Distributed wind-solar resource capacity planning method and terminal

Abstract

The application discloses a distributed wind-solar resource capacity planning method and a terminal, and the method comprises the following steps: obtaining historical meteorological data of a target area where wind-solar equipment needs to be constructed; determining a predicted wind-solar power curve of the target area according to the historical meteorological data and an output principle of the wind-solar equipment; performing cluster analysis on the predicted wind-solar power curve to obtain a predicted typical day wind-solar power curve; constructing a double-layer optimization model for wind-solar resource capacity planning, wherein an upper-layer optimization model plans wind-solar equipment capacity with the minimum wind-solar resource annual investment cost as the target, and a lower-layer optimization model plans power distribution network operation state with the minimum total power distribution network operation cost as the target based on the typical day predicted wind-solar power curve; and coupling and solving the double-layer optimization model to obtain an optimal configuration result of the wind-solar resource capacity of the target area. The application can improve the economy and reliability of wind-solar resource capacity planning.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a distributed wind and solar resource capacity planning method and terminal. Background Technology

[0002] With the development of new power distribution systems with high penetration rates of new energy sources, a large-scale distributed wind and solar power system will be connected to the distribution system. In this context, if the wind and solar capacity is not rationally planned "according to local conditions" and coordinated with other resources within the distribution area, a series of problems will arise. Over-planning of wind and solar capacity will lead to a waste of new energy resources, while under-planning will make it difficult to achieve the construction goals of the new power system. Therefore, when planning wind and solar capacity in a certain area, it is necessary to combine it with the actual local conditions and conduct production simulations on a certain number of typical days to ensure that the planned capacity matches the operating scenario of the power system in that area.

[0003] However, for some planning areas, due to the lack of historical wind and solar power output data, it is impossible to estimate the typical wind and solar power output scenarios in the area. This leads to a certain degree of blindness in the construction of wind and solar facilities in the area, and thus cannot guarantee the economic efficiency of wind and solar construction capacity. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a distributed wind and solar resource capacity planning method and terminal, which can realize the rational allocation of wind and solar resource capacity in areas lacking historical wind and solar output data, thereby achieving a balance between the economy and reliability of wind and solar resource planning.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A distributed wind and solar resource capacity planning method includes:

[0007] Obtain historical meteorological data for the target area where wind and solar equipment needs to be built;

[0008] The predicted wind and solar power curve for the target area is determined based on the historical meteorological data and the power output principle of the wind and solar equipment.

[0009] Cluster analysis was performed on the predicted wind and solar power curves to obtain the predicted wind and solar power curves for typical days;

[0010] A two-layer optimization model for wind and solar resource capacity planning is constructed. The upper-layer optimization model plans the capacity of wind and solar equipment with the goal of minimizing the annual investment cost of wind and solar resources. The lower-layer optimization model plans the operation status of the distribution network based on the predicted wind and solar power curves of the typical day with the goal of minimizing the total operating cost of the distribution network.

[0011] The optimal configuration result of wind and solar resource capacity in the target area is obtained by coupling the solution of the two-layer optimization model.

[0012] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0013] A distributed wind and solar resource capacity planning terminal includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the various steps in the aforementioned distributed wind and solar resource capacity planning method.

[0014] The beneficial effects of this invention are as follows: For target areas lacking historical wind and solar power output data, historical meteorological data is acquired for the region, and based on the power output principles of wind and solar equipment, this data is converted into predicted wind and solar power curves suitable for capacity planning. Simultaneously, cluster analysis is performed on these predicted wind and solar power curves to determine the predicted wind and solar power curves for typical days. Based on the power output under typical day scenarios, a lower-level optimization model is constructed with the objective of minimizing the total operating cost of the distribution network, while an upper-level optimization model is constructed with the objective of minimizing the annual investment cost of wind and solar resources. The upper-level and lower-level optimization models are coupled and influence each other during the solution process, thereby obtaining the optimal configuration result for wind and solar resource capacity. This invention enables precise planning of wind and solar equipment directly based on historical meteorological data, solving the problem of blind planning in areas lacking historical wind and solar data, and improving the economy and reliability of wind and solar resource capacity configuration. Attached Figure Description

[0015] Figure 1 A flowchart illustrating the steps of a distributed wind and solar resource capacity planning method provided in this embodiment of the invention;

[0016] Figure 2 This is a schematic diagram illustrating the coupling of a two-layer optimization model provided in an embodiment of the present invention;

[0017] Figure 3 A flowchart for determining the predicted typical daily wind and solar power curve is provided as an embodiment of the present invention;

[0018] Figure 4 This is a schematic diagram of the structure of a distributed wind and solar resource capacity planning terminal provided in an embodiment of the present invention;

[0019] Label Explanation:

[0020] 300. A distributed wind and solar resource capacity planning terminal; 301. Memory; 302. Processor. Detailed Implementation

[0021] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0022] Embodiments of the present invention provide a distributed wind and solar resource capacity planning method, comprising:

[0023] Obtain historical meteorological data for the target area where wind and solar equipment needs to be built;

[0024] The predicted wind and solar power curve for the target area is determined based on the historical meteorological data and the power output principle of the wind and solar equipment.

[0025] Cluster analysis was performed on the predicted wind and solar power curves to obtain the predicted wind and solar power curves for typical days;

[0026] A two-layer optimization model for wind and solar resource capacity planning is constructed. The upper-layer optimization model plans the capacity of wind and solar equipment with the goal of minimizing the annual investment cost of wind and solar resources. The lower-layer optimization model plans the operation status of the distribution network based on the predicted wind and solar power curves of the typical day with the goal of minimizing the total operating cost of the distribution network.

[0027] The optimal configuration result of wind and solar resource capacity in the target area is obtained by coupling the solution of the two-layer optimization model.

[0028] As described above, the beneficial effects of this invention are as follows: For target areas lacking historical wind and solar power output data, historical meteorological data for the region is acquired, and based on the power output principles of wind and solar equipment, this historical meteorological data is converted into predicted wind and solar power curves suitable for capacity planning. Simultaneously, cluster analysis is performed on the predicted wind and solar power curves to determine the predicted wind and solar power curves for typical days. Based on the power under typical day scenarios, a lower-level optimization model is constructed with the objective of minimizing the total operating cost of the distribution network, and an upper-level optimization model is constructed with the objective of minimizing the annual investment cost of wind and solar resources. The upper-level and lower-level optimization models are coupled and influence each other during the solution process, thereby obtaining the optimal configuration result for wind and solar resource capacity. This invention enables precise planning of wind and solar equipment directly based on historical meteorological data, solving the problem of blind planning in areas lacking historical wind and solar data, and improving the economy and reliability of wind and solar resource capacity configuration.

[0029] Furthermore, obtaining historical meteorological data for the target area where wind and solar power equipment needs to be constructed includes:

[0030] Obtain regional data for the target area where wind and solar equipment needs to be built;

[0031] Determine the spatial resolution and temporal resolution based on the regional data;

[0032] Historical meteorological data is downloaded from a preset meteorological analysis database based on the spatial and temporal resolution.

[0033] As described above, ERA5 reanalysis meteorological data covers global historical meteorological data and can simultaneously download meteorological data strongly correlated with wind and solar power output, such as wind speed components and solar radiation, providing a clear and intuitive reflection of the region's wind and solar resource endowment. Accurate acquisition of corresponding meteorological data based on regional data of the target area improves the accuracy and reliability of subsequent calculations.

[0034] Furthermore, the wind and solar equipment includes wind turbine equipment and photovoltaic equipment; the predicted wind and solar power curve includes a predicted unit wind turbine power curve and a predicted unit photovoltaic power curve;

[0035] The step of determining the predicted wind and solar power curve for the target area based on the historical meteorological data and the power output principle of the wind and solar equipment includes:

[0036] Obtain the equipment models of the planned wind turbine and photovoltaic equipment, and determine the equipment parameters based on the equipment models;

[0037] Based on the equipment parameters and output principles of the wind turbine and photovoltaic equipment, as well as the historical meteorological data, the predicted unit wind turbine power curve and the predicted unit photovoltaic power curve for the target area are determined respectively.

[0038] As described above, given the models of wind turbines and photovoltaic equipment that need to be planned and constructed in the current target area, the corresponding equipment parameters can be determined based on the equipment models. Thus, the power that the equipment can provide under the wind and solar resources in the area can be estimated based on the accurate equipment parameters. This can then be used as historical wind and solar power output data for subsequent model planning, avoiding the blindness of regional planning in areas lacking wind and solar power output data.

[0039] Furthermore, the step of performing cluster analysis on the predicted wind and solar power curves to obtain the predicted typical day wind and solar power curves includes:

[0040] Obtain the number of clusters for typical daytime landscape scenes;

[0041] Based on the number of clusters, the predicted wind and solar power curves are clustered using the K-means algorithm to obtain multiple predicted daily wind and solar power curves corresponding to each cluster.

[0042] Based on the Pearson correlation coefficient, the predicted typical daily wind and solar power curves are determined according to the multiple predicted daily power curves.

[0043] As described above, cluster analysis is used to obtain the predicted wind and solar power curves per unit installed capacity under typical days. Typical days cover days in different seasons to reflect the impact of seasonal changes on grid load and energy output. Based on typical day scenarios, the planning and operation of the target area are optimized to improve the rationality and economy of wind and solar equipment planning under different seasons.

[0044] Furthermore, the coupled solution of the two-layer optimization model to obtain the optimal configuration result of the wind and solar resource capacity of the target area includes:

[0045] Construct distributed power generation capacity constraints and distribution network line power flow constraints, and solve the upper-level optimization model based on the distributed power generation capacity constraints and the distribution network line power flow constraints to obtain the wind and solar equipment capacity;

[0046] The constraints of energy storage system, wind and solar power output, distribution network outlet characteristics, and distribution network power flow are constructed. Based on the capacity of wind and solar equipment, the constraints of energy storage system, wind and solar power output, distribution network outlet characteristics, and distribution network power flow, the lower-level optimization model is solved to obtain the power flow results of the distribution network. The power flow results of the distribution network are then fed back to the upper-level optimization model.

[0047] The optimal configuration result of the wind and solar resource capacity of the target area is determined based on the solution results of the upper-level optimization model and the lower-level optimization model.

[0048] As described above, the upper and lower optimization models are coupled during the solution process. The wind and solar power capacity obtained from the solution of the upper optimization model is input into the lower optimization model, while the power flow results of the distribution network obtained from the solution of the lower optimization model are fed back to the upper optimization model, thus achieving comprehensive optimization of the annual investment cost of wind and solar resources and the operation of the distribution network.

[0049] Furthermore, the construction of the two-layer optimization model for wind and solar resource capacity planning includes an upper-layer optimization model that plans the capacity of wind and solar equipment with the objective of minimizing the annual investment cost of wind and solar resources, and a lower-layer optimization model that plans the operation status of the distribution network based on the predicted wind and solar power curves for typical days with the objective of minimizing the total operating cost of the distribution network.

[0050] The upper-level optimization model is constructed with the objective function of minimizing the annual investment cost of wind and solar resources, as follows:

[0051] ;

[0052] ;

[0053] ;

[0054] in, This indicates the annual investment cost of wind and solar resources. This indicates the investment cost of the wind turbine equipment. This indicates the investment cost of photovoltaic equipment. N wind Indicates the number of wind turbine units. R wind This represents the present value to equivalent annual value factor for wind turbine equipment. S wind This indicates the investment cost required per unit capacity of the wind turbine. This indicates the maintenance cost of the wind turbine equipment. This represents the installed capacity of the i-th fan unit. R pv This represents the present value to equivalent annual value factor for photovoltaic equipment. N pv Indicates the number of photovoltaic devices. S pv This indicates the investment cost required per unit capacity of photovoltaic power. Indicates the maintenance cost of photovoltaic equipment. This represents the installed capacity of the i-th photovoltaic device;

[0055] Based on the typical daily predicted wind and solar power curves, a lower-level optimization model is constructed with the objective function of minimizing the total operating cost of the distribution network, specifically as follows:

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] in, This represents the total operating cost of the power distribution network. This represents the operating cost of a distribution network under a typical daily rate. This represents the electricity purchase cost of a typical daily distribution network from its upstream power grid. This represents the probability corresponding to a typical day r. This represents the unit cost of electricity purchased by the distribution network from the upstream power grid. This represents the interaction power between the distribution network and the upstream power grid. , as well as These represent the typical daily operating costs of wind turbines, photovoltaic power plants, and energy storage power stations, respectively. This represents the planned power output of wind turbine equipment i during time period t under a typical day r. This represents the operating cost coefficient of the wind turbine. This represents the unit cost coefficient for wind curtailment. This represents the maximum allowable power of wind turbine equipment i during time period t. This represents the planned power output of photovoltaic equipment i during time period t under a typical day r. Indicates the photovoltaic operating cost coefficient. This represents the unit photovoltaic curtailment cost coefficient. This represents the maximum allowable power of photovoltaic device i during time period t.

[0062] As can be seen from the above description, since the upper-level optimization model plans the capacity of wind and solar equipment with the goal of minimizing the annual investment cost of wind and solar resources, and the lower-level optimization model plans the operation status of the distribution network with the goal of minimizing the total operating cost of the distribution network, and the upper and lower-level optimization models are coupled with each other in the solution process, the economy and reliability of wind and solar resource capacity allocation are improved at the same time.

[0063] Furthermore, the historical meteorological data includes historical average wind speed and historical solar radiation.

[0064] The specific steps for determining the predicted unit wind turbine power curve and the predicted unit photovoltaic power curve for the target area based on the equipment parameters and output principles of the wind turbine and photovoltaic equipment, as well as the historical meteorological data, are as follows:

[0065] ;

[0066] ;

[0067] in, This represents the predicted unit wind turbine power curve. Indicates air density, This represents the historical average wind speed. D , , as well as These are all equipment parameters. D Indicates the diameter of the fan. Indicates leaf surface efficiency. as well as These represent the conversion efficiencies of the generator and the bearing, respectively. SP t This represents the predicted unit photovoltaic power curve. This represents the amount of solar radiation during time period t. H STC This represents the amount of solar radiation under standard test conditions. P sr Indicates the rated power of photovoltaic equipment. c 1 represents the degradation coefficient of photovoltaic equipment. c2 represents the power temperature coefficient of photovoltaic equipment. Indicates the temperature of the photovoltaic equipment. T STC This indicates the temperature under standard test conditions.

[0068] As described above, given the known equipment parameters of wind and solar power equipment, wind and solar power can be estimated based on historical meteorological data, yielding wind and solar power curves for different times and locations. This effectively fills the gap in historical wind and solar data for the selected planning area, thus avoiding the problem of difficulty in conducting production simulation assessments. It provides strong support for the assessment of wind and solar resource endowment and the rational planning of equipment capacity in the region.

[0069] Furthermore, the predicted daily power curve includes a predicted daily wind power curve and a predicted daily photovoltaic power curve; the predicted typical daily wind and solar power curve includes a predicted typical daily wind power curve and a predicted typical daily photovoltaic power curve.

[0070] The method of determining the predicted typical daily wind and solar power curves based on the Pearson correlation coefficient and the multiple predicted daily power curves includes:

[0071] Based on the Pearson correlation coefficient, the predicted typical daily wind power curve is determined according to the predicted daily wind power curve, specifically as follows:

[0072] ;

[0073] ;

[0074] in, This represents the predicted typical daily wind power output curve for the i-th type of wind power output scenario. Let represent the Pearson correlation coefficient between the predicted daily wind power curve and the predicted typical daily wind power curve under the i-th wind power output scenario, and let cov(x,y) represent the covariance. Indicates the first i The first in wind power output scenarios l A predicted daily wind power curve, The standard deviation of each wind power curve is represented by E, and E represents the expectation operation symbol when calculating covariance. This represents the expected value of each wind power output. This represents the power value of the predicted daily wind power curve for the i-th wind power output scenario at time t. Indicates the first i The predicted typical daily wind power output curve for wind-like power output scenarios is in t Output value at time j, where j represents the output value at time j. i Number of predicted daily wind power output curves in wind power output scenarios;

[0075] Based on the Pearson correlation coefficient, a typical daily photovoltaic power curve is determined according to the predicted daily photovoltaic power curve, specifically as follows:

[0076] ;

[0077] ;

[0078] in, This represents the predicted typical daily photovoltaic power curve for the x-th photovoltaic power output scenario. This represents the Pearson correlation coefficient between the predicted daily photovoltaic power curve and the predicted typical daily photovoltaic power curve under the x-th photovoltaic power output scenario. Indicates the xth photovoltaic power output scenario. l A predicted daily photovoltaic power curve; This represents the predicted typical daily photovoltaic power curve for the x-th type of photovoltaic output scenario. t Constant output value; This represents the number of predicted daily photovoltaic power curves within the x-th photovoltaic output scenario.

[0079] As described above, when implementing the two-level optimization model for wind and solar capacity planning, the predicted typical day wind and solar power curves obtained after processing with the Pearson correlation coefficient enable the typical days in the lower-level optimization model to more accurately reflect the wind and solar power output of the selected planning area, thereby obtaining more reliable capacity planning results.

[0080] Furthermore, the distributed power supply capacity constraint is specifically as follows:

[0081] ;

[0082] in, and These represent the lower and upper limits of the total installed capacity of distributed power sources for wind and solar power systems, respectively. This indicates the total installed capacity of distributed power sources for wind and solar power equipment.

[0083] The power flow constraints of the distribution network lines are specifically as follows:

[0084] ;

[0085] Where i and j represent the two nodes on both sides of the distribution line ij, respectively. Indicates line current The square of, Indicates node voltage The square of, This represents the active power of line ij. This indicates the reactive power of line ij. , These represent the upper and lower limits of the square of the node voltage, respectively. Indicates the upper limit of the line current;

[0086] The specific constraints of the energy storage system are as follows:

[0087] ;

[0088] in, S es,i,t This represents the state of charge of energy storage system i at time t. and Let represent the charging power and discharging power of energy storage system i at time t, respectively. and These represent the charging efficiency and discharging efficiency of the energy storage system, respectively. E es, i Indicates the capacity of the energy storage system. S es,max and S es,min These represent the upper and lower limits of the state of charge of the energy storage system, respectively. S es,end This indicates the state of charge of the energy storage system at the last moment of a typical day. S es,max，end and S es,min,end These represent the upper and lower limits of the state of charge of the energy storage system at the last moment of a typical day, respectively.

[0089] The specific constraints on wind and solar power output are as follows:

[0090] ;

[0091] ;

[0092] in, This represents the planned wind power output on a typical day. This represents the planned photovoltaic power output under a typical day. This represents the maximum allowable power of wind turbine equipment i during time period t. This represents the maximum allowable power of photovoltaic device i during time period t;

[0093] The specific constraints on the distribution network output characteristics are as follows:

[0094] ;

[0095] ;

[0096] in, This indicates the power that the distribution network purchases from the upstream power grid. and These represent the discharge power and charging power of energy storage system i during time period t, respectively. and These represent the lower and upper limits of the power that the distribution network purchases from the upper-level power grid, respectively.

[0097] The specific steps for constructing power flow constraints in the distribution network are as follows:

[0098] ;

[0099] in, This represents the active power of node j. This represents the reactive power at node j. This indicates the active power of line jk. Let j represent the set of routes starting from j. Let j represent the set of routes ending at point j. This represents the square of the line current value ij. This indicates the reactive power of line jk. This represents the square of the voltage value at node j. and These represent the resistance and reactance of line ij, respectively.

[0100] As described above, solving the bi-level optimization model based on constraints under different objectives can fully optimize investment and operating costs while ensuring the safe and stable operation of the system. This minimizes the impact of the current planning configuration on the system, achieves optimal economy, and improves the reliability of the system configuration.

[0101] Another embodiment of the present invention provides a distributed wind and solar resource capacity planning terminal, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the various steps in the above-mentioned distributed wind and solar resource capacity planning method.

[0102] As described above, the beneficial effects of this invention are as follows: For target areas lacking historical wind and solar power output data, historical meteorological data for the region is acquired, and based on the power output principles of wind and solar equipment, this historical meteorological data is converted into predicted wind and solar power curves suitable for capacity planning. Simultaneously, cluster analysis is performed on the predicted wind and solar power curves to determine the predicted wind and solar power curves for typical days. Based on the power under typical day scenarios, a lower-level optimization model is constructed with the objective of minimizing the total operating cost of the distribution network, and an upper-level optimization model is constructed with the objective of minimizing the annual investment cost of wind and solar resources. The upper-level and lower-level optimization models are coupled and influence each other during the solution process, thereby obtaining the optimal configuration result for wind and solar resource capacity. This invention enables precise planning of wind and solar equipment directly based on historical meteorological data, solving the problem of blind planning in areas lacking historical wind and solar data, and improving the economy and reliability of wind and solar resource capacity configuration.

[0103] This invention provides a distributed wind and solar resource capacity planning method and terminal, which can be applied to wind and solar resource planning in a designated area. For areas lacking historical wind and solar output data, it can achieve reasonable allocation of wind and solar resource capacity, thereby achieving a balance between the economy and reliability of wind and solar resource planning. The following specific embodiments illustrate this:

[0104] Please refer to Figures 1 to 2 Embodiment 1 of the present invention is as follows:

[0105] A distributed wind and solar resource capacity planning method includes:

[0106] S1. Obtain historical meteorological data for the target area where wind and solar equipment needs to be built.

[0107] Specifically, step S1 includes:

[0108] S11. Obtain regional data of the target area where wind and solar equipment needs to be built.

[0109] In some embodiments, the regional data includes the area size of the target region and its geographic location information, wherein the geographic location information is the latitude and longitude information of the target region.

[0110] S12. Determine the spatial resolution and temporal resolution based on the regional data.

[0111] S13. Download historical meteorological data from a preset meteorological analysis database according to the spatial resolution and temporal resolution.

[0112] In some embodiments, to assess the wind power output of a selected target area, the downloaded historical meteorological data should include the air pressure, temperature, horizontal wind speed component at a height of 100 meters, and vertical wind speed component of the target area. To assess the photovoltaic output of a selected target area, the downloaded historical meteorological data should also include the solar radiation of the target area.

[0113] In some embodiments, the preset meteorological analysis database is the ERA5 atmospheric reanalysis tool of the European Centre for Numerical Weather Prediction (ECMWF). After selecting the target area where wind and solar equipment needs to be built, an appropriate spatial resolution (longitude × latitude) and temporal resolution (a certain time period) are selected based on the area size and geographical location information of the target area. Historical meteorological data of the corresponding resolution grid are downloaded from the ERA5 reanalysis meteorological data according to the spatial and temporal resolutions.

[0114] In some embodiments, Fujian Province is selected as the target area, with a spatial resolution of 0.25° × 0.25° (longitude × latitude) and a temporal resolution of 1 hour. Based on this resolution, the ERA5 reanalysis meteorological database is accessed using Python to download historical meteorological data for typical months of different seasons in Fujian Province in 2022. Specifically, relevant data for typical months of four seasons are selected for wind and solar resource analysis.

[0115] S2. Determine the predicted wind and solar power curve for the target area based on the historical meteorological data and the power output principle of the wind and solar equipment.

[0116] S3. Perform cluster analysis on the predicted wind and solar power curves to obtain the predicted typical day wind and solar power curves.

[0117] S4. Construct a two-layer optimization model for wind and solar resource capacity planning. The upper-layer optimization model plans the capacity of wind and solar equipment with the goal of minimizing the annual investment cost of wind and solar resources. The lower-layer optimization model plans the operation status of the distribution network based on the predicted wind and solar power curves of the typical day with the goal of minimizing the total operating cost of the distribution network.

[0118] Specifically, step S4 includes:

[0119] S41. Construct an upper-level optimization model with the objective function of minimizing the annual investment cost of wind and solar resources, specifically as follows:

[0120] (1)

[0121] (2)

[0122] (3)

[0123] in, This indicates the annual investment cost of wind and solar resources. This indicates the investment cost of the wind turbine equipment. This indicates the investment cost of photovoltaic equipment. N wind Indicates the number of wind turbine units. R wind This represents the present value to equivalent annual value factor for wind turbine equipment. S wind This indicates the investment cost required per unit capacity of the wind turbine. This indicates the maintenance cost of the wind turbine equipment. This represents the installed capacity of the i-th fan unit. R pv This represents the present value to equivalent annual value factor for photovoltaic equipment. N pv Indicates the number of photovoltaic devices. Spv This indicates the investment cost required per unit capacity of photovoltaic power. Indicates the maintenance cost of photovoltaic equipment. This represents the installed capacity of the i-th photovoltaic device.

[0124] In some embodiments, the present value to equivalent annual value factor for wind turbine equipment and photovoltaic equipment can be calculated using formula (4):

[0125] (4)

[0126] in, R This represents the present value to equivalent annual value factor for wind turbine or photovoltaic equipment. y Indicates the service life of wind turbine or photovoltaic equipment. d This represents the discount rate.

[0127] It should be noted that the upper-level optimization model mainly plans the capacity of wind and solar equipment in order to obtain the number of wind turbines and photovoltaic equipment planned in the current target area.

[0128] S42. Based on the typical daily predicted wind and solar power curves, a lower-level optimization model is constructed with the objective function of minimizing the total operating cost of the distribution network, specifically as follows:

[0129] (5)

[0130] (6)

[0131] (7)

[0132] (8)

[0133] (9)

[0134] in, This represents the total operating cost of the power distribution network. This represents the operating cost of a distribution network under a typical daily rate. This represents the electricity purchase cost of a typical daily distribution network from its upstream power grid. This represents the probability corresponding to a typical day r. This represents the unit cost of electricity purchased by the distribution network from the upstream power grid. This represents the interaction power between the distribution network and the upstream power grid. , as well as These represent the typical daily operating costs of wind turbines, photovoltaic power plants, and energy storage power stations, respectively. This represents the planned wind power output on a typical day. This represents the operating cost coefficient of the wind turbine. This represents the unit cost coefficient for wind curtailment. This represents the predicted wind power output on a typical day (r). This represents the planned photovoltaic power output under a typical day. Indicates the photovoltaic operating cost coefficient. This represents the unit photovoltaic curtailment cost coefficient. This represents the predicted photovoltaic power output on a typical day (r).

[0135] It should be noted that the lower-level optimization model mainly considers fully leveraging the low operating costs of wind turbines and photovoltaics, while utilizing the peak shaving and valley filling capabilities of energy storage systems to enhance system flexibility and promote the consumption of renewable energy. It aims to improve the economic efficiency of system operation while ensuring safe and stable operation, and to achieve priority energy exchange within the distribution area, minimizing the impact of its external output characteristics on the distribution network.

[0136] In some embodiments, the lower-level optimization model is mainly based on the typical daily predicted wind and solar power curves to determine typical scenarios of the target area, and optimizes the minimization of the total operating cost of the power distribution area under each typical scenario.

[0137] In some embodiments, the lower-level optimization model mainly optimizes the system production simulation in order to obtain the operating results in the current target area under typical scenarios.

[0138] S5. Solve the dual-layer optimization model in a coupled manner to obtain the optimal configuration result of the wind and solar resource capacity of the target area.

[0139] Step S5 includes:

[0140] S51. Construct distributed power source capacity constraints and distribution network line power flow constraints, and solve the upper-level optimization model based on the distributed power source capacity constraints and the distribution network line power flow constraints to obtain the wind and solar equipment capacity.

[0141] (1) Because the output of distributed power sources such as photovoltaic and wind turbines is greatly affected by natural conditions and has significant randomness and volatility, the total installed capacity of both must be limited to ensure the safe operation of the system.

[0142] The specific distributed power supply capacity constraint is as follows:

[0143] (10)

[0144] in, and These represent the lower and upper limits of the total installed capacity of distributed power sources for wind and solar power systems, respectively. This indicates the total installed capacity of distributed power sources for wind and solar power equipment.

[0145] (2) In typical scenarios, the operation of wind and solar power equipment should meet the line power flow constraints of the distribution network, which specifically include:

[0146] (11)

[0147] Where i and j represent the two nodes on both sides of the distribution line ij, respectively. Indicates line current The square of, Represents node voltage The square of, This represents the active power of line ij. This represents the reactive power of line ij. , These represent the upper and lower limits of the square of the node voltage, respectively. This indicates the upper limit of the line current.

[0148] S52. Construct constraints for energy storage systems, wind and solar power output, distribution network outlet characteristics, and distribution network power flow. Solve the lower-level optimization model based on the wind and solar equipment capacity, energy storage system constraints, wind and solar power output constraints, distribution network outlet characteristics, and distribution network power flow constraints to obtain the power flow results of the distribution network. Feed the power flow results of the distribution network back to the upper-level optimization model.

[0149] (1) The constraints of the energy storage system are as follows:

[0150] (12)

[0151] in, S es,i,t This represents the state of charge of energy storage system i at time t. and Let represent the charging power and discharging power of energy storage system i at time t, respectively. and These represent the charging efficiency and discharging efficiency of the energy storage system, respectively. E es, i Indicates the capacity of the energy storage system. S es,max and S es,min These represent the upper and lower limits of the state of charge of the energy storage system, respectively. S es,end This indicates the state of charge of the energy storage system at the last moment of a typical day. S es,max，end and S es,min,end These represent the upper and lower limits of the state of charge of the energy storage system at the last moment of a typical day, respectively.

[0152] In some embodiments, energy storage system constraints also include:

[0153] (13)

[0154] in, and These are 0-1 variables, representing the operating state of each energy storage system at time t; and These represent the minimum and maximum charging power, respectively. and These represent the minimum and maximum discharge power, respectively. and These represent the charging power and discharging power of energy storage system i, respectively.

[0155] (2) The specific constraints on wind and solar power output are as follows:

[0156] (14)

[0157] (15)

[0158] in, This represents the planned power output of wind turbine equipment i during time period t under a typical day r. This represents the planned power output of photovoltaic equipment i during time period t under a typical day r. This represents the maximum allowable power of wind turbine equipment i during time period t. This represents the maximum allowable power of photovoltaic device i during time period t.

[0159] It should be noted that formula (14) is the output constraint of wind turbine (wind turbine equipment), and formula (15) is the output constraint of photovoltaic system (photovoltaic equipment).

[0160] (3) The specific characteristics constraint of the distribution network outlet is as follows:

[0161] (16)

[0162] (17)

[0163] in, This indicates the power that the distribution network purchases from the upstream power grid. and These represent the discharge power and charging power of energy storage system i during time period t, respectively. and These represent the lower and upper limits of the power that the distribution network can purchase from the upper-level power grid, respectively.

[0164] (4) The specific construction of power flow constraints in the distribution network is as follows:

[0165] (18)

[0166] in, This represents the active power of node j. This represents the reactive power of node j. This indicates the active power of line jk. Let j represent the set of routes starting from j. Let j represent the set of routes ending at point j. This represents the square of the line current value ij. This indicates the reactive power of line jk. This represents the square of the voltage value at node j. and These represent the resistance and reactance of line ij, respectively.

[0167] S53. Determine the optimal configuration result of the wind and solar resource capacity of the target area based on the solution results of the upper-level optimization model and the lower-level optimization model.

[0168] It should be noted that this invention achieves reasonable planning of wind and solar power capacity through a two-layer planning model. The upper-layer optimization model is the configuration model for wind and solar capacity, while the lower-layer optimization model is used to obtain the operating results under various typical scenarios. Through the coupling and interaction of the upper and lower-layer optimization models, the optimal wind and solar power capacity planning scheme for the power distribution network within the distribution area is obtained. At the same time, it can adapt to the operating conditions of various scenarios, thereby ensuring that the planned wind and solar capacity can adapt to the meteorological characteristics of the region.

[0169] Please refer to Figure 3 Embodiment two of the present invention is as follows:

[0170] A distributed wind and solar resource capacity planning method differs from Embodiment 1 in that it specifies the concrete steps of steps S2 and S3. Specifically,

[0171] The wind and solar equipment includes wind turbine equipment and photovoltaic equipment; the predicted wind and solar power curves include the predicted unit wind turbine power curve and the predicted unit photovoltaic power curve.

[0172] Step S2 includes:

[0173] S21. Obtain the equipment models of the wind turbine and photovoltaic equipment to be planned and constructed, and determine the equipment parameters based on the equipment models.

[0174] In some embodiments, when the model of the photovoltaic equipment is determined, the obtained equipment parameters are shown in Table 1.

[0175] Table 1 Equipment parameters of photovoltaic equipment

[0176]

[0177] S22. Based on the equipment parameters, output principles, and historical meteorological data of the wind turbine and photovoltaic equipment, determine the predicted unit wind turbine power curve and the predicted unit photovoltaic power curve for the target area.

[0178] The historical meteorological data includes historical average wind speed and historical solar radiation.

[0179] Step S22 specifically involves:

[0180] (19)

[0181] (20)

[0182] in, This represents the predicted unit wind turbine power curve. Indicates air density, This represents the historical average wind speed. D , , as well as These are all equipment parameters. D Indicates the diameter of the fan. Indicates leaf surface efficiency. as well as These represent the conversion efficiencies of the generator and the bearing, respectively. SP t This represents the predicted unit photovoltaic power curve. This indicates the amount of solar radiation. H STC This represents the amount of solar radiation under standard test conditions. P sr Indicates the rated power of photovoltaic equipment. c 1 represents the degradation coefficient of photovoltaic equipment. c 2 represents the power temperature coefficient of photovoltaic equipment. Indicates the temperature of the photovoltaic equipment. T STC This indicates the temperature under standard test conditions.

[0183] In some embodiments, The temperature of the photovoltaic equipment is determined by formula (21).

[0184] (twenty one)

[0185] in, T 2 (t) Indicates the surface temperature (°C); T s ,TETC This indicates the temperature of the photovoltaic panel under the estimated temperature test conditions, i.e., 47°C. T a,TETC This indicates the ambient temperature under the temperature estimation test conditions, i.e., 20℃; H TETC This represents the solar radiation under the temperature estimation test conditions, i.e., 800 W / m². 2 .

[0186] In some embodiments, photovoltaic (PV) equipment typically requires a corresponding inverter to connect to the grid. Therefore, the AC power output of the PV equipment should take into account the inverter's conversion efficiency. Specifically:

[0187] (twenty two)

[0188] in, Indicates the AC power conversion efficiency of the photovoltaic panel (%). This indicates the nominal efficiency (%) of the photovoltaic inverter. Indicates the inverter efficiency reference value (%); a 1 、a 2 and a 3 represent the empirical parameters of the inverter; This indicates the DC output power of the photovoltaic panel; Indicates the photovoltaic AC output power (W); This indicates the AC rated power (W) of the photovoltaic inverter.

[0189] In some embodiments, since the amount of solar radiation irradiating the photovoltaic device directly affects the efficiency of photovoltaic power generation, the total amount of radiation irradiating the photovoltaic device should be calculated before calculating the predicted unit photovoltaic power curve for the target area. Specifically,

[0190] (twenty three)

[0191] in, This represents the total solar radiation on the inclined plane (W / m2). Indicates the horizontal direct radiation component (W / m2); Represents the ratio of inclined to horizontal direct sunlight (dimensionless). Indicates the horizontal scattering component (W / m2); This represents the total horizontal solar radiation (W / m2). Represents reflectance (dimensionless). This indicates the tilt angle (deg) of the photovoltaic panel.

[0192] Step S3 includes:

[0193] S31. Obtain the number of clusters for typical daytime landscape scenes.

[0194] S32. Based on the number of clusters, the predicted wind and solar power curves are clustered using the K-means algorithm to obtain multiple predicted daily wind and solar power curves corresponding to each cluster.

[0195] In some embodiments, the initial cluster centers of the K-means algorithm are not selected directly by random selection, but by a roulette wheel method, making the initial cluster centers more evenly distributed and better covering the entire sample space. Furthermore, the selection of the K value adopts an elbow principle based on the sum of squared errors (SSE) between the cluster centers and the sample points within the cluster, combined with the silhouette coefficient. Specifically, for a cluster, a lower SSE indicates a more compact cluster, while a higher SSE indicates a more loosely clustered cluster. SSE decreases with the increase of categories, but for data with a certain degree of discriminative power, the distortion level is greatly improved at a certain critical point, and then slowly decreases. This critical point is used as the K value with better clustering performance; this method is called the elbow principle. For multiple K values ​​that meet the elbow principle, the K value with the largest silhouette coefficient is taken as the final K value.

[0196] In some embodiments, after performing cluster analysis on the wind turbine power curve and photovoltaic power curve based on the above K-means algorithm, the following results are obtained: K wind and K pv In similar power output scenarios, wind turbine equipment is the first i Similar to output scenarios The CCP j The predicted daily wind power curve, photovoltaic equipment number one. x Similar to output scenarios The CCP y A predicted daily photovoltaic power curve.

[0197] S33. Based on the Pearson correlation coefficient, determine the predicted typical daily wind and solar power curve according to the multiple predicted daily power curves.

[0198] It should be noted that step S33 generates a representative sequence based on a known wind and light cluster by setting an objective function representing the total correlation coefficient, so as to maximize the Pearson correlation coefficient between the generated representative sequence and each known sequence in the wind and light cluster.

[0199] The predicted daily power curve includes the predicted daily wind power curve and the predicted daily photovoltaic power curve; the predicted typical daily wind and solar power curve includes the predicted typical daily wind power curve and the predicted typical daily photovoltaic power curve.

[0200] Step S33 includes:

[0201] S331. Based on the Pearson correlation coefficient, determine the predicted typical daily wind power curve according to the predicted daily wind power curve, specifically as follows:

[0202] (twenty four)

[0203] (25)

[0204] in, This represents the predicted typical daily wind power output curve for the i-th type of wind power output scenario. Let represent the Pearson correlation coefficient between the predicted daily wind power curve and the predicted typical daily wind power curve under the i-th wind power output scenario, and let cov(x,y) represent the covariance. Indicates the first i The first in wind power output scenarios l A predicted daily wind power curve, The standard deviation of each wind power curve is represented by E, and E represents the expectation operation symbol when calculating covariance. This represents the expected value of each wind power output. This represents the power value of the predicted daily wind power curve for the i-th wind power output scenario at time t. Indicates the first i The predicted typical daily wind power output curve for wind-like power output scenarios is in t Output value at time j, where j represents the output value at time j. i Number of predicted daily wind power output curves in wind power output scenarios;

[0205] S332. Based on the Pearson correlation coefficient, determine the typical daily photovoltaic power curve according to the predicted daily photovoltaic power curve, specifically as follows:

[0206] (26)

[0207] (27)

[0208] in, This represents the predicted typical daily photovoltaic power curve for the x-th photovoltaic power output scenario. This represents the Pearson correlation coefficient between the predicted daily photovoltaic power curve and the predicted typical daily photovoltaic power curve under the x-th photovoltaic power output scenario. Indicates the xth photovoltaic power output scenario. l A predicted daily photovoltaic power curve; This represents the predicted typical daily photovoltaic power curve for the x-th photovoltaic output scenario. t Constant output value; This represents the number of predicted daily photovoltaic power curves within the x-th photovoltaic output scenario.

[0209] Please refer to Figure 4 Embodiment 3 of the present invention is as follows:

[0210] A distributed wind and solar resource capacity planning terminal 300 includes a memory 301, a processor 302, and a computer program stored in the memory 301 and running on the processor 302. When the processor 302 executes the computer program, it implements each step of the distributed wind and solar resource capacity planning method described in Embodiments 1 and 2 above.

[0211] In summary, the distributed wind and solar resource capacity planning method and terminal provided by this invention, for target areas lacking historical wind and solar power output data, accurately obtains historical meteorological data for the region by acquiring regional data. Given the equipment models of the wind and solar power equipment, the historical meteorological data can be directly converted into predicted wind and solar power curves usable for capacity planning based on the power output principles of the equipment. Simultaneously, cluster analysis is performed on the predicted wind and solar power curves to determine the predicted typical daily wind and solar power curves for the target area in different seasons. Finally, a lower-level optimization model is constructed based on the power under typical daily scenarios, aiming to minimize the total operating cost of the distribution network, while an upper-level optimization model is constructed aiming to minimize the annual investment cost of wind and solar resources. The upper-level and lower-level optimization models are coupled and influence each other during the solution process, thereby obtaining the optimal configuration result of wind and solar resource capacity. The wind and solar capacity planning of the distribution network in this invention achieves optimal economic efficiency within the distribution area and ensures that the planned wind and solar capacity is adapted to the meteorological characteristics of the region, providing more effective guidance for capacity planning within the distribution area. This solves the problem of blind planning in areas lacking historical wind and solar data and improves the economy and reliability of wind and solar resource capacity configuration.

[0212] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A distributed wind and solar resource capacity planning method, characterized in that, include: Obtain historical meteorological data for the target area where wind and solar equipment needs to be built; The predicted wind and solar power curve for the target area is determined based on the historical meteorological data and the power output principle of the wind and solar equipment. Cluster analysis was performed on the predicted wind and solar power curves to obtain the predicted wind and solar power curves for typical days; A two-layer optimization model for wind and solar resource capacity planning is constructed. The upper-layer optimization model plans the capacity of wind and solar equipment with the goal of minimizing the annual investment cost of wind and solar resources. The lower-layer optimization model plans the operation status of the distribution network based on the predicted typical daily wind and solar power curve with the goal of minimizing the total operating cost of the distribution network. The optimal configuration result of wind and solar resource capacity in the target area is obtained by coupling the solution of the two-layer optimization model. The two-layer optimization model for wind and solar resource capacity planning includes an upper-layer model that plans the capacity of wind and solar equipment with the objective of minimizing the annual investment cost of wind and solar resources, and a lower-layer model that plans the operation status of the distribution network based on the predicted typical daily wind and solar power curve with the objective of minimizing the total operating cost of the distribution network. The upper-level optimization model is constructed with the objective function of minimizing the annual investment cost of wind and solar resources, as follows: ； ； ； in, This indicates the annual investment cost of wind and solar resources. This indicates the investment cost of the wind turbine equipment. This indicates the investment cost of photovoltaic equipment. N wind Indicates the number of wind turbine units. R wind This represents the present value to equivalent annual value factor for wind turbine equipment. S wind This indicates the investment cost required per unit capacity of the wind turbine. This indicates the maintenance cost of the wind turbine equipment. This represents the installed capacity of the i-th fan unit. R pv This represents the present value to equivalent annual value factor for photovoltaic equipment. N pv Indicates the number of photovoltaic devices. S pv This indicates the investment cost required per unit capacity of photovoltaic power. Indicates the maintenance cost of photovoltaic equipment. This represents the installed capacity of the i-th photovoltaic device; Based on the typical daily predicted wind and solar power curves, a lower-level optimization model is constructed with the objective function of minimizing the total operating cost of the distribution network, specifically as follows: ； ； ； ； ； in, This represents the total operating cost of the power distribution network. This represents the operating cost of a distribution network under a typical daily rate. This represents the electricity purchase cost of a typical daily distribution network from its upstream power grid. This represents the probability corresponding to a typical day r. This represents the unit cost of electricity purchased by the distribution network from the upstream power grid. This represents the interaction power between the distribution network and the upstream power grid. , as well as These represent the typical daily operating costs of wind turbines, photovoltaic power plants, and energy storage power stations, respectively. This represents the planned wind power output on a typical day. This represents the operating cost coefficient of the wind turbine. This represents the unit cost coefficient for wind curtailment. This represents the predicted wind power output under a typical day (r). This represents the planned photovoltaic power output under a typical day. This represents the photovoltaic operating cost coefficient. This represents the unit photovoltaic curtailment cost coefficient. This represents the predicted photovoltaic power output on a typical day (r).

2. The distributed wind and solar resource capacity planning method according to claim 1, characterized in that, The acquisition of historical meteorological data for the target area where wind and solar power equipment needs to be built includes: Obtain regional data for the target area where wind and solar equipment needs to be built; Determine the spatial resolution and temporal resolution based on the regional data; Historical meteorological data is downloaded from a preset meteorological analysis database based on the spatial and temporal resolution.

3. The distributed wind and solar resource capacity planning method according to claim 1, characterized in that, The wind and solar equipment includes wind turbine equipment and photovoltaic equipment; the predicted wind and solar power curves include a predicted unit wind turbine power curve and a predicted unit photovoltaic power curve. The step of determining the predicted wind and solar power curve for the target area based on the historical meteorological data and the power output principle of the wind and solar equipment includes: Obtain the equipment models of the planned wind turbine and photovoltaic equipment, and determine the equipment parameters based on the equipment models; Based on the equipment parameters and output principles of the wind turbine and photovoltaic equipment, as well as the historical meteorological data, the predicted unit wind turbine power curve and the predicted unit photovoltaic power curve for the target area are determined respectively.

4. The distributed wind and solar resource capacity planning method according to claim 1, characterized in that, The step of performing cluster analysis on the predicted wind and solar power curves to obtain the predicted typical day wind and solar power curves includes: Obtain the number of clusters for typical daytime landscape scenes; Based on the number of clusters, the predicted wind and solar power curves are clustered using the K-means algorithm to obtain multiple predicted daily wind and solar power curves corresponding to each cluster. Based on the Pearson correlation coefficient, the predicted typical daily wind and solar power curve is determined according to the multiple predicted daily wind and solar power curves.

5. The distributed wind and solar resource capacity planning method according to claim 1, characterized in that, The coupled solution of the two-layer optimization model to obtain the optimal configuration result of the wind and solar resource capacity of the target area includes: Construct distributed power generation capacity constraints and distribution network line power flow constraints, and solve the upper-level optimization model based on the distributed power generation capacity constraints and the distribution network line power flow constraints to obtain the wind and solar equipment capacity; The constraints of energy storage system, wind and solar power output, distribution network outlet characteristics, and distribution network power flow are constructed. Based on the capacity of wind and solar equipment, the constraints of energy storage system, wind and solar power output, distribution network outlet characteristics, and distribution network power flow, the lower-level optimization model is solved to obtain the power flow results of the distribution network. The power flow results of the distribution network are then fed back to the upper-level optimization model. The optimal configuration result of the wind and solar resource capacity of the target area is determined based on the solution results of the upper-level optimization model and the lower-level optimization model.

6. The distributed wind and solar resource capacity planning method according to claim 3, characterized in that, The historical meteorological data includes historical average wind speed and historical solar radiation. The specific steps for determining the predicted unit wind turbine power curve and the predicted unit photovoltaic power curve for the target area based on the equipment parameters and output principles of the wind turbine and photovoltaic equipment, as well as the historical meteorological data, are as follows: ； ； in, This represents the predicted unit wind turbine power curve. Indicates air density, This represents the historical average wind speed. D , , as well as These are all equipment parameters. D Indicates the diameter of the fan. Indicates leaf surface efficiency. as well as These represent the conversion efficiencies of the generator and the bearing, respectively. SP t This represents the predicted unit photovoltaic power curve. This indicates the amount of solar radiation. H STC This represents the amount of solar radiation under standard test conditions. P sr Indicates the rated power of photovoltaic equipment. c 1 represents the degradation coefficient of photovoltaic equipment. c 2 represents the power temperature coefficient of photovoltaic equipment. Indicates the temperature of the photovoltaic equipment. T STC This indicates the temperature under standard test conditions.

7. A distributed wind and solar resource capacity planning method according to claim 4, characterized in that, The predicted daily wind and solar power curve includes the predicted daily wind power curve and the predicted daily photovoltaic power curve; the predicted typical daily wind and solar power curve includes the predicted typical daily wind power curve and the predicted typical daily photovoltaic power curve. The method of determining the typical daily wind and solar power curve based on the Pearson correlation coefficient and the multiple predicted daily wind and solar power curves includes: Based on the Pearson correlation coefficient, the predicted typical daily wind power curve is determined according to the predicted daily wind power curve, specifically as follows: ； ； in, This represents the predicted typical daily wind power output curve for the i-th type of wind power output scenario. Let represent the Pearson correlation coefficient between the predicted daily wind power curve and the predicted typical daily wind power curve under the i-th wind power output scenario, and let cov(x,y) represent the covariance. Indicates the first i The first in wind power output scenarios l A predicted daily wind power curve, The standard deviation of each wind power curve is represented by E, and E represents the expectation operation symbol when calculating covariance. This represents the expected value of each wind power output. This represents the power value of the predicted daily wind power curve for the i-th wind power output scenario at time t. Indicates the first i The predicted typical daily wind power output curve for wind-like power output scenarios is in t Output value at time j, where j represents the output value at time j. i Number of predicted daily wind power output curves in wind power output scenarios; Based on the Pearson correlation coefficient, a typical daily photovoltaic power curve is determined according to the predicted daily photovoltaic power curve, specifically as follows: ； ； in, This represents the predicted typical daily photovoltaic power curve for the x-th photovoltaic power output scenario. This represents the Pearson correlation coefficient between the predicted daily photovoltaic power curve and the predicted typical daily photovoltaic power curve under the x-th photovoltaic power output scenario. Indicates the xth photovoltaic power output scenario. l A predicted daily photovoltaic power curve; This represents the predicted typical daily photovoltaic power curve for the x-th type of photovoltaic output scenario. t Constant output value; This represents the number of predicted daily photovoltaic power curves within the x-th photovoltaic output scenario.

8. A distributed wind and solar resource capacity planning method according to claim 5, characterized in that, The specific distributed power supply capacity constraint is as follows: ； in, and These represent the lower and upper limits of the total installed capacity of distributed power sources for wind and solar power systems, respectively. This indicates the total installed capacity of distributed power sources for wind and solar power equipment. The power flow constraints of the distribution network lines are specifically as follows: ； Where i and j represent the two nodes on both sides of the distribution line ij, respectively. Indicates line current The square of, Indicates node voltage The square of, This represents the active power of line ij. This indicates the reactive power of line ij. , These represent the upper and lower limits of the square of the node voltage, respectively. Indicates the upper limit of the line current; The specific constraints of the energy storage system are as follows: ； in, S es,i,t This represents the state of charge of energy storage system i at time t. and Let represent the charging power and discharging power of energy storage system i at time t, respectively. and These represent the charging efficiency and discharging efficiency of the energy storage system, respectively. E es, i Indicates the capacity of the energy storage system. S es,max and S es,min These represent the upper and lower limits of the state of charge of the energy storage system, respectively. S es,end This indicates the state of charge of the energy storage system at the last moment of a typical day. S es,max，end and S es,min,end These represent the upper and lower limits of the state of charge of the energy storage system at the last moment of a typical day, respectively. The specific constraints on wind and solar power output are as follows: ； ； in, This represents the planned wind power output on a typical day. This represents the planned photovoltaic power output under a typical day. This represents the maximum allowable power of fan equipment i during time period t. This represents the maximum allowable power output of photovoltaic device i during time period t; The specific constraints on the distribution network output characteristics are as follows: ； ； in, This indicates the power that the distribution network purchases from the upstream power grid. and These represent the discharge power and charging power of energy storage system i during time period t, respectively. and These represent the lower and upper limits of the power that the distribution network purchases from the upper-level power grid, respectively. The power flow constraints of the power distribution network are constructed as follows: ； in, This represents the active power of node j. This represents the reactive power of node j. This indicates the active power of line jk. Let j represent the set of routes starting from j. Let j represent the set of routes ending at point j. This represents the square of the line current value ij. This indicates the reactive power of line jk. This represents the square of the voltage value at node j. and These represent the resistance and reactance of line ij, respectively.

9. A distributed wind and solar resource capacity planning terminal, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the various steps of the distributed wind and solar resource capacity planning method as described in any one of claims 1-8.

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