Distribution area emergency response and power transfer method for distributed energy uncertainty

Abstract

The invention discloses a power distribution area emergency response and power transfer method for distributed energy uncertainty. The method comprises the following steps: constructing a distributed energy output model; a load weight model is constructed, and clustering is carried out on fault recovery time periods by adopting an ISODATA clustering algorithm; based on a clustering result, establishing an island division model by taking the maximum recovery power-loss load capacity as a target, and establishing a network reconstruction model by taking the minimum network loss, the minimum voltage offset and the minimum switching operation times as targets; carrying out island division by adopting a breadth-first traversal method based on the island division model and the distributed energy output model; a whale algorithm is improved by adopting chaotic mapping and a reverse learning strategy, and network reconstruction is carried out by adopting the improved whale algorithm based on a network reconstruction model; and integrating an island division result and a network reconstruction result, and outputting a power distribution area fault recovery operation result. The method can effectively solve the problem of power supply unreliability of the power distribution area.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution substation fault recovery, and particularly relates to an emergency response and power transfer method for a distribution substation with distributed energy uncertainty. Background Art

[0002] As an important part of the power system, the distribution substation is directly connected to users. When a fault occurs in the distribution substation, it will directly affect the daily power consumption of users. With the increasing proportion of distributed energy in the distribution substation, in order to ensure power supply reliability and quickly restore the power supply of important loads after a fault occurs in the distribution substation, using distributed energy for distribution substation fault recovery has become a trend. Although the access of distributed energy has increased the complexity of the distribution substation, it can supply power for a short time when a fault occurs in the distribution substation, and form islands by dividing the substation into regions. At the same time, load transfer is carried out using tie switches to restore the power supply of the distribution substation and optimize the power flow operation. Therefore, both network reconfiguration and islanding are effective strategies for restoring power supply to loads after a fault.

[0003] How to quickly restore power supply in an emergency and how to combine islanding and network reconfiguration will become research hotspots and difficulties. At present, many methods for distribution substation fault recovery have been proposed at home and abroad and are gradually applied in distribution substations. However, there are still problems in islanding and network reconfiguration. Some solutions only consider islanding and do not consider network reconfiguration; some solutions consider both islanding and network reconfiguration, but do not consider the characteristics of load and distributed energy changing over time. Some solutions use algorithms with more iteration times, longer solution times, and unsatisfactory solution results.

[0004] In view of the above problems in the prior art, it is urgent to propose an emergency response and power transfer method for a distribution substation with distributed energy uncertainty. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes an emergency response and power transfer method for a distribution substation with distributed energy uncertainty to solve the problems existing in the above prior art.

[0006] To achieve the above object, the present invention provides an emergency response and power transfer method for a distribution substation with distributed energy uncertainty, including the following steps:

[0007] When a fault occurs in the distribution substation, a distributed energy output model is constructed based on the influencing factors of distributed energy output;

[0008] A load weight model is constructed, and the ISODATA clustering algorithm is used to cluster the fault recovery period based on the load weight model;

[0009] Based on the clustering results, an islanding division model is established with the goal of maximizing the restored power loss of the de-energized load, and a network reconfiguration model is established with the goals of minimizing network losses, minimizing voltage deviation, and minimizing the number of switching operations;

[0010] Construct the constraint conditions corresponding to the islanding division model and the network reconfiguration model;

[0011] Based on the islanding division model and the distributed energy output model, the breadth-first search method is used for islanding division;

[0012] The whale algorithm is improved by using chaotic mapping and reverse learning strategy, and based on the network reconfiguration model, the improved whale algorithm is used for network reconfiguration;

[0013] Integrate the results of islanding division and the results of network reconfiguration, and output the emergency response and power transfer scheme.

[0014] Optionally, the expression of the distributed energy output model is as follows:

[0015]

[0016] In the formula: p g represents the distributed photovoltaic power generation; k represents the illumination area; η represents the solar energy conversion efficiency; α represents the illumination intensity; w represents the temperature coefficient; T represents the actual temperature of the photovoltaic panel; T a represents the actual environmental temperature; T z represents the temperature of the photovoltaic panel when the reference temperature is 25 degrees.

[0017] Optionally, the expression of the load weight model is as follows:

[0018]

[0019] In the formula, w k represents the recovery coefficient of node k; F k represents the load weight; c1, c2 represent the coefficients of two determining factors; H k represents the electrical load; F max represents the maximum load weight; H max represents the maximum electrical load.

[0020] Optionally, the process of clustering the fault recovery period by using the ISODATA clustering algorithm based on the load weight model includes:

[0021] Using the Mahalanobis distance as the similarity degree between samples, aggregate the fault recovery periods to obtain clustering samples; use the ISODATA algorithm to cluster the clustering samples to obtain the clustering center numbers for each period; based on the clustering center numbers for each period, merge the fault recovery periods that belong to the same clustering center and are adjacent into one segment, and use the value of the clustering center as the load status of the nodes within the corresponding fault recovery period.

[0022] Optionally, the objective function expression of the island division model is as follows:

[0023]

[0024] In the formula, K represents the set of distribution transformer area load nodes; w k represents the priority coefficient of load node k; p k represents the power of node k; x k represents a 0-1 variable; q1 represents the amount of restored power outage load.

[0025] Optionally, the objective function expression of the network reconfiguration model is as follows:

[0026]

[0027] In the formula, m q2 、m q3 、m q4 represent weight coefficients respectively; q2 represents the network loss; q3 represents the voltage deviation; q4 represents the number of switch operations.

[0028] Optionally, the constraint conditions of the island division model and the network reconfiguration model both include power constraint, node voltage constraint, branch capacity constraint, charge and discharge constraint of energy storage devices, capacity constraint, connectivity constraint and network radiality constraint;

[0029] The constraint conditions of the network reconfiguration model also include power balance constraint.

[0030] Optionally, the process of island division using the breadth-first search method includes:

[0031] Calculate the output of distributed energy based on the distributed energy output model; obtain the node positions of distributed energy, use the node positions of distributed energy as the root nodes, and search with the output of distributed energy as the radius to determine the power circle island range; if the power circle islands cross, perform island recombination, and if the power circle islands do not cross, the island division ends.

[0032] Optionally, the improved whale algorithm includes: initializing the whale population using chaotic mapping, calculating the reverse solution using the reverse learning strategy, providing calculation methods for vector coefficients and convergence factors, and optimizing the positions of whale individuals using the Levy flight strategy to complete the improvement of the whale algorithm.

[0033] The present invention also provides a distribution network area emergency response and power transfer system for distributed energy uncertainty, for implementing the method, including: a distributed energy output model construction module, a load weight model construction module, a fault recovery period clustering module, an island division model construction module, a network reconfiguration model construction module, a constraint condition construction module, an island division execution module, a network reconfiguration optimization module, and an emergency plan integration module;

[0034] The distributed energy output model construction module is used to construct a distributed energy output model based on the influencing factors of distributed energy output when a fault occurs in the distribution network area;

[0035] The load weight model construction module is used to construct a load weight model;

[0036] The fault recovery period clustering module is used to cluster the fault recovery periods using the ISODATA clustering algorithm based on the load weight model;

[0037] The island division model construction module is used to establish an island division model with the goal of maximizing the amount of restored power loss based on the clustering results;

[0038] The network reconfiguration model construction module is used to establish a network reconfiguration model with the goals of minimizing network loss, minimizing voltage deviation, and minimizing the number of switch operations;

[0039] The constraint condition construction module is used to construct the constraint conditions corresponding to the island division model and the network reconfiguration model;

[0040] The island division execution module is used to perform island division using the breadth-first traversal algorithm based on the island division model and the distributed energy output model;

[0041] The network reconfiguration optimization module is used to improve the whale algorithm using chaotic mapping and reverse learning strategy, and perform network reconfiguration using the improved whale algorithm based on the network reconfiguration model;

[0042] The emergency plan integration module is used to integrate the results of island division and network reconfiguration, and output the distribution network area emergency response and power transfer plan.

[0043] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method.

[0044] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0045] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method are implemented.

[0046] Compared with the prior art, the present invention has the following advantages and technical effects:

[0047] A method for emergency response and power transfer in a distribution substation area with distributed energy uncertainty proposed by the present invention first establishes a distributed energy output model for the uncertainty of distributed energy, and secondly, considering the time-varying nature of the load, establishes a load weight model related to time, and uses the ISODATA clustering algorithm to aggregate each time period, thereby simplifying the solution. Finally, a multi-objective optimization model is established to obtain a fault recovery strategy for the distribution substation area. The method of the present invention can effectively solve the problem of unreliable power supply and long power supply recovery interval in the distribution substation area. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0049] Figure 1 is the flowchart of the method of the embodiment of the present invention;

[0050] Figure 2 is the flowchart of the fault time period division of the embodiment of the present invention;

[0051] Figure 3 is the flowchart of the ISODATA clustering algorithm of the embodiment of the present invention;

[0052] Figure 4 is the flowchart of the island division using the breadth-first traversal method of the embodiment of the present invention;

[0053] Figure 5 is the flowchart of the improved whale algorithm of the embodiment of the present invention;

[0054] Figure 6 is the schematic structural diagram of the distribution substation area of the embodiment of the present invention;

[0055] Figure 7 is the completion result diagram of the distribution substation area of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0057] It should be noted that the steps shown in the flowchart of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0058] Embodiment 1

[0059] As Figures 1-3 shown, in this embodiment, a method for emergency response and power transfer in a distribution substation area with distributed energy uncertainty is provided. This method considers four factors: restoring as many lost power loads with high importance, network loss, voltage deviation, and the number of switch operations as possible, so as to establish a multi-objective function to solve the problem of fault recovery in the distribution substation area. The breadth-first traversal method and the whale algorithm are used for island division and network reconstruction, considering various constraint conditions such as power constraint, node voltage constraint, branch capacity constraint, charge and discharge constraint and capacity constraint of energy storage devices, connectivity constraint, and network radial constraint, etc., to improve the reliability and accuracy of island division and network reconstruction.

[0060] As a specific embodiment, the method includes the following steps:

[0061] Step 1: When a fault occurs in the distribution substation area, it is necessary to obtain the location, capacity information, fault location, fault occurrence time period, and load level of distributed energy nodes.

[0062] Step 2: Since the output of distributed energy is affected by solar irradiation, temperature, and wind speed, resulting in uncertainty in the output of distributed energy, a distributed energy output model is established.

[0063] The distributed energy output model described in Step 2 is as follows:

[0064] The output of distributed photovoltaics is mainly affected by the solar radiation intensity, secondly affected by the temperature, and at the same time considering the influence of solar radiation on the temperature of the photovoltaic panel, the following mathematical model is obtained:

[0065]

[0066] In the formula: p g represents the distributed photovoltaic power generation; k represents the illumination area; η represents the solar energy conversion efficiency; α represents the illumination intensity; w represents the temperature coefficient; T represents the actual temperature of the photovoltaic panel; T a represents the actual ambient temperature; T zIndicates the temperature of the photovoltaic panel at a reference temperature of 25 degrees Celsius.

[0067] The efficiency and feasibility of distributed wind power generation are affected by multiple factors: Wind speed is the most direct factor affecting wind power generation. When the wind speed is too low, the wind turbine cannot generate electricity; when the wind speed is too high, the wind turbine will automatically shut down to protect the equipment. Secondly, temperature also affects wind power generation. The impact of temperature on wind power generation is mainly reflected in the change of air density. Air density directly affects the output power of the wind turbine because the wind turbine utilizes the kinetic energy of the wind, and air density determines the mass of air per unit volume, thus affecting the density of wind energy. Therefore, the following mathematical model is obtained:

[0068]

[0069] In the formula: p f represents the output power; v represents the wind speed; v i represents the cut-in wind speed; vj represents the cut-out wind speed; v r represents the rated wind speed of the wind turbine; C p represents the power coefficient of the wind turbine. The actual value depends on the design of the wind turbine. In this embodiment, the value is taken as 0.4; P represents the atmospheric pressure; r represents the gas constant; T represents the ambient temperature; A represents the area swept by the wind turbine rotor. λ k represents the influence coefficient related to the season.

[0070] There are three modes of energy storage devices in the distribution substation area: First is the discharging mode; when the energy storage system is in the inverter mode, it will convert the stored energy into electricity. At this time, the model of the energy storage system is shown in the following formula (3). Secondly is the charging mode; when the energy storage system is in the rectifying state, the energy storage system shows a load property and absorbs power from the distribution network and stores it in the energy storage device. At this time, the model of the energy storage system is shown in the following formula (4). Finally is the floating charge mode; the energy storage system does not participate in the operation of the distribution network, there is no power interaction behavior between the two, and its charge capacity does not change. At this time, the model of the energy storage system is shown in the following formula (5):

[0071]

[0072] E i+1 = E i (5)

[0073] In the formula: E represents the remaining power of the energy storage system at time i; p if , p ic represent the discharging power and charging power of the energy storage system at time i; α and β represent the discharging and charging levels respectively; Δt represents the charging and discharging time; represents the loss during the charging and discharging process of the energy storage device.

[0074] Step 3. The load power varies with time. At the same time, different loads are divided into primary loads, secondary loads, and tertiary loads, and the load power cannot be set as a fixed value. Therefore, a load model related to time needs to be established. Secondly, the importance of the load needs to be considered from two aspects: load power and load level. Therefore, a load weight model is established. Finally, the ISODATA clustering algorithm is used to cluster the fault recovery periods.

[0075] The load model described in Step 3 is as follows:

[0076]

[0077] In the formula: H k (t) represents the power consumption load of node k in the stage from t to t + 1; h k (t) represents the load curve of node k.

[0078] For the load weight model described above, the size of the load weight not only needs to consider the power consumption load in different periods, but also needs to consider the load level. According to the importance, the load is divided into primary load, secondary load, and tertiary load. Therefore, the two are considered simultaneously, and normalization processing is adopted for the decision factors with different dimensions, so as to establish the load weight model specifically as follows;

[0079]

[0080] In the formula: w k represents the recovery coefficient of node k; F k represents the load weight, which is 50, 5, and 1 for primary, secondary, and tertiary respectively; c1 and c2 represent the coefficients of two decision factors. In this embodiment, when performing fault recovery, the load weight is mainly considered and then the power consumption load is considered. Therefore, their values are 0.6 and 0.4 respectively.

[0081] The specific steps of using the ISODATA algorithm to cluster the fault recovery periods are as follows:

[0082] (3.1) When a fault occurs in the distribution substation area, the power consumption load has time-variability. In order to simplify the fault recovery, the fault periods are aggregated, and the Mahalanobis distance is used as the similarity degree between samples.

[0083] (3.2) Use the ISODATA algorithm to cluster the clustering samples to obtain the clustering center numbers of each period.

[0084] (3.3) Combine the periods that belong to the same clustering center and are adjacent into one segment, simplify the multi-segment fault periods into one segment, and use the value of the clustering center as the load status of the nodes within the fault recovery period to complete the division of the fault recovery period.

[0085] The Mahalanobis distance described in step (3.1) is;

[0086]

[0087] Where: R t represents the set of load states of all nodes in time period t; S m represents the set of node load states of the m-th clustering center; r tk represents the complex power of node k in time period t; s mk represents the complex power of node k of the m-th clustering center; S1 represents the inverse matrix of the covariance matrix, and its specific calculation method is as follows;

[0088]

[0089] Where: μ is the mean vector of the sample; I represents the identity matrix; represents a constant. In this patent, when calculating the Mahalanobis distance, it belongs to high-dimensional data, and there are situations where the covariance matrix is irreversible or unstable. Therefore the value is 10.

[0090] The specific steps of the ISODATA algorithm described in step (3.2) are as follows:

[0091] (3.2.1) Input the initial data, including the sample standard deviation threshold, the minimum distance threshold between two clustering centers, and the number of clustering centers allowed to be merged in one iteration.

[0092] (3.2.2) Set the algorithm parameters, mainly including relevant parameters such as the expected number of clustering centers, the number of samples in each class, and the number of iterations.

[0093] (3.2.3) Assign the samples to each cluster, using the distance from the clustering center as the criterion.

[0094] (3.2.4) Judge whether the number of samples meets the set requirements. If it meets, proceed to the next step to update the clustering center. Otherwise, reduce the number of clustering centers by one and return to step (3.2.3).

[0095] (3.2.5) Judge the splitting, merging, and iteration operation steps. For the splitting operation, if the number of clustering centers is less than or equal to half of the expected number, split; otherwise, perform the merging process. Finally, judge the number of iterations. If it is equal to the maximum number of iterations, end; otherwise, continue to iterate.

[0096] Step 4: Establish a fault recovery method model for the distribution substation area. An island division model is established with the goal of maximizing the restored power loss. A network reconfiguration model is established with the goals of minimizing network loss, minimizing voltage deviation, and minimizing the number of switch operations. Constraint conditions are constructed considering power constraints, node voltage constraints, branch capacity constraints, charge and discharge constraints and capacity constraints of energy storage devices, connectivity constraints, and network radiality constraints.

[0097] The objective function of the island division model for the distribution substation area described in Step 4 is to maximize the restored power loss:

[0098]

[0099] In the formula: K represents the set of load nodes in the substation area; w k represents the priority coefficient of load node k; p k represents the power of node k; x k represents a 0-1 variable, which represents whether to restore the load of node k with 1 and 0 respectively.

[0100] The set constraint conditions are set as follows:

[0101] The island power constraint is:

[0102]

[0103] In the formula: m represents the number of distributed energy sources in the island; p bt represents the power of the bth distributed energy source at time t; p kt represents the power magnitude of node k at time t; Q represents the set of branches in the distribution substation area; U jt represents the voltage of branch j at time t; x j represents a 0-1 variable, which represents whether branch j is within the island division with 1 and 0 respectively. ε p represents the power safety threshold.

[0104] To prevent the voltage from dropping too low, a safety threshold is added to the minimum voltage constraint to ensure that there is no too low voltage in the system and to protect the safety of the equipment. To prevent the voltage from rising too high, a safety threshold is added to the maximum voltage constraint to avoid equipment damage or system instability caused by too high voltage. Therefore, its node voltage constraint is:

[0105] u kmin +ε u ≤u kt ≤u kmax -ε u (12)

[0106] In the formula: u kt represents the voltage amplitude of node k at time t; u kmax 、ukmin Denote the upper and lower limits of the voltage amplitude of node k. In this embodiment, the values are 1.05 times and 0.95 times the rated voltage; ε u Denote the voltage safety threshold.

[0107] The branch capacity constraint is:

[0108] p jmax -p j ≥γ p (13)

[0109] In the formula: P j Denote the power of branch j; P jmax Denote the maximum allowable power of branch j. γ p Denote the safety threshold of the branch capacity constraint.

[0110] The charge-discharge constraint and capacity constraint of the energy storage device are:

[0111]

[0112] In the formula: ε c 、ε f Denote the safety threshold of the charging power and the safety threshold of the discharging power; E(t) denotes the capacity of the energy storage device.

[0113] The connectivity constraint and the network radiality constraint are:

[0114] z∈Z(15)

[0115] Set the objective function of the distribution substation area network reconstruction model:

[0116] In the distribution substation area, the network loss is mainly related to the voltage and resistance of the transmission line. Here, considering the factors affecting the resistance, the equation for minimizing the network loss is obtained:

[0117] r1=λR j (T-T0) (16)

[0118]

[0119] In the formula: r1 denotes the influence resistance of the branch due to temperature; r2 denotes the ice-snow resistance in the case of ice-snow coverage; T denotes the actual temperature; T0 denotes the reference temperature of 25 degrees; ρ ice Denote the resistivity of ice-snow; L ice 、A ice Denote the thickness and cross-sectional area of the ice-snow layer; Q denotes the set of branches in the distribution substation area; U jt Denote the voltage of branch j at time t; R j Denote the resistance of branch j at the reference temperature.

[0120] The equation for minimizing voltage deviation is as follows:

[0121]

[0122] In the formula: represents the reference voltage of node k; u k represents the actual voltage of node k.

[0123] The equation for minimizing the number of switch operations is as follows:

[0124]

[0125] When setting the network reconfiguration of the distribution substation area, three objective functions are considered. Since the three objective functions have different dimensions and different degrees of importance, normalization and weighting are performed to obtain a single objective function:

[0126]

[0127] In the formula: m q2 , m q3 , m q4 respectively represent the weight coefficients. When establishing the objective function in this embodiment, minimizing network loss is mainly considered, followed by minimizing voltage deviation, and finally minimizing the number of switch operations. Therefore, the values are 0.5, 0.3, and 0.2;

[0128] Set the following constraint conditions:

[0129] Power flow constraint, i.e., power balance constraint:

[0130]

[0131] In the formula: P i , Q i represent the active power and reactive power injected into node i; N represents the total number of nodes; U i , U j represent the voltage amplitudes of nodes i and j; G ij , B ij , θ ij are the conductance, susceptance, and voltage phase angle difference of nodes i and j, respectively.

[0132] Other constraint conditions are the same as those for islanding division.

[0133] Step 5: Use the breadth-first traversal method to perform islanding division.

[0134] As Figure 4 shown, the use of the breadth-first traversal method to perform islanding division in Step 5 is as follows:

[0135] (5.1) After a fault occurs, it is necessary to calculate the output of distributed energy.

[0136] (5.2) Based on the location of the node where the distributed energy is located, use it as the root node to provide a starting point for subsequent searches.

[0137] (5.3) Use the obtained output of the distributed energy as the radius for searching to determine the power circle range.

[0138] (5.4) Determine whether the power circle islands intersect. If they do, perform island combination. Otherwise, end.

[0139] Step 6: Improve the whale algorithm using chaotic mapping and reverse learning strategy, and then use the improved whale algorithm for network reconstruction.

[0140] As Figure 5 shown, the improved whale algorithm described in Step 6 is specifically as follows:

[0141] (6.1) Initialize the whale population and calculate the fitness of the whale population.

[0142] (6.2) Use the elite reverse learning strategy to calculate the reverse solution, compare the fitness of the two, and select the one with higher fitness to join the next generation of the whale population.

[0143] (6.3) Calculate the vector coefficient and the convergence factor.

[0144] (6.4) Update the whale position.

[0145] (6.5) Through the whale position updated in Step (6.4), optimize the whale individual position through the Levy flight strategy, compare the fitness, save the individual with higher fitness, and continue the iteration.

[0146] (6.6) Determine whether the maximum number of iterations is reached. If it is, end the iteration and output the result. Otherwise, continue with Step (6.3) and continue the iteration.

[0147] The initialization of the whale population using chaotic mapping described in Step 6.1 is as follows:

[0148] (6.1.1) Initialize D-dimensional whale individuals in the interval [0, 1] within the search space.

[0149] (6.1.2) Use the following formula to generate chaotic individuals to obtain a chaotic sequence.

[0150]

[0151] (6.1.3) Then map to the original search space using the following formula.

[0152]

[0153] Where: N represents the population size; D represents the search dimension, and X ij represents the variable mapped to the search space, and Y ij represents the mapping sequence.

[0154] The elite opposition-based learning strategy described in Step 6.2 is as follows:

[0155]

[0156] Where: S ij represents the elite solution; r represents a random number with a value range of [0, 1]; b maxj , b minj represent the maximum and minimum values of the elite group in the j-th dimension; represents the opposition elite solution; m s represents the weight coefficient. This patent provides logarithmic and exponential functions for elite opposition-based learning. Since it mainly changes according to the exponential function, the value here is 0.7; T and t represent the total number of iterations and the current number of iterations.

[0157] The update vector coefficient and convergence factor described in Step 6.3 are as follows:

[0158]

[0159] Where: represents the vector coefficient; δ represents the perturbation coefficient with a value range of [-0.1, 0.1]; represents a random number with a value range of [0, 1]; t represents the number of iterations; represents the convergence factor; μ represents the non-linear adjustment coefficient.

[0160] The position update described in Step 6.4 is as follows:

[0161] (6.4.1) Randomly give a random number P with a value range of [0, 1].

[0162] (6.4.2) When P is less than 0.45, the search encirclement phase is carried out.

[0163]

[0164] Where: represents the distance from the whale to the prey; represents the random position of the whale; represents the position vector of the current optimal solution; t represents the number of iterations.

[0165] (6.4.3) When P is greater than or equal to 0.45, the spiral position update is carried out.

[0166]

[0167] In the formula: l represents a random number with a value range of [-1, 1].

[0168] The Levy flight strategy described in step 6.5 optimizes the position of the whale individual as follows:

[0169]

[0170] In the formula: t represents the current iteration number; represents the step size coefficient, which follows the Levy flight distribution; μ and υ satisfy the normal distribution; Γ is the standard Gamma function; ρ represents a constant of 1.5.

[0171] Step 7, output the fault recovery operation result of the distribution substation area.

[0172] As Figure 6 shown, taking the IEEE33 node as an example, which includes 5 distributed power sources and tie switches represented by dotted lines, and conducts fault recovery for the distribution substation area. Figure 7 is the completion result diagram of the distribution substation area for example. Among them, 5 distributed power sources are used to form an island form, and some tie switches are closed and represented by solid lines, so as to complete the power supply load transfer.

[0173] Embodiment 2

[0174] This embodiment also provides a distribution substation area emergency response and power transfer system for distributed energy uncertainty for implementing the described method, including: a distributed energy output model construction module, a load weight model construction module, a fault recovery period clustering module, an island division model construction module, a network reconstruction model construction module, a constraint condition construction module, an island division execution module, a network reconstruction optimization module, and an emergency plan integration module;

[0175] The distributed energy output model construction module is used to construct a distributed energy output model based on the influencing factors of distributed energy output when a fault occurs in the distribution substation area;

[0176] The load weight model construction module is used to construct a load weight model;

[0177] The fault recovery period clustering module is used to cluster the fault recovery periods based on the load weight model by using the ISODATA clustering algorithm;

[0178] The island division model construction module is used to establish an island division model based on the clustering result with the goal of maximizing the amount of lost power load restored;

[0179] The network reconstruction model construction module is used to establish a network reconstruction model with the goals of minimizing network loss, minimizing voltage deviation, and minimizing the number of switch operations;

[0180] The constraint condition construction module is used to construct the constraint conditions corresponding to the island division model and the network reconstruction model;

[0181] The island division execution module is used to perform island division based on the island division model and the distributed energy output model by using the breadth-first traversal algorithm;

[0182] The network reconstruction optimization module is used to improve the whale algorithm by using chaotic mapping and reverse learning strategies, and perform network reconstruction based on the network reconstruction model by using the improved whale algorithm;

[0183] The emergency plan integration module is used to integrate the results of island division and network reconstruction, and output the emergency response and power transfer plan for the distribution transformer area.

[0184] Embodiment III

[0185] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method.

[0186] Embodiment IV

[0187] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0188] Embodiment V

[0189] This embodiment also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method are implemented.

[0190] The above is only the preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the technical field of the present application within the technical scope disclosed by the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for emergency response and power transfer in a distribution substation area with distributed energy uncertainty, characterized in that It includes the following steps: When a fault occurs in the distribution substation area, based on the influencing factors of distributed energy output, construct a distributed energy output model; Construct a load weight model, and cluster the fault recovery period by using the ISODATA clustering algorithm based on the load weight model; Based on the clustering results, establish an island division model with the goal of maximizing the restored power loss load, and establish a network reconfiguration model with the goals of minimizing network loss, minimizing voltage deviation, and minimizing the number of switch operations; Construct the constraint conditions corresponding to the island division model and the network reconfiguration model; Based on the island division model and the distributed energy output model, use the breadth-first search algorithm to perform island division; Improve the whale algorithm by using the chaos mapping and the reverse learning strategy, and perform network reconfiguration by using the improved whale algorithm based on the network reconfiguration model; Integrate the results of island division and network reconfiguration, and output the emergency response and power transfer scheme for the distribution substation area.

2. The method according to claim 1, wherein The expression of the distributed energy output model is as follows: Where: p g represents the distributed photovoltaic power generation; k represents the light-receiving area; η represents the solar energy conversion efficiency; α represents the light intensity; w represents the temperature coefficient; T represents the actual temperature of the photovoltaic panel; T a represents the actual ambient temperature; T z represents the temperature of the photovoltaic panel when the reference temperature is 25 degrees.

3. The method according to claim 1, wherein The expression of the load weight model is as follows: where, w k represents the k-node recovery coefficient; F k represents the load weight; c1 and c2 represent the coefficients of two determining factors; H k represents the electricity load; F max represents the maximum load weight; H max represents the maximum electricity load.

4. The method according to claim 3, wherein The process of clustering the fault recovery period by using the ISODATA clustering algorithm based on the load weight model includes: Use the Mahalanobis distance as the similarity between samples, aggregate the fault recovery period to obtain clustering samples; use the ISODATA algorithm to cluster the clustering samples to obtain the clustering center numbers of each period; based on the clustering center numbers of each period, merge the fault recovery periods that belong to the same clustering center and are adjacent into one segment, and use the value of the clustering center as the load status of the nodes within the corresponding fault recovery period.

5. The method according to claim 1, wherein The objective function expression of the island division model is as follows: Where K represents the set of load nodes in the substation area; w k represents the priority coefficient of load node k; p k represents the power of node k; x k represents a 0-1 variable; q1 represents the amount of restored power outage load.

6. The method according to claim 1, wherein The objective function expression of the network reconfiguration model is as follows: where m q2 , m q3 , m q4 represent weight coefficients respectively; q2 represents network loss; q3 represents voltage deviation; q4 represents the number of switching operations.

7. The method according to claim 1, wherein The constraint conditions of the island division model and the network reconfiguration model both include power constraint, node voltage constraint, branch capacity constraint, charge and discharge constraint and capacity constraint of energy storage devices, as well as connectivity constraint and network radial constraint; The constraint conditions of the network reconfiguration model also include power balance constraint.

8. The method according to claim 1, wherein The improved whale algorithm includes: initializing the whale population by using chaos mapping, calculating the reverse solution by using the reverse learning strategy, providing the calculation methods of the vector coefficient and the convergence factor, and optimizing the positions of whale individuals by using the Levy flight strategy to complete the improvement of the whale algorithm.

9. A distribution transformer substation emergency response and power transfer system for distributed energy uncertainty, characterized in that, For implementing the method according to any one of claims 1-8, including: a distributed energy output model construction module, a load weight model construction module, a fault recovery period clustering module, an island division model construction module, a network reconfiguration model construction module, a constraint condition construction module, an island division execution module, a network reconfiguration optimization module, and an emergency plan integration module; The distributed energy output model construction module is used to construct a distributed energy output model based on the influencing factors of distributed energy output when a fault occurs in the distribution substation area; The load weight model construction module is used to construct a load weight model; The fault recovery period clustering module is used to cluster the fault recovery periods by using the ISODATA clustering algorithm based on the load weight model; The island division model construction module is used to establish an island division model with the goal of maximizing the amount of restored power-off load based on the clustering results; The network reconfiguration model construction module is used to establish a network reconfiguration model with the goals of minimizing network loss, minimizing voltage deviation, and minimizing the number of switch operations; The constraint condition construction module is used to construct the constraint conditions corresponding to the island division model and the network reconfiguration model; The island division execution module is used to perform island division by using the breadth-first search algorithm based on the island division model and the distributed energy output model; The network reconfiguration optimization module is used to improve the whale algorithm by using the chaotic mapping and reverse learning strategy, and perform network reconfiguration by using the improved whale algorithm based on the network reconfiguration model; The emergency plan integration module is used to integrate the results of island division and the results of network reconfiguration, and output the emergency response and power transfer scheme for the distribution substation area.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-8.

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