Optimal Selection Method and System for Point of Common Coupling of Energy Storage Power Station Considering Comprehensive Sensitivity

Abstract

The present invention provides a method and system for optimizing the connection point of an energy storage power station considering comprehensive sensitivity. By considering multi-dimensional indicators affecting system stability and economy, multi-scenario sensitivity calculations are performed on the nodes in the power grid where energy storage can be connected. According to the severity analysis of power grid line overload, network loss, and voltage stability problems, the weights of the corresponding indicators are selected, so as to transform multiple objectives into a single objective and obtain the priority order of the connection points of the energy storage. The beneficial effects of the present invention are as follows: A scheme for optimizing the connection point of an energy storage power station considering comprehensive sensitivity is given. By considering factors such as delaying the construction of transmission lines, improving voltage stability, and reducing network active power losses, the optimal grid connection position of the energy storage is solved through multi-scenario and multi-dimensional node sensitivity analysis, which can effectively improve the safety and economy of the power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage planning for power systems, and particularly relates to a method and system for optimizing the connection point of an energy storage power station considering comprehensive sensitivity. Background Art

[0002] The access of an energy storage system to the power grid can improve the reliability and stability of the system, and provide means for regulating and optimizing the system power flow, providing new ideas for problems such as curtailment of wind and light and peak regulation and frequency modulation brought about by large-scale grid connection of renewable energy. However, due to the still high current cost of energy storage, inappropriate energy storage location and capacity configuration will aggravate local grid overload, deteriorate voltage stability, increase the operating cost of the system, and cause waste of social resources. In the environment of large-scale grid connection of new energy, the power flow of the power grid is more complex and changeable, and there is currently a lack of a method for selecting the location of an energy storage power station that adapts to various power grid operation modes and considers the multi-dimensional impact of energy storage on power grid operation. Summary of the Invention

[0003] In view of this, in order to overcome the defects and deficiencies of the prior art and aiming at the deficiencies in the location selection aspect of the current energy storage planning research, the purpose of the present invention is to provide a method and system for optimizing the connection point of an energy storage power station considering comprehensive sensitivity, considering the complex impact of new energy grid connection on the operation of the power grid, calculating the multi-scenario sensitivity of the nodes where the power grid can access energy storage, determining the index weights through the severity analysis of power grid security problems, transforming multiple optimization objectives into a single optimization objective, and obtaining the priority order of the energy storage connection points, thereby reducing the search space of subsequent energy storage planning problems.

[0004] It considers multi-dimensional indicators that affect system stability and economy, calculates the multi-scenario sensitivity of the nodes where the power grid can access energy storage, selects the weights of the corresponding indicators according to the severity analysis of power grid line overload, network loss, and voltage stability problems, thereby transforming multiple objectives into a single objective and obtaining the priority order of the energy storage connection points. The beneficial effects of the present invention are: providing an optimized scheme for the connection point of an energy storage power station considering comprehensive sensitivity, considering factors such as delaying the construction of transmission lines, improving voltage stability, and reducing network active power loss, and solving the optimal grid connection position of energy storage through multi-scenario and multi-dimensional node sensitivity analysis, which can effectively improve the security and economy of the power grid.

[0005] The present invention specifically adopts the following technical solutions:

[0006] A method for optimizing the connection point of an energy storage power station considering comprehensive sensitivity, characterized by including the following steps:

[0007] Step S1: Based on the grid load characteristics, the grid connection conditions of new energy and conventional power sources, and the output characteristics of new energy, typical operating scenarios affecting the energy storage operation state are summarized, covering at least the following combinations: 1) large and small summer load modes; large and small winter load modes; 2) high and low output modes of new energy power sources; Characterize the typical grid operating scenarios as: S = [s 1 , s 2 ,..., s n ;

[0008] Step S2: Assume that the alternative nodes for the energy storage power station to connect to the grid are k, and calculate the sensitivity L 1 of the power injection of the energy storage at node k to the line power flow, the sensitivity L 2 of the system active power loss, and the sensitivity L 3 of the node voltage stability; Then, for multiple scenarios, based on the sensitivity coefficients, calculate the effects of the energy storage connected at different connection points on reducing the load rate of heavy-load lines, reducing the system loss, and improving the voltage stability;

[0009] Step S3: Select the weights of the corresponding indicators according to the severity analysis of the problems of heavy-load grid lines, network loss, and voltage stability. The greater the severity, the greater the selected weight. Transform the multi-objective optimization problem into a single-objective optimization problem to obtain the comprehensive sensitivity index value of the node, which is used as the priority order of the energy storage configuration nodes.

[0010] Furthermore, in Step S2, the specific calculation method for the sensitivity of the energy storage to the line power flow considering multiple scenarios is as follows:

[0011] 1) For each typical scenario s, based on the output of conventional power sources and new energy power sources in the grid, conduct a base-state power flow calculation; Define the lines with a load rate greater than 60% as heavy-load lines to form a set of heavy-load grid lines At the same time, considering the peak shaving provided by the energy storage power station, if the scenario is a large-load mode, the energy storage charge and discharge flag L s = 1, indicating that the energy storage discharges; conversely, if the scenario is a small-load mode, the energy storage charge and discharge flag K s = -1, indicating that the energy storage charges;

[0012] 2) Calculate the influence of the energy storage charge and discharge operation on the power flow of heavy-load lines, which is represented by the sensitivity matrix L 1 ; L 1 (k, l, s) represents the sensitivity coefficient of the energy storage charge and discharge to the power flow of the heavy-load line l when the energy storage is connected to the alternative node k and in the operating scenario s. Its calculation expression is:

[0013]

[0014] Among them, let the admittance matrix of the power grid nodes be Y, and take the imaginary part of all elements in the Y matrix as the node susceptance matrix B, B 0 =-B; i, j are the head and end nodes of line l; are respectively the elements in the i-th and j-th rows and k-th column of the i,j matrix; x

[0015] 3) Correct the power flow sensitivity coefficient according to the occurrence probability of each operation scenario; let p s be the probability of scenario s, then the corrected sensitivity matrix L 1 ' is:

[0016] L 1 '(k, l, s)=p s ×L 1 (k, l, s) (2)

[0017] For each node, denote the sum of the L 1 ' matrix for all heavy-loaded lines l and all scenarios s as vector L 1 , and the line power flow sensitivity coefficient L 1 reflects the effect of connecting energy storage at a certain grid connection point on reducing the power flow of heavy-loaded lines, and the larger its value, the better the effect.

[0018] Furthermore, in step S2, the specific calculation method of the energy storage on the system network loss sensitivity considering multiple scenarios is as follows:

[0019] 1) For each typical scenario s, based on the output of conventional units and new energy power sources in the power grid, carry out the base-state power flow calculation to form the Jacobian matrix J of the base-state power flow. The linearized power flow equation at the base state is written as Equation (3), and J represents the sensitivity of ΔP and ΔQ to Δδ and ΔV, as shown in Equation (4);

[0020]

[0021]

[0022] 2) Calculate the impact of the charge and discharge operation of the energy storage on the active power network loss of the system after the energy storage is connected to different grid connection points, and use the sensitivity coefficient L 2 to represent; L 2 (k, s) represents the sensitivity of the charge and discharge action of the energy storage to the active power loss when the energy storage is connected to the alternative node k under scenario s;

[0023]

[0024] Among them, is The transpose matrix of the matrix, is the k-th row of the matrix; represents the sensitivity of active power network loss to node phase angle and voltage. Through the definition formula of active power network loss P Loss = ∑P i = ∑∑V i V j G ij cosδ ij obtained by taking the partial derivative, G ij is the conductance of line ij;

[0025] 3) Correct the network loss sensitivity coefficient according to the occurrence probability of each operation scenario; Let p s be the probability of scenario s, then the corrected sensitivity matrix L 2 is:

[0026] L 2 ′(k, s) = p s ×L 2 (k, s) (6)

[0027] For each node, denote the sum of the L 2 ′ matrix over all scenarios s as vector L 2 , and the magnitude of the network loss sensitivity coefficient L 2 reflects the effect of connecting energy storage at a certain parallel connection point on reducing the active power network loss of the system. The larger the index, the better the effect.

[0028] Furthermore, in step S2, the specific calculation method of the sensitivity of energy storage to voltage stability considering multiple scenarios is as follows:

[0029] 1) For each typical scenario s, based on the output of conventional units and new energy power sources in the power grid, carry out base - state power flow calculation to form the Jacobian matrix J of the base - state power flow; Define the nodes with the maximum voltage fluctuation amount of all scenarios greater than 0.1 p.u. as weakly stable nodes, and form a set

[0030] 2) Calculate the impact of the charge - discharge operation of energy storage on the voltage stability of the system after the energy storage is connected to different parallel connection points, using the sensitivity coefficient L 3 to represent. L 3 (k, i, s) represents the voltage sensitivity of the energy storage operation to grid node i when the energy storage is connected to node k under scenario s;

[0031]

[0032] Among them, is the i - th row and k - th column of the matrix , i is a weakly stable node, k i, k s They are the voltage offset direction and the energy storage charge and discharge flag of scenario s respectively. If the voltage in scenario s is lower than the average voltage, then k i = 1, otherwise k i = -1; Finally, L 3 (k, i, s) reflects the sensitivity of the overall voltage offset to the injection power of node k under the voltage offset direction and energy storage charging or discharging conditions in scenario s;

[0033] 3) Correct the voltage sensitivity coefficient according to the occurrence probability of each operating scenario; Let p s be the probability of scenario s, then the corrected sensitivity matrix L 3 ' is:

[0034] L 3 '(k, i, s) = p s × L 3 (k, i, s) (8)

[0035] For each node, denote the sum of the L 3 ' matrix for all nodes i with weak voltage stability and all scenarios s as vector L 3 , and the voltage sensitivity coefficient L 3 reflects the effect of connecting an energy storage at a certain grid connection point on improving voltage stability. The larger the index, the better the effect.

[0036] Furthermore, in step S3, select the weights of the corresponding indicators according to the severity analysis of grid line overload, network loss, and voltage stability problems. The greater the severity of a certain problem, the greater the selected weight.

[0037] And, an energy storage power station grid connection point optimization system considering comprehensive sensitivity, characterized in that: based on the above-mentioned energy storage power station grid connection point optimization method considering comprehensive sensitivity, including:

[0038] A data reading module for reading the grid structure parameters of the power system, including: load, generator output time series data, maximum and minimum generator outputs, maximum up and down ramp rates, and generation costs;

[0039] A typical scenario generation module for reading new energy historical output data and load characteristic data and generating multiple typical verification scenarios;

[0040] A sensitivity calculation module for solving the multi-scenario and multi-dimensional sensitivity values of each node of the read grid based on the read conventional power source and load data;

[0041] Energy storage connection point optimization module, which is used to calculate the multi-dimensional sensitivity weight distribution based on the severity analysis of different grid security issues, and then obtain the comprehensive sensitivity index and the optimal set of energy storage connection points.

[0042] Compared with the prior art, the present invention and its preferred solutions solve the problem of optimal energy storage configuration in scenarios of delaying the upgrade of transmission lines, improving voltage stability, and reducing network losses on the grid side by solving for the best energy storage connection points through multi-scenario and multi-dimensional node sensitivity analysis, and can effectively solve the problem of optimizing the selection of energy storage connection points in a high-proportion new energy power system. Brief Description of the Drawings

[0043] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0044] The drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0045] Figure 1 It is a flowchart of the method for optimizing the connection point of an energy storage power station considering comprehensive sensitivity according to an embodiment of the present invention.

[0046] Figure 2 It is a structural diagram of the system for optimizing the connection point of an energy storage power station considering comprehensive sensitivity according to an embodiment of the present invention.

[0047] Figure 3 It is a schematic diagram of the topological structure of the 39-node system selected in an embodiment of the present invention.

[0048] Figure 4 It is a bar chart of the active power load rate of the line in the typical power flow calculation result of the 39-node system selected in an embodiment of the present invention.

[0049] Figure 5 It is a schematic diagram of the calculation result of the comprehensive sensitivity index of different nodes in an application example of an embodiment of the present invention.

[0050] Figure 6 and Figure 7 It is a schematic diagram of the charge and discharge power and SOC curve of the energy storage system under a certain configuration scheme in an application example of an embodiment of the present invention.

[0051] Figure 8 It is a schematic diagram of the comparison of the wind power consumption before and after energy storage configuration in an application example of an embodiment of the present invention.

[0052] Figure 9 It is a schematic diagram of the comparison of the maximum load of the heavily loaded line before and after energy storage configuration in an application example of an embodiment of the present invention. Detailed Description of the Embodiment

[0053] To make the features and advantages of this patent more obvious and understandable, specific embodiments are given below for detailed description as follows:

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Usually, the components described and shown in the accompanying drawings here can be combined and designed in different configurations. Therefore, the following detailed description of the selected embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the present invention to be protected, but only represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0055] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0056] As Figure 1 shown, in a preferred embodiment of the present invention, a method for optimizing the grid connection point of an energy storage power station considering comprehensive sensitivity is provided, which includes the following steps:

[0057] Step S1: Based on factors such as grid load characteristics, the grid connection conditions of new energy and conventional power sources, and the output characteristics of new energy, typical operation scenarios affecting the operation state of the energy storage are summarized, and at least the following modes should be covered: 1) Summer high-load mode, low-load mode; winter high-load mode, low-load mode; 2) High-output and low-output modes of the main new energy power sources. The typical operation scenarios of the power grid are characterized as: S = [s 1 , s 2 ,..., s n .

[0058] Step S2: Assume that the alternative nodes for the energy storage power station to connect to the grid are k, and calculate the sensitivity L 1 of the power injection of the energy storage at node k to the line power flow, the sensitivity L 2 of the system active power loss, and the sensitivity L 3 of the node voltage stability. For multiple scenarios, based on the sensitivity coefficients, calculate the effects of the energy storage connected at different grid connection points on reducing the load rate of overloaded lines, reducing the system loss, and improving the voltage stability.

[0059] In this step, the calculation method of the line power flow sensitivity index for each node is as follows:

[0060] 1) For each typical scenario s, based on the output of conventional power sources and new energy power sources in the power grid, perform a base-state power flow calculation. Define the lines with a load rate greater than 60% as overloaded lines, and form a set of overloaded lines in the power grid Meanwhile, considering that the energy storage power station provides peak shaving, if the scenario is a large-load mode, the energy storage charge-discharge flag k s = 1, indicating that the energy storage discharges. Conversely, if the scenario is a small-load mode, the energy storage charge-discharge flag k s = -1, indicating that the energy storage charges.

[0061] 2) Calculate the impact of energy storage charge / discharge operation on the heavy-load line power flow, represented by the sensitivity matrix L 1 L 1 (k, l, s) represents the power flow sensitivity coefficient of energy storage charge / discharge to the heavy-load line l when the energy storage is connected to the alternative node k and in the operation scenario s, and its calculation expression is:

[0062]

[0063] Among them, assuming that the nodal admittance matrix of the power grid is Y, taking the imaginary part of all elements in the Y matrix as the nodal susceptance matrix B, B 0 = -B. i, j are the head and end nodes of the line l. are respectively the elements in the i-th and j-th rows and k-th column of the matrix. x i,j is the reactance of the line l. Meanwhile, according to the power flow direction of the heavy-load line, the positive and negative signs of the sensitivity are corrected, and the reduction of the heavy-load line power flow by the energy storage charge / discharge action is taken as positive.

[0064] 3) Correct the power flow sensitivity coefficient according to the occurrence probability of each operation scenario. Let p s be the probability of the scenario s, then the corrected sensitivity matrix L 1 ′ is:

[0065] L 1 ′(k, l, s) = p s × L 1 (k, l, s) (2)

[0066] For each node, denote the sum of the L 1 ′ matrix for all heavy-load lines l and all scenarios s as the vector L 1 , and the line power flow sensitivity coefficient L 1 reflects the effect of connecting energy storage at a certain grid connection point on reducing the heavy-load line power flow, and the larger its value, the better the effect.

[0067] In this step, the calculation process of the system active power loss sensitivity index for each node is as follows:

[0068] 1) For each typical scenario s, based on the output of conventional units and new energy power sources in the power grid, perform a base - state power flow calculation to form the Jacobian matrix J of the base - state power flow. The linearized power flow equation at the base - state can be written as Equation (3), and J can be expressed as the sensitivities of ΔP and ΔQ with respect to Δδ and ΔV, as shown in Equation (4). Meanwhile, considering the peak - shaving provided by the energy storage power station, the energy storage charge - discharge flag k s is defined in the same way as above.

[0069]

[0070]

[0071] 2) Calculate the impact of the charge / discharge operation of the energy storage on the active power loss of the system after the energy storage is connected to different connection points, which is represented by the sensitivity coefficient L 2 . L 2 (k, s) represents the sensitivity of the active power loss to the charge / discharge action of the energy storage when the energy storage is connected to the alternative node k under scenario s.

[0072]

[0073] Among them, is the transpose matrix of the matrix, and is the k - th row of the matrix. represents the sensitivity of the active power loss to the node phase angle and voltage, which can be obtained by taking the partial derivative of the definition formula of the active power loss P Loss =∑P i =∑∑V i V j G ij cosδ ij . G ij is the conductance of line ij.

[0074] 3) Correct the power loss sensitivity coefficient according to the occurrence probability of each operation scenario. Let p s be the probability of scenario s, then the corrected sensitivity matrix L 2 is:

[0075] L 2 ′(k, s)=p s ×L 2 (k, s) (6)

[0076] For each node, denote the sum of the L 2 ′ matrix for all scenarios s as the vector L 2 . The magnitude of the power loss sensitivity coefficient L 2 reflects the effect of connecting the energy storage at a certain connection point on reducing the active power loss of the system. The larger the index, the better the effect.

[0077] In this step, the calculation process of the voltage stability sensitivity index for each node is as follows:

[0078] 1) For each typical scenario s, based on the output of conventional units and new energy power sources in the power grid, perform a base - state power flow calculation to form the Jacobian matrix J of the base - state power flow. Define the nodes with the maximum voltage fluctuation greater than 0.1 p.u. in all scenarios as weakly stable nodes, and form a set At the same time, considering the peak - shaving provided by the energy storage power station, the energy storage charge - discharge flag k s is defined in the same way as above.

[0079] 2) Calculate the impact of the charge / discharge operation of the energy storage on the system voltage stability after the energy storage is connected to different connection points, which is represented by the sensitivity coefficient L 3 L 3 (k, i, s) represents the voltage sensitivity of the energy storage operation to node i of the power grid when the energy storage is connected to node k under scenario s.

[0080]

[0081] Among them, is the element in the i - th row and k - th column of the matrix , i is a weakly stable node, k i and k s are the voltage offset direction of scenario s and the energy storage charge - discharge flag respectively. If the voltage in scenario s is lower than the average voltage, then k i = 1, otherwise k i = - 1. Finally, L 3 (k, i, s) reflects the sensitivity of the overall voltage offset to the injected power of node k under the voltage offset direction of scenario s and the energy storage charging (or discharging) condition.

[0082] 3) Correct the voltage sensitivity coefficient according to the occurrence probability of each operation scenario. Let p s be the probability of scenario s, then the corrected sensitivity matrix L 3 ′ is:

[0083] L 3 ′(k, i, s) = p s ×L 3 (k, i, s) (8)

[0084] For each node, denote the sum of the L 3 ′ matrix for all weakly voltage - stable nodes i and all scenarios s as the vector L 3 , and the voltage sensitivity coefficient L 3 reflects the effect of connecting the energy storage at a certain connection point on improving the voltage stability. The larger the index, the better the effect.

[0085] Step S3: Select the weights of the corresponding indicators according to the severity analysis of the heavy load, network loss, and voltage stability problems of the power grid lines. The greater the severity, the greater the selected weight. Convert the multi-objective optimization problem into a single-objective optimization problem to obtain the comprehensive node sensitivity index value, which is used as the priority order of the energy storage configuration nodes. Generally speaking, the power flow and voltage stability of the power grid lines are related to the safe operation of the power grid, while the network loss only involves economy. Therefore, preferably, select L 1 、L 2 、L 3 The weights accounted for are 0.56, 0.11, and 0.33 respectively.

[0086] Figure 2 The present invention is an optimal system for the grid connection point of an energy storage power station considering comprehensive sensitivity. The system includes:

[0087] A data reading module 201, which is used to read the grid structure parameters of the power system, the time series data of the load and the output of the generator sets, and the technical and economic parameters such as the maximum / minimum output of the generator, the maximum up / down ramp rate, and the power generation cost.

[0088] A typical scenario generation module 202, which is used to read the historical output data of new energy and the load characteristic data to generate multiple typical verification scenarios.

[0089] A sensitivity calculation module 203, which is used to solve the multi-scenario and multi-dimensional sensitivity values of each node of the read power grid based on the read data of the conventional power sources and loads.

[0090] An energy storage grid connection point optimization module 204, which is used to calculate the multi-dimensional sensitivity weight distribution according to the severity analysis of different safety problems of the power grid, and then obtain the comprehensive sensitivity index and the optimal set of grid connection points for the energy storage.

[0091] To verify the effectiveness of the present invention, in the following application example, the above-mentioned quantitative analysis method is implemented using the relevant data of the 10-machine 39-node New England power system. The specific steps will not be elaborated, and mainly its technical effects and implementation details are given.

[0092] Application example

[0093] The method of the present invention is written in Julia-JuMP, and mathematical optimizers such as GUROBI and IPOPT are called to solve it, and the implementation effects are demonstrated for the case data.

[0094] Operating environment:

[0095] AMD Ryzen 3 2200G 3.50GHz CPU 3.70GHz, 16GB memory, Microsoft Windows 10 X64

[0096] GUROBI9.1.2

[0097] IPOPT 0.7.0

[0098] JULIA 1.6.4

[0099] Implementation results:

[0100] Based on the network parameters, power sources and load data of the 10-machine 39-bus New England power system, on the basis of the original standard example, wind farms with an installed capacity of 300 MW are incorporated at buses 4, 8, 20, and 39. Figure 3 For the network topology of this system, Figure 4 is the bar chart of the active power flow load rate of each line. The system's renewable energy penetration rate is 19.2%, the annual load growth rate is 1.1%, the energy storage type is lithium iron phosphate, and the optimal connection points for energy storage are selected for this standard system.

[0101] Table 1 reflects the heavy-load lines and related information obtained through repeated power flow calculations.

[0102] Table 1 Heavy-load and overloaded lines and their related information

[0103]

[0104] Figure 5 Reflects the calculation results of the comprehensive sensitivity index of all nodes. The line power flow sensitivity accounts for a large proportion, and the overall effect is closer to reducing the power flow of heavy-load lines. The nodes with relatively high comprehensive sensitivity, that is, relatively high priority, are: 14, 15, 20, 34, 19, 33, 3, 24, 16, 21.

[0105] Figure 6 and Figure 7 respectively reflect the charge and discharge power and SOC curves of two energy storage systems when 97.8 MW / 582.7 MWh is connected at bus 14 and 44.3 MW / 118.1 MWh is connected at bus 21. The energy storage charges from 4 to 7 hours and from 20 to 22 hours (during low load and high wind power periods), and discharges from 12 to 16 hours (during high load and low wind power periods). Therefore, the principle of using energy storage to absorb wind power can be understood as that the energy storage stores the original wind power abandonment and discharges it during peak load hours, realizing the time shift of wind power.

[0106] Figure 8 Intuitively shows the comparison of wind power absorption before and after energy storage configuration.

[0107] Figure 9 Reflects the role of energy storage in reducing the load rate of heavy-load lines. The configuration scheme in this example mainly reduces the load on lines 19 and 23 and does not increase the load on other heavy-load lines.

[0108] The logic programming solution in the above solution provided by this embodiment can be stored in a computer-readable storage medium in a codified form, implemented in the form of a computer program, input basic parameter information required for calculation through computer hardware, and output calculation results.

[0109] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention 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.

[0110] The present invention is described with reference to methods, devices (apparatuses), and computer program products according to embodiments of the present invention. It should be understood that each process can be implemented by computer program instructions. 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, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one or more processes.

[0111] 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, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in one or more processes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes.

[0113] As mentioned above, it is only a preferred embodiment of the present invention, and the present invention is not limited to other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

[0114] This patent is not limited to the above-mentioned optimal implementation mode. Anyone inspired by this patent can derive various other forms of methods and systems for optimizing the grid connection point of an energy storage power station considering comprehensive sensitivity. All equivalent changes and modifications made within the scope of the patent application of this invention shall fall within the scope covered by this patent.

Claims

1. An optimal selection method for the connection point of an energy storage power station considering comprehensive sensitivity, characterized in that, it includes the following steps: Step S1: Based on the grid load characteristics, the grid connection conditions of new energy and conventional power sources, and the output characteristics of new energy, typical operation scenarios affecting the energy storage operation state are summarized, covering at least the combinations of the following methods: 1) high-load mode and low-load mode in summer; high-load mode and low-load mode in winter; 2) high-output mode and low-output mode of new energy power sources; The typical operation scenarios of the power grid are characterized as: S = [s 1 , s 2 ,..., s n ; Step S2: Let the alternative node for the energy storage power station to connect to the power grid be k, and calculate the sensitivity coefficient l of the power injected by the energy storage at node k to the line power flow 1 , the sensitivity coefficient l of the system active power loss 2 , and the sensitivity coefficient l of the node voltage stability 3 ; Then, for multiple scenarios, based on the sensitivity coefficient, calculate the effects of energy storage connected at different connection points on reducing the load rate of heavy-load lines, reducing system losses, and improving voltage stability; Step S3: According to the severity analysis of the heavy load, network loss, and voltage stability problems of the power grid lines, select the weights of the corresponding indicators. The greater the severity, the greater the selected weight. Transform the multi-objective optimization problem into a single-objective optimization problem to obtain the node comprehensive sensitivity index value, which is used as the priority order of the energy storage configuration nodes.

2. The optimal selection method for the connection point of an energy storage power station considering comprehensive sensitivity according to claim 1, characterized in that: In step S2, the specific calculation method for the sensitivity of energy storage to line power flow considering multiple scenarios is: 1) For each typical scenario s, based on the power output of conventional power sources and new energy power sources in the power grid, perform base-case power flow calculations; define lines with a load rate greater than 60% as overloaded lines to form a set of overloaded lines in the power grid. Meanwhile, considering the peak shaving provided by the energy storage power station, if the scenario is a high-load mode, the energy storage charge-discharge flag k s = 1, indicating that the energy storage discharges; conversely, if the scenario is a low-load mode, the energy storage charge-discharge flag k s = -1, indicating that the energy storage charges. 2) Calculate the impact of the energy storage charge and discharge operation on the heavy-load line power flow, represented by the sensitivity matrix L 1 denoted as; L 1 (k, l, s) represents the power flow sensitivity of the energy storage charge and discharge to the heavy-load line l when the energy storage is connected to the alternative node k and in the operation scenario s, and its calculation expression is: Among them, let the admittance matrix of the power grid nodes be Y, and take the imaginary part of all elements in the Y matrix as the nodal susceptance matrix B, B 0 = -B; i and j are the head and tail nodes of line l; are respectively the elements in the i-th and j-th rows and k-th column of the matrix; x i,j is the reactance of line l; according to the power flow direction of the heavily loaded line, the positive and negative signs of the sensitivity are corrected, and the energy storage charge and discharge action to reduce the power flow of the heavily loaded line is taken as positive; 3) Modify the power flow sensitivity coefficient according to the occurrence probability of each operation scenario; let p s be the probability of scenario s, then the modified sensitivity matrix L 1 ' is: L 1 '(k, l, s) = p s × L 1 (k, l, s) (2) For each node, denote L 1 The sum of the ' matrices over all overloaded lines l and all scenarios s is the vector L 1 , the line power flow sensitivity coefficient l 1 reflects the effect of connecting energy storage at a certain parallel connection point on reducing the power flow of overloaded lines, and the larger its value, the better the effect.

3. The optimal selection method for the connection point of an energy storage power station considering comprehensive sensitivity according to claim 2, characterized in that: In step S2, the specific calculation method for the sensitivity of energy storage to system network loss considering multiple scenarios is: 1) For each typical scenario s, based on the output of conventional units and new energy power sources in the power grid, carry out the base-state power flow calculation to form the Jacobian matrix J of the base-state power flow. The linearized power flow equation at the base state is written as Equation (3), and J represents the sensitivity of ΔP and ΔQ to Δδ and ΔV, as shown in Equation (4); 2) Calculate the impact of energy storage charging and discharging operation on the active power loss of the system after the energy storage is connected to different connection points, and use the sensitivity matrix L 2 to represent; L 2 (k, s) represents the sensitivity of the active power loss to the charging and discharging actions of the energy storage when the energy storage is connected to the alternative node k under the scenario s; Among them, is the transpose matrix of the matrix, is the k-th row of the matrix; represents the sensitivity of active power loss to node phase angle and voltage, and is obtained by taking the partial derivative of the definition formula of active power loss \(P Loss =\sum P i =\sum\sum V i V j G ij \cos\delta ij where \(G ij is the conductance of line ij; 3) Modify the network loss sensitivity coefficient according to the occurrence probability of each operation scenario; let p s be the probability of scenario s, then the modified sensitivity matrix L 2 ' is: L 2 '(k, s) = p s ×L 2 (k, s) (6) For each node, denote L 2 The summation result of the' matrix for all scenarios s is the vector L 2 , and the sensitivity coefficient l 2 of the active power loss of the system reflects the effect of connecting energy storage at a certain grid connection point on reducing the active power network loss of the system. The larger the index, the better the effect.

4. The optimal selection method for the connection point of an energy storage power station considering comprehensive sensitivity according to claim 3, characterized in that: In step S2, the specific calculation method for the sensitivity of energy storage to voltage stability considering multiple scenarios is: 1) For each typical scenario s, based on the output of conventional units and new energy power sources in the power grid, perform a base-case power flow calculation to form the Jacobian matrix J of the base-case power flow; define the nodes with the maximum voltage fluctuation greater than 0.1 p.u. in all scenarios as the nodes with relatively weak stability to form a set 2) Calculate the impact of the charge and discharge operation of the energy storage on the system voltage stability after the energy storage is connected to different connection points, and use the sensitivity matrix L 3 to represent, where L 3 (k, i, s) represents the voltage sensitivity of the energy storage operation to grid node i when the energy storage is connected to node k under scenario s; Among them, is the matrix at the i-th row and k-th column, where i is a node with relatively weak stability, k i , k s are respectively the voltage deviation direction and the energy storage charge and discharge flag of scenario s. If the voltage in scenario s is lower than the average voltage, then k i = 1, otherwise k i = -1; finally, L 3 (k, i, s) reflects the sensitivity of the overall voltage deviation to the injected power of node k under the voltage deviation direction and energy storage charging or discharging conditions in scenario s; 3) Modify the voltage sensitivity coefficient according to the occurrence probability of each operation scenario; let p s be the probability of scenario s, then the modified sensitivity matrix L 3 ' is: L 3 '(k,i,s) = p s ×L 3 (k,i,s) (8) For each node, denote L 3 The summation result of the' matrix for all nodes i with weak voltage stability and all scenarios s is the vector L 3 , and the sensitivity coefficient l of the node voltage stability 3 reflects the effect of connecting energy storage at a certain parallel connection point on improving voltage stability. The larger the index, the better the effect.

5. The optimal selection method for the connection point of an energy storage power station considering comprehensive sensitivity according to claim 4, characterized in that: In step S3, according to the severity analysis of the heavy load, network loss, and voltage stability problems of the power grid lines, select the weights of the corresponding indicators. If the severity of a certain problem is greater, the selected weight is greater.

6. An optimal selection system for the connection point of an energy storage power station considering comprehensive sensitivity, characterized in that: Based on the optimal selection method for the connection point of an energy storage power station considering comprehensive sensitivity according to any one of claims 1-5, it includes: A data reading module for reading the grid structure parameters of the power system, including: load, generator output time series data, maximum and minimum generator outputs, maximum up and down ramp rates, and generation costs; A typical scenario generation module for reading the historical output data of new energy and the load characteristic data to generate multiple typical verification scenarios; A sensitivity calculation module for solving the multi-scenario and multi-dimensional sensitivity values of each node of the read power grid based on the read conventional power source and load data; An energy storage connection point optimization module for calculating the multi-dimensional sensitivity weight distribution according to the severity analysis of different safety problems of the power grid, and then obtaining the comprehensive sensitivity index and the set of relatively optimal connection points of the energy storage.

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