Power distribution network power supply mode optimization method and system based on novel power system

Abstract

The invention discloses a power distribution network power supply mode optimization method and system based on a novel electric power system, and relates to the technical field of power supply mode optimization, and the method comprises the steps: obtaining corresponding prediction environment influence indexes based on environment influence factors corresponding to an electric power production end, an electric power storage end and an electric power conversion end, and then obtaining comprehensive environment influence index information, first preset line information is obtained based on the multi-source environment information, the first connection line information, the second connection line information and the comprehensive environment influence index information, and the influence of each power production end on the environment is evaluated through the comprehensive environment influence index information, so that a reasonable first preset line is set; according to the method, the power grid virtual line model is constructed, then the first preset line is determined by integrating the environment influence index information and determining the virtual output point information, the virtual line model is utilized, resource occupation is reduced, and meanwhile the reliability of power distribution network power supply mode optimization is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply mode optimization, and in particular to a method and system for optimizing a power supply mode of a distribution network based on a novel power system. Background Art

[0002] With the large-scale access of new energy sources, traditional power systems are facing new challenges in power supply stability and reliability. The construction of new power systems aims to promote green and low-carbon transformation, emphasizing the efficient use and sustainable development of clean energy. In this context, it is particularly important to optimize the power supply mode of the distribution network. By introducing intelligent technology, distributed energy and energy storage systems, the adaptability and flexibility of the power grid can be improved, ensuring the reliability and economy of power supply. In addition, the optimization of the power supply mode of the distribution network can also reduce energy waste and improve energy utilization efficiency, which is of great significance to achieving green energy goals and improving the overall operation level of the power system.

[0003] A Chinese patent application with publication number CN116760076A discloses a configuration optimization method based on energy storage power supply mode, including: distributed collection of different load data in the power supply area, and systematic preprocessing of power grid system load data in different periods; establishing a preliminary load curve prediction model, and making preliminary predictions on the power grid system load data of different periods with pre-marks through the preliminary load curve prediction model; performing error correction on the power grid system load data of different periods with pre-marks through an error correction model; setting a monitoring module and a compensation module of a distributed energy storage system grid-connected system, monitoring data transmission through the monitoring module, the monitoring module including multiple communication transmission units, and the compensation module for performing difference compensation on the acquired reactive power; establishing the compensation module objective function, calculating the optimal compensation capacity value, improving the coordination of the energy storage system with the overall power grid, and improving the reliability of power grid operation.

[0004] In the above-mentioned prior art, different load data of the power supply area are collected through distributed collection, and then the load data is predicted and error corrected through the load curve preliminary prediction model and the error correction model, and then the compensation module objective function is determined, and the optimal compensation capacity value is calculated to improve the coordination of the energy storage system with the overall power grid. However, the above-mentioned prior art analyzes different load data of the power supply area, lacks analysis of the power loss and environmental impact of the distribution network, and is unable to evaluate the fluctuation of electricity demand in different time periods, and cannot ensure load balance, resulting in supply and demand mismatch. Therefore, it is necessary to provide a distribution network power supply mode optimization method and system based on a new power system. Summary of the invention

[0005] In order to solve the above technical problems, a method and system for optimizing the power supply mode of a distribution network based on a new power system are provided. The technical solution solves the problem that the existing technology proposed in the above background technology analyzes different load data in the power supply area, lacks analysis of the power loss and environmental impact of the distribution network, and is unable to evaluate the fluctuations in electricity demand in different time periods, and cannot ensure load balance, resulting in a mismatch between supply and demand.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] A method for optimizing a power supply mode of a distribution network based on a new power system, comprising:

[0008] Determine the optimized area information of the new power system, and obtain the power production end information, power storage end information, power conversion end information and power consumption area information based on the optimized area information;

[0009] Collect multi-source environmental information in real time based on the optimized regional information, and determine the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, power conversion end and power storage end respectively;

[0010] Process the predicted power generation and the real-time power generation to obtain comprehensive power generation information, and process the predicted environmental impact index and the real-time environmental quality index to obtain comprehensive environmental impact index information;

[0011] Acquire first connection line information and second connection line information according to power production end information, power conversion end information and power consumption area information;

[0012] Based on the multi-source environmental information, the first connection line information, the second connection line information and the comprehensive environmental impact index information, the first preset line information is obtained, and the power demand fluctuation information is obtained based on the power consumption area information;

[0013] Based on the comprehensive power generation information, the first preset line information, the power consumption area information and the power demand fluctuation information, the first preset line is evaluated at different time periods to obtain the first grid-connected line information;

[0014] Evaluate the first grid-connected line information to obtain a first transmission quality index of each first grid-connected line in the first grid-connected line information;

[0015] Based on the first transmission quality index, the first grid-connected line information, the first connection line information, and the second connection line information are evaluated to obtain second preset line information;

[0016] According to the second preset line information, a power grid stability index of the second preset line in the second preset line information is obtained, and a power supply mode optimization strategy is determined based on the power grid stability index.

[0017] In an optional embodiment, the real-time collection of multi-source environmental information based on the optimized area information and the determination of the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, the power conversion end and the power storage end respectively include:

[0018] Based on the optimization area information, obtain the optimization area location information;

[0019] According to the location information of the optimized area, obtain the meteorological data, air quality data and geographical terrain information of the optimized area, and determine the multi-source environmental information;

[0020] To optimize the regional location information, obtain the power generation type information and the power generation area location information;

[0021] Determine the generator set information, fuel consumption information and renewable energy generation information at the power production end based on the power generation type information;

[0022] Determine the power production end information through power generation type information, power generation area location information, power generation unit information, fuel consumption information and renewable energy power generation information;

[0023] According to the location information of the optimized area, the substation location information, the substation transmission line information and the smart transformer information are obtained, and the power conversion end information is determined;

[0024] According to the location information of the optimized area, the energy storage equipment information and the charging and discharging efficiency information are obtained, and the power storage terminal information is determined;

[0025] Based on the information of the power production end, the power storage end and the power conversion end, the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end are obtained respectively;

[0026] Based on the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end, the corresponding predicted environmental impact index is obtained;

[0027] The calculation formula for the predicted environmental impact index is:

[0028]

[0029] In the formula, E real (t) is the predicted environmental impact index at time t, E i (t) is the quantitative value of the i-th environmental impact factor at the t-th moment, p i is the contribution characteristic value of the i-th environmental impact factor to the environment, E j (t) is the quantitative value of the jth environmental impact factor at the tth moment, p jis the contribution characteristic value of the jth environmental impact factor to the environmental impact, is the quantitative value of the interaction between the i-th environmental impact factor and the j-th environmental impact factor at time t, k is the summation index value, e is the base of the natural logarithm, α i is the weight coefficient of the i-th environmental impact factor, α j is the weight coefficient of the jth environmental impact factor, γ k It is the degree of interactive influence of each environmental factor on other environmental factors.

[0030] In an optional embodiment, the real-time collection of multi-source environmental information based on the optimized area information and the determination of the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, the power conversion end and the power storage end respectively include:

[0031] Based on the information from the power production end, obtain the predicted multi-source environmental information and predicted equipment status information;

[0032] The predicted multi-source environmental information is divided into short-term predicted multi-source environmental information, medium-term predicted multi-source environmental information and long-term predicted multi-source environmental information;

[0033] Sequentially grouping the short-term prediction multi-source environment information, the mid-term prediction multi-source environment information, the long-term prediction multi-source environment information, and the prediction device state information to obtain first prediction sub-data, second prediction sub-data, and third prediction sub-data;

[0034] Respectively extracting power generation prediction feature data from the first prediction sub-data, the second prediction sub-data, and the third prediction sub-data, and sequentially inputting the power generation prediction feature data into the power generation prediction model to obtain short-term predicted power generation, medium-term predicted power generation, and long-term predicted power generation output by the power generation prediction model;

[0035] The steps of obtaining the power generation prediction model are as follows:

[0036] S100, based on the information of the power production end, obtaining the historical power generation data, historical multi-source meteorological data and historical equipment status data corresponding to the power production end, and performing data preprocessing on the historical power generation data, historical multi-source meteorological data and historical equipment status data respectively;

[0037] S101, grouping historical power generation data, historical multi-source meteorological data, and historical equipment status data based on preset time values ​​to obtain historical power generation prediction sub-data;

[0038] S102, sequentially converting historical power generation prediction sub-data into feature vectors;

[0039] S103, establishing a regression analysis model, and using historical power generation prediction sub-data to train the regression analysis model to obtain a power generation prediction model;

[0040] The expression formula of the power generation prediction model is:

[0041]

[0042] In the formula, is time point t for The predicted power generation, is the bias term, is the i-th corresponding multi-source meteorological data in the historical power generation prediction sub-data for The regression coefficients of the eigenvectors, is the jth corresponding to the equipment status data in the historical power generation prediction sub-data for The regression coefficient of the feature vector, F is the total number of historical power generation prediction sub-data, is the i-th corresponding multi-source meteorological data in the historical power generation prediction sub-data for feature vectors, is the jth corresponding to the equipment status data in the historical power generation prediction sub-data for feature vectors, Indicates the predicted power generation and the actual value in historical power generation data The training error between , e is the base of the natural logarithm.

[0043] In an optional embodiment, acquiring the first connection line information and the second connection line information according to the power production end information, the power conversion end information and the power consumption area information specifically includes:

[0044] Based on the information of the power production end, determine the generator set information and renewable energy power generation information of the power production end;

[0045] The generator set information, generator set location information and generator set output power information are used to determine the output location information of the power production end;

[0046] Using renewable energy generation information, determine renewable energy output location information;

[0047] Based on the power conversion terminal information, determine the substation voltage level information, substation access line information and substation output line information;

[0048] Based on the power consumption area information, obtain the power consumption area geographical information and the power consumption area historical load information;

[0049] Construct a virtual line model of the power grid, and determine virtual output point information, virtual transfer point information, virtual line information and virtual receiving point information through the output power information of the generator set, the output location information of the power production end, the output location information of renewable energy, the voltage level information of the substation, the access line information of the substation, the output line information of the substation, the geographical information of the power consumption area and the historical load information of the power consumption area;

[0050] According to the historical load information of the power consumption area and the virtual receiving point information, the load of the virtual receiving point is divided, and according to the output power information of the generator set, the renewable energy power generation information and the virtual output point information, the output power of the virtual output point is divided;

[0051] Based on the results of load division and output power division, load flow analysis is performed on the virtual line model of the power grid to simulate power flow information under different loads and different output powers;

[0052] Determine the location information of the main line and redundant line based on the power flow information under different loads and different output powers;

[0053] Based on the position information of the main line and the redundant line, the first connection line information and the second connection line information are acquired.

[0054] In an optional embodiment, the acquiring of the first preset line information based on the multi-source environmental information, the first connection line information, the second connection line information and the comprehensive environmental impact index information, and the acquiring of the power demand fluctuation information based on the power consumption area information specifically includes:

[0055] Based on the short-term prediction multi-source environmental information, the medium-term prediction multi-source environmental information and the long-term prediction multi-source environmental information, a short-term prediction time value, a medium-term prediction time value and a long-term prediction time value are set;

[0056] The short-term prediction time value, the medium-term prediction time value and the long-term prediction time value are sequentially input into the calculation formula of the prediction environmental impact index to obtain the short-term prediction environmental impact index, the medium-term prediction environmental impact index and the long-term prediction environmental impact index;

[0057] Processing the data of the short-term predicted environmental impact index, the medium-term predicted environmental impact index and the long-term predicted environmental impact index to obtain the predicted environmental impact index;

[0058] Obtaining initial time node information for real-time collection of multi-source environmental information, and obtaining an initial environmental quality index based on the initial time node information;

[0059] Based on the initial environmental quality index and the predicted environmental impact index, a predicted environmental quality index is obtained;

[0060] Compare the real-time environmental quality index with the predicted environmental quality index to obtain the predicted environmental error coefficient;

[0061] The dynamic comparison curve is established through the real-time environmental quality index and the predicted environmental quality index, and the thre =μ mean +k de σ sta (3≥k de ≥1.5), determine the prediction environment error coefficient threshold, where E thre is the prediction environment error coefficient threshold, μ mean is the average value of the difference between the real-time environmental quality index and the predicted environmental quality index in the dynamic comparison curve, σ sta is the standard deviation of the difference between the real-time environmental quality index and the predicted environmental quality index in the dynamic comparison curve, k de is the degree to which the error deviates from the mean;

[0062] If E thre ≤0.1, the predicted environmental impact index is used as the comprehensive environmental impact index information of the power production end. thre >0.1, the average of the predicted environmental impact index at time t and time t-1 is used as the comprehensive environmental impact index information;

[0063] Determine virtual output point information by integrating environmental impact index information, and determine first preset line information according to the virtual output point information;

[0064] Based on the first preset line information and the power consumption area information, and through the virtual output point information, the virtual transfer point information, the virtual line information and the virtual receiving point information, the power supply to the power consumption area in different time periods is simulated in the virtual line model of the power grid to obtain the simulated power consumption of the power consumption area;

[0065] The actual power consumption of the power consumption area is obtained, and the difference between the simulated power consumption of the power consumption area and the actual power consumption of the power consumption area is obtained to obtain the power demand fluctuation coefficient curve and determine the power demand fluctuation information.

[0066] In an optional embodiment, the first preset line is evaluated at different time periods based on the first preset line information, the power consumption area information and the power consumption demand fluctuation information to obtain the first grid-connected line information, specifically including:

[0067] Acquire real-time equipment status information, and process multi-source environmental information and real-time equipment status information to obtain real-time power generation prediction sub-data;

[0068] Based on the short-term prediction time value, the medium-term prediction time value and the long-term prediction time value, the real-time power generation prediction sub-data is converted into a feature vector and input into the power generation prediction model to obtain the real-time predicted power generation of different prediction periods output by the power generation prediction model;

[0069] Determine the preferred power production end information and the preferred power storage end information according to the real-time predicted power generation in different prediction periods, the simulated power consumption in the power consumption area, the location information of the power generation area and the power storage end information, and obtain the spare power production end information and the spare power storage end information;

[0070] Based on the power consumption area information and power demand fluctuation information, determine whether to call the spare power production end information and spare power storage end information;

[0071] A grid connection index threshold is set, and based on the preferred power production end information, the preferred power storage end information and the virtual line information, virtual line location information having a grid connection index greater than the grid connection index threshold is determined in the power grid virtual line model;

[0072] The first grid-connected line information is determined according to the virtual line location information.

[0073] In an optional embodiment, the evaluating the first grid-connected line information to obtain the first transmission quality index of each first grid-connected line in the first grid-connected line information specifically includes:

[0074] Based on the first grid-connected line information and the virtual line location information, obtaining starting point characteristic data, end point characteristic data and first grid-connected line characteristic data of the first grid-connected line;

[0075] Determine a first transmission quality index of each first grid-connected line according to the starting point characteristic data, the end point characteristic data and the first grid-connected line characteristic data of the first grid-connected line;

[0076] The calculation formula of the first transmission quality index is:

[0077]

[0078] In the formula, I tran is the first transmission quality index, w1, w2, w3, w4 and w5 are weight factors, L is the line load rate, VSI is the voltage stability index, P loss is the power loss of the line, P max is the maximum power carrying capacity of the line, H is the current harmonic content, and A is the availability of the line.

[0079] In an optional embodiment, the step of evaluating the first grid-connected line information, the first connection line information, and the second connection line information based on the first transmission quality index to obtain the second preset line information specifically includes:

[0080] Setting a first transmission quality index threshold;

[0081] Based on the first transmission quality index threshold and the first transmission quality index, determining whether to modify the virtual line in the first grid-connected line information;

[0082] If the first transmission quality index is greater than the first transmission quality index threshold, the virtual line in the first grid-connected line information is not modified;

[0083] If the first transmission quality index is less than or equal to the first transmission quality index threshold, modifying the virtual line in the first grid-connected line information;

[0084] Based on the first grid-connected line information, the first connection line information, and the second connection line information, obtaining virtual line position information and position information of the first connection line and the second connection line in a virtual line model of a power grid;

[0085] According to the virtual line position information and the position information of the first connection line and the second connection line in the power grid virtual line model, the first connection line is released, and the second connection line is called at the same time to obtain the second preset line information.

[0086] In an optional embodiment, the obtaining, according to the second preset line information, a power grid stability index of the second preset line in the second preset line information, and determining the power supply mode optimization strategy based on the power grid stability index specifically includes:

[0087] Based on the second preset line information, obtaining the power distribution line location information in the second preset line;

[0088] Acquiring distribution line characteristic data through the distribution line location information in the second preset line;

[0089] Based on the distribution line characteristic data, determining the stability influencing factors of the distribution line characteristic data;

[0090] Obtain the grid stability index based on the stability influencing factors of the distribution line characteristic data;

[0091] Setting a grid stability index threshold, and based on the grid stability index, determining whether to use the second preset line information as a power supply mode optimization strategy;

[0092] The calculation formula of the power grid stability index is:

[0093]

[0094] In the formula, The second preset line The grid stability index of the distribution line at the location, τ1, τ2, τ3, τ4 and τ5 are all weight factors, The second preset line The voltage stability index of the distribution line at The second preset line Frequency stability index of the distribution line at The second preset line The load balance index of the distribution line at The second preset line The power loss index of the distribution line at The second preset line Harmonic pollution index of distribution lines.

[0095] Furthermore, a distribution network power supply mode optimization system based on a new power system is proposed, which is used to implement any of the above optimization methods, including:

[0096] A data acquisition module, which is used to acquire information on power production, power storage, power conversion, and power consumption areas, and collect multi-source environmental information in real time based on optimized area information;

[0097] A calculation module, the calculation module is used to determine the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, the power conversion end and the power storage end;

[0098] A data processing module, wherein the data processing module is used to process the predicted power generation and the real-time power generation to obtain comprehensive power generation information, and to process the predicted environmental impact index and the real-time environmental quality index to obtain comprehensive environmental impact index information;

[0099] A model building module, wherein the model building module is used to build a virtual line model of a power grid and simulate power grid operation through the virtual line model of the power grid;

[0100] A threshold setting module, the threshold setting module is used to set a prediction environment error coefficient threshold, a grid connection index threshold, a first transmission quality index threshold and a grid stability index threshold;

[0101] An evaluation module, the evaluation module is used to evaluate the first preset line in different time periods based on the comprehensive power generation information, the first preset line information, the power consumption area information and the power demand fluctuation information, obtain the first grid-connected line information, evaluate the first grid-connected line information, obtain the first transmission quality index of each first grid-connected line in the first grid-connected line information, evaluate the first grid-connected line information, the first connection line information and the second connection line information based on the first transmission quality index, and obtain the second preset line information;

[0102] A power supply mode optimization module, the power supply mode optimization module is used to determine the power supply mode optimization strategy based on the power grid stability index and execute the optimization strategy;

[0103] A display module, wherein the display module is used to display the process and results of the data acquisition module acquiring data and information, the calculation process and result data of the calculation module, the processing process and results of the processing module, the construction process and results of the model construction module, the process and results of the model construction module simulating the operation of the power grid, the process and results of the threshold setting module, the evaluation process and results of the evaluation module, the optimization process and results of the power supply mode optimization module, and the process and results of the power supply mode optimization module executing the optimization strategy.

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] This scheme proposes a method and system for optimizing the power supply mode of a distribution network based on a new power system. Based on the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end, the corresponding predicted environmental impact index is obtained, and then the predicted environmental impact index and the real-time environmental quality index are processed to obtain comprehensive environmental impact index information. Then, based on multi-source environmental information, first connection line information, second connection line information and comprehensive environmental impact index information, the first preset line information is obtained. Through the comprehensive environmental impact index information, the impact of each power production end on the environment is evaluated, so as to set a reasonable first preset line. By constructing a virtual line model of the power grid, the virtual output point information is determined through the comprehensive environmental impact index information to determine the first preset line. The virtual line model is used to reduce resource occupation and improve the reliability of the optimization of the power supply mode of the distribution network.

[0106] This scheme proposes a method and system for optimizing the power supply mode of a distribution network based on a new power system. By comprehensively considering the power generation information, the first preset line information, the power consumption area information and the power demand fluctuation information, the first preset line is evaluated at different time periods to obtain the first grid-connected line information. Then, the first grid-connected line information is evaluated to obtain the first transmission quality index of each first grid-connected line in the first grid-connected line information. The first transmission quality index comprehensively analyzes the power generation at the power production end and the power demand fluctuation information of the power consumption area, and the first transmission quality index includes the analysis of indicators such as line load rate and power loss, so as to realize the analysis of power loss and transmission quality of the distribution network at different time periods, ensure the load balance of the distribution network, improve the accuracy and reliability of the optimization of the power supply mode of the distribution network, and avoid the problem of mismatch between supply and demand.

[0107] The present invention proposes a method and system for optimizing the power supply mode of a distribution network based on a new power system. The method evaluates the first grid-connected line information, the first connection line information and the second connection line information through a first transmission quality index to obtain the second preset line information. Then, based on the second preset line information, the power grid stability index of the second preset line in the second preset line information is obtained, and the power supply mode optimization strategy is determined based on the power grid stability index, so as to improve the reliability of the power supply mode optimization of the distribution network, enable the optimization system to provide an accurate and reliable distribution network power supply mode optimization strategy, and ensure the stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Figure 1 A flow chart of a method for optimizing a power supply mode of a distribution network based on a new power system proposed by the present invention;

[0109] Figure 2 A flowchart for obtaining the predicted environmental impact index in the present invention;

[0110] Figure 3 A flowchart for obtaining the first connection line information and the second connection line information in the present invention;

[0111] Figure 4 This is a system framework diagram of a distribution network power supply mode optimization system based on a new power system proposed by the present invention. DETAILED DESCRIPTION

[0112] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0113] Reference Figure 1 - Figure 4 As shown, a method for optimizing the power supply mode of a distribution network based on a new power system includes:

[0114] Determine the optimized area information of the new power system, and obtain the power production end information, power storage end information, power conversion end information and power consumption area information based on the optimized area information;

[0115] Collect multi-source environmental information in real time based on the optimized regional information, and determine the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, power conversion end and power storage end respectively;

[0116] Process the predicted power generation and the real-time power generation to obtain comprehensive power generation information, and process the predicted environmental impact index and the real-time environmental quality index to obtain comprehensive environmental impact index information;

[0117] Acquire first connection line information and second connection line information according to power production end information, power conversion end information and power consumption area information;

[0118] Based on the multi-source environmental information, the first connection line information, the second connection line information and the comprehensive environmental impact index information, the first preset line information is obtained, and the power demand fluctuation information is obtained based on the power consumption area information;

[0119] Based on the comprehensive power generation information, the first preset line information, the power consumption area information and the power demand fluctuation information, the first preset line is evaluated at different time periods to obtain the first grid-connected line information;

[0120] Evaluate the first grid-connected line information to obtain a first transmission quality index of each first grid-connected line in the first grid-connected line information;

[0121] Based on the first transmission quality index, the first grid-connected line information, the first connection line information, and the second connection line information are evaluated to obtain second preset line information;

[0122] According to the second preset line information, a power grid stability index of the second preset line in the second preset line information is obtained, and a power supply mode optimization strategy is determined based on the power grid stability index.

[0123] Furthermore, based on the optimized regional information, multi-source environmental information is collected in real time, and the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, power conversion end and power storage end are determined respectively, including:

[0124] Based on the optimization area information, obtain the optimization area location information;

[0125] According to the location information of the optimized area, obtain the meteorological data, air quality data and geographical terrain information of the optimized area, and determine the multi-source environmental information;

[0126] To optimize the regional location information, obtain the power generation type information and the power generation area location information;

[0127] Determine the generator set information, fuel consumption information and renewable energy generation information at the power production end based on the power generation type information;

[0128] Determine the power production end information through power generation type information, power generation area location information, power generation unit information, fuel consumption information and renewable energy power generation information;

[0129] According to the location information of the optimized area, the substation location information, the substation transmission line information and the smart transformer information are obtained, and the power conversion end information is determined;

[0130] According to the location information of the optimized area, the energy storage equipment information and the charging and discharging efficiency information are obtained, and the power storage terminal information is determined;

[0131] Based on the information of the power production end, the power storage end and the power conversion end, the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end are obtained respectively;

[0132] Based on the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end, the corresponding predicted environmental impact index is obtained;

[0133] The calculation formula for the predicted environmental impact index is:

[0134]

[0135] In the formula, E real (t) is the predicted environmental impact index at time t, E i (t) is the quantitative value of the i-th environmental impact factor at the t-th moment, p i is the contribution characteristic value of the i-th environmental impact factor to the environment, E j (t) is the quantitative value of the jth environmental impact factor at the tth moment, p j is the contribution characteristic value of the jth environmental impact factor to the environmental impact, is the quantitative value of the interaction between the i-th environmental impact factor and the j-th environmental impact factor at time t, k is the summation index value, e is the base of the natural logarithm, α i is the weight coefficient of the i-th environmental impact factor, α j is the weight coefficient of the jth environmental impact factor, γ k It is the degree of interactive influence of each environmental factor on other environmental factors.

[0136] Specifically, the optimized regional location information is the geographical area where electricity is stored and used in the new power system, for example, the power load demand in a certain area, the distribution of wind and solar energy, the existing layout of energy storage facilities, etc. According to the optimized regional location information, the energy storage equipment information and the charging and discharging efficiency information are obtained, and the power storage terminal information is determined. First, the location information of the power storage terminal is obtained, and then the energy storage equipment information is obtained according to the location information of the power storage terminal. The energy storage equipment information includes the type, capacity, deployment location, status, etc. of various types of batteries, supercapacitors, hydrogen energy storage devices and other facilities. Existing energy storage facilities include battery energy storage systems (BESS), pumped storage power stations, compressed air energy storage (CAES), hydrogen energy storage systems, etc. The charging and discharging efficiency is the energy conversion efficiency of energy storage equipment during the charging and discharging process. The efficiency of the equipment directly affects the benefits of power storage and release.

[0137] Furthermore, based on the information of the power production end, the power storage end and the power conversion end, the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end are obtained respectively. The environmental impact factors are the impact factors of the power production end, the power storage end and the power conversion end on the regional environment, including the potential impact on the atmosphere, water sources, soil, ecosystems and human health. For example, thermal power generation (coal, natural gas, oil, etc.): These traditional fossil fuel power generation methods usually produce a large amount of greenhouse gases (such as carbon dioxide, methane, nitrogen oxides, etc.), which have a negative impact on climate change. Renewable energy (wind power, solar energy, hydropower): These energy sources usually do not directly produce greenhouse gas emissions, but produce a certain carbon footprint during the production and installation process. is the quantitative value of the interaction between the ith environmental impact factor and the jth environmental impact factor at time t. The environmental impact factors at the power production end, power storage end and power conversion end include but are not limited to greenhouse gas emissions (such as carbon dioxide, methane), air pollutants (such as sulfur oxides, nitrogen oxides), water resource use and pollution, noise, thermal pollution, ecological impact, etc. These factors not only exist independently in the power generation process, but may also interact with each other to produce a compound effect. For example, greenhouse gases (such as CO2) and air pollutants (such as NOx, SOx) often appear together, which may aggravate their environmental impact in some areas.

[0138] Furthermore, based on the optimized regional information, multi-source environmental information is collected in real time, and the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, power conversion end and power storage end are determined respectively, including:

[0139] Based on the information from the power production end, obtain the predicted multi-source environmental information and predicted equipment status information;

[0140] The predicted multi-source environmental information is divided into short-term predicted multi-source environmental information, medium-term predicted multi-source environmental information and long-term predicted multi-source environmental information;

[0141] Sequentially grouping the short-term prediction multi-source environment information, the mid-term prediction multi-source environment information, the long-term prediction multi-source environment information, and the prediction device state information to obtain first prediction sub-data, second prediction sub-data, and third prediction sub-data;

[0142] Respectively extracting power generation prediction feature data from the first prediction sub-data, the second prediction sub-data, and the third prediction sub-data, and sequentially inputting the power generation prediction feature data into the power generation prediction model to obtain short-term predicted power generation, medium-term predicted power generation, and long-term predicted power generation output by the power generation prediction model;

[0143] Steps to obtain the power generation prediction model:

[0144] S100, based on the information of the power production end, obtaining the historical power generation data, historical multi-source meteorological data and historical equipment status data corresponding to the power production end, and performing data preprocessing on the historical power generation data, historical multi-source meteorological data and historical equipment status data respectively;

[0145] S101, grouping historical power generation data, historical multi-source meteorological data, and historical equipment status data based on preset time values ​​to obtain historical power generation prediction sub-data;

[0146] S102, sequentially converting historical power generation prediction sub-data into feature vectors;

[0147] S103, establishing a regression analysis model, and using historical power generation prediction sub-data to train the regression analysis model to obtain a power generation prediction model;

[0148] The expression formula of the power generation prediction model is:

[0149]

[0150] In the formula, is time point t for The predicted power generation, is the bias term, is the i-th corresponding multi-source meteorological data in the historical power generation prediction sub-data for The regression coefficients of the eigenvectors, is the jth corresponding to the equipment status data in the historical power generation prediction sub-data for The regression coefficient of the feature vector, F is the total number of historical power generation prediction sub-data, is the i-th corresponding multi-source meteorological data in the historical power generation prediction sub-data for feature vectors, is the jth corresponding to the equipment status data in the historical power generation prediction sub-data for feature vectors, Indicates the predicted power generation and the actual value in historical power generation data The training error between , e is the base of the natural logarithm.

[0151] Specifically, the preset time value is set by the optimization system, usually 1 hour or 24 hours.

[0152] Further, according to the power production end information, the power conversion end information and the power consumption area information, the first connection line information and the second connection line information are obtained, specifically including:

[0153] Based on the information of the power production end, determine the generator set information and renewable energy power generation information of the power production end;

[0154] The generator set information, generator set location information and generator set output power information are used to determine the output location information of the power production end;

[0155] Using renewable energy generation information, determine renewable energy output location information;

[0156] Based on the power conversion terminal information, determine the substation voltage level information, substation access line information and substation output line information;

[0157] Based on the power consumption area information, obtain the power consumption area geographical information and the power consumption area historical load information;

[0158] Construct a virtual line model of the power grid, and determine virtual output point information, virtual transfer point information, virtual line information and virtual receiving point information through the output power information of the generator set, the output location information of the power production end, the output location information of renewable energy, the voltage level information of the substation, the access line information of the substation, the output line information of the substation, the geographical information of the power consumption area and the historical load information of the power consumption area;

[0159] According to the historical load information of the power consumption area and the virtual receiving point information, the load of the virtual receiving point is divided, and according to the output power information of the generator set, the renewable energy power generation information and the virtual output point information, the output power of the virtual output point is divided;

[0160] Based on the results of load division and output power division, load flow analysis is performed on the virtual line model of the power grid to simulate power flow information under different loads and different output powers;

[0161] Determine the location information of the main line and redundant line based on the power flow information under different loads and different output powers;

[0162] Based on the position information of the main line and the redundant line, the first connection line information and the second connection line information are acquired.

[0163] Specifically, load division is to distribute the electricity load (i.e. user demand) to different virtual receiving points (or different areas of the power grid) in proportion or in an optimized manner based on historical load information and virtual receiving point information. This process needs to take into account the changing patterns of electricity demand and historical load in different areas. Power division is to distribute the total power generation to virtual output points according to certain rules based on the output power information of the generator set, renewable energy power generation information and virtual output point information. This process also involves the optimization of power production and distribution.

[0164] It can be understood that based on the location information of the main line and the redundant line, the first connection line information and the second connection line information are obtained. The main line is the core line in the power system and is responsible for the transmission of trunk power. They are usually important lines that directly connect the power source (such as a substation) and the load center. The redundant line is a backup line set up to improve the reliability of the system. The redundant line can be used to maintain power supply when the main line fails. The first connection line information corresponds to the location information of the main line, etc., and the second connection line information corresponds to the location information of the redundant line, etc.

[0165] Further, based on the multi-source environmental information, the first connection line information, the second connection line information and the comprehensive environmental impact index information, the first preset line information is obtained, and the power demand fluctuation information is obtained based on the power consumption area information, specifically including:

[0166] Based on the short-term prediction multi-source environmental information, the medium-term prediction multi-source environmental information and the long-term prediction multi-source environmental information, a short-term prediction time value, a medium-term prediction time value and a long-term prediction time value are set;

[0167] The short-term prediction time value, the medium-term prediction time value and the long-term prediction time value are sequentially input into the calculation formula of the prediction environmental impact index to obtain the short-term prediction environmental impact index, the medium-term prediction environmental impact index and the long-term prediction environmental impact index;

[0168] Processing the data of the short-term predicted environmental impact index, the medium-term predicted environmental impact index and the long-term predicted environmental impact index to obtain the predicted environmental impact index;

[0169] Obtaining initial time node information for real-time collection of multi-source environmental information, and obtaining an initial environmental quality index based on the initial time node information;

[0170] Based on the initial environmental quality index and the predicted environmental impact index, a predicted environmental quality index is obtained;

[0171] Compare the real-time environmental quality index with the predicted environmental quality index to obtain the predicted environmental error coefficient;

[0172] The dynamic comparison curve is established through the real-time environmental quality index and the predicted environmental quality index, and the thre =μ mean +k de σ sta (3≥k de ≥1.5), determine the prediction environment error coefficient threshold, where E thre is the prediction environment error coefficient threshold, μ mean is the average value of the difference between the real-time environmental quality index and the predicted environmental quality index in the dynamic comparison curve, σ sta is the standard deviation of the difference between the real-time environmental quality index and the predicted environmental quality index in the dynamic comparison curve, k de is the degree to which the error deviates from the mean;

[0173] If E thre ≤0.1, the predicted environmental impact index is used as the comprehensive environmental impact index information of the power production end. thre >0.1, the average of the predicted environmental impact index at time t and time t-1 is used as the comprehensive environmental impact index information;

[0174] Determine virtual output point information by integrating environmental impact index information, and determine first preset line information according to the virtual output point information;

[0175] Based on the first preset line information and the power consumption area information, and through the virtual output point information, the virtual transfer point information, the virtual line information and the virtual receiving point information, the power supply to the power consumption area in different time periods is simulated in the virtual line model of the power grid to obtain the simulated power consumption of the power consumption area;

[0176] The actual power consumption of the power consumption area is obtained, and the difference between the simulated power consumption of the power consumption area and the actual power consumption of the power consumption area is obtained to obtain the power demand fluctuation coefficient curve and determine the power demand fluctuation information.

[0177] Specifically, the short-term forecast time value, the medium-term forecast time value, and the long-term forecast time value are input into the calculation formula of the predicted environmental impact index in turn, and the time values ​​of the three are substituted into E real In t of (t), the short-term predicted environmental impact index, the medium-term predicted environmental impact index and the long-term predicted environmental impact index can be obtained.

[0178] Furthermore, based on the initial time node information, the initial environmental quality index is obtained, the time period for collecting real-time multi-source environmental information is determined, and the average value of the air quality index and other indexes in the time period is obtained. The average value is the initial environmental quality index. Based on the initial environmental quality index and the predicted environmental impact index, the predicted environmental quality index is obtained, and the absolute value of the difference between the initial environmental quality index and the predicted environmental impact index is obtained. The absolute value is the predicted environmental quality index.

[0179] It can be understood that the power grid virtual line model is a mathematical model used for power system analysis and optimization. It is mainly used to simplify and effectively represent the complex lines and network structures in the power system. Virtual output points, virtual transfer points, virtual lines and virtual receiving points are marked in the model. The virtual output points, virtual transfer points, virtual lines and virtual receiving points respectively represent the power production end / power storage end, power conversion end and power consumption area in the distribution network. The virtual output point information is determined by comprehensive environmental impact index information, and the first preset line information is determined according to the virtual output point information. First, the location information of the virtual output point is determined in the power grid virtual line model, and then the comprehensive environmental impact index is used to select a suitable virtual output point. For example, if the comprehensive environmental impact index is greater than 0.8, the virtual output point has a greater impact on the environment during power generation and is not suitable as the power output end of the distribution network. Otherwise, it is suitable.

[0180] Furthermore, based on the first preset line information, the power consumption area information and the power consumption demand fluctuation information, the first preset line is evaluated at different time periods to obtain the first grid-connected line information, which specifically includes:

[0181] Acquire real-time equipment status information, and process multi-source environmental information and real-time equipment status information to obtain real-time power generation prediction sub-data;

[0182] Based on the short-term prediction time value, the medium-term prediction time value and the long-term prediction time value, the real-time power generation prediction sub-data is converted into a feature vector and input into the power generation prediction model to obtain the real-time predicted power generation of different prediction periods output by the power generation prediction model;

[0183] Determine the preferred power production end information and the preferred power storage end information according to the real-time predicted power generation in different prediction periods, the simulated power consumption in the power consumption area, the location information of the power generation area and the power storage end information, and obtain the spare power production end information and the spare power storage end information;

[0184] Based on the power consumption area information and power demand fluctuation information, determine whether to call the spare power production end information and spare power storage end information;

[0185] A grid connection index threshold is set, and based on the preferred power production end information, the preferred power storage end information and the virtual line information, virtual line location information having a grid connection index greater than the grid connection index threshold is determined in the power grid virtual line model;

[0186] The first grid-connected line information is determined according to the virtual line location information.

[0187] Specifically, based on the power consumption area information and the power demand fluctuation information, it is determined whether to call the spare power production end information and the spare power storage end information. The power demand fluctuation information is obtained by obtaining the power demand fluctuation coefficient curve after calculating the difference between the simulated power consumption in the power consumption area and the actual power consumption in the power consumption area. Then, when the power demand fluctuation coefficient is higher than the preset power demand fluctuation coefficient threshold, the spare power production end and the spare power storage end are enabled. The preset power demand fluctuation coefficient threshold is set by the monitoring personnel according to historical data.

[0188] It can be understood that the grid connection index is the total number of power flow paths when the power flow paths simulated by different virtual output points occupy the same virtual line in the virtual line model of the power grid.

[0189] Further, the first grid-connected line information is evaluated to obtain a first transmission quality index of each first grid-connected line in the first grid-connected line information, specifically including:

[0190] Based on the first grid-connected line information and the virtual line location information, obtaining starting point characteristic data, end point characteristic data and first grid-connected line characteristic data of the first grid-connected line;

[0191] Determine a first transmission quality index of each first grid-connected line according to the starting point characteristic data, the end point characteristic data and the first grid-connected line characteristic data of the first grid-connected line;

[0192] The calculation formula of the first transmission quality index is:

[0193]

[0194] In the formula, I tran is the first transmission quality index, w1, w2, w3, w4 and w5 are weight factors, L is the line load rate, VSI is the voltage stability index, P loss is the power loss of the line, P max is the maximum power carrying capacity of the line, H is the current harmonic content, and A is the availability of the line.

[0195] Further, based on the first transmission quality index, the first grid-connected line information, the first connection line information, and the second connection line information are evaluated to obtain the second preset line information, specifically including:

[0196] Setting a first transmission quality index threshold;

[0197] Based on the first transmission quality index threshold and the first transmission quality index, determining whether to modify the virtual line in the first grid-connected line information;

[0198] If the first transmission quality index is greater than the first transmission quality index threshold, the virtual line in the first grid-connected line information is not modified;

[0199] If the first transmission quality index is less than or equal to the first transmission quality index threshold, modifying the virtual line in the first grid-connected line information;

[0200] Based on the first grid-connected line information, the first connection line information, and the second connection line information, obtaining virtual line position information and position information of the first connection line and the second connection line in a virtual line model of a power grid;

[0201] According to the virtual line position information and the position information of the first connection line and the second connection line in the power grid virtual line model, the first connection line is released, and the second connection line is called at the same time to obtain the second preset line information.

[0202] Specifically, liberating the first connection line usually means disconnecting or stopping the load flow of the first connection line, but in this solution, in order to maintain the stability of the circuit, the first connection line is liberated, and the second connection line is called at the same time to gradually distribute a part of the load from the first connection line to the spare second connection line to avoid disconnecting the load all at once. For example: first, the load of the first connection line is gradually reduced, and by adjusting the load flow direction, a part of the load is gradually transferred from the first line to the second line. This process is implemented by load control equipment (such as automated dispatching systems, regulating transformer taps, automatic voltage regulators, etc.), and the first connection line is liberated, the second connection line is called at the same time, and the second preset line information is obtained. The simulation is performed in the virtual line model of the power grid, and the obtained second preset line information is used to determine the final optimization strategy.

[0203] Further, according to the second preset line information, a power grid stability index of the second preset line in the second preset line information is obtained, and a power supply mode optimization strategy is determined based on the power grid stability index, specifically including:

[0204] Based on the second preset line information, obtaining the power distribution line location information in the second preset line;

[0205] Acquiring distribution line characteristic data through the distribution line location information in the second preset line;

[0206] Based on the distribution line characteristic data, determining the stability influencing factors of the distribution line characteristic data;

[0207] Obtain the grid stability index based on the stability influencing factors of the distribution line characteristic data;

[0208] Setting a grid stability index threshold, and based on the grid stability index, determining whether to use the second preset line information as a power supply mode optimization strategy;

[0209] The calculation formula of the power grid stability index is:

[0210]

[0211] In the formula, The second preset line The grid stability index of the distribution line at the location, τ1, τ2, τ3, τ4 and τ5 are all weight factors, The second preset line The voltage stability index of the distribution line at The second preset line Frequency stability index of the distribution line at The second preset line The load balance index of the distribution line at The second preset line The power loss index of the distribution line at The second preset line Harmonic pollution index of distribution lines.

[0212] Furthermore, a distribution network power supply mode optimization system based on a new power system is proposed, which is used to implement any of the above optimization methods, including:

[0213] Data acquisition module, which is used to obtain information on power production, power storage, power conversion, and power consumption areas, and collect multi-source environmental information in real time based on optimized area information;

[0214] A calculation module, which is used to determine the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, the power conversion end and the power storage end;

[0215] A data processing module is used to process the predicted power generation and the real-time power generation to obtain comprehensive power generation information, and to process the predicted environmental impact index and the real-time environmental quality index to obtain comprehensive environmental impact index information;

[0216] A model building module, the model building module is used to build a virtual line model of the power grid and simulate the operation of the power grid through the virtual line model of the power grid;

[0217] A threshold setting module, the threshold setting module is used to set a prediction environment error coefficient threshold, a grid connection index threshold, a first transmission quality index threshold and a grid stability index threshold;

[0218] An evaluation module, the evaluation module is used to evaluate the first preset line at different time periods based on the comprehensive power generation information, the first preset line information, the power consumption area information and the power demand fluctuation information, obtain the first grid-connected line information, evaluate the first grid-connected line information, obtain the first transmission quality index of each first grid-connected line in the first grid-connected line information, evaluate the first grid-connected line information, the first connection line information and the second connection line information based on the first transmission quality index, and obtain the second preset line information;

[0219] A power supply mode optimization module, which is used to determine a power supply mode optimization strategy based on a power grid stability index and execute the optimization strategy;

[0220] Display module, the display module is used to display the process and results of the data acquisition module acquiring data and information, the calculation process and result data of the calculation module, the processing process and results of the processing module, the construction process and results of the model construction module, the process and results of the model construction module simulating the operation of the power grid, the process and results of the threshold setting module, the evaluation process and results of the evaluation module, the optimization process and results of the power supply mode optimization module, and the process and results of the power supply mode optimization module executing the optimization strategy.

[0221] The advantages of the present invention are: by constructing a virtual line model of the power grid, and then determining the virtual output point information through comprehensive environmental impact index information to determine the first preset line, the virtual line model is used to reduce resource occupation and improve the reliability of the power supply mode optimization of the distribution network;

[0222] Through the first transmission quality index, the power generation at the power production end and the power demand fluctuation information in the power consumption area are comprehensively analyzed. The first transmission quality index includes the analysis of indicators such as line load rate and power loss, and realizes the analysis of power loss and transmission quality of the distribution network in different periods, ensuring the load balance of the distribution network, improving the accuracy and reliability of the optimization of the power supply mode of the distribution network, and avoiding the problem of mismatch between supply and demand;

[0223] Through the first transmission quality index, the first grid-connected line information, the first connection line information and the second connection line information are evaluated to obtain the second preset line information, and then according to the second preset line information, the power grid stability index of the second preset line in the second preset line information is obtained, and the power supply mode optimization strategy is determined based on the power grid stability index to improve the reliability of the distribution network power supply mode optimization, so that the optimization system can provide accurate and reliable distribution network power supply mode optimization strategy to ensure the stability of the distribution network.

[0224] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing the power supply mode of a distribution network based on a new power system, characterized in that: include: Determine the optimized area information of the new power system, and obtain the power production end information, power storage end information, power conversion end information and power consumption area information based on the optimized area information; Collect multi-source environmental information in real time based on the optimized regional information, and determine the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, power conversion end and power storage end respectively; Process the predicted power generation and the real-time power generation to obtain comprehensive power generation information, and process the predicted environmental impact index and the real-time environmental quality index to obtain comprehensive environmental impact index information; Acquire first connection line information and second connection line information according to power production end information, power conversion end information and power consumption area information; Based on the multi-source environmental information, the first connection line information, the second connection line information and the comprehensive environmental impact index information, the first preset line information is obtained, and the power demand fluctuation information is obtained based on the power consumption area information; Based on the comprehensive power generation information, the first preset line information, the power consumption area information and the power demand fluctuation information, the first preset line is evaluated at different time periods to obtain the first grid-connected line information; Evaluate the first grid-connected line information to obtain a first transmission quality index of each first grid-connected line in the first grid-connected line information; Based on the first transmission quality index, the first grid-connected line information, the first connection line information, and the second connection line information are evaluated to obtain second preset line information; According to the second preset line information, a power grid stability index of the second preset line in the second preset line information is obtained, and a power supply mode optimization strategy is determined based on the power grid stability index.

2. According to a method for optimizing the power supply mode of a distribution network based on a new power system according to claim 1, it is characterized in that: The real-time collection of multi-source environmental information based on the optimized regional information, and the determination of the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, the power conversion end and the power storage end, specifically include: Based on the optimization area information, obtain the optimization area location information; According to the location information of the optimized area, obtain the meteorological data, air quality data and geographical terrain information of the optimized area, and determine the multi-source environmental information; To optimize the regional location information, obtain the power generation type information and the power generation area location information; Determine the generator set information, fuel consumption information and renewable energy generation information at the power production end based on the power generation type information; Determine the power production end information through power generation type information, power generation area location information, power generation unit information, fuel consumption information and renewable energy power generation information; According to the location information of the optimized area, the substation location information, the substation transmission line information and the smart transformer information are obtained, and the power conversion end information is determined; According to the location information of the optimized area, the energy storage equipment information and the charging and discharging efficiency information are obtained, and the power storage terminal information is determined; Based on the information of the power production end, the power storage end and the power conversion end, the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end are obtained respectively; Based on the environmental impact factors corresponding to the power production end, the power storage end and the power conversion end, the corresponding predicted environmental impact index is obtained; The calculation formula for the predicted environmental impact index is: In the formula, E real (t) is the predicted environmental impact index at time t, E i (t) is the quantitative value of the i-th environmental impact factor at the t-th moment, p i is the contribution characteristic value of the i-th environmental impact factor to the environment, E j (t) is the quantitative value of the jth environmental impact factor at the tth moment, p j is the contribution characteristic value of the jth environmental impact factor to the environmental impact, is the quantitative value of the interaction between the i-th environmental impact factor and the j-th environmental impact factor at time t, k is the summation index value, e is the base of the natural logarithm, α i is the weight coefficient of the i-th environmental impact factor, α j is the weight coefficient of the jth environmental impact factor, γ k It is the degree of interactive influence of each environmental factor on other environmental factors.

3. The method for optimizing the power supply mode of a distribution network based on a new power system according to claim 2 is characterized in that: The real-time collection of multi-source environmental information based on the optimized regional information, and the determination of the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, the power conversion end and the power storage end, specifically include: Based on the information from the power production end, obtain the predicted multi-source environmental information and predicted equipment status information; The predicted multi-source environmental information is divided into short-term predicted multi-source environmental information, medium-term predicted multi-source environmental information and long-term predicted multi-source environmental information; Sequentially grouping the short-term prediction multi-source environment information, the mid-term prediction multi-source environment information, the long-term prediction multi-source environment information, and the prediction device state information to obtain first prediction sub-data, second prediction sub-data, and third prediction sub-data; Respectively extracting power generation prediction feature data from the first prediction sub-data, the second prediction sub-data, and the third prediction sub-data, and sequentially inputting the power generation prediction feature data into the power generation prediction model to obtain short-term predicted power generation, medium-term predicted power generation, and long-term predicted power generation output by the power generation prediction model; The steps of obtaining the power generation prediction model are as follows: S100, based on the information of the power production end, obtaining the historical power generation data, historical multi-source meteorological data and historical equipment status data corresponding to the power production end, and performing data preprocessing on the historical power generation data, historical multi-source meteorological data and historical equipment status data respectively; S101, grouping historical power generation data, historical multi-source meteorological data, and historical equipment status data based on preset time values ​​to obtain historical power generation prediction sub-data; S102, sequentially converting historical power generation prediction sub-data into feature vectors; S103, establishing a regression analysis model, and using historical power generation prediction sub-data to train the regression analysis model to obtain a power generation prediction model; The expression formula of the power generation prediction model is: In the formula, is time point t for The predicted power generation, is the bias term, is the i-th corresponding multi-source meteorological data in the historical power generation prediction sub-data for The regression coefficients of the eigenvectors, is the jth corresponding to the equipment status data in the historical power generation prediction sub-data for The regression coefficient of the feature vector, F is the total number of historical power generation prediction sub-data, is the i-th corresponding multi-source meteorological data in the historical power generation prediction sub-data for feature vectors, is the jth corresponding to the equipment status data in the historical power generation prediction sub-data for feature vectors, Indicates the predicted power generation and the actual value in historical power generation data The training error between , e is the base of the natural logarithm.

4. The method for optimizing the power supply mode of a distribution network based on a new power system according to claim 3 is characterized in that: The acquiring the first connection line information and the second connection line information according to the power production end information, the power conversion end information and the power consumption area information specifically includes: Based on the information of the power production end, determine the generator set information and renewable energy power generation information of the power production end; The generator set information, generator set location information and generator set output power information are used to determine the output location information of the power production end; Using renewable energy generation information, determine renewable energy output location information; Based on the power conversion terminal information, determine the substation voltage level information, substation access line information and substation output line information; Based on the power consumption area information, obtain the power consumption area geographical information and the power consumption area historical load information; Construct a virtual line model of the power grid, and determine virtual output point information, virtual transfer point information, virtual line information and virtual receiving point information through the output power information of the generator set, the output location information of the power production end, the output location information of renewable energy, the voltage level information of the substation, the access line information of the substation, the output line information of the substation, the geographical information of the power consumption area and the historical load information of the power consumption area; According to the historical load information of the power consumption area and the virtual receiving point information, the load of the virtual receiving point is divided, and according to the output power information of the generator set, the renewable energy power generation information and the virtual output point information, the output power of the virtual output point is divided; Based on the results of load division and output power division, load flow analysis is performed on the virtual line model of the power grid to simulate power flow information under different loads and different output powers; Determine the location information of the main line and redundant line based on the power flow information under different loads and different output powers; Based on the position information of the main line and the redundant line, the first connection line information and the second connection line information are acquired.

5. The method for optimizing the power supply mode of a distribution network based on a new power system according to claim 4 is characterized in that: The method of acquiring the first preset line information based on the multi-source environmental information, the first connection line information, the second connection line information and the comprehensive environmental impact index information, and acquiring the power demand fluctuation information based on the power consumption area information specifically includes: Based on the short-term prediction multi-source environmental information, the medium-term prediction multi-source environmental information and the long-term prediction multi-source environmental information, a short-term prediction time value, a medium-term prediction time value and a long-term prediction time value are set; The short-term prediction time value, the medium-term prediction time value and the long-term prediction time value are sequentially input into the calculation formula of the prediction environmental impact index to obtain the short-term prediction environmental impact index, the medium-term prediction environmental impact index and the long-term prediction environmental impact index; Processing the data of the short-term predicted environmental impact index, the medium-term predicted environmental impact index and the long-term predicted environmental impact index to obtain the predicted environmental impact index; Obtaining initial time node information for real-time collection of multi-source environmental information, and obtaining an initial environmental quality index based on the initial time node information; Based on the initial environmental quality index and the predicted environmental impact index, a predicted environmental quality index is obtained; Compare the real-time environmental quality index with the predicted environmental quality index to obtain the predicted environmental error coefficient; The dynamic comparison curve is established through the real-time environmental quality index and the predicted environmental quality index, and the E thre =μ mean +k de σ sta (3≥k de ≥1.5), determine the prediction environment error coefficient threshold, where E thre is the prediction environment error coefficient threshold, μ mean is the average value of the difference between the real-time environmental quality index and the predicted environmental quality index in the dynamic comparison curve, σ sta is the standard deviation of the difference between the real-time environmental quality index and the predicted environmental quality index in the dynamic comparison curve, k de is the degree to which the error deviates from the mean; If E thre ≤0.1, the predicted environmental impact index is used as the comprehensive environmental impact index information of the power production end. thre >0.1, the average of the predicted environmental impact index at time t and time t-1 is used as the comprehensive environmental impact index information; Determine virtual output point information by integrating environmental impact index information, and determine first preset line information according to the virtual output point information; Based on the first preset line information and the power consumption area information, and through the virtual output point information, the virtual transfer point information, the virtual line information and the virtual receiving point information, the power supply to the power consumption area in different time periods is simulated in the virtual line model of the power grid to obtain the simulated power consumption of the power consumption area; The actual power consumption of the power consumption area is obtained, and the difference between the simulated power consumption of the power consumption area and the actual power consumption of the power consumption area is obtained to obtain the power demand fluctuation coefficient curve and determine the power demand fluctuation information.

6. The method for optimizing the power supply mode of a distribution network based on a new power system according to claim 5 is characterized in that: The step of evaluating the first preset line at different time periods based on the first preset line information, the power consumption area information, and the power consumption demand fluctuation information to obtain the first grid-connected line information specifically includes: Acquire real-time equipment status information, and process multi-source environmental information and real-time equipment status information to obtain real-time power generation prediction sub-data; Based on the short-term prediction time value, the medium-term prediction time value and the long-term prediction time value, the real-time power generation prediction sub-data is converted into a feature vector and input into the power generation prediction model to obtain the real-time predicted power generation of different prediction periods output by the power generation prediction model; Determine the preferred power production end information and the preferred power storage end information according to the real-time predicted power generation in different prediction periods, the simulated power consumption in the power consumption area, the location information of the power generation area and the power storage end information, and obtain the spare power production end information and the spare power storage end information; Based on the power consumption area information and power demand fluctuation information, determine whether to call the spare power production end information and spare power storage end information; A grid connection index threshold is set, and based on the preferred power production end information, the preferred power storage end information and the virtual line information, virtual line location information having a grid connection index greater than the grid connection index threshold is determined in the power grid virtual line model; The first grid-connected line information is determined according to the virtual line location information.

7. The method for optimizing the power supply mode of a distribution network based on a new power system according to claim 6 is characterized in that: The step of evaluating the first grid-connected line information to obtain the first transmission quality index of each first grid-connected line in the first grid-connected line information specifically includes: Based on the first grid-connected line information and the virtual line location information, obtaining starting point characteristic data, end point characteristic data and first grid-connected line characteristic data of the first grid-connected line; Determine a first transmission quality index of each first grid-connected line according to the starting point characteristic data, the end point characteristic data and the first grid-connected line characteristic data of the first grid-connected line; The calculation formula of the first transmission quality index is: In the formula, I tran is the first transmission quality index, w1, w2, w3, w4 and w5 are weight factors, L is the line load rate, VSI is the voltage stability index, P loss is the power loss of the line, P max is the maximum power carrying capacity of the line, H is the current harmonic content, and A is the availability of the line.

8. The method for optimizing the power supply mode of a distribution network based on a new power system according to claim 7 is characterized in that: The step of evaluating the first grid-connected line information, the first connection line information, and the second connection line information based on the first transmission quality index to obtain the second preset line information specifically includes: Setting a first transmission quality index threshold; Based on the first transmission quality index threshold and the first transmission quality index, determining whether to modify the virtual line in the first grid-connected line information; If the first transmission quality index is greater than the first transmission quality index threshold, the virtual line in the first grid-connected line information is not modified; If the first transmission quality index is less than or equal to the first transmission quality index threshold, modifying the virtual line in the first grid-connected line information; Based on the first grid-connected line information, the first connection line information, and the second connection line information, obtaining virtual line position information and position information of the first connection line and the second connection line in a virtual line model of a power grid; According to the virtual line position information and the position information of the first connection line and the second connection line in the power grid virtual line model, the first connection line is released, and the second connection line is called at the same time to obtain the second preset line information.

9. The method for optimizing the power supply mode of a distribution network based on a new power system according to claim 8, characterized in that: The obtaining, according to the second preset line information, a power grid stability index of the second preset line in the second preset line information, and determining a power supply mode optimization strategy based on the power grid stability index specifically includes: Based on the second preset line information, obtaining the power distribution line location information in the second preset line; Acquiring distribution line characteristic data through the distribution line location information in the second preset line; Based on the distribution line characteristic data, determining the stability influencing factors of the distribution line characteristic data; Obtain the grid stability index based on the stability influencing factors of the distribution line characteristic data; Setting a grid stability index threshold, and based on the grid stability index, determining whether to use the second preset line information as a power supply mode optimization strategy; The calculation formula of the power grid stability index is: In the formula, The second preset line The grid stability index of the distribution line at the location, τ1, τ2, τ3, τ4 and τ5 are all weight factors, The second preset line The voltage stability index of the distribution line at The second preset line Frequency stability index of the distribution line at The second preset line The load balance index of the distribution line at The second preset line The power loss index of the distribution line at The second preset line Harmonic pollution index of distribution lines.

10. A distribution network power supply mode optimization system based on a new power system, used to implement the optimization method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module, which is used to acquire information on power production, power storage, power conversion, and power consumption areas, and collect multi-source environmental information in real time based on optimized area information; A calculation module, the calculation module is used to determine the predicted power generation, real-time power generation, predicted environmental impact index and real-time environmental quality index corresponding to the power production end, the power conversion end and the power storage end; A data processing module, wherein the data processing module is used to process the predicted power generation and the real-time power generation to obtain comprehensive power generation information, and to process the predicted environmental impact index and the real-time environmental quality index to obtain comprehensive environmental impact index information; A model building module, wherein the model building module is used to build a virtual line model of a power grid and simulate power grid operation through the virtual line model of the power grid; A threshold setting module, the threshold setting module is used to set a prediction environment error coefficient threshold, a grid connection index threshold, a first transmission quality index threshold and a grid stability index threshold; An evaluation module, the evaluation module is used to evaluate the first preset line in different time periods based on the comprehensive power generation information, the first preset line information, the power consumption area information and the power demand fluctuation information, obtain the first grid-connected line information, evaluate the first grid-connected line information, obtain the first transmission quality index of each first grid-connected line in the first grid-connected line information, evaluate the first grid-connected line information, the first connection line information and the second connection line information based on the first transmission quality index, and obtain the second preset line information; A power supply mode optimization module, the power supply mode optimization module is used to determine the power supply mode optimization strategy based on the power grid stability index and execute the optimization strategy; A display module, wherein the display module is used to display the process and results of the data acquisition module acquiring data and information, the calculation process and result data of the calculation module, the processing process and results of the processing module, the construction process and results of the model construction module, the process and results of the model construction module simulating the operation of the power grid, the process and results of the threshold setting module, the evaluation process and results of the evaluation module, the optimization process and results of the power supply mode optimization module, and the process and results of the power supply mode optimization module executing the optimization strategy.

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