Intelligent management methods and systems for the planning, construction and operation of power distribution networks

Abstract

The present disclosure provides an intelligent management method and system for the planning, construction and operation of distribution networks, and relates to the field of electric power technology. The specific scheme is: based on the simulated operation of the power equipment of each regional power grid in the distribution network, determine the equipment problem solving needs of each regional power grid; based on the solar illumination of each regional power grid, predict the new load development potential of each regional power grid; based on the new load development potential of each regional power grid, and the matching degree between the current power generation and the current power consumption of each regional power grid, determine the energy storage point demand, load demand and distribution transformer point demand of each regional power grid. Then, based on these needs, determine multiple candidate power planning schemes, and conduct simulation evaluation, select the target power planning scheme from multiple schemes, and then construct the distribution network, and after the construction is completed, use the actual operation of the power grid to adjust the planning scheme. In this way, the distribution network can be efficiently planned and managed.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to an intelligent management method and system for planning, constructing and operating a distribution network. Background Art

[0002] There are many technical difficulties in the planning, construction and operation of distribution networks. First, the operating conditions of power equipment in each regional power grid are different, and it is necessary to accurately assess the needs of equipment problem solving. Secondly, in the planning stage, the optimal configuration of resources such as energy storage, load, and distribution transformer is a complex combinatorial optimization problem with multiple objectives and multiple constraints. Finally, after the construction of the distribution network is completed, the actual operation may deviate from the planned expectations, and the optimization planning scheme needs to be dynamically adjusted.

[0003] Therefore, the present disclosure requires a systematic digital management method to solve these problems. Summary of the invention

[0004] The present disclosure provides an intelligent management method, system, electronic device and storage medium for planning, construction and operation of a distribution network, which can solve at least one of the above problems.

[0005] The present disclosure provides a digital management method for planning, construction and operation of a distribution network, including:

[0006] Based on the simulated operation status of the power equipment of each regional power grid in the distribution network, determine the equipment problem solving needs of each regional power grid;

[0007] Using image recognition technology to determine the solar illumination conditions of each of the regional power grids;

[0008] Based on the solar illumination conditions of each of the regional power grids, respectively predicting the new load development potential of each of the regional power grids;

[0009] Based on the new load development potential of each of the regional power grids and the matching degree between the current power generation and the current power consumption of each of the regional power grids, the energy storage point layout requirements, load requirements and distribution transformer point layout requirements of each of the regional power grids are determined;

[0010] Determine multiple candidate power planning solutions based on planning constraints of each regional power grid, the equipment problem solving requirements, the energy storage location requirements, the load requirements, and the distribution transformer location requirements;

[0011] Based on the preset multiple simulation dimensions, each of the candidate power planning schemes is simulated respectively to obtain simulation indicators corresponding to each simulation dimension of each of the candidate power planning schemes;

[0012] Determining a target power planning scheme from among the plurality of candidate power planning schemes based on simulation indicators corresponding to the simulation dimensions of the candidate power planning schemes;

[0013] Based on the target power planning scheme, construct the distribution network;

[0014] Based on the actual operation status of each regional power grid in the completed distribution network, the target power planning scheme is adjusted.

[0015] According to another aspect of the present disclosure, a digital intelligent management system for planning, constructing and operating a distribution network is provided, including:

[0016] A first demand determination module is used to determine the equipment problem solving needs of each regional power grid in the distribution network based on the simulated operation status of the power equipment of each regional power grid;

[0017] A light condition determination module, used to determine the sunlight condition of each of the regional power grids using image recognition technology;

[0018] A development potential determination module, used to predict the new load development potential of each of the regional power grids based on the sunlight conditions of each of the regional power grids;

[0019] A second demand determination module is used to determine the energy storage point demand, load demand and distribution transformer point demand of each of the regional power grids based on the new load development potential of each of the regional power grids and the matching degree between the current power generation and the current power consumption of each of the regional power grids;

[0020] A candidate solution determination module is used to determine multiple candidate power planning solutions based on planning constraints of each regional power grid, the equipment problem solving requirements, the energy storage point layout requirements, the load requirements and the distribution transformer point layout requirements;

[0021] A scheme simulation module, used to simulate each of the candidate power planning schemes based on a plurality of preset simulation dimensions, and obtain simulation indicators corresponding to each simulation dimension of each of the candidate power planning schemes;

[0022] A target scheme determination module, configured to determine a target power planning scheme from among the plurality of candidate power planning schemes based on simulation indicators corresponding to the simulation dimensions of the candidate power planning schemes;

[0023] A power grid construction module, used to construct the distribution network based on the target power planning scheme;

[0024] The scheme adjustment module is used to adjust the target power planning scheme based on the actual operation status of each regional power grid in the completed distribution network.

[0025] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0026] at least one processor; and

[0027] a memory communicatively connected to the at least one processor; wherein,

[0028] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any intelligent management method for distribution network planning, construction and operation in the embodiments of the present disclosure.

[0029] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any intelligent management method for distribution network planning, construction and operation in the embodiments of the present disclosure.

[0030] By adopting the technical solution disclosed in the present invention, the equipment problem solving needs of each regional power grid are determined based on the simulated operation of the power equipment of each regional power grid in the distribution network; the new load development potential of each regional power grid is predicted based on the solar illumination of each regional power grid; the energy storage point distribution needs, load needs and distribution transformer point distribution needs of each regional power grid are determined based on the new load development potential of each regional power grid and the matching degree between the current power generation and the current power consumption of each regional power grid. In this way, the equipment problem solving needs, energy storage point distribution needs, load needs and distribution transformer point distribution needs of the regional power grid can be accurately evaluated. Then, based on these needs, multiple candidate power planning schemes are determined, and then simulation evaluation is performed to select the target power planning scheme from multiple candidate power planning schemes, and then the distribution network is constructed, and after the construction is completed, the actual operation of the power grid is used to adjust the planning scheme. In this way, the distribution network can be efficiently planned and managed. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of an intelligent management method for planning, construction and operation of a distribution network according to an embodiment of the present disclosure;

[0032] Figure 2 It is a structural block diagram of an intelligent management system for planning, construction and operation of a distribution network according to an embodiment of the present disclosure;

[0033] Figure 3 is a block diagram of an electronic device for implementing an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0035] Figure 1 It is a flow chart of an intelligent management method for planning, construction and operation of a distribution network according to an embodiment of the present disclosure.

[0036] like Figure 1 As shown, the intelligent management method for planning, construction and operation of distribution networks may include:

[0037] S110, determining the equipment problem solving requirements of each regional power grid based on the simulated operation status of the power equipment of each regional power grid in the distribution network;

[0038] S120, uses image recognition technology to determine the solar illumination conditions of each regional power grid;

[0039] S130, based on the solar radiation conditions of each regional power grid, predict the new load development potential of each regional power grid;

[0040] S140, based on the new load development potential of each regional power grid and the matching degree between the current power generation and current power consumption of each regional power grid, determine the energy storage location requirements, load requirements and distribution transformer location requirements of each regional power grid;

[0041] S150, determining multiple candidate power planning schemes based on planning constraints of each regional power grid, equipment problem solving requirements, energy storage location requirements, load requirements, and distribution transformer location requirements;

[0042] S160, based on the preset multiple simulation dimensions, simulating each candidate power planning scheme respectively to obtain a simulation index corresponding to each simulation dimension of each candidate power planning scheme;

[0043] S170, determining a target power planning scheme from a plurality of candidate power planning schemes based on simulation indicators corresponding to each simulation dimension of each candidate power planning scheme;

[0044] S180, construct the distribution network based on the target power planning scheme;

[0045] S190, based on the actual operation of each regional power grid in the completed distribution network, adjust the target power planning scheme.

[0046] It can be understood that each of the above steps can be implemented by means of artificial intelligence to establish a coordination mechanism for the three stages of planning, construction and operation, reduce adjustments in the intermediate links, and achieve efficient management of the distribution network.

[0047] For example, the historical operating conditions of the power equipment of each regional power grid in the distribution network can be obtained in advance to train the equipment status assessment model. Then, the current operating conditions of the power equipment of each regional power grid in the distribution network are processed using the equipment status assessment model of the training data to obtain the future operating conditions of the power equipment of each regional power grid in the distribution network. This future operating condition can be considered as the above-mentioned simulated operating condition. Therefore, based on the future operating conditions of each power equipment of the regional power grid, the equipment problem solving needs of the regional power grid can be obtained.

[0048] It is understandable that the equipment problem solving requirements may include multiple abnormal equipment and their equipment problems, the priority of solving each equipment problem, etc. The priority of solving each equipment problem may be determined according to the abnormality of the equipment. For example, the more serious the equipment abnormality, the higher the priority of solving the problem.

[0049] Exemplarily, a three-dimensional power model of each regional power grid may also be constructed, and the model may be simulated, and the simulation results may be used to determine equipment problem solving requirements.

[0050] For example, by using image recognition technology to identify the time-series images of the regional power grid, it is possible to obtain the illumination time of each sub-region in the regional power grid, the changes in the altitude angle and azimuth angle of the sun over time, the degree of illumination coverage over time, whether there is mountain shadow, and the degree of mountain shadow coverage over time. In this way, the sunlight conditions of the regional power grid can be obtained.

[0051] For example, a machine learning method, such as support vector or random forest algorithm, can be used to establish a load development potential model. The solar illumination conditions of the regional power grid are input into the load development potential model to obtain the new load development potential of the regional power grid output by the model.

[0052] For example, a time series analysis method, such as ARIMA, may be used to analyze historical load data of the regional power grid to obtain the load demand of the regional power grid in different future time periods.

[0053] For example, the load demand may include the load of each load node in the regional power grid in a future time period.

[0054] For example, the energy storage point requirements may include information such as the locations where energy storage points need to be added in the regional power grid and the capacity of the energy storage.

[0055] For example, the distribution transformer location requirement may include information such as the location where distribution transformer equipment needs to be added in the regional power grid, as well as the model and distribution transformer parameter requirements of the distribution transformer equipment.

[0056] For example, planning constraints may include road network information, station corridors, and distribution transformer layout as constraints. Planning constraints may also include power balance, voltage stability, and other constraints. Planning constraints may also include planning investment limits, which can also be understood as cost limits for planning solutions.

[0057] For example, a randomly listed scheme may be used to cite planning schemes that meet the above constraints and various requirements of energy storage, load, and distribution transformation as candidate planning schemes.

[0058] Exemplarily, an objective function may also be constructed to solve the objective function for candidate planning solutions that meet the above conditions.

[0059] For example, multiple simulation models such as power flow calculation and fault analysis involved in the distribution network can be obtained to simulate the candidate power planning scheme to obtain multiple simulation indicators of the candidate power planning scheme. Alternatively, the above simulation dimensions can be the dimensions of safety, reliability and economy.

[0060] For example, the weight coefficient of each simulation index can be determined by using the hierarchical analysis method, and then the multiple simulation indexes of the candidate power planning schemes are weighted and summed to obtain the comprehensive score of the candidate power planning schemes. Then, the target power planning scheme is selected from the multiple candidate power planning schemes using the comprehensive scores of the candidate power planning schemes.

[0061] For example, during the construction phase, the following indicators can be determined: precise correspondence between the construction progress completion rate (for milestone plans, reflecting the actual construction progress of the project), the investment progress completion rate (for annual investment plans, reflecting the completion of fixed asset investment), the accounting progress completion rate (for annual budgets, reflecting the actual accounting of the project), and the material supply completion rate (for material supply, reflecting the matching degree between the material supply progress and the actual situation on site), to monitor the construction progress, quality, and safety and ensure that the project proceeds as planned.

[0062] For example, during the operation phase, the real-time monitoring and statistics of equipment operation status can be carried out periodically, and online optimization operation strategy suggestions can be provided to improve operation efficiency. Alternatively, the equipment status and the necessity of new construction or renovation projects in the area can be evaluated to provide a basis for the next rolling planning project arrangement.

[0063] In actual application, the planning stage includes the above steps S110 to S170, the construction stage includes the above step S180, and the operation stage includes the above step S190.

[0064] The above three stages work in coordination with each other.

[0065] 1. Coordination between planning and construction: Distribution network planning can guide distribution network construction, and the deviation from project planning to implementation is small. At the same time, the trend of distribution network construction is in line with the transition to planning goals year by year.

[0066] 2. Coordination of construction and operation: The construction phase meets the actual operation needs and takes into account the requirements for interaction between distribution network sources, grids, loads and storage after commissioning; various actual operation scenarios meet the construction standards.

[0067] 3. Coordination between operation and planning: In addition to adjusting the operation mode, some of the actual problems in operation are fed back to the planning stage. The planning considers in advance the related problems that may arise from the operation control factors after commissioning to prevent them from happening.

[0068] For the above three stages, administrative regions or artificially demarcated spatial areas, such as power supply grids and power supply units, can be used as the minimum areas for digital management, and the planning, construction and operation results at the provincial, municipal and county levels can be superimposed to carry out online management and control of the entire life cycle of the distribution network project.

[0069] Specifically, we analyze power grid problems through digital means, make detailed predictions on the access to the source, grid, load and storage sides, and coordinate various demands to form a distribution network planning scheme that better meets precise investment needs.

[0070] For the planning stage, the details are as follows:

[0071] 1. Digital diagnosis of power grid problems: Based on the current grid topology, simulation calculations are performed on the health status of the operating status, power transfer capacity, equipment operating life, etc., and a problem list is automatically generated and associated with specific equipment.

[0072] 2. Digital prediction of new loads: Accurately locate existing new loads through image recognition technology, predict the development potential of new loads through relevant algorithms, and provide energy storage location requirements based on the matching degree of power generation and power consumption, so as to form a more refined load forecast result suitable for distribution network planning and provide recommendations for the location of distribution transformers.

[0073] 3. Digital recommendation of grid solutions: Based on the existing grid structure, with road network information, station corridors, and distribution transformer locations as constraints, and according to annual or several years of investment limits, generate corresponding distribution network planning solutions.

[0074] 4. Digital generation of planning projects: Coordinate the resource, network, load and storage needs and problem-solving needs within the planning scope, arrange projects according to the principle of one project meeting multiple needs as much as possible, reduce redundant projects, and improve the efficiency of planning investment. Project information includes all graphic information such as geographical wiring diagram, topology diagram, equipment selection, cost statistics, and estimated construction period.

[0075] 5. Digital evaluation of planning effects: Through the distribution network simulation function, the planned distribution network framework is analyzed in advance, the planning scheme is evaluated in multiple dimensions through the indicator system, and multiple schemes are compared and selected to optimize the scheme.

[0076] For the construction phase, the details are as follows:

[0077] 1. Considering that the demand for distribution network projects is clear and the construction period is short, the feasibility study and preliminary design will be managed in an integrated manner in batches according to the distribution network project investment plan, reducing repeated text preparation and approval processes, and generating or uploading feasibility study preliminary design approval documents online, which will be used as a constraint condition for the start of project construction.

[0078] 2. Maintain project bidding information online, and maintain information on construction, supervision and other units.

[0079] 3. In accordance with the requirements of “four rates in one”, we will ensure accurate correspondence between the construction progress completion rate (for milestone plans, reflecting the actual construction progress of the project), the investment progress completion rate (for annual investment plans, reflecting the completion of fixed asset investment), the accounting progress completion rate (for annual budgets, reflecting the actual accounting of the project) and the material supply completion rate (for material supply, reflecting the matching degree between the material supply progress and the actual situation on site), supervise the construction progress, quality and safety, and ensure that the project is advanced as planned.

[0080] 4. Online management of project acceptance information, accurate maintenance of project production scale, budget estimate, final budget and other related information, and push accurate distribution network project information to the operating state.

[0081] For the operational phase, the details are as follows:

[0082] 1. Establish a four-level digital management ledger for provincial, municipal and county power supply stations, including information on operation strategies, organizational structure, staffing, management systems, etc. at all levels, and assign operation and maintenance responsibilities to individuals.

[0083] 2. Set different online measurement accuracies to monitor and count the equipment operation status in real time, provide online suggestions for optimizing operation strategies, and improve operation efficiency.

[0084] 3. Set different data acquisition frequencies to evaluate equipment status and the necessity of new construction or renovation projects in the area, and provide a basis for the next rolling planning project arrangement.

[0085] 4. According to the relevant requirements of the technical specifications for post-evaluation of distribution network planning, periodically summarize the experience and deficiencies of distribution network planning in the study area, and compare them with the pre-evaluation results of the planning to continuously improve the accuracy of pre-evaluation of the digitalization of planning results.

[0086] Furthermore, the following usage scenarios can also be realized:

[0087] 1. Distribution network simulation and calculation technology: It provides local decision-making analysis in the whole process of planning, construction and operation, including resource carrying capacity analysis and calculation, source-grid-load-storage optimization configuration, distribution network dynamic characteristics analysis, economic operation and energy efficiency optimization, disaster risk analysis and simulation, and other major functions. It provides decision-making support for areas that require separate verification, such as areas with intensive interaction with new energy and areas with frequent disasters.

[0088] 2. Planning report and diagnostic report generation function: Based on the platform's powerful graph data analysis function, it automatically completes data extraction, table nesting and text information generation according to the set multiple sets of templates.

[0089] 3. Investment performance evaluation function for the entire distribution network process: automatically extract investment data at each stage of distribution network planning, construction and operation, compare indicator improvements, and generate investment performance reports of different granularities.

[0090] 4. Post-evaluation function of distribution network planning: Utilize the data accumulated year by year on the digital platform to automatically analyze the experience and deficiencies in project decision-making, engineering construction, production operation, economic benefits and project impact, and provide decision-making support for subsequent distribution network planning.

[0091] 5. Information interaction with other digital platforms: Use data from other platforms as the basis for analysis, transmit calculation and planning results to other platforms for interactive use, and have a two-way transmission interface. At the same time, the platform has the flexible use of offline analysis from data packets and online data acquisition analysis.

[0092] In one embodiment, based on the simulated operation status of the power equipment in each regional power grid in the distribution network, the equipment problem solving needs of each regional power grid are determined, including: based on the digital twin technology, the equipment information of multiple power equipment in the regional power grid is processed to obtain a three-dimensional power model of the regional power grid; the three-dimensional power model of the regional power grid is simulated to obtain the simulation results of each power equipment in the regional power grid; based on the simulation results of each power equipment in the regional power grid and the operation requirements of each power equipment, the equipment problem solving needs of the regional power grid are determined.

[0093] Exemplarily, the device information of the power device may include the device type, operating parameters, geographic location, and connection relationship with other power devices.

[0094] Exemplarily, digital twin technology can be used to project the environmental data of the regional power grid to obtain a three-dimensional environmental scene of the regional power grid; digital twin technology can continue to be used to process the equipment information of each power equipment so that the virtual three-dimensional model of each power equipment can be projected into the three-dimensional environmental scene.

[0095] It can be understood that the three-dimensional power model may include geometric information, topological relationships, and attributes of each device.

[0096] For example, the simulation environment can be determined in combination with the physical characteristics, operating conditions, and environmental factors of the equipment in the regional power grid. The three-dimensional power model of the regional power grid is simulated in the simulation environment to obtain the simulation operation results of each power equipment in the regional power grid.

[0097] For example, the simulation operation results may include the operating status of the power equipment or some curve index data. These data are compared with the preset operating requirements one by one. If the deviation is too large, the power equipment is considered to be abnormal equipment, and its abnormal indicators include what, and then the corresponding problem solving requirements are obtained.

[0098] According to the above implementation, a simulation algorithm may be used to determine equipment problem solving requirements of a regional power grid.

[0099] In one embodiment, based on the solar illumination conditions of each regional power grid, the new load development potential of each regional power grid is predicted respectively, including: based on the solar illumination conditions of the regional power grid, determining the area without mountain shadow influence and the area affected by mountain shadow corresponding to the regional power grid; based on the daily changes in solar altitude angle and azimuth angle, performing solar radiation simulation on the terrain model corresponding to the area affected by mountain shadow, and determining the annual total horizontal solar radiation in the area affected by mountain shadow; based on the daily changes in solar altitude angle and azimuth angle, performing solar radiation simulation on the terrain model corresponding to the area without mountain shadow influence, and determining the annual total horizontal solar radiation in the area without mountain shadow influence; based on the annual total horizontal solar radiation in the area affected by mountain shadow and the annual total horizontal solar radiation in the area without mountain shadow, determining the new load development potential of the regional power grid.

[0100] Exemplarily, a high-precision three-dimensional terrain model is constructed based on terrain data within the coverage area of ​​the regional power grid. Then, according to the solar illumination conditions of the regional power grid, the areas in the high-precision three-dimensional terrain model are divided into areas without mountain shadows and areas affected by mountain shadows, so that different radiation simulation methods can be used for different areas.

[0101] Exemplarily, for areas without mountain shadows, the astronomical parameter model is used to calculate the solar radiation intensity in each time step of the area based on the time series data of the solar altitude angle and azimuth angle, and the total annual horizontal solar radiation in the area is accumulated to obtain the total annual horizontal solar radiation in the area.

[0102] For example, for areas affected by mountain shadows, within each simulation time step, a ray tracing algorithm is used based on the solar altitude and azimuth angles, combined with a high-precision three-dimensional terrain model, to calculate in real time the mountain's shielding of solar radiation, and use the shielding information as the boundary condition for radiation simulation.

[0103] For example, based on the ray tracing algorithm, an atmospheric radiation transfer model is introduced to consider the scattering and absorption effects of the atmosphere on solar radiation, thereby improving the accuracy of radiation simulation in areas affected by mountain shadows.

[0104] For example, for the radiation data of different regions in the simulation process, a spatial interpolation algorithm is used to realize the continuous processing of the radiation data in space, and obtain the spatial distribution map of solar radiation within the coverage of the regional power grid. Then, based on the spatial distribution map of solar radiation within the coverage of the regional power grid, the region with high solar energy resource development potential and the potential of the region are determined.

[0105] According to the above implementation mode, based on the solar illumination conditions of each regional power grid, the new load development potential of each regional power grid can be predicted respectively.

[0106] In one embodiment, based on the new load development potential of each regional power grid and the degree of matching between the current power generation and current power consumption of each regional power grid, the energy storage location demand, load demand and distribution transformer location demand of each regional power grid are determined, including: when the degree of matching between the current power generation and current power consumption of the regional power grid is less than a preset threshold, based on the absolute difference between the current power generation and current power consumption of the regional power grid and the new load development potential of the regional power grid, the load demand of the regional power grid is determined; based on the load demand, the future predicted load of each load node in the regional power grid is determined; based on the location information and future predicted load of each load node in the regional power grid, and the location information of each energy storage node in the regional power grid, the energy storage location demand of the regional power grid is determined; based on the load demand of the regional power grid and the topology map of the regional power grid, the distribution transformer location demand of the regional power grid is determined.

[0107] For example, the absolute difference between the current power generation and the current power consumption of the regional power grid can be memorized using a time curve to obtain an absolute difference curve. Then, the absolute difference curve is integrated to obtain the maximum daily load demand, and the maximum daily load demand is compared with the new load development potential. If the maximum daily load demand is greater than the new load development potential, the new load development potential is used as the load demand of the regional power grid. If the maximum daily load demand is less than the new load development potential, the maximum daily load demand is used as the load demand of the regional power grid.

[0108] For example, the K-means clustering algorithm is used to perform cluster analysis on the location distribution characteristics and load demand characteristics of each load node in the regional power grid, and load nodes with similar characteristics are divided into several categories. For each type of load node, a support vector machine regression model is established to obtain the future predicted load of each load node.

[0109] Exemplarily, based on the layout of load nodes, by solving a multi-objective optimization model, factors such as the location distribution of energy storage nodes, energy storage capacity, and energy storage efficiency are comprehensively considered to determine the optimal layout plan for energy storage facilities in order to balance the energy storage investment cost and operating benefits.

[0110] For example, according to the load demand forecast results and the grid topology of the regional power grid, a genetic algorithm is used to optimize the distribution transformer location plan, and an economic distribution transformer location plan is obtained under the constraints of voltage qualification rate and network loss rate.

[0111] Exemplarily, the energy storage facility layout plan and the distribution transformer layout plan are superimposed and analyzed to determine whether there is a conflict in spatial position between the two layout plans. If there is a conflict, the positions of the energy storage nodes and the distribution transformer nodes are adjusted until the layout conflict is eliminated.

[0112] According to the above implementation, the energy storage location requirements, load requirements and distribution transformer location requirements of each regional power grid can be determined based on the new load development potential of each regional power grid and the degree of matching between the current power generation and current power consumption of each regional power grid.

[0113] In one embodiment, based on planning constraints, problem solving requirements, energy storage location requirements, load requirements, and distribution transformer location requirements of each regional power grid, multiple candidate power planning solutions are determined, including:

[0114] The objective function is constructed by taking the power planning scheme of the regional power grid as the independent variable and the investment benefit of the power planning scheme as the dependent variable;

[0115] The particle swarm algorithm is used to solve the objective function and obtain the optimal particle swarm. In each iteration, the power planning scheme corresponding to each individual in the particle swarm meets the planning constraints of the distribution network, the problem solving requirements of each regional power grid, the energy storage point distribution requirements, the load requirements and the distribution transformer point distribution requirements;

[0116] Based on the power planning scheme corresponding to each individual in the optimal particle swarm, multiple candidate power planning schemes are determined.

[0117] Exemplarily, the power planning scheme includes the equipment selection of each load node in the regional power grid, the equipment selection of each energy storage node, the equipment selection of each distribution transformer node, and the topological connection diagram of each load node, each energy storage node and each distribution transformer node.

[0118] Exemplarily, a particle swarm is initialized, wherein each individual in the initialized particle swarm corresponds to an electric power planning scheme. Starting from the initialized particle swarm, the following iterative operations are performed: based on the objective function, the fitness value of each individual in the particle swarm of this iteration is calculated. When the fitness value of each individual does not meet the preset conditions, each individual in the particle swarm of this iteration is controlled to meet the planning constraints of the distribution network, the problem solving requirements of each regional power grid, the energy storage point requirements, the load requirements and the distribution transformer point requirements, and the individual in the particle swarm of this iteration is cross-mutated to obtain the particle swarm of the next iteration. When the fitness value of each individual meets the preset conditions, the particle swarm of this iteration is used as the optimal particle swarm.

[0119] Exemplarily, the preset condition is that the fitness values ​​of each individual are greater than a preset threshold value. Alternatively, the preset condition is that the minimum fitness value among the fitness values ​​of each individual is greater than a preset threshold value.

[0120] According to the above implementation, a particle swarm algorithm may be used to solve and obtain multiple candidate electronic force planning solutions.

[0121] In one embodiment, the planning constraints of the distribution network include the investment limit of the power planning scheme, the distribution transformer location constraints and the energy storage location constraints.

[0122] In one embodiment, a target power planning scheme is determined among multiple candidate power planning schemes based on simulation indicators corresponding to each simulation dimension of each candidate power planning scheme, including: for each candidate power planning scheme, based on the weight of each simulation dimension, weighted summing up the simulation indicators corresponding to each simulation dimension of the candidate power planning scheme to obtain a comprehensive score of the candidate power planning scheme; based on the comprehensive score of each candidate power planning scheme, determining a target candidate power planning scheme among multiple candidate power planning schemes.

[0123] Exemplarily, the weight of each simulation dimension may be determined by using the analytic hierarchy process.

[0124] According to the above implementation, a target power planning scheme can be determined from multiple candidate power planning schemes from multiple dimensions.

[0125] Figure 2 It is a structural block diagram of a digital management system for distribution network planning, construction and operation according to an embodiment of the present disclosure.

[0126] like Figure 2 As shown, the digital management system includes:

[0127] A first demand determination module 210, configured to determine the equipment problem solving requirements of each regional power grid based on the simulated operation status of the power equipment of each regional power grid in the distribution network;

[0128] The illumination condition determination module 220 is used to determine the sunlight condition of each of the regional power grids by using image recognition technology;

[0129] A development potential determination module 230, for predicting the new load development potential of each of the regional power grids based on the solar illumination conditions of each of the regional power grids;

[0130] A second demand determination module 240 is used to determine the energy storage point demand, load demand and distribution transformer point demand of each of the regional power grids based on the new load development potential of each of the regional power grids and the matching degree between the current power generation and the current power consumption of each of the regional power grids;

[0131] A candidate solution determination module 250 is used to determine a plurality of candidate power planning solutions based on planning constraints of each regional power grid, the equipment problem solving requirements, the energy storage point requirements, the load requirements and the distribution transformer point requirements;

[0132] A scheme simulation module 260 is used to simulate each of the candidate power planning schemes based on a plurality of preset simulation dimensions, and obtain simulation indicators corresponding to each simulation dimension of each of the candidate power planning schemes;

[0133] A target scheme determining module 270 is used to determine a target power planning scheme from among the plurality of candidate power planning schemes based on simulation indicators corresponding to the simulation dimensions of the candidate power planning schemes;

[0134] A power grid construction module 280, configured to construct the distribution network based on the target power planning scheme;

[0135] The scheme adjustment module 290 is used to adjust the target power planning scheme based on the actual operation status of each regional power grid in the completed distribution network.

[0136] In one implementation, the first demand determination module 210 includes:

[0137] A model building unit, used to process the equipment information of multiple power equipment in the regional power grid based on the digital twin technology to obtain a three-dimensional power model of the regional power grid;

[0138] A model simulation unit, used to simulate the three-dimensional power model of the regional power grid to obtain simulation results of each power device in the regional power grid;

[0139] The demand determination unit is used to determine the equipment problem solving demand of the regional power grid based on the simulation results of each power device in the regional power grid and the operation requirements of each power device.

[0140] In one embodiment, the development potential determination module 230 includes:

[0141] A region determination unit, configured to determine a region not affected by mountain shadows and a region affected by mountain shadows corresponding to the regional power grid based on the sunlight conditions of the regional power grid;

[0142] The first simulation unit is used to perform solar radiation simulation on the terrain model corresponding to the area affected by the mountain shadow based on the daily changes in the solar altitude angle and azimuth angle, so as to determine the annual total horizontal solar radiation in the area affected by the mountain shadow;

[0143] The second simulation unit is used to perform solar radiation simulation on the terrain model corresponding to the area without mountain shadow influence based on the daily changes in the solar altitude angle and azimuth angle, so as to determine the annual horizontal total solar radiation in the area without mountain shadow influence;

[0144] The potential determination unit is used to determine the new load development potential of the regional power grid based on the annual total horizontal solar radiation in the area affected by the mountain shadow and the annual total horizontal solar radiation in the area not affected by the mountain shadow.

[0145] In one implementation, the second demand determination module 240 includes:

[0146] a load demand determination unit, configured to determine the load demand of the regional power grid based on an absolute difference between the current power generation and the current power consumption of the regional power grid and a new load development potential of the regional power grid when the matching degree between the current power generation and the current power consumption of the regional power grid is less than a preset threshold;

[0147] A future load prediction unit, used to determine the future predicted load of each load node in the regional power grid based on the load demand;

[0148] An energy storage demand determination unit, configured to determine the energy storage point requirements of the regional power grid based on the location information of each load node in the regional power grid and the future predicted load, and the location information of each energy storage node in the regional power grid;

[0149] The distribution transformer demand determination unit is used to determine the distribution transformer point requirements of the regional power grid based on the load requirements of the regional power grid and the topology diagram of the regional power grid.

[0150] In one implementation, the candidate solution determination module 250 includes:

[0151] An objective function determination unit, used to construct an objective function with the power planning scheme of the regional power grid as an independent variable and the investment benefit of the power planning scheme as a dependent variable;

[0152] An objective function solving unit, used for solving the objective function by using a particle swarm algorithm to obtain an optimal particle swarm, wherein, at each iteration, the power planning scheme corresponding to each individual in the particle swarm satisfies the planning constraints of the distribution network, the problem solving requirements of each of the regional power grids, the energy storage point requirements, the load requirements and the distribution transformer point requirements;

[0153] The candidate scheme determining unit is used to determine the multiple candidate power planning schemes based on the power planning scheme corresponding to each individual in the optimal particle swarm.

[0154] In one embodiment, the planning constraints of the distribution network include the investment limit of the power planning scheme, the distribution transformer location constraints and the energy storage location constraints.

[0155] In one implementation, the target solution determination module 270 includes:

[0156] A comprehensive scoring unit, for each of the candidate power planning schemes, based on the weights of each of the simulation dimensions, performs weighted summation on the simulation indicators corresponding to each simulation dimension of the candidate power planning scheme, to obtain a comprehensive score for the candidate power planning scheme;

[0157] The target scheme determining unit is used to determine a target power planning scheme from among the multiple candidate power planning schemes based on the comprehensive scores of the candidate power planning schemes.

[0158] For the description of specific functions and examples of each module and submodule of the system in the embodiment of the present disclosure, please refer to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0159] According to an embodiment of the present disclosure, the above method of the present disclosure may be applied to an electronic device and a readable storage medium.

[0160] Figure 3 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0161] like Figure 3As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0162] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0163] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as an intelligent management method for planning, construction and operation of a distribution network. For example, in some embodiments, an intelligent management method for planning, construction and operation of a distribution network may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of an intelligent management method for planning, construction and operation of a distribution network described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured in any other appropriate manner (eg, by means of firmware) to execute an intelligent management method for planning, construction and operation of a distribution network.

[0164] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0165] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0166] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0168] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0169] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0170] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0171] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A digital management method for planning, construction and operation of distribution networks, characterized in that: include: Based on the simulated operation status of the power equipment of each regional power grid in the distribution network, determining the equipment problem solving requirements of each regional power grid, wherein the equipment problem solving requirements include the equipment problem of each abnormal equipment and the solution priority of the equipment problem; Using image recognition technology to determine the solar illumination conditions of each of the regional power grids; Based on the sunlight conditions of each of the regional power grids, the new load development potential of each of the regional power grids is predicted respectively, wherein the new load development potential is determined based on the annual horizontal total solar radiation in the area affected by the mountain shadow and the annual horizontal total solar radiation in the area not affected by the mountain shadow in the regional power grid; When the matching degree between the current power generation and the current power consumption of the regional power grid is less than a preset threshold, determining the load demand of the regional power grid based on the absolute difference between the current power generation and the current power consumption of the regional power grid and the new load development potential of the regional power grid; Based on the load demand, determining the future predicted load of each load node in the regional power grid; Determining the energy storage point requirements of the regional power grid based on the location information and future predicted load of each load node in the regional power grid, and the location information of each energy storage node in the regional power grid; Determining the distribution and transformation point requirements of the regional power grid based on the load requirements of the regional power grid and the topology diagram of the regional power grid; Taking the power planning scheme of the regional power grid as an independent variable and the investment benefit of the power planning scheme as a dependent variable, constructing an objective function; Based on the planning constraints of each regional power grid, the equipment problem solving requirements, the energy storage location requirements, the load requirements and the distribution transformer location requirements, the objective function is solved to obtain multiple candidate power planning solutions; Based on the preset multiple simulation dimensions, each of the candidate power planning schemes is simulated respectively to obtain simulation indicators corresponding to each simulation dimension of each of the candidate power planning schemes; Determining a target power planning scheme from among the plurality of candidate power planning schemes based on simulation indicators corresponding to the simulation dimensions of the candidate power planning schemes; Based on the target power planning scheme, construct the distribution network; Based on the actual operation status of each regional power grid in the completed distribution network, the target power planning scheme is adjusted.

2. The method according to claim 1, characterized in that The step of determining the equipment problem solving requirements of each regional power grid based on the simulated operation status of the power equipment of each regional power grid in the distribution network includes: Based on the digital twin technology, the device information of multiple power devices in the regional power grid is processed to obtain a three-dimensional power model of the regional power grid; Simulating the three-dimensional power model of the regional power grid to obtain simulation results of each power device in the regional power grid; Based on the simulation results of each power device in the regional power grid and the operation requirements of each power device, the equipment problem solving needs of the regional power grid are determined.

3. The method according to claim 1, characterized in that The method of predicting the new load development potential of each of the regional power grids based on the solar illumination conditions of each of the regional power grids includes: Based on the sunlight conditions of the regional power grid, determining the area without mountain shadow influence and the area affected by mountain shadow corresponding to the regional power grid; Based on the daily changes in the solar altitude angle and azimuth angle, a solar radiation simulation is performed on the terrain model corresponding to the area affected by the mountain shadow to determine the annual total horizontal solar radiation in the area affected by the mountain shadow; Based on the daily changes in the solar altitude angle and azimuth angle, a solar radiation simulation is performed on the terrain model corresponding to the area without mountain shadow influence, so as to determine the annual horizontal total solar radiation in the area without mountain shadow influence; Based on the annual total horizontal solar radiation in the area affected by the mountain shadow and the annual total horizontal solar radiation in the area not affected by the mountain shadow, the new load development potential of the regional power grid is determined.

4. The method according to claim 1, characterized in that: The objective function is solved based on the planning constraints of each regional power grid, the problem solving requirements, the energy storage point requirements, the load requirements and the distribution transformer point requirements to obtain multiple candidate power planning schemes, including: A particle swarm algorithm is used to solve the objective function and obtain an optimal particle swarm, wherein, in each iteration, the power planning scheme corresponding to each individual in the particle swarm satisfies the planning constraints of the distribution network, the problem solving requirements of each regional power grid, the energy storage point distribution requirements, the load requirements and the distribution transformer point distribution requirements; Based on the power planning scheme corresponding to each individual in the optimal particle swarm, the plurality of candidate power planning schemes are determined.

5. The method according to claim 4, characterized in that The planning constraints of the distribution network include the investment limit of the power planning scheme, the distribution transformer location constraints and the energy storage location constraints.

6. The method according to claim 1, characterized in that The step of determining a target power planning scheme from among the plurality of candidate power planning schemes based on the simulation indicators corresponding to the simulation dimensions of the candidate power planning schemes includes: For each of the candidate power planning schemes, based on the weights of each of the simulation dimensions, weighted sums are performed on the simulation indicators corresponding to each simulation dimension of the candidate power planning scheme to obtain a comprehensive score for the candidate power planning scheme; Based on the comprehensive scores of the candidate power planning schemes, a target power planning scheme is determined from the multiple candidate power planning schemes.

7. A digital intelligent management system for planning, construction and operation of distribution networks, characterized in that: include: A first demand determination module is used to determine the equipment problem solving requirements of each regional power grid in the distribution network based on the simulated operation status of the power equipment of each regional power grid in the distribution network, wherein the equipment problem solving requirements include the equipment problem and solution priority of each abnormal equipment; A light condition determination module, used to determine the sunlight condition of each of the regional power grids using image recognition technology; A development potential determination module, used to predict the new load development potential of each of the regional power grids based on the sunlight conditions of each of the regional power grids, wherein the new load development potential is determined based on the annual horizontal total solar radiation in the area affected by the mountain shadow and the annual horizontal total solar radiation in the area not affected by the mountain shadow in the regional power grid; A second demand determination module is used to determine the energy storage point demand, load demand and distribution transformer point demand of each of the regional power grids based on the new load development potential of each of the regional power grids and the matching degree between the current power generation and the current power consumption of each of the regional power grids; A candidate scheme determination module is used to construct an objective function with the power planning scheme of the regional power grid as an independent variable and the investment benefit of the power planning scheme as a dependent variable; based on the planning constraints of each regional power grid, the equipment problem solving requirements, the energy storage point requirements, the load requirements and the distribution transformer point requirements, the objective function is solved to obtain multiple candidate power planning schemes; A scheme simulation module, used to simulate each of the candidate power planning schemes based on a plurality of preset simulation dimensions, and obtain simulation indicators corresponding to each simulation dimension of each of the candidate power planning schemes; A target scheme determination module, configured to determine a target power planning scheme from among the plurality of candidate power planning schemes based on simulation indicators corresponding to the simulation dimensions of the candidate power planning schemes; A power grid construction module, used to construct the distribution network based on the target power planning scheme; A scheme adjustment module, used to adjust the target power planning scheme based on the actual operation status of each regional power grid in the completed distribution network; Wherein, the second demand determination module includes: a load demand determination unit, configured to determine the load demand of the regional power grid based on an absolute difference between the current power generation and the current power consumption of the regional power grid and a new load development potential of the regional power grid when the matching degree between the current power generation and the current power consumption of the regional power grid is less than a preset threshold; A future load prediction unit, used to determine the future predicted load of each load node in the regional power grid based on the load demand; An energy storage demand determination unit, configured to determine the energy storage point requirements of the regional power grid based on the location information of each load node in the regional power grid and the future predicted load, and the location information of each energy storage node in the regional power grid; The distribution transformer demand determination unit is used to determine the distribution transformer point requirements of the regional power grid based on the load requirements of the regional power grid and the topology diagram of the regional power grid.

8. The system according to claim 7, characterized in that The first demand determination module includes: A model building unit, used to process the equipment information of multiple power equipment in the regional power grid based on the digital twin technology to obtain a three-dimensional power model of the regional power grid; A model simulation unit, used to simulate the three-dimensional power model of the regional power grid to obtain simulation results of each power device in the regional power grid; The demand determination unit is used to determine the equipment problem solving demand of the regional power grid based on the simulation results of each power device in the regional power grid and the operation requirements of each power device.

9. The system according to claim 7, characterized in that The development potential determination module includes: A region determination unit, configured to determine a region not affected by mountain shadows and a region affected by mountain shadows corresponding to the regional power grid based on the sunlight conditions of the regional power grid; The first simulation unit is used to perform solar radiation simulation on the terrain model corresponding to the area affected by the mountain shadow based on the daily changes in the solar altitude angle and azimuth angle, so as to determine the annual total horizontal solar radiation in the area affected by the mountain shadow; The second simulation unit is used to perform solar radiation simulation on the terrain model corresponding to the area without mountain shadow influence based on the daily changes in the solar altitude angle and azimuth angle, so as to determine the annual horizontal total solar radiation in the area without mountain shadow influence; The potential determination unit is used to determine the new load development potential of the regional power grid based on the annual total horizontal solar radiation in the area affected by the mountain shadow and the annual total horizontal solar radiation in the area not affected by the mountain shadow.

10. The system according to claim 7, characterized in that The candidate solution determination module includes: An objective function determination unit, used to construct an objective function with the power planning scheme of the regional power grid as an independent variable and the investment benefit of the power planning scheme as a dependent variable; An objective function solving unit, used for solving the objective function by using a particle swarm algorithm to obtain an optimal particle swarm, wherein, at each iteration, the power planning scheme corresponding to each individual in the particle swarm satisfies the planning constraints of the distribution network, the problem solving requirements of each of the regional power grids, the energy storage point requirements, the load requirements and the distribution transformer point requirements; The candidate scheme determining unit is used to determine the multiple candidate power planning schemes based on the power planning scheme corresponding to each individual in the optimal particle swarm.

11. The system according to claim 7, characterized in that The target solution determination module includes: A comprehensive scoring unit, for each of the candidate power planning schemes, based on the weights of each of the simulation dimensions, performs weighted summation on the simulation indicators corresponding to each simulation dimension of the candidate power planning scheme, to obtain a comprehensive score for the candidate power planning scheme; The target scheme determining unit is used to determine a target power planning scheme from among the multiple candidate power planning schemes based on the comprehensive scores of the candidate power planning schemes.

12. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 6.

13. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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