Electric power resource distribution method and system

Abstract

The invention discloses an electric power resource distribution method and system, and relates to the technical field of electric power resource distribution, and the method comprises the steps: training a power grid supply and demand two-side prediction model, training a prediction model which can consider the time hysteresis of the power grid supply and demand two sides and analyzes the complex coupling relation of the supply and demand two sides, and a reliable basis is provided for subsequent power resource allocation strategy and allocation effect evaluation. And defining working condition stability indexes of supply and demand sides of the power grid, thereby setting a monitoring period, and ensuring timeliness and adaptability of power resource distribution and scheduling. The distribution effect of the initial power distribution strategy is analyzed in multiple dimensions, the power distribution strategy in the next monitoring period is adjusted in combination with the evaluation index, and the power distribution strategy is comprehensively evaluated in multiple dimensions, so that the power distribution strategy in the next monitoring period is adjusted, the power resource distribution of the power grid system is optimized, and the power distribution efficiency of the power grid system is improved. And the reliability and the accuracy of power resource distribution are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power resource allocation, and particularly to a power resource allocation method and system. Background Art

[0002] With the continuous expansion of the scale of the power system, the power resource allocation scheme faces many challenges. Traditional power distribution mainly relies on the planned distribution method, which is difficult to adapt to the increasingly complex power demand and diverse power generation resources. At present, the problem of the peak-valley difference of the power grid is prominent, the access of renewable energy brings uncertainty, the distribution of power resources between regions is unbalanced, and the power market mechanism is not yet perfect. To solve these problems, measures such as adopting intelligent power distribution systems, optimizing power dispatching mechanisms, and strengthening cross-regional power allocation are required. By real-time monitoring of power supply and demand and flexibly adjusting distribution strategies, it is ensured that power resources are reasonably allocated, the operation efficiency and reliability of the power system are improved, and the power demand for economic and social development is met.

[0003] In the prior art, the strategies for power resource allocation are often relatively fixed, responsible for power distribution for a long period of time, and cannot adapt to the changes on both sides of the power grid supply and demand that are complex and changeable, resulting in poor timeliness, accuracy, and adaptability of power resource allocation, and unable to ensure the normal operation requirements on both sides of the power grid supply and demand.

[0004] Therefore, how to improve the timeliness, accuracy, and adaptability of power resource allocation is a technical problem to be solved at present. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem of poor timeliness, accuracy, and adaptability of power resource allocation in the prior art, and a power resource allocation method is proposed, which includes: Collect historical data on both sides of the power grid supply and demand, train a prediction model for both sides of the power grid supply and demand, and the prediction model for both sides of the power grid supply and demand is used to predict the supply and demand information on both sides of the power grid supply and demand in the next period of time; Define the working condition stability index for both sides of the power grid supply and demand, and set the monitoring period through the working condition stability index for both sides of the power grid supply and demand; On the basis of the monitoring period, collect relevant data on both sides of the power grid supply and demand in real time, and combine the predicted supply and demand information on both sides of the power grid supply and demand to perform an optimization algorithm to generate an initial power distribution strategy, and set a threshold according to the predicted supply and demand information on both sides of the power grid supply and demand; Allocate power resources on the power grid according to the initial power distribution strategy, analyze the distribution effect of the initial power distribution strategy in multiple dimensions to obtain evaluation indicators, and combine the evaluation indicators to adjust the power distribution strategy in the next monitoring period, so as to optimize the power resource allocation of the power grid system.

[0006] In some embodiments of the present application, training a prediction model for both the supply and demand sides of the power grid includes: Both the supply and demand sides of the power grid include the power supply side and the power demand side of the power grid. Draw time series diagrams of the power generation power on the power supply side of the power grid and the power consumption load on the power demand side of the power grid respectively, calculate the correlation coefficients between the power generation power and the power consumption load under different time lag values, traverse the correlation coefficients under different time lag values, count the maximum value and the median value of the correlation coefficients, record the correlation coefficient interval of the maximum value and the median value as the strong correlation interval, and determine the time lag value of the power supply side - power demand side according to the strong correlation interval. Establish data time axes for the power supply side and the demand side respectively for the historical data of both the power supply side and the power demand side of the power grid according to the time stamps, align the data time axes of the power supply side and the demand side according to the time lag value of the power supply side - power demand side, determine the time window based on the periodicity of the historical data on both sides and the prediction time, intercept the data time axes of the power supply side and the demand side according to the time window, determine the corresponding data axes of the power supply side - power demand side, and divide the training set and the test set based on the corresponding data axes of the power supply side - power demand side. Evaluate the contribution of the data on both the power supply side and the power demand side of the power grid to the prediction result, so as to determine the respective proportions of the data on both sides in the training set and the test set. Train and optimize the prediction model for both the supply and demand sides of the power grid based on the completed training set and test set.

[0007] In some embodiments of the present application, defining the operating condition stability index for both the supply and demand sides of the power grid includes: Collect the operating condition stability parameters of the power supply side and the demand side of the power grid respectively, classify the operating condition stability parameters of the power supply side and the demand side of the power grid respectively, determine the representative interval and the coefficient of variation of the operating condition stability parameters under each category, evaluate the stability of the representative interval of the operating condition stability parameters under each category, and obtain the evaluation index. Calculate the operating condition stability indexes of the power supply side and the demand side of the power grid respectively according to the evaluation indexes and the coefficients of variation of the respective operating condition stability parameters of the power supply side and the demand side of the power grid.

[0008] In some embodiments of the present application, setting the monitoring period through the operating condition stability indexes of both the supply and demand sides of the power grid includes: Screen out the parameters in the power grid data that can describe the supply - demand relationship of both the supply and demand sides of the power grid, count the representative interval and the coefficient of variation of each supply - demand relationship parameter, and generate a supply - demand stability index according to the representative interval and the coefficient of variation of each supply - demand relationship parameter. Combine the operating condition stability indexes of both the supply and demand sides of the power grid and the supply - demand stability index of the power grid to determine the comprehensive stability level of a power grid, and map a monitoring period according to the comprehensive stability level of the power grid.

[0009] In some embodiments of the present application, an optimization algorithm is combined with the predicted supply and demand information on both the supply and demand sides of the power grid to generate an initial power distribution strategy, including The optimization algorithm includes linear programming, quadratic programming, dynamic programming, genetic algorithm, and particle swarm algorithm, and obtains the optimization objective and the power grid operation constraint conditions; Based on the predicted supply and demand information on both the supply and demand sides of the power grid, the optimization objective, and the power grid operation constraint conditions, the optimization algorithm is applied to find the optimal initial power distribution strategy.

[0010] In some embodiments of the present application, thresholds are set according to the predicted supply and demand information on both the supply and demand sides of the power grid, including Deviation thresholds in multiple dimensions are set according to the predicted supply and demand information on both the supply and demand sides of the power grid, and the deviation threshold is the degree of deviation between the actual value and the predicted value.

[0011] In some embodiments of the present application, the distribution effect of the initial power distribution strategy is analyzed in multiple dimensions to obtain evaluation indicators, including The multiple dimensions include the model prediction dimension, the power supply reliability dimension, the energy utilization rate dimension, and the electricity cost dimension. Based on the deviation threshold, the distribution effects in the model prediction dimension, the power supply reliability dimension, the energy utilization rate dimension, and the electricity cost dimension are analyzed to obtain multiple evaluation indicators, and each evaluation indicator corresponds to one dimension.

[0012] In some embodiments of the present application, the power distribution strategy in the next monitoring period is adjusted in combination with the evaluation indicators, including Integrate the evaluation indicators under all dimensions to obtain the distribution evaluation indicator; When the distribution evaluation indicator is higher than the preset distribution evaluation indicator, the power distribution strategy in the next monitoring period is not adjusted; Otherwise, the power distribution strategy in the next monitoring period is adjusted according to the evaluation indicators under each dimension.

[0013] Correspondingly, the present application also provides a power resource distribution system, including The first module is used to collect the historical data on both the supply and demand sides of the power grid and train the power grid supply and demand prediction model, and the power grid supply and demand prediction model is used to predict the supply and demand information on both the supply and demand sides of the power grid in the future for a period of time; The second module is used to define the working condition stability index on both the supply and demand sides of the power grid and set the monitoring period through the working condition stability index on both the supply and demand sides of the power grid; The third module is used to collect relevant data on both the supply and demand sides of the power grid in real time based on the monitoring cycle, and combine the predicted supply and demand information on both the supply and demand sides of the power grid to perform an optimization algorithm to generate an initial power distribution strategy, and set a threshold according to the predicted supply and demand information on both the supply and demand sides of the power grid. The fourth module is used to allocate power resources on the power grid according to the initial power distribution strategy, analyze the distribution effect of the initial power distribution strategy in multiple dimensions to obtain evaluation indicators, and adjust the power distribution strategy in the next monitoring cycle in combination with the evaluation indicators, so as to optimize the power resource allocation of the power grid system.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Train the prediction models for both the supply and demand sides of the power grid, and train a prediction model that can consider the time lag on both the supply and demand sides of the power grid and analyze the complex coupling relationship between the supply and demand sides, providing a reliable basis for subsequent power resource allocation strategies and evaluation of allocation effects. Define the working condition stability indicators for both the supply and demand sides of the power grid, and determine the working condition stability indicators by considering the respective working condition stability of both the supply and demand sides of the power grid and the overall supply and demand stability, so as to set the monitoring cycle and ensure the timeliness and adaptability of power resource allocation and scheduling.

[0015] 2. Analyze the distribution effect of the initial power distribution strategy in multiple dimensions, and adjust the power distribution strategy in the next monitoring cycle in combination with the evaluation indicators, comprehensively evaluate the power distribution strategy from multiple dimensions, so as to adjust the power distribution strategy in the next monitoring cycle, optimize the power resource allocation of the power grid system, and ensure the reliability and accuracy of power resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flowchart of a power resource allocation method proposed by the present invention; Figure 2 It is a schematic structural diagram of a power resource allocation system proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0018] Refer to Figure 1 , a power resource allocation method, including the following steps: Step S101, collect historical data on both the supply and demand sides of the power grid, and train the prediction models for both the supply and demand sides of the power grid. The prediction models for both the supply and demand sides of the power grid are used to predict the supply and demand information on both the supply and demand sides of the power grid in the future for a period of time.

[0019] In this embodiment, in order to ensure the timeliness and advance of power resource allocation, it is necessary to predict the supply and demand information on both the supply and demand sides of the power grid in advance. Supply side: Collect historical power generation data of different types of power generation equipment (such as thermal power, hydropower, wind power, photovoltaic, etc.), including power generation power, equipment operation status, fuel consumption, meteorological conditions (such as wind speed, light intensity, water level), etc. At the same time, collect the power transmission data of the power grid, such as the load and loss of the transmission line. Demand side: Collect historical power consumption data of different industries (industry, commerce, residents), covering factors such as power consumption load, user power consumption behavior (such as power consumption habits during peak and valley periods), economic development level, holidays, etc. Clean the collected data and process missing values and outliers. For example, for missing power generation power data, interpolation can be used for filling; for abnormal power consumption load data, statistical analysis methods can be used for identification and correction. Perform data normalization or standardization processing to make data with different characteristics comparable. For example, normalize the power generation power and power consumption load data to the [0,1] interval.

[0020] In some embodiments of the present application, training the prediction model for both the supply and demand sides of the power grid includes, Both the supply and demand sides of the power grid include the supply side and the demand side of the power grid; Draw the time series diagrams of the power generation power on the supply side of the power grid and the power consumption load on the demand side of the power grid respectively, calculate the correlation coefficients between the power generation power and the power consumption load under different time lag values, traverse the correlation coefficients under different time lag values, statistically obtain the maximum value and the median value of the correlation coefficients, record the correlation coefficient interval of the maximum value and the median value as the strong correlation interval, and determine the time lag value of the supply side - demand side according to the strong correlation interval; Establish the respective data time axes of the supply side and the demand side for the historical data of both the supply side and the demand side of the power grid according to the time stamp, align the respective data time axes of the supply side and the demand side according to the time lag value of the supply side - demand side, determine the time window based on the periodicity of the historical data on both sides and the prediction time, intercept the respective data time axes of the supply side and the demand side according to the time window, determine the corresponding data axis of the supply side - demand side, and divide the training set and the test set based on the corresponding data axis of the supply side - demand side; Evaluate the contribution of the data on both the supply side and the demand side of the power grid to the prediction result, so as to determine the respective proportions of the data on both sides in the training set and the test set; Train and optimize the prediction model for both the supply and demand sides of the power grid based on the divided training set and test set.

[0021] In this embodiment, the power generation of the power supply side of the power grid is used to describe the situation of the power supply side, and the power consumption load of the demand side of the power grid is used to describe the situation of the power consumption side, that is, the demand side. There is usually a time lag between the data of the power supply side and the data of the demand side. From the perspective of causality, the change in power consumption demand on the demand side will prompt the power supply side to adjust the power generation plan. For example, when the power consumption of industrial users suddenly increases during the daytime working hours, it may take some time (such as the dispatching response time, the starting time of the generator set, etc.) for the power supply side to increase the power generation to meet the demand. On the contrary, the change in the power generation capacity of the power supply side will also affect the power consumption situation on the demand side, but this kind of influence is relatively indirect. For example, when the power generation of the power supply side decreases due to equipment failure, the demand side may respond by power rationing, adjusting the power consumption plan, etc., and there is also a certain time lag in this process. Considering the time lag on both the supply and demand sides to divide the training set and the test set can make the model prediction effect better.

[0022] In this embodiment, the time series diagrams of the power generation of the power supply side and the power consumption load of the demand side are respectively drawn, and the correlation coefficient between the power generation of the power supply side and the power consumption load of the demand side under different time lags is calculated. The correlation coefficient interval of the maximum value and the median value is recorded as the strong correlation interval, and this range is used as the strong correlation interval with relatively strong correlation. Within this strong correlation interval, the time lag value corresponding to the average value is used to determine the time lag situation on both the supply and demand sides. The approximate time lag duration can be determined by analyzing historical data. For example, through analysis, it is found that after the change in the power consumption load on the demand side, the power generation of the power supply side starts to have an obvious adjustment on average after 1 hour. Then, the data on the demand side can be moved forward by 1 hour to be initially aligned with the data on the supply side. Due to the time lag, it is necessary to align the data on the supply side and the demand side in time.

[0023] In this embodiment, the time window is determined based on the periodicity of the historical data on both sides and the prediction time. Considering the periodicity of each type of data and the prediction time (the duration to be predicted by this model) together to determine the size of the time window. If the short-term supply-demand balance is predicted, such as the supply-demand situation in the next 1 hour, a smaller time window can be selected, such as dividing the data at 1-hour intervals; if the long-term supply-demand trend is predicted, such as the supply-demand situation in the next week, a larger time window can be selected, such as dividing the data at 1-day intervals, so as to divide the training set and the test set. Evaluate the contribution of the data on the power supply side and the demand side of the power grid to the prediction result, and analyze the importance of the data on the power supply side and the demand side to the model prediction. The contribution degree of each feature (the data on the power supply side and the demand side) to the prediction result can be determined through a feature selection algorithm (such as the feature importance evaluation of a random forest). If the data on the demand side has a greater impact on the prediction result, the proportion of the data on the demand side in the training set can be appropriately increased; on the contrary, if the data on the power supply side is more important, the proportion of the data on the power supply side is increased.

[0024] Step S102: Define the operating stability indicators on both the supply and demand sides of the power grid, and set the monitoring period based on the operating stability indicators on both the supply and demand sides of the power grid.

[0025] In this embodiment, the parameters that can describe the stable situation on both the supply and demand sides of the power grid are screened out and integrated respectively to obtain the operating stability indicators on both the supply and demand sides of the power grid. Then, in combination with the overall supply-demand relationship parameters of the power grid, the monitoring period is set.

[0026] It can be understood that the stable situation of the power grid is jointly reflected from three aspects: the operating stability indicators on both the supply and demand sides of the power grid and the overall supply-demand relationship parameters of the power grid, so as to set the monitoring period, which is convenient for timely adjustment of subsequent power distribution strategies. This monitoring period can be dynamically adjusted according to the actual situation of the power grid.

[0027] In some embodiments of the present application, the operating stability indicators on both the supply and demand sides of the power grid are defined, including: Collect the operating stability parameters on the supply side and the demand side of the power grid respectively, classify the operating stability parameters on the supply side and the demand side of the power grid respectively, determine the representative intervals and coefficient of variation of the operating stability parameters under each category, evaluate the stability of the representative intervals of the operating stability parameters under each category, and obtain the evaluation indicators; Calculate the operating stability indicators on the supply side and the demand side of the power grid respectively according to the evaluation indicators and coefficient of variation of the operating stability parameters on the supply side and the demand side of the power grid.

[0028] In this embodiment, the operating stability parameters on the supply side of the power grid include equipment operation category, power generation capacity category, standby capacity category, etc. For the equipment operation category, the failure rate of power generation equipment: reflects the reliability of power generation equipment, and the lower the failure rate, the higher the power supply stability. The average trouble-free operation time of power generation equipment: statistics the average operation time between two failures of power generation equipment, and the longer the time, the more stable the equipment. For the power generation capacity category, the fluctuation range of power generation capacity: reflects the change range of power generation power within a certain period of time, and the smaller the fluctuation range, the more stable the power generation. The power generation power. For the standby capacity category, the standby capacity ratio: measures the guarantee degree of standby power generation capacity for the total power generation capacity, and the higher the ratio, the stronger the ability to cope with emergencies. The standby capacity response time: the time from the need to activate the standby capacity to the actual operation, and the shorter the time, the more timely the power supply stability can be guaranteed. The operating stability parameters on the demand side include power consumption load category and user behavior category.

[0029] In this embodiment, the representative interval of the operating condition stability parameter is the interval value with a relatively high occurrence frequency. By analyzing the interval values, the evaluation index of the operating condition stability parameter is obtained. Combining with the coefficient of variation, the operating condition stability indicators of the supply side and the demand side of the power grid are calculated respectively (The stability indicators of the operating conditions on the supply side or demand side of the power grid). The specific formula is as follows: ; Wherein, is the operating condition stability indicator of the supply side or the demand side of the power grid, is the number of operating condition stability parameters of the supply side or the demand side of the power grid, is the combined weight of the th operating condition stability parameter of the supply side or the demand side of the power grid, is the th evaluation index of the operating condition stability parameter of the supply side or the demand side of the power grid, is the th coefficient of variation of the operating condition stability parameter of the supply side or the demand side of the power grid. The coefficient of variation describes the stability of this type of operating condition stability parameter, is the th first constant of the operating condition stability parameter of the supply side or the demand side of the power grid, represents the correction of the evaluation index by the coefficient of variation. After summing and then taking the class average (slightly smaller than the average value, which can better reflect the operating condition stability), as the operating condition stability indicator.

[0030] In some embodiments of the present application, the monitoring period is set through the operating condition stability indicators on both the supply and demand sides of the power grid, including, Parameters that can describe the supply-demand relationship on both the supply and demand sides of the power grid are screened out from the power grid data. The representative interval and the coefficient of variation of each parameter of the supply-demand relationship are statistically analyzed, and the supply-demand stability indicator is generated according to the representative interval and the coefficient of variation of each parameter of the supply-demand relationship; Combining the operating condition stability indicators on both the supply and demand sides of the power grid and the supply-demand stability indicator of the power grid to determine the comprehensive stability level of a power grid, and mapping a monitoring period according to the comprehensive stability level of the power grid.

[0031] In this embodiment, the supply-demand coupling relationship clearly reveals the internal connection between the power supply side and the demand side. The change in the power generation capacity on the power supply side will directly affect the degree of electricity consumption satisfaction on the demand side, while the fluctuation of the electricity load on the demand side will also affect the power generation plan and equipment operation on the power supply side. When combining the stability indicators of the operating conditions on both sides, this kind of association needs to be considered, and no single indicator can be viewed in isolation. This mutual influence relationship provides a logical basis for the combination of the stability indicators of the operating conditions on both sides. Based on the supply-demand coupling relationship, key indicators that truly reflect the coupling situation on both sides can be screened out, so as to be used for the combination of the stability indicators of the operating conditions. When constructing the coupling index system, the selected indicators of supply-demand relationship parameters such as the supply-demand balance index (the ratio of the difference between the actual power generation power and the electricity load to the electricity load), the adaptability of the standby capacity response speed to the load fluctuation, etc. are all direct manifestations of the supply-demand coupling relationship. The supply-demand stability indicators are generated according to the representative intervals and coefficient of variation of each parameter of the supply-demand relationship. The calculation process is the same as that of the stability indicators of the operating conditions on both sides above, and will not be elaborated here. Determine the comprehensive stability level of a power grid by combining the stability indicators of the operating conditions on both the supply and demand sides of the power grid and the supply-demand stability indicators of the power grid. The specific calculation formula is as follows: ; Wherein, is the comprehensive stability level of the power grid, , , are the combined weights of the power supply side, the demand side and the supply-demand relationship of the power grid respectively, , , are the stability indicators of the operating conditions of the power supply side, the demand side and the supply-demand relationship of the power grid respectively, represents the minimum value among the three, , are both preset constants, represents the correction of the sum of the stability indicators of the three by the minimum stability indicator of the operating conditions. The two constants are used to balance the magnitude of the correction function and the magnitude of the balance stability level, and [] is the rounding symbol.

[0032] Step S103, on the basis of the monitoring period, collect the relevant data on both the supply and demand sides of the power grid in real time, and combine the predicted supply-demand information on both the supply and demand sides of the power grid to perform an optimization algorithm to generate an initial power distribution strategy, and set thresholds according to the predicted supply-demand information on both the supply and demand sides of the power grid.

[0033] In this embodiment, a prediction model is used to predict the supply and demand information on both the supply and demand sides of the power grid. Methods such as time series analysis, regression analysis, and neural network models are used to predict the information on both the supply and demand sides of the power grid. According to the specific objectives and constraints of power distribution, a suitable optimization algorithm is selected. Commonly used optimization algorithms include linear programming, quadratic programming, dynamic programming, genetic algorithms, particle swarm algorithms, etc.

[0034] In some embodiments of the present application, an optimization algorithm is combined with the predicted supply and demand information on both the supply and demand sides of the power grid to generate an initial power distribution strategy, including The optimization algorithm includes linear programming, quadratic programming, dynamic programming, genetic algorithms, and particle swarm algorithms, and obtains the optimization objective and the power grid operation constraints; Based on the predicted supply and demand information on both the supply and demand sides of the power grid, the optimization objective, and the power grid operation constraints, an optimization algorithm is applied to find the optimal initial power distribution strategy.

[0035] In this embodiment, the optimization objective can be to minimize the power generation cost, maximize the energy utilization rate, minimize the transmission loss, etc. under the premise of meeting the supply-demand balance. Other objectives such as environmental friendliness and power grid security can also be considered. The supply and demand prediction information, the power grid operation constraints (such as the output limit of power generation equipment and the capacity limit of transmission lines), and the optimization objective are input into the optimization algorithm. The optimization algorithm performs iterative calculations to find the optimal power distribution strategy that satisfies all the constraints. According to the calculation results of the optimization algorithm, an initial power distribution strategy is generated. The power distribution strategy includes the output plan of each power generation equipment, the power flow distribution of the transmission line, the power consumption plan of users, etc.

[0036] In some embodiments of the present application, thresholds are set according to the predicted supply and demand information on both the supply and demand sides of the power grid, including Deviation thresholds in multiple dimensions are set according to the predicted supply and demand information on both the supply and demand sides of the power grid. The deviation threshold is the degree of deviation between the actual value and the predicted value.

[0037] In this embodiment, the predicted supply and demand information on both the supply and demand sides of the power grid contains rich data, such as power generation capacity prediction, power consumption load prediction, supply-demand balance prediction, etc. These data can reflect the operating state of the power grid at different times and under different conditions, providing a solid foundation for setting multi-dimensional evaluation thresholds. By analyzing and mining these data, the key factors affecting the power distribution effect can be found, and then reasonable deviation thresholds can be set, and reasonable deviations can be set for each evaluation dimension to evaluate the rationality of the subsequent distribution strategy.

[0038] Step S104: Allocate the power resources on the power grid according to the initial power allocation strategy, analyze the allocation effect of the initial power allocation strategy in multiple dimensions to obtain evaluation indicators, and adjust the power allocation strategy in the next monitoring period in combination with the evaluation indicators, so as to optimize the power resource allocation of the power grid system.

[0039] In this embodiment, the process of monitoring the initial power allocation strategy is analyzed in multiple dimensions to determine the evaluation indicators for each dimension. A comprehensive indicator is determined in combination with the evaluation indicators to judge whether it is necessary to adjust the power allocation strategy.

[0040] In some embodiments of the present application, the allocation effect of the initial power allocation strategy is analyzed in multiple dimensions to obtain evaluation indicators, including The multiple dimensions include the model prediction dimension, the power supply reliability dimension, the energy utilization rate dimension, and the electricity consumption cost dimension. Based on the deviation threshold, the allocation effects in the model prediction dimension, the power supply reliability dimension, the energy utilization rate dimension, and the electricity consumption cost dimension are analyzed to obtain multiple evaluation indicators, and each evaluation indicator corresponds to one dimension.

[0041] In this embodiment, the model prediction dimension includes the situations predicted by the model such as the electricity load and the power generation capacity. The power supply reliability dimension includes the number of power outages, the power outage duration, the planned power outage duration, the unplanned power outage duration, etc. The energy utilization rate dimension includes the input and output energy of equipment, the transmission line loss, the actual electricity consumption of users, etc. The electricity consumption cost dimension includes the power generation cost, the transmission cost, the electricity consumption cost, the peak-valley electricity price, etc.

[0042] In some embodiments of the present application, adjusting the power allocation strategy in the next monitoring period in combination with the evaluation indicators includes Integrate the evaluation indicators under all dimensions to obtain the allocation evaluation indicator; When the allocation evaluation indicator is higher than the preset allocation evaluation indicator, do not adjust the power allocation strategy in the next monitoring period; Otherwise, adjust the power allocation strategy in the next monitoring period according to the evaluation indicators under each dimension.

[0043] In this embodiment, the evaluation indicators under all dimensions are integrated to obtain the allocation evaluation indicator (Distributionevaluation index), and the specific formula is as follows: ; Where is the allocation evaluation indicator, , , , are the influence weights of the model prediction dimension, the power supply reliability dimension, the energy utilization rate dimension, and the electricity consumption cost dimension respectively, , , , Evaluation indicators for the model prediction dimension, power supply reliability dimension, energy utilization rate dimension, and electricity cost dimension respectively , are respectively the maximum and minimum values in is a preset constant represents the correction of the sum of the four dimensions by the average value determined by the maximum and minimum values

[0044] Because there are multiple power distribution optimization objectives and not all dimensions can be taken into account, it is possible that not all are optimal solutions under each dimension. As long as the distribution evaluation indicators meet the requirements, a slightly worse single-dimension evaluation indicator can be accepted without adjusting the distribution strategy. Otherwise, adjustments will be made. Based on which dimension is analyzed to be worse, a specific power distribution strategy adjustment plan will be formulated. Implement the adjusted power distribution strategy in the next monitoring cycle

[0045] Correspondingly, the present application also provides a power resource distribution system, as Figure 2 shown, including The first module is used to collect historical data on both sides of the power grid supply and demand, train the power grid supply and demand prediction model, and the power grid supply and demand prediction model is used to predict the supply and demand information on both sides of the power grid in the next period of time The second module is used to define the operating stability indicators on both sides of the power grid supply and demand, and set the monitoring cycle through the operating stability indicators on both sides of the power grid supply and demand The third module is used to collect relevant data on both sides of the power grid supply and demand in real time based on the monitoring cycle, and combine the predicted supply and demand information on both sides of the power grid to perform an optimization algorithm, generate an initial power distribution strategy, and set a threshold based on the predicted supply and demand information on both sides of the power grid The fourth module is used to allocate power resources on the power grid according to the initial power distribution strategy, analyze the distribution effect of the initial power distribution strategy in multiple dimensions to obtain evaluation indicators, and combine the evaluation indicators to adjust the power distribution strategy in the next monitoring cycle, so as to optimize the power resource distribution of the power grid system

[0046] Compared with the prior art, the beneficial effects of the present invention are 1. Train the prediction model for both the supply and demand sides of the power grid. Train a prediction model that can consider the time lag on both the supply and demand sides of the power grid and analyze the complex coupling relationship between the supply and demand sides, providing a reliable basis for subsequent power resource allocation strategies and the evaluation of allocation effects. Define the operating stability indicators for both the supply and demand sides of the power grid, and determine the operating stability indicators by considering the respective operating stability conditions of the supply and demand sides of the power grid and the overall supply and demand stability, so as to set the monitoring period and ensure the timeliness and adaptability of power resource allocation and scheduling.

[0047] 2. Analyze the allocation effect of the initial power allocation strategy in multiple dimensions, and adjust the power allocation strategy in the next monitoring period in combination with the evaluation indicators. Evaluate the power allocation strategy comprehensively from multiple dimensions, so as to adjust the power allocation strategy in the next monitoring period, optimize the power resource allocation of the power grid system, and ensure the reliability and accuracy of power resource allocation.

[0048] Through the description of the above implementation manners, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0049] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the drawings are not necessarily essential for implementing the present invention.

[0050] Those skilled in the art can understand that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more systems different from the present implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0051] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for allocating electric power resources, characterized in that: include, Collect historical data on both sides of the power grid supply and demand, and train prediction models on both sides of the power grid supply and demand. The prediction models on both sides of the power grid supply and demand are used to predict the supply and demand information on both sides of the power grid in the future; Define the working condition stability indicators on both sides of the power grid supply and demand, and set the monitoring cycle based on the working condition stability indicators on both sides of the power grid supply and demand; Collect relevant data on both sides of the power grid supply and demand in real time based on the monitoring cycle, and optimize the algorithm based on the predicted supply and demand information on both sides of the power grid supply and demand, generate the initial power distribution strategy, and set the threshold value based on the predicted supply and demand information on both sides of the power grid supply and demand; The power resources on the power grid are allocated according to the initial power allocation strategy, and the allocation effect of the initial power allocation strategy is analyzed in multiple dimensions to obtain evaluation indicators. The power allocation strategy in the next monitoring cycle is adjusted based on the evaluation indicators to optimize the power resource allocation of the power grid system.

2. The method for allocating electric power resources according to claim 1, characterized in that: Training forecasting models for both the supply and demand sides of the power grid, including: The supply and demand sides of the power grid include the power supply side of the power grid and the demand side of the power grid; Draw the respective time series diagrams of the power generation on the power supply side of the power grid and the power load on the power demand side of the power grid, calculate the correlation coefficient between the power generation and the power load under different time lag values, traverse the correlation coefficients under different time lag values, and calculate the maximum value and median value of the correlation coefficient. The correlation coefficient interval of the maximum value and the median value is recorded as a strong correlation interval, and the time lag value of the power supply side-demand side is determined according to the strong correlation interval; Establish the data time axis of the power supply side and the demand side of the power grid according to the timestamp of the historical data on both sides, align the data time axis of the power supply side and the demand side according to the time lag value of the power supply side-demand side, determine the time window based on the periodicity and prediction time of the historical data on both sides, intercept the data time axis of the power supply side and the demand side according to the time window, determine the corresponding data axis of the power supply side-demand side, and divide the training set and the test set based on the corresponding data axis of the power supply side-demand side; Evaluate the contribution of data from both the power supply side and the power demand side of the grid to the prediction results, so as to determine the respective proportions of data from both sides in the training set and the test set; The prediction models for both the supply and demand sides of the power grid are trained and optimized based on the divided training and test sets.

3. The method for allocating electric power resources according to claim 2, characterized in that: Define the operating stability indicators for both the supply and demand sides of the power grid, including: The operating stability parameters of the power supply side and the demand side of the power grid are collected respectively, and the operating stability parameters of the power supply side and the demand side of the power grid are classified respectively, and the representative interval and coefficient of variation of the operating stability parameters under each category are determined, and the stability of the representative interval of the operating stability parameters under each category is evaluated to obtain the evaluation index; The operating stability indicators of the power supply side and the demand side of the power grid are calculated according to the evaluation indicators and coefficients of variation of the operating stability parameters of the power supply side and the demand side of the power grid.

4. The method for allocating electric power resources according to claim 3, characterized in that: The monitoring cycle is set based on the operating stability indicators on both the supply and demand sides of the power grid, including: Parameters that can describe the supply-demand relationship on both sides of the power grid are selected from the power grid data, and the representative interval and coefficient of variation of each parameter of the supply-demand relationship are counted. Supply-demand stability indicators are generated based on the representative interval and coefficient of variation of each parameter of the supply-demand relationship. The comprehensive stability level of a power grid is determined by combining the operating stability indicators of both the supply and demand sides of the power grid and the supply and demand stability indicators of the power grid, and a monitoring cycle is mapped according to the comprehensive stability level of the power grid.

5. The method for allocating electric power resources according to claim 1, characterized in that: Combine the predicted supply and demand information on both sides of the power grid to perform optimization algorithms and generate initial power distribution strategies. include, Optimization algorithms include linear programming, quadratic programming, dynamic programming, genetic algorithm and particle swarm algorithm to obtain optimization objectives and grid operation constraints; Based on the predicted supply and demand information on both sides of the power grid, the optimization objectives and the power grid operation constraints, the optimization algorithm is applied to find the optimal initial power distribution strategy.

6. The method for allocating electric power resources according to claim 1, characterized in that: Thresholds are set based on the predicted supply and demand information on both sides of the grid, including: Deviation thresholds are set in multiple dimensions according to the predicted supply and demand information on both sides of the power grid, and the deviation thresholds are the degree of deviation between the actual value and the predicted value.

7. The method for allocating electric power resources according to claim 6, characterized in that: The distribution effect of the initial power distribution strategy is analyzed in multiple dimensions to obtain evaluation indicators, including: The multiple dimensions include model prediction dimension, power supply reliability dimension, energy utilization dimension and electricity cost dimension. The allocation effects in the model prediction dimension, power supply reliability dimension, energy utilization dimension and electricity cost dimension are analyzed based on the deviation threshold to obtain multiple evaluation indicators, and each evaluation indicator corresponds to one dimension.

8. The method for allocating electric power resources according to claim 1, characterized in that: Combine the evaluation indicators to adjust the power distribution strategy in the next monitoring cycle. include, Integrate the evaluation indicators under all dimensions to obtain the distribution evaluation indicators; When the allocation evaluation index is higher than the preset allocation evaluation index, the power allocation strategy in the next monitoring cycle is not adjusted; Otherwise, the power allocation strategy in the next monitoring cycle is adjusted according to the evaluation indicators under each dimension.

9. A power resource allocation system, characterized in that: include, The first module is used to collect historical data on both sides of the power grid supply and demand, and train the power grid supply and demand prediction model. The power grid supply and demand prediction model is used to predict the supply and demand information of the power grid supply and demand in the future. The second module is used to define the working condition stability indicators on both sides of the power grid supply and demand, and set the monitoring cycle according to the working condition stability indicators on both sides of the power grid supply and demand; The third module is used to collect relevant data on both sides of the power grid supply and demand in real time based on the monitoring period, and optimize the algorithm in combination with the predicted supply and demand information on both sides of the power grid supply and demand, generate an initial power allocation strategy, and set thresholds according to the predicted supply and demand information on both sides of the power grid supply and demand; The fourth module is used to allocate power resources on the power grid according to the initial power allocation strategy, analyze the allocation effect of the initial power allocation strategy in multiple dimensions, obtain evaluation indicators, and adjust the power allocation strategy in the next monitoring cycle based on the evaluation indicators to optimize the power resource allocation of the power grid system.

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