Wind, solar, thermal and energy storage integrated collaborative planning method and system

Abstract

The present invention provides a method and system for the coordinated planning of wind, solar, thermal and storage integration, which relates to the field of wind, solar, thermal and storage integration, wherein the method comprises: quantifying the impact of meteorological factors on the wind and solar output of the planned area by region according to the site selection related information of the planned area, generating a first quantification result, determining the resource evaluation result of the planned area, and then determining the target location; quantifying the impact of meteorological factors on the load of the load area according to the building related information of the load area, generating a second quantification result; predicting the power load information of the load area according to the second quantification result; generating an initial planning scheme, and performing source-load imbalance evaluation on the initial planning scheme according to the first time scale according to the power load information of the load area and the historical meteorological information of the target location, generating a source-load imbalance evaluation result, and generating a target planning scheme according to the source-load imbalance evaluation result, thereby improving the resource utilization rate and power supply reliability of the wind, solar, thermal and storage integration.

Description

Technical Field

[0001] The present invention relates to the field of wind, solar, thermal and storage integration, and in particular to a method and system for wind, solar, thermal and storage integration collaborative planning. Background Art

[0002] In the source-grid-load-storage system with large-scale grid-connected renewable energy, the random fluctuations of wind and solar increase the uncertainty of system operation, which brings serious problems to the safety and stability of system operation. If a high proportion of new energy output is to be achieved, its installed capacity will reach several times the load supplied to avoid load loss, which will bring higher instantaneous fluctuations in output; at the same time, the uncertainty and uncontrollability of wind and solar output may also bring the risk of load loss to the system. With the continuous advancement of the construction of multi-energy complementary energy demonstration bases, the wind, solar, thermal and storage integration mode may become the mainstream operation mode of large energy bases. Wind, solar, thermal and storage integration gives priority to the use of clean energy such as wind and light, gives full play to the regulation capacity of thermal power units, and combines reasonably configured energy storage facilities to turn volatile, random and intermittent renewable energy into stable and reliable output power, achieving the complementary effect of wind, solar, thermal and storage characteristics within the day, and reducing the peak load demand on the power grid. Energy storage facilities can realize the conversion and storage of electrical energy and other energies. When the power generation output is greater than the power supply demand, the energy can be stored in the form of chemical energy, mechanical energy, electromagnetic energy, etc. When the system is in short supply, the electrical energy can be released through energy conversion devices to realize energy exchange to meet the needs of the power grid or load, thereby improving the power supply security of the power system.

[0003] The existing methods for new energy scale configuration all use production simulation or safety and stability verification, and take single objectives such as the maximum total wind and solar development scale or the proportion of renewable energy as research objects, which has little guiding significance for the early planning of integrated thermal, wind, solar and storage bases.

[0004] Therefore, it is necessary to provide a wind, solar, thermal and storage integrated collaborative planning method and system to improve the resource utilization and power supply reliability of wind, solar, thermal and storage integration. Summary of the invention

[0005] The present invention provides a method for integrated collaborative planning of wind, solar, thermal and storage, comprising: obtaining site selection related information of a to-be-planned area, wherein the site selection related information at least includes terrain information of the to-be-planned area; quantifying the influence of meteorological factors on the wind and solar output of the to-be-planned area by region according to the site selection related information of the to-be-planned area, and generating a first quantification result; determining a resource assessment result of the to-be-planned area according to the first quantification result; determining a target location based on the resource assessment result of the to-be-planned area; obtaining building related information of a load area; quantifying the influence of meteorological factors on the load of the load area according to the building related information of the load area, and generating a second quantification result; predicting the power load information of the load area according to the second quantification result; generating an initial planning scheme, and performing source-load imbalance assessment on the initial planning scheme according to a first time scale according to the power load information of the load area and the historical meteorological information of the target location, and generating a source-load imbalance assessment result; generating a target planning scheme according to the source-load imbalance assessment result, wherein the target planning scheme includes a target wind power device capacity, a target photovoltaic installed capacity, a target thermal power installed capacity and a target energy storage installed capacity.

[0006] Furthermore, according to the site selection related information of the area to be planned, the impact of meteorological factors on the wind and solar power output of the area to be planned is quantified by region to generate a first quantification result, including: obtaining multiple groups of first sample data, wherein the first sample data includes terrain information, wind and solar power generation equipment information, meteorological information and wind and solar power generation information of the first sample area; establishing a simulation model according to the multiple groups of first sample data; generating multiple groups of second sample data through the simulation model, wherein the second sample data includes terrain information, wind and solar power generation equipment information, meteorological information and wind and solar power generation information of the second sample area; dividing the multiple groups of first sample data and the multiple groups of second sample data into multiple first sample groups according to the terrain information of each group of the first sample data and the terrain information of the multiple groups of second sample data, wherein each of the first sample groups includes at least one group of first sample data and / or second sample data; for each of the first sample groups, based on the first sample For each group of first sample data and second sample data included in the group, determine the first key meteorological factor corresponding to the first sample group and the weight corresponding to each of the first key meteorological factors; establish a site selection evaluation system, wherein the site selection evaluation system includes multiple site selection evaluation indicators; determine multiple candidate sub-areas of the area to be planned according to the site selection evaluation system and the site selection related information of the area to be planned; for each of the candidate sub-areas, determine a similar first sample group from the multiple first sample groups according to the terrain information of the candidate sub-area, and determine the first key meteorological factor corresponding to the candidate sub-area and the weight corresponding to each of the first key meteorological factors according to the first key meteorological factor corresponding to the similar first sample group and the weight corresponding to each of the first key meteorological factors, wherein the first quantization result includes the first key meteorological factor corresponding to each of the candidate sub-areas of the area to be planned and the weight corresponding to each of the first key meteorological factors.

[0007] Further, based on each group of first sample data and second sample data included in the first sample group, the first key meteorological factor corresponding to the first sample group and the weight corresponding to each of the first key meteorological factors are determined, including: for each group of first sample data included in the first sample group, based on the meteorological information and wind and solar power generation information of the first sample data, calculating the correlation coefficient between each meteorological factor corresponding to the first sample data and wind and solar power generation; for each group of second sample data included in the first sample group, based on the meteorological information and wind and solar power generation information of the second sample data, calculating the correlation coefficient between each meteorological factor corresponding to the second sample data and wind and solar power generation; for each of the meteorological factors, based on the correlation coefficient between each meteorological factor corresponding to each group of the first sample data included in the first sample group and wind and solar power generation and the correlation coefficient between each meteorological factor corresponding to the second sample data and wind and solar power generation, calculating the key parameters corresponding to the meteorological factor, and judging whether the meteorological factor is the first key meteorological factor according to the key parameters corresponding to the meteorological factor; for each first key meteorological factor corresponding to the first sample group, based on the key parameters corresponding to each first key meteorological factor corresponding to the first sample group, determining the weight corresponding to the first key meteorological factor.

[0008] Further, according to the first quantification result, the resource evaluation result of the area to be planned is determined, including: establishing a basic evaluation model according to the first key meteorological factor corresponding to each group of the first sample groups; for each of the first sample groups, establishing a resource evaluation model corresponding to the first sample group according to the first key meteorological factor corresponding to the first sample group and the basic evaluation model, generating training samples using the first sample data and the second sample data included in the first sample group, and training the resource evaluation model corresponding to the first sample group through the training samples, wherein the input of the resource evaluation model includes the first key meteorological factor corresponding to the first sample group, the weight corresponding to each of the first key meteorological factors, and the meteorological factor feature matrix; for each of the candidate sub-areas, based on the first key meteorological factor corresponding to the candidate sub-area, extracting the meteorological factor feature matrix from the historical meteorological information of the candidate sub-area, generating the resource evaluation result of the candidate sub-area through the resource evaluation model corresponding to the similar first sample group based on the first key meteorological factor corresponding to the candidate sub-area, the weight corresponding to each of the first key meteorological factors, and the meteorological factor feature matrix, wherein the resource evaluation result of the area to be planned includes the resource evaluation result of each candidate sub-area.

[0009] Furthermore, based on the resource assessment results of the area to be planned, the target location is determined, including: for each of the candidate sub-areas, based on the historical meteorological information of the candidate sub-areas, the wind-solar complementarity parameters of the candidate sub-areas are determined according to a second time scale; according to the resource assessment results and wind-solar complementarity parameters of each candidate sub-area, the target location is determined, wherein the second time scale is smaller than the first time scale.

[0010] Further, according to the building-related information of the load area, the impact of meteorological factors on the load of the load area is quantified to generate a second quantification result, including: obtaining multiple groups of third sample data, wherein the third sample data includes building-related information, meteorological factors and power load information of the third sample area; dividing the multiple groups of third sample data into multiple second sample groups according to the building-related information of each group of third sample data; for each of the second sample groups, based on the meteorological factors and power load information of each group of third sample data included in the second sample group, calculating the correlation coefficient between each meteorological factor and the power load, and determining the second key meteorological factor corresponding to the second sample group and the weight corresponding to each of the second key meteorological factors according to the correlation coefficient between each meteorological factor and the power load; establishing A change prediction model; using the change prediction model to predict the building-related information of the load area in future time periods based on the building-related information of the load area at multiple historical time points; for each of the future time periods, determining the second similar sample group from the multiple second sample groups based on the building-related information of the load area at the future time point, and determining the second key meteorological factor corresponding to the load area in the future time period and the weight corresponding to each of the second key meteorological factors based on the second key meteorological factors corresponding to the second similar sample group and the weight corresponding to each of the second key meteorological factors, wherein the second quantification result includes the second key meteorological factor corresponding to the load area in multiple future time periods and the weight corresponding to each of the second key meteorological factors.

[0011] Furthermore, based on the second quantization result, the power load information of the load area is predicted, including: for each of the future time periods, through the load prediction model according to the second key meteorological factors corresponding to the load area in the future time period and the weight corresponding to each of the second key meteorological factors, the power load information of the load area at multiple historical time points and the historical meteorological information of the load area, predicting the power load information of the load area in the future time period.

[0012] Further, according to the power load information of the load area and the historical meteorological information of the target location, the initial planning scheme is evaluated for source-load imbalance according to a first time scale to generate a source-load imbalance evaluation result, and according to the source-load imbalance evaluation result, a target planning scheme is generated, including: S11, taking the initial planning scheme as the current planning scheme; S12, according to the power load information of the load area and the historical meteorological information of the target location, according to the first time scale, determining a source-load imbalance evaluation scenario; S13, calculating the scenario similarity between the source-load imbalance evaluation scenario and the source-load imbalance evaluation scenario that has been evaluated, and judging whether the scenario similarity is less than a scenario similarity threshold, if so, executing S14, if not, then Execute S12; S14. Calculate the wind and solar power generation under the source-load imbalance assessment scenario according to the current planning scheme, and calculate the source-load imbalance corresponding to the source-load imbalance assessment scenario according to the wind and solar power generation and power load information under the source-load imbalance assessment scenario; S15. Determine whether the number of source-load imbalance assessment scenarios that have completed the assessment is greater than the quantity threshold, if so, execute S16, if not, execute S12; S16. Determine whether to optimize the current planning scheme based on the source-load imbalance corresponding to each source-load imbalance assessment scenario that has completed the assessment, if so, execute S17, if not, use the current planning scheme as the target planning scheme; S17. Optimize the current planning scheme, update the current planning scheme, and execute S12.

[0013] Further, the current planning scheme is optimized, including: optimizing the current planning scheme according to the source-load imbalance corresponding to the source-load imbalance assessment scenario in which the assessment of the current planning scheme is completed.

[0014] The present invention provides a wind, solar, thermal and storage integrated collaborative planning system, which applies the above-mentioned wind, solar, thermal and storage integrated collaborative planning method, including: a site selection optimization module, which is used to obtain site selection related information of the area to be planned, and according to the site selection related information of the area to be planned, wherein the site selection related information at least includes the terrain information of the area to be planned, quantify the impact of meteorological factors on the wind and solar output of the area to be planned by region, generate a first quantification result, determine the resource evaluation result of the area to be planned according to the first quantification result, and determine the target location based on the resource evaluation result of the area to be planned; a load prediction module, which is used to obtain building related information of the load area, and according to the load area domain, quantify the impact of meteorological factors on the load area, generate a second quantification result, and predict the power load information of the load area according to the second quantification result; a collaborative planning module is used to generate an initial planning scheme, and according to the power load information of the load area and the historical meteorological information of the target location, perform a source-load imbalance assessment on the initial planning scheme according to a first time scale to generate a source-load imbalance assessment result, and generate a target planning scheme according to the source-load imbalance assessment result, wherein the target planning scheme includes a target wind power device capacity, a target photovoltaic installed capacity, a target thermal power installed capacity and a target energy storage installed capacity.

[0015] Compared with the prior art, the wind, solar, thermal and energy storage integrated collaborative planning method and system provided by the present invention has at least the following beneficial effects:

[0016] 1. By analyzing the topography, ecology and meteorological information of the planned area in detail, the potential of renewable energy such as wind and light can be accurately assessed. This not only helps to maximize the use of renewable energy in resource-rich areas, but also optimizes the use of resources through reasonable planning in areas with relatively scarce resources. Taking into account various energy forms such as wind power, photovoltaics, thermal power and energy storage, a more scientific and reasonable energy structure can be formulated by quantifying the impact of various factors on energy supply and demand. This helps to balance the intermittent and instability of renewable energy and improve the reliability and stability of the entire energy system. By predicting the power load information of the load area and evaluating the source-load imbalance of the initial planning scheme in combination with historical meteorological information, this method can timely discover and adjust the deficiencies in the planning scheme. This helps to reduce the source-load imbalance and improve the operating efficiency and economy of the energy system. The flexibility and response speed of the energy system can be further enhanced through reasonable energy storage installation capacity. During periods of insufficient output of renewable energy or peak load, the energy storage system can release electricity to make up for the supply-demand gap and ensure the stable operation of the power system. Taking renewable energy as the main planning object, optimizing the energy structure and improving resource utilization efficiency can help reduce dependence on traditional fossil energy, reduce greenhouse gas emissions, and promote the sustainable development of the energy industry. Through accurate resource assessment and load forecasting, more economical and reasonable planning schemes can be formulated to reduce unnecessary investment waste. At the same time, by optimizing the energy structure and improving system operation efficiency, energy costs can be reduced and overall economic benefits can be improved.

[0017] 2. By obtaining multiple sets of first sample data and establishing a simulation model, it is possible to more accurately simulate and predict wind and solar power generation under different terrain and meteorological conditions. By analyzing each set of first sample data and second sample data in detail, key meteorological factors and their weights are determined, making resource assessment more accurate. The established site selection evaluation system covers multiple site selection evaluation indicators and can comprehensively consider all aspects of the area to be planned. Based on the site selection evaluation system and the site selection related information of the area to be planned, multiple candidate sub-areas can be screened out, and the optimal target location can be determined through further analysis.

[0018] 3. Obtain multiple sets of third sample data containing building-related information, meteorological factors, and power load information. For each second sample group, calculate the correlation coefficient between the meteorological factor and the power load, and determine the second key meteorological factor and its weight. This step helps to identify meteorological factors that have a significant impact on the power load and provide key input for subsequent predictions. Establish a change prediction model to predict building-related information in future time periods based on building-related information in the load area at multiple historical time points. This helps to consider the impact of future building changes on power load and improve the accuracy of predictions. For each future time period, determine a similar sample group from the second sample group based on the predicted building information, and adjust the second key meteorological factor and its weight accordingly. This enables the prediction to more accurately reflect the impact of meteorological factors on power load in the future time period. Using the load prediction model, combined with the second key meteorological factor and its weight corresponding to the future time period, historical power load information, and historical meteorological information, predict the power load information of the load area in the future time period. By analyzing the correlation between meteorological factors and power load in detail and considering changes in building information, this method can more accurately predict future power loads. This helps to reduce prediction errors and improve the feasibility and economy of planning schemes.

[0019] 4. By considering the power load information of the load area and the historical meteorological information of the target location, the initial planning scheme is evaluated for source-load imbalance according to the first time scale. During the evaluation process, multiple source-load imbalance evaluation scenarios are determined, and the source-load imbalance under each scenario is calculated. During the evaluation process, the similarity between the new scenario and the scenario that has been evaluated is calculated. If the similarity between the new scenario and the existing scenario is higher than the set threshold, the new scenario is considered to have been fully evaluated and no repeated calculation is required. According to the evaluation results of the source-load imbalance, it is determined whether the current planning scheme needs to be optimized. If optimization is required, the planning scheme is adjusted according to the historical data and evaluation results, and the source-load imbalance evaluation is re-performed. This process is iterative until the set evaluation quantity threshold is met or it is determined that no further optimization is required. The optimization strategy is formulated based on the source-load imbalance corresponding to the current planning scheme in the source-load imbalance evaluation scenario that has been evaluated. This means that the optimization process takes into account past experience and data to adjust the planning scheme more accurately. Through the source-load imbalance evaluation, the performance of the planning scheme in actual operation can be more accurately understood. This helps to identify potential problems in the planning scheme and make timely adjustments and optimizations. By optimizing the planning scheme, the imbalance between sources and loads can be reduced. This helps to enhance the stability of the power system and reduce the risk of power failures caused by imbalance between supply and demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0021] Figure 1 It is a flow chart of the wind, solar, thermal and energy storage integrated collaborative planning method according to some embodiments of this specification;

[0022] Figure 2 It is a schematic diagram of a process for generating a target planning scheme according to some embodiments of this specification;

[0023] Figure 3 It is a module schematic diagram of the wind, solar, thermal and energy storage integrated collaborative planning system shown in some embodiments of this specification. DETAILED DESCRIPTION

[0024] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0025] Figure 1 is a flow chart of a method for wind, solar, thermal and energy storage integrated collaborative planning according to some embodiments of this specification, such as Figure 1 As shown, the integrated collaborative planning method of wind, solar, thermal and energy storage can include the following steps.

[0026] Step 110, obtaining site selection related information of the area to be planned.

[0027] The site selection related information at least includes the topographic information of the area to be planned.

[0028] Specifically, terrain information may include:

[0029] Altitude: Altitude directly affects the distribution of natural resources such as wind speed and solar radiation intensity. High altitude areas usually have higher wind speeds and stronger solar radiation, which are suitable for the construction of wind power and photovoltaic power generation facilities.

[0030] Topographic undulation: The undulation of the terrain affects the distribution and transmission efficiency of wind energy, as well as the layout and orientation of photovoltaic arrays. Flat areas are convenient for the construction of large-scale wind and photovoltaic power plants, while mountainous areas may require more complex layout designs.

[0031] Geological conditions: These factors include soil type, rock formations, and groundwater conditions. These factors affect foundation stability and construction costs. For example, soft soil areas may require additional foundation reinforcement measures.

[0032] Surface cover: such as vegetation, water bodies, buildings, etc. These factors not only affect the site selection of new energy facilities, but may also have an important impact on the ecological environment.

[0033] The site selection related information of the area to be planned may also include other information, such as the types, quantity and distribution of animal and plant populations in the area to be planned, and the location information of ecological zones such as nature reserves, scenic spots, water source protection areas, etc. in the area to be planned.

[0034] Step 120, based on the site selection related information of the area to be planned, quantify the impact of meteorological factors on the wind and solar output of the area to be planned by region, and generate a first quantification result.

[0035] In some embodiments, step 120 specifically includes:

[0036] Acquire multiple sets of first sample data, wherein the first sample data includes terrain information, wind and solar power generation equipment information, meteorological information, and wind and solar power generation information of the first sample area, and the first sample data may be real data or experimental data obtained through a physical model;

[0037] Establishing a simulation model according to the multiple groups of first sample data, wherein the simulation model may be a variational autoencoder (VAEs);

[0038] Generate multiple sets of second sample data through the simulation model, wherein the second sample data includes terrain information, wind and solar power generation equipment information, meteorological information and wind and solar power generation information of the second sample area;

[0039] According to the terrain information of each group of first sample data and the terrain information of the multiple groups of second sample data, the multiple groups of first sample data and the multiple groups of second sample data are divided into multiple first sample groups, wherein each first sample group includes at least one group of first sample data and / or second sample data. For example, the multiple groups of first sample data and the multiple groups of second sample data can be divided into multiple first sample groups according to the terrain information of each group of first sample data and the terrain information of the multiple groups of second sample data by using a K-means clustering algorithm;

[0040] For each first sample group, based on each group of first sample data and second sample data included in the first sample group, determine a first key meteorological factor corresponding to the first sample group and a weight corresponding to each first key meteorological factor;

[0041] Establish a site selection assessment system, which includes multiple site selection assessment indicators, such as wind energy resource indicators, solar energy indicators, soil bearing capacity indicators, groundwater level indicators, rock stability indicators, biodiversity impact indicators, etc.;

[0042] Determine multiple candidate sub-areas of the area to be planned based on the site selection evaluation system and relevant information on the site selection of the area to be planned;

[0043] For each candidate sub-region, a similar first sample group is determined from multiple first sample groups based on the terrain information of the candidate sub-region, and the first key meteorological factor corresponding to the candidate sub-region and the weight corresponding to each first key meteorological factor are determined based on the first key meteorological factors corresponding to the similar first sample groups and the weight corresponding to each first key meteorological factor, wherein the first quantization result includes the first key meteorological factor corresponding to each candidate sub-region of the area to be planned and the weight corresponding to each first key meteorological factor.

[0044] Specifically, terrain conditions are a very important factor affecting the site selection of wind and solar power plants. When choosing a site for a wind and solar power plant, it is necessary to find an area without obstructions to ensure full utilization of wind energy. At the same time, geological conditions, such as groundwater level and soil stability, must also be considered when choosing a site to ensure the stability and safety of wind and solar generators.

[0045] Climate conditions are also an important factor affecting the site selection of wind and solar power plants. When choosing a location for a wind and solar power plant, you need to find an area with high wind speed, stable wind direction, strong sunlight, and long duration of sunlight, so as to ensure the working efficiency of wind and solar generators. At the same time, climate conditions will also affect the life of wind and solar generators, so you need to choose an area with moderate temperature, humidity and precipitation.

[0046] Social factors are also an important factor affecting the site selection of wind and solar power plants. When choosing a location for a wind and solar power plant, it is necessary to consider the acceptance of the project by surrounding residents to avoid negative impacts on the lives of local residents. At the same time, land use issues need to be considered when selecting a site to ensure that the wind and solar power plant is built without occupying a large amount of farmland, forest and other land.

[0047] Therefore, considering multiple factors and establishing a site selection evaluation system can screen candidate sub-areas suitable for establishing wind and solar power plants in the planned area.

[0048] The area to be planned can be divided into multiple sub-areas. For each sub-area, the score of the sub-area in multiple site selection evaluation indicators is calculated, and the sub-areas with scores greater than a threshold are taken as candidate sub-areas.

[0049] In some embodiments, based on each group of first sample data and second sample data included in the first sample group, determining the first key meteorological factor corresponding to the first sample group and the weight corresponding to each first key meteorological factor includes:

[0050] For each group of first sample data included in the first sample group, based on the meteorological information and wind-solar power generation information of the first sample data, the correlation coefficient between each meteorological factor corresponding to the first sample data and wind-solar power generation is calculated, wherein the meteorological factor may be a wind speed factor, a wind direction factor, a light factor, a temperature factor, a precipitation factor, an airflow factor, a humidity factor, an air pressure factor, etc.;

[0051] For each group of second sample data included in the first sample group, based on the meteorological information and wind-solar power generation information of the second sample data, a correlation coefficient (for example, a Pearson correlation coefficient) between each meteorological factor corresponding to the second sample data and wind-solar power generation is calculated;

[0052] For each meteorological factor, based on the correlation coefficient between each meteorological factor and wind-solar power generation corresponding to each group of first sample data included in the first sample group and the correlation coefficient between each meteorological factor and wind-solar power generation corresponding to the second sample data, calculate the key parameter corresponding to the meteorological factor, and judge whether the meteorological factor is the first key meteorological factor according to the key parameter corresponding to the meteorological factor. For example, when the key parameter corresponding to the meteorological factor is greater than the key parameter threshold, judge the meteorological factor to be the first key meteorological factor;

[0053] For each first key meteorological factor corresponding to the first sample group, a weight corresponding to the first key meteorological factor is determined based on a key parameter corresponding to each first key meteorological factor corresponding to the first sample group.

[0054] Specifically, the key parameters corresponding to the meteorological factors can be calculated according to the following formula:

[0055] ,

[0056] ,

[0057] in, is the key parameter corresponding to the i-th meteorological factor, , and is the weight, , and greater than 0, , The first sample group includes The correlation coefficient between each meteorological factor and wind and solar power generation corresponding to the first sample data is: is the total number of first sample data included in the first sample group, is the preset parameter, The first sample group includes The correlation coefficient between each meteorological factor and wind and solar power generation corresponding to the second sample data is is the total number of second sample data included in the first sample group, is the preset parameter, greater than 0, is the preset parameter, greater than 0, is the fluctuation parameter corresponding to the i-th meteorological factor, and greater than 0, .

[0058] In some embodiments, the weight corresponding to the first key meteorological factor may be calculated according to the following formula:

[0059] ,

[0060] in, is the weight corresponding to the i-th first key meteorological factor corresponding to the first sample group, is the key parameter corresponding to the jth first key meteorological factor corresponding to the first sample group, is the total number of the first key meteorological factors corresponding to the first sample group.

[0061] Step 130: Determine a resource assessment result of the area to be planned according to the first quantification result.

[0062] In some embodiments, step 130 specifically includes:

[0063] Establishing a basic evaluation model according to the first key meteorological factor corresponding to each first sample group, wherein the basic evaluation model may be a long short-term memory network model;

[0064] For each first sample group, according to the first key meteorological factor corresponding to the first sample group and the basic evaluation model, a resource evaluation model corresponding to the first sample group is established, the first sample group includes first sample data and second sample data, a training sample is generated, and the resource evaluation model corresponding to the first sample group is trained through the training sample, wherein the input of the resource evaluation model includes the first key meteorological factor corresponding to the first sample group, the weight corresponding to each first key meteorological factor, and the meteorological factor characteristic matrix, wherein the meteorological factor characteristic matrix includes a plurality of meteorological factor characteristic vectors, and one meteorological factor characteristic vector corresponds to one first key meteorological factor;

[0065] For each candidate sub-region, based on the first key meteorological factor corresponding to the candidate sub-region, a meteorological factor feature matrix is ​​extracted from the historical meteorological information of the candidate sub-region, and a resource evaluation result of the candidate sub-region is generated based on the first key meteorological factor corresponding to the candidate sub-region, the weight corresponding to each first key meteorological factor and the meteorological factor feature matrix through a resource evaluation model corresponding to a similar first sample group, wherein the resource evaluation result of the area to be planned includes the resource evaluation result of each candidate sub-region.

[0066] Specifically, for each meteorological factor, the necessity parameter corresponding to the meteorological factor can be determined according to the first key meteorological factor corresponding to each first sample group, where: , is the necessary parameter corresponding to the i-th meteorological factor, is the number of first sample groups including the ith meteorological factor corresponding to the first key meteorological factor in the plurality of first sample groups, is the total number of the first sample group. The meteorological factors whose necessity parameters are greater than the necessity parameter threshold are taken as necessary meteorological factors, and a basic evaluation model is established based on the necessary meteorological factors, wherein the input of the basic evaluation model includes the meteorological factor feature vector corresponding to the necessary meteorological factor and the weight corresponding to each necessary meteorological factor.

[0067] Based on the multiple groups of first sample data and the multiple groups of second sample data, multiple groups of first training samples are generated, and the basic evaluation model is trained by the multiple groups of first training samples.

[0068] For each first sample group, the structure of the basic assessment model is fine-tuned according to the first key meteorological factor corresponding to the first sample group to generate a resource assessment model corresponding to the first sample group, and then multiple groups of second training samples are generated based on the first sample data and the second sample data included in the first sample group. The resource assessment model corresponding to the first sample group is trained using the multiple groups of second training samples.

[0069] As an example only, the resource evaluation results of the candidate sub-regions may include:

[0070] (1) Wind energy resource assessment results

[0071] Wind speed distribution: Evaluate the wind speed variation within the candidate sub-areas, including average wind speed, wind speed frequency distribution, etc., which is a key factor in determining the potential of wind power generation.

[0072] Wind power density: The degree of wind energy availability calculated based on wind speed distribution, reflecting the richness of wind energy resources in the candidate sub-region.

[0073] Wind resource stability: Evaluate the stability of wind speed changes. Candidate sub-regions with good stability are conducive to the stable operation of wind power stations.

[0074] (2) Solar energy resource assessment results

[0075] Sunshine duration: The daily sunshine duration in a sub-region is the basic data for evaluating the potential of solar power generation.

[0076] Solar radiation intensity: Evaluates the solar radiation energy received by the candidate sub-region, which directly affects the efficiency of solar power generation.

[0077] Sunlight stability: Analyze the daily and seasonal changes in solar radiation intensity. Stable sunshine conditions are conducive to the stable operation of solar power generation systems.

[0078] Step 140: Determine the target location based on the resource evaluation result of the area to be planned.

[0079] In some embodiments, step 140 specifically includes:

[0080] For each candidate sub-region, based on historical meteorological information of the candidate sub-region, determining a wind-solar complementarity parameter of the candidate sub-region according to a second time scale;

[0081] The target location is determined according to the resource assessment results and the wind-solar complementarity parameters of each candidate sub-area, wherein the second time scale is smaller than the first time scale.

[0082] Specifically, the second time scale can be determined according to the following process:

[0083] S21, initializing the second time scale;

[0084] S22, according to the second time scale, dividing the photovoltaic power generation related meteorological information (for example, solar radiation intensity, etc.) in the historical meteorological information of the candidate sub-region into a plurality of photovoltaic power generation related meteorological information segments, and dividing the wind power generation related meteorological information (for example, wind speed, wind direction, etc.) in the historical meteorological information of the candidate sub-region into a plurality of wind power generation related meteorological information segments;

[0085] S23. For each photovoltaic power generation related meteorological information segment, the standard deviation of solar radiation intensity may be calculated according to the solar radiation intensity at multiple historical time points included in the photovoltaic power generation related meteorological information segment;

[0086] S24. For each wind power generation related meteorological information segment, the wind speed standard deviation may be calculated based on the wind speeds at multiple historical time points included in the wind power generation related meteorological information segment, and the wind direction standard deviation may be calculated based on the wind directions at multiple historical time points included in the wind power generation related meteorological information segment;

[0087] S25, determining whether the solar radiation intensity standard deviation is less than the solar radiation intensity standard deviation threshold, whether the wind speed standard deviation is less than the wind speed standard deviation threshold, and whether the wind direction standard deviation is less than the wind direction standard deviation threshold, if so, outputting the second time scale, if not, executing S26;

[0088] S26. Optimize the second time scale according to the standard deviation of solar radiation intensity, the standard deviation of wind speed and the standard deviation of wind direction, and execute S22.

[0089] For example, the second time scale can be optimized according to the following formula:

[0090] ,

[0091] in, is the second time scale of the k+1th iteration, is the second time scale of the kth iteration, For the preset time length, is the standard deviation of solar radiation intensity at the kth iteration, is the preset standard deviation of solar radiation intensity, is the standard deviation of wind speed at the kth iteration, is the preset wind speed standard deviation, is the standard deviation of wind direction at the kth iteration, is the preset wind direction standard deviation.

[0092] For each candidate sub-region, a consistent wind power device capacity and photovoltaic installed capacity can be preset, and the wind power generation and photovoltaic power generation in multiple historical time periods are predicted according to the second time scale and the historical meteorological information of the candidate sub-region. For each historical time period, when the wind power generation in the historical time period is greater than the wind power generation threshold, the wind power generation flag is set to 1, otherwise it is 0. When the photovoltaic power generation in the historical time period is greater than the photovoltaic power generation threshold, the photovoltaic power generation flag is set to 1, otherwise it is 0, and the wind-solar complementarity parameter of the candidate sub-region is calculated according to the following formula:

[0093] ,

[0094] in, is the wind-solar complementarity parameter of the i-th candidate sub-region, is the total number of historical time periods, is the photovoltaic power generation identifier of the i-th candidate sub-region in the m-th historical time period, is the wind power generation identifier of the i-th candidate sub-region in the m-th historical time period.

[0095] A comprehensive resource scoring system can be established in advance to compare the suitability of different candidate sub-regions. For each candidate sub-region, the comprehensive resource score of the candidate sub-region can be calculated based on the comprehensive resource scoring system and the resource assessment results of the candidate sub-region. Candidate sub-regions with high comprehensive resource scores are more suitable for building wind and solar power stations.

[0096] For each candidate sub-region, the comprehensive resource score and wind-solar complementarity parameter of the candidate sub-region may be weighted and summed to calculate the priority value of the candidate sub-region, and the candidate sub-region with the largest priority value may be used as the target location.

[0097] Step 150, obtaining building related information of the load area.

[0098] Specifically, the building-related information of the load area may include the proportion of various types of buildings (eg, residential buildings, commercial buildings, public facilities, industrial buildings, etc.) in multiple historical time periods.

[0099] Step 160 , quantify the impact of meteorological factors on the load of the load area according to the building related information of the load area, and generate a second quantification result.

[0100] In some embodiments, step 160 specifically includes:

[0101] Acquire multiple sets of third sample data, wherein the third sample data includes building-related information, meteorological factors, and power load information of the third sample area;

[0102] According to the building-related information of each group of third sample data, the multiple groups of third sample data are divided into multiple second sample groups. For example, by using a K-means clustering algorithm, the third sample data with relatively similar proportions are divided into the same second sample group according to the proportions of various types of buildings;

[0103] For each second sample group, based on the meteorological factors and power load information of each group of third sample data included in the second sample group, calculate the correlation coefficient (for example, Pearson correlation coefficient) between each meteorological factor and the power load, and determine the second key meteorological factor corresponding to the second sample group and the weight corresponding to each second key meteorological factor according to the correlation coefficient between each meteorological factor and the power load. Specifically, the weight corresponding to each second key meteorological factor can be determined according to the ratio of the correlation coefficient between each second key meteorological factor and the power load. For example, for any second key meteorological factor, its weight is the ratio of the absolute value of the correlation coefficient between the second key meteorological factor and the power load to the sum of the absolute values ​​of the correlation coefficients of all the second key meteorological factors and the power load.

[0104] Establishing a change prediction model, wherein the change prediction model may be a long short-term memory network model;

[0105] Predicting the building-related information of the load area in the future time period based on the building-related information of the load area at multiple historical time points through the change prediction model, wherein the building-related information of the future time period may include the proportion of various types of buildings in the load area in the future time period;

[0106] For each future time period, a second similar sample group is determined from multiple second sample groups based on the building-related information of the load area at the future time point, and the second key meteorological factors corresponding to the load area in the future time period and the weights corresponding to each second key meteorological factor are determined based on the second key meteorological factors corresponding to the second similar sample group and the weights corresponding to each second key meteorological factor. The second quantification result includes the second key meteorological factors corresponding to the load area in multiple future time periods and the weights corresponding to each second key meteorological factor. For example, a second sample group with similar proportions of various types of buildings can be selected as the second similar sample group.

[0107] Step 170: predicting the power load information of the load area according to the second quantization result.

[0108] In some embodiments, step 170 specifically includes:

[0109] For each future time period, the load forecasting model is used to predict the power load information of the load area in the future time period based on the second key meteorological factors corresponding to the load area in the future time period and the weights corresponding to each second key meteorological factor, the power load information of the load area at multiple historical time points, and the historical meteorological information of the load area. The load forecasting model can be a long short-term memory network model.

[0110] Step 180, generate an initial planning scheme, perform source-load imbalance assessment on the initial planning scheme according to the first time scale based on the power load information of the load area and the historical meteorological information of the target location, generate a source-load imbalance assessment result, and generate a target planning scheme based on the source-load imbalance assessment result, wherein the target planning scheme includes a target wind power device capacity, a target photovoltaic installed capacity, a target thermal power installed capacity and a target energy storage installed capacity.

[0111] Figure 2 is a flow chart of generating a target planning scheme according to some embodiments of this specification, such as Figure 2 As shown, in some embodiments, step 180 specifically includes:

[0112] S11. Use the initial planning scheme as the current planning scheme;

[0113] S12, determining a source-load imbalance assessment scenario according to the first time scale based on the power load information of the load area and the historical meteorological information of the target location, wherein the source-load imbalance assessment scenario may include meteorological information and power load information;

[0114] S13, calculating the scene similarity between the source-load imbalance assessment scene and the source-load imbalance assessment scene that has been assessed, and determining whether the scene similarity is less than the scene similarity threshold. If so, executing S14; if not, executing S12. Specifically, the cosine similarity between the source-load imbalance assessment scene and the source-load imbalance assessment scene that has been assessed can be calculated as the scene similarity based on the meteorological information and power load information of the source-load imbalance assessment scene and the meteorological information and power load information of the source-load imbalance assessment scene that has been assessed;

[0115] S14. According to the current planning scheme, calculate the wind and solar power generation under the source-load imbalance assessment scenario. According to the wind and solar power generation and power load information under the source-load imbalance assessment scenario, calculate the source-load imbalance corresponding to the source-load imbalance assessment scenario. For example only, source-load imbalance = (wind and solar power generation - power load) / power load × 100%;

[0116] S15, determining whether the number of source-load imbalance assessment scenarios that have completed the assessment is greater than the quantity threshold, if so, executing S16, if not, executing S12;

[0117] S16, judging whether to optimize the current planning scheme according to the source-load imbalance corresponding to each completed source-load imbalance assessment scenario, if so, executing S17, if not, taking the current planning scheme as the target planning scheme;

[0118] S17: Optimize the current planning scheme, update the current planning scheme, and execute S12.

[0119] Specifically, the target planning scheme can be further optimized to generate a final planning scheme. For example, the target planning scheme can be further optimized from the perspective of cost and pollution of thermal power generation.

[0120] In some embodiments, optimizing the current planning scheme includes:

[0121] The current planning scheme is optimized according to the source-load imbalance corresponding to the source-load imbalance assessment scenario in which the assessment is completed.

[0122] Specifically, the current planning scheme can be optimized through a scheme optimization model according to the source-load imbalance corresponding to the source-load imbalance evaluation scenario of the current planning scheme after the evaluation is completed, wherein the scheme optimization model can be a convolutional neural network model.

[0123] Figure 3 is a module schematic diagram of a wind, solar, thermal and energy storage integrated collaborative planning system according to some embodiments of this specification, such as Figure 3 As shown, the integrated wind, solar, thermal and storage collaborative planning system can include a site optimization module, a load forecasting module and a collaborative planning module.

[0124] The site selection optimization module can be used to obtain site selection related information of the area to be planned, and based on the site selection related information of the area to be planned, wherein the site selection related information at least includes the terrain information of the area to be planned, quantify the impact of meteorological factors on the wind and solar output of the area to be planned by region, generate a first quantified result, determine the resource assessment result of the area to be planned based on the first quantified result, and determine the target location based on the resource assessment result of the area to be planned.

[0125] The load prediction module can be used to obtain building-related information in the load area, quantify the impact of meteorological factors on the load in the load area based on the building-related information in the load area, generate a second quantification result, and predict the power load information of the load area based on the second quantification result.

[0126] The collaborative planning module can be used to generate an initial planning scheme. According to the power load information of the load area and the historical meteorological information of the target location, the source-load imbalance of the initial planning scheme is evaluated according to the first time scale to generate a source-load imbalance evaluation result. According to the source-load imbalance evaluation result, a target planning scheme is generated, wherein the target planning scheme includes the target wind power device capacity, the target photovoltaic installed capacity, the target thermal power installed capacity and the target energy storage installed capacity.

[0127] The integrated wind, solar, thermal and storage collaborative planning system can be used to implement the integrated wind, solar, thermal and storage collaborative planning method. For more descriptions of the integrated wind, solar, thermal and storage collaborative planning system, please refer to the relevant descriptions of the integrated wind, solar, thermal and storage collaborative planning method, which will not be repeated here.

[0128] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A wind, solar, thermal and energy storage integrated collaborative planning method, characterized in that: include: Acquire site selection related information of the area to be planned, wherein the site selection related information at least includes terrain information of the area to be planned; According to the site selection related information of the area to be planned, quantify the influence of meteorological factors on the wind and solar power output of the area to be planned by region, and generate a first quantification result; Determining a resource assessment result of the area to be planned according to the first quantification result; Determining a target location based on a resource assessment result of the area to be planned; Obtain building-related information for the load area; quantifying the impact of meteorological factors on the load of the load area according to the building related information of the load area, and generating a second quantification result; predicting the power load information of the load area according to the second quantization result; Generate an initial planning scheme, perform a source-load imbalance assessment on the initial planning scheme according to the power load information of the load area and the historical meteorological information of the target location according to a first time scale, generate a source-load imbalance assessment result, and generate a target planning scheme according to the source-load imbalance assessment result, wherein the target planning scheme includes a target wind power device capacity, a target photovoltaic installed capacity, a target thermal power installed capacity and a target energy storage installed capacity, specifically including: S11, taking the initial planning scheme as the current planning scheme; S12, determining a source-load imbalance assessment scenario according to the power load information of the load area and the historical meteorological information of the target location at a first time scale; S13, calculating the scene similarity between the source-load imbalance assessment scene and the source-load imbalance assessment scene that has completed the assessment, and determining whether the scene similarity is less than the scene similarity threshold, if so, executing S14, if not, executing S12; S14, according to the current planning scheme, calculating the wind and solar power generation under the source-load imbalance assessment scenario, and calculating the source-load imbalance corresponding to the source-load imbalance assessment scenario according to the wind and solar power generation and power load information under the source-load imbalance assessment scenario; S15, determining whether the number of source-load imbalance assessment scenarios that have completed the assessment is greater than the quantity threshold, if so, executing S16, if not, executing S12; S16, judging whether to optimize the current planning scheme according to the source-load imbalance corresponding to each completed source-load imbalance assessment scenario, if so, executing S17, if not, taking the current planning scheme as the target planning scheme; S17: Optimize the current planning scheme, update the current planning scheme, and execute S12.

2. The wind, solar, thermal and energy storage integrated collaborative planning method according to claim 1 is characterized in that: According to the site selection related information of the area to be planned, the influence of meteorological factors on the wind and solar output of the area to be planned is quantified by region to generate a first quantification result, including: Acquire multiple groups of first sample data, wherein the first sample data includes terrain information, wind and solar power generation equipment information, meteorological information, and wind and solar power generation information of a first sample area; Establishing a simulation model according to the plurality of groups of first sample data; Generate multiple sets of second sample data through the simulation model, wherein the second sample data includes terrain information, wind and solar power generation equipment information, meteorological information and wind and solar power generation information of the second sample area; According to the terrain information of each group of the first sample data and the terrain information of the multiple groups of the second sample data, the multiple groups of the first sample data and the multiple groups of the second sample data are divided into a plurality of first sample groups, wherein each of the first sample groups includes at least one group of the first sample data and / or the second sample data; For each of the first sample groups, based on each group of first sample data and second sample data included in the first sample group, determining a first key meteorological factor corresponding to the first sample group and a weight corresponding to each of the first key meteorological factors; Establishing a site selection evaluation system, wherein the site selection evaluation system includes a plurality of site selection evaluation indicators; Determining a plurality of candidate sub-areas of the area to be planned according to the site selection evaluation system and the site selection related information of the area to be planned; For each of the candidate sub-areas, a similar first sample group is determined from the multiple first sample groups based on the terrain information of the candidate sub-area, and the first key meteorological factor corresponding to the candidate sub-area and the weight corresponding to each of the first key meteorological factors are determined based on the first key meteorological factors corresponding to the similar first sample groups and the weight corresponding to each of the first key meteorological factors, wherein the first quantization result includes the first key meteorological factor corresponding to each of the candidate sub-areas of the area to be planned and the weight corresponding to each of the first key meteorological factors.

3. The wind, solar, thermal and energy storage integrated collaborative planning method according to claim 2 is characterized in that: Determining, based on each group of first sample data and second sample data included in the first sample group, a first key meteorological factor corresponding to the first sample group and a weight corresponding to each of the first key meteorological factors, including: For each group of first sample data included in the first sample group, based on the meteorological information and wind-solar power generation information of the first sample data, calculating the correlation coefficient between each meteorological factor corresponding to the first sample data and wind-solar power generation; For each group of second sample data included in the first sample group, based on the meteorological information and wind-solar power generation information of the second sample data, calculating the correlation coefficient between each meteorological factor corresponding to the second sample data and wind-solar power generation; For each of the meteorological factors, based on the correlation coefficient between each meteorological factor corresponding to each group of the first sample data included in the first sample group and wind-solar power generation and the correlation coefficient between each meteorological factor corresponding to the second sample data and wind-solar power generation, calculate the key parameter corresponding to the meteorological factor, and determine whether the meteorological factor is a first key meteorological factor according to the key parameter corresponding to the meteorological factor; For each first key meteorological factor corresponding to the first sample group, a weight corresponding to the first key meteorological factor is determined based on a key parameter corresponding to each first key meteorological factor corresponding to the first sample group.

4. The wind, solar, thermal and energy storage integrated collaborative planning method according to claim 3 is characterized in that: Determining a resource assessment result of the area to be planned according to the first quantification result includes: Establishing a basic assessment model according to the first key meteorological factor corresponding to each group of the first sample groups; For each of the first sample groups, a resource evaluation model corresponding to the first sample group is established according to the first key meteorological factor corresponding to the first sample group and the basic evaluation model, the first sample group includes first sample data and second sample data, a training sample is generated, and the resource evaluation model corresponding to the first sample group is trained by the training sample, wherein the input of the resource evaluation model includes the first key meteorological factor corresponding to the first sample group, the weight corresponding to each of the first key meteorological factors, and a meteorological factor feature matrix; For each of the candidate sub-regions, based on the first key meteorological factor corresponding to the candidate sub-region, a meteorological factor feature matrix is ​​extracted from the historical meteorological information of the candidate sub-region, and a resource evaluation result of the candidate sub-region is generated based on the first key meteorological factor corresponding to the candidate sub-region, the weight corresponding to each of the first key meteorological factors, and the meteorological factor feature matrix through the resource evaluation model corresponding to the similar first sample group, wherein the resource evaluation result of the area to be planned includes the resource evaluation result of each candidate sub-region.

5. The wind, solar, thermal and energy storage integrated collaborative planning method according to claim 4 is characterized in that: Based on the resource assessment results of the area to be planned, determine the target location, including: For each of the candidate sub-regions, based on the historical meteorological information of the candidate sub-region, determining the wind-solar complementarity parameter of the candidate sub-region according to the second time scale; The target location is determined according to the resource evaluation result and the wind-solar complementarity parameter of each candidate sub-area, wherein the second time scale is smaller than the first time scale.

6. The wind, solar, thermal and energy storage integrated collaborative planning method according to any one of claims 2 to 5, characterized in that: Quantifying the impact of meteorological factors on the load of the load area according to the building related information of the load area to generate a second quantification result includes: Acquire multiple sets of third sample data, wherein the third sample data includes building-related information, meteorological factors, and power load information of the third sample area; According to the building related information of each group of third sample data, the plurality of groups of third sample data are divided into a plurality of second sample groups; For each of the second sample groups, based on the meteorological factors and power load information of each group of third sample data included in the second sample group, calculate the correlation coefficient between each meteorological factor and the power load, and determine the second key meteorological factor corresponding to the second sample group and the weight corresponding to each of the second key meteorological factors according to the correlation coefficient between each meteorological factor and the power load; Build change prediction models; Predicting the building-related information of the load area in a future time period according to the building-related information of the load area at multiple historical time points by using the change prediction model; For each of the future time periods, a second similar sample group is determined from the multiple second sample groups based on the building-related information of the load area in the future time period, and the second key meteorological factor corresponding to the load area in the future time period and the weight corresponding to each of the second key meteorological factors are determined based on the second key meteorological factors corresponding to the second similar sample group and the weight corresponding to each of the second key meteorological factors, wherein the second quantification result includes the second key meteorological factors corresponding to the load area in multiple future time periods and the weight corresponding to each of the second key meteorological factors.

7. The wind, solar, thermal and energy storage integrated collaborative planning method according to claim 6 is characterized in that: Predicting the power load information of the load area according to the second quantization result includes: For each of the future time periods, the load forecasting model is used to predict the power load information of the load area in the future time period based on the second key meteorological factors corresponding to the load area in the future time period and the weights corresponding to each of the second key meteorological factors, the power load information of the load area at multiple historical time points, and the historical meteorological information of the load area.

8. The wind, solar, thermal and energy storage integrated collaborative planning method according to claim 1 is characterized in that: Optimize the current planning scheme, including: The current planning scheme is optimized according to the source-load imbalance corresponding to the source-load imbalance assessment scenario in which the assessment is completed.

9. The wind, solar, thermal and energy storage integrated collaborative planning system is characterized by: The wind, solar, thermal and storage integrated collaborative planning method according to any one of claims 1 to 8 is applied, comprising: A site selection optimization module is used to obtain site selection related information of the area to be planned, quantify the influence of meteorological factors on the wind and solar output of the area to be planned by region according to the site selection related information of the area to be planned, wherein the site selection related information at least includes the terrain information of the area to be planned, generate a first quantification result, determine the resource evaluation result of the area to be planned according to the first quantification result, and determine the target location based on the resource evaluation result of the area to be planned; A load prediction module, used to obtain building-related information of a load area, quantify the impact of meteorological factors on the load of the load area according to the building-related information of the load area, generate a second quantification result, and predict the power load information of the load area according to the second quantification result; The collaborative planning module is used to generate an initial planning scheme, and according to the power load information of the load area and the historical meteorological information of the target location, the source-load imbalance assessment is performed on the initial planning scheme according to the first time scale to generate a source-load imbalance assessment result, and according to the source-load imbalance assessment result, a target planning scheme is generated, wherein the target planning scheme includes a target wind power device capacity, a target photovoltaic installed capacity, a target thermal power installed capacity and a target energy storage installed capacity.

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