Distributed optimization method and system for new energy power station and energy storage power station

Abstract

The invention discloses a distributed optimization method and system for a new energy power station and an energy storage power station, and the method comprises the steps: generating an electric quantity relation curve according to the predicted first power generation data of the new energy power station, the second power generation data of a conventional power station, and the power consumption data; according to the electric quantity relation curve, determining an optimization mode of the new energy power generation station, and if the optimization mode is power generation station optimization, outputting prompt information for performing distributed optimization on the new energy power generation station; and if the optimization mode is energy storage power station optimization, determining the capacities of a plurality of energy storage power stations for energy storage, and performing distributed optimization on the energy storage power stations according to the capacities. The determined capacity of the energy storage power station can be accurately matched with the residual electric quantity except the electricity consumption, and accurate storage of the residual electric energy is achieved; and the energy storage power station can be set as a pumped storage power station, so that natural resources are fully utilized, and the environment is prevented from being influenced.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a distributed optimization method and system for new energy power generation stations and energy storage power stations. Background Art

[0002] In recent years, with the rapid development of new energy power generation technologies such as wind energy and photovoltaics, there are more and more wind and photovoltaic power generation sites. However, the power generation of wind and photovoltaics is unstable due to environmental influences and is difficult to access the power grid, resulting in increasingly prominent problems such as wind and solar power abandonment. In order to fully and effectively utilize the electricity generated by new energy such as wind energy and solar energy and reduce carbon emissions, energy storage technology has emerged. As a key technology to promote the transformation of energy structure, energy storage technology plays an indispensable role in the large-scale access of renewable energy and the improvement of the efficiency, safety and economy of power systems and regional energy systems.

[0003] At present, energy storage technology can be divided into mechanical energy storage, electrochemical energy storage, electrical energy storage, thermal energy storage, etc. according to different storage methods. Among them, mechanical energy storage and electrochemical energy storage are more commonly used. Mechanical energy storage is to convert electrical energy into potential energy, kinetic energy and other mechanical energy through certain devices or facilities for storage, such as pumped energy storage, flywheel energy storage, etc. Electrochemical energy storage is to use specific materials to complete the mutual conversion of electrical energy and chemical energy to achieve the storage of electrical energy, such as lithium electronic batteries, lead-acid batteries, etc.

[0004] Although the development of energy storage technology has alleviated the problem of energy abandonment in renewable energy power generation to a certain extent, in complex scenarios where renewable energy and traditional energy are combined for power generation, there are still problems such as insufficient total power generation, or excessive power generation and energy abandonment, or a significant impact on the environment due to unreasonable planning of renewable energy power generation sites and energy storage power stations. Therefore, how to optimize renewable energy power generation sites and energy storage power stations in a distributed manner to store renewable energy power generation while meeting electricity demand, and to minimize renewable energy abandonment and its impact on the environment, is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a distributed optimization method and system for renewable energy power generation stations and energy storage power stations, which are used to perform distributed optimization of renewable energy power generation stations and energy storage power stations, so as to store renewable energy power generation while meeting electricity demand, thereby minimizing the waste of renewable energy and its impact on the environment.

[0006] To achieve the above objectives, the technical solution of the present invention is: a distributed optimization method for new energy power generation stations and energy storage power stations, comprising:

[0007] Predicting first power generation data of each new energy power station in the area to be planned within a preset time period, and second power generation data of each traditional power station in the area to be planned within a preset time period;

[0008] Predicting the power consumption data of the area to be planned within a preset time period, and generating a power size relationship curve according to each of the first power generation data, each of the second power generation data and the power consumption data;

[0009] Determine the optimization mode of the new energy power station according to the electric quantity relationship curve, and if the optimization mode is power station optimization, output prompt information for performing distributed optimization on the new energy power station in the area to be planned;

[0010] If the optimization mode is energy storage power station optimization, the capacity of multiple energy storage power stations used to store energy for each new energy power generation station in the area to be planned is determined, and distributed optimization of the energy storage power stations in the area to be planned is performed based on the capacity.

[0011] The new energy power generation station is a wind power generation station, and the first power generation data of each new energy power generation station in the area to be planned within a preset time period is predicted to include:

[0012] Obtaining historical wind direction data, historical wind speed data, historical air density data and historical wind power generation hours for each of the wind power plants in a plurality of historical time periods corresponding to a preset time period;

[0013] According to each of the historical wind direction data and each of the historical wind speed data, predict the average effective wind speed of the wind power plant in the preset time period and the effective duration corresponding to the average effective wind speed, and predict the wind power generation hours according to the effective duration and the historical wind power generation hours;

[0014] Predicting the average air density of the wind power plant in the preset time period according to each of the historical air density data, and finding the wind turbine parameters, prediction correction coefficient and first-year attenuation coefficient of the wind power plant;

[0015] The first power generation data of the wind power plant in a preset period is calculated according to the first annual attenuation coefficient, the average effective wind speed, the average air density, the number of wind power generation hours, the prediction correction coefficient and the blade length, the wind energy utilization coefficient, the blade area, the blade windward angle and the generator angular velocity in the wind turbine parameters, and the calculation formula is:

[0016]

[0017] Wherein, Q1 represents the first power generation data; N represents the number of wind power generation hours; n1 represents the number of wind power plants; n2 represents the number of wind turbines in the wind power plants; r1 represents the first annual attenuation coefficient; represents the wind energy utilization coefficient of the jth wind turbine in the i-th wind power station; ρ represents the average air density; A ij represents the blade area of ​​the jth wind turbine in the i-th wind power station; B ij represents the windward angle of the blades of the jth wind turbine in the i-th wind power station; L ij represents the blade length of the jth wind turbine in the i-th wind power station; V represents the average effective wind speed; ω ij represents the angular velocity of the generator in the jth wind turbine in the i-th wind power plant; τ represents the prediction correction coefficient; Used to calculate the motor torque of the jth wind turbine in the i-th wind power plant.

[0018] The new energy power generation station is a photovoltaic power generation station, and the first power generation data of each new energy power generation station in the area to be planned within a preset time period is predicted to include:

[0019] Acquire historical light temperature data and historical light intensity data of the photovoltaic power station in multiple historical time periods corresponding to the preset time period, and predict the light temperature data and light intensity data of the photovoltaic power station in the preset time period respectively according to each of the historical light temperature data and each of the historical light intensity data;

[0020] Fitting the illumination temperature data and the illumination intensity data into a temperature-time function and an intensity-time function within a preset period of time, respectively, and generating the illumination intensity data as an average illumination intensity within the preset period of time;

[0021] Finding the second-year attenuation coefficient, hourly reference output power, temperature coefficient corresponding to the photovoltaic power generation station, and the hourly reference light intensity and hourly reference temperature corresponding to the hourly reference output power;

[0022] According to the second annual attenuation coefficient, hourly reference output power, temperature coefficient, hourly reference light intensity, hourly reference temperature, and the average light intensity, temperature time function, and intensity time function, the first power generation data of the photovoltaic power station in the preset time period is calculated, and the calculation formula is:

[0023]

[0024] Wherein, Q2 represents the first power generation data; k represents the temperature coefficient; r2 represents the second annual attenuation coefficient; G represents the average light intensity in the preset period; G0 represents the hourly reference light intensity; P0 represents the hourly reference output power; g(t) represents the intensity time function; θ(t) represents the temperature time function; θ0 represents the hourly reference temperature; Used to calculate the light intensity coefficient within a preset time period.

[0025] The predicting of the power consumption data of the area to be planned within a preset period of time includes:

[0026] Obtaining historical electricity consumption data of the area to be planned in multiple historical time periods corresponding to the preset time periods, as well as population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data, and tertiary industry change parameters in each historical time period;

[0027] Generate historical power consumption data, population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data and tertiary industry change parameters for each historical period into a historical array for each historical period;

[0028] Based on the preset prediction model, the historical array of each historical period is analyzed and calculated to obtain the power consumption data of the area to be planned within the preset period, wherein the formula used for analysis and calculation in the preset prediction model is:

[0029]

[0030] Where, H represents the power consumption data; W hb represents the connection weight between the output layer and the bth hidden layer in the preset prediction model; s represents the number of hidden layers in the preset prediction model; W cb represents the connection weight between the input layer and the bth hidden layer in the preset prediction model; d c represents the Cth history array; m represents the number of history arrays; α b represents the b-th hidden layer model parameters; δ represents the output layer model parameters.

[0031] The determining of the capacity of a plurality of energy storage power stations for storing energy for each new energy power generation station in the area to be planned includes:

[0032] Identifying the maximum value of the difference in the electric quantity relationship curve, and determining the total capacity of the plurality of energy storage power stations according to the maximum value of the difference;

[0033] Generating each of the first power generation data into a power generation proportion, and dividing the total capacity into to-be-stored capacities corresponding to each of the new energy power generation stations according to each of the power generation proportions;

[0034] According to each of the capacities to be stored, the energy storage capacity of the energy storage power station corresponding to each of the new energy power generation stations is determined, and the capacity loss value corresponding to each of the energy storage capacities is obtained to correct each of the energy storage capacities, so as to obtain the capacity size of the energy storage power station corresponding to each of the new energy power generation stations.

[0035] The distributed optimization of the energy storage power stations in the area to be planned according to the capacity sizes includes:

[0036] Determine whether the environmental parameters of the environment in which each of the new energy power generation stations is located include water source parameters. If the water source parameters are included, determine the energy storage power station corresponding to the new energy power generation station as a pumped storage power station, and obtain the water source location corresponding to the water source parameters as the location information of the pumped storage power station;

[0037] If the environmental parameters do not include water source parameters, energy storage noise data corresponding to the capacity of the energy storage power station is obtained, and location information of the energy storage power station corresponding to the new energy power station is determined according to the energy storage noise data;

[0038] The location information and the capacity size are used as optimization parameters of the energy storage power station, and distributed optimization is performed on the energy storage power station according to the optimization parameters.

[0039] The determining, according to the energy storage noise data, the location information of the energy storage power station corresponding to the new energy power generation station comprises:

[0040] Acquire power generation noise data corresponding to the energy storage noise data in each of the new energy power generation stations, and determine the initial position information of the energy storage power station;

[0041] According to the initial position information, the energy storage noise data and the power generation noise data are superimposed in volume to generate a superposition value that varies with distance;

[0042] Determine a maximum value among the superposition values ​​that vary with the distance, and judge whether the maximum value is less than or equal to a preset threshold value, and if the maximum value is less than or equal to the preset threshold value, determine the initial position information as the position information;

[0043] If the maximum value is greater than a preset threshold, the initial position information is adjusted, and according to the adjusted initial position information, a step of adding the volume of the energy storage noise data and the power generation noise data is performed.

[0044] Determining the optimization mode of the new energy power generation station according to the electric quantity relationship curve includes:

[0045] Determine the total power generation and total power consumption of the planned area within a preset period of time based on the power quantity relationship curve, and determine the size relationship between the total power consumption and the total power generation;

[0046] If the relationship is that the total power consumption is greater than the total power generation, then determine whether the difference between the total power consumption and the total power generation is greater than a preset power difference; if so, determine the optimization mode as power station optimization;

[0047] If the size relationship is that the total power consumption is less than the total power generation, the optimization mode is determined as energy storage power station optimization.

[0048] A distributed optimization system for a new energy power station and an energy storage power station, the system is applied to the above-mentioned method, the system comprising:

[0049] A power generation data acquisition module is used to predict first power generation data of each new energy power station in the area to be planned within a preset time period, and second power generation data of each traditional power station in the area to be planned within a preset time period;

[0050] An electricity quantity relationship curve generating module is used to predict the electricity consumption data of the area to be planned within a preset time period, and generate an electricity quantity relationship curve according to each of the first power generation data, each of the second power generation data and the power consumption data;

[0051] The distributed optimization mode determination module is used to determine the optimization mode of the new energy power generation station according to the electricity quantity relationship curve. If the optimization mode is power generation station optimization, a prompt message for distributed optimization of the new energy power generation station in the area to be planned is output; if the optimization mode is energy storage power station optimization, the capacity of multiple energy storage power stations used for storing energy for each new energy power generation station in the area to be planned is determined, and distributed optimization of the energy storage power stations in the area to be planned is performed according to each capacity.

[0052] A distributed optimization device for a new energy power generation station and an energy storage power station, including a memory and a processor;

[0053] The memory is used to store computer program code and transmit the computer program code to the processor;

[0054] The processor is used to execute the method as described above according to the instructions in the computer program code.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] In a distributed optimization method and system for a new energy power station and an energy storage power station of the present invention, the first power generation data of the new energy power station in the planned area within a preset time period is predicted, and the second power generation data of the traditional power station in the planned area within the same preset time period is predicted. In addition, the power consumption data of the planned area within the preset time period is predicted; based on the predicted first power generation data, second power generation data and power consumption data, a power size relationship curve is generated; and then the optimization mode of the new energy power station is determined according to the power size relationship curve. If the determined optimization mode is power station optimization, it means that the planning of the new energy power station in the planned area is unreasonable, so the new energy power station is distributedly optimized. If it is determined that the optimization mode is energy storage power station optimization, it means that the energy storage power station in the planned area needs to be optimized, so as to determine the capacity of multiple energy storage power stations used for storing energy for each new energy power station in the planned area, and according to the capacity, the energy storage power station in the planned area is distributedly optimized. Among them, the electricity size relationship curve reflects the size relationship between the total power generation and power consumption of the combination of traditional energy and new energy. From this, the optimization mode including distributed optimization of new energy power generation stations and distributed optimization of energy storage power stations used to store energy for new energy power generation stations can be determined. The problem of insufficient total power generation is avoided through power generation station optimization. As for the optimization of energy storage power stations, the capacity of energy storage power stations that can accurately match the remaining electricity except for power consumption is determined to achieve accurate storage of the remaining electricity and minimize energy abandonment. At the same time, the corresponding location information is determined in combination with the factors affecting the environment caused by the generated noise to avoid a large noise impact on the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the first embodiment of a distributed optimization method for a new energy power generation station and an energy storage power station of the present invention.

[0058] Figure 2 It is a flow chart of the second embodiment of a distributed optimization method for a new energy power generation station and an energy storage power station of the present invention.

[0059] Figure 3 It is a flow chart of the third embodiment of a distributed optimization method for a new energy power generation station and an energy storage power station of the present invention.

[0060] Figure 4 It is a flow chart of the fourth embodiment of a distributed optimization method for a new energy power generation station and an energy storage power station of the present invention.

[0061] Figure 5It is a structural block diagram of a distributed optimization system of a new energy power generation station and an energy storage power station according to the present invention.

[0062] Figure 6 It is a structural block diagram of a distributed optimization device for a new energy power generation station and an energy storage power station according to the present invention. DETAILED DESCRIPTION

[0063] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0064] The present invention provides a distributed optimization method for a new energy power generation station and an energy storage station, see Figure 1 , Figure 1 It is a flow chart of the first embodiment of the distributed optimization method for new energy power generation stations and energy storage power stations of the present invention.

[0065] The embodiment of the present invention provides an embodiment of a distributed optimization method for a new energy power station and an energy storage power station. It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here. Specifically, the distributed optimization method for a new energy power station and an energy storage power station in this embodiment includes:

[0066] S10, predicting first power generation data of each new energy power station in the area to be planned within a preset time period, and second power generation data of each traditional power station in the area to be planned within a preset time period.

[0067] The distributed optimization method of the new energy power station and the energy storage power station in this embodiment is applied to the control center of the new energy power station system, and the control center performs distributed optimization on the existing new energy power station, and performs distributed optimization on the energy storage power station used to store energy for the new energy power station. Among them, the distributed optimization of the new energy power station is to plan a new new energy power station by analyzing that the total power generation cannot meet the power demand, and the distribution location of the new new energy station can be determined by analyzing the noise generated by the existing new energy power station, so as to avoid the impact of the noise superposition of each new energy power station on the environment. For the distributed optimization of the energy storage power station, it is to analyze that the total power generation exceeds the power demand, and the remaining power generated by the new energy power station is stored by planning the energy storage power station, and the capacity of each energy storage power station is determined by analyzing the amount of remaining power of each new energy power station, so as to realize the accurate storage of the remaining power, and the distribution location of the energy storage power station can also be determined by analyzing the noise generated by the existing new energy power station, so as to avoid the impact of the noise superposition of each new energy power station and the energy storage power station on the environment.

[0068] Specifically, a new energy power station is a power station that uses renewable natural resources such as wind and light to generate electricity, and a traditional energy power station is a power station that uses traditional coal to generate electricity. The energy storage power station can be a mechanical pumped storage or an electrochemical lithium-ion battery storage. Areas that have both new energy power stations and traditional energy power stations and need to plan energy storage stations to store electricity are regarded as areas to be planned, such as areas to be planned divided by province. In addition, a period of time is pre-set as a preset period, which can be set to a quarter, half a year, a year, etc., and can be preferably one year to reflect the impact of periodic climate change on the power generation of new energy power stations.

[0069] Furthermore, the number of new energy power generation stations in the area to be planned is obtained, and the historical power generation related data of each new energy power generation station in multiple previous time periods are obtained. The previous time period has a corresponding relationship with the preset time period. For example, if the preset time period is set to one year, the multiple previous time periods are multiple previous years. The historical power generation related data obtained include but are not limited to historical wind speed, historical power generation, etc., and the power generation data of each new energy power generation station in the preset time period is predicted based on such historical power generation related data to obtain the first power generation data of each new energy power generation station in the preset time period. The first power generation data is data with time attributes. At different times in the preset time period, the corresponding power generation is different. For example, at a certain time in the summer when the light intensity is high, the power generation of the new energy power station in the area to be planned may be higher than that at a certain time in the winter.

[0070] Furthermore, the number of traditional power plants that generate electricity with traditional energy in the planned area is obtained, and the historical power generation data of each traditional power plant for multiple previous time periods is obtained. Based on the historical power generation data, the power generation data of each traditional power plant in the preset time period is predicted to obtain the second power generation data of each traditional power plant in the preset time period.

[0071] S20, predicting the power consumption data of the area to be planned within a preset time period, and generating a power quantity relationship curve according to each of the first power generation data, each of the second power generation data and the power consumption data.

[0072] Understandably, the electricity consumption of the area to be planned is not static, but changes dynamically. For example, within a year, although the climate changes little in spring, summer, autumn and winter, people may move and industries may change, which may lead to changes in electricity consumption. In order to determine the electricity consumption of the area to be planned in a preset period of time, the electricity consumption data of the area to be planned in multiple previous time periods are obtained, and combined with factors such as population changes and industrial changes, the electricity consumption data of the area to be planned in the preset period of time is predicted. Specifically, the prediction of the electricity consumption data of the area to be planned in the preset period of time includes:

[0073] S21, obtaining historical electricity consumption data of the area to be planned in multiple historical time periods corresponding to the preset time periods, as well as population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data, and tertiary industry change parameters in each historical time period;

[0074] S22, generating the historical electricity consumption data, population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data and tertiary industry change parameters of each historical period into a historical array for each historical period;

[0075] S23. Analyze and calculate the historical array of each historical time period based on a preset prediction model to obtain the electricity consumption data of the area to be planned within the preset time period.

[0076] Furthermore, the historical electricity consumption data of the planned area in multiple historical periods corresponding to the preset period, that is, the electricity consumption data of multiple previous periods are obtained, and the population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data and tertiary industry change parameters in each historical period are obtained. Among them, the change parameter is a parameter used to reflect the change size of the data equivalent to the previous period. For example, the number of previous time periods is 3, including 2023, 2022 and 2021. For the historical period corresponding to 2023, the population data is the population size in 2023, and the population change parameter is the parameter reflecting the change in population size in 2023 relative to 2022; the weather data includes at least the number of hot weather days and cold weather days in 2023, and the weather change parameter corresponds to the parameter reflecting the change in the number of hot weather days and cold weather days in 2023 relative to 2022; the primary industry data, secondary industry data, and tertiary industry data are the primary industry quantity, secondary industry data, and tertiary industry quantity in 2023, and the primary industry change parameter, secondary industry change parameter, and tertiary industry change parameter are the parameters reflecting the change in the primary industry quantity, secondary industry quantity, and tertiary industry quantity in 2023 relative to 2022.

[0077] Furthermore, the acquired electricity consumption data, population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data and tertiary industry change parameters are generated into a historical array for each historical period according to the historical period in which they are located. In addition, a preset prediction model is pre-trained, and the historical arrays of each historical period are analyzed and calculated by the preset prediction model to obtain the total electricity consumption data of the area to be planned within the preset period. The total electricity consumption data is also data with time attributes. At different times within the preset period, the corresponding electricity consumption is different. For example, at a certain time in the high temperature in summer, the electricity consumption of the area to be planned may be higher than at a certain time in spring. Among them, the formula used for analysis and calculation in the preset prediction model is:

[0078]

[0079] Where, H represents the power consumption data; W hb represents the connection weight between the output layer and the bth hidden layer in the preset prediction model; s represents the number of hidden layers in the preset prediction model; W cb represents the connection weight between the input layer and the bth hidden layer in the preset prediction model; d c represents the cth history array; m represents the number of history arrays; α brepresents the b-th hidden layer model parameters; δ represents the output layer model parameters.

[0080] The preset prediction model is formed by training with a large amount of electricity consumption-related data, population-related data, weather-related data, and industry-related data from different regions. The connection weights between the input layer and the hidden layer, as well as the connection weights between the output layer and the hidden layer obtained through training, can accurately mine the characteristic relationship between electricity consumption and population, weather, and industry, thereby achieving accurate prediction of electricity consumption data in the planned area within the preset period of time.

[0081] Further, the predicted first power generation data, second power generation data and power consumption data are formed into a power size relationship curve to reflect the size change relationship between the power generation and power consumption of the planned area within the preset time period. Among them, the power size relationship curve may include a power generation curve and a power consumption curve, and its form may be a curve formed on a two-dimensional coordinate system, the horizontal axis on the two-dimensional coordinate system represents each time point within the preset time period, and the vertical axis represents the power size. From each first power generation data and each second power generation data, each first power generation and each second power generation at the same time point within the preset time period are selected, and each first power generation and each second power generation at the same time point are added and processed to obtain the sum of power generation at each time point within the preset time period, and then each power generation sum is added to the two-dimensional coordinate system according to their respective time sequence, that is, to form a power generation curve. Similarly, the power consumption data can be added to the two-dimensional coordinate system according to the time sequence reflected by the time attribute of the power consumption data, that is, to form a power consumption curve. The size of the corresponding ordinate of the power generation curve and the power consumption curve in the coordinate system reflects the size relationship between power consumption and power generation, and together form a power size relationship curve. It should be noted that the time length represented by the time point on the horizontal axis can be set according to demand, for example, set to one day. If the preset period is one year, the horizontal axis corresponds to 365 time points, and the vertical axis is also correspondingly represented as the total power generation formed by the sum of the first power generation data and the second power generation data of each day.

[0082] S30. Determine the optimization mode of the new energy power station according to the electricity quantity relationship curve. If the optimization mode is power station optimization, output prompt information for performing distributed optimization on the new energy power station in the area to be planned.

[0083] Furthermore, for the electric quantity relationship curve in the two-dimensional coordinate system, in the same horizontal coordinate, the ordinates corresponding to the power generation curve and the power consumption curve may be the same or different. If they are the same, it means that the power generation and power consumption are equal at that time. If they are different and the ordinate corresponding to the power generation curve is greater than the ordinate corresponding to the power consumption, it means that the power generation at that time is greater than the power consumption, indicating that the circuits issued by the new energy power generation station and the traditional power generation station are sufficient to meet the use demand; if they are different and the ordinate corresponding to the power generation curve is less than the ordinate corresponding to the power consumption, it means that the power generation at that time is less than the power consumption, indicating that the circuits issued by the new energy power generation station and the traditional power generation station are insufficient to meet the use demand. At the same time, the power generation curve and the power consumption curve in the power quantity relationship curve can also be used to reflect the total power generation and total power consumption demand of the area to be planned within the preset time period. According to the size relationship between the total power generation and the total power consumption, it is determined whether the total power generation within the preset time period can meet the total power consumption demand, and then the optimization mode of the new energy power generation station is determined. The optimization mode includes a mode of distributed optimization of new energy power generation stations and a mode of distributed optimization of energy storage power stations, which are specifically determined based on the size relationship between the total power generation and the total power consumption. If it is determined that the optimization mode is power generation station optimization, a prompt message for distributed optimization of new energy power generation stations in the planned area is output. Specifically, the optimization mode of the new energy power generation station is determined based on the power size relationship curve, including:

[0084] S31, determining the total power generation and total power consumption of the planned area within a preset period of time based on the power quantity relationship curve, and determining the size relationship between the total power consumption and the total power generation;

[0085] S32. If the size relationship is that the total electricity consumption is greater than the total power generation, determine whether the difference between the total electricity consumption and the total power generation is greater than a preset electricity difference; if it is greater than the preset electricity difference, determine the optimization mode as power station optimization; if the size relationship is that the total electricity consumption is less than the total power generation, determine the optimization mode as energy storage power station optimization.

[0086] Furthermore, for the power generation curve in the power size relationship curve, the area enclosed by the horizontal axis and the vertical axis in the two-dimensional coordinate system reflects the total power generation of the area to be planned in the preset period, and the total power generation can be obtained by integral calculation. For the power consumption curve in the power size relationship curve, the total power consumption of the area to be planned in the preset period can also be reflected by the area enclosed by the horizontal axis and the vertical axis in the two-dimensional coordinate system, and the total power consumption can also be obtained by integral calculation.

[0087] Furthermore, after the total electricity consumption and the total power generation are calculated, the two are compared to determine the size relationship between them. If the size relationship between the two is that the total electricity consumption is greater than the total power generation, it means that the power generation in the planned area within the preset period is insufficient and the power generation needs to be increased. Among them, the way to increase the power generation can be to increase it through the peak-shaving units of traditional power plants or to increase it through the construction of new energy power plants. Taking into account the high cost of building new energy power plants, when the power generation that needs to be increased is not much, it is preferred to increase it through the peak-shaving units of traditional power plants.

[0088] Furthermore, a preset power difference indicating that a small amount of power generation needs to be increased is pre-set, and a difference operation is performed between the total power consumption and the total power generation, and the calculated difference is compared with the preset power difference to determine whether the calculated difference is greater than the preset power difference. If it is greater than the preset power difference, it means that a large amount of power generation needs to be increased. If the power generation is increased by the peak-shaving unit in the traditional power station, it may have a greater impact on the environment. It should be considered to increase the power generation by building a new energy power station. At this time, the optimization mode is determined as power station optimization, and the difference between the total power consumption and the total power generation is formed as a prompt information output for adding a new energy power station, prompting to add a new energy power station according to the power generation reflected by the difference, so as to perform distributed optimization of the new energy power station in the planned area. On the contrary, if the difference calculated by the comparison is less than or equal to the preset power difference, it means that the power generation needs to be increased is not much, and the power generation can be increased by the peak-shaving unit in the traditional power station, thereby outputting a prompt information for increasing or starting the peak-shaving unit for power generation.

[0089] Furthermore, if it is determined through comparison that the total amount of electricity consumption and the total amount of power generation is that the total amount of electricity consumption is less than the total amount of power generation, it means that there is surplus power generation in the planned area within the preset time period. At this time, the surplus power generation needs to be stored, so the optimization mode is determined as energy storage power station optimization, and based on the electricity size relationship curve, the capacity of multiple energy storage power stations used to store energy for each new energy power station in the planned area is determined. By setting the capacity of the energy storage power station to match the surplus power generation, accurate storage of the surplus power generation is achieved, thereby realizing distributed optimization of the energy storage power station.

[0090] S40. If the optimization mode is energy storage power station optimization, determine the capacity of multiple energy storage power stations used to store energy for each new energy power generation station in the area to be planned, and perform distributed optimization of the energy storage power stations in the area to be planned based on the capacity.

[0091] Furthermore, after determining that the optimization mode is energy storage power station optimization, it is necessary to plan the capacity and geographical location of the energy storage power station, through the power size relationship curve, determine whether there is surplus power generation in the planned area within the preset time period, and determine the capacity of the energy storage power station used to store the surplus power of the new energy power generation station according to the size of the surplus, and then plan each energy storage power station to accurately store the surplus power through the determined capacity sizes, so as to achieve distributed optimization of the energy storage power stations in the planned area. Specifically, the determination of the capacity sizes of multiple energy storage power stations used to store energy for each new energy power generation station in the planned area includes:

[0092] S41, identifying the maximum difference value in the electric quantity relationship curve, and determining the total capacity of the plurality of energy storage power stations according to the maximum difference value;

[0093] S42, generating each of the first power generation data into a power generation proportion, and dividing the total capacity into to-be-stored capacities corresponding to each of the new energy power generation stations according to each of the power generation proportions;

[0094] S43. Determine the energy storage capacity of the energy storage power station corresponding to each of the new energy power generation stations based on the capacities to be stored, obtain the capacity loss value corresponding to each of the energy storage capacities, and correct each of the energy storage capacities to obtain the capacity of the energy storage power station corresponding to each of the new energy power generation stations.

[0095] Furthermore, the power generation curve and the power consumption curve in the power size relationship curve are used to generate multiple vertical axis differences based on the horizontal axis coordinates. Each vertical axis difference reflects the difference between the power generation and the power consumption at a moment. Then, a comparison is made between the various differences to determine the maximum difference. The maximum difference reflects the maximum remaining power that needs to be stored, so it can be used as the total storage capacity required by all energy storage power stations.

[0096] Furthermore, the total power generation is derived from the sum of each new energy power station, and the remaining power is also derived from each new energy power station accordingly. In order to store the remaining power of each new energy power station nearby, this embodiment is provided with a storage mechanism according to the power generation ratio. Specifically, each first power generation data is generated as a power generation ratio, that is, each first power generation data is generated as the sum of their respective power generation. Specifically, the integration method as described above can be used to reflect the power generation of each new energy power station, and then the power generation of each individual power station is used as a ratio with the total power generation to obtain the power generation ratio of each new energy power station in the total power generation, that is, the power generation ratio is generated. Then, according to the ratio of the power generation ratio, the total capacity is divided accordingly, divided into corresponding to each ratio, to form the storage capacity to be stored of the storage power station corresponding to each new energy power station. For example, among the five renewable energy power plants in the planned area, their respective power generation in the preset time period accounts for 30%, 20%, 10%, 25% and 15%. Therefore, a storage power station can be set up for each renewable energy power plant, and the storage capacity of each storage power station accounts for 30%, 20%, 10%, 25% and 15% of the total capacity respectively.

[0097] Furthermore, after determining the capacity to be stored of each storage power station, a product operation can be performed between the capacity to be stored and the total capacity to obtain the storage capacity of each storage power station corresponding to each new energy power generation station. In addition, considering that the storage power station may have energy loss in the process of storing electric energy, including but not limited to battery charging loss, electric energy loss in the process of energy storage and discharge, and electric energy loss in the power conversion system, and the loss caused by different storage capacities is different. Therefore, the energy loss corresponding to each storage capacity is obtained as the capacity loss value corresponding to each energy storage capacity, and each energy storage capacity is corrected according to the capacity loss value to obtain the capacity size of the energy storage power station corresponding to each new energy power generation station. The corrected capacity size takes into account the energy loss, which is greater than the capacity before correction, and can match the residual electric energy of the new energy power generation station to achieve effective storage of the residual electric energy.

[0098] The distributed optimization method of the new energy power station and the energy storage power station implemented in this embodiment predicts the first power generation data of the new energy power station in the planning area within the preset time period, and predicts the second power generation data of the traditional power station in the planning area within the same preset time period, and also predicts the power consumption data of the planning area within the preset time period; generates a power size relationship curve based on the predicted first power generation data, second power generation data and power consumption data; and then determines the optimization mode of the new energy power station based on the power size relationship curve. If the determined optimization mode is power station optimization, it means that the planning of the new energy power station in the planning area is unreasonable, so the new energy power station is distributedly optimized. If the optimization mode is determined to be energy storage power station optimization, it means that the energy storage power station in the planning area needs to be optimized, so as to determine the capacity of multiple energy storage power stations used for energy storage of each new energy power station in the planning area, and according to the capacity, the energy storage power station in the planning area is distributedly optimized. Among them, the electricity size relationship curve reflects the size relationship between the total power generation and power consumption of the combination of traditional energy and new energy. From this, the optimization mode including distributed optimization of new energy power generation stations and distributed optimization of energy storage power stations used to store energy for new energy power generation stations can be determined. The problem of insufficient total power generation is avoided through power generation station optimization. As for the optimization of energy storage power stations, the capacity of energy storage power stations that can accurately match the remaining electricity except for power consumption is determined to achieve accurate storage of the remaining electricity and minimize energy abandonment. At the same time, the corresponding location information is determined in combination with the factors affecting the environment caused by the generated noise to avoid a large noise impact on the environment.

[0099] Further, see Figure 2 Based on the first embodiment of the distributed optimization method of renewable energy power generation stations and energy storage power stations of the present invention, a second embodiment of the distributed optimization method of renewable energy power generation stations and energy storage power stations of the present invention is proposed.

[0100] The difference between the second embodiment of the distributed optimization method for new energy power generation stations and energy storage power stations and the first embodiment of the distributed optimization method for new energy power generation stations and energy storage power stations is that the new energy power generation station is a wind power generation station, and the first power generation data of each new energy power generation station in the area to be planned within a preset time period is predicted to include:

[0101] S11, obtaining historical wind direction data, historical wind speed data, historical air density data and historical wind power generation hours for each wind power station in multiple historical time periods corresponding to a preset time period;

[0102] S12, predicting the average effective wind speed of the wind power plant in the preset time period and the effective duration corresponding to the average effective wind speed according to each of the historical wind direction data and each of the historical wind speed data, and predicting the number of wind power generation hours according to the effective duration and the number of historical wind power generation hours;

[0103] S13, predicting the average air density of the wind power plant in the preset time period according to each of the historical air density data, and finding the wind turbine parameters, prediction correction coefficient and first-year attenuation coefficient of the wind power plant;

[0104] S14. Calculate the first power generation data of the wind power plant in a preset time period based on the first annual attenuation coefficient, average effective wind speed, average air density, wind power generation hours, prediction correction coefficient and the blade length, wind energy utilization coefficient, blade area, blade windward angle and generator angular velocity of the wind turbine parameters.

[0105] The new energy power station of this embodiment is preferably a wind power station. For the wind power station, its power generation in the preset time period is mainly related to factors such as wind speed, wind direction, and parameters of the wind turbine itself in the wind power station. Specifically, the time period corresponding to the preset time period in the past is used as the historical time period corresponding to the preset time period. For each wind power station, its historical wind direction data, historical wind speed data, historical air density data and historical wind power generation hours in multiple historical time periods are obtained, and the historical wind direction data, historical wind speed data, historical air density data and historical wind power generation hours from the same historical time period are formed into a data group. Then, based on the historical wind direction data and historical wind speed data in each data group, the average effective wind speed of the new energy power station in the preset time period is predicted. The average effective wind speed is the wind speed that can be used for wind turbine power generation. The wind direction and wind speed at each time in a preset time period can be predicted through the wind direction change rules and wind speed change rules reflected by the historical wind direction data and historical wind speed data of multiple historical time periods. Then, the effective wind speed that can be used for power generation at each time is predicted based on the wind speed and wind direction at each time. Then, the average effective wind speed in the preset time period is obtained by averaging each effective wind speed. At the same time, the time length corresponding to each effective wind speed is accumulated and processed as the effective time length corresponding to the average effective wind speed.

[0106] Furthermore, according to the changing rules of wind power generation time reflected by the historical wind power generation hours in each data group, the power generation hours in the preset time period are predicted, and the predicted power generation hours are corrected by the effective time to obtain the final wind power generation hours. During the correction, it is determined whether the predicted power generation hours exceed the reasonable range of the effective time. If it exceeds the reasonable range, it means that the power generation time of the average effective wind speed cannot reach the power generation hours, so the power generation hours are reduced. If it does not exceed the reasonable range, the power generation hours are used as wind power generation hours. Among them, the reasonable range can be set in advance through analysis and set the set value, and the amount of power generation hours reduced can also be preset through analysis and set to correspond to the set value.

[0107] Furthermore, the power generation of a wind power plant is also related to air density. The historical air density data in each data group is the average air density in each historical period. The average air density in a preset period is predicted by the air density change law in each historical period reflected by each historical air density data.

[0108] Understandably, as the wind power plant generates electricity, its own performance is impaired, resulting in a reduction in power generation. This influencing factor can be determined in advance through testing and used as the first-year attenuation coefficient. At the same time, there may be errors in the number of wind power generation hours based on the prediction of historical data in previous years and the actual average effective wind speed, which affects the accuracy of the power generation forecast. For this influencing factor, a forecast correction coefficient can be generated based on the difference between the predicted data and the actual data in previous years. For example, for the average effective wind speed in 2023, the prediction based on the previous historical wind direction data and historical wind speed data is A, but the actual average effective wind speed is B, then the forecast correction coefficient can be formed as B / A. In addition, another correction coefficient can be generated by combining the difference between the predicted number of wind power generation hours and the actual number of wind power generation hours, and the average processing is performed between the two correction coefficients to obtain the final forecast correction coefficient.

[0109] Furthermore, the power generation of a wind power plant is also related to the parameters of the wind turbines of the wind power plant itself, which at least include the blade length, wind energy utilization coefficient, blade area and blade windward angle of the wind turbine. Find the parameters of such wind turbines, the prediction correction coefficient and the first-year attenuation coefficient of the new energy power plant, and then calculate the first power generation data of the new energy power plant in the preset time period in combination with the predicted average effective wind speed, average air density and wind power generation hours. The specific calculation formula is:

[0110]

[0111] Wherein, Q1 represents the first power generation data; N represents the number of wind power generation hours; n1 represents the number of wind power plants; n2 represents the number of wind turbines in the wind power plants; r1 represents the first annual attenuation coefficient; represents the wind energy utilization coefficient of the jth wind turbine in the i-th wind power station; ρ represents the average air density; A ij represents the blade area of ​​the jth wind turbine in the i-th wind power station; B ij represents the windward angle of the blades of the jth wind turbine in the i-th wind power station; L ij represents the blade length of the jth wind turbine in the i-th wind power station; V represents the average effective wind speed; ω ij represents the angular velocity of the generator in the jth wind turbine in the i-th wind power plant; τ represents the prediction correction coefficient; Used to calculate the motor torque of the jth wind turbine in the i-th wind power plant.

[0112] This embodiment predicts the effective wind speed, air density and generating hours within a preset time period for a wind power plant based on the past wind speed, wind direction, air density and generating hours, and takes into account the error caused by the prediction, and combines the parameters of the wind power plant itself, as well as the attenuation factor of the wind power plant with the years of power generation, to calculate the first power generation data of the wind power plant within the preset time period, comprehensively considering the factors affecting the power generation of the wind generator, so that the calculation of the first power generation data is more accurate.

[0113] Further, see Figure 3 Based on the first and second embodiments of the distributed optimization method for renewable energy power generation stations and energy storage power stations of the present invention, a third embodiment of the distributed optimization method for renewable energy power generation stations and energy storage power stations of the present invention is proposed.

[0114] The third embodiment of the distributed optimization method for new energy power generation stations and energy storage power stations is different from the first and second embodiments of the distributed optimization method for new energy power generation stations and energy storage power stations in that the new energy power generation station is a photovoltaic power generation station, and the first power generation data of each new energy power generation station in the area to be planned within a preset time period is predicted to include:

[0115] S15, obtaining historical light temperature data and historical light intensity data of the photovoltaic power station in multiple historical time periods corresponding to the preset time period, and predicting the light temperature data and light intensity data of the photovoltaic power station in the preset time period respectively according to each of the historical light temperature data and each of the historical light intensity data;

[0116] S16, fitting the illumination temperature data and the illumination intensity data into a temperature-time function and an intensity-time function within a preset period of time, respectively, and generating the illumination intensity data as an average illumination intensity within the preset period of time;

[0117] S17, searching for the second-year attenuation coefficient, hourly reference output power, temperature coefficient corresponding to the photovoltaic power generation station, and the hourly reference light intensity and hourly reference temperature corresponding to the hourly reference output power;

[0118] S18. Calculate the first power generation data of the photovoltaic power station within a preset time period based on the second annual attenuation coefficient, hourly reference output power, temperature coefficient, hourly reference light intensity, hourly reference temperature, and average light intensity, temperature-time function, and intensity-time function.

[0119] The new energy power generation station of this embodiment is preferably a photovoltaic power generation station. For the photovoltaic power generation station, its power generation in the preset time period is mainly related to factors such as light temperature and light intensity. Specifically, the time period corresponding to the preset time period in the past is used as the historical time period corresponding to the preset time period. For each photovoltaic power generation station, its historical light temperature data and historical light intensity data in multiple historical time periods are obtained, and the historical light temperature data and historical light intensity data from the same historical time period are formed into a data group. Then, according to the light temperature change law in each historical time period reflected by the historical light temperature data in each data group, the light temperature data of the preset time period is predicted; and according to the light intensity change law in each historical time period reflected by the historical light intensity data in each data group, the light intensity data of the preset time period is predicted.

[0120] Furthermore, the light temperature data and light intensity data obtained through prediction have time attributes, indicating the light temperature and intensity at a certain time in a preset period. Preferably, the time is set to hours, which means the light temperature and light intensity for each hour of each day in the preset period. During the prediction, the historical light temperature data of each historical period are generated as the average light temperature per hour according to the year, month, day and time of each period, and then the light temperature of that day and hour in the preset period is predicted based on the light temperature variation law reflected by the average light temperature of the same hour on the same day of multiple historical years; the historical light intensity data of each historical period are also generated as the average light intensity per hour according to the year, month, day and time of each period, and the light intensity of that day and hour in the preset period is predicted based on the light intensity variation law reflected by the average light intensity of the same hour on the same day of multiple historical years. After obtaining the light temperature and light intensity of each hour of each day in the preset period, the light temperature data and light intensity data in the preset period are obtained.

[0121] Furthermore, a two-dimensional coordinate system is established, the horizontal axis represents time, and the time interval is in hours, the vertical axis represents light temperature and light intensity, and each light temperature data is added to the two-dimensional coordinate system in the order of time days and hours, and each light intensity data is also added to the two-dimensional coordinate system in the order of time days and hours. Then, each light temperature data and each light intensity data in the two-dimensional coordinate system are fitted respectively to form a temperature time function and an intensity time function within a preset period of time, so as to reflect the change rules of light temperature and light intensity over time. At the same time, the light intensity data is processed by average value to generate the average light intensity within the preset value period, so as to reflect the average size of the light intensity of each day within the preset period of time.

[0122] Furthermore, in order to accurately predict the power generation of photovoltaic power stations, the hourly reference output power of wind power stations is also pre-set. The hourly reference output power is obtained by setting a specific light intensity and light temperature for a one-hour test, which represents the power generation output power of the photovoltaic power station for one hour under the specific light intensity and light temperature. In addition, considering that the photovoltaic panels in the photovoltaic power station are made of different materials, the materials have different physical properties and the rate of change with temperature is also different, thus affecting the power generation. The influencing factor is analyzed and determined in advance and used as the temperature coefficient. In addition, considering that as the photovoltaic power station generates electricity, its own performance will be reduced, resulting in a reduction in power generation, the influencing factor is determined in advance through testing and used as the second-year attenuation coefficient.

[0123] Furthermore, the hourly reference output power, temperature coefficient, and second-year attenuation coefficient of this type are searched, and the light intensity and light temperature that generate the hourly reference output power are searched as the hourly reference light intensity and hourly reference temperature, and the first power generation data of the photovoltaic power station in the preset period is calculated by combining the average light intensity, temperature time function, and intensity time function. The specific calculation formula is:

[0124]

[0125] Wherein, Q2 represents the first power generation data; k represents the temperature coefficient; r2 represents the second annual attenuation coefficient; G represents the average light intensity in the preset period; G0 represents the hourly reference light intensity; P0 represents the hourly reference output power; g(t) represents the intensity time function; θ(t) represents the temperature time function; θ0 represents the hourly reference temperature; Used to calculate the light intensity coefficient within a preset time period.

[0126] This embodiment fits the predicted light temperature and light intensity within a preset time period into a temperature-time function and an intensity-time function in hours for a photovoltaic power station, sets an hourly reference output power, combines the hourly reference light intensity and the hourly reference temperature for generating the hourly reference output power, and takes into account the attenuation factor of the photovoltaic power station with the years of power generation, the temperature coefficient related to the physical properties of the photovoltaic panel material, etc., to calculate the first power generation data of the photovoltaic power station within the preset time period, comprehensively considers the factors affecting the power generation of the photovoltaic generator, and subdivides the calculation into hours, so that the calculation of the first power generation data is more accurate.

[0127] Further, see Figure 4 Based on the first, second and third embodiments of the distributed optimization method for renewable energy power generation stations and energy storage power stations of the present invention, a fourth embodiment of the distributed optimization method for renewable energy power generation stations and energy storage power stations of the present invention is proposed.

[0128] The fourth embodiment of the distributed optimization method for new energy power generation stations and energy storage power stations is different from the first, second and third embodiments of the distributed optimization method for new energy power generation stations and energy storage power stations in that the distributed optimization of the energy storage power stations in the area to be planned is performed according to the capacity of each of the capacities, including:

[0129] S44, determining whether the environmental parameters of the environment in which each of the new energy power generation stations is located include water source parameters; if the water source parameters are included, determining the energy storage power station corresponding to the new energy power generation station as a pumped storage power station, and obtaining the water source location corresponding to the water source parameter as the location information of the pumped storage power station;

[0130] S45. If the environmental parameters do not include water source parameters, energy storage noise data corresponding to the capacity of the energy storage power station is obtained, and location information of the energy storage power station corresponding to the new energy power station is determined according to the energy storage noise data;

[0131] S46: Using the location information and the capacity size as optimization parameters of the energy storage power station, and performing distributed optimization on the energy storage power station according to the optimization parameters.

[0132] Furthermore, the energy storage power station used by this embodiment to store energy for each new energy power station in the planned area can be pumped storage or electrochemical energy storage. After determining the capacity of each energy storage power station, the environmental parameters of the environment in which each new energy power station is located are obtained, and the environmental parameters include information on whether there is a water source. Then, the environmental parameters of each new energy power station are identified one by one to determine whether they contain water source parameters. If they contain water source parameters, it means that there is a water source within a preset range around the new energy power station, so that the energy storage power station corresponding to the new energy power station is determined to be a pumping power station. In addition, the location of the water source is obtained as the location information of the energy storage power station, indicating that the energy storage power station used to store energy for the new energy power station can be built at this location.

[0133] Furthermore, if it is determined that the environmental parameters do not include water source parameters, it means that there is no water source within the preset range around the new energy power station. At this time, it is necessary to build an electrochemical energy storage power station to store energy for the new energy power station. In order to facilitate energy storage, energy storage power stations and new energy power stations are usually built in relatively close locations, but the operation of energy storage power stations and new energy power stations will generate noise. If the two are close to each other, the superposition of noise may have a greater impact on the environment. Therefore, in order to avoid greater noise impact, the location of the energy storage power station needs to be determined based on the noise.

[0134] In addition, considering that the construction scale of energy storage power stations of different capacities is different, the noise generated during operation is also different. The noise generated by different capacities and the changing relationship between the noise size and the distance from the noise source are determined in advance through simulation tests, forming a corresponding relationship between the capacity size interval, the noise size interval, and the changing relationship. For new energy power generation stations that do not contain water source parameters, the capacity size of the corresponding energy storage power station is compared with the corresponding relationship to determine the capacity size interval where the capacity size is located, and then the noise size corresponding to the capacity size and its changing relationship are determined according to the noise size interval corresponding to the capacity size interval in the corresponding relationship, and the noise size and its changing relationship are used together as energy storage noise data to determine the location information of the energy storage power station corresponding to the new energy power generation station based on the energy storage noise data. Among them, the determined noise size can be directly the middle value of the noise size interval or the maximum value. Then, the determined location information and capacity size are jointly formed as the optimization parameters of the energy storage power station, and such optimization parameters can be output as prompt information to facilitate distributed optimization of the energy storage power station based on the optimization parameters.

[0135] Specifically, determining the location information of the energy storage power station corresponding to the new energy power generation station according to the energy storage noise data includes:

[0136] S451, acquiring power generation noise data corresponding to the energy storage noise data in each of the new energy power generation stations, and determining initial position information of the energy storage power station;

[0137] S452, according to the initial position information, superimposing the energy storage noise data and the power generation noise data in volume to generate a superposition value that varies with distance;

[0138] S453, determining a maximum value among the superposition values ​​that vary with the distance, and judging whether the maximum value is less than or equal to a preset threshold value, and if the maximum value is less than or equal to the preset threshold value, determining the initial position information as the position information;

[0139] If the maximum value is greater than a preset threshold, the initial position information is adjusted, and according to the adjusted initial position information, a step of adding the volume of the energy storage noise data and the power generation noise data is performed.

[0140] Furthermore, the energy storage noise data is derived from an energy storage power station that stores the electric energy of a new energy power station, and a corresponding relationship is formed between the energy storage noise data and the new energy power station. The new energy power station is found from various new energy power stations, and its power generation noise data is obtained. In addition to the noise level generated by the power generation operation of the new energy power station, the power generation noise data also includes the relationship between the noise level and the distance from the noise source, which is the power generation noise data corresponding to the energy storage noise data.

[0141] Furthermore, the initial location information of the energy storage power station is determined based on the noise level in the energy storage noise data and the noise level in the power generation noise data. A mapping relationship between the noise level and the initial location distance is preset, the noise levels of the two are added, the addition result is obtained and compared with the mapping relationship, and the initial location distance corresponding to the addition result is determined, and then the location area where the energy storage power station is located is determined based on the distance between the new energy power station and the initial location as the initial location information.

[0142] Furthermore, based on the distance between the location area reflected by the initial location information and the location area where the new energy power generation station is located, the noise level in the energy storage noise data is superimposed with the noise level in the power generation noise data to obtain a superimposed value that varies with the distance. For example, the distance determined based on the initial location information is S, and there are two locations W1 and W2 at the distance. The noise level in the energy storage noise data varies with the distance, and the levels at the W1 and W2 positions are p1 and p2 respectively. The noise level in the power generation noise data varies with the distance, and the levels at the W1 and W2 positions are p3 and p4 respectively. The superimposed value superimposed on w1 is p1+p3, and the superimposed value superimposed on w2 is p2+p4.

[0143] Furthermore, each superposition value is compared to determine the maximum value, which represents the maximum noise volume obtained by superimposing the energy storage noise data and the power generation noise data. A preset threshold representing the volume is set in advance, and the determined maximum value is compared with the preset threshold to determine whether the maximum value is less than or equal to the preset threshold. If it is less than or equal to the preset threshold, it means that the noise generated by the superposition of the energy storage power station and the new energy power station is small, and the energy storage power station can be set up at the initial position, so the initial position information is determined as the position information. On the contrary, if it is determined by comparison that the maximum value is greater than the preset threshold, it means that the noise generated by the superposition of the energy storage power station and the new energy power station is large, which has a great impact on the environment. At this time, the initial position information is adjusted, and after the adjustment, the energy storage noise data and the power generation noise data are continued to be superimposed in volume, and the maximum value thereof is determined to be compared with the preset threshold to determine whether the maximum value is less than or equal to the preset threshold. If it is less than or equal to, it means that the adjusted initial position information can meet the environmental requirements and is used as the position information. If the maximum value determined after adjustment is still greater than the preset threshold, the initial position information needs to be adjusted again until the maximum value determined after adjustment is less than or equal to the preset threshold to ensure that the impact on the environment is small.

[0144] This embodiment is for an energy storage power station. When there is a water source around a new energy power generation site, the energy storage power station is set as a pumping power station. When there is no water source around the new energy power station, the location of the energy storage power station is determined based on the impact of the noise generated by the energy storage power station and the impact of the noise generated by the new energy power generation site on the environment, to ensure that the installed energy storage power station does not increase the environmental burden.

[0145] See also Figure 5 The present invention also provides a distributed optimization system for a new energy power station and an energy storage power station, which is applied to the above-mentioned distributed optimization method for a new energy power station and an energy storage power station, and the system includes:

[0146] A power generation data acquisition module is used to predict first power generation data of each new energy power station in the area to be planned within a preset time period, and second power generation data of each traditional power station in the area to be planned within a preset time period;

[0147] An electricity quantity relationship curve generating module is used to predict the electricity consumption data of the area to be planned within a preset time period, and generate an electricity quantity relationship curve according to each of the first power generation data, each of the second power generation data and the power consumption data;

[0148] The distributed optimization mode determination module is used to determine the optimization mode of the new energy power generation station according to the electricity quantity relationship curve. If the optimization mode is power generation station optimization, a prompt message for distributed optimization of the new energy power generation station in the area to be planned is output; if the optimization mode is energy storage power station optimization, the capacity of multiple energy storage power stations used for storing energy for each new energy power generation station in the area to be planned is determined, and distributed optimization of the energy storage power stations in the area to be planned is performed according to each capacity.

[0149] The specific implementation of the distributed optimization system of a new energy power generation station and an energy storage power station of the present invention is basically the same as the various embodiments of the distributed optimization method of the new energy power generation station and the energy storage power station mentioned above, and will not be repeated here.

[0150] See also Figure 6 , the present invention also provides a distributed optimization device for a new energy power generation station and an energy storage power station, including a memory and a processor;

[0151] The memory is used to store computer program code and transmit the computer program code to the processor;

[0152] The processor is used to execute the above-mentioned distributed optimization method of new energy power generation sites and energy storage power stations according to the instructions in the computer program code.

[0153] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the distributed optimization method for a new energy power generation station and an energy storage power station described above is implemented.

[0154] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0155] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0156] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0157] The above-mentioned devices and non-temporary computer-readable storage media can be found in the specific description of a distributed optimization method for a new energy power generation station and an energy storage power station and its beneficial effects, which will not be repeated here.

[0158] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A distributed optimization method for new energy power generation stations and energy storage power stations, characterized in that: include: Predicting first power generation data of each new energy power station in the area to be planned within a preset time period, and second power generation data of each traditional power station in the area to be planned within a preset time period; Predicting the power consumption data of the area to be planned within a preset time period, and generating a power size relationship curve according to each of the first power generation data, each of the second power generation data and the power consumption data; Determine the optimization mode of the new energy power station according to the electric quantity relationship curve, and if the optimization mode is power station optimization, output prompt information for performing distributed optimization on the new energy power station in the area to be planned; If the optimization mode is energy storage power station optimization, the capacity of multiple energy storage power stations used to store energy for each new energy power generation station in the area to be planned is determined, and distributed optimization of the energy storage power stations in the area to be planned is performed based on the capacity.

2. A distributed optimization method for new energy power generation stations and energy storage power stations according to claim 1, characterized in that: The new energy power generation station is a wind power generation station, and the first power generation data of each new energy power generation station in the area to be planned within a preset time period is predicted to include: Obtaining historical wind direction data, historical wind speed data, historical air density data and historical wind power generation hours for each of the wind power plants in a plurality of historical time periods corresponding to a preset time period; According to each of the historical wind direction data and each of the historical wind speed data, predict the average effective wind speed of the wind power plant in the preset time period and the effective duration corresponding to the average effective wind speed, and predict the wind power generation hours according to the effective duration and the historical wind power generation hours; Predicting the average air density of the wind power plant in the preset time period according to each of the historical air density data, and finding the wind turbine parameters, prediction correction coefficient and first-year attenuation coefficient of the wind power plant; The first power generation data of the wind power plant in a preset period is calculated according to the first annual attenuation coefficient, the average effective wind speed, the average air density, the number of wind power generation hours, the prediction correction coefficient and the blade length, wind energy utilization coefficient, blade area, blade windward angle and generator angular velocity in the wind turbine parameters, and the calculation formula is: Wherein, Q1 represents the first power generation data; N represents the number of wind power generation hours; n1 represents the number of wind power plants; n2 represents the number of wind turbines in the wind power plants; r1 represents the first annual attenuation coefficient; represents the wind energy utilization coefficient of the jth wind turbine in the i-th wind power station; ρ represents the average air density; A ij represents the blade area of ​​the jth wind turbine in the i-th wind power station; B ij represents the windward angle of the blades of the jth wind turbine in the i-th wind power station; L ij represents the blade length of the jth wind turbine in the i-th wind power station; V represents the average effective wind speed; ω ij represents the angular velocity of the generator in the jth wind turbine in the i-th wind power plant; τ represents the prediction correction coefficient; Used to calculate the motor torque of the jth wind turbine in the i-th wind power plant.

3. A distributed optimization method for new energy power generation stations and energy storage power stations according to claim 1, characterized in that: The new energy power generation station is a photovoltaic power generation station, and the first power generation data of each new energy power generation station in the area to be planned within a preset time period is predicted to include: Acquire historical light temperature data and historical light intensity data of the photovoltaic power station in multiple historical time periods corresponding to the preset time period, and predict the light temperature data and light intensity data of the photovoltaic power station in the preset time period respectively according to each of the historical light temperature data and each of the historical light intensity data; Fitting the illumination temperature data and the illumination intensity data into a temperature-time function and an intensity-time function within a preset period of time, respectively, and generating the illumination intensity data as an average illumination intensity within the preset period of time; Finding the second-year attenuation coefficient, hourly reference output power, temperature coefficient corresponding to the photovoltaic power generation station, and the hourly reference light intensity and hourly reference temperature corresponding to the hourly reference output power; According to the second annual attenuation coefficient, hourly reference output power, temperature coefficient, hourly reference light intensity, hourly reference temperature, and the average light intensity, temperature time function, and intensity time function, the first power generation data of the photovoltaic power station in the preset time period is calculated, and the calculation formula is: Wherein, Q2 represents the first power generation data; k represents the temperature coefficient; r2 represents the second annual attenuation coefficient; G represents the average light intensity in the preset period; G0 represents the hourly reference light intensity; P0 represents the hourly reference output power; g(t) represents the intensity time function; θ(t) represents the temperature time function; θ0 represents the hourly reference temperature; Used to calculate the light intensity coefficient within a preset time period.

4. A distributed optimization method for new energy power generation stations and energy storage power stations according to claim 1, characterized in that: The predicting of the power consumption data of the area to be planned within a preset period of time includes: Obtaining historical electricity consumption data of the area to be planned in multiple historical time periods corresponding to the preset time periods, as well as population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data, and tertiary industry change parameters in each historical time period; Generate historical power consumption data, population data, population change parameters, weather data, weather change parameters, primary industry data, primary industry change parameters, secondary industry data, secondary industry change parameters, tertiary industry data and tertiary industry change parameters for each historical period into a historical array for each historical period; Based on the preset prediction model, the historical array of each historical period is analyzed and calculated to obtain the power consumption data of the area to be planned within the preset period, wherein the formula used for analysis and calculation in the preset prediction model is: Where, H represents the power consumption data; W hb represents the connection weight between the output layer and the bth hidden layer in the preset prediction model; s represents the number of hidden layers in the preset prediction model; W cb represents the connection weight between the input layer and the bth hidden layer in the preset prediction model; d c represents the cth history array; m represents the number of history arrays; α b represents the b-th hidden layer model parameters; δ represents the output layer model parameters.

5. A distributed optimization method for new energy power generation stations and energy storage power stations according to claim 1, characterized in that: The determining of the capacity of a plurality of energy storage power stations for storing energy for each new energy power generation station in the area to be planned includes: Identifying the maximum value of the difference in the electric quantity relationship curve, and determining the total capacity of the plurality of energy storage power stations according to the maximum value of the difference; Generating each of the first power generation data into a power generation proportion, and dividing the total capacity into to-be-stored capacities corresponding to each of the new energy power generation stations according to each of the power generation proportions; According to each of the capacities to be stored, the energy storage capacity of the energy storage power station corresponding to each of the new energy power generation stations is determined, and the capacity loss value corresponding to each of the energy storage capacities is obtained to correct each of the energy storage capacities, so as to obtain the capacity size of the energy storage power station corresponding to each of the new energy power generation stations.

6. A distributed optimization method for new energy power generation stations and energy storage power stations according to claim 1, characterized in that: The distributed optimization of the energy storage power stations in the area to be planned according to the capacity sizes includes: Determine whether the environmental parameters of the environment in which each of the new energy power generation stations is located include water source parameters. If the water source parameters are included, determine the energy storage power station corresponding to the new energy power generation station as a pumped storage power station, and obtain the water source location corresponding to the water source parameters as the location information of the pumped storage power station; If the environmental parameters do not include water source parameters, energy storage noise data corresponding to the capacity of the energy storage power station is obtained, and location information of the energy storage power station corresponding to the new energy power station is determined according to the energy storage noise data; The location information and the capacity size are used as optimization parameters of the energy storage power station, and distributed optimization is performed on the energy storage power station according to the optimization parameters.

7. A distributed optimization method for new energy power generation stations and energy storage power stations according to claim 6, characterized in that: The determining, according to the energy storage noise data, the location information of the energy storage power station corresponding to the new energy power generation station comprises: Acquire power generation noise data corresponding to the energy storage noise data in each of the new energy power generation stations, and determine the initial position information of the energy storage power station; According to the initial position information, the energy storage noise data and the power generation noise data are superimposed in volume to generate a superposition value that varies with distance; Determine a maximum value among the superposition values ​​that vary with the distance, and judge whether the maximum value is less than or equal to a preset threshold value, and if the maximum value is less than or equal to the preset threshold value, determine the initial position information as the position information; If the maximum value is greater than a preset threshold, the initial position information is adjusted, and according to the adjusted initial position information, a step of adding the volume of the energy storage noise data and the power generation noise data is performed.

8. A distributed optimization method for new energy power generation stations and energy storage power stations according to claim 1, characterized in that: Determining the optimization mode of the new energy power generation station according to the electric quantity relationship curve includes: Determine the total power generation and total power consumption of the planned area within a preset period of time based on the power quantity relationship curve, and determine the size relationship between the total power consumption and the total power generation; If the relationship is that the total power consumption is greater than the total power generation, then determine whether the difference between the total power consumption and the total power generation is greater than a preset power difference; if so, determine the optimization mode as power station optimization; If the size relationship is that the total power consumption is less than the total power generation, the optimization mode is determined as energy storage power station optimization.

9. A distributed optimization system for new energy power generation stations and energy storage power stations, characterized in that: The system is applied to the method described in any one of claims 1 to 8, and the system comprises: A power generation data acquisition module is used to predict first power generation data of each new energy power station in the area to be planned within a preset time period, and second power generation data of each traditional power station in the area to be planned within a preset time period; An electricity quantity relationship curve generating module is used to predict the electricity consumption data of the area to be planned within a preset time period, and generate an electricity quantity relationship curve according to each of the first power generation data, each of the second power generation data and the power consumption data; The distributed optimization mode determination module is used to determine the optimization mode of the new energy power generation station according to the electricity quantity relationship curve. If the optimization mode is power generation station optimization, a prompt message for distributed optimization of the new energy power generation station in the area to be planned is output; if the optimization mode is energy storage power station optimization, the capacity of multiple energy storage power stations used for storing energy for each new energy power generation station in the area to be planned is determined, and distributed optimization of the energy storage power stations in the area to be planned is performed according to each capacity.

10. A distributed optimization device for a new energy power station and an energy storage power station, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 8 according to instructions in the computer program code.

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