Measurement optimization configuration method and system considering new energy space distribution difference

Abstract

The invention provides a measurement optimization configuration method and system considering new energy space distribution difference, and the method comprises the steps: generating an initial configuration scheme based on the latitude and longitude coordinate vectors of a terrain outer contour curve in a to-be-configured region; generating a measurement area matrix based on the initial configuration scheme, and calculating a integrating degree index in combination with measurement data; and establishing a multi-target optimization configuration model by taking the minimum equipment configuration number and the highest data integrating degree as targets, and solving a Pareto solution set by adopting an epsilon-constraint method to obtain a configuration scheme set corresponding to the equipment number and the data integrating degree. The optimal measurement point configuration scheme is generated under the condition that the constraints of cost and precision are considered, the arrangement of redundant measurement points is effectively reduced, the overall cost of the system is reduced, meanwhile, the precision of real-time environment data collection is improved, and a reliable guarantee is provided for the improvement of the accuracy of load prediction and the new energy consumption efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network measurement optimization configuration, and in particular to a measurement optimization configuration method and system that takes into account the spatial distribution differences of new energy sources. Background Art

[0002] In recent years, China has made significant progress in the development of renewable energy, leading the world in wind power and photovoltaics. However, the output of renewable energy sources such as wind turbines and photovoltaics is characterized by significant randomness, volatility, and spatial distribution differences, posing numerous challenges to their stable application in power grids.

[0003] The randomness of renewable energy stems from variations in the natural environment, such as weather, season, and time of day, making wind and solar power generation difficult to predict. Furthermore, the spatial distribution of renewable energy significantly impacts load forecasting and its absorption. Wind and solar resource endowments vary across regions; some areas may be rich in wind or solar power, while others may be relatively scarce. This uneven spatial distribution necessitates that power grids consider the generation capacity and effective load of each distributed renewable energy source when conducting load forecasts, enabling precise scheduling.

[0004] In power systems incorporating large-scale renewable energy, achieving effective renewable energy consumption and load forecasting requires the use of advanced measurement technologies. Grid measurement and configuration technology is a crucial component of the power system landscape, involving real-time monitoring, measurement, and analysis of various system parameters and states. In modern power systems, measurement and configuration technology plays a crucial role in ensuring stable operation and efficient management.

[0005] The primary goal of grid measurement configuration technology is to monitor grid parameters such as current, voltage, power, and frequency in real time, as well as the status of various devices such as switches, transformers, and generators, by installing appropriate sensors and measuring equipment. This data helps operators understand the grid's operating status in real time, promptly identify and resolve potential issues, and improve system reliability and efficiency. However, to address the randomness and volatility of renewable energy, grid measurement systems must not only monitor traditional grid parameters but also accurately collect environmental variables closely related to renewable energy generation, such as wind speed, light intensity, and temperature. Because these environmental variables directly affect renewable energy output and the accuracy of grid load forecasts, their measurement accuracy plays a key role in the accuracy of load forecasts and the efficiency of renewable energy absorption.

[0006] By measuring the output of renewable energy in real time, the power grid can better understand the current power generation status and promptly adjust its dispatch strategy to address the challenges brought by fluctuations. Accurately collecting data such as wind speed, sunlight intensity, and temperature not only provides more reliable basic data for load forecasting, but also helps grid managers optimize renewable energy absorption strategies and improve system flexibility and reliability.

[0007] To address the randomness and volatility of renewable energy output, foreign countries primarily use weather forecast data to predict photovoltaic and wind turbine power. However, existing methods have several limitations. First, the measurement of basic data for renewable energy output forecasting has not received sufficient attention, especially regarding the configuration and optimization of measurement equipment, which lacks in-depth research. Second, although the impact of weather changes on renewable energy has been incorporated into prediction models, errors in weather forecasts and interference from external factors often lead to low prediction accuracy, which in turn affects actual scheduling and operation. More importantly, existing methods generally ignore the spatial distribution differences of renewable energy resources and fail to effectively incorporate these spatial distribution differences into prediction models. This leads to significant deviations between prediction results and actual output, which in turn affects the grid's load forecast accuracy and the grid's ability to absorb renewable energy. When renewable energy is distributed and connected to the distribution network, configuring independent measurement equipment for each access point often leads to a surge in the number of devices, significantly increasing configuration costs. Summary of the Invention

[0008] To address the problem in existing technologies of large deviations between prediction results and actual output of renewable energy, which in turn affects the load prediction accuracy of the power grid and the ability to accommodate renewable energy, the present invention proposes a measurement optimization configuration method that considers the spatial distribution differences of renewable energy, including:

[0009] Generate an initial configuration plan based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured;

[0010] Generate a measurement area matrix based on the initial configuration scheme, and calculate a fit index based on the measurement data;

[0011] A multi-objective optimization configuration model is established with the goal of minimizing the number of equipment configurations and maximizing data fit. The ε-constraint method is used to find the Pareto solution set, and a set of configuration schemes corresponding to the number of equipment and data fit is obtained.

[0012] Optionally, generating an initial configuration solution based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured includes:

[0013] Extract the latitude and longitude coordinate vectors of the terrain outer contour curve from the .shp file of the area to be configured;

[0014] Obtaining a plan view of the region from the latitude and longitude coordinate vectors, and rasterizing the plan view;

[0015] Number the center coordinates of each grid in the order of increasing latitude first and then longitude;

[0016] An initial configuration scheme of the measurement equipment is generated based on the number.

[0017] Optionally, generating a measurement area matrix based on the initial configuration scheme and calculating a fit index in combination with measurement data includes:

[0018] Calculate the distance from each grid to each measuring device through the haversine function;

[0019] Generate a distance matrix from the centers of all grids in the region to the measurement devices based on the distance from each grid to each measurement device;

[0020] Calculate the measurement data of each grid containing a certain amount of uncontrollable noise influence according to the distance from each grid to each measuring device;

[0021] Calculate indirect measurements of wind turbine and photovoltaic output based on environmental measurement data;

[0022] The cross entropy index in information theory is used to calculate the correlation between the indirect measurement value and the actual data vector, and the fit index of the configuration scheme is obtained.

[0023] Optionally, the compatibility index is calculated as follows:

[0024]

[0025] Where: λ is the fit index; i is the latitude number of the measurement data vector; m is the latitude of the measurement data vector; h(·) is the calculation function of wind turbine and photovoltaic output; σ is the ratio of the number of measurement grids to the number of all grids in the region; x lc,i 、x zs,i They represent the measured value and true value of wind speed, temperature, light intensity and other data in dimension i respectively.

[0026] Optionally, establishing a multi-objective optimization configuration model with the goal of minimizing the number of device configurations and maximizing data compatibility includes:

[0027] Construct a multi-objective function with the goal of minimizing the number of device configurations and maximizing data compatibility;

[0028] Setting constraints for the objective function;

[0029] A multi-objective optimization configuration model is constructed based on the objective function and the constraint conditions.

[0030] Optionally, the method of using the ε-constraint method to find the Pareto solution set to obtain a set of configuration solutions corresponding to the number of devices and the degree of data compatibility includes:

[0031] According to the ε-constraint method, the multi-objective function is converted into gradient constraints;

[0032] The whale algorithm is used to solve the gradient constraints and obtain a set of configuration solutions corresponding to the number of devices and data fit.

[0033] Optionally, the multi-objective optimization configuration model is as shown below:

[0034]

[0035] Where K is the number of measurement devices; λ is the data fit; L ce is the maximum measuring range of the measuring device; x i 、y i The longitude and latitude of the i-th device installation location respectively; L is the distance from the grid to the measuring device; x b oundary represents the horizontal coordinate vector of the outer contour of the terrain in the coordinate system; y boundary Represents the vertical coordinate vector of the terrain outline in the coordinate system, f1 is the minimum number of measuring devices, and f2 is the maximum data fit.

[0036] On the other hand, the present application also provides a measurement optimization configuration system that takes into account the spatial distribution differences of new energy sources, including:

[0037] A preliminary configuration module, for generating an initial configuration plan based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured;

[0038] An index calculation module, configured to generate a measurement area matrix based on the initial configuration scheme and calculate a fit index based on the measurement data;

[0039] The solution optimization module is used to establish a multi-objective optimization configuration model with the goal of minimizing the number of equipment configurations and maximizing data fit. It uses the ε-constraint method to find the Pareto solution set and obtain a set of configuration solutions corresponding to the number of equipment and data fit.

[0040] Optionally, the preliminary configuration module is specifically used to:

[0041] Extract the latitude and longitude coordinate vectors of the terrain outer contour curve from the .shp file of the area to be configured;

[0042] Obtaining a plan view of the region from the latitude and longitude coordinate vectors, and rasterizing the plan view;

[0043] Number the center coordinates of each grid in the order of increasing latitude first and then longitude;

[0044] An initial configuration scheme of the measurement equipment is generated based on the number.

[0045] Optionally, the indicator calculation module is specifically used to:

[0046] Calculate the distance from each grid to each measuring device through the haversine function;

[0047] Generate a distance matrix from the centers of all grids in the region to the measurement devices based on the distance from each grid to each measurement device;

[0048] Calculate the measurement data of each grid containing a certain amount of uncontrollable noise influence according to the distance from each grid to each measuring device;

[0049] Calculate indirect measurements of wind turbine and photovoltaic output based on environmental measurement data;

[0050] The cross entropy index in information theory is used to calculate the correlation between the indirect measurement value and the actual data vector, and the fit index of the configuration scheme is obtained.

[0051] Optionally, the compatibility index is calculated as follows:

[0052]

[0053] Where: λ is the fit index; i is the latitude number of the measurement data vector; m is the latitude of the measurement data vector; h(·) is the calculation function of wind turbine and photovoltaic output; σ is the ratio of the number of measurement grids to the number of all grids in the region; x lc,i 、x zs,i They represent the measured value and true value of wind speed, temperature, light intensity and other data in dimension i respectively.

[0054] Optionally, the solution optimization module is specifically used to:

[0055] Construct a multi-objective function with the goal of minimizing the number of device configurations and maximizing data compatibility;

[0056] Setting constraints for the objective function;

[0057] Constructing a multi-objective optimization configuration model based on the objective function and the constraint conditions;

[0058] According to the ε-constraint method, the multi-objective function is converted into gradient constraints;

[0059] The whale algorithm is used to solve the gradient constraints and obtain a set of configuration solutions corresponding to the number of devices and data fit.

[0060] Optionally, the multi-objective optimization configuration model is as shown below:

[0061]

[0062] Where K is the number of measurement devices; λ is the data fit; L ce is the maximum measuring range of the measuring device; x i 、y i The longitude and latitude of the i-th device installation location respectively; L is the distance from the grid to the measuring device; x boundary Represents the horizontal coordinate vector of the terrain outer contour in the coordinate system; y boundary Represents the vertical coordinate vector of the terrain outline in the coordinate system, f1 is the minimum number of measuring devices, and f2 is the maximum data fit.

[0063] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0064] The memory is used to store one or more programs;

[0065] When the one or more programs are executed by the at least one processor, the measurement optimization configuration method considering the spatial distribution differences of new energy sources as described above is implemented.

[0066] On the other hand, the present application also provides a readable storage medium having an execution program stored thereon. When the execution program is executed, the measurement optimization configuration method considering the spatial distribution differences of new energy as described above is implemented.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The present invention provides a measurement optimization configuration method that takes into account differences in the spatial distribution of new energy sources, including: generating an initial configuration scheme based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured; generating a measurement area matrix based on the initial configuration scheme, and calculating a fit index in combination with the measurement data; establishing a multi-objective optimization configuration model with the goal of minimizing the number of equipment configurations and maximizing data fit, using the ε-constraint method to find the Pareto solution set, and obtaining a set of configuration schemes corresponding to the number of equipment and data fit. The present invention generates an optimal measurement point configuration scheme under the constraints of cost and accuracy, effectively reducing the layout of redundant measurement points, lowering the overall cost of the system, and at the same time improving the accuracy of real-time environmental data collection, providing reliable guarantees for the accuracy of load forecasting and the improvement of new energy consumption efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of a measurement optimization configuration method considering the spatial distribution differences of new energy sources according to the present invention;

[0070] Figure 2It is a flow chart of the optimization configuration of the measuring equipment in the present invention;

[0071] Figure 3 is a schematic diagram of the solar energy resource distribution in the selected configuration area in the embodiment;

[0072] Figure 4 Schematic diagram of wind resource distribution in the selected configuration area in the embodiment;

[0073] Figure 5 is the device quantity-fitness curve corresponding to the optimal device configuration solution set;

[0074] Figure 6 This is a schematic diagram of a configuration scheme with 7 devices in the selected scheme set. The white triangles in the figure represent measurement devices, and the darker the grid, the higher the measurement accuracy.

[0075] Figure 7 It is a schematic diagram of the grid division of the area to be configured;

[0076] Figure 8 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0077] With the large-scale integration of renewable energy into distribution networks, traditional power systems face unprecedented challenges. Renewable energy output is significantly affected by environmental factors (such as wind speed, sunlight intensity, and temperature). Inadequate measurement systems in existing distribution networks prevent accurate measurement of these critical environmental variables, severely limiting the accuracy of load forecasts. This limitation exacerbates the challenges of renewable energy absorption in decentralized scenarios characterized by high volatility, randomness, and significant spatial variability.

[0078] To address the above issues, the present invention proposes a measurement optimization configuration method that considers the differences in the spatial distribution of renewable energy. This method clusters the output of each renewable energy access point in a traditional distribution network, quantifying the renewable energy nodes of multiple distributed access points into the overall renewable energy output within a region. Using three types of equipment—intelligent environmental sensors, light intensity monitors, and wind speed monitors—real-time monitoring of renewable energy output within the region is performed, providing accurate regional power generation data without adding a large amount of equipment. This cluster quantification effectively reduces the number of measurement devices deployed, lowering system configuration costs. It also transforms the traditional distribution network grid structure into a regional grid, facilitating the construction and solution of configuration models.

[0079] Based on the differences in the spatial distribution of new energy, the present invention proposes a measurement optimization configuration method that takes into account the differences in the spatial distribution of new energy, aiming to provide high-precision new energy acquisition and measurement configuration for distribution networks containing a large number of distributed photovoltaic and wind turbines. This solution optimizes the layout of the measurement equipment, fully considers the acquisition accuracy of environmental quantities such as wind speed, light intensity, and temperature, ensures the accuracy of real-time data, and thus improves the accuracy of load forecasting and the efficiency of new energy consumption. When optimizing the configuration of measurement equipment, the present invention not only focuses on the traditional electrical parameters of the power grid, but also places special emphasis on the precise monitoring and analysis of environmental quantities on a spatial scale, so that the final measurement configuration solution can more effectively cope with the randomness and volatility of new energy power generation, improve the system's response capability to the output of new energy in different regions, and ultimately achieve efficient scheduling and stable operation of the power grid.

[0080] In order to better understand the present invention, the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0081] Example 1:

[0082] A measurement optimization configuration method considering the spatial distribution differences of new energy sources, such as Figure 1 As shown, including:

[0083] Step S1: generating an initial configuration plan based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured;

[0084] Step S2: generating a measurement area matrix based on the initial configuration scheme, and calculating a fit index based on the measurement data;

[0085] Step S3: A multi-objective optimization configuration model is established with the goal of minimizing the number of equipment configurations and maximizing data compatibility. The ε-constraint method is used to find the Pareto solution set to obtain a set of configuration solutions corresponding to the number of equipment and data compatibility.

[0086] The present invention provides a measurement optimization configuration method that takes into account the spatial distribution differences of new energy sources, and the specific steps include:

[0087] First, the area to be configured is divided into multiple grids, each containing one or more corresponding grid nodes. The wind and solar resources within the grid are used as the comprehensive equivalent of the new energy output in the area, achieving a quantitative description of new energy on a spatial scale.

[0088] Secondly, in the evaluation of the fit between the measured value and the true value, the data cross entropy in information theory was used as a basis to construct the objective function of the optimization configuration model, quantifying the performance of the measurement equipment and the data accuracy;

[0089] Finally, the ε-constraint method is used to solve the multi-objective optimization model of equipment quantity and data compatibility to obtain the optimal measurement configuration plan, providing a set of measurement equipment site selection solutions for distribution networks containing a large number of distributed wind power and photovoltaic power, and realizing the minimized and accurate collection of measurement equipment.

[0090] The following further describes each step of the present invention:

[0091] A measurement optimization configuration method considering the spatial distribution differences of new energy sources, such as Figure 2 As shown, the following steps are included:

[0092] Step S1: Generate an initial configuration plan based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured, including:

[0093] Extract the latitude and longitude coordinate vectors of the terrain outer contour curve from the .shp file of the area to be configured;

[0094] Obtaining a plan view of the region from the latitude and longitude coordinate vectors, and rasterizing the plan view;

[0095] Number the center coordinates of each grid in the order of increasing latitude first and then longitude;

[0096] An initial configuration scheme of the measurement equipment is generated based on the number.

[0097] like Figure 3 and Figure 4 As shown, step S1 specifically includes:

[0098] Step 1: Input the coordinates of the bottom line contour curve of the area to be configured, rasterize the map, and generate an initial configuration plan.

[0099] Step 1.1: Input the .shp file of the area to be configured, read the longitude and latitude coordinate vectors representing the terrain contour curve, and then draw the plan of the area. The coordinate vector is as follows:

[0100]

[0101] Where: x boundary Represents the horizontal coordinate vector of the terrain outer contour in the coordinate system, x1,x2,...,x m is the horizontal coordinate corresponding to the 1st, 2nd, ..., mth point; y boundary Represents the vertical coordinate vector of the terrain outline in the coordinate system, y1,y2,...,y m are the vertical coordinates corresponding to the 1st, 2nd, ..., mth points; m is the point number used in the .shp file to depict the outer contour of the terrain.

[0102] Step 1.2: Set the actual size of each grid edge to determine the number of grids, and then rasterize the terrain map. The specific conversion of edge length is as follows:

[0103]

[0104] Where: k r is the actual geographical distance corresponding to the unit longitude and latitude; k s The actual distance of each grid side length set; x size The maximum number of grids that the topographic map can be cut into horizontally; y size The maximum number of grids for vertically cutting the topographic map.

[0105] Step 1.3: Number the center coordinates of each grid in ascending order of latitude first and longitude second, and generate the initial configuration scheme Dec of the measurement equipment based on the numbering, which corresponds to the following format:

[0106] Dec=[i,j,...,n]

[0107] Where: i, j, n are the grid numbers corresponding to the configuration positions of the measurement equipment.

[0108] The divided grid coverage area is one or more new energy access nodes within the distribution network, such as Figure 7 To facilitate data analysis and processing, the wind and solar resources contained within a grid are used to represent the renewable energy output within that area. The subsequent installation of measurement equipment is performed at a node near the center of the grid. The environmental data it measures, such as wind speed, light intensity, and temperature, represents the overall output of all renewable energy within the grid area. This method allows for spatially segmenting the renewable energy output data within the region, and then proceeds to step 2 for data collection and analysis using the measurement equipment.

[0109] Step S2: Generate a measurement area matrix based on the initial configuration scheme, and calculate the fit index based on the measurement data, including:

[0110] Calculate the distance from each grid to each measuring device through the haversine function;

[0111] Generate a distance matrix from the centers of all grids in the region to the measurement devices based on the distance from each grid to each measurement device;

[0112] Calculate the measurement data of each grid containing a certain amount of uncontrollable noise influence according to the distance from each grid to each measuring device;

[0113] Calculate indirect measurements of wind turbine and photovoltaic output based on environmental measurement data;

[0114] The cross entropy index in information theory is used to calculate the correlation between the indirect measurement value and the actual data vector, and the fit index of the configuration scheme is obtained.

[0115] Step S2 specifically includes:

[0116] Step 2.1: Calculate the distance from each grid to each measurement device using the haversine function. The calculation formula is as follows:

[0117]

[0118] Where: L represents the actual distance between A and B; R is the radius of the earth; N lat Indicates the latitude of place N; N lon Indicates the longitude of place N; X A 、Y A , Z A Indicates the three-phase coordinates of site A; X B 、Y B , Z B Indicates the three-phase coordinates of site B; X N 、Y N , Z N Indicates the three-phase coordinates of N locations, where N represents locations A, B, etc.; P alu Indicates an angle.

[0119] Step 2.2: Generate a distance matrix from all grid centers in the region to the measurement device. The distance matrix D is as follows:

[0120]

[0121] Where: K represents the measurement equipment number; n represents the grid number; L K-n Represents the actual distance from the nth grid to the Kth device. Based on this, we take the minimum row vector of matrix D, set the elements larger than the measurement range to inf, and delete the elements with inf distances. This gives us the final measurement grid number and the corresponding distance matrix. The mathematical expression is as follows:

[0122]

[0123] Where: D mea is the final measurement area matrix, which includes the grid number and the shortest distance from the grid to the measurement equipment; L ce The maximum distance that the measuring device can measure; L 1-1 is the actual distance from the first grid to the first device, L 2-1 is the actual distance from the first grid to the second device, L K-1 is the actual distance from the 1st grid to the Kth device, L 1-2is the actual distance from the second grid to the first device, L 2-2 is the actual distance from the second grid to the second device, L K-2 is the actual distance from the second grid to the Kth device, L 1-n is the actual distance from the nth grid to the first device, L 2-n is the actual distance from the nth grid to the second device, L K-n is the actual distance from the nth grid to the Kth device, D mea.i is the measurement area matrix of the i-th measurement device, b1, b2, b n 、b i To measure the elements of the distance matrix correction vector, the distance matrix is ​​greater than L ce The grid distance is assigned a value of inf. The purpose of this step is that when the measurement accuracy is calculated later, the measurement accuracy of the inf grid is calculated as 0, indicating that the grid is not measured.

[0124] Step 2.3: Calculate the measurement data of each grid containing a certain amount of uncontrollable noise based on the distance. The mathematical formula for calculation is as follows:

[0125]

[0126] Where: x lc 、x zs They represent the measured value and true value of data such as wind speed, temperature, and light intensity respectively; α is the device measurement accuracy attenuation parameter; L is the distance from the grid to the measurement device; β is the random interference intensity; and randn is noise that obeys the Gaussian distribution.

[0127] Step 2.4: Calculate the indirect measurement values ​​of wind turbine and photovoltaic output using the wind speed, temperature, and light intensity data collected in step 2.3, which include measurement errors and uncertain interference factors. The mathematical formula is as follows:

[0128]

[0129] Where: P v is the photovoltaic output, calculated using the data measured in step 2.3; s is the light intensity; φ is the derating factor; β is the power-temperature coefficient; T stc is the standard operating temperature; T c is the actual working temperature; T air is the air temperature; N oct is the nominal operating temperature of the battery; I stc is the light intensity under standard test conditions; P wind is the wind turbine output, calculated using the data measured in step 2.3; v is the wind speed; P max is the maximum output value of the fan; vmax is the maximum wind speed, v min is the minimum wind speed.

[0130] Step 2.5: Use the cross entropy indicator in information theory to calculate the correlation between the measured data vector and the actual data vector to obtain the data fit λ under the current configuration scheme. The calculation formula is as follows:

[0131]

[0132] Where: i and m are the latitudes of the measurement data vector; h(·) is the wind turbine and photovoltaic output calculation function in step 2.4; σ is the ratio of the number of measurement grids to the number of all grids in the region; x lc,i 、x zs,i They represent the measured value and true value of wind speed, temperature, light intensity and other data in dimension i respectively.

[0133] Step S3: A multi-objective optimization configuration model is established with the goal of minimizing the number of equipment configurations and maximizing data compatibility. The Pareto solution set is obtained using the ε-constraint method to obtain a set of configuration solutions corresponding to the number of equipment and data compatibility, including:

[0134] According to the ε-constraint method, the multi-objective function is converted into gradient constraints;

[0135] The whale algorithm is used to solve the gradient constraints and obtain a set of configuration solutions corresponding to the number of devices and the degree of data fit;

[0136] The ε-constraint method is used to find the Pareto solution set and obtain the set of configuration solutions corresponding to the number of devices and data fit.

[0137] The step S3 specifically includes the following steps:

[0138] Step 3.1: Establish a multi-objective measurement optimization configuration model taking into account both cost and measurement data compatibility. The data compatibility is calculated according to step 2.5, as follows:

[0139]

[0140] Where: K is the number of measuring devices; λ is the data fit; L ce is the maximum measuring range of the measuring device; x i 、y i The longitude and latitude of the i-th device installation location, f1 is the minimum number of measurement devices, f2 is the maximum value of data fit, L is the distance from the grid to the measurement device, x boundary Represents the horizontal coordinate vector of the terrain outline in the coordinate system, y boundary The vertical coordinate vector representing the outer contour of the terrain in the coordinate system.

[0141] Step 3.2: Convert the objective function f1 into a gradient constraint condition according to the ε-constraint method. Since the number of device configurations is an integer, the gradient constraint is converted into an equation, which is as follows:

[0142] K=[1,2,...,n dec ]

[0143] Where: n dec The number of measuring equipment installed.

[0144] Step 3.3: Use the whale algorithm to solve the above optimization configuration model. The configuration scheme is updated as follows during the optimization process:

[0145]

[0146] Where: is the best individual in the population. Each individual represents a configuration scheme. The best individual is the configuration scheme with the highest data fit. represents any random individual in the population; i is the individual number in the population; A, D1, D2, and D3 are coefficient vectors; r and l are random numbers; T is the maximum number of iterations; t is the current number of iterations; b is a constant that controls the shape of the logarithmic spiral; is the configuration scheme of individual numbered i at the t+1th iteration, and C is twice the random number r.

[0147] Through the above optimization process, when the algorithm reaches the set maximum number of iterations, the optimal solution set that satisfies the equation constraints in step 3.2 is output, that is, the set of configuration solutions corresponding to the highest data fit and the least number of configurations.

[0148] By optimizing the configuration of measurement equipment, this invention addresses the practical issues of new energy consumption and load forecasting, achieving the following substantial results:

[0149] 1. Spatial Quantification of Regional Renewable Energy Data: Using a geographic data rasterization method, regional renewable energy data is quantified at a spatial scale, providing data support for the rational placement of measurement points. This method enables a precise description of the spatial distribution of renewable energy output (such as photovoltaic and wind power) across different regions, providing more refined spatial information for load forecasting and scheduling decisions, and enhancing the grid's ability to perceive distributed energy resources.

[0150] 2. Accurately calculate the actual distance between two locations: Using the haversine function to calculate the actual distance between two locations avoids the inaccuracy of distance calculations in traditional methods due to the large span of geographic latitude and longitude. By improving the accuracy of distance calculations, this invention can more accurately assess the coverage of measurement equipment and its impact on grid scheduling, thereby more effectively configuring measurement equipment, reducing device redundancy, and improving the grid's ability to monitor distributed renewable energy in real time.

[0151] 3. Accurately Determine the Optimal Configuration Scheme: This paper utilizes the ε-constraint method to process the optimization configuration model, combined with the Whale Algorithm, to determine the optimal solution for each device configuration quantity. This method generates the optimal measurement point configuration scheme within the constraints of cost and accuracy, effectively reducing the need for redundant measurement points and overall system costs. It also improves the accuracy of real-time environmental data collection, providing reliable guarantees for load forecasting accuracy and enhanced renewable energy consumption efficiency.

[0152] Example 2:

[0153] The present invention based on the same inventive concept also provides a measurement optimization configuration system that takes into account the differences in spatial distribution of new energy sources, including:

[0154] A preliminary configuration module, for generating an initial configuration plan based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured;

[0155] An index calculation module, configured to generate a measurement area matrix based on the initial configuration scheme and calculate a fit index based on the measurement data;

[0156] The solution optimization module is used to establish a multi-objective optimization configuration model with the goal of minimizing the number of equipment configurations and maximizing data fit. It uses the ε-constraint method to find the Pareto solution set and obtain a set of configuration solutions corresponding to the number of equipment and data fit.

[0157] Optionally, the preliminary configuration module is specifically used to:

[0158] Extract the latitude and longitude coordinate vectors of the terrain outer contour curve from the .shp file of the area to be configured;

[0159] Obtaining a plan view of the region from the latitude and longitude coordinate vectors, and rasterizing the plan view;

[0160] Number the center coordinates of each grid in the order of increasing latitude first and then longitude;

[0161] An initial configuration scheme of the measurement equipment is generated based on the number.

[0162] Optionally, the indicator calculation module is specifically used to:

[0163] Calculate the distance from each grid to each measuring device through the haversine function;

[0164] Generate a distance matrix from the centers of all grids in the region to the measurement devices based on the distance from each grid to each measurement device;

[0165] Calculate the measurement data of each grid containing a certain amount of uncontrollable noise influence according to the distance from each grid to each measuring device;

[0166] Calculate indirect measurements of wind turbine and photovoltaic output based on environmental measurement data;

[0167] The cross entropy index in information theory is used to calculate the correlation between the indirect measurement value and the actual data vector, and the fit index of the configuration scheme is obtained.

[0168] Optionally, the compatibility index is calculated as follows:

[0169]

[0170] Where: λ is the fit index; i is the latitude number of the measurement data vector; m is the latitude of the measurement data vector; h(·) is the calculation function of wind turbine and photovoltaic output; σ is the ratio of the number of measurement grids to the number of all grids in the region; x lc,i 、x zs,i They represent the measured value and true value of wind speed, temperature, light intensity and other data in dimension i respectively.

[0171] Optionally, the solution optimization module is specifically used to:

[0172] Construct a multi-objective function with the goal of minimizing the number of device configurations and maximizing data compatibility;

[0173] Setting constraints for the objective function;

[0174] Constructing a multi-objective optimization configuration model based on the objective function and the constraint conditions;

[0175] According to the ε-constraint method, the multi-objective function is converted into gradient constraints;

[0176] The whale algorithm is used to solve the gradient constraints and obtain a set of configuration solutions corresponding to the number of devices and data fit.

[0177] Optionally, the multi-objective optimization configuration model is as shown below:

[0178]

[0179] Where K is the number of measurement devices; λ is the data fit; L ce is the maximum measuring range of the measuring device; x i、y i The longitude and latitude of the i-th device installation location respectively; L is the distance from the grid to the measuring device; x boundary Represents the horizontal coordinate vector of the terrain outer contour in the coordinate system; y boundary Represents the vertical coordinate vector of the terrain outline in the coordinate system, f1 is the minimum number of measuring devices, and f2 is the maximum value of data fit.

[0180] The present invention balances the equipment configuration cost and the accuracy of environmental data collection by rationally arranging the measurement equipment. On the basis of ensuring the economy of the system, real-time environmental data is accurately collected to improve the measurement accuracy of key environmental quantities such as wind speed, light intensity, and temperature. High-precision environmental quantity data can significantly improve the accuracy of load forecasting, provide a more reliable basis for power grid dispatching, and thus optimize the consumption of new energy and reduce the phenomenon of wind and solar power abandonment. Through this solution, minimized and accurate data collection in load forecasting and new energy consumption scenarios is achieved, taking into account the balance between cost and accuracy, and providing strong support for the efficient operation of smart grids and the sustainable use of new energy.

[0181] Example 3

[0182] In order to enable those skilled in the art to better understand the principles of the present invention, the following is described with reference to the accompanying drawings and embodiments:

[0183] by Figure 3 and Figure 4 Taking the geographical area and the distribution of wind and solar resources in the area as an example, the method proposed in the present invention is used to perform measurement optimization configuration.

[0184] A method for optimizing the configuration of distribution network measurement equipment considering the spatial distribution differences of new energy sources includes the following steps:

[0185] Step 1: Input the coordinates of the bottom line contour curve of the area to be configured, rasterize the map, and generate an initial configuration plan.

[0186] Step 1 shown specifically includes:

[0187] Step 1.1: Input the .shp file of the area to be configured, read the longitude and latitude coordinate vectors representing the terrain contour curve, and then draw the plan of the area. The coordinate vector is as follows:

[0188]

[0189] Where: x boundary Represents the horizontal coordinate vector of the terrain outer contour in the coordinate system, x1,x2,...,x m is the horizontal coordinate corresponding to the 1st, 2nd, ..., mth point; y boundaryRepresents the vertical coordinate vector of the terrain outline in the coordinate system, y1,y2,...,y m are the vertical coordinates corresponding to the 1st, 2nd, ..., mth points; m is the point number used in the .shp file to depict the outer contour of the terrain.

[0190] Step 1.2: Set the actual size of each grid edge to determine the number of grids, and then rasterize the terrain map. The specific conversion of edge length is as follows:

[0191]

[0192] Where: k r is the actual geographical distance corresponding to the unit longitude and latitude, and its corresponding value is 111km; k s x is the actual distance of each grid side length, which is set to 10 km in this embodiment; size The maximum number of grids for horizontally cutting the topographic map is 43; size The maximum number of grids for vertically cutting the topographic map is 41. In this embodiment, the value range is [100.5°, 104.5°], and the value range of latitude is [30.5°, 34.3°].

[0193] Step 1.3: Number the center coordinates of each grid in ascending order of latitude first and longitude second, and generate the initial configuration scheme Dec of the measurement equipment based on the numbering, which corresponds to the following format:

[0194] Dec = [10, 175, ..., 699]

[0195] Where: 10, 175, 699 means that the measuring equipment is installed in the areas corresponding to grid No. 10, No. 175, and No. 699.

[0196] Step 2: Generate a measurement area matrix based on the current measurement equipment configuration plan, assign values ​​to the matrix elements using the measurement data, and then calculate the fit index.

[0197] The step 2 specifically includes the following steps:

[0198] Step 2.1: Calculate the distance from each grid to each measurement device using the haversine function. The calculation formula is as follows:

[0199]

[0200] Where: L represents the actual distance between A and B; R is the radius of the earth, which is 6371.393 km; N lat Indicates the latitude of place N; N lon Indicates the longitude of place N; X A 、Y A, Z A Indicates the three-phase coordinates of site A; X B 、Y B , Z B Indicates the three-phase coordinates of site B.

[0201] Step 2.2: Generate a distance matrix from all grid centers in the region to the measurement device. The distance matrix is ​​as follows:

[0202]

[0203] The above formula is the distance matrix for the area corresponding to the No. 79 grid when device No. 1 is installed. Based on this, the minimum row vector of matrix D is taken, the elements larger than the measurement range are set to inf, and the elements with a distance of inf are deleted to obtain the final measurement grid number and the corresponding distance matrix:

[0204]

[0205] Where: D mea is the final measurement area matrix, which includes the grid number and the shortest distance from the grid to the measurement equipment; L ce The maximum distance that the measurement device can measure is set to 100 km. The final measurement matrix is ​​converted into a one-dimensional vector, whose elements are the distances from the corresponding grid to the nearest measurement device.

[0206] Step 2.3: Calculate the measurement data of each grid containing a certain amount of uncontrollable noise based on the distance. The mathematical formula for calculation is as follows:

[0207]

[0208] Where: x zs Indicates the true value of data such as wind speed, temperature, and light intensity; x lc represents the measurement values ​​of wind speed, temperature, light intensity and other data collected by three types of equipment: intelligent environmental sensors, light intensity monitors, and wind speed monitors; α is the device measurement accuracy attenuation parameter, which is set to 0.028; L is the distance from the grid to the measurement device; β is the random interference intensity, which is set to 0.0065; randn is the noise that follows the Gaussian distribution, with a variance of 1 and an expectation of 0.

[0209] Step 2.4: Calculate the indirect measurement values ​​of wind turbine and photovoltaic output using the wind speed, temperature, and light intensity data collected in step 2.3, which include measurement errors and uncertain interference factors. The mathematical formula is as follows:

[0210]

[0211] Where: Pv is the photovoltaic output, calculated using the data measured in step 2.3; Is is the light intensity; φ is the derating factor, which is set to 0.001; β is the power-temperature coefficient, which is -0.0045; T stc is the standard operating temperature, which is 25°C; T c is the actual working temperature; T air is the air temperature; N oct is the nominal operating temperature of the battery; I stc is the light intensity under standard test conditions, and its value is 800; P wind is the wind turbine output, calculated using the data measured in step 2.3; v is the wind speed; P max is the maximum output value of the fan, and its value is set to 1.5.

[0212] Step 2.5: Use the cross entropy indicator in information theory to calculate the correlation between the measured data vector and the actual data vector to obtain the data fit under the current configuration scheme. The calculation formula is as follows:

[0213]

[0214] Where: i and m are the latitudes of the measurement data vector; h(·) is the calculation function of wind turbine and photovoltaic output in step 2.4; σ is the ratio of the number of measurement grids to the number of all grids in the region; x lc,i 、x zs,i They represent the measured value and true value of wind speed, temperature, light intensity and other data in dimension i respectively.

[0215] Step 3: Establish a multi-objective optimization configuration model with the goal of minimizing the number of equipment configurations and maximizing data fit. Use the ε-constraint method to find the Pareto solution set and obtain a set of configuration solutions corresponding to the number of equipment and data fit.

[0216] The step 3 specifically includes the following steps:

[0217] Step 3.1: Establish a multi-objective measurement optimization configuration model taking into account both cost and measurement data compatibility. The data compatibility is calculated according to step 2.5, as follows:

[0218]

[0219] Where: λ is the fit index; i is the latitude number of the measurement data vector; m is the latitude of the measurement data vector; h(·) is the calculation function of wind turbine and photovoltaic output; σ is the ratio of the number of measurement grids to the number of all grids in the region; x lc,i 、x zs,i They represent the measured value and true value of wind speed, temperature, light intensity and other data in dimension i respectively.

[0220] Step 3.2: Convert the objective function f1 into a gradient constraint condition according to the ε-constraint method. Since the number of device configurations is an integer, the gradient constraint is converted into an equation, which is as follows:

[0221] K=[1,2,...,n dec ]

[0222] Where: K is the number of measuring devices, n dec The number of measuring equipment installed.

[0223] Step 3.3: Use the whale algorithm to solve the above optimization configuration model. The configuration scheme is updated as follows during the optimization process:

[0224]

[0225] Where: is the best individual in the population. Each individual represents a configuration scheme. The best individual is the configuration scheme with the highest data fit. represents any random individual in the population; i is the individual number in the population; A, D1, D2, and D3 are coefficient vectors; r and l are random numbers; T is the maximum number of iterations; t is the current number of iterations; b is a constant that controls the shape of the logarithmic spiral; is the configuration scheme of individual numbered i at the t+1th iteration; is the configuration scheme of individual numbered i at the tth iteration.

[0226] Through the above optimization process, when the algorithm reaches the set maximum number of iterations, the optimal solution set that satisfies the equation constraints in step 3.2 is output, that is, the set of configuration schemes corresponding to the highest data fit and the least number of configurations. The data fit corresponding to each scheme is as follows: Figure 5 The specific configuration of each solution is shown in Table 1. Among them, the specific configuration of the solution with 7 devices is shown in Figure 6 shown.

[0227] Table 1 Measurement equipment configuration scheme set

[0228]

[0229]

[0230] Example 4

[0231] like Figure 8As shown, the present invention also provides an electronic device, which may be a computer, a single-chip microcomputer, a smart mobile device, or the like. The electronic device in this embodiment may include a processor, a memory, a transceiver component, and the like. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor may be used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0232] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a measurement optimization configuration method considering the spatial distribution differences of new energy in the above embodiment.

[0233] Example 5

[0234] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a measurement optimization configuration method considering the differences in the spatial distribution of new energy in the above embodiment.

[0235] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0236] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0237] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0238] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0239] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A measurement optimization configuration method considering the spatial distribution differences of new energy sources, characterized by: include: Generate an initial configuration plan based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured; Generate a measurement area matrix based on the initial configuration scheme, and calculate a fit index based on the measurement data; A multi-objective optimization configuration model is established with the goal of minimizing the number of equipment configurations and maximizing data fit. The ε-constraint method is used to find the Pareto solution set, and a set of configuration schemes corresponding to the number of equipment and data fit is obtained.

2. The method according to claim 1, wherein The generating of the initial configuration scheme based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured includes: Extract the latitude and longitude coordinate vectors of the terrain outer contour curve from the .shp file of the area to be configured; Obtaining a plan view of the region from the latitude and longitude coordinate vectors, and rasterizing the plan view; Number the center coordinates of each grid in the order of increasing latitude first and then longitude; An initial configuration scheme of the measurement equipment is generated based on the number.

3. The method according to claim 1, wherein Generating a measurement area matrix based on the initial configuration scheme and calculating a fit index based on the measurement data include: Calculate the distance from each grid to each measuring device through the haversine function; Generate a distance matrix from the centers of all grids in the region to the measurement devices based on the distance from each grid to each measurement device; Calculate the measurement data of each grid containing a certain amount of uncontrollable noise influence according to the distance from each grid to each measuring device; Calculate indirect measurements of wind turbine and photovoltaic output based on environmental measurement data; The cross entropy index in information theory is used to calculate the correlation between the indirect measurement value and the actual data vector, and the fit index of the configuration scheme is obtained.

4. The method according to claim 3, wherein The compatibility index is calculated as follows: Where: λ is the fit index; i is the latitude number of the measurement data vector; m is the latitude of the measurement data vector; h(·) is the calculation function of wind turbine and photovoltaic output; σ is the ratio of the number of measurement grids to the number of all grids in the region; x lc,i 、x zs,i They represent the measured value and true value of wind speed, temperature, light intensity and other data in dimension i respectively.

5. The method according to claim 1, wherein The multi-objective optimization configuration model is established with the goal of minimizing the number of equipment configurations and maximizing data compatibility, including: Construct a multi-objective function with the goal of minimizing the number of device configurations and maximizing data compatibility; Setting constraints for the objective function; A multi-objective optimization configuration model is constructed based on the objective function and the constraint conditions.

6. The method according to claim 5, wherein The ε-constraint method is used to find the Pareto solution set to obtain a set of configuration solutions corresponding to the number of devices and the degree of data fit, including: According to the ε-constraint method, the multi-objective function is converted into gradient constraints; The whale algorithm is used to solve the gradient constraints and obtain a set of configuration solutions corresponding to the number of devices and data fit.

7. The method according to claim 5, wherein The multi-objective optimization configuration model is shown in the following formula: Where K is the number of measurement devices; λ is the data fit; L ce is the maximum measuring range of the measuring device; x i 、y i The longitude and latitude of the i-th device installation location respectively; L is the distance from the grid to the measuring device; x b oundary represents the horizontal coordinate vector of the outer contour of the terrain in the coordinate system; y boundary Represents the vertical coordinate vector of the terrain outline in the coordinate system, f1 is the minimum number of measuring devices, and f2 is the maximum data fit.

8. A measurement optimization configuration system considering the spatial distribution differences of new energy sources, characterized by: include: A preliminary configuration module, for generating an initial configuration plan based on the latitude and longitude coordinate vectors of the terrain outer contour curve in the area to be configured; An index calculation module, configured to generate a measurement area matrix based on the initial configuration scheme and calculate a fit index based on the measurement data; The solution optimization module is used to establish a multi-objective optimization configuration model with the goal of minimizing the number of equipment configurations and maximizing data fit. It uses the ε-constraint method to find the Pareto solution set and obtain a set of configuration solutions corresponding to the number of equipment and data fit.

9. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a measurement optimization configuration method considering the spatial distribution differences of new energy sources as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a measurement optimization configuration method considering the spatial distribution differences of new energy sources as described in any one of claims 1 to 7 is implemented.

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