

[0037] The second index parameters before and after the electro-chemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate, including:

[0038] The line load rate before the electro-chemical energy storage system is connected to the distribution network is expressed as: ;

[0039] Wherein, P i,k is the power flowing through branch i at node k ; P k is the capacity of line k ;

[0040] The line load rate after the electro-chemical energy storage system is connected to the distribution network is expressed as: ;

[0041] Wherein, P i,k_B is the power flowing through branch i at node k after the connection of the electro-chemical energy storage system;

[0042] The change in line load rate is expressed as: ;

[0043] Then for node i , its objective function based on the change in line load rate is: 。

[0044] Furthermore, it includes:

[0045] Establish a constraint condition mathematical model according to the power, power output of power sources and energy storage, and capacity of inverters of each node in the distribution network, including: Wherein, P i and Q i are i the active power and reactive power injected into the node; U i and U j are respectively i , j the voltage amplitudes of the nodes; θ ij is i , jVoltage phase difference of the node; G ij and B ij are respectively i the conductance and susceptance between the node and j the node; P SE are the active power output values of the power source and energy storage, P SEmin is the minimum active power output of the power source and energy storage, P SEmax is the maximum active power output of the power source and energy storage; Q SE is the reactive power output value of the power source and energy storage, Q SEmin is the minimum reactive power output of the power source and energy storage, Q SEmax is the maximum reactive power output of the power source and energy storage; S is the inverter capacity; p ij is i the power between the node and j the node, p ij，max is i the power maximum between the node and j the node.

[0046] Furthermore, it includes:

[0047] The optimized objective functions corresponding to all nodes are merged and normalized, expressed as:

[0048] The set of objective functions based on the network loss change amount is: ;

[0049] The set of objective functions based on the load rate change amount is: ;

[0050] Among them, is the best objective function based on the network loss change amount obtained by optimizing the Nth node using the optimization algorithm, is the best objective function based on the load rate change amount obtained by optimizing the Nth node using the optimization algorithm;

[0051] The corresponding objective function sets are merged and normalized, expressed as: ;

[0052] According to the normalization results of the obtained various index parameter data, determine the information entropy of the m th index parameter: ;

[0053] Among them, is the m normalized result of the n th node data in the index parameters. At this time, M = 2, .

[0054] Furthermore, it includes:

[0055] Determine the information entropy of the index parameters according to the normalized result, and obtain the weights of each index parameter according to the information entropy, so as to obtain the scores of each index, including; ;

[0056] Among them, is the m th weight of the th index parameter in the comprehensive score, m is the n th objective function corresponding to the

[0057] Sort the scores of each index and determine the nodes corresponding to the selected index scores, including: sort the obtained scores of each index in descending order, and select the l largest previous index scores corresponding nodes for capacity configuration.

[0058] On the other hand, the present invention also provides an electrochemical energy storage power station planning system considering the dynamic changes of the power grid. The system includes:

[0059] The first index parameter model construction module is used to construct a mathematical model of the network loss change before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the network loss change amount, and the objective function of each node in the distribution network for the network loss change amount;

[0060] The second index parameter model construction module is used to construct a mathematical model of the change amount of the line load rate before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change amount of the line load rate, and the objective function of each node in the distribution network for the change amount of the line load rate;

[0061] The constraint condition construction module is used to establish a constraint condition mathematical model according to the power, power supply and energy storage output of each node in the distribution network and the capacity of the inverter. The constraint conditions include line power flow constraints, transmission capacity constraints and source-storage output constraints;

[0062] An optimization module, configured to combine the constraint conditions and use an optimization algorithm to solve the objective function of each node in the distribution network for the change in network loss and the objective function of each node in the distribution network for the change in line load rate;

[0063] A scoring calculation module, configured to merge the optimized objective functions corresponding to all nodes, normalize them, determine the information entropy of the index parameters according to the normalization result, and obtain the weight values of each index parameter according to the information entropy, so as to obtain the scores of each index;

[0064] A capacity configuration module, configured to sort the scores of each index, determine the nodes corresponding to the selected index scores, and perform capacity configuration under the nodes. Beneficial effects

[0065] A method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid proposed by the present invention aims at the comprehensive application value of the electrochemical energy storage power station in the power market. The entropy weight method is adopted and economic, reliable and environmental protection factors are considered to propose a planning method for the energy storage power station. Aiming at the problems of limited objectivity of traditional planning methods and the complex system value problems, the changes in node network loss and line load rate before and after the access of the energy storage system are considered, and the location selection indicators of the grid-side energy storage power station are evaluated. Combining the established evaluation criteria, the application value of the grid-side energy storage power station is comprehensively evaluated, providing a method for the planning of the grid-side energy storage power station, so as to obtain the best location and capacity, realize the coordinated operation of the energy storage and the power grid, improve the stability and reliability of the power grid, and promote the consumption of new energy. Description of the drawings

[0066] Figure 1 is a flowchart of the method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to an embodiment of the present invention;

[0067] Figure 2 is a structural diagram of the system for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to an embodiment of the present invention. Detailed implementation manners

[0068] The present invention will be further clarified below with reference to the drawings. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent forms of modification of the present invention by those skilled in the art fall within the scope defined by the appended claims of this application.

[0069] The technical solution provided by the present invention is to consider the dynamic changes before and after the energy storage is connected to the power grid and establish a site selection index model for the energy storage power station on the grid side in combination with the entropy weight method, so as to optimize the planning method of the energy storage power station on the grid side. First, considering the change in node network loss and the change in line load rate before and after the energy storage system is connected, a mathematical model for site selection index of the energy storage power station on the grid side is established. Secondly, according to the mathematical model of the site selection index of the energy storage power station, an evaluation model for the site selection index of the energy storage power station on the grid side is established using the entropy weight method. Finally, considering the new energy consumption capacity and the income after configuring the energy storage, a capacity configuration model of the electrochemical energy storage power station is established to provide a method for the planning of the energy storage power station on the grid side.

[0070] In the first aspect, as Figure 1 shown, the present invention provides a planning method for an electrochemical energy storage power station considering the dynamic changes of the power grid, and the method includes the following steps:

[0071] Step 1: Construct a mathematical model of the change in network loss before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in network loss, and the objective function of each node in the distribution network for the change in network loss.

[0072] The obtaining of the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in network loss includes:

[0073] The network loss model corresponding to before the electrochemical energy storage system is connected is expressed as: ;

[0074] Among them, N is the number of nodes; z i-1,i is the i -1th node and the i th node impedance value; P j is the power injected by node j ;

[0075] The network loss model corresponding to after the electrochemical energy storage system is connected is expressed as: ;

[0076] Among them, M is the number of energy storage access nodes; P B is the total active power output of the energy storage;

[0077] Therefore, the change in network loss is expressed as: ;

[0078] The objective function of each node in the distribution network for the change in network loss is expressed as:

[0079] For node i , its objective function based on the change in network loss is: 。

[0080] Step 2: Construct a mathematical model for the change in line load rate before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate;

[0081] In this embodiment, this step includes:

[0082] The obtaining of the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate includes:

[0083] The line load rate before the electrochemical energy storage system is connected to the distribution network is expressed as: ;

[0084] Wherein, P i,j is the power on branch i flowing through node j ; P j is the capacity of line j ;

[0085] The line load rate after the electrochemical energy storage system is connected to the distribution network is expressed as: ;

[0086] Wherein, P i,j_B is the power on branch i flowing through node j after the connection of the electrochemical energy storage system;

[0087] The change in line load rate is expressed as: ;

[0088] Then for node i , its objective function based on the change in line load rate is: 。

[0089] Step 3: Establish a constraint condition mathematical model according to the power, power generation of power sources and energy storage, and the capacity of inverters of each node in the distribution network. The constraint conditions include line power flow constraints, transmission capacity constraints, and source-storage output constraints.

[0090] In this embodiment, establishing a constraint condition mathematical model according to the power, power generation of power sources and energy storage, and the capacity of inverters of each node in the distribution network includes: ;

[0091] Among them, P i and Q i are i the active power and reactive power injected into the node; U i and U j are respectively i and j the voltage amplitudes of the nodes; θ ij is i , j the voltage phase difference of the node; G ij and B ij are respectively i the conductance and susceptance between the node and j the node; P SE is the active power output value of the power source and energy storage, P SEmin is the minimum active power output value of the power source and energy storage, P SEmax is the maximum active power output value of the power source and energy storage; Q SE is the reactive power output value of the power source and energy storage, Q SEmin is the minimum reactive power output value of the power source and energy storage, Q SEmax is the maximum reactive power output value of the power source and energy storage; S is the inverter capacity; p ij is i the power between the node and j the node, p ij，max is i the maximum power between the node and j the node.

[0092] Step 4: Combine the above constraints and use an optimization algorithm to solve the objective function of each node in the distribution network for the change in network loss and the objective function of each node in the distribution network for the change in line load rate;

[0093] This embodiment does not limit the optimization algorithm, as long as the optimization algorithm is used to find the optimal solution (maximum or minimum) of the objective function under the premise of meeting the constraints.

[0094] Step 5: Combine and normalize the optimized objective functions corresponding to all nodes, determine the information entropy of the index parameters according to the normalization result, and obtain the weights of each index parameter according to the information entropy, so as to obtain the scores of each index;

[0095] In this embodiment, the combination and normalization of the optimized objective functions corresponding to all nodes are expressed as:

[0096] The set of objective functions based on the network loss change is: ;

[0097] The set of objective functions based on the load rate change is: ;

[0098] Among them, is the optimal objective function based on the network loss change obtained by optimizing the Nth node using the optimization algorithm, is the optimal objective function based on the load rate change obtained by optimizing the Nth node using the optimization algorithm;

[0099] Combining the corresponding objective function sets and normalizing them is expressed as: ;

[0100] Determine the information entropy of the m th index parameter according to the normalization result of each index parameter data obtained: Among them, is the normalization result of the m th node data in the n th index parameter. At this time, M = 2, .

[0101] Furthermore, this embodiment also includes:

[0102] Determine the information entropy of the index parameters according to the normalization result, and obtain the weights of each index parameter according to the information entropy, so as to obtain the scores of each index, including; ;

[0103] Among them, is the weight of the m th index parameter in the comprehensive score, is the m th index parameter and the n th node corresponding objective function.

[0104] Sort the scores of each index and determine the nodes corresponding to the selected index scores, including: Sort the obtained scores of each index from largest to smallest, and select the top lPerform capacity configuration for the nodes corresponding to each index score. In this embodiment, l it is set to 10, that is, sort the top 10 larger index scores, and perform capacity configuration for the distribution network nodes corresponding to these 10 indexes.

[0105] Step 6: Sort each index score, determine the nodes corresponding to the selected index scores, and perform capacity configuration under the nodes.

[0106] In this embodiment, sorting each index score, determining the nodes corresponding to the selected index scores, and performing capacity configuration under the nodes specifically include:

[0107] Step 61: Construct relevant capacity configuration mathematical models, including: a capacity configuration mathematical model considering new energy consumption and a configuration mathematical model considering maximizing benefits.

[0108] In this embodiment, the capacity configuration mathematical model considering new energy consumption is expressed as: the sum of the output of new energy and the output of the electrochemical energy storage system after adding the electrochemical energy storage system;

[0109] A representation form of this embodiment is: ;

[0110] In the formula, E sources is the output of new energy after adding the energy storage system; E bess is the output of the electrochemical energy storage system.

[0111] The configuration mathematical model considering maximizing benefits is expressed as: the sum of the benefits obtained from the increased electricity generated and fed into the grid after configuring the electrochemical energy storage system, the assessment fees reduced by reducing the output prediction deviation, and the benefits obtained by removing the active power reserve, and then subtracting the annual value of the cost of the electrochemical energy storage system.

[0112] A representation form of this embodiment is: ;

[0113] In the formula, I g is the benefit obtained from the increased electricity generated and fed into the grid after configuring the electrochemical energy storage system; I er is the assessment fees reduced by reducing the output prediction deviation; I r is the benefit obtained by removing the active power reserve; C L is the annual value of the cost of the electrochemical energy storage system.

[0114] Step 62: Construct a capacity configuration objective function by using the capacity configuration mathematical model considering new energy accommodation and the configuration mathematical model considering maximization of revenue.

[0115] In this embodiment, the capacity configuration objective function is the maximum value of the configuration mathematical model considering maximization of revenue and the capacity configuration mathematical model considering new energy accommodation, which is expressed as: .

[0116] Step 63: According to the constructed constraint conditions, use an optimization algorithm to obtain the optimal value of the objective function, so as to obtain the optimal configuration capacity and the optimal location of the electrochemical energy storage system.

[0117] The constraint conditions are expressed as: ;

[0118] Among them, S OC,min and S OC,max are respectively the minimum SOC and the maximum SOC thresholds of the battery of the electrochemical energy storage system; P rate is the rated power of the energy storage system; P ( t ) is the output power of the electrochemical energy storage system t at time P ref is the energy storage power demand; T is the output time of the energy storage power station providing power within the demand time, and the parameters in the constraint conditions in this embodiment can all be collected through the system.

[0119] Finally, use an optimization algorithm to find the optimal solution of the objective function under the condition of satisfying the constraint conditions. In this embodiment, no special limitation is imposed on the optimization algorithm, and common optimization algorithms in the prior art can be used.

[0120] On the other hand, the present invention also provides an electrochemical energy storage power station planning system considering the dynamic changes of the power grid, as Figure 2 shown. This system includes:

[0121] A first index parameter model construction module, which is used to construct a mathematical model of the network loss change before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the network loss change amount, and the objective function of each node in the distribution network for the network loss change amount;

[0122] The second index parameter model construction module is used to construct a mathematical model for the change in line load rate before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate;

[0123] The constraint condition construction module is used to establish a mathematical model of constraint conditions according to the power, power sources, energy storage output, and inverter capacity of each node in the distribution network. The constraint conditions include line power flow constraints, transmission capacity constraints, and source-storage output constraints;

[0124] The optimization module is used to combine the constraint conditions and use an optimization algorithm to solve the objective function of each node in the distribution network for the change in network loss and the objective function of each node in the distribution network for the change in line load rate;

[0125] The scoring calculation module is used to merge the optimized objective functions corresponding to all nodes, normalize them, determine the information entropy of the index parameters according to the normalization result, and obtain the weight values of each index parameter according to the information entropy, so as to obtain the scores of each index;

[0126] The capacity configuration module is used to sort the scores of each index, determine the nodes corresponding to the selected index scores, and perform capacity configuration under the nodes.

[0127] Other technical features of the electrochemical energy storage power station planning system considering the dynamic changes of the power grid in this embodiment are similar to those of the electrochemical energy storage power station planning method considering the dynamic changes of the power grid, and will not be elaborated herein by the present invention.

[0128] Embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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 code.

[0129] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or a plurality of blocks.

[0130] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or a plurality of blocks.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or a plurality of blocks.

[0132] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0133] Obviously, those skilled in the art can make various changes and variations to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A planning method for an electrochemical energy storage power station considering the dynamic changes of the power grid, characterized in that The method includes the following steps: Construct a mathematical model of the change in network loss before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in network loss, and the objective function of each node in the distribution network for the change in network loss; Construct a mathematical model of the change in line load rate before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate; Establish a constraint condition mathematical model according to the power of each node, the output of power sources and energy storage, and the capacity of inverters in the distribution network. The constraint conditions include line power flow constraints, transmission capacity constraints, and source-storage output constraints; Combined with the constraint conditions, use an optimization algorithm to solve the objective function of each node in the distribution network for the change in network loss and the objective function of each node in the distribution network for the change in line load rate; Merge the optimized objective functions corresponding to all nodes and normalize them. Determine the information entropy of the index parameters according to the normalization result, and obtain the weights of each index parameter according to the information entropy, so as to obtain the scores of each index; Sort the scores of each index, and determine the nodes corresponding to the selected index scores, and perform capacity configuration under the nodes; The sorting of the scores of each index and the determination of the nodes corresponding to the selected index scores, and performing capacity configuration under the nodes specifically include: Construct relevant capacity configuration mathematical models, including: a capacity configuration mathematical model considering new energy consumption and a configuration mathematical model considering maximizing benefits; Use the capacity configuration mathematical model considering new energy consumption and the configuration mathematical model considering maximizing benefits to construct a capacity configuration objective function; According to the constructed constraint conditions, use an optimization algorithm to obtain the optimal value of the objective function, so as to obtain the best configuration capacity and the best location of the electrochemical energy storage system; The obtaining of the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in network loss includes: The network loss model corresponding to the electrochemical energy storage system before connection is expressed as: ; where N is the number of nodes; z i-1,i is the impedance value between the i -1-th node and the i -th node; P j is the power injected at node j ; is j the voltage magnitude of the node; The network loss model corresponding after the electrochemical energy storage system is connected is expressed as: ； where M is the number of energy storage access nodes; P B is the total active power output of the energy storage; Therefore, the change in network loss is expressed as: ; The objective function of each node in the distribution network for the change in network loss is expressed as: For a node i , its objective function based on the change in network loss is as follows: ; The second index parameter before and after the access of the electrochemical energy storage system to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate, including: The line load rate before the electrochemical energy storage system is connected to the distribution network is expressed as: ; Among them, P i,k is the power flowing through the branch i ; k on the branch; P k is the capacity of the line k ; The line load rate after the electrochemical energy storage system is connected to the distribution network is expressed as: ; Among them, P i,k_B is the power flowing through the branch i after the access of the said electrochemical energy storage system; k on the branch. The change in the line load rate is expressed as: ; Then for node i , its objective function based on the change in line load rate is: .

2. The method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to claim 1, wherein The capacity configuration mathematical model considering new energy consumption is expressed as: the sum of the output of new energy and the output of the electrochemical energy storage system after adding the electrochemical energy storage system; The configuration mathematical model considering maximizing benefits is expressed as: the sum of the benefits obtained from the increased power grid connection after configuring the electrochemical energy storage system, the reduced assessment cost due to reducing the output prediction deviation, and the benefits obtained from removing the active reserve, and then subtracting the annual value of the cost of the electrochemical energy storage system.

3. The method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to claim 2, wherein The capacity configuration objective function is the maximum value of the configuration mathematical model considering maximizing benefits and the capacity configuration mathematical model considering new energy consumption.

4. The method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to claim 3, wherein The constraint condition is expressed as: ; Among them, S OC,min and S OC,max are the minimum SOC and maximum SOC thresholds of the battery of the electrochemical energy storage system, respectively; P rate is the rated power of the energy storage system; P ( t ) is the output power of the electrochemical energy storage system t at time P ref is the energy storage power demand; T is the output time for the energy storage power station to provide power within the demand time.

5. The method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to claim 1, characterized in that, Establish a constraint condition mathematical model based on the power of each node of the distribution network, the output of the power source and energy storage, and the capacity of the inverter, including: ; Among them, P i and Q i are i the active power and reactive power injected into the node; U i and U j are respectively i , j the voltage amplitudes of the nodes; θ ij is i , j the voltage phase difference of the node; G ij and B ij are respectively i the conductance and susceptance between the node and j the node; P SE is the active power output value of the power source and energy storage, P SE,min is the minimum active power output value of the power source and energy storage, P SE,max is the maximum active power output value of the power source and energy storage; Q SE is the reactive power output value of the power source and energy storage, Q SE,min is the minimum reactive power output value of the power source and energy storage, Q SE,max is the maximum reactive power output value of the power source and energy storage; S is the inverter capacity; p ij is i the power between the node and j the node, p ij，max is i the maximum power between the node and j the node.

6. The method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to claim 1, characterized in that, The merging and normalization of the optimized objective functions corresponding to all nodes is expressed as: The set of objective functions based on the change in network loss is as follows: ; The set of objective functions based on the change in load factor is as follows: ; Among them, is the optimal objective function based on the change in network loss obtained by optimizing the Nth node using an optimization algorithm, is the optimal objective function based on the change in load factor obtained by optimizing the Nth node using an optimization algorithm; Merge the corresponding objective function sets and normalize them, expressed as: ; Determine the information entropy of the m th index parameter according to the normalization results of the obtained index parameter data: ； Among them, is the normalization result of the m th node data in the index parameter of n , where M = 2, .

7. The method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to claim 6, wherein Determine the information entropy of the index parameter according to the normalization result, and obtain the weight of each index parameter according to the information entropy, so as to obtain the score of each index, including; ; wherein, is the weight of the m th index parameter in the comprehensive score, is the m th n th objective function corresponding to the node in the index parameter.

8. The method for planning an electrochemical energy storage power station considering the dynamic changes of the power grid according to claim 7, wherein Sort the scores of each indicator and determine the nodes corresponding to the selected indicator scores, including: sort the obtained scores of each indicator in descending order, and select the first l nodes corresponding to the larger indicator scores for capacity configuration.

9. An electrochemical energy storage power station planning system considering the dynamic changes of the power grid, characterized in that, The system includes: The first index parameter model construction module is used to construct a mathematical model for the change in network loss before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in network loss, and the objective function of each node in the distribution network for the change in network loss; The second index parameter model construction module is used to construct a mathematical model for the change in line load rate before and after the electrochemical energy storage system is connected to the distribution network, so as to obtain the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate; The constraint condition construction module is used to establish a constraint condition mathematical model according to the power, power supply and energy storage output of each node in the distribution network and the capacity of the inverter. The constraint conditions include line power flow constraints, transmission capacity constraints and source-storage output constraints; The optimization module is used to combine the constraint conditions and use an optimization algorithm to solve the objective function of each node in the distribution network for the change in network loss and the objective function of each node in the distribution network for the change in line load rate; The score calculation module is used to merge the optimized objective functions corresponding to all nodes, normalize them, determine the information entropy of the index parameters according to the normalization result, and obtain the weight values of each index parameter according to the information entropy, so as to obtain the scores of each index; The capacity configuration module is used to sort the scores of each index, determine the nodes corresponding to the selected index scores, and perform capacity configuration under the nodes; In the capacity configuration module, sorting the scores of each index, determining the nodes corresponding to the selected index scores, and performing capacity configuration under the nodes specifically include: Constructing relevant capacity configuration mathematical models, including: a capacity configuration mathematical model considering new energy consumption and a configuration mathematical model considering maximum profit; Using the capacity configuration mathematical model considering new energy consumption and the configuration mathematical model considering maximum profit to construct a capacity configuration objective function; According to the constructed constraint conditions, using an optimization algorithm to obtain the optimal value of the objective function, so as to obtain the best configuration capacity and the best location of the electrochemical energy storage system; In the first index parameter model construction module, obtaining the first index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in network loss, including: The network loss model corresponding to the electrochemical energy storage system before connection is expressed as: ; Among them, N is the number of nodes; z i-1,i is the i impedance value between the i -1th node and the P j th node; j is the power injected into node ; j is the voltage amplitude of node The network loss model corresponding after the electrochemical energy storage system is connected is expressed as: ； where M is the number of energy storage access nodes; P B is the total active power output of the energy storage; Therefore, the change in network loss is expressed as: ; The objective function of each node in the distribution network for the change in network loss is expressed as: For a node i , its objective function based on the change in network loss is as follows: ; Obtaining the second index parameter before and after the electrochemical energy storage system is connected to the distribution network: the change in line load rate, and the objective function of each node in the distribution network for the change in line load rate, including: The line load rate before the electrochemical energy storage system is connected to the distribution network is expressed as: ; Among them, P i,k is the branch i flowing through the node k power; P k is the capacity of the line k ; The line load rate after the electrochemical energy storage system is connected to the distribution network is expressed as: ; Among them, P i,k_B is the power flowing through the branch i after the connection of the said electrochemical energy storage system to the node k ; The change in the line load rate is expressed as: ; For node i , its objective function based on the change in line load rate is as follows: .

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