Resource allocation optimization method and device, equipment and storage medium

Abstract

The invention provides a resource allocation optimization method and apparatus, a device and a storage medium. The method comprises the steps of obtaining geographic information data, load demand data and energy supply data; inputting the load demand data and the energy supply data into a pre-trained space resource distribution model to obtain a resource allocation scheme; the spatial resource distribution model is obtained based on geographic information data training; determining a resource adjustment demand according to the load demand data and the energy supply data; and determining a target resource allocation scheme according to the resource adjustment demand and the resource allocation scheme. According to the method, the multi-source data is comprehensively utilized, the accuracy and applicability of resource allocation are improved, and the operation efficiency and stability of the micro-grid system are further improved.

Description

Technical Field

[0001] This application relates to the technical fields of energy management and distributed power grids, and particularly to a method, device, equipment, and storage medium for optimizing resource allocation. Background Art

[0002] With the rapid development of renewable energy and the wide application of distributed energy technologies, the microgrid, as an innovative model that can achieve local production, transmission, and consumption of energy, has gradually become an important part of the energy system. The microgrid can not only improve energy utilization efficiency but also enhance the stability and reliability of the energy system.

[0003] However, in complex geographical environments (such as remote mountainous areas, islands, or high-density cities), the resource planning and scheduling of the microgrid are affected by various geographical factors such as terrain, climate, and transportation networks. In addition, the volatility of renewable energy and the uncertainty of load demands further exacerbate the complexity of resource allocation and system regulation. These factors pose many challenges to the design and operation of the microgrid. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a method, device, equipment, and storage medium for optimizing resource allocation.

[0005] As an aspect of this application, a method for optimizing resource allocation is provided, including:

[0006] Obtain geographical information data, load demand data, and energy supply data;

[0007] Input the load demand data and the energy supply data into a pre-trained spatial resource distribution model to obtain a resource allocation plan; the spatial resource distribution model is trained based on the geographical information data;

[0008] Determine the resource adjustment requirements according to the load demand data and the energy supply data;

[0009] Determine the target resource allocation plan according to the resource adjustment requirements and the resource allocation plan.

[0010] Optionally, the spatial resource distribution model is obtained through the following method, including:

[0011] Perform spatial analysis on the geographical information data based on geographical information system technology to determine a resource potential distribution map and a resource accessibility analysis map;

[0012] Integrate the resource potential distribution map and the resource accessibility analysis map to determine the spatial resource distribution model.

[0013] Optionally, inputting the load demand data and the energy supply data into a pre-trained spatial resource distribution model to obtain a resource allocation plan includes:

[0014] Performing matching analysis and processing on the load demand data and the spatial resource distribution model to determine the resource demand quantity;

[0015] Performing matching analysis and processing on the energy supply data and the spatial resource distribution model to determine the resource supply quantity;

[0016] Determining an initial resource allocation plan according to the resource demand quantity, the resource supply quantity, and an optimization algorithm;

[0017] Determining the resource allocation plan according to the network topology structure, the initial resource allocation plan, and a hierarchical clustering algorithm.

[0018] Optionally, determining a target resource allocation plan according to the resource adjustment demand and the resource allocation plan includes:

[0019] Determining an optimized resource allocation plan according to the resource adjustment demand, the resource allocation plan, and a dynamic optimization algorithm;

[0020] Determining the target resource allocation plan according to the resource allocation priority and the optimized resource allocation plan.

[0021] Optionally, sending the target resource allocation plan to a microgrid control system, and the microgrid control system receives and executes the target resource allocation plan to determine a resource configuration result;

[0022] Generating a system operation status report according to the resource configuration result;

[0023] Adjusting the target resource allocation plan in real time according to the system operation status report.

[0024] Optionally, obtaining the geographic information data, the load demand data, and the energy supply data includes:

[0025] Obtaining initial geographic information data, initial load demand data, and initial energy supply data;

[0026] Performing feature extraction processing on the initial geographic information data, the initial load demand data, and the initial energy supply data to determine a geographic information feature sequence, a load demand feature sequence, and an energy supply feature sequence;

[0027] Performing data preprocessing on the geographic information feature sequence, the load demand feature sequence, and the energy supply feature sequence to determine the geographic information data, the load demand data, and the energy supply data.

[0028] As a second aspect of the present application, there is provided an apparatus for optimizing resource allocation, including: an acquisition module, a processing module, and a determination module;

[0029] The acquisition module is configured to acquire geographic information data, load demand data, and energy supply data;

[0030] The processing module is configured to input the load demand data and the energy supply data into a pre-trained spatial resource distribution model to obtain a resource allocation plan; the spatial resource distribution model is trained based on the geographic information data;

[0031] The determination module is configured to determine a resource adjustment requirement according to the load demand data and the energy supply data;

[0032] The determination module is further configured to determine a target resource allocation plan according to the resource adjustment requirement and the resource allocation plan.

[0033] Optionally, the determination module is further configured to perform a spatial analysis on the geographic information data based on geographic information system technology to determine a resource potential distribution map and a resource accessibility analysis map;

[0034] The determination module is further configured to integrate the resource potential distribution map and the resource accessibility analysis map to determine the spatial resource distribution model.

[0035] Optionally, the processing module is specifically configured to perform a matching analysis and processing on the load demand data and the spatial resource distribution model to determine the resource demand; perform a matching analysis and processing on the energy supply data and the spatial resource distribution model to determine the resource supply; determine an initial resource allocation plan according to the resource demand, the resource supply, and an optimization algorithm; and determine the resource allocation plan according to the network topology structure, the initial resource allocation plan, and a hierarchical clustering algorithm.

[0036] Optionally, the determination module is specifically configured to determine an optimized resource allocation plan according to the resource adjustment requirement, the resource allocation plan, and a dynamic optimization algorithm; and determine the target resource allocation plan according to the resource allocation priority and the optimized resource allocation plan.

[0037] Optionally, the apparatus for optimizing resource allocation further includes an adjustment module;

[0038] The processing module is further configured to send the target resource allocation plan to a microgrid control system, and the microgrid control system receives and executes the target resource allocation plan to determine a resource configuration result;

[0039] The determining module is further configured to generate a system operation status report according to the resource configuration result;

[0040] The adjusting module is configured to adjust the target resource allocation scheme in real time according to the system operation status report.

[0041] Optionally, the obtaining module is specifically configured to obtain initial geographic information data, initial load demand data, and initial energy supply data; perform feature extraction processing on the initial geographic information data, the initial load demand data, and the initial energy supply data to determine a geographic information feature sequence, a load demand feature sequence, and an energy supply feature sequence; perform data preprocessing on the geographic information feature sequence, the load demand feature sequence, and the energy supply feature sequence to determine the geographic information data, the load demand data, and the energy supply data.

[0042] As a third aspect of the present application, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the above resource allocation optimization method is implemented.

[0043] As a fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, and the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the above resource allocation optimization method provided by the present application.

[0044] As a fifth aspect of the present application, a computer program product is provided, including computer program instructions, and when the computer program instructions run on a computer, the computer is caused to execute the above resource allocation optimization method provided by the present application.

[0045] As can be seen from the above, the resource allocation optimization method, apparatus, device, and storage medium provided by this application integrate geographic information data, load demand data, and energy supply data, and perform regional modeling and resource distribution analysis in combination with Geographic Information System (GIS) technology. It can generate resource allocation plans according to the geographical features, transportation networks, and energy supply characteristics of different regions. Compared with traditional static methods, the accuracy and applicability of resource allocation are significantly improved. Further, the resource adjustment requirements are determined based on the load demand data and energy supply data, and corresponding adjustment measures are taken to ensure that the resource allocation plan can always adapt to changes in the actual situation. This dynamic adaptability enables the microgrid system to quickly respond to changes in supply and demand, maintain the balance between supply and demand, and improve the stability and operation efficiency of the microgrid system. Finally, combining the resource adjustment requirements and the resource allocation plan, the target resource allocation plan is determined, further optimizing the resource configuration. The target resource allocation plan takes into account both geographic information and the relationship between supply and demand, can allocate resources more reasonably, improve the utilization efficiency of resources, and reduce the cost of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in this application or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings in the following description are only the embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a schematic flowchart of a resource allocation optimization method provided by an embodiment of this application;

[0048] Figure 2 It is a schematic flowchart of another resource allocation optimization method provided by an embodiment of this application;

[0049] Figure 3 It is a schematic flowchart of yet another resource allocation optimization method provided by an embodiment of this application;

[0050] Figure 4 It is a schematic flowchart of yet another resource allocation optimization method provided by an embodiment of this application;

[0051] Figure 5 It is a schematic flowchart of yet another resource allocation optimization method provided by an embodiment of this application;

[0052] Figure 6 It is a schematic flowchart of yet another resource allocation optimization method provided by an embodiment of this application;

[0053] Figure 7Schematic diagram of the composition of a resource allocation optimization device provided by an embodiment of the present application;

[0054] Figure 8 Schematic diagram of the composition of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0055] To make the objectives, technical solutions, and advantages of the present application clearer, the following further describes the present application in detail with reference to specific embodiments and the accompanying drawings. Obviously, the described embodiments are only a part rather than all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0056] It should be noted that in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design solution described as "exemplarily" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific manner. Unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the ordinary meanings understood by those of ordinary skill in the art to which the present application belongs. Summary of the Invention

[0058] In the related art, in the actual operation of a microgrid, geographical factors have an important impact on the distribution and utilization of energy. For example, conditions such as light intensity and wind speed in different regions will affect the power generation efficiency of renewable energy, while factors such as terrain and traffic networks will affect the layout of energy equipment and the transmission path of energy. In addition, the operation of the microgrid is also affected by various multi-dimensional data, such as the real-time changes in energy supply and demand and the operating status of equipment. Traditional planning methods fail to fully consider the interaction between these factors, resulting in difficulties in achieving precise planning and dynamic adjustment of resources in a complex environment.

[0059] The inventors of the present application found that traditional microgrid planning methods mainly rely on static data or simple optimization models, often ignoring the coupling relationship between geographical factors and multi-dimensional data, and it is difficult to achieve precise hierarchical planning and dynamic adjustment of resources in a complex environment. At the same time, traditional methods lack a real-time feedback mechanism. When the load demand or energy supply changes, the resource configuration cannot be adjusted in a timely manner, resulting in supply-demand imbalance or reduced system operation efficiency.

[0060] To solve the above problems, the present application provides a method for optimizing resource allocation. By integrating geographical information data, load demand data, and energy supply data, and combining GIS technology for regional modeling and resource distribution analysis, it is possible to generate resource allocation plans for different regions according to their geographical characteristics, transportation networks, and energy supply characteristics. Compared with traditional static methods, the accuracy and applicability of resource allocation are significantly improved. Further, based on the load demand data and energy supply data, the resource adjustment requirements are determined, and corresponding adjustment measures are taken to ensure that the resource allocation plan can always adapt to changes in the actual situation. This dynamic adaptability enables the microgrid system to quickly respond to changes in supply and demand, maintain the balance between supply and demand, and improve the stability and operating efficiency of the microgrid system. Finally, by combining the resource adjustment requirements and the resource allocation plan, the target resource allocation plan is determined, further optimizing the resource configuration. The target resource allocation plan takes into account both geographical information and the supply-demand relationship, can allocate resources more reasonably, improve the utilization efficiency of resources, and reduce the cost of resource allocation.

[0061] After introducing the basic principle of the present application, various non-limiting implementation manners of the present application will be specifically introduced below.

[0062] Figure 1 It is a schematic flowchart of a method for optimizing resource allocation provided by an embodiment of the present application. As Figure 1 shown, the method for optimizing resource allocation provided by the present application specifically includes the following steps:

[0063] S101. Obtain geographical information data, load demand data, and energy supply data.

[0064] Among them, the geographical information data includes data such as geographical coordinates, regional division, transportation network, terrain height, and climate conditions; the load demand data includes data such as regional distribution, load demand volume, power consumption pattern (such as peak-valley load situation), and user type (residential, commercial, industrial); the energy supply data includes data such as the production capacity, storage status, and transmission loss of traditional energy (such as grid power) and renewable energy (such as solar energy, wind energy).

[0065] In some embodiments, the geographical information data within a preset region is obtained from a GIS database, satellite remote sensing, measurement sensors, etc.; through a load prediction system, historical power consumption data is used for analysis and modeling to predict and collect the load demand data for each period; the energy supply data is monitored and collected from the sensor monitoring system of energy production facilities (such as solar power plants, wind farms) and the data feedback of energy producers.

[0066] It should be understood that geographical information data helps to understand the spatial distribution and limiting conditions of resources, while load demand data and energy supply data provide real-time operating condition information. By integrating geographical information data, load demand data and energy supply data, the resource allocation plan can more accurately match the actual demand, reducing resource waste and supply-demand imbalance.

[0067] In some embodiments, as Figure 2 shown, S101 can be specifically implemented as S1011 - S1013 as follows:

[0068] S1011. Obtain initial geographical information data, initial load demand data, and initial energy supply data.

[0069] In some embodiments, the method for obtaining initial geographical information data, initial load demand data, and initial energy supply data can be as described in S101 above and will not be elaborated here.

[0070] In some embodiments, the input quantity indicators of the initial geographical information data include geographical coordinates (X, Y) of a preset area, terrain height (Z), traffic network connectivity, climate data (such as sunshine, wind speed, etc.). The initial geographical information data related to the microgrid planning is extracted through the data extraction module of the microgrid system to provide a basis for subsequent planning. The input quantity indicators of the initial load demand data include the hourly load demand P load (t), where t is the time period index, in hours or minutes, representing the load demand in that time period. This data is predicted and collected through historical electricity consumption data or a load forecasting model. The input quantity indicators of the initial energy supply data include the energy supply quantity P supply (t), where P supply (t) represents the energy supply capacity at time t, in kilowatts (kW). This data is monitored and collected based on the real-time power generation of renewable energy and the traditional power supply situation.

[0071] Furthermore, preprocess the original data set composed of initial geographical information data, initial load demand data, and initial energy supply data, including data cleaning, outlier removal, data interpolation, and format standardization. By removing the missing values or outliers in the original data set, the consistency and integrity of the data are ensured. Among them, the input quantity indicators include the original data set D raw The preprocessed data D cleaned is obtained through cleaning and interpolation operations, where D cleaned is a data set without missing values and outliers. Then, uniformly format the preprocessed data D cleaned so that data from different data sources (geographical information, load demand, energy supply) can be calculated and analyzed in the same model.

[0072] S1012. Perform feature extraction processing on the initial geographic information data, initial load demand data, and initial energy supply data to determine the geographic information feature sequence, load demand feature sequence, and energy supply feature sequence.

[0073] In some embodiments, perform feature extraction processing on the initial geographic information data: Extract key features from the processed initial geographic information data in S1011, including the geographic distribution of regions, traffic network density, climate factors, etc. These features will affect the regional planning and resource scheduling of the microgrid. Among them, the input quantity indicators include geographic coordinates (X, Y), regional climate data (such as sunshine, wind speed, etc.), and traffic network density T traffic . Then, extract the required geographic features through spatial analysis methods (such as K-means clustering or spatial interpolation) to form the geographic information feature sequence G = [X, Y, T traffic , c limate . Perform feature extraction processing on the initial load demand data: Extract features related to load fluctuations, load peaks, and electricity consumption patterns from the initial load demand data. Specifically, analyze the load demand patterns in different time periods and extract features such as the load fluctuation range and the difference between load peaks and valleys. Among them, the input quantity indicator includes the period load data P load (t). Through time series analysis methods (such as Fourier transform or wavelet transform), extract the load demand feature sequence L = [P peak , P valley , loadfluctuation], where P peak is the load peak value, P valley is the load valley value, and load fluctuation is the load fluctuation range. Perform feature extraction processing on the initial energy supply data: Extract key energy production features from the initial energy supply data, including the stability of energy supply, energy volatility, and the available proportion of various energy sources. Among them, the input quantity indicator includes the energy supply amount P supply (t). Then, by calculating the fluctuation degree and stability of energy supply, generate the energy supply feature sequence E = [P avg , P stability , P fluctuation , where P avg is the average supply amount, P stability is the stability index, and P fluctuation is the supply fluctuation range.

[0074] It should be noted that by merging the geographical information feature sequence, the load demand feature sequence, and the energy supply feature sequence, a multi-dimensional feature sequence is obtained as the basic data for subsequent optimization calculations. Among them, the output quantity index includes the optimized feature sequence F = [G, L, E]. The feature sequence F contains all the important factors affecting the hierarchical planning and resource scheduling of the microgrid, providing a comprehensive data basis for subsequent model training and optimization.

[0075] S1013. Perform data preprocessing on the geographical information feature sequence, the load demand feature sequence, and the energy supply feature sequence to determine the geographical information data, the load demand data, and the energy supply data.

[0076] In some embodiments, first, perform normalization processing on the generated feature sequence F to make the numerical ranges of different features consistent and avoid the influence of different dimensions on subsequent calculations. Specifically, adopt the min-max normalization method to map the value of each feature into the interval [0, 1]. The normalization formula satisfies the following expression:

[0077]

[0078] where F min and F max respectively represent the minimum value and the maximum value of each feature in the feature sequence. Then, perform standardization processing on the normalized feature sequence F norm to make the mean of the features 0 and the standard deviation 1, further eliminating the influence of different features on the calculation results. The standardization formula satisfies the following expression:

[0079]

[0080] where μ(Fnorm) is the mean of the feature sequence and σ(Fnorm) is the standard deviation. The feature sequence F obtained through normalization and standardization operations std includes geographical information data, load demand data, and energy supply data, and these data can be used as the standardized input data for subsequent regional modeling and resource allocation analysis.

[0081] S102. Input the load demand data and the energy supply data into the pre-trained spatial resource distribution model to obtain the resource allocation plan.

[0082] Among them, the spatial resource distribution model is trained based on the geographical information data.

[0083] In some embodiments, combining the load demand data with the spatial resource distribution model can obtain the resource demand for each area in the preset region. Further, combining the energy supply data with the spatial resource distribution model can analyze the power supply capacity of each area. Thus, by comprehensively considering the geographic information data, load demand data, and energy supply data, a resource allocation plan for the microgrid system can be obtained.

[0084] In some embodiments, such as Figure 3 shown, the method for constructing the spatial resource distribution model of the microgrid area includes the following S1021 - S1022:

[0085] S1021. Perform a spatial analysis on the geographic information data based on geographic information system technology to determine the resource potential distribution map and the resource accessibility analysis map.

[0086] In some embodiments, using GIS technology to perform a spatial analysis on the geographic information data specifically includes a combined analysis of factors such as geographical distribution, traffic accessibility, and terrain features. For example, using a spatial interpolation method (such as Kriging method) to perform interpolation calculations on different geographical factors (such as wind speed, sunshine, terrain, etc.) to generate a resource potential distribution map within the preset region to estimate the renewable energy potential at each location within the region. Then, based on the traffic network data, analyze the traffic connectivity and accessibility of each area. This includes evaluating the distribution, connectivity, and traffic convenience of the road network. And combining terrain data (such as elevation, slope) to analyze the impact of terrain on resource acquisition and transportation, further refining the resource accessibility analysis. Combining the traffic network and terrain analysis results, generate a resource accessibility analysis map to reflect the difficulty of resource acquisition in each area.

[0087] S1022. Integrate the resource potential distribution map and the resource accessibility analysis map to determine the spatial resource distribution model.

[0088] In some embodiments, integrating the resource potential distribution map and the resource accessibility analysis map to obtain a unified geographic information resource distribution map provides accurate geographic data support for the microgrid area planning. Based on the integrated data, use random forest or neural network for training to construct a spatial resource distribution model. This model comprehensively considers the resource potential and the actual resource accessibility and can more accurately reflect the resource distribution in the region.

[0089] In some embodiments, such as Figure 4 shown, S102 can be specifically implemented as the following S1023 - S1026:

[0090] S1023. Perform a matching analysis and processing on the load demand data and the spatial resource distribution model to determine the resource demand.

[0091] In some embodiments, based on the load demand data P load (t), according to the change of power demand in different time periods, the load demand of each region is evaluated. Further, the load demand data is combined with the spatial resource distribution model to perform matching analysis of regional power demand. Among them, the input index is the load demand data P load (t), and the spatial resource distribution model M geo . The matching method includes matching the load demand of each region through optimization algorithms (such as the shortest path algorithm, transportation problem model), and calculating the resource demand quantity P req (t) of each region.

[0092] S1024. Perform matching analysis and processing on the energy supply data and the spatial resource distribution model to determine the resource supply quantity.

[0093] In some embodiments, based on the energy supply data P supply (t) (including the supply situation of renewable energy and traditional energy), on the basis of the spatial resource distribution model M geo , analyze the power supply capacity of each region. Among them, the input index is the energy supply data P supply (t), and the spatial resource distribution model M geo . The matching method includes matching the energy supply of each region through optimization algorithms (such as the shortest path algorithm, transportation problem model), and calculating the required resource supply quantity of each region.

[0094] S1025. Determine the initial resource allocation plan according to the resource demand quantity, resource supply quantity, and optimization algorithm.

[0095] In some embodiments, the resource allocation method includes combining the resource demand quantity and resource supply quantity, and adjusting the resource allocation of each region through an optimization algorithm (such as dynamic programming or linear programming) to obtain the initial resource allocation plan R alloc (t) of the microgrid system. Among them, R alloc (t) represents the resource allocation plan at time t, and the unit is kilowatt (kW). That is, the output index is the initial resource allocation plan R alloc (t), including the load distribution, power supply source and its proportion of each region.

[0096] S1026. Determine the resource allocation plan according to the network topology structure, the initial resource allocation plan, and the hierarchical clustering algorithm.

[0097] In some embodiments, in the initial resource allocation plan R allocBased on (t), combined with the hierarchical planning requirements of the microgrid, the resources are hierarchically adjusted through an optimization algorithm. Specifically, considering the differences in power demand, resource supply, and network topology among regions, the resource scheduling and allocation between each level are optimized. The optimization methods include using hierarchical optimization algorithms (such as multi-objective optimization or genetic algorithms), dividing the region into multiple levels, and adjusting the allocation strategy according to the specific requirements and available resources of each level. Further, the resource configuration plan R after hierarchical optimization is obtained. opt , and includes the resource scheduling strategy and power distribution ratio of each microgrid level. Then, according to the resource configuration plan R opt , combined with the network topology and the requirements of the power supply network, the final microgrid planning plan is generated. The plan includes information such as the power flow path between regions, the layout of energy facilities, and the configuration of backup power supplies. The microgrid planning plan can be expressed as P plan = [R opt , T network , where T network is the network topology of the power transmission network, and R opt is the resource configuration plan. Finally, the final microgrid planning plan P plan is output as a resource configuration report, which details the resource allocation in each region, the balance between power supply and demand, and the configuration of the power transmission network. The output quantity index is the resource configuration report R report , and the report includes information such as the resource configuration in each region, the comparison of demand and supply, and the optimization adjustment plan.

[0098] S103. Determine the resource adjustment demand according to the load demand data and energy supply data.

[0099] In some embodiments, the load demand data and energy supply data are obtained in real time, and the load demand data and energy supply data are comprehensively analyzed using dynamic programming (DP for short) or linear programming (LP for short) algorithms to determine whether the resource configuration needs to be adjusted. Among them, the output quantity index is the resource adjustment demand ΔR adjust (t), which represents the amount of resource adjustment required for each region at time t (unit: kilowatt, kW), providing an adjustment basis for subsequent optimization calculations.

[0100] Specifically, analyze the difference between the load demand and supply capacity of the current region, evaluate whether there is an imbalance between supply and demand. If the load demand is greater than the supply capacity, resource allocation needs to be increased; conversely, if the supply capacity is excessive, resource allocation can be considered to be reduced. And according to the analysis results, determine whether resource adjustment is required, such as increasing or decreasing the energy supply in certain regions, adjusting the energy distribution ratio, etc.

[0101] S104. Determine the target resource allocation plan according to the resource adjustment requirements and the resource allocation plan.

[0102] In some embodiments, a dynamic optimization algorithm (such as a genetic algorithm, a particle swarm algorithm, etc.) is used to calculate the resource allocation so as to adjust the resource configuration according to the real-time load demand and the energy supply situation. By minimizing the supply-demand difference and maximizing the resource utilization rate, the power supply demand and supply capacity balance of each region are ensured, and the power supply and load matching plan of each adjusted region, that is, the target resource allocation plan, is obtained.

[0103] It should be noted that the target resource allocation plan T dispatch (t) includes the scheduling order of resources and the power flow direction to ensure that the power supply demand of each region is met in a timely manner. Further, the target resource allocation plan T dispatch (t) is integrated into the resource scheduling optimization report R dispatchreport , and the report details the resource allocation adjustment situation, scheduling strategy and its effect of each region. The report content includes: the difference between the load demand and resource configuration of each region; the resource configuration and supply-demand balance after adjustment; the power flow direction, scheduling plan and backup power supply strategy. Finally, the resource scheduling optimization report R dispatchreport is provided to the operation and maintenance personnel of the microgrid system for adjusting and optimizing the microgrid system. At the same time, the report provides data support for the real-time monitoring and feedback mechanism of the microgrid system for subsequent system adjustment and monitoring.

[0104] In some embodiments, as Figure 5 shown, S104 can be specifically implemented as S1041 - S1042 as follows:

[0105] S1041. Determine the optimized resource allocation plan according to the resource adjustment requirements, the resource allocation plan and the dynamic optimization algorithm.

[0106] In some embodiments, a dynamic optimization algorithm (such as a genetic algorithm, a particle swarm algorithm, etc.) is used to calculate the resource allocation plan so as to adjust the resource configuration according to the real-time resource adjustment requirement situation. The optimization goal is to minimize the supply-demand difference and maximize the resource utilization rate to ensure the power supply demand and supply capacity balance of each region. Among them, the output index is the optimized resource allocation plan R opt (t), that is, the power supply and load matching plan of each adjusted region.

[0107] It should be noted that if the load demand data P load ,i(t) and the energy supply data P supply ,i(t) change at time t in region i, the optimization goal is to minimize the total adjustment cost C total, under the condition of meeting the constraints, an optimal resource allocation scheme is obtained. The optimization objective function satisfies the following expression:

[0108]

[0109] where n is the number of regions, and P load ,i(t) is the load demand of region i, and P supply ,i(t) is the energy supply of region i. Finally, the optimized resource allocation scheme R opt (t) is obtained, that is, the power supply allocation of each region at time t. By comprehensively considering the balance between load demand and energy supply and dynamically adjusting through an optimization algorithm, the resource configuration and load balance scheme of each region are obtained.

[0110] S1042. Determine the target resource allocation scheme according to the resource allocation priority and the optimized resource allocation scheme.

[0111] In some embodiments, the resource allocation priorities of each region are sorted, and the power dispatching strategy is adjusted according to the priorities to form the final target resource configuration scheme. Among them, the priority factors may include the urgency of load demand. For example, regions with high load demand are given priority; the stability of energy supply. For example, stable and renewable energy is preferably used; geographical and network conditions. For example, factors such as transportation network and terrain are considered to ensure the efficiency of resource transmission; user types. For example, the needs of key users (such as hospitals, schools) are given priority.

[0112] Exemplarily, weights are assigned to each factor. For example, the weight of the urgency of load demand is 0.4, the weight of the stability of energy supply is 0.3, the weight of geographical and network conditions is 0.2, and the weight of user type is 0.1. Each region is scored according to its performance in each factor, and the comprehensive score is calculated. The higher the score, the higher the priority. Regions with higher priorities are allocated resources first to ensure that the load demands of these regions are met. Regions with lower priorities are allocated resources when there is sufficient supply, and the allocation amount can be appropriately reduced if necessary. And the priorities are dynamically adjusted according to real-time data to ensure that the microgrid system can quickly respond to changes.

[0113] The resource allocation optimization method provided by this application can generate a resource allocation plan for different regions according to their geographical features, transportation networks, and energy supply characteristics by integrating geographical information data, load demand data, and energy supply data and combining GIS technology for regional modeling and resource distribution analysis. Compared with traditional static methods, it significantly improves the accuracy and applicability of resource allocation. Further, it determines the resource adjustment requirements based on the load demand data and energy supply data and takes corresponding adjustment measures to ensure that the resource allocation plan can always adapt to the changes in the actual situation. This dynamic adaptability enables the microgrid system to quickly respond to changes in supply and demand, maintain the balance between supply and demand, and improve the stability and operation efficiency of the microgrid system. Finally, by combining the resource adjustment requirements and the resource allocation plan, it determines the target resource allocation plan, further optimizing the resource configuration. The target resource allocation plan takes into account both geographical information and the supply-demand relationship, can allocate resources more reasonably, improve the utilization efficiency of resources, and reduce the cost of resource allocation.

[0114] In some embodiments, as Figure 6 shown, the resource allocation optimization method provided by the embodiments of this application further includes the following S201 - S203:

[0115] S201. Send the target resource allocation plan to the microgrid control system. The microgrid control system receives and executes the target resource allocation plan to determine the resource configuration result.

[0116] In some embodiments, the target resource allocation plan is real - time fed back to the microgrid control system C control (t). The microgrid control system performs resource scheduling and allocation according to the fed - back target resource allocation plan. Among them, the microgrid control system is used to guide the resource scheduling and allocation of the microgrid system. The target resource allocation plan includes the power supply and scheduling sequence of each region.

[0117] Specifically, the microgrid control system will start or adjust the power flow of each region according to the power supply - demand balance indicated by the target resource allocation plan to ensure the matching of supply and demand. The microgrid control system needs to schedule the power flow in real - time according to the load and supply situation. This process includes operations such as through switchgear, adjusting the generator output, and charging and discharging of energy storage devices to achieve the dynamic allocation of resources. The resource configuration result after scheduling and allocation will be fed back by the microgrid control system to each subsystem of the microgrid (such as power generation equipment, energy storage equipment, load management system, etc.) to perform specific scheduling operations.

[0118] S202. Generate a system operation status report according to the resource configuration result.

[0119] In some embodiments, based on the resource configuration result, the microgrid system generates the current system operation status report R of the microgrid statusreport(t). The report contains key information such as load demand, energy supply, scheduling execution status, and equipment status in each region, and summarizes the optimized resource allocation and adjustment situation. That is, the output data includes the adjusted system operation status report R statusreport (t), where the report includes: the real-time load and supply situation in each region; the specific execution situation of the microgrid resource scheduling; the adjusted power flow direction and equipment operation status, etc.

[0120] S203. According to the system operation status report, adjust the target resource allocation plan in real time.

[0121] In some embodiments, the generated system operation status report will be provided to the operation and maintenance personnel or decision-making personnel of the microgrid for further decision support and system adjustment. The operation and maintenance personnel can analyze the operation situation of each region based on the report, make optimization suggestions or take necessary maintenance measures.

[0122] It should be noted that the microgrid system will monitor the operation status of each region in real time, including power supply, load demand, equipment operation conditions, etc. The monitored data includes real-time data collected from various sensors, including load demand data P load (t), energy supply data P supply (t), equipment status data S device (t), and the power grid topology T network . The microgrid control system makes dynamic adjustments according to the monitored real-time data and the current target resource allocation plan. If it is found that the load demand in a certain region exceeds the predicted value or a certain energy supply is unstable, the microgrid system will initiate corresponding adjustment measures, balance the supply and demand by adjusting the resource allocation plan, and ensure the stable operation of the power grid.

[0123] Exemplarily, the adjustment rule: For example, when the load P load (i,t) is higher than the supply P supply (i,t), the microgrid system will automatically activate the energy storage device or draw power from the standby power supply. Then, output the adjusted resource allocation plan and provide real-time feedback to the microgrid control system for execution. The adjusted resource allocation plan will be fed back to the microgrid control system, and the control system will perform power scheduling in real time according to the feedback adjustment plan, and output the adjusted resource configuration result and feedback. This process will synchronously monitor and adjust the power grid to ensure that the resource allocation in each region is always in a dynamically optimized state.

[0124] The beneficial effects of the resource allocation optimization method provided by this application also include the following aspects:

[0125] (1) Improve the accuracy of resource allocation. By obtaining geographical information data, load demand data, and energy supply data, and combining GIS technology for regional modeling and resource distribution analysis, this application can generate resource allocation plans for the geographical features, transportation networks, and energy supply characteristics of different regions. Compared with traditional static methods, it significantly improves the accuracy and applicability of resource allocation.

[0126] (2) Dynamically adjust to meet real-time demands. This application uses dynamic optimization algorithms, combined with real-time load demand data and energy supply data, to adjust resource allocation in a timely manner, forming an optimized resource allocation plan. It can quickly respond to fluctuations in load demand or changes in energy supply, ensuring the supply-demand balance and stable operation of the microgrid.

[0127] (3) Full-process closed-loop control. This invention adopts a feedback mechanism, which real-time feeds back the optimized resource allocation plan to the microgrid control system to execute resource scheduling, and generates an operation status report through real-time monitoring, forming a closed-loop control process of planning, optimization, execution, and feedback, improving the operation efficiency and stability of the microgrid system.

[0128] It should be noted that the method of the embodiment of this application can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario, and completed by multiple devices cooperating with each other. In this distributed scenario, one of these multiple devices can only execute one or more steps of the method of the embodiment of this application, and these multiple devices will interact with each other to complete the described method.

[0129] It should be noted that some embodiments of this application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] Based on the same inventive concept, corresponding to the method of any of the above embodiments, this application also provides a resource allocation optimization device.

[0131] Referring to Figure 7 , the resource allocation optimization device includes: an acquisition module 701, a processing module 702, and a determination module 703;

[0132] The acquisition module 701 is used to acquire geographical information data, load demand data, and energy supply data;

[0133] The processing module 702 is configured to input the load demand data and the energy supply data into a pre-trained spatial resource distribution model to obtain a resource allocation plan; the spatial resource distribution model is trained based on the geographic information data;

[0134] The determination module 703 is configured to determine a resource adjustment requirement according to the load demand data and the energy supply data;

[0135] The determination module 703 is further configured to determine a target resource allocation plan according to the resource adjustment requirement and the resource allocation plan.

[0136] In some embodiments, the determination module 703 is further configured to perform a spatial analysis on the geographic information data based on geographic information system technology to determine a resource potential distribution map and a resource accessibility analysis map;

[0137] The determination module 703 is further configured to integrate the resource potential distribution map and the resource accessibility analysis map to determine the spatial resource distribution model.

[0138] In some embodiments, the processing module 702 is specifically configured to perform a matching analysis process on the load demand data and the spatial resource distribution model to determine a resource demand quantity; perform a matching analysis process on the energy supply data and the spatial resource distribution model to determine a resource supply quantity; determine an initial resource allocation plan according to the resource demand quantity, the resource supply quantity, and an optimization algorithm; and determine the resource allocation plan according to the network topology structure, the initial resource allocation plan, and a hierarchical clustering algorithm.

[0139] In some embodiments, the determination module 703 is specifically configured to determine an optimized resource allocation plan according to the resource adjustment requirement, the resource allocation plan, and a dynamic optimization algorithm; and determine a target resource allocation plan according to a resource allocation priority and the optimized resource allocation plan.

[0140] In some embodiments, the resource allocation optimization device further includes an adjustment module 704;

[0141] The processing module 702 is further configured to send the target resource allocation plan to a microgrid control system, and the microgrid control system receives and executes the target resource allocation plan to determine a resource configuration result;

[0142] The determination module 703 is further configured to generate a system operation status report according to the resource configuration result;

[0143] The adjustment module 704 is configured to adjust the target resource allocation plan in real time according to the system operation status report.

[0144] In some embodiments, the obtaining module 701 is specifically configured to obtain initial geographic information data, initial load demand data, and initial energy supply data; perform feature extraction processing on the initial geographic information data, the initial load demand data, and the initial energy supply data to determine a geographic information feature sequence, a load demand feature sequence, and an energy supply feature sequence; perform data preprocessing on the geographic information feature sequence, the load demand feature sequence, and the energy supply feature sequence to determine the geographic information data, the load demand data, and the energy supply data.

[0145] For convenience of description, when describing the above device, various modules are described separately according to their functions. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0146] The device in the above embodiments is used to implement the corresponding resource allocation optimization method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0147] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the resource allocation optimization method in any of the above embodiments when executing the computer program.

[0148] Figure 8 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1060. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1060.

[0149] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0150] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010.

[0151] The input / output interface 1030 is used to connect to the input / output module to achieve information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0152] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to achieve communication interaction between this device and other devices. Among them, the communication module can achieve communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0153] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0154] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary for implementing the solutions of the embodiments of this specification and do not have to include all the components shown in the figure.

[0155] The electronic device in the above embodiment is used to implement the corresponding resource allocation optimization method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0156] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the resource allocation optimization method as described in any of the foregoing embodiments.

[0157] The computer-readable medium of this embodiment includes both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0158] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the resource allocation optimization method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0159] Based on the same inventive concept, corresponding to the resource allocation optimization method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes a computer program. In some embodiments, the computer program is executable by one or more processors to cause the processors to execute the resource allocation optimization method. Corresponding to the execution subjects corresponding to the steps in each embodiment of the resource allocation optimization method, the processors that execute the corresponding steps can belong to the corresponding execution subjects.

[0160] The computer program product of the above embodiment is used to cause the computer and / or the processor to execute the resource allocation optimization method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0161] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.

[0162] In addition, for simplicity of explanation and discussion, and so as not to make the embodiments of the present application difficult to understand, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (i.e., these details should be fully within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be considered illustrative rather than restrictive.

[0163] Although the present application has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0164] Embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A resource allocation optimization method, characterized in that, The method includes: Obtaining geographic information data, load demand data, and energy supply data; Inputting the load demand data and the energy supply data into a pre-trained spatial resource distribution model to obtain a resource allocation plan; the spatial resource distribution model is trained based on the geographic information data; Determining a resource adjustment requirement according to the load demand data and the energy supply data; Determining a target resource allocation plan according to the resource adjustment requirement and the resource allocation plan.

2. The method according to claim 1, characterized in that, The method further includes obtaining the spatial resource distribution model through the following method, including: Performing a spatial analysis on the geographic information data based on geographic information system technology to determine a resource potential distribution map and a resource accessibility analysis map; Integrating the resource potential distribution map and the resource accessibility analysis map to determine the spatial resource distribution model.

3. The method according to claim 1, characterized in that, The step of inputting the load demand data and the energy supply data into a pre-trained spatial resource distribution model to obtain a resource allocation plan includes: Performing a matching analysis and processing on the load demand data and the spatial resource distribution model to determine a resource demand quantity; Performing a matching analysis and processing on the energy supply data and the spatial resource distribution model to determine a resource supply quantity; Determining an initial resource allocation plan according to the resource demand quantity, the resource supply quantity, and an optimization algorithm; Determining the resource allocation plan according to the network topology structure, the initial resource allocation plan, and a hierarchical clustering algorithm.

4. The method according to claim 1, characterized in that, The step of determining a target resource allocation plan according to the resource adjustment requirement and the resource allocation plan includes: Determining an optimized resource allocation plan according to the resource adjustment requirement, the resource allocation plan, and a dynamic optimization algorithm; Determining the target resource allocation plan according to the resource allocation priority and the optimized resource allocation plan.

5. The method according to claim 1, wherein The method further includes: Sending the target resource allocation plan to a microgrid control system, and the microgrid control system receiving and executing the target resource allocation plan to determine a resource configuration result; Generating a system operation status report according to the resource configuration result; Adjusting the target resource allocation plan in real time according to the system operation status report.

6. The method according to claim 1, wherein The step of obtaining geographic information data, load demand data, and energy supply data includes: Obtaining initial geographic information data, initial load demand data, and initial energy supply data; Performing feature extraction processing on the initial geographic information data, the initial load demand data, and the initial energy supply data to determine a geographic information feature sequence, a load demand feature sequence, and an energy supply feature sequence; Performing data preprocessing on the geographic information feature sequence, the load demand feature sequence, and the energy supply feature sequence to determine the geographic information data, the load demand data, and the energy supply data.

7. An apparatus for optimizing resource allocation, characterized in that The device includes: an acquisition module, a processing module, and a determination module; The acquisition module is used to obtain geographic information data, load demand data, and energy supply data; The processing module is configured to input the load demand data and the energy supply data into a pre-trained spatial resource distribution model to obtain a resource allocation scheme; the spatial resource distribution model is trained based on the geographic information data; The determination module is configured to determine a resource adjustment requirement according to the load demand data and the energy supply data; The determination module is further configured to determine a target resource allocation scheme according to the resource adjustment requirement and the resource allocation scheme.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method according to any one of claims 1 to 6.

10. A computer program product comprising computer program instructions which, when run on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

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