Wind and light resource potential assessment method for traffic energy collaborative planning

Abstract

The invention relates to the technical field of integrated energy system planning, in particular to a wind and light resource potential assessment method for traffic energy collaborative planning. According to the technical scheme, the method comprises the following steps: firstly, acquiring and integrating multi-source data such as geography, weather, traffic and the like; secondly, calculating the technical potential of the wind and light foundation, and determining a traffic energy collaboration index for quantifying the adaptation degree of a site and a traffic system; then, fusing the two to generate a collaborative development potential map; thirdly, simulating and predicting the space-time dynamic demand of charging of the electric vehicle through a trip chain; and finally, establishing a multi-objective optimization model taking the collaborative potential map and the demand matrix as input, and solving and generating a collaborative planning scheme considering economy, greenization and user convenience. According to the method, collaborative layout of energy and traffic infrastructures can be realized, the utilization rate of renewable energy and the overall economy of the system are remarkably improved, and scientific decision support is provided for constructing a green and efficient traffic energy fusion network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of comprehensive energy system planning, in particular to a wind and light resource potential evaluation method for traffic energy collaborative planning. BACKGROUND

[0003] In the field of renewable energy resource assessment, existing methods usually follow a hierarchical and progressive evaluation logic. The typical steps include: first, using geographic information systems (GIS) and meteorological data to simulate the distribution of wind or solar energy resources in the macro geographical space; second, by imposing a series of restrictive factors such as topography, ecological protection red line, population settlement, water area, road traffic, etc., to exclude unsuitable development areas, and thus estimate the theoretical developable potential; third, combined with the technical parameters of wind turbines or photovoltaic components, calculate the technical developable potential, i.e. the maximum power generation that can be achieved in a specific area; finally, through economic analysis such as estimating power generation cost and comparing with the on-grid price, estimate the economic developable potential. The core of this method is to identify the optimal power generation site for resources, and the output is usually a static resource potential distribution map, but it does not fully consider how these energy outputs are accurately matched in time and space with dynamic and dispersed terminal energy demand.

[0004] In the field of electric vehicle charging infrastructure planning, existing methods mainly start from the demand side of traffic. The core is to use massive traffic flow data such as vehicle sensors, mobile communication data, GPS trajectory, etc. to predict the travel habits and charging demand of electric vehicles. The goal of this type of planning is usually to minimize the charging cost or waiting time of users, or to maximize the charging service coverage under budget constraints. However, these methods often treat the power grid as an infinite capacity, homogeneous energy supply source, ignoring the geographical distribution of power sources, power generation cost and intermittent fluctuation characteristics of renewable energy.

[0005] In summary, existing energy planning focuses on "seeking sources", finding the optimal location for wind and light resources; traffic planning focuses on "seeking loads", finding the location with the most concentrated charging demand. This separate planning mode leads to serious space-time mismatch problems. For example, areas with the most abundant wind and light resources are often far from power load centers and traffic busy areas; while the charging station clusters planned along the traffic arteries may need to rely on long-distance, high-cost transmission networks to meet their huge instantaneous charging load, which not only increases the pressure and investment cost of the power grid, but also weakens the potential of local renewable energy consumption. This separation of "source" and "load" ultimately leads to a suboptimal solution at the system level: the wind and light curtailment rate increases, the green benefits of electric vehicles are discounted, and the overall investment benefits of the energy network and the transportation network cannot be maximized. SUMMARY

[0006] To solve the defect that renewable energy supply planning and traffic energy demand planning are disconnected in the prior art, a wind and light resource potential evaluation method for traffic energy collaborative planning is provided.The present application aims to realize collaborative decision of wind and light power generation facility and electric vehicle charging facility layout by constructing a unified optimization framework deeply coupling energy supply potential, traffic charging demand and power grid constraint, so as to maximize local consumption rate of renewable energy, minimize system life cycle cost, and improve user charging convenience.

[0007] To achieve the above object, the present application provides the following technical scheme:

[0008] The wind and light resource potential evaluation method for traffic energy collaborative planning comprises the following steps:

[0009] Obtain and integrate multi-source data in the planning area, wherein the multi-source data comprises meteorological data, geographic spatial data, traffic network data and traffic demand data;

[0010] Based on the meteorological data and the geographic spatial data, exclude unsuitable development areas through GIS exclusivity analysis, and calculate the basic technical potential of wind energy and solar energy in the planning area;

[0011] Determine a traffic energy collaboration degree index for quantifying the adaptation degree of candidate power generation sites and traffic energy systems;

[0012] According to the basic technical potential and the traffic energy collaboration degree index, generate a wind and light collaborative development potential supply atlas representing the collaborative development priority;

[0013] Based on the traffic network data and the traffic demand data, simulate the travel and charging behavior of electric vehicle users by using a trip chain model, predict and generate a space-time dynamic demand matrix of electric vehicle charging load in the planning area;

[0014] Based on the collaborative development potential supply atlas and the space-time dynamic demand matrix, establish a collaborative planning multi-objective optimization model, and solve the model to generate a specific planning scheme meeting the collaborative optimization target.

[0015] Preferably, the exclusion of unsuitable development areas through GIS exclusivity analysis specifically comprises:

[0016] Based on the geographic spatial data, generate a plurality of exclusive geographic layers respectively identifying areas not meeting development conditions as exclusive areas according to constraint conditions of terrain slope, ecological protection zones and land use types;

[0017] Spatially superimpose the plurality of exclusive geographic layers to identify and exclude geographic positions falling within at least one exclusive area, so as to determine suitable development areas.

[0018] Preferably, the traffic energy synergy index for quantifying the degree of adaptation of the candidate power generation site to the traffic energy system comprises: the access convenience of the candidate site to the power grid, the spatio-temporal matching degree of its power generation output curve to the load demand curve of the adjacent charging hub, and the suitability of land composite utilization.

[0019] Preferably, the basic technical potential of wind energy in the planning area is determined by the following steps:

[0020] obtaining wind speed data of the planning area according to the meteorological data;

[0021] extrapolating the wind speed data at the reference height to the hub height of the preset wind turbine through a wind shear model to obtain a wind speed sequence at the hub height;

[0022] combining the power curve of the selected wind turbine and the wind speed sequence at the hub height, calculating the annual power generation as the basic technical potential of wind energy.

[0023] Preferably, the basic technical potential of solar energy in the planning area is determined by the following steps:

[0024] obtaining geographical latitude data and solar radiation data of the planning area according to the geospatial data and meteorological data, respectively;

[0025] based on the geographical latitude data and solar radiation data of the planning area, calculating a total plane radiation sequence of a photovoltaic array at an optimal fixed inclination;

[0026] combining the conversion efficiency and system performance ratio of the selected photovoltaic system, using the total plane radiation sequence to calculate the annual power generation as the basic technical potential of solar energy.

[0027] Preferably, the wind-solar synergy development potential supply map representing the priority of synergy development is generated according to the basic technical potential and the traffic energy synergy index, specifically comprising:

[0028] using a multi-criteria decision method to determine the weights of multiple sub-indicators contained in the traffic energy synergy index;

[0029] for each geographical grid cell in the planning area, performing weighted summation on the multiple sub-indicators according to the weights to calculate the comprehensive synergy degree score of the cell;

[0030] multiplying the basic technical potential of each geographical grid cell by the comprehensive synergy degree score of the cell to obtain the synergy development potential value of the cell, and generating the map based on the synergy development potential values of all cells.

[0031] Preferably, the travel chain model is used to simulate the travel and charging behaviors of electric vehicle users, to predict and generate a time-space dynamic demand matrix of the charging load of electric vehicles in the planning area, specifically including:

[0032] For each virtual electric vehicle user agent in the planning area, based on the travel chain feature data, a Monte Carlo method is used for random sampling to generate a daily travel sequence containing travel purposes, departure times and residence durations;

[0033] The driving trajectory of the daily travel sequence is determined in combination with the shortest path algorithm of the road network, and the charging event after each trip is determined according to the vehicle energy consumption model and the user charging decision model, the charging event including the charging location, the start time, the charging duration and the charging power;

[0034] The charging events generated by all user agents are aggregated to obtain the time-space dynamic demand matrix.

[0035] Preferably, the collaborative optimization objectives of the collaborative planning multi-objective optimization model include: minimizing the total life cycle cost of the system, maximizing the localization rate of traffic energy, and minimizing the comprehensive charging inconvenience of electric vehicle users.

[0036] Preferably, the collaborative planning multi-objective optimization model contains a power grid safe operation constraint, which requires that after the implementation of the planning scheme, the power flow of any transmission line in the power grid should not exceed its thermal stability limit, and the voltage offset of any node should be within the preset allowable range.

[0037] Preferably, the multi-objective evolutionary algorithm, specifically the non-dominated sorting genetic algorithm II, is used to solve the collaborative planning multi-objective optimization model.

[0038] Compared with the prior art, the present application has the following advantages:

[0039] By proposing a collaborative planning method that places energy supply and traffic demand in the same optimization framework, the "source-load mismatch" problem caused by traditional separate planning is solved, and the deep integration of energy system and transportation system planning is realized.

[0040] By introducing the "traffic energy coordination degree" index, the resource potential evaluation is improved from static resource analysis to dynamic, deeply coupled with the end application scenario, making the evaluation results more forward-looking.

[0041] By multi-objective collaborative optimization and using the collaborative potential map to guide the search direction, this method can more efficiently find the comprehensive solution with the lowest total cost and the highest renewable energy utilization rate under the premise of meeting traffic demand and power grid safety, significantly improving the overall economic benefits of infrastructure investment. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The method flowchart of the wind and light resource potential evaluation method for the traffic energy collaborative planning proposed by the present application is shown in the figure. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application.

[0044] Referring to Figure 1 , the present application provides specific description of the wind and light resource potential evaluation method for the traffic energy collaborative planning.

[0045] S1, multi-source heterogeneous data collection and fusion:

[0046] The first step of the method is to build a comprehensive, multi-level, and spatio-temporal aligned database as the basis for all subsequent analysis and modeling. This step involves collecting heterogeneous data from different sources and integrating, cleaning, and preprocessing them through the GIS platform.

[0047] Geospatial data: used for spatial constraint analysis. Mainly includes: digital elevation model (DEM), land use / land cover (LULC) data, ecological protection red line data, and vector data of existing power grid and road network.

[0048] Meteorological data: used for evaluating resource potential. Mainly includes: long time series of hourly wind speed, wind direction, global horizontal irradiance (GHI), direct normal irradiance (DNI), and temperature data.

[0049] Traffic network and demand data: used for evaluating synergy and predicting charging load. Mainly includes: road network topology, traffic flow data, trip chain characteristic data of electric vehicle users, and preliminary load characteristic data of key traffic charging hubs.

[0050] Economic and technical data: used for cost-benefit analysis. Mainly includes: investment and operation cost and key technical parameters of wind turbine generators, photovoltaic systems, and charging facilities.

[0051] S2, basic technical potential calculation:

[0052] After obtaining the data, this step calculates the basic technical potential of wind energy and solar energy in the planning area based on meteorological data and geospatial data.

[0053] GIS exclusivity analysis: Firstly, the acquired geographic spatial data layers are superimposed to identify and eliminate areas unsuitable for renewable energy facility development using GIS spatial analysis functions. This analysis specifically includes: based on geographic spatial data, multiple exclusivity geographic layers are generated by identifying areas that do not meet development conditions based on terrain slope, ecological protection zones, and land use types, etc. as exclusive areas; the multiple exclusivity geographic layers are spatially superimposed to identify and eliminate geographic locations falling within at least one exclusive area, thereby determining suitable development areas.

[0054] Wind energy basic technical potential calculation: The wind energy potential of the screened suitable development area is calculated. The calculation specifically includes: the wind speed data at the reference height is extrapolated to the hub height of the pre-set wind turbine through a wind shear model (such as the logarithmic law formula) to obtain the wind speed sequence at the hub height; the annual power generation is calculated as the basic technical potential of wind energy by combining the power curve of the selected wind turbine and the wind speed sequence at the hub height.

[0055] Solar energy basic technical potential calculation: The solar energy potential of the screened suitable development area is calculated. The calculation specifically includes: based on the geographic latitude information and solar radiation data of the planning area, the total radiation sequence of the photovoltaic array at the best fixed inclination is calculated; the annual power generation is calculated as the basic technical potential of solar energy by combining the conversion efficiency and system performance ratio of the selected photovoltaic system and using the total radiation sequence.

[0056] S3, traffic energy synergy index determination:

[0057] The method determines a comprehensive "traffic energy synergy index", which is composed of multiple sub-indices:

[0058] Grid access convenience: measures the economic efficiency of the power plant site accessing the power grid, which is inversely proportional to the distance from the site to the nearest substation or high-voltage transmission line with sufficient capacity.

[0059] "Source-load" spatio-temporal matching degree: quantifies the spatio-temporal matching degree of the power generation output characteristics of the candidate power plant site and the load demand characteristics of the adjacent traffic charging hub.

[0060] Land composite utilization suitability: assesses the potential of land composite utilization on existing traffic facilities (such as highway service areas, parking lots).

[0061] S4, collaborative development potential supply map generation:

[0062] The basic technical potential and synergy index are fused to generate a wind-solar collaborative development potential supply map representing the priority of collaborative development. This step specifically includes:

[0063] Index weight determination: A multi-criteria decision method (such as AHP) is used to determine the weights of multiple sub-indices in the coordination degree index.

[0064] Comprehensive coordination degree calculation: For each geographical grid cell in the planning area, the multiple sub-indices are weighted and summed according to the determined weights to calculate the comprehensive coordination degree score of the cell:

[0065] TESI j = w grid I grid,j + w match I match,j + w comp I comp,j ;

[0066] where TESI j is the comprehensive coordination degree of the jth cell, I grid,j , I match,j and I comp,j are the grid access convenience, "source-load" space-time matching degree and land composite utilization suitability of the jth cell, w grid , w match and w comp are the corresponding index weights.

[0067] Coordination potential value calculation and atlas generation: multiply the basic technology potential P technical,j of each geographical grid cell j by the comprehensive coordination degree score TESI j of the cell to obtain the coordination development potential value P synergu,j of the cell, and based on the coordination development potential values of all cells, generate the final coordination development potential supply atlas through GIS visualization technology.

[0068] S5, spatiotemporal dynamic demand matrix prediction and generation:

[0069] This step determines the dynamic variation of electric vehicle charging load in the time and space dimensions. Specifically, it includes:

[0070] Individual trip simulation: for each virtual electric vehicle user agent in the planning area, based on trip chain characteristic data (such as trip purpose, departure time probability distribution, destination residence time probability distribution), a Monte Carlo method is used for random sampling to generate a daily trip sequence containing trip purpose, departure time, and residence time.

[0071] Charging event determination: The driving trajectory of the daily travel sequence is determined by combining the road network shortest path algorithm (such as Dijkstra algorithm), and the charging event after each trip is determined according to the vehicle energy consumption model and the user charging decision model, including the charging location, start time, charging duration and charging power.

[0072] Load aggregation: The charging events generated by all user agents are aggregated in space and time to obtain the spatio-temporal dynamic demand matrix.

[0073] S6, collaborative planning and scheme generation:

[0074] This step integrates the potential assessment of the supply side and the load forecast of the demand side into a unified optimization framework to generate specific planning schemes.

[0075] Establish a collaborative planning multi-objective optimization model: Establish an optimization model with collaborative development potential supply map and spatio-temporal dynamic demand matrix as input, and layout and capacity of renewable energy facilities and charging facilities as decision variables. The model optimizes multiple collaborative optimization objectives including system economy, renewable energy consumption rate and user experience. Preferred objectives can include: minimizing total life cycle cost of the system, maximizing local supply rate of transportation energy, and minimizing comprehensive charging inconvenience of electric vehicle users. At the same time, the model also meets a series of preset constraints, such as total investment budget constraint, power grid safe operation constraint, etc.

[0076] Model solution and scheme generation: A multi-objective evolutionary algorithm (such as non-dominated sorting genetic algorithm NSGA-II) is used to solve the model and generate a set of Pareto optimal solutions. Each solution in the solution set corresponds to a specific and complete collaborative planning scheme, providing a scientific basis for managers to weigh and choose between different objectives.

[0077] Example 1:

[0078] This example illustrates the scheme by taking a national renewable energy demonstration area as an example. The demonstration area is one of the regions with the most abundant wind and solar energy resources in China, and as an important ecological conservation area, its development of green transportation and energy has great strategic significance and demonstration effect.

[0079] Model parameterization:

[0080] When applying the method of the present application to this demonstration area, the first thing needed is to parameterize the model. The planning area of this case study is set as the whole territory of the national renewable energy demonstration area. According to the official planning assessment data, the wind energy potential available for development in this area is huge, expected to exceed 40 million kilowatts, and the solar energy potential is also considerable, expected to exceed 30 million kilowatts. In terms of economic and technical parameters, the unit investment cost of wind power is set at 2300 yuan / kW, and the unit investment cost of photovoltaic power is set at 2750 yuan / kW. In terms of transportation network, two main traffic trunks running through the area, namely S1 and S2 expressways, are considered. In terms of user experience and quality of service targets, the maximum waiting time for electric vehicle charging stations during peak hours is set not to exceed 15 minutes. Finally, the electric vehicle (EV) penetration rate in this area is set at 30%.

[0081] Collaborative planning process:

[0082] The above parameters are input into the model described in the present application.

[0083] First, steps S1 to S4 are executed to generate the "wind-solar collaborative development potential map" of the demonstration area. Specifically, the model will calculate the "transportation energy synergy index (TESI)" of each plot. For example, for the transportation corridor proximity, the closer to S1 and S2 expressways, the higher the score of the plot. For the "source-load" spatio-temporal matching degree, the model will analyze the matching degree of the night high wind speed characteristics of the A and B counties with the highest wind speed in the demonstration area and the charging demand peak of the night logistics vehicles in the expressway service areas, as well as the matching degree of the day high solar radiation characteristics along the C to D counties and the daytime passenger car travel power supplement demand. For the land composite utilization suitability, the scores of S1 and S2 expressway service areas, large-scale event venue parking lots, etc. will be higher, as they are suitable for the construction of "photovoltaic corridors" or photovoltaic carports.

[0084] Then, steps S5 and S6 are executed, and the collaborative development potential map and the predicted charging demand are used as important inputs to run the collaborative planning optimization model. The model will prioritize the siting and capacity configuration decisions of wind and solar power stations and charging stations in areas with high collaborative potential.

[0085] Result analysis:

[0086] After the model is solved, the management personnel selects a balanced scheme from the Pareto frontier. The scheme may exhibit the following characteristics:

[0087] Spatial layout collaboration: A 500MW large-scale wind farm is planned in A County where the wind energy resource is abundant, and large-scale direct current fast charging station clusters are planned in several major service areas of S1 Expressway (such as C service area, D service area). The model finds that the output peak of the wind farm (usually at night and in winter) has a certain complementarity with the charging demand of trucks and part of passenger cars in the expressway service area at night, and the geographical distance is close, which can realize "wind power direct supply" through the newly built special line, greatly reducing the cost and loss of purchasing electricity from the main network.

[0088] Quantitative benefits: Compared with the traditional "separated planning scheme of building wind farm first and then charging pile", the collaborative planning scheme generated by the method can realize the following beneficial effects:

[0089] Local consumption rate improvement: The proportion of renewable energy power used for local traffic charging is improved.

[0090] System total cost reduction: Through optimization of layout, unnecessary long-distance transmission line investment and power grid reconstruction cost are reduced.

[0091] User charging waiting time is shortened: Since the capacity and location of the charging station are configured according to the accurate prediction of dynamic demand, the problem of long queuing at some sites and idling at other sites is effectively avoided.

[0092] This embodiment proves that the method proposed by the present application can be effectively applied to actual regional energy and transportation planning, and the collaborative planning scheme produced by the method is significantly better than the prior art in economy, greenness and user satisfaction, and has high industrial applicability.

[0093] In the description of the specification, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0094] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of the specification. The specification selects and describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their entire scope and equivalents.

Claims

1. A method for wind and solar resource potential assessment for transportation energy collaborative planning, characterized in that, The method comprises the following steps: acquiring and integrating multi-source data in a planning area, the multi-source data comprising meteorological data, geospatial data, traffic network data and traffic demand data; based on the meteorological data and geospatial data, excluding unsuitable development areas through GIS exclusive analysis, and calculating the basic technical potential of wind energy and solar energy in the planning area; determining a traffic-energy coordination degree index for quantifying the degree of adaptation of candidate power generation sites to the traffic energy system; generating a wind-solar coordinated development potential supply atlas representing the priority of coordinated development according to the basic technical potential and the traffic-energy coordination degree index; based on the traffic network data and traffic demand data, simulating the travel and charging behavior of electric vehicle users by using a trip chain model, and predicting and generating a time-space dynamic demand matrix of electric vehicle charging load in the planning area; based on the coordinated development potential supply atlas and the time-space dynamic demand matrix, establishing a coordinated planning multi-objective optimization model, and solving the model to generate a specific planning scheme meeting the coordinated optimization objectives.

2. The method for wind and solar resource potential assessment for transportation energy-oriented coordinated planning according to claim 1, characterized in that, The excluding unsuitable development areas through GIS exclusive analysis specifically comprises: based on the geospatial data, generating a plurality of exclusive geographic layers respectively marking areas not meeting the development conditions as exclusive areas in terms of the constraints of terrain slope, ecological protection zones and land use types; spatially superimposing the plurality of exclusive geographic layers to identify and exclude geographic locations falling within at least one exclusive area, thereby determining suitable development areas.

3. The method for wind and solar resource potential assessment for transportation energy oriented coordinated planning according to claim 1, characterized in that, The traffic-energy coordination degree index for quantifying the degree of adaptation of candidate power generation sites to the traffic energy system comprises the access convenience of the candidate sites to the power grid, the time-space matching degree of the power generation output curve of the candidate sites to the load demand curve of adjacent charging hubs, and the suitability of land composite utilization.

4. The method for wind and solar resource potential assessment for transportation energy-oriented coordinated planning according to claim 1, characterized in that, The basic technical potential of wind energy in the planning area is determined by the following steps: acquiring wind speed data of the planning area according to the meteorological data; extrapolating the wind speed data at a reference height to the hub height of a preset wind turbine through a wind shear model to obtain a wind speed sequence at the hub height; combining the power curve of a selected wind turbine and the wind speed sequence at the hub height, calculating the annual power generation as the basic technical potential of wind energy.

5. The method for wind and solar resource potential assessment for transportation energy oriented collaborative planning according to claim 1, characterized in that, The basic technical potential of solar energy in the planning area is determined by the following steps: acquiring geographic latitude data and solar radiation data of the planning area according to the geospatial data and meteorological data respectively; based on the geographic latitude data and solar radiation data of the planning area, calculating a plane total radiation sequence of a photovoltaic array under an optimal fixed inclination; combining the conversion efficiency and system performance ratio of a selected photovoltaic system, and using the plane total radiation sequence to calculate the annual power generation as the basic technical potential of solar energy.

6. The method for wind and solar resource potential assessment for transportation energy oriented collaborative planning according to claim 1, characterized in that, The generating a wind-solar coordinated development potential supply atlas representing the priority of coordinated development according to the basic technical potential and the traffic-energy coordination degree index specifically comprises: determining the weights of a plurality of sub-indices contained in the traffic-energy coordination degree index by using a multi-criteria decision method; For each geographical grid unit in the planning area, the plurality of sub-indicators are weighted and summed according to the weights to obtain a comprehensive synergy degree score of the unit; The basic technology potential of each geographical grid unit is multiplied by the comprehensive synergy degree score of the unit to obtain a synergy development potential value of the unit, and a map is generated based on the synergy development potential values of all units.

7. The method for wind and solar resource potential assessment for transportation energy oriented collaborative planning according to claim 1, characterized in that, The travel chain model is used to simulate the travel and charging behaviors of electric vehicle users, to predict and generate a time-space dynamic demand matrix of electric vehicle charging load in the planning area, specifically including: For each virtual electric vehicle user agent in the planning area, a day travel sequence including travel purpose, departure time and residence duration is generated by using the Monte Carlo method for random sampling based on travel chain characteristic data; The driving trajectory of the day travel sequence is determined by combining a shortest path algorithm of a road network, and a charging event after each trip is determined according to a vehicle energy consumption model and a user charging decision model, the charging event including a charging location, a start time, a charging duration and a charging power; The charging events generated by all user agents are aggregated to obtain the time-space dynamic demand matrix. 8.The method for wind and solar resource potential assessment for transportation energy-oriented collaborative planning according to claim 1, characterized in that, The synergy optimization target of the synergy planning multi-objective optimization model includes: minimizing the total life cycle cost of the system, maximizing the localization rate of traffic energy, and minimizing the comprehensive charging inconvenience of electric vehicle users.

9. The method for wind and solar resource potential assessment for transportation energy oriented collaborative planning according to claim 1, characterized in that, The synergy planning multi-objective optimization model contains a power grid safe operation constraint, which requires that after the implementation of the planning scheme, the power flow of any transmission line in the power grid should not exceed its thermal stability limit, and the voltage offset of any node should be within the preset allowable range.

10. The method for wind and solar resource potential assessment for transportation energy oriented collaborative planning according to claim 1, characterized in that, A multi-objective evolutionary algorithm, specifically a non-dominated sorting genetic algorithm II, is used to solve the synergy planning multi-objective optimization model.

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