Energy storage planning and site selection method and device based on power grid vulnerability evaluation

Abstract

The invention discloses an energy storage planning and site selection method and device based on power grid vulnerability evaluation, and belongs to the technical field of power system energy storage planning. The method comprises the following steps: after each energy storage site selection scheme is accessed to a system, carrying out whole-network N-1 power flow scanning calculation on a whole regional power grid in different typical operation scenes; according to an N-1 scanning result, constructing a line equivalent association network for each typical scene of each energy storage site selection scheme, and performing k-kernel decomposition of the network to obtain system vulnerability of each typical scene of each energy storage site selection scheme; the system vulnerability is used as an evaluation sub-target, and a TOPSIS multi-target decision algorithm is used to calculate and sort the ideal solution closeness of each energy storage site selection scheme; and selecting the energy storage site selection scheme with the maximum ideal solution closeness as the optimal scheme. The method can effectively and reasonably analyze and calculate the scheme for the optimal operation benefit of the regional power grid after the energy storage access system.

Description

Technical Field

[0001] The invention relates to a method and a device for energy storage planning site selection based on power grid vulnerability assessment, belonging to the technical field of power system energy storage planning. Background Art

[0002] With the continuous strengthening of regional power grids in recent years, the power system has achieved the benefits of close networking. However, due to its complex network characteristics, it also bears the potential risk of large-scale power outages caused by more and more cascading failures. Therefore, the planning and operation of the power system should be based on safety and stability, and develop in the direction of interaction, self-healing, high safety and high reliability. With the characteristics of two-way transmission and fast response speed, energy storage can effectively improve the safety of the power grid, enhance the power grid's ability to resist fault shocks, and enhance the peak-shaving capacity of the power system after reasonable layout in the planning and site selection stage.

[0003] Since energy storage has the flexible dual characteristics of charging and discharging, there are many scenarios for the grid department to dispatch energy storage during the energy storage planning and site selection process. It is necessary to comprehensively consider the safety of energy storage access to the grid under various typical scenarios in order to obtain the optimal solution for energy storage site selection. In the actual energy storage planning stage, a large number of simulation calculations are required for different site selection schemes. The operation is highly complex and requires a lot of manpower, material resources and time, which undoubtedly increases the cost of power system construction. In addition, the long planning cycle is not suitable for the rapidly developing power grid needs.

[0004] On the other hand, most of the current research on energy storage planning and site selection has not considered incorporating grid vulnerability into the decision-making model. Vulnerability reflects the dangerous state of the power system with potential accident hazards. After energy storage is connected to the grid, it is bound to have an impact on the vulnerability of the power system. It is very necessary to comprehensively consider the safety impact on the large power grid during the energy storage planning and site selection stage. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention proposes a method and device for energy storage planning and site selection based on power grid vulnerability assessment, which performs flow scanning calculations on all possible energy storage site selection schemes under multiple typical scenarios, and then converts the power grid model into a line equivalent associated network model. The associated network model is subjected to a power grid vulnerability assessment using k-core decomposition, and finally, according to the system vulnerability index in different typical operating scenarios after the energy storage is connected to the power grid, the TOPSIS multi-objective decision-making algorithm is used to comprehensively evaluate the optimal solution of the energy storage target access point, thereby quickly and accurately calculating the optimal site selection scheme for the energy storage.

[0006] In order to achieve the above purpose / solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a method for energy storage planning and site selection based on power grid vulnerability assessment, comprising the following steps:

[0008] Obtain the system structure, operation data and all energy storage site selection options of the regional power grid;

[0009] According to the system structure and operation data of the regional power grid, the grid data of each energy storage site selection scheme after being connected to the regional power grid is scanned and calculated in the whole network N-1 in different typical scenarios, and the power flow scanning data of each energy storage site selection scheme in each typical scenario is obtained;

[0010] According to the flow scanning data of each energy storage site selection scheme in each typical scenario, the corresponding line equivalent association network is constructed and weighted, and the weighted line equivalent association network model of each energy storage site selection scheme in each typical scenario is obtained;

[0011] Perform k-core decomposition on the equivalent associated network model of each authorized line to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario;

[0012] The system vulnerability index of each energy storage site selection scheme in each typical scenario is used as the evaluation sub-goal, and the TOPSIS multi-objective decision-making algorithm is used to calculate the ideal solution closeness of each energy storage site selection scheme;

[0013] The energy storage site selection scheme with the maximum closeness to the ideal solution is selected as the optimal energy storage site selection scheme.

[0014] In combination with the first aspect, optionally, the typical scenarios include peak load energy storage discharging, valley load energy storage charging, and peak load energy storage charging.

[0015] In combination with the first aspect, optionally, according to the flow scanning data of each energy storage site selection scheme in each typical scenario, a corresponding line equivalent association network is constructed and weighted to obtain a weighted line equivalent association network model for each energy storage site selection scheme in each typical scenario, including:

[0016] The lines in the regional power grid are converted into nodes in an equivalent association network, and the weighted edges between each pair of nodes represent the impact of power flow transfer under the power grid line fault mode, and an equivalent association network model of the line is constructed for each energy storage site selection scheme in each typical scenario;

[0017] The weight of each edge in the line equivalent association network model is calculated based on the power flow scanning data. The formula is as follows:

[0018] ; ;

[0019] Among them, M represents the correlation matrix of the line equivalent correlation network model, mij represents the weight of the edge from node i to node j in the line equivalent association network model, represents the power flow of line j in normal operation mode, represents the power flow on line j after line i is disconnected due to a fault, represents the thermal stability limit transmission capacity of line j, is the correlation factor;

[0020] Assign the element values ​​of the association matrix M to the line equivalent association network model to obtain the weighted line equivalent association network model.

[0021] In combination with the first aspect, optionally, the k-core decomposition is performed on each authorized line equivalent association network model to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario, including:

[0022] For each node in each entitled line equivalent association network model, the sum of the weights of all edges connected to the node in the entitled line equivalent association network model is calculated as the power flow transfer degree of the node;

[0023] According to the power flow transfer degree of each node, the k-core decomposition is performed on the equivalent association network model of the authorized lines to obtain the core layer value of each node in each energy storage site selection scheme in each typical scenario;

[0024] The sum of the core layer values ​​of all nodes of each energy storage site selection scheme in each typical scenario is taken as the system vulnerability indicator of each energy storage site selection scheme in each typical scenario.

[0025] In combination with the first aspect, optionally, performing k-core decomposition on the equivalent association network model of the entitled lines according to the power flow transfer degree of each node includes:

[0026] (1) Calculate the node with the smallest power flow transfer degree in the equivalent association network model of the current authorized line, and set its power flow transfer degree as p min , let k = p min ;

[0027] (2) The nodes whose power flow transfer degree is not greater than k in the equivalent association network model of the right-hand line are classified into the k-core layer, and the nodes are removed from the equivalent association network model of the right-hand line to obtain the updated equivalent association network model of the right-hand line;

[0028] (3) According to the updated equivalent association network model of the authorized lines, the flow transfer degrees of the remaining nodes are updated. The formula is: ,in, represents the degree of power flow transfer after updating, and p represents the degree of power flow transfer before updating. represents nuclear dissociation retention factor;

[0029] (4) Determine the power transfer degree of all nodes in the equivalent associated network model of the current authorized line Is it greater than k? If so, proceed to the next step; if not, return to step (2);

[0030] (5) Determine whether all nodes in the current weighted line equivalent association network model are classified into the corresponding core layer. If so, end the k-core decomposition. If not, return to step (1).

[0031] In combination with the first aspect, optionally, when node i exists in the k-core layer, the core layer value of node i is k.

[0032] In combination with the first aspect, optionally, the system vulnerability index of each energy storage site selection scheme in each typical scenario is used as an evaluation sub-goal, and the TOPSIS multi-objective decision algorithm is used to calculate the closeness of the ideal solution of each energy storage site selection scheme, including:

[0033] Construct a multi-objective decision matrix based on the system vulnerability index of each energy storage site selection scheme in each typical scenario , where n is the total number of energy storage site selection options, r is the total number of typical scenarios, and the multi-objective decision matrix Medium Element represents the system vulnerability index value of the s-th energy storage site selection scheme under the t-th typical scenario, , ;

[0034] For multi-objective decision matrix Take the inverse of each element in and get the high-optimality decision matrix ;

[0035] High-quality decision matrix Each element in is vector normalized to obtain the normalized decision matrix :

[0036] ;

[0037] in, Represents the normalized decision matrix The value of the element in the sth row and tth column, Represents a high-priority decision matrix The element value at the sth row and tth column in ;

[0038] According to the preset weight sequence and normalized decision matrix , and obtain the weight normalized decision matrix :

[0039] ;

[0040] in, represents the weight normalized decision matrix The value of the element in the sth row and tth column, is the weight of the tth typical scenario;

[0041] Normalize the decision matrix according to the weights Calculate the positive ideal solution and negative ideal solution of each energy storage site selection scheme, and the calculation formula is as follows:

[0042] ;

[0043] in, represents the positive ideal solution matrix, represents the negative ideal solution matrix, represents the positive ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the negative ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the weight normalized decision matrix The maximum value of the tth column in represents the weight normalized decision matrix The minimum value of the tth column in;

[0044] According to the positive ideal solution and negative ideal solution of each energy storage site selection scheme, the distance scale of each energy storage site selection scheme is calculated. The calculation formula is as follows:

[0045] ;

[0046] in, represents the distance of the sth energy storage site selection scheme relative to the positive ideal solution, represents the distance of the sth energy storage site selection scheme relative to the negative ideal solution;

[0047] According to the distance scale of each energy storage site selection scheme, the ideal solution closeness of each energy storage site selection scheme is calculated. The calculation formula is as follows:

[0048] ;

[0049] in, represents the closeness to the ideal solution of the sth energy storage site selection scheme.

[0050] In a second aspect, the present invention provides an energy storage planning and site selection device based on power grid vulnerability assessment, comprising:

[0051] Data acquisition module, used to obtain the system structure, operation data and all energy storage site selection plans of the regional power grid;

[0052] The power flow scanning module is used to perform N-1 power flow scanning calculations on the grid data of each energy storage site selection scheme after it is connected to the regional power grid in different typical scenarios, and obtain the power flow scanning data of each energy storage site selection scheme in each typical scenario;

[0053] The equivalent association modeling module is used to construct and weight the corresponding line equivalent association network according to the flow scanning data of each energy storage site selection scheme in each typical scenario, and obtain the authorized line equivalent association network model of each energy storage site selection scheme in each typical scenario;

[0054] The vulnerability analysis module is used to perform k-core decomposition on the equivalent associated network model of each authorized line to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario;

[0055] The multi-objective decision-making module is used to use the system vulnerability index of each energy storage site selection scheme in each typical scenario as an evaluation sub-objective, and use the TOPSIS multi-objective decision-making algorithm to calculate the ideal solution closeness of each energy storage site selection scheme;

[0056] The output module is used to select the energy storage site selection scheme with the maximum closeness to the ideal solution as the optimal energy storage site selection scheme.

[0057] In conjunction with the second aspect, optionally, the vulnerability analysis module is specifically used to:

[0058] For each node in each entitled line equivalent association network model, the sum of the weights of all edges connected to the node in the entitled line equivalent association network model is calculated as the power flow transfer degree of the node;

[0059] According to the power flow transfer degree of each node, the k-core decomposition is performed on each authorized line equivalent association network model to obtain the core layer value of each node in each energy storage site selection scheme in each typical scenario;

[0060] The sum of the core layer values ​​of all nodes of each energy storage site selection scheme in each typical scenario is taken as the system vulnerability indicator of each energy storage site selection scheme in each typical scenario.

[0061] In conjunction with the second aspect, optionally, the multi-objective decision-making module is specifically used to:

[0062] Construct a multi-objective decision matrix based on the system vulnerability index of each energy storage site selection scheme in each typical scenario , where n is the total number of energy storage site selection options, r is the total number of typical scenarios, and the multi-objective decision matrix Medium Element represents the system vulnerability index value of the s-th energy storage site selection scheme under the t-th typical scenario, , ;

[0063] For multi-objective decision matrix Take the inverse of each element in and get the high-optimality decision matrix ;

[0064] High-quality decision matrix Each element in is vector normalized to obtain the normalized decision matrix :

[0065] ;

[0066] in, Represents the normalized decision matrix The value of the element in the sth row and tth column, Represents a high-priority decision matrix The element value at the sth row and tth column in ;

[0067] According to the preset weight sequence and normalized decision matrix , and obtain the weight normalized decision matrix :

[0068] ;

[0069] in, represents the weight normalized decision matrix The value of the element in the sth row and tth column, is the weight of the tth typical scenario;

[0070] Normalize the decision matrix according to the weights Calculate the positive ideal solution and negative ideal solution of each energy storage site selection scheme, and the calculation formula is as follows:

[0071] ;

[0072] in, represents the positive ideal solution matrix, represents the negative ideal solution matrix, represents the positive ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the negative ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the weight normalized decision matrix The maximum value of the tth column in represents the weight normalized decision matrix The minimum value of the tth column in;

[0073] According to the positive ideal solution and negative ideal solution of each energy storage site selection scheme, the distance scale of each energy storage site selection scheme is calculated. The calculation formula is as follows:

[0074] ;

[0075] in, represents the distance of the sth energy storage site selection scheme relative to the positive ideal solution, represents the distance of the sth energy storage site selection scheme relative to the negative ideal solution;

[0076] According to the distance scale of each energy storage site selection scheme, the ideal solution closeness of each energy storage site selection scheme is calculated. The calculation formula is as follows:

[0077] ;

[0078] in, represents the closeness to the ideal solution of the sth energy storage site selection scheme.

[0079] The following advantages can be obtained by using the above technical means:

[0080] The present invention proposes a method and device for energy storage planning and site selection based on grid vulnerability assessment, performs flow scanning calculations on all possible energy storage site selection schemes in multiple typical scenarios, and then converts the grid model into a line equivalent associated network model, and uses k-core decomposition to evaluate the grid vulnerability of the associated network model. Finally, according to the system vulnerability index in different typical operation scenarios after the energy storage is connected to the grid, the TOPSIS multi-objective decision algorithm is used to comprehensively evaluate the optimal solution of the energy storage target access point, so as to quickly and accurately calculate the optimal site selection scheme for energy storage. Compared with the prior art, the present invention introduces the system vulnerability index as an evaluation sub-objective for energy storage planning and site selection, and takes into account the impact of energy storage after grid connection in the site selection stage, which can improve the rationality of the energy storage site selection scheme and improve the safety of the power system after the energy storage is connected to the grid. The present invention evaluates a large number of energy storage site selection schemes through flow scanning, k-core decomposition, and TOPSIS multi-objective decision algorithm, reduces the participation of manual labor in energy storage planning and site selection, reduces planning costs, improves planning efficiency, and can quickly, accurately, reasonably, and effectively provide a basis for its site selection decision in the planning stage of energy storage projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 The figure shows a schematic flow chart of the steps of a method for energy storage planning and site selection based on power grid vulnerability assessment provided by an embodiment of the present invention;

[0082] Figure 2 It is a schematic diagram showing that a simple network model is constructed as a line equivalent association network in an embodiment of the present invention;

[0083] Figure 3 It is a schematic diagram of the steps of power grid vulnerability assessment based on k-core decomposition in an embodiment of the present invention;

[0084] Figure 4 The figure is a schematic diagram of the structure of the IEEE39 node system in an embodiment of the present invention;

[0085] Figure 5 The figure is a schematic diagram showing the change of the power supply range of the whole system after the simulated lines of the IEEE39 node system fail in sequence in an embodiment of the present invention;

[0086] Figure 6 It is a schematic diagram showing the vulnerability changes of various schemes for planning 50MW / 100MWh energy storage to access the IEEE39 node system in various typical scenarios in an embodiment of the present invention;

[0087] Figure 7 It is a schematic diagram of the TOPSIS ideal solution closeness of various schemes for planning 50MW / 100MWh energy storage to access the IEEE39 node system in an embodiment of the present invention;

[0088] Figure 8 Shown is a structural schematic diagram of an energy storage planning and site selection device based on power grid vulnerability assessment in an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0090] Example 1

[0091] The embodiment of the present invention introduces a method for energy storage planning and site selection based on power grid vulnerability assessment, such as Figure 1 As shown, the specific steps include:

[0092] Step A: Obtain the system structure and operation data of the regional power grid, including the regional power grid grid structure, line parameters, generator access location and capacity, substation load conditions, etc.

[0093] Step B: Generate all possible energy storage site selection plans for the possible access point locations of energy storage for subsequent evaluation.

[0094] Within the regional power grid, based on the project construction conditions, the number of possible energy storage access upstream substations is initially screened, and then all possible energy storage site selection schemes are determined. Assuming there are n possible upstream substations, the n access points are determined as the corresponding n energy storage site selection schemes.

[0095] Step C: Based on the system structure and operation data of the regional power grid, perform a full-network N-1 power flow scan calculation on the power grid data after each energy storage site selection scheme is connected to the regional power grid in different typical scenarios to obtain the power flow scan data of each energy storage site selection scheme in each typical scenario.

[0096] In the embodiment of the present invention, typical scenarios mainly include peak load energy storage discharge, valley load energy storage charging, and peak load energy storage charging. Among them, the valley load scenario of the regional power grid is considered at 60% of the peak load scenario, and the energy storage charging and discharging are considered at full power.

[0097] The present invention simulates the regional power grid in the simulation software according to the system structure and operation data of the regional power grid, and performs a fault simulation on each line in the regional power grid under different scenarios and different energy storage site selection schemes in the simulation software, performs corresponding power flow calculation, and obtains the N-1 power flow scanning calculation result of the whole network.

[0098] Step D: According to the flow scanning data of each energy storage site selection scheme in each typical scenario, a corresponding line equivalent association network is constructed and weighted to obtain multiple weighted line equivalent association network models.

[0099] Step D01: For each typical scenario of each energy storage site selection scheme, first obtain its simple power grid model, then convert the lines in the simple power grid model into nodes in an equivalent associated network, and finally use the right edges between each pair of nodes to represent the flow transfer effect under the power grid line fault mode, and generate a line equivalent associated network model, such as Figure 2 If the power flow impact on line b after the fault of line a is high, it is considered that there is an edge from node a to node b; if the power flow impact on line a after the fault is small or even reduces the load rate of line b, it is considered that there is no edge from node a to node b.

[0100] Step D02: construct the correlation matrix M of the line equivalent correlation network according to the power flow scanning data of each energy storage site selection scheme in each typical scenario. The expression is as follows:

[0101] M = [ m 11 m 12 m 13 ⋯ m 1 n m 21 m 22 m 23 ⋯ m 2 n m 31 m 32 m 33 ⋯ m 3 n ⋮ ⋮ ⋮ ⋱ ⋮ m n 1 m n 2 m n 3 ⋯ m nn ] (1)

[0102] Among them, m ij Represents the weight of the edge from node i to node j in the line equivalent association network.

[0103] In the embodiment of the present invention, m ij The value of is measured using power flow scan data:

[0104] (2)

[0105] in, represents the power flow of line j in normal operation mode; represents the power flow on line j after line i is disconnected by fault; represents the thermal stability limit transmission capacity of line j; is the correlation factor, which is 1 when the load rate of line j after being affected by power flow transfer is greater than the specified threshold, otherwise it is 0.

[0106] In formula (2), when i=j, the impact of line fault disconnection on itself is not considered, that is, there is no self-loop in the line equivalent association network; when line i fails, the load rate of line j decreases, that is, there is a flow relief effect on line j, m ij Set to 0.

[0107] Step D03: assign each element value of the association matrix M to the line equivalent association network model as the weight of each edge to obtain a complete and weighted line equivalent association network model.

[0108] Step E: Perform k-core decomposition on the equivalent associated network model of the authorized lines of each energy storage site selection scheme in each typical scenario to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario.

[0109] Step E01: For each node in each entitled line equivalent association network model, the sum of the weights of all edges connected to the node in the model is calculated as the power flow transfer degree of the node.

[0110] Step E02: According to the power flow transfer degree of each node, perform k-core decomposition on each authorized line equivalent association network model to obtain the core layer value of each node in each energy storage site selection scheme in each typical scenario.

[0111] In the embodiment of the present invention, specifically Figure 3 As shown in the figure, the specific operation of k-core decomposition is: (1) Calculate the node with the smallest power flow transfer degree in the current right line equivalent association network model, and set its power flow transfer degree to p min , let k = p min ; (2) The nodes whose power transfer degree is not greater than k in the equivalent association network model of the right line are classified into the k-core layer, and the nodes are removed from the equivalent association network model of the right line to obtain the updated equivalent association network model of the right line; (3) According to the updated equivalent association network model of the right line, the power transfer degree of the remaining nodes is updated, and the formula is: ,in, represents the degree of power flow transfer after updating, and p represents the degree of power flow transfer before updating. Represents the core decomposition retention factor, which is the degree to which the influence of the previous degree is retained each time the power flow transfer degree is updated. It is set manually according to actual needs and can be 0; (4) Determine the power flow transfer degree of all nodes in the equivalent association network model of the current authorized line Is it greater than k? If so, proceed to the next step; if not, return to step (2); (5) Determine whether all nodes in the current right-link equivalent association network model are included in the corresponding core layer; if so, end the k-core decomposition; if not, return to step (1).

[0112] After completing the k-core decomposition, all nodes in the network model with equivalent association of right lines have been divided into different core layers according to the degree of power flow transfer. If a node in the network exists in the k-core layer, the core layer value of the node is k.

[0113] Step E03: Since the nodes in the network model with equivalent association of right lines correspond to the transmission lines in the simple network model, the core layer value of the node is used as the vulnerability index value of the corresponding transmission line, and the sum of the core layer values ​​of all nodes in the network model with equivalent association of right lines (the sum of the vulnerability index values ​​of all transmission lines in the regional power grid) is the system vulnerability index value of the regional power grid.

[0114] Step F: Taking the system vulnerability index of each energy storage site selection scheme in each typical scenario as the evaluation sub-goal, the TOPSIS multi-objective decision-making algorithm is used to calculate the closeness of the ideal solution of each energy storage site selection scheme and sort them.

[0115] Step F01: Construct a multi-objective decision matrix based on the system vulnerability index of each energy storage site selection scheme in each typical scenario , where r is the number of typical scenarios, and the multi-objective decision matrix Each column in represents the system vulnerability index of n energy storage site selection schemes in the rth typical scenario. In the embodiment of the present invention, r=3, and the multi-objective decision matrix is ​​constructed based on the calculation results of the three system evaluation indicators (peak discharge vulnerability, valley charging vulnerability, peak charging vulnerability) corresponding to the n schemes. .

[0116] Step F02: unify the monotonicity of each element in the multi-objective decision matrix to obtain a new high-optimality decision matrix.

[0117] For the power grid system, the optimization direction of energy storage access is to reduce the system vulnerability index as much as possible. Since the present invention adopts high-quality index, that is, the higher the index value, the better the evaluation object solution, The elements in each column of the component are taken inversely, and a new high-optimality decision matrix is ​​constructed .

[0118] Step F03: High-quality decision matrix Each element in is vector normalized to form a standardized decision matrix :

[0119] (3)

[0120] in, Represents the normalized decision matrix The value of the element in the sth row and tth column, Represents a high-priority decision matrix The value of the element in the sth row and tth column, , .

[0121] Step F04: Set corresponding weights for each typical scenario to obtain a weight sequence ,in, is the weight of the tth typical scenario; construct a weight normalized decision matrix based on the weight sequence , where the elements are:

[0122] (4)

[0123] in, represents the weight normalized decision matrix The value of the element at the sth row and tth column in .

[0124] Step F05: Normalize the decision matrix according to the weights Calculate the positive ideal solution and negative ideal solution of each energy storage site selection scheme and determine them as and .

[0125] Considering that the present invention adopts high-quality indicators, there are:

[0126] (5)

[0127] in, represents the positive ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the negative ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the weight normalized decision matrix The maximum value of the tth column in represents the weight normalized decision matrix The minimum value of the tth column in .

[0128] Step F06: Calculate the distance scale of each energy storage site selection scheme according to the positive ideal solution and the negative ideal solution of each energy storage site selection scheme, that is, the distance of each energy storage site selection scheme relative to the positive ideal solution and the negative ideal solution.

[0129] The present invention uses Euclidean distance for calculation, and the formula is as follows:

[0130] (6)

[0131] in, represents the distance of the sth energy storage site selection scheme relative to the positive ideal solution, Represents the distance of the sth energy storage site selection plan relative to the negative ideal solution.

[0132] Step F07: Calculate the ideal solution closeness of each energy storage site selection scheme according to the distance scale of each energy storage site selection scheme:

[0133] (7)

[0134] in, represents the closeness to the ideal solution of the sth energy storage site selection scheme.

[0135] Step F08: sort all energy storage site selection schemes in descending order of closeness to the ideal solution.

[0136] Step G: Select the energy storage site selection scheme with the largest closeness to the ideal solution as the optimal solution for multi-objective decision making, which is the optimal site location for planned energy storage.

[0137] In order to verify the effect of the method of the present invention, the following experiment is given in the embodiment of the present invention:

[0138] The embodiment of the present invention adopts the IEEE 39 node system and uses the method of the present invention to perform energy storage planning and site selection analysis, wherein the IEEE 39 node system topology diagram is as follows: Figure 4 shown.

[0139] In order to verify the rationality of the k-core decomposition method proposed in the present invention to evaluate the system vulnerability, various simulated attacks are carried out on the IEEE39 node system, line faults are simulated in different orders, and the changes in the power supply range of the whole system are calculated. Among them, the simulated attack fault modes include:

[0140] (1) Randomly select lines in the system and simulate faults in turn;

[0141] (2) simulating faults in order of high and low k-core decomposition indices proposed by the present invention;

[0142] (3) Simulate faults in order of maximum flow transmission contribution index;

[0143] (4) Simulate faults in order of source load intermediate index;

[0144] (5) Simulate faults in order of topological structure and operating status indicators;

[0145] (6) Simulate faults in order of electrical dielectric constant index.

[0146] The evaluation results of the system power supply area under different attack modes are considered according to the sum of the maximum loads that each subsystem can supply after the failure, that is:

[0147] (8)

[0148] Among them, GS(q) is the proportion of the power supply range after the qth line fault to the initial power supply range; F(q) is the sum of the power supply loads of each subsystem after the qth line fault; F0 represents the total power supply load of the initial system.

[0149] like Figure 5 As shown in the figure, under different simulated fault modes, as the number of simulated fault attacks increases successively, there are obvious differences in the reduction of the power supply range of the IEEE39 node system. Compared with random attacks and several other vulnerability assessment indicators, the maximum power supply area of ​​the system decreases fastest when the vulnerable lines evaluated by the k-core decomposition method proposed in the present invention are attacked successively, reflecting that the line with a high core layer value will have a more serious impact on the system after the fault. Therefore, the line vulnerability calculated by the k-core decomposition method is more reasonable.

[0150] In order to verify the rationality of the energy storage planning and site selection method based on grid vulnerability assessment proposed in this invention, it is assumed that the IEEE39 node system is a regional power grid, in which each node corresponds to a substation. It is now planned to invest in the construction of a 50MW / 100MWh energy storage power station within the regional power grid. When selecting the site, it is considered that the energy storage has the conditions to be connected to the lower supply area of ​​each substation, so there are 39 evaluation alternatives. The energy storage access is simulated in each node of the IEEE39 node system respectively, and the changes in the system vulnerability of the entire regional power grid under various typical scenarios are simulated after the lower substations are connected to the energy storage. The results are as follows Figure 6 As shown. Figure 6 It can be seen that the three sub-target indicators of peak discharge, valley charging, and peak charging cannot reach the ideal state at the same time, so the TOPSIS multi-objective decision-making method is used to evaluate and determine the best access point for this energy storage. The weights of each sub-target indicator are set equal. After calculation, the system vulnerability value vector corresponding to the positive ideal solution of the evaluation scheme is , the fragile value vector corresponding to the negative ideal solution is For each evaluation alternative, the distance to the positive ideal solution and the negative ideal solution is calculated according to formula (6), and the closeness to the ideal solution of each solution is obtained according to formula (7). The closeness to the ideal solution of the 39 evaluation alternatives is as follows: Figure 7 As shown in the figure, the evaluation alternatives are ranked according to the ideal solution closeness. The best access location for the planned 50MW / 100MWh energy storage is the node 12 substation supply area, and the ideal solution closeness of this solution is 0.758.

[0151] It can be seen from the above experiments that the present invention can provide the optimal solution for energy storage site selection as reasonably as possible.

[0152] Example 2

[0153] Based on the same inventive concept as Example 1, this example introduces a device for energy storage planning and site selection based on grid vulnerability assessment, such as Figure 8 As shown, it includes a data acquisition module, a power flow scanning module, an equivalent correlation modeling module, a vulnerability analysis module, a multi-objective decision module and an output module.

[0154] The data acquisition module is used to obtain the system structure, operation data and all energy storage site selection plans of the regional power grid.

[0155] The power flow scanning module is used to perform N-1 power flow scanning calculations on the grid data of each energy storage site selection scheme after it is connected to the regional power grid in different typical scenarios, and obtain the power flow scanning data of each energy storage site selection scheme in each typical scenario.

[0156] The equivalent association modeling module is used to construct and weight the corresponding line equivalent association network according to the flow scanning data of each energy storage site selection scheme in each typical scenario, and obtain the authorized line equivalent association network model of each energy storage site selection scheme in each typical scenario.

[0157] The vulnerability analysis module is used to perform k-core decomposition on each authorized line equivalent association network model to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario.

[0158] The multi-objective decision-making module is used to take the system vulnerability index of each energy storage site selection scheme in each typical scenario as the evaluation sub-objective, and use the TOPSIS multi-objective decision-making algorithm to calculate the closeness of the ideal solution of each energy storage site selection scheme.

[0159] The output module is used to select the energy storage site selection scheme with the maximum closeness to the ideal solution as the optimal energy storage site selection scheme.

[0160] The specific functional implementation of each of the above modules can be found in the relevant contents of the method in Example 1 and will not be elaborated here.

[0161] Example 3

[0162] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the energy storage planning and site selection method based on power grid vulnerability assessment in Embodiment 1 are implemented.

[0163] In summary of the above embodiments, the present invention performs flow scanning calculations on all possible energy storage site selection schemes in multiple typical scenarios, and then converts the power grid model into a line equivalent associated network model, and uses k-core decomposition to evaluate the vulnerability of the associated network model. Finally, according to the system vulnerability index in different typical operation scenarios after the energy storage is connected to the power grid, the TOPSIS multi-objective decision algorithm is used to comprehensively evaluate the optimal solution of the energy storage target access point, so as to quickly and accurately calculate the optimal site selection scheme for energy storage. Compared with the prior art, the present invention introduces the system vulnerability index as an evaluation sub-objective for energy storage planning and site selection, and takes into account the impact of energy storage after grid connection in the site selection stage, which can improve the rationality of the energy storage site selection scheme and improve the safety of the power system after the energy storage is connected to the power grid. The present invention evaluates a large number of energy storage site selection schemes through flow scanning, k-core decomposition, and TOPSIS multi-objective decision algorithm, reduces the participation of manual labor in energy storage planning and site selection, reduces planning costs, improves planning efficiency, and can quickly, accurately, reasonably, and effectively provide a basis for its site selection decision in the planning stage of energy storage projects.

[0164] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0168] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for energy storage planning and site selection based on power grid vulnerability assessment, characterized in that: The steps include: Obtain the system structure, operation data and all energy storage site selection options of the regional power grid; According to the system structure and operation data of the regional power grid, the grid data of each energy storage site selection scheme after being connected to the regional power grid is scanned and calculated in the whole network N-1 in different typical scenarios, and the power flow scanning data of each energy storage site selection scheme in each typical scenario is obtained; According to the flow scanning data of each energy storage site selection scheme in each typical scenario, the corresponding line equivalent association network is constructed and weighted, and the weighted line equivalent association network model of each energy storage site selection scheme in each typical scenario is obtained; Perform k-core decomposition on the equivalent association network model of each authorized line to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario; The system vulnerability index of each energy storage site selection scheme in each typical scenario is used as the evaluation sub-goal, and the TOPSIS multi-objective decision-making algorithm is used to calculate the ideal solution closeness of each energy storage site selection scheme; The energy storage site selection scheme with the maximum closeness to the ideal solution is selected as the optimal energy storage site selection scheme.

2. The energy storage planning and site selection method according to claim 1, characterized in that: The typical scenarios include peak load energy storage discharging, valley load energy storage charging, and peak load energy storage charging.

3. The energy storage planning and site selection method according to claim 1, characterized in that: According to the flow scanning data of each energy storage site selection scheme in each typical scenario, a corresponding line equivalent association network is constructed and weighted to obtain a weighted line equivalent association network model for each energy storage site selection scheme in each typical scenario, including: The lines in the regional power grid are converted into nodes in an equivalent association network, and the weighted edges between each pair of nodes represent the impact of power flow transfer under the power grid line fault mode, and an equivalent association network model of the line is constructed for each energy storage site selection scheme in each typical scenario; The weight of each edge in the line equivalent association network model is calculated based on the power flow scanning data. The formula is as follows: ； ； Among them, M represents the correlation matrix of the line equivalent correlation network model, m ij represents the weight of the edge from node i to node j in the line equivalent association network model, represents the power flow of line j in normal operation mode, represents the power flow on line j after line i is disconnected due to a fault, represents the thermal stability limit transmission capacity of line j, is the correlation factor; Assign the element values ​​of the association matrix M to the line equivalent association network model to obtain the weighted line equivalent association network model.

4. The method for energy storage planning and site selection according to claim 1, characterized in that: The k-core decomposition is performed on each authorized line equivalent association network model to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario, including: For each node in each entitled line equivalent association network model, the sum of the weights of all edges connected to the node in the entitled line equivalent association network model is calculated as the power flow transfer degree of the node; According to the power flow transfer degree of each node, the k-core decomposition is performed on the equivalent association network model of the authorized lines to obtain the core layer value of each node in each energy storage site selection scheme in each typical scenario; The sum of the core layer values ​​of all nodes of each energy storage site selection scheme in each typical scenario is taken as the system vulnerability indicator of each energy storage site selection scheme in each typical scenario.

5. The energy storage planning and site selection method according to claim 4, characterized in that: According to the power flow transfer degree of each node, the k-core decomposition of the equivalent association network model of the entitled line is performed, including: (1) Calculate the node with the smallest power flow transfer degree in the equivalent associated network model of the current authorized line, and set its power flow transfer degree as p min , let k = p min ; (2) The nodes whose power flow transfer degree is not greater than k in the equivalent association network model of the right-hand line are classified into the k-core layer, and the nodes are removed from the equivalent association network model of the right-hand line to obtain the updated equivalent association network model of the right-hand line; (3) According to the updated equivalent association network model of the authorized lines, the flow transfer degrees of the remaining nodes are updated. The formula is: ,in, represents the degree of power flow transfer after updating, and p represents the degree of power flow transfer before updating. represents nuclear dissociation retention factor; (4) Determine the power transfer degree of all nodes in the equivalent associated network model of the current authorized line Is it greater than k? If so, proceed to the next step; if not, return to step (2); (5) Determine whether all nodes in the current weighted line equivalent association network model are classified into the corresponding core layer. If so, end the k-core decomposition. If not, return to step (1).

6. The energy storage planning and site selection method according to claim 5, characterized in that: When node i exists in the k-core layer, the core layer value of node i is k.

7. The method for energy storage planning and site selection according to claim 1, characterized in that: The system vulnerability index of each energy storage site selection scheme in each typical scenario is used as an evaluation sub-goal, and the TOPSIS multi-objective decision-making algorithm is used to calculate the ideal solution closeness of each energy storage site selection scheme, including: Construct a multi-objective decision matrix based on the system vulnerability index of each energy storage site selection scheme in each typical scenario , where n is the total number of energy storage site selection options, r is the total number of typical scenarios, and the multi-objective decision matrix Medium Element represents the system vulnerability index value of the s-th energy storage site selection scheme under the t-th typical scenario, , ; For multi-objective decision matrix Take the inverse of each element in and get the high-optimality decision matrix ; High-quality decision matrix Each element in is vector normalized to obtain the normalized decision matrix : ； in, Represents the normalized decision matrix The value of the element in the sth row and tth column, Represents a high-priority decision matrix The element value at the sth row and tth column in ; According to the preset weight sequence and normalized decision matrix , and obtain the weight normalized decision matrix : ； in, represents the weight normalized decision matrix The value of the element in the sth row and tth column, is the weight of the tth typical scenario; Normalize the decision matrix according to the weights Calculate the positive ideal solution and negative ideal solution of each energy storage site selection scheme, and the calculation formula is as follows: ； in, represents the positive ideal solution matrix, represents the negative ideal solution matrix, represents the positive ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the negative ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the weight normalized decision matrix The maximum value of the tth column in represents the weight normalized decision matrix The minimum value of the tth column in; According to the positive ideal solution and negative ideal solution of each energy storage site selection scheme, the distance scale of each energy storage site selection scheme is calculated. The calculation formula is as follows: ； in, represents the distance of the sth energy storage site selection scheme relative to the positive ideal solution, represents the distance of the sth energy storage site selection scheme relative to the negative ideal solution; According to the distance scale of each energy storage site selection scheme, the ideal solution closeness of each energy storage site selection scheme is calculated. The calculation formula is as follows: ； in, represents the closeness to the ideal solution of the sth energy storage site selection scheme.

8. A device for energy storage planning and site selection based on power grid vulnerability assessment, characterized in that: include: Data acquisition module, used to obtain the system structure, operation data and all energy storage site selection plans of the regional power grid; The power flow scanning module is used to perform N-1 power flow scanning calculations on the grid data of each energy storage site selection scheme after it is connected to the regional power grid in different typical scenarios, and obtain the power flow scanning data of each energy storage site selection scheme in each typical scenario; The equivalent association modeling module is used to construct and weight the corresponding line equivalent association network according to the flow scanning data of each energy storage site selection scheme in each typical scenario, and obtain the authorized line equivalent association network model of each energy storage site selection scheme in each typical scenario; The vulnerability analysis module is used to perform k-core decomposition on the equivalent associated network model of each authorized line to obtain the system vulnerability index of each energy storage site selection scheme in each typical scenario; The multi-objective decision-making module is used to use the system vulnerability index of each energy storage site selection scheme in each typical scenario as an evaluation sub-objective, and use the TOPSIS multi-objective decision-making algorithm to calculate the ideal solution closeness of each energy storage site selection scheme; The output module is used to select the energy storage site selection scheme with the maximum closeness to the ideal solution as the optimal energy storage site selection scheme.

9. The energy storage planning and site selection device according to claim 8, characterized in that: The vulnerability analysis module is specifically used for: For each node in each entitled line equivalent association network model, the sum of the weights of all edges connected to the node in the entitled line equivalent association network model is calculated as the power flow transfer degree of the node; According to the power flow transfer degree of each node, the k-core decomposition is performed on each authorized line equivalent association network model to obtain the core layer value of each node in each energy storage site selection scheme in each typical scenario; The sum of the core layer values ​​of all nodes of each energy storage site selection scheme in each typical scenario is taken as the system vulnerability indicator of each energy storage site selection scheme in each typical scenario.

10. The energy storage planning and site selection device according to claim 8, characterized in that: The multi-objective decision-making module is specifically used for: Construct a multi-objective decision matrix based on the system vulnerability index of each energy storage site selection scheme in each typical scenario , where n is the total number of energy storage site selection options, r is the total number of typical scenarios, and the multi-objective decision matrix Medium Element represents the system vulnerability index value of the s-th energy storage site selection scheme under the t-th typical scenario, , ; For multi-objective decision matrix Take the inverse of each element in and get the high-optimality decision matrix ; High-quality decision matrix Each element in is vector normalized to obtain the normalized decision matrix : ； in, Represents the normalized decision matrix The value of the element in the sth row and tth column, Represents a high-priority decision matrix The element value at the sth row and tth column in ; According to the preset weight sequence and normalized decision matrix , and obtain the weight normalized decision matrix : ； in, represents the weight normalized decision matrix The value of the element in the sth row and tth column, is the weight of the tth typical scenario; Normalize the decision matrix according to the weights Calculate the positive ideal solution and negative ideal solution of each energy storage site selection scheme, and the calculation formula is as follows: ； in, represents the positive ideal solution matrix, represents the negative ideal solution matrix, represents the positive ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the negative ideal solution of the s-th energy storage site selection scheme under the t-th typical scenario, represents the weight normalized decision matrix The maximum value of the tth column in represents the weight normalized decision matrix The minimum value of the tth column in; According to the positive ideal solution and negative ideal solution of each energy storage site selection scheme, the distance scale of each energy storage site selection scheme is calculated. The calculation formula is as follows: ； in, represents the distance of the sth energy storage site selection scheme relative to the positive ideal solution, represents the distance of the sth energy storage site selection scheme relative to the negative ideal solution; According to the distance scale of each energy storage site selection scheme, the ideal solution closeness of each energy storage site selection scheme is calculated. The calculation formula is as follows: ； in, represents the closeness to the ideal solution of the sth energy storage site selection scheme.

Images