A power distribution network hybrid carrying capacity evaluation method, system, device and medium

Abstract

The application relates to the technical field of power distribution networks, and discloses a power distribution network hybrid bearing capacity evaluation method, a system, equipment and a medium. The power distribution network hybrid bearing capacity evaluation method considers not only historical operation data and intraday real-time operation data of the power distribution network, but also predicts operation scheduling schemes of distributed power sources in future time, and evaluates the bearing capacity of the power distribution network by using operation data of the power distribution network under the operation scheduling schemes and evaluation index scores of each preset evaluation index. The method can effectively cope with random changes of new energy generation, ensure that the power distribution network has sufficient bearing capacity for distributed energy, and improve the operation stability and safety of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a method, system, equipment and medium for assessing the hybrid carrying capacity of power distribution networks. Background Technology

[0002] With the continuous growth of installed capacity of new energy sources, the power grid is facing a bottleneck in its capacity to carry these sources. On the one hand, some regions are experiencing significant wind and solar power curtailment due to low load demand and limited power transmission channels. On the other hand, because the output of new energy sources is uncertain and volatile, a high penetration rate of distributed power sources can significantly impact the grid's reactive power and voltage control, as well as real-time power balance, and may even begin to affect the overall operational stability of the power grid.

[0003] When the penetration rate of distributed generation in the distribution network is high, distributed generation will cause power flow reversal, leading to power quality problems. In severe cases, it can cause reverse overload and voltage exceeding limits in distribution transformers, affecting the safe and stable operation of the power grid. Therefore, when planning the distribution network, it is necessary to consider the characteristics of distributed generation, such as strong volatility and uncertainty, and reserve sufficient backup capacity to cope with the random changes in new energy generation and ensure that the distribution network has sufficient capacity to support distributed energy.

[0004] Currently, most methods for assessing the carrying capacity of distributed generation in distribution networks suffer from overly complex mathematical derivations, resulting in weak operability and significant deviations in assessment results in practical applications. Some assessment methods are highly subjective and only applicable to certain specific scenarios. For ordinary scenarios, the assessment results often differ significantly from the actual situation, failing to accurately reflect the operation of the distribution network and hindering efforts to improve the stability and security of its operation. Summary of the Invention

[0005] In view of this, in order to solve the above-mentioned technical problems, the present invention provides a method, system, equipment and medium for assessing the hybrid carrying capacity of power distribution networks.

[0006] The first aspect of this invention provides a method for assessing the 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 within the day, the operation and scheduling scheme of distributed power sources in the future can be predicted.

[0008] According to the operation and scheduling scheme of the distributed power source, the data on the operation status of the distribution network after the distributed power source is connected to the distribution network in the future time is obtained;

[0009] The evaluation index scores of each preset evaluation index are determined based on the power distribution network operation status data; wherein, the preset evaluation index is used to quantify the carrying capacity of the power distribution network;

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

[0011] Preferably, the step of predicting the operation and scheduling scheme of distributed power sources in the future based on historical operation data and real-time operation data of the distribution network includes:

[0012] Based on the historical operating data and intraday real-time operating data of the distribution network, a source-load stochastic time model is established; wherein, the source-load stochastic time model is used to predict the power output of the distributed power source at the next moment;

[0013] The power output of the distributed power source before the day-ahead scheduling phase is determined based on the source-load stochastic time model.

[0014] The day-ahead scheduling optimization model is solved based on the power output of the distributed power sources before the day-ahead scheduling phase to obtain the day-ahead scheduling plan; wherein, the day-ahead scheduling optimization model is constructed with the objective of minimizing the operating cost of the distribution network;

[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 day-ahead scheduling phase.

[0016] The intraday scheduling optimization model is solved based on the power output of the distributed power source before the intraday scheduling phase, and the intraday scheduling scheme is obtained as the operation scheduling scheme of the distributed power source in the future. 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 scheduling error of the distribution network.

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

[0018] Preferably, the method further includes:

[0019] The scores of each evaluation indicator are normalized.

[0020] Preferably, the method further includes:

[0021] The pecking order analysis method and the variation correlation method are used to assign weights to each of the preset evaluation indicators, respectively, to obtain the first weight set and the second weight set corresponding to the pecking order analysis method and the variation correlation method, respectively.

[0022] 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;

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

[0024] Preferably, the step of weighting the scores of each evaluation index to obtain the carrying capacity assessment value of the distributed power source after it is connected to the distribution network in the future includes:

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

[0026] Preferably, the method further includes:

[0027] Determine whether the load-bearing capacity assessment value is greater than a preset assessment threshold;

[0028] When it is determined that the carrying capacity assessment value is greater than the preset assessment threshold, the current operation scheduling scheme will be executed in the future.

[0029] When it is determined that the carrying capacity assessment value is not greater than the preset assessment threshold, the step of predicting the operation and scheduling scheme 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 is re-executed until the carrying capacity assessment value is greater than the preset assessment threshold, and the current operation and scheduling scheme is output.

[0030] Secondly, the present invention also provides a system for assessing the hybrid carrying capacity of a power distribution network, comprising:

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

[0032] The operation data acquisition module is used to obtain the distribution network operation status data after the distributed power source is connected to the distribution network in the future time according to the operation scheduling scheme of the distributed power source;

[0033] The indicator scoring module is used to determine the evaluation index scores of each preset evaluation index based on the distribution network operation status data; wherein, the preset evaluation indexes are used to quantify the carrying capacity of the distribution network.

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

[0035] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the power distribution network hybrid carrying capacity assessment method as described in the first aspect.

[0036] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for assessing the hybrid carrying capacity of a distribution network as described in the first aspect.

[0037] As can be seen from the above technical solutions, the hybrid carrying capacity assessment method for distribution networks proposed in this invention not only considers historical and intraday real-time operating data of the distribution network, but also predicts the operation and scheduling schemes of distributed power sources in the future. It then uses the operating data of the distribution network under the operation and scheduling schemes to assess the carrying capacity of the distribution network through the scores of various preset evaluation indicators. This method can effectively cope with the random changes in new energy power generation, ensuring that the distribution network has sufficient carrying capacity for distributed energy, thereby improving the operational stability and security of the distribution network. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This invention provides an application environment for a method for assessing the hybrid carrying capacity of a power distribution network, as provided in this embodiment.

[0040] Figure 2 A flowchart of a method for assessing the hybrid carrying capacity of a power distribution network provided in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of a hybrid carrying capacity assessment system for a power distribution network provided in an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] The method for assessing the hybrid carrying capacity of distribution networks provided in this application can be applied to, for example... Figure 1 In the application environment shown, the distribution network communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102 or located in the cloud or on other network servers. Server 102 predicts the operation and scheduling scheme of distributed power sources in the future based on historical and real-time daily operation data of the distribution network; obtains data on the operation status of the distribution network after the distributed power sources are connected to the distribution network in the future based on the operation and scheduling scheme; determines the evaluation index scores of each preset evaluation index based on the distribution network operation status data; the preset evaluation indexes are used to quantify the carrying capacity of the distribution network; and the scores of each evaluation index are weighted to obtain the carrying capacity assessment value of the distributed power sources after they are connected to the distribution network in the future. Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0045] like Figure 2 As shown in the embodiment of this application, a method for assessing the hybrid carrying capacity of a distribution network is provided, which is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps S1 to S4. Wherein:

[0046] Step S1: Based on the historical operation data of the distribution network and the real-time operation data within the day, predict the operation and scheduling scheme of distributed power sources in the future.

[0047] Historical operating data refers to the historical power output of distributed power sources connected to the distribution network, while intraday real-time operating data refers to the real-time power output of distributed power sources connected to the distribution network. Considering the uncertainty of renewable energy and load under real-time operating conditions, the source and load output of the distribution network is predicted based on the intraday real-time data and historical operating data. By making real-time adjustments to distributed power sources such as reactive power compensation devices, OLTC tap changers, and energy storage systems at future times, an operating and scheduling scheme for distributed power sources in the future is obtained.

[0048] Step S2: Obtain data on the operation status of the distribution network after the distributed power source is connected to the distribution network in the future, based on the operation and scheduling scheme of the distributed power source.

[0049] Understandably, based on the proposed operation and scheduling scheme for distributed power sources, we can obtain data on the operation status of the distribution network at some point in the future, after these distributed power sources are connected to the distribution network.

[0050] Step S3: Determine the evaluation index scores of each preset evaluation index based on 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, considering the factors affecting the carrying capacity of the distribution network, the carrying capacity of the distribution network under the current operating status data is quantified by setting various preset evaluation indicators.

[0052] Step S4: Calculate the weighted scores of each evaluation index to obtain the carrying capacity assessment value of the distributed power source after it is connected to the distribution network in the future.

[0053] Among them, the carrying capacity of the distribution network refers to

[0054] The maximum capacity of distributed generation to be connected to a power distribution network under the premise of safe and stable operation. By weighting the scores of various preset evaluation indicators, a comprehensive carrying capacity assessment value can be obtained, which can intuitively reflect the carrying capacity status of the power distribution network after the connection of distributed generation in the future.

[0055] It should be noted that, in the embodiments of this invention, the proposed method for assessing the hybrid carrying capacity of distribution networks not only considers historical and intraday real-time operating data of the distribution network, but also predicts the operation and scheduling schemes of distributed power sources in the future. Using the operating data of the distribution network under the operation and scheduling schemes, and through the scores of various preset evaluation indicators, the carrying capacity of the distribution network is assessed. This method can effectively cope with the random changes in new energy power generation, ensuring that the distribution network has sufficient carrying capacity for distributed energy, thereby improving the operational stability and security of the distribution network.

[0056] In some embodiments, based on historical operating data and intraday real-time operating data of the distribution network, a distributed generation operation scheduling scheme for future time periods is predicted, including:

[0057] Step S101: Based on the historical operation data of the distribution network and the real-time operation data within the day, establish a source-load stochastic time model; wherein, the source-load stochastic time model is used to predict the power output of distributed power sources at the next moment.

[0058] To address the uncertainties of source load under real-time operation, a source load stochastic time model is established using historical and real-time data on renewable energy generation and load. This source load stochastic time model is expressed as follows:

[0059]

[0060] In the formula, , These represent the power generated by renewable energy and the power of the load at node i at time t+1, respectively. Let be the installed capacity of renewable energy at node i; The maximum power of the load at node i; , These represent the historical load rates of renewable energy generation and load at node i at time t; , These represent the current load rates of renewable energy generation and load at node i at time t, respectively. , These represent the forecast random biases for renewable energy generation and load, respectively.

[0061] The expression for the random deviation of renewable energy generation and load forecasts is as follows:

[0062]

[0063] Understandably, the source-load stochastic time model is built upon historical and real-time data. This model can predict the power output of renewable energy generation and load at the next moment, thus providing a foundation for subsequent dispatch optimization. The model considers the uncertainties of renewable energy generation and load, making the prediction results more accurate and reliable. By solving the source-load stochastic time model, the predicted power output of distributed generation at the next moment can be obtained. This predicted value can be used as input data for the day-ahead and intraday dispatch phases to optimize dispatch schemes.

[0064] Step S102: Determine the power output of distributed power sources before the day-ahead scheduling phase based on the source-load stochastic time model.

[0065] The day-ahead dispatch phase refers to the preliminary planning and arrangement of power output by distributed generation sources to address the uncertainties in renewable energy generation and load. A day-ahead dispatch plan is formulated by considering the source-load output forecasts of the distribution network, determining the power output of distributed generation sources before the day-ahead dispatch phase. Specifically, the power output of distributed generation sources before the day-ahead dispatch phase can be determined using a source-load stochastic time model, thereby determining the power flow distribution of distributed generation sources before the day-ahead dispatch phase.

[0066] Step S103: Solve the day-ahead scheduling optimization model based on the power output of distributed generation before the day-ahead scheduling phase to obtain the day-ahead scheduling plan; wherein, the day-ahead scheduling optimization model is constructed with the goal of minimizing the operating cost of the distribution network.

[0067] The day-ahead scheduling optimization model is constructed based on the power output of distributed generation sources before the day-ahead scheduling phase, with the objective of minimizing the operating cost of the distribution network. The day-ahead scheduling optimization model is expressed as follows:

[0068]

[0069] In the formula, For the operating costs of the distribution network, For maintenance costs, This refers to the cost of purchasing electricity.

[0070] in,

[0071] In the formula, The value represents the energy storage operation and maintenance cost, where t is the time period, T is the total time period, i is the node index, and N is the number of nodes. Let be the active power output of the i-th node during time period t (discharge is positive, charging is negative). The time interval of the period. The cost of electricity purchased by node i from DG. The active power output of the i-th DG during time period t.

[0072] Step S104: Adjust the power output of the 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 the distributed generation before the day-ahead dispatch phase.

[0073] This process involves adjusting the power output of distributed generation sources according to a pre-established scheduling plan, and then reconnecting these sources to the distribution network for power flow calculations. In this way, the power output of distributed generation sources before the start of the intraday scheduling phase can be obtained.

[0074] Step S105: Solve the intraday scheduling optimization model based on the power output of the distributed power source before the intraday scheduling phase to obtain the intraday scheduling scheme as the operation and scheduling scheme of the distributed power source in the future. 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 scheduling error of the distribution network.

[0075] Based on the optimal scheduling plan and combined with current intraday real-time data, an ultra-short-term forecasting method is used to optimize scheduling with multiple objectives: maximizing the renewable energy grid's carrying capacity and minimizing grid scheduling errors. This forms the intraday scheduling optimization model. The intraday scheduling optimization model is as follows:

[0076]

[0077] In the formula, This represents the difference between the power output of distributed generation sources after they are connected to the distribution network and the power output after the distribution network is dispatched. , Each with different weights, This refers to the power output of distributed generation sources after they are connected to the distribution network. This represents the average power output of distributed generation sources after they are connected to the distribution network.

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

[0079] Specifically, the average voltage deviation rate A1 is expressed as:

[0080]

[0081] in, This represents the node voltage value at node i at time t. This indicates the system's nominal voltage.

[0082] The average voltage quality rate A2 is expressed as:

[0083]

[0084] Where, N t The number of nodes indicating high-quality node voltage.

[0085] The average line loss rate B1 of the distribution network is expressed as:

[0086]

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

[0088] The average line utilization rate B2 of the distribution network is expressed as:

[0089]

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

[0091] The average clean energy utilization rate B3 is expressed as:

[0092]

[0093] in, This represents the active power actually utilized by new energy generation at node i at time t. It represents the total active power of new energy generation at node i at time t.

[0094] The average clean energy power supply rate C1 is expressed as:

[0095]

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

[0097] The average rate of change of net load, C2, is expressed as:

[0098]

[0099] in, This 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 index to eliminate the influence of the dimensions of the evaluation index.

[0101] Specifically, the evaluation index is normalized using the min-max normalization method.

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

[0103]

[0104] If the evaluation indicator is negative, the normalized evaluation indicator is:

[0105]

[0106] In the formula, The normalized evaluation index The evaluation indicators before normalization , These 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. This method also includes:

[0108] Step S21: Use pecking order analysis and variation correlation analysis to assign weights to each preset evaluation index, respectively, to obtain the first weight set and the second weight set corresponding to pecking order analysis and variation correlation analysis.

[0109] The method of using pecking order analysis and assigning weights to the indicators in the evaluation system assumes there are m decision-makers and n indicators to be evaluated. Decision-makers need to score the importance of the indicators, and based on the scores from the m experts, a judgment matrix A = (a... ij )n×n.

[0110] Based on the constructed judgment matrix, calculate the time-to-live (TTL) score of indicator j. j for:

[0111]

[0112] The score of indicator j is normalized, and the weight Wa of each indicator is calculated. j for:

[0113] .

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

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

[0116]

[0117] Where, x ij This represents the normalized value of the j-th evaluation index for the i-th object to be evaluated.

[0118] Calculate the comparative strength of the indicators:

[0119]

[0120]

[0121] Conflicts in the calculation of indicators:

[0122]

[0123]

[0124] In the formula, r kj This represents the correlation coefficient between index k and index j.

[0125] Based on the intensity and conflict of the indicators, the information content c of each indicator is calculated. j :

[0126]

[0127] The information content of the indicators is normalized, and the weight Wb of each indicator is calculated. 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] The optimal weight value model for calculating the evaluation index based on the improved least squares method is as follows:

[0131]

[0132] In the formula, W j The optimal weight for evaluating index j.

[0133] Step S23: Optimize the optimal weight value model based on the adaptive mutation algorithm, and determine the optimal weight set based on the optimal solution. The optimal weight set includes the optimal weights of each preset evaluation index.

[0134] Among them, the adaptive mutation algorithm is used to quickly solve the optimal evaluation index weights, so as to reduce the subjective and objective limitations brought about by the single weighting method and make the decision results more realistic, scientific and comprehensive.

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

[0136] Initialize, assuming the population size parameter is n, then randomly select n points V from the feasible region. i (i=(1,2,⋯n), forming the initial population M(0)={V1,V2,...,V n}. And calculate the fitness f(V) of all individuals. i ).

[0137] Individuals are selected for reproduction, with each individual having a probability P of being selected.i :

[0138]

[0139] With crossover probability P c Perform a crossover operation on the selected individuals to generate new individuals. Assume 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 the mutation probability P m To generate new individuals by mutating some of the genes of the next generation, the gene mutation process is as follows:

[0142]

[0143] In the formula, It is a randomly varying quantity.

[0144] To improve the optimization speed and avoid getting trapped in local optima, the crossover probability P is adjusted. c and the probability of mutation P m Perform adaptive operations.

[0145]

[0146]

[0147] In the formula, f is the individual with the larger fitness value among those undergoing crossover; f is the fitness value of the individual undergoing mutation. max f min and f avg These represent the maximum, minimum, and average fitness values ​​of the population, respectively. c0 and P m0 It is a constant within (0,1).

[0148] In some embodiments, the scores of each evaluation index are weighted to obtain an assessment value of the carrying capacity of distributed generation after it is connected to the distribution network at a future time, including:

[0149] The weights of each evaluation index score are assigned based on the optimal weight set, and a weighted calculation is performed based on the weighted results of each evaluation index score to obtain the carrying capacity assessment value of the distributed power source after it is connected to the distribution network in the future.

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

[0151] Step S501: Determine whether the load-bearing capacity assessment value is greater than the preset assessment threshold;

[0152] Step S502: When the carrying capacity assessment value is determined to be greater than the preset assessment threshold, the current operation scheduling scheme will be executed in the future.

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

[0154] Understandably, the first step is to determine whether the carrying capacity assessment value exceeds a preset assessment threshold. If the assessment value is higher than the threshold, the current operation and scheduling scheme will continue to be executed in the future. If the assessment value does not exceed the threshold, the following steps need to be repeated: based on the historical operation data and real-time operation data of the distribution network, predict the operation and scheduling scheme of distributed power sources in the future until the carrying capacity assessment value reaches or exceeds the preset assessment threshold, and finally output the current operation and scheduling scheme to find the optimal operation and scheduling scheme that enables the distribution network to meet the maximum carrying capacity margin.

[0155] Based on the same inventive concept, this application also provides a system for evaluating the mixed carrying capacity of a distribution network to implement the above-mentioned method for evaluating the mixed carrying capacity of a distribution network.

[0156] The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the distribution network hybrid carrying capacity assessment system provided below can be found in the limitations of the distribution network hybrid carrying capacity assessment method above, and will not be repeated here.

[0157] like Figure 3 As shown in the figure, this application provides a system for assessing the hybrid carrying capacity of a distribution network, including:

[0158] The scheduling prediction module 100 is used to predict the operation and scheduling scheme of distributed power sources in the future based on the historical operation data of the distribution network and the real-time operation data during the day.

[0159] The operation data acquisition module 200 is used to obtain data on the operation status of the distribution network after the distributed power source is connected to the distribution network in the future, based on the operation scheduling scheme of the distributed power source.

[0160] The indicator scoring modulus 300 is used to determine the evaluation index scores of each preset evaluation index based on the distribution network operation status data; among them, the preset evaluation indexes are used to quantify the carrying capacity of the distribution network.

[0161] The carrying capacity assessment module 400 is used to perform weighted calculations on the scores of each evaluation index to obtain the carrying capacity assessment value of the distributed power source after it is connected to the distribution network in the future.

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

[0163] Based on historical operating data and intraday real-time operating data of the distribution network, a source-load stochastic time model is established; the source-load stochastic time model is used to predict the power output of distributed power sources at the next moment.

[0164] The power output of distributed power sources before the day-ahead scheduling phase is determined based on the source-load stochastic time model.

[0165] The day-ahead scheduling optimization model is solved based on the power output of distributed generation sources before the day-ahead scheduling phase to obtain the day-ahead scheduling plan; the day-ahead scheduling optimization model is constructed with the goal of minimizing the operating cost of the distribution network.

[0166] The power output of distributed generation is adjusted according to the daily dispatch plan, and the adjusted distributed generation is connected to the distribution network for power flow calculation to obtain the power output of distributed generation before the intraday dispatch phase.

[0167] The intraday scheduling optimization model is solved based on the power output of distributed generation before the intraday scheduling phase, and the intraday scheduling scheme is obtained as the operation and scheduling scheme of distributed generation in the future. The intraday scheduling optimization model is constructed with the goal of maximizing the carrying capacity of distributed generation after it is 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 following: average voltage deviation rate, average voltage quality rate, average distribution network line loss rate, average distribution network line utilization rate, average clean energy utilization rate, average clean energy power supply rate, and 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, used for:

[0171] The pecking order analysis method and the variation correlation method are used to assign weights to each preset evaluation index, respectively, to obtain the first weight set and the second weight set corresponding to the pecking order 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 based on the adaptive mutation algorithm, and the optimal weight set is determined based on 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, and to perform weighted calculations based on the weight assignment results of the scores of each evaluation index to obtain the carrying capacity assessment value of the distributed power source after it is connected to the distribution network in the future.

[0175] In some embodiments, the system further includes: a scheme optimization module, used for:

[0176] Determine whether the load-bearing capacity assessment value is greater than the preset assessment threshold;

[0177] If the assessment value of the carrying capacity is determined to be greater than the preset assessment threshold, the current operation scheduling scheme will be executed in the future.

[0178] When the carrying capacity assessment value is determined to be less than the preset assessment threshold, the step of predicting the operation and scheduling scheme 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 is re-executed until the carrying capacity assessment value is greater than the preset assessment threshold, and the current operation and scheduling scheme is output.

[0179] like Figure 4 As shown in the embodiments of this application, an electronic device is also provided. The electronic device 10 includes a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 performs the steps of the power distribution network hybrid carrying capacity assessment method as described in the above embodiments.

[0180] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed, implements the steps of the power distribution network hybrid carrying capacity assessment method as described in the above embodiments.

[0181] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, and computer storage media described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0182] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0183] In the several embodiments provided by this invention, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0184] In the embodiments provided by this 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 merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

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

[0186] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If the integrated unit is implemented as 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, in essence, 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. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0188] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power distribution network hybrid load carrying capacity assessment method, characterized in that, The method comprises the following steps: According to the historical operation data and the real-time operation data of the power distribution network, a future operation scheduling scheme of the distributed power supply is predicted, which comprises the following steps: According to the historical operation data and the real-time operation data of the power distribution network, 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 supply at the next time; According to the source-load random time model, the power output of the distributed power supply before the day-ahead scheduling stage is determined; According to the power output of the distributed power supply before the day-ahead scheduling stage, a day-ahead scheduling optimization model is solved to obtain a day-ahead scheduling plan; wherein the day-ahead scheduling optimization model is constructed with the minimum power distribution network operation cost as the target; According to the day-ahead scheduling plan, the power output of the distributed power supply is adjusted, and the adjusted distributed power supply is connected to the power distribution network for power flow calculation to obtain the power output of the distributed power supply before the day-ahead scheduling stage; According to the power output of the distributed power supply before the day-ahead scheduling stage, a day-ahead scheduling optimization model is solved to obtain a day-ahead scheduling scheme as the future operation scheduling scheme of the distributed power supply; wherein the day-ahead scheduling optimization model is constructed with the maximum load carrying capacity of the distributed power supply connected to the power distribution network and the minimum scheduling error of the power distribution network as the target; According to the operation scheduling scheme of the distributed power supply, the power distribution network operation condition data after the distributed power supply is connected to the power distribution network at the future time is obtained; According to the power distribution network operation condition data, the evaluation index scores of each preset evaluation index are determined; wherein the preset evaluation index is used to quantify the load carrying capacity of the power distribution network; The evaluation index scores are weighted and calculated to obtain the load carrying capacity evaluation value of the distributed power supply connected to the power distribution network at the future time.

2. The power distribution grid hybrid load carrying capacity assessment method of claim 1, wherein, The preset evaluation index comprises at least one of the average voltage deviation rate, the average voltage quality rate, the average line loss rate of the power distribution network, the average line utilization rate of the power distribution network, the average clean energy utilization rate, the average clean energy power supply rate, and the net load average change rate.

3. The power distribution grid hybrid load carrying capacity assessment method of claim 1, wherein, The method further comprises the following steps: The evaluation index scores are normalized.

4. The power distribution grid hybrid load carrying capacity assessment method of claim 1, wherein, The method further comprises the following steps: The weight values of each preset evaluation index are assigned by using the priority sequence analysis method and the mutation correlation method respectively to obtain a first weight set and a second weight set corresponding to the priority sequence analysis method and the mutation correlation method respectively; Based on the improved least square 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 minimum error of the optimal weight as the target; Based on the adaptive mutation algorithm, the optimal weight value model is optimized and solved to determine an optimal weight set according to the optimal solution, wherein the optimal weight set comprises the optimal weight of each preset evaluation index.

5. The power distribution network hybrid load carrying capacity assessment method of claim 4, wherein, The weighted calculation of the evaluation index scores to obtain the load carrying capacity evaluation value of the distributed power supply connected to the power distribution network at the future time comprises the following steps: According to the weight assignment of each evaluation index score according to the optimal weight set, the weight calculation is performed according to the weight assignment result of each evaluation index score, and the carrying capacity evaluation value of the distributed power supply after being connected to the power distribution network in the future time is obtained.

6. The power distribution network hybrid load carrying capacity assessment method of any one of claims 1 to 5, wherein, Also includes: Judge whether the carrying capacity evaluation value is greater than the preset evaluation threshold value; When judging that the carrying capacity evaluation value is greater than the preset evaluation threshold value, the current operation scheduling scheme is executed in the future time; When judging that the carrying capacity evaluation value is not greater than the preset evaluation threshold value, the step of predicting the operation scheduling scheme of the distributed power supply in the future time according to the historical operation data and the real-time operation data of the power distribution network is re-executed until the carrying capacity evaluation value is greater than the preset evaluation threshold value, and the current operation scheduling scheme is output.

7. A power distribution network hybrid load carrying capacity assessment system, characterized by, Including: The scheduling prediction module is used for predicting the operation scheduling scheme of the distributed power supply in the future time according to the historical operation data and the real-time operation data of the power distribution network; According to the historical operation data and the real-time operation data of the power distribution network, the operation scheduling scheme of the distributed power supply in the future time is predicted, including: According to the historical operation data and the real-time operation data of the power distribution network, 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 supply in the next time; According to the source-load random time model, the power output of the distributed power supply before the day-ahead scheduling stage is determined; According to the power output of the distributed power supply before the day-ahead scheduling stage, a day-ahead scheduling optimization model is solved to obtain a day-ahead scheduling plan; wherein the day-ahead scheduling optimization model is constructed with the minimum power distribution network operation cost as the target; According to the day-ahead scheduling plan, the power output of the distributed power supply is adjusted, and the adjusted distributed power supply is connected to the power distribution network for power flow calculation to obtain the power output of the distributed power supply before the day-ahead scheduling stage; According to the power output of the distributed power supply before the day-ahead scheduling stage, a day-ahead scheduling optimization model is solved to obtain a day-ahead scheduling plan; wherein the day-ahead scheduling optimization model is constructed with the maximum carrying capacity of the distributed power supply connected to the power distribution network and the minimum scheduling error of the power distribution network as the target; The operation data acquisition module is used for obtaining the power distribution network operation condition data of the distributed power supply connected to the power distribution network in the future time according to the operation scheduling scheme of the distributed power supply; The index scoring module is used for determining the evaluation index score of each preset evaluation index according to the power distribution network operation condition data; wherein the preset evaluation index is used to quantify the carrying capacity of the power distribution network; The carrying capacity evaluation module is used for weighting calculation of each evaluation index score to obtain the carrying capacity evaluation value of the distributed power supply connected to the power distribution network in the future time.

8. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the power distribution network hybrid load carrying capacity evaluation method according to any one of claims 1-6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the steps of the power distribution network hybrid load carrying capacity evaluation method according to any one of claims 1-6.

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