Method, system and equipment for evaluating hybrid bearing capacity of power distribution network and medium

Abstract

The invention relates to the technical field of power distribution networks, and discloses a method, a system, equipment and a medium for evaluating the hybrid bearing capacity of a power distribution network, the method for evaluating the hybrid bearing capacity of the power distribution network provided by the invention considers historical operation data and intra-day real-time operation data of the power distribution network; and evaluating the bearing capacity of the power distribution network by predicting the operation scheduling scheme of the distributed power supply in the future time and utilizing the operation data of the power distribution network under the operation scheduling scheme through the evaluation index score of each preset evaluation index. According to the method, random changes of new energy power generation can be effectively coped with, it is guaranteed that the power distribution network has sufficient bearing capacity for distributed energy, and therefore the operation stability and safety of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a method, system, device and medium for evaluating hybrid carrying capacity of a distribution network. Background Art

[0002] As the installed capacity of renewable energy continues to grow, the power grid is facing the bottleneck problem of renewable energy carrying capacity. On the one hand, due to low load demand and limited power transmission channels, a large number of wind and solar power have been abandoned in some areas. On the other hand, due to the uncertainty and volatility of renewable energy power output, when the penetration rate of distributed power sources is high, the reactive voltage control and real-time balance of power and electricity in the power grid will be significantly affected, and even the operational stability of the entire power grid has begun to be affected.

[0003] When the penetration rate of distributed power sources in the distribution network is high, the generation of distributed power sources will cause reverse power flow, leading to power quality problems. In severe cases, it will lead to load capacity problems such as reverse overload and voltage over-limit of distribution transformers, affecting the safe and stable operation of the power grid. Therefore, when planning the distribution network, it is necessary to take into account the strong volatility and uncertainty of distributed power sources, and reserve sufficient spare capacity to cope with the random changes in new energy generation and ensure that the distribution network has sufficient load capacity for distributed energy.

[0004] At present, most of the evaluation methods for the carrying capacity of distributed power generation connected to the distribution network have the problem of overly cumbersome mathematical derivation, poor operability and large deviation in evaluation results in practical applications. Some evaluation methods are highly subjective and can only be applied to certain specific scenarios. For ordinary scenarios, the evaluation results are often very different from the actual situation, which cannot truly reflect the operation of the distribution network and is difficult to improve the stability and safety of the distribution network. Summary of the invention

[0005] In view of this, in order to solve the above technical problems, the present invention provides a method, system, device and medium for evaluating the hybrid carrying capacity of a distribution network.

[0006] A first aspect of the present invention provides a method for evaluating hybrid carrying capacity of a distribution network, comprising:

[0007] Based on the historical operation data of the distribution network and the real-time operation data during the day, the operation and dispatching plan of the distributed power source in the future is predicted;

[0008] Obtaining distribution network operation status data after the distributed power source is connected to the distribution network at the future time according to the operation scheduling plan of the distributed power source;

[0009] Determining the evaluation index score of each preset evaluation index according to the distribution network operation status data; wherein the preset evaluation index is used to quantify the carrying capacity of the distribution network;

[0010] The scores of the evaluation indicators are weighted and calculated to obtain an evaluation value of the carrying capacity of the distributed power source after it is connected to the distribution network at the future time.

[0011] Preferably, the predicting of the operation scheduling scheme of the distributed power source at a future time based on the historical operation data of the distribution network and the real-time operation data of the day includes:

[0012] A source-load random time model is established based on the historical operation data of the distribution network and the real-time operation data within a day; wherein the source-load random time model is used to predict the power output of the distributed power source at the next moment;

[0013] Determine the power output of the distributed power source before the day-ahead scheduling phase according to the source-load random time model;

[0014] Solving the day-ahead dispatch optimization model according to the power output of the distributed power source before the day-ahead dispatch stage to obtain a day-ahead dispatch plan; wherein the day-ahead dispatch optimization model is constructed with the goal of minimizing the distribution network operation cost;

[0015] The power output of the distributed power source is adjusted according to the day-ahead scheduling plan, and the adjusted distributed power source is connected to the distribution network for power flow calculation to obtain the power output of the distributed power source before the intraday scheduling stage;

[0016] The intraday scheduling optimization model is solved according to the power output of the distributed power source before the intraday scheduling stage, and the intraday scheduling plan is obtained as the operation scheduling plan of the distributed power source in the future; wherein, the intraday scheduling optimization model is constructed with the goal of maximizing the carrying capacity of the distributed power source after connecting to the distribution network and minimizing the distribution network scheduling error.

[0017] Preferably, the preset evaluation indicators include at least one of the average voltage deviation rate, the average voltage quality rate, the average line loss rate of the distribution network, the average line utilization rate of the distribution network, the average utilization rate of clean energy, the average clean energy power supply rate and the average net load change rate.

[0018] Preferably, the method further comprises:

[0019] The scores of the evaluation indicators are normalized.

[0020] Preferably, the method further comprises:

[0021] Using the rank order analysis method and the variation correlation method to assign weights to the preset evaluation indicators respectively, and obtaining a first weight set and a second weight set corresponding to the rank order analysis method and the variation correlation method respectively;

[0022] Based on the improved least squares method, an optimal weight value model of the evaluation index is determined according to the first weight set and the second weight set; wherein the optimal weight value model is constructed with the goal of minimizing the error of the optimal weight;

[0023] The optimal weight value model is optimized and solved based on an adaptive mutation algorithm, and an optimal weight set is determined according to the optimal solution. The optimal weight set includes the optimal weights of each of the preset evaluation indicators.

[0024] Preferably, performing weighted calculation on the scores of the evaluation indicators to obtain the carrying capacity evaluation value of the distributed power source after being connected to the distribution network at the future time includes:

[0025] The weights of the evaluation index scores are assigned according to the optimal weight set, and weighted calculation is performed according to the weight assignment results of the evaluation index scores to obtain the carrying capacity assessment value of the distributed power source after being connected to the distribution network at the future time.

[0026] Preferably, the method further comprises:

[0027] Determining whether the carrying capacity assessment value is greater than a preset assessment threshold;

[0028] When it is determined that the carrying capacity evaluation value is greater than the preset evaluation threshold, the current operation scheduling plan is executed at a future time;

[0029] When it is determined that the carrying capacity evaluation value is not greater than the preset evaluation threshold, the step of predicting the operation scheduling plan of the distributed power source in the future based on the historical operation data of the distribution network and the real-time operation data during the day is re-executed until the carrying capacity evaluation value is greater than the preset evaluation threshold, and the current operation scheduling plan is output.

[0030] In a second aspect, the present invention further provides a distribution network hybrid carrying capacity evaluation system, comprising:

[0031] The scheduling prediction module is used to predict the operation scheduling plan of distributed power sources in the future based on the historical operation data of the distribution network and the real-time operation data of the day;

[0032] An operation data acquisition module, used for obtaining the distribution network operation status data after the distributed power source is connected to the distribution network at the future time according to the operation scheduling plan of the distributed power source;

[0033] An indicator scoring module, used to determine the evaluation indicator score of each preset evaluation indicator according to the distribution network operation status data; wherein the preset evaluation indicator is used to quantify the carrying capacity of the distribution network;

[0034] The carrying capacity evaluation module is used to perform weighted calculation on the scores of the evaluation indicators to obtain a carrying capacity evaluation value of the distributed power source after it is connected to the distribution network at the future time.

[0035] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the distribution network hybrid carrying capacity assessment method as described in the first aspect.

[0036] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the steps of the method for evaluating hybrid carrying capacity of a distribution network as described in the first aspect are implemented.

[0037] It can be seen from the above technical solutions that the hybrid load-bearing capacity evaluation method of the distribution network proposed in the present invention not only takes into account the historical operation data and real-time operation data of the distribution network, but also predicts the operation scheduling plan of the distributed power source in the future, and uses the operation data of the distribution network under the operation scheduling plan to evaluate the distribution network's carrying capacity through the evaluation index scores of each preset evaluation index. This method can effectively cope with the random changes in renewable energy generation, ensure that the distribution network has sufficient carrying capacity for distributed energy, and thus improve the operation stability and safety of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0039] Figure 1 An application environment of a distribution network hybrid carrying capacity evaluation method provided by an embodiment of the present invention;

[0040] Figure 2 A flow chart of a method for evaluating hybrid carrying capacity of a distribution network provided by an embodiment of the present invention;

[0041] Figure 3 A schematic diagram of the structure of a distribution network hybrid carrying capacity evaluation system provided by an embodiment of the present invention;

[0042] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The method for evaluating the hybrid carrying capacity of a distribution network provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the distribution network communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 predicts the operation scheduling plan of the distributed power source in the future according to the historical operation data of the distribution network and the real-time operation data during the day; obtains the distribution network operation status data after the distributed power source is connected to the distribution network in the future according to the operation scheduling plan of the distributed power source; determines the evaluation index score of each preset evaluation index according to the distribution network operation status data; wherein the preset evaluation index is used to quantify the carrying capacity of the distribution network; and weightedly calculates the scores of each evaluation index to obtain the carrying capacity evaluation value of the distributed power source after it is connected to the distribution network in the future. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0045] like Figure 2 As shown, a method for evaluating the hybrid carrying capacity of a distribution network provided in an embodiment of the present application is applied to Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S4. Among them:

[0046] Step S1: predicting the operation scheduling plan of the distributed power source in the future based on the historical operation data of the distribution network and the real-time operation data of the day.

[0047] Among them, historical operation data refers to the historical power output of distributed power sources connected to the distribution network, and daily real-time operation data refers to the real-time power output of distributed power sources connected to the distribution network. Taking into account the uncertainty of renewable energy and load under real-time operation, the source and load output of the distribution network is predicted based on the daily real-time data and historical operation data of the distribution network. By making real-time adjustments to distributed power sources such as reactive compensation devices, OLTC taps and energy storage systems at future times, the operation scheduling plan of distributed power sources at future times is obtained.

[0048] Step S2: obtaining the distribution network operation status data after the distributed power source is connected to the distribution network at a future time according to the operation scheduling plan of the distributed power source.

[0049] It can be understood that, according to the proposed operation and scheduling scheme of distributed power sources, we can obtain data information on the operation status of the distribution network at a certain moment in the future when these distributed power sources are connected to the distribution network.

[0050] Step S3: determining the evaluation index score of each preset evaluation index according to the distribution network operation status data; wherein the preset evaluation index is used to quantify the carrying capacity of the distribution network.

[0051] Among them, taking into account the influencing factors of the carrying capacity of the distribution network, the carrying capacity of the distribution network under the current distribution network operation status data is quantified by setting various preset evaluation indicators.

[0052] Step S4: Perform weighted calculation on the scores of the evaluation indicators to obtain the carrying capacity evaluation value of the distributed generation after it is connected to the distribution network at a future time.

[0053] The carrying capacity of the distribution network refers to

[0054] The maximum capacity of distributed power generation that the distribution network can carry while ensuring safe and stable operation. By weighted calculation of the scores of each preset evaluation index, a comprehensive carrying capacity assessment value can be obtained, which can intuitively reflect the carrying capacity of the distribution network after the distributed power generation is connected in the future.

[0055] It should be noted that in the embodiment of the present invention, the proposed distribution network hybrid carrying capacity evaluation method not only takes into account the historical operation data and real-time operation data of the distribution network, but also predicts the operation scheduling plan of the distributed power source in the future, and uses the operation data of the distribution network under the operation scheduling plan to evaluate the carrying capacity of the distribution network through the evaluation index scores of each preset evaluation index. This method can effectively cope with the random changes of new energy generation, ensure that the distribution network has sufficient carrying capacity for distributed energy, and thus improve the operation stability and safety of the distribution network.

[0056] In some embodiments, based on the historical operation data of the distribution network and the real-time operation data of the day, the operation scheduling plan of the distributed power source at a future time is predicted, including:

[0057] Step S101: Establish a source-load random time model based on historical operation data and real-time operation data of the distribution network; wherein the source-load random time model is used to predict the power output of the distributed power source at the next moment.

[0058] In view of the uncertainty of source and load under real-time operation, a source-load random time model is established based on the historical data and real-time data of renewable energy generation and load; the source-load random time model is expressed as:

[0059]

[0060] In the formula, , are the power of renewable energy generation and load at node i at time t+1 respectively; is the installed capacity of renewable energy at node i; is the maximum power of the load at node i; , are the historical load rates of renewable energy generation and load at node i at time t, respectively; , are the current load rates of renewable energy generation and load at node i at time t, respectively; , are the forecast random deviations of renewable energy generation and load, respectively.

[0061] The forecast random deviation expression of renewable energy generation and load is:

[0062]

[0063] It is understandable that the source-load random time model is established based on historical data and real-time data. Through this model, the power output of renewable energy generation and load at the next moment can be predicted, thus providing a basis for subsequent scheduling optimization. This model takes into account the uncertainty of renewable energy generation and load, making the prediction results more accurate and reliable. By solving the source-load random time model, the power output prediction value of the distributed power source at the next moment can be obtained. This prediction value can be used as input data for the day-ahead scheduling stage and the intraday scheduling stage, and is used to optimize the formulation of the scheduling plan.

[0064] Step S102: determining the power output of the distributed generation before the day-ahead scheduling stage according to the source-load random time model.

[0065] The day-ahead dispatching stage refers to the preliminary planning and arrangement of the power output of distributed power sources to cope with the uncertainty of renewable energy generation and load. The day-ahead dispatching plan is formulated by considering the source-load output forecast of the distribution network to determine the power output of distributed power sources before the day-ahead dispatching stage. The power output of distributed power sources before the day-ahead dispatching stage can be determined by the source-load random time model, thereby determining the distribution of power flow changes of distributed power sources before the day-ahead dispatching stage.

[0066] Step S103, solving the day-ahead dispatch optimization model according to the power output of the distributed generation before the day-ahead dispatch stage to obtain a day-ahead dispatch plan; wherein the day-ahead dispatch optimization model is constructed with the goal of minimizing the distribution network operation cost.

[0067] Among them, the day-ahead dispatch optimization model is constructed based on the power output of distributed power sources before the day-ahead dispatch stage, with the goal of minimizing the operation cost of the distribution network. The day-ahead dispatch optimization model is expressed as:

[0068]

[0069] In the formula, is the distribution network operating cost, For operation and maintenance costs, The cost of purchasing electricity.

[0070] in,

[0071] In the formula, is the energy storage operation and maintenance cost, t is the time, T is the total period, i is the node index, N is the number of nodes, is the active output of the ith node in period t (discharging is positive and charging is negative), is the time interval of the period, is the cost of purchasing electricity from DG for node i, is the active power output of the i-th DG in period t.

[0072] Step S104: adjust the power output of the distributed power source according to the day-ahead dispatch plan, and connect the adjusted distributed power source to the distribution network for power flow calculation to obtain the power output of the distributed power source before the intraday dispatch stage.

[0073] Among them, according to the pre-made dispatching plan, the power output of the distributed power source is adjusted accordingly, and the adjusted distributed power source is reconnected to the distribution network for power flow calculation. In this way, the power output of the distributed power source before the start of the intraday dispatching phase can be obtained.

[0074] Step S105, solving the intraday scheduling optimization model according to the power output of the distributed power source before the intraday scheduling stage, and obtaining the intraday scheduling plan as the operation scheduling plan of the distributed power source in the future; wherein the intraday scheduling optimization model is constructed with the goal of maximizing the carrying capacity of the distributed power source after it is connected to the distribution network and minimizing the distribution network scheduling error.

[0075] Among them, on the basis of the optimal dispatch plan, combined with the current intraday real-time data, the ultra-short-term prediction method is used to optimize the dispatch with the maximum carrying capacity of renewable energy access distribution network and the minimum dispatch error of distribution network as multiple objectives, and the intraday dispatch optimization model is constructed. Among them, the intraday dispatch optimization model is:

[0076]

[0077] In the formula, It is the difference between the power of the distributed generation after it is connected to the distribution network and the power after the distribution network is dispatched. , They are different weights, It is the power after the distributed generation is connected to the distribution network. It is the average power after the distributed generation is connected to the distribution network.

[0078] In some embodiments, the preset evaluation indicators include at least one of the average voltage deviation rate, the average voltage quality rate, the average line loss rate of the distribution network, the average line utilization rate of the distribution network, the average utilization rate of clean energy, the average clean energy power supply rate and the average net load change rate.

[0079] Specifically, the voltage average deviation rate A 1 It is expressed as:

[0080]

[0081] in, represents the node voltage value at node i at time t, Indicates the nominal system voltage.

[0082] Average voltage quality rate A 2 It is expressed as:

[0083]

[0084] Among them, N t The node number representing the quality node voltage.

[0085] Average line loss rate of distribution network B 1 It is expressed as:

[0086]

[0087] in, represents the branch loss at branch l at time t, represents the branch transmission power at branch l at time t, and m is the number of branches.

[0088] Average line utilization rate of distribution network B 2 It is expressed as:

[0089]

[0090] in, represents the branch transmission power at branch l at time t, S l Represents the maximum active transmission power at branch l.

[0091] Average utilization rate of clean energy B 3 It is expressed as:

[0092]

[0093] in, It represents the active power actually utilized by the renewable energy power generation at node i at time t. Represents the total active power generated by renewable energy at node i at time t.

[0094] Average clean energy supply rate C 1 It is expressed as:

[0095]

[0096] in, Represents the active power required by the load at node i at time t.

[0097] Net load average change rate C 2 It is expressed as:

[0098]

[0099] in, Represents the net load power of the distribution network at time t.

[0100] In some embodiments, the method further includes: normalizing the scores of each evaluation indicator to eliminate the dimensional influence of the evaluation indicator.

[0101] Specifically, the min-max normalization method is used to normalize the evaluation indicators.

[0102] If the evaluation index is a positive index, the normalized evaluation index is:

[0103]

[0104] If the evaluation index is a negative index, the normalized evaluation index is:

[0105]

[0106] In the formula, is the normalized evaluation index, is the evaluation index before normalization, , are the minimum and maximum evaluation indicators respectively.

[0107] In some embodiments, it is necessary to assign weights to the indicators in the evaluation system. The method also includes:

[0108] Step S21, weighting the preset evaluation indicators using the rank order analysis method and the variation correlation method, to obtain a first weight set and a second weight set corresponding to the rank order analysis method and the variation correlation method, respectively.

[0109] Among them, the use of priority analysis and weighting of indicators in the evaluation system is to assume that there are m decision makers and n indicators to be evaluated. The decision makers are required to score the importance of the indicators and construct a judgment matrix A=(a ij )n×n.

[0110] According to the constructed judgment matrix, calculate the indicator score TTL of indicator j j for:

[0111]

[0112] Normalize the index score of index j and calculate the weight Wa of each index j for:

[0113] .

[0114] Among them, the variation correlation method is used to assign weights to the indicators in the evaluation system, including:

[0115] Assuming that there are n evaluation objects and m evaluation indicators, the original data of the evaluation indicators are normalized and the normalized indicator data matrix X=(x ij ) n×m :

[0116]

[0117] Among them, x ij Represents the normalized value of the jth evaluation indicator of the i-th object to be evaluated.

[0118] Calculate the comparative strength of the indicator:

[0119]

[0120]

[0121] Calculation indicator conflict:

[0122]

[0123]

[0124] In the formula, r kj Represents the correlation coefficient between indicator k and indicator j.

[0125] According to the comparison intensity and conflict of indicators, calculate the information volume c of each indicator j :

[0126]

[0127] Normalize the amount of information on the indicators and calculate the weight of each indicator Wb j :

[0128] .

[0129] Step S22: Based on the improved least squares method, determine the optimal weight value model of the evaluation index according to the first weight set and the second weight set; wherein the optimal weight value model is constructed with the goal of minimizing the error of the optimal weight.

[0130] Among them, the optimal weight value model for calculating the evaluation index based on the improved least squares method is:

[0131]

[0132] Where W j is the optimal weight of evaluation index j.

[0133] Step S23: searching and solving the optimal weight value model based on the adaptive mutation algorithm, and determining the optimal weight set according to the optimal solution, wherein the optimal weight set includes the optimal weights of each preset evaluation index.

[0134] Among them, an adaptive mutation algorithm is used to quickly solve the optimal evaluation index weights to reduce the subjective and objective limitations brought by a single weighting method, making the decision results more real, scientific and comprehensive.

[0135] Specifically, the solution process of the adaptive mutation algorithm is:

[0136] Initialization, assuming that the size parameter of the group is n, and then randomly select n points V from the feasible domain i (i=(1, 2, ⋯n), forming the initial group M(0)={V1 , V 2 ,...,V n}. And calculate the fitness f(V i ).

[0137] Select individuals for reproduction, and the probability of each individual being selected is P i :

[0138]

[0139] With crossover probability P c Perform crossover operation on the selected individuals to generate new individuals. Assume that the crossover point is k and the selected individuals are V i and V j , then the new individual V new for:

[0140]

[0141] With mutation probability P m Mutate some genes of the next generation of individuals to produce new individuals. The gene mutation process is:

[0142]

[0143] In the formula, is a random variation.

[0144] In order to improve the optimization speed and avoid falling into the local optimum, the crossover probability P c and mutation probability P m Perform adaptive operation.

[0145]

[0146]

[0147] In the formula, is the fitness value with the largest value in the crossover operation; f is the fitness value of the individual in the mutation operation, f max 、f min and f avg are the maximum, minimum and average fitness values ​​of the population respectively. c0 and P m0 is a constant in (0,1).

[0148] In some embodiments, the scores of the evaluation indicators are weighted and calculated to obtain the carrying capacity evaluation value of the distributed power source after it is connected to the distribution network at a future time, including:

[0149] The weights of the scores of each evaluation index are assigned according to the optimal weight set, and weighted calculation is performed according to the weight assignment results of the scores of each evaluation index to obtain the carrying capacity assessment value of the distributed generation after it is connected to the distribution network in the future.

[0150] In some embodiments, the method further comprises:

[0151] Step S501: determine whether the carrying capacity evaluation value is greater than a preset evaluation threshold;

[0152] Step S502: When it is determined that the carrying capacity evaluation value is greater than a preset evaluation threshold, the current operation scheduling plan is executed in the future;

[0153] Step S503: When it is determined that the carrying capacity evaluation value is not greater than the preset evaluation threshold, the step of predicting the operation scheduling plan of the distributed power source in the future based on the historical operation data of the distribution network and the real-time operation data during the day is re-executed until the carrying capacity evaluation value is greater than the preset evaluation threshold, and the current operation scheduling plan is output.

[0154] It can be understood that first, it is determined whether the carrying capacity evaluation value exceeds the preset evaluation threshold; if the evaluation value is higher than the threshold, the current operation scheduling plan will continue to be executed in the future; if the evaluation value does not exceed the threshold, the following steps need to be re-executed: based on the historical operation data of the distribution network and the real-time operation data during the day, the operation scheduling plan of the distributed power source in the future is predicted until the carrying capacity evaluation value reaches or exceeds the preset evaluation threshold, and finally the current operation scheduling plan is output, so as to find the optimal operation scheduling plan, which can enable the distribution network to meet the maximum margin carrying capacity.

[0155] Based on the same inventive concept, an embodiment of the present application also provides a distribution network hybrid carrying capacity evaluation system for implementing the distribution network hybrid carrying capacity evaluation method involved above.

[0156] The implementation solution for solving the problem provided by the system is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more distribution network hybrid carrying capacity assessment system embodiments provided below can be referred to the limitations on the distribution network hybrid carrying capacity assessment method above, and will not be repeated here.

[0157] like Figure 3 As shown, the embodiment of the present application provides a distribution network hybrid carrying capacity evaluation system, including:

[0158] The scheduling prediction module 100 is used to predict the operation scheduling plan of the distributed power source at a future time according to the historical operation data of the distribution network and the real-time operation data of the day;

[0159] The operation data acquisition module 200 is used to obtain the distribution network operation status data after the distributed power source is connected to the distribution network at a future time according to the operation scheduling plan of the distributed power source;

[0160] The index scoring module 300 is used to determine the evaluation index score of each preset evaluation index according to the distribution network operation status data; wherein the preset evaluation index is used to quantify the carrying capacity of the distribution network;

[0161] The carrying capacity evaluation module 400 is used to perform weighted calculation on the scores of the evaluation indicators to obtain a carrying capacity evaluation value of the distributed power source after it is connected to the distribution network at a future time.

[0162] In some embodiments, the scheduling prediction module 100 is used to:

[0163] According to the historical operation data of the distribution network and the real-time operation data during the day, a source-load random time model is established; wherein the source-load random time model is used to predict the power output of the distributed power source at the next moment;

[0164] Determine the power output of distributed generation before the day-ahead dispatching stage according to the source-load random time model;

[0165] The day-ahead dispatch optimization model is solved according to the power output of distributed generation before the day-ahead dispatch stage to obtain the day-ahead dispatch plan; the day-ahead dispatch optimization model is constructed with the goal of minimizing the distribution network operation cost;

[0166] Adjust the power output of distributed generation according to the day-ahead dispatch plan, and connect the adjusted distributed generation to the distribution network for power flow calculation to obtain the power output of distributed generation before the intraday dispatch stage;

[0167] The intraday scheduling optimization model is solved according to the power output of distributed power sources before the intraday scheduling stage, and the intraday scheduling plan is obtained as the operation scheduling plan of distributed power sources in the future; among them, the intraday scheduling optimization model is constructed with the goal of maximizing the carrying capacity of distributed power sources after they are connected to the distribution network and minimizing the scheduling error of the distribution network.

[0168] In some embodiments, the preset evaluation indicators include at least one of the average voltage deviation rate, the average voltage quality rate, the average line loss rate of the distribution network, the average line utilization rate of the distribution network, the average utilization rate of clean energy, the average clean energy power supply rate and the average net load change rate.

[0169] In some embodiments, the system further includes: a normalization module for normalizing the scores of each evaluation index.

[0170] In some embodiments, the system further includes: a weight optimization module, which is used to:

[0171] Using the superiority analysis method and the variation correlation method to assign weights to the preset evaluation indicators, respectively, to obtain a first weight set and a second weight set corresponding to the superiority analysis method and the variation correlation method, respectively;

[0172] Based on the improved least squares method, the optimal weight value model of the evaluation index is determined according to the first weight set and the second weight set; wherein the optimal weight value model is constructed with the goal of minimizing the error of the optimal weight;

[0173] The optimal weight value model is optimized and solved based on the adaptive mutation algorithm, and the optimal weight set is determined according to the optimal solution. The optimal weight set includes the optimal weights of each preset evaluation index.

[0174] In some embodiments, the carrying capacity assessment module 400 is used to assign weights to the scores of each evaluation index according to the optimal weight set, perform weighted calculations based on the weight assignment results of each evaluation index score, and obtain the carrying capacity assessment value of the distributed power source after it is connected to the distribution network at a future time.

[0175] In some embodiments, the system further includes: a solution optimization module, which is used to:

[0176] Determine whether the carrying capacity assessment value is greater than a preset assessment threshold;

[0177] When it is judged that the carrying capacity evaluation value is greater than the preset evaluation threshold, the current operation scheduling plan is executed in the future;

[0178] When it is judged that the carrying capacity evaluation value is not greater than the preset evaluation threshold, the step of predicting the operation scheduling plan of the distributed power source in the future time according to the historical operation data of the distribution network and the real-time operation data during the day is re-executed until the carrying capacity evaluation value is greater than the preset evaluation threshold, and the current operation scheduling plan is output.

[0179] like Figure 4 As shown, an embodiment of the present application also provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the distribution network hybrid carrying capacity assessment method in the above embodiment.

[0180] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed, the steps of the method for evaluating the hybrid carrying capacity of a distribution network as in the above embodiment are implemented.

[0181] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0182] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0183] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0184] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the method described in each embodiment of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0188] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the hybrid carrying capacity of a distribution network, characterized in that: include: Based on the historical operation data of the distribution network and the real-time operation data during the day, the operation and dispatching plan of the distributed power source in the future is predicted; Obtaining distribution network operation status data after the distributed power source is connected to the distribution network at the future time according to the operation scheduling plan of the distributed power source; Determining the evaluation index score of each preset evaluation index according to the distribution network operation status data; wherein the preset evaluation index is used to quantify the carrying capacity of the distribution network; The scores of the evaluation indicators are weighted and calculated to obtain an evaluation value of the carrying capacity of the distributed power source after it is connected to the distribution network at the future time.

2. The method for evaluating the hybrid carrying capacity of a distribution network according to claim 1, characterized in that: The method of predicting the operation and dispatching plan of the distributed power source in the future based on the historical operation data of the distribution network and the real-time operation data of the day includes: A source-load random time model is established based on the historical operation data of the distribution network and the real-time operation data within a day; wherein the source-load random time model is used to predict the power output of the distributed power source at the next moment; Determine the power output of the distributed power source before the day-ahead scheduling phase according to the source-load random time model; Solving the day-ahead dispatch optimization model according to the power output of the distributed power source before the day-ahead dispatch stage to obtain a day-ahead dispatch plan; wherein the day-ahead dispatch optimization model is constructed with the goal of minimizing the distribution network operation cost; The power output of the distributed power source is adjusted according to the day-ahead scheduling plan, and the adjusted distributed power source is connected to the distribution network for power flow calculation to obtain the power output of the distributed power source before the intraday scheduling stage; The intraday scheduling optimization model is solved according to the power output of the distributed power source before the intraday scheduling stage, and the intraday scheduling plan is obtained as the operation scheduling plan of the distributed power source in the future; wherein, the intraday scheduling optimization model is constructed with the goal of maximizing the carrying capacity of the distributed power source after connecting to the distribution network and minimizing the distribution network scheduling error.

3. The method for evaluating the hybrid carrying capacity of a distribution network according to claim 1, characterized in that: The preset evaluation indicators include at least one of the average voltage deviation rate, the average voltage quality rate, the average line loss rate of the distribution network, the average line utilization rate of the distribution network, the average utilization rate of clean energy, the average clean energy power supply rate and the average net load change rate.

4. The method for evaluating the hybrid carrying capacity of a distribution network according to claim 1, characterized in that: Also includes: The scores of the evaluation indicators are normalized.

5. The method for evaluating the hybrid carrying capacity of a distribution network according to claim 1, characterized in that: Also includes: Using the rank order analysis method and the variation correlation method to assign weights to the preset evaluation indicators respectively, and obtaining a first weight set and a second weight set corresponding to the rank order analysis method and the variation correlation method respectively; Based on the improved least squares method, an optimal weight value model of the evaluation index is determined according to the first weight set and the second weight set; wherein the optimal weight value model is constructed with the goal of minimizing the error of the optimal weight; The optimal weight value model is optimized and solved based on an adaptive mutation algorithm, and an optimal weight set is determined according to the optimal solution. The optimal weight set includes the optimal weights of each of the preset evaluation indicators.

6. The method for evaluating the hybrid carrying capacity of a distribution network according to claim 5, characterized in that: The weighted calculation of the scores of the evaluation indicators to obtain the carrying capacity evaluation value of the distributed power source after being connected to the distribution network at the future time includes: The weights of the evaluation index scores are assigned according to the optimal weight set, and weighted calculation is performed according to the weight assignment results of the evaluation index scores to obtain the carrying capacity assessment value of the distributed power source after being connected to the distribution network at the future time.

7. The method for evaluating the hybrid carrying capacity of a distribution network according to any one of claims 1 to 6, characterized in that: Also includes: Determining whether the carrying capacity assessment value is greater than a preset assessment threshold; When it is determined that the carrying capacity evaluation value is greater than the preset evaluation threshold, the current operation scheduling plan is executed at a future time; When it is determined that the carrying capacity evaluation value is not greater than the preset evaluation threshold, the step of predicting the operation scheduling plan of the distributed power source in the future based on the historical operation data of the distribution network and the real-time operation data during the day is re-executed until the carrying capacity evaluation value is greater than the preset evaluation threshold, and the current operation scheduling plan is output.

8. A distribution network hybrid carrying capacity assessment system, characterized in that: include: The scheduling prediction module is used to predict the operation scheduling plan of distributed power sources in the future based on the historical operation data of the distribution network and the real-time operation data of the day; An operation data acquisition module, used for obtaining the distribution network operation status data after the distributed power source is connected to the distribution network at the future time according to the operation scheduling plan of the distributed power source; An indicator scoring module, used to determine the evaluation indicator score of each preset evaluation indicator according to the distribution network operation status data; wherein the preset evaluation indicator is used to quantify the carrying capacity of the distribution network; The carrying capacity evaluation module is used to perform weighted calculation on the scores of the evaluation indicators to obtain a carrying capacity evaluation value of the distributed power source after it is connected to the distribution network at the future time.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for evaluating the hybrid carrying capacity of a distribution network as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the method for evaluating the hybrid carrying capacity of a distribution network as described in any one of claims 1 to 7 are implemented.

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