Regional power distribution network scheduling method and system based on flexible load optimization

Abstract

The invention provides a regional power distribution network scheduling method and system based on flexible load optimization, and relates to the technical field of power distribution network scheduling, and the method comprises the steps: extracting a first type in preset flexible load types, and carrying out the traversing in a scheduling database, and obtaining a first database; if the first power distribution network feature accords with a predetermined power distribution network constraint, sending a scheduling evaluation instruction, the predetermined power distribution network constraint being a conditional constraint obtained by analyzing a historical power distribution network feature of the power distribution network of the target region; evaluating and analyzing the first scheduling feature to obtain a first scheduling fitness; adjusting by taking the first scheduling feedback coefficient as a weight to obtain a first effective scheduling fitness; and if the preset fitness threshold is reached, taking the first scheduling scheme as a target day-ahead scheduling scheme. The technical problem that a power distribution network scheduling strategy in the prior art is mainly based on historical data or a fixed mode and lacks a flexible adjustment mechanism for real-time load change, so that the stability and reliability of a power grid are affected is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network scheduling, and in particular to a regional distribution network scheduling method and system based on flexible load optimization. Background Art

[0002] In traditional distribution network scheduling, the load is usually regarded as a fixed demand, and the scheduling strategy of the power grid is mainly based on historical data or fixed patterns, lacking a flexible adjustment mechanism for real-time load changes. The fluctuations of the power grid load, especially during high-demand periods, may lead to overloading or imbalance of the power grid load, increasing the risks of grid overload and faults. Moreover, the existing scheduling evaluation methods generally evaluate through fixed indicators, lacking targeted evaluation of different types of loads and scheduling characteristics. For example, some load types may be more easily schedulable and adjustable, while some other load types may have stronger priorities. The lack of identification and handling of these differences results in inaccurate evaluation of scheduling fitness, ultimately affecting the overall scheduling optimization effect of the power grid. Summary of the Invention

[0003] The present application provides a regional distribution network scheduling method and system based on flexible load optimization, aiming to solve the technical problem that the distribution network scheduling strategy in the prior art is mainly based on historical data or fixed patterns and lacks a flexible adjustment mechanism for real-time load changes, thereby affecting the stability and reliability of the power grid.

[0004] In a first aspect disclosed by the present application, a regional distribution network scheduling method based on flexible load optimization is provided. The method includes: Step S100: Extract the first type from a predetermined flexible load type, and traverse the first type in a scheduling database to obtain a first database, where the first database includes a first data group; Step S200: If the first distribution network feature in the first data group conforms to a predetermined distribution network constraint, issue a scheduling evaluation instruction, where the predetermined distribution network constraint refers to a conditional constraint obtained by analyzing the historical distribution network features of a target area distribution network; Step S300: Evaluate and analyze the first scheduling feature in the first data group based on the scheduling evaluation instruction to obtain a first scheduling fitness; Step S400: Adjust the first scheduling fitness with the first scheduling feedback coefficient corresponding to the first type as the weight to obtain a first effective scheduling fitness; Step S500: If the first effective scheduling fitness reaches a predetermined fitness threshold, use the first scheduling plan in the first data group as the target day-ahead scheduling plan for the target area distribution network.

[0005] The second aspect disclosed in this application provides a regional distribution network scheduling system based on flexible load optimization. The system is used for the above-mentioned regional distribution network scheduling method based on flexible load optimization. The system includes: a first database acquisition module, configured to implement step S100: extract the first type from the predetermined flexible load types, and traverse the first type in the scheduling database to obtain a first database, where the first database includes a first data group; a scheduling evaluation instruction issuing module, configured to implement step S200: if the first distribution network feature in the first data group meets the predetermined distribution network constraint, issue a scheduling evaluation instruction, where the predetermined distribution network constraint refers to the conditional constraint obtained by analyzing the historical distribution network features of the target area distribution network; an evaluation and analysis module, configured to implement step S300: evaluate and analyze the first scheduling feature in the first data group based on the scheduling evaluation instruction to obtain a first scheduling fitness; a fitness adjustment module, configured to implement step S400: adjust the first scheduling fitness with the first scheduling feedback coefficient corresponding to the first type as the weight to obtain a first effective scheduling fitness; a target scheduling scheme acquisition module, configured to implement step S500: if the first effective scheduling fitness reaches the predetermined fitness threshold, use the first scheduling scheme in the first data group as the target day-ahead scheduling scheme for the target area distribution network.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: By extracting the first type among the predetermined flexible load types, it is ensured that the scheduling scheme can be flexibly adjusted according to different types of load demands. Traversing the first type in the scheduling database to obtain the first database enables the formulation of optimal scheduling strategies for different load characteristics, enhancing the flexibility of load allocation. Analyzing the characteristics of the distribution network based on the historical data of the target area ensures that the scheduling scheme complies with the constraints of grid operation. By determining whether it meets the predetermined distribution network constraints, problems such as grid overload and voltage fluctuations can be effectively avoided, improving the feasibility of the scheduling scheme and the stability of grid operation. For the scheduling that meets the constraint conditions, a scheduling evaluation instruction is issued to promote the further optimization of the scheduling scheme. Through the evaluation and analysis of the scheduling characteristics, the first scheduling fitness is obtained. The evaluation process can comprehensively reflect the effectiveness of the scheduling scheme, identify which scheduling schemes are more in line with grid requirements, and further improve resource utilization and grid scheduling efficiency. By combining the scheduling feedback coefficient, the scheduling fitness is adjusted to further optimize the scheduling scheme. Through the weighted adjustment of the feedback coefficient, the scheduling fitness can be targeted optimized according to the characteristics of different types of loads. The feedback adjustment can enhance the response ability of the scheduling scheme to changes in the grid operation state, improve the accuracy and adaptability of the scheme, and ensure better meeting the grid operation requirements in practical applications. When the evaluated and adjusted scheduling fitness reaches the predetermined threshold, the target day-ahead scheduling scheme is finally selected. This scheme is based on the latest data and evaluation results to ensure the stable operation of the grid during the scheduling period. Through the iterative process, the scheduling scheme not only meets the constraints of historical data but also can flexibly respond to real-time changes, ensuring the reasonable allocation of grid load and generation resources, reducing operating costs, and improving energy utilization efficiency.

[0007] The above description is only an overview of the technical solution of this application. In order to be able to more clearly understand the technical means of this application, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0008] Figure 1 It is a schematic flowchart of the regional distribution network scheduling method based on flexible load optimization provided by an embodiment of this application.

[0009] Figure 2 It is a schematic structural diagram of the regional distribution network scheduling system based on flexible load optimization provided by an embodiment of this application.

[0010] Description of the reference numerals: The first database acquisition module 10, the scheduling evaluation instruction issuing module 20, the evaluation and analysis module 30, the fitness adjustment module 40, the target scheduling scheme acquisition module 50. Detailed Description of the Invention

[0011] By providing a regional distribution network scheduling method and system based on flexible load optimization in the embodiments of the present application, the technical problem in the prior art that the distribution network scheduling strategy is mainly based on historical data or fixed patterns and lacks a flexible adjustment mechanism for real-time load changes, thus affecting the stability and reliability of the power grid, is solved.

[0012] After introducing the basic principle of the present application, various non-limiting implementation manners of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1 shown, the embodiments of the present application provide a regional distribution network scheduling method based on flexible load optimization, and the method includes: Step S100: Extract the first type from the predetermined flexible load types, and traverse the first type in the scheduling database to obtain a first database, where the first database includes a first data group.

[0014] The predetermined flexible load types are load types that can be flexibly adjusted according to requirements, including transferable load types, interruptible load types, and shiftable load types. The predetermined flexible load types are usually defined in the system design stage and set according to specific requirements and scheduling objectives. Randomly extract the first type from the predetermined flexible load types as the analysis object, and this random selection facilitates the subsequent traversal and analysis of all types of the predetermined flexible load types.

[0015] Traverse the first type in the scheduling database. The scheduling database contains multiple different load data, scheduling parameters, historical operation data, etc. The traversal process of the database is based on the first type for screening. For the load of the first type, traverse all relevant data records related to this type, including load data, generation data, grid data, and other relevant characteristic data, to ensure that all relevant data can be extracted for subsequent analysis.

[0016] During the traversal process, all data sets that meet the conditions are merged into a new database as the first database. This database contains a first data group related to the extracted first type. The first data group is a specific data set extracted from the scheduling database, and each data group represents a set of associated data.

[0017] Step S200: If the first distribution network feature in the first data group meets the predetermined distribution network constraints, issue a scheduling evaluation instruction, where the predetermined distribution network constraints refer to the conditional constraints obtained by analyzing the historical distribution network features of the target area distribution network.

[0018] The first distribution network feature in the first data group refers to a specific state of the distribution network, including information such as the load condition, current, voltage, power flow, power distribution, etc. of the distribution network. The distribution network feature can reflect information such as the operating state of the distribution network, load demand, and balance between power generation and consumption.

[0019] The predetermined distribution network constraints are some conditional constraints obtained by analyzing the historical distribution network features of the target area distribution network. These constraints are set in advance to ensure that the distribution network can maintain a stable, reliable, and safe state during operation. The predetermined constraints include voltage range limits, load distribution limits, current limits, power balance, etc. These constraints can be extracted from historical data, for example, by analyzing the historical voltage, current, power, load, etc. features of the distribution network to obtain the laws and boundary conditions of historical distribution network operation.

[0020] Compare the first distribution network feature in the first data group with the predetermined distribution network constraints. Specifically, by comparing the actual operating features of the current distribution network with the typical operating features of the historical distribution network, determine whether it exceeds the preset constraint range. If the feature value exceeds the predetermined range, such as too high or too low voltage, or current exceeding the standard, it does not meet the constraints; otherwise, it meets the constraints.

[0021] If the first distribution network feature in the first data group meets the predetermined distribution network constraints, issue a dispatching evaluation instruction, indicating that the evaluation and optimization work of the dispatching plan can continue. The purpose of this instruction is to guide the entry into the next dispatching analysis stage to further evaluate, optimize, or adjust the dispatching plan to ensure that the operation of the distribution network not only meets the technical specifications but also meets the actual requirements.

[0022] Step S300: Evaluate and analyze the first dispatching feature in the first data group based on the dispatching evaluation instruction to obtain the first dispatching fitness.

[0023] The first dispatching feature refers to specific parameters and data related to dispatching operations. For example, the power generation plan, including the power generation plan for different time periods, including the start and stop times and power generation power of generators; the load dispatching plan, specifically the dispatching strategy for loads, including load transfer, load shifting, etc.; the grid state, that is, the grid state at different time points, such as voltage, current, power distribution, etc.; the dispatching optimization parameters, specifically the optimization objectives set according to requirements and conditions, such as dispatching cost, network loss, etc.

[0024] According to the scheduling evaluation instruction, evaluate and analyze the first scheduling feature. Specifically, conduct an analysis of the completion degree of the scheduling goal, such as whether the load demand can be met on time, whether power supply and demand can be balanced, etc.; conduct an analysis of operation efficiency, analyze whether the scheduling minimizes operation costs, such as fuel consumption, power transmission loss, etc.; conduct an analysis of system stability, check whether the power grid maintains a stable operation state during the scheduling process and whether it meets the power grid stability requirements such as voltage and current; conduct an analysis of resource optimization, such as whether operations like generator start-stop and load transfer achieve the goal of maximizing resource utilization.

[0025] After the evaluation and analysis, calculate the first scheduling fitness. The fitness is a comprehensive evaluation value, usually obtained by weighted summation, and is used to measure the quality and feasibility of the scheduling scheme. Through this process, the quality of the scheduling scheme can be quantitatively evaluated, providing data support for subsequent optimization and adjustment. The scheduling fitness reflects whether the scheduling scheme can efficiently and safely meet the requirements of the distribution network and can provide a basis for subsequent optimization and adjustment.

[0026] Step S400: Adjust the first scheduling fitness with the first scheduling feedback coefficient corresponding to the first type to obtain the first effective scheduling fitness.

[0027] The first type has a corresponding first scheduling feedback coefficient, which reflects the importance and influence degree of this load type in scheduling optimization. The feedback coefficients of different load types may be different. The feedback coefficient is obtained based on actual operation conditions or historical data, and it represents the adaptability or flexibility of scheduling adjustment of this load type in the distribution network scheduling. For example, some load types may be easier to adjust, so their feedback coefficients are larger.

[0028] Use the first scheduling feedback coefficient as the weight to perform weighted adjustment on the first scheduling fitness. This means that the fitness of the scheduling will be affected by the load type. For example, if the feedback coefficient of a certain load type is larger, it indicates that this load type is more important in the current scheduling, and then the corresponding scheduling fitness will be given a greater weight, thus affecting the final scheduling evaluation result.

[0029] The adjusted fitness value is the first effective scheduling fitness, which more accurately reflects the effect of the scheduling scheme, especially considering the flexibility of the load type and the effect after scheduling adjustment. This effective fitness value will be used as the criterion for subsequent judgment of whether the scheduling goal is achieved.

[0030] Step S500: If the first effective scheduling fitness reaches the predetermined fitness threshold, use the first scheduling scheme in the first data group as the target day-ahead scheduling scheme for the distribution network in the target area.

[0031] A fitness threshold is preset, which is set according to the system requirements, scheduling objectives, and historical data. The fitness threshold represents the minimum requirement for the scheduling scheme to reach an acceptable level. Compare the first effective scheduling fitness with the preset fitness threshold. If the first effective scheduling fitness is greater than or equal to the fitness threshold, it indicates that the scheduling scheme meets the requirements and can be accepted as the final scheduling scheme. In this case, the first scheduling scheme in the first data group is used as the target day-ahead scheduling scheme for the target area distribution network. The target day-ahead scheduling scheme refers to the scheduling scheme for the next day determined based on the current scheduling evaluation and optimization results, which represents parameters such as load distribution, generation scheduling, and network operation status of the distribution network within the next day.

[0032] Furthermore, if the first effective scheduling fitness does not reach the preset threshold, the scheduling scheme needs to be further optimized or adjusted. For example, trigger a return to the previous step for adjustment, or re-evaluate different data groups. Only when the scheduling scheme reaches the fitness threshold after multiple adjustments and optimizations can it be finally selected as the target day-ahead scheduling scheme, so as to ensure that the distribution network can efficiently and stably perform load distribution and generation scheduling during future operation.

[0033] Furthermore, the preset flexible load types include shiftable load types, interruptible load types, and shiftable load types.

[0034] The preset flexible load types include shiftable load types, interruptible load types, and shiftable load types. These three load types have different characteristics and application scenarios. In the distribution network scheduling, they can effectively improve the flexibility and stability of the power grid.

[0035] Shiftable load refers to the load that can be flexibly shifted in different time periods without causing a significant impact on the entire system. For example, some electrical equipment (such as air conditioners, heaters, etc.) can be shifted to off-peak periods for use during peak load periods, or delayed for use without affecting normal work, which is suitable for peak load reduction. For example, part of the load of industrial production or the electricity consumption of commercial facilities can be postponed to periods when the power grid burden is lighter.

[0036] Interruptible load refers to the load that can be interrupted within a short time when the power system is in a tense situation. Such a load can be temporarily cut off under specific conditions (such as when the power grid is overloaded or the power supply is insufficient) without causing a significant impact on the system security. When the power grid has a power supply shortage or excessive load, it is used to reduce the power demand within a short time, which is suitable for power grid load management and emergency backup power supply.

[0037] A shiftable load refers to the optimization scheduling of a load by adjusting the load distribution without changing the total energy consumption. It is different from a transferable load because the usage time does not have to be completely transferred. Instead, the load can be shifted between different regions or devices, and it is applicable to situations where the loads of multiple regions or devices need to be balanced. For example, shifting the load of commercial or industrial facilities between different regions to avoid overloading the power grid in certain regions.

[0038] Furthermore, step S200 includes: Obtain the historical load characteristics of the target area distribution network; obtain the historical power generation characteristics of the target area distribution network; obtain the historical power grid characteristics of the target area distribution network; and form the historical distribution network characteristics based on the historical load characteristics, the historical power generation characteristics, and the historical power grid characteristics. Among them, the historical load characteristics include historical charge and discharge power and historical state of charge, the historical power generation characteristics include historical ramp rate and historical slide rate, and the historical power grid characteristics include historical power flow, historical voltage, and historical current.

[0039] Historical load characteristics refer to the historical data related to the load demand of the distribution network, which reflect the load fluctuations and load demand changes of the power grid at different time periods. Historical load characteristics include historical charge and discharge power and historical state of charge. Among them, historical charge and discharge power refers to the charge and discharge power of the power grid or energy storage system during the historical time period. By observing the changes in charge and discharge power, one can understand how the power grid balances power supply and demand at different time periods and how energy storage and discharge operations are carried out. Historical state of charge refers to the charging state of the energy storage device. During the power grid scheduling process, the state of charge reflects the remaining power of the energy storage device and its contribution to the power grid load balance. Historical state of charge data can help the system judge the charge and discharge behavior of the energy storage device in the past and predict its impact on future scheduling.

[0040] Historical power generation characteristics refer to the historical data related to the operation and power generation capacity of the generator sets in the power grid. Historical power generation characteristics include historical ramp rate and historical slide rate. Among them, historical ramp rate refers to the growth rate of the power from low to high when the generator set starts or the load changes. A high ramp rate means that the generator can quickly respond to changes in load demand and is suitable for a rapidly changing load environment. Historical slide rate refers to the rate of decrease of the power from high to low when the load of the generator set decreases. A lower slide rate means that the generator set can reduce the output power more smoothly and avoid power grid fluctuations.

[0041] Historical grid characteristics refer to the operating status characteristics of the grid, reflecting the actual operating conditions of the distribution network at different historical moments. Historical grid characteristics include historical flow, historical voltage and historical current. Among them, historical flow refers to the flow path and size of electricity in the distribution network. Flow characteristics include the direction and size of power flow and the distribution of electricity from one area to another. Historical flow data helps to analyze the transmission of grid load and identify potential bottlenecks and overload areas; historical voltage refers to the historical fluctuations of grid voltage, including whether the voltage of the grid is within a safe range in different time periods. Excessive or low voltage may cause damage to grid equipment or reduce efficiency. Therefore, voltage characteristic data is crucial for evaluating the stability of the grid; historical current reflects the current size of each part of the distribution network, especially the current conditions on key lines and equipment. Current characteristics help to determine whether the grid is overloaded and whether there are potential current limitation problems.

[0042] The historical load characteristics, historical power generation characteristics and historical power grid characteristics are integrated to form the historical distribution network characteristics. By obtaining the historical load characteristics, historical power generation characteristics and historical power grid characteristics of the target area distribution network respectively, data support is provided for subsequent dispatch evaluation. These historical data not only help analyze the operating status of the distribution network, but also provide a predictive basis for future dispatch optimization, so that the distribution network can adjust the dispatch strategy according to the actual operating conditions, thereby improving the stability and efficiency of the power grid.

[0043] Furthermore, step S200 further includes: If the first distribution network characteristics do not meet the predetermined distribution network constraints, a scheduling re-evaluation instruction is issued; based on the scheduling re-evaluation instruction, a second data group in the first database is obtained, and the second data group is used as the first data group, and steps S200 to S500 are iteratively repeated to obtain the target day-ahead scheduling plan.

[0044] If the first distribution network characteristic does not meet the predetermined distribution network constraints, it means that the operating conditions of the power grid exceed the preset safety or operating range. In this case, a dispatch re-evaluation instruction is issued, indicating that the current dispatch plan needs to be re-evaluated or adjusted.

[0045] According to the scheduling re-evaluation instruction, a new data set is extracted from the first database as the second data group. The second data group contains different load data, power generation data or power grid data. The purpose of extracting this new data group is to recalculate and adjust according to the constraints of the power grid so as to meet the predetermined power grid constraints.

[0046] Taking the second data set as the input, restart the process from step S200 to step S500, including judging whether it meets the constraints of the power grid according to the new data set, then conducting a scheduling evaluation, calculating a new scheduling fitness, then performing a weighted adjustment on the scheduling fitness to obtain an effective scheduling fitness, and finally judging whether the predetermined fitness threshold is reached. If the condition is met, determine the target day-ahead scheduling plan. This process will be continuously iterated until the scheduling plan meets the predetermined power grid constraints and fitness threshold. Through iteration, the scheduling plan can be continuously optimized to ensure that the scheduling result not only meets the safety constraints of the power grid but also can efficiently allocate resources. Finally, after multiple iterations and adjustments, a scheduling plan that meets the power grid constraints and fitness requirements is selected and used as the target day-ahead scheduling plan, ready to execute the distribution network operation scheduling on the target date.

[0047] Furthermore, step S300 further includes: Reading the scheduling evaluation indicators, and traversing and matching the scheduling evaluation indicators in the first scheduling characteristics to obtain the first scheduling index parameters; performing a coefficient of variation weighted calculation on the normalized first scheduling index parameters to obtain the first scheduling fitness; wherein, the scheduling evaluation indicators at least include scheduling operation cost, scheduling network loss, scheduling load utilization rate, and scheduling power generation consumption capacity.

[0048] Read the scheduling evaluation indicators. The scheduling evaluation indicators are key parameters for measuring the performance of the scheduling plan, reflecting the effects of the scheduling plan in multiple dimensions, and at least include scheduling operation cost, scheduling network loss, scheduling load utilization rate, and scheduling power generation consumption capacity. Among them, the scheduling operation cost reflects the cost-benefit in the scheduling process, usually including power generation cost, fuel cost, equipment usage cost, etc. A scheduling plan with a lower operation cost is a preferred plan; the scheduling network loss refers to the energy loss in the process of transmitting electric energy in the power grid, usually determined by factors such as current, voltage loss, and line impedance. The lower the network loss, the more efficient the scheduling plan; the scheduling load utilization rate is used to measure the utilization efficiency of load resources, and the calculation method is usually the ratio between the actual load and the maximum load. The higher the load utilization rate, the more reasonable the load distribution of the power grid; the scheduling power generation consumption capacity reflects the consumption capacity of power generation resources, that is, whether the generator set can completely consume the generated power when the demand is insufficient. A scheduling plan with strong consumption capacity can better balance power generation and demand.

[0049] Traverse and match the scheduling evaluation metrics in the first scheduling feature to find the parts related to the scheduling evaluation metrics. For example, the scheduling operation cost is related to factors such as power generation and fuel cost, and the scheduling network loss is related to current, voltage, and line parameters. Match the scheduling evaluation metrics with the first scheduling feature to obtain specific first scheduling metric parameters. These scheduling metric parameters are numerical values obtained by analyzing the scheduling feature and reflect the performance of each evaluation metric in the current scheduling scheme.

[0050] Since different scheduling evaluation metrics may have different dimensions, such as the operation cost may be in currency units and the load utilization rate may be in percentage, it is necessary to normalize these metrics. The purpose of normalization is to convert data with different units and dimensions into a comparable standard form for comprehensive analysis and weighted calculation. The method of normalization is to adjust the data through a normalization formula so that all metric parameters are within the same scale range, such as between 0 and 1, thus eliminating the influence brought by the dimension.

[0051] The coefficient of variation is an index that describes the degree of data dispersion. It reflects the relative fluctuation of the data. For scheduling metrics, the coefficient of variation is usually used to measure the uncertainty and volatility of the scheduling results. Weight the normalized scheduling metric parameters according to their coefficients of variation, that is, metrics with a larger coefficient of variation will be assigned a higher weight because they may have a greater impact on the scheduling. Conversely, metrics with less volatility have a lower weight. After normalization and weighted calculation, the first scheduling fitness is obtained. This fitness comprehensively reflects the comprehensive performance of the scheduling scheme in multiple evaluation dimensions. The higher the fitness value, the more optimized the scheduling scheme and the better it can meet the system's requirements.

[0052] Furthermore, step S400 includes: Read the first predetermined set of scheduling factors of the first type, and the first predetermined set of scheduling factors has an identifier of the first predetermined weight assignment; perform scheduling monitoring on the target area distribution network based on the first predetermined set of scheduling factors to obtain a first scheduling factor data set; weight the first scheduling factor data set in combination with the first predetermined weight assignment to obtain the first scheduling feedback coefficient.

[0053] Read the first predetermined set of scheduling factors of the first type. The set of scheduling factors refers to a group of key factors used to reflect the scheduling performance during the scheduling process. For example, the scheduling factors include aspects such as the load in the system, the power generation plan, and the grid stability. These factors directly affect the effectiveness and optimization degree of the scheduling result. Each load type will have specific scheduling factors, and these factor sets are related to specific types of loads.

[0054] The first predetermined set of scheduling factors has an identification of the first predetermined weight assignment. Weight assignment refers to the degree of emphasis on each factor when evaluating scheduling factors. Different scheduling factors may have different impacts on the final scheduling plan. Therefore, different weights are assigned to each factor. The weight assignment identification is the information recording the weights of these factors, which is used to guide subsequent weighted calculations.

[0055] Based on the first predetermined set of scheduling factors, scheduling monitoring is carried out in the target area distribution network to obtain the first scheduling factor data set. This data set contains real-time data related to scheduling factors, such as the load condition of the power grid, the operating status of generators, the voltage and current status of the power grid, etc. The first scheduling factor data set contains relevant data within multiple time periods, which helps to grasp the operation of the distribution network in real time so as to adjust the scheduling plan as needed.

[0056] According to the first predetermined weight assignment identification, the data in the first scheduling factor data set is weighted. The purpose of weighted calculation is to adjust the influence of different scheduling factors on the final result according to their relative importance. The obtained first scheduling feedback coefficient is the weighted result of the scheduling factors, which reflects the contribution degree of each scheduling factor to the overall scheduling plan. The scheduling feedback coefficient is usually adjusted according to the performance of each factor in the scheduling process (such as load demand, power generation and consumption capacity, etc.) to ensure that the scheduling plan better meets the optimization objectives of the system.

[0057] Furthermore, reading the first predetermined set of scheduling factors of the first type, the first predetermined set of scheduling factors has an identification of the first predetermined weight assignment, including: Taking the first scheduling feature as the independent variable; taking the first scheduling fitness as the dependent variable; performing a many-to-one correlation analysis on the independent variable and the dependent variable to obtain a correlation analysis result; analyzing the correlation analysis result and constructing the first predetermined set of scheduling factors, and obtaining the first predetermined weight assignment corresponding to the first predetermined set of scheduling factors.

[0058] The first scheduling features include information such as the load of the distribution network, the power generation plan, and the grid status. These features are key input data in the scheduling plan and will have a direct impact on the final scheduling effect. Taking the first scheduling features as the independent variables, through these independent variables, how the scheduling fitness changes can be predicted.

[0059] The first scheduling fitness is the standard for evaluating the quality of the scheduling plan and is the result obtained by weighted calculation of multiple scheduling evaluation indicators. Taking the first scheduling fitness as the dependent variable, that is, the result to be explained or predicted, it is determined by the first scheduling features.

[0060] Perform a one-to-many correlation analysis on independent variables and dependent variables, that is, consider the relationship between multiple independent variables (the first scheduling characteristics) and one dependent variable (the first scheduling fitness) simultaneously. The one-to-many analysis helps to reveal which scheduling characteristics have a greater impact on fitness and the strength of the relationship between them. The correlation analysis uses statistical methods such as Pearson correlation coefficient, regression analysis, etc. to measure the linear or non-linear relationship between independent variables and dependent variables, and outputs the correlation analysis results, which describe the degree of correlation between each scheduling characteristic (independent variable) and scheduling fitness (dependent variable). These results provide a basis for subsequent scheduling factor selection and weight assignment.

[0061] Based on the correlation analysis results, select those scheduling characteristics that are highly correlated with scheduling fitness as the first predetermined scheduling factor set. These factors are the key factors for scheduling optimization and can directly affect the quality of scheduling results. After forming the factor set, assign a weight to each factor to indicate the importance of each factor to the scheduling result. By analyzing the correlation, it is possible to determine which factors contribute more to scheduling fitness and assign them higher weights. Conversely, assign lower weights to ensure that the weight of each factor can truly reflect its importance in scheduling decisions.

[0062] Furthermore, after step S500, it further includes: Perform dynamic monitoring on the target area distribution network to obtain real-time distribution network characteristics; according to the rolling adjustment response mechanism, replace the historical distribution network characteristics with the real-time distribution network characteristics to obtain real-time distribution network constraints; use the real-time distribution network constraints as the predetermined distribution network constraints to iteratively repeat steps S200 to S500, and use the first scheduling plan as the target day-ahead scheduling plan for the target area distribution network.

[0063] Perform real-time dynamic monitoring on the target area distribution network to obtain the operation data of the current power grid, including various operation parameters such as the load, power generation, voltage, current, and power flow of the power grid. Dynamic monitoring is a process of real-time data acquisition, which reflects the specific operation state of the distribution network at the current moment and ensures that the scheduling plan can cope with the possible changes during the operation of the power grid. Through dynamic monitoring, real-time distribution network characteristics are obtained, including the current load distribution of the power grid, the state of generator sets, the charge and discharge conditions of energy storage devices, etc., which can reflect the immediate operation of the distribution network and provide the latest information for subsequent scheduling adjustments.

[0064] The rolling adjustment response mechanism is a dynamic update and real-time adjustment strategy. Its function is to adjust the original scheduling plan and distribution network constraints according to real-time data. Through this mechanism, it can quickly respond to the changes in the power grid and flexibly adjust the load, generation, and scheduling plan. For example, during certain periods, if the power grid load suddenly increases or there are problems with power generation, the rolling adjustment mechanism will make corresponding adjustments to the scheduling plan based on real-time data to avoid power grid overload or instability. Through rolling adjustment, the real-time distribution network characteristics are used to replace the historical distribution network characteristics constructed based on historical data. In this way, the scheduling plan not only depends on historical data but also fully considers the actual operating conditions of the current power grid to ensure that the scheduling plan can respond in real time when the power grid changes dynamically.

[0065] After obtaining the real-time distribution network constraints, they are input into the scheduling process as new constraint conditions, and the scheduling evaluation, optimization, weighting, etc. processes from step S200 to step S500 are re-executed. This process is an iterative process to ensure that each scheduling is adjusted based on the latest distribution network status and constraint conditions. This iterative process means that the scheduling plan of the distribution network can be flexibly adjusted in real-time changes to ensure the stable and efficient operation of the power grid. After one or more iterations, the optimal scheduling plan is selected as the scheduling plan for the target day. This plan is the comprehensive optimization result based on real-time constraints and scheduling requirements, aiming to achieve the stable operation of the distribution network.

[0066] In summary, the regional distribution network scheduling method based on flexible load optimization provided by the embodiments of the present application has the following technical effects: By extracting the first type from the predetermined flexible load types, it is ensured that the scheduling scheme can be flexibly adjusted according to different types of load demands. Traversing the first type in the scheduling database to obtain the first database enables the formulation of an optimal scheduling strategy for different load characteristics, enhancing the flexibility of load allocation. Analyzing the distribution network characteristics based on the historical data of the target area ensures that the scheduling scheme complies with the constraints of power grid operation. By judging whether it meets the predetermined distribution network constraints, problems such as grid overload and voltage fluctuations can be effectively avoided, improving the feasibility of the scheduling scheme and the stability of power grid operation. For the scheduling that meets the constraint conditions, a scheduling evaluation instruction is issued to promote the further optimization of the scheduling scheme. Through the evaluation and analysis of the scheduling characteristics, the first scheduling fitness is obtained. The evaluation process can comprehensively reflect the effectiveness of the scheduling scheme and identify which scheduling schemes are more in line with the power grid requirements, further improving the resource utilization rate and power grid scheduling efficiency. By combining the scheduling feedback coefficient, the scheduling fitness is adjusted to further optimize the scheduling scheme. Through the weighted adjustment of the feedback coefficient, the scheduling fitness can be targeted optimized according to the characteristics of different types of loads. The feedback adjustment can enhance the response ability of the scheduling scheme to changes in the power grid operation state, improve the accuracy and adaptability of the scheme, and ensure better meeting the power grid operation requirements in practical applications. When the evaluated and adjusted scheduling fitness reaches the predetermined threshold, the target day-ahead scheduling scheme is finally selected. This scheme is based on the latest data and evaluation results to ensure the stable operation of the power grid during the scheduling period. Through the iterative process, the scheduling scheme not only meets the constraints of historical data but also can flexibly respond to real-time changes, ensuring the reasonable allocation of power grid load and generation resources, reducing the operation cost and improving the energy utilization efficiency.

[0067] Embodiment 2, based on the same inventive concept as the regional distribution network scheduling method based on flexible load optimization in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a regional distribution network scheduling system based on flexible load optimization, and the system includes: The first database acquisition module 10 is used to implement step S100: extract the first type from the predetermined flexible load types, and traverse the first type in the scheduling database to obtain the first database, where the first database includes the first data group.

[0068] The scheduling evaluation instruction issuing module 20 is used to implement step S200: if the first distribution network characteristics in the first data group meet the predetermined distribution network constraints, issue a scheduling evaluation instruction, where the predetermined distribution network constraints refer to the conditional constraints obtained by analyzing the historical distribution network characteristics of the target area distribution network.

[0069] The evaluation and analysis module 30 is used to implement step S300: evaluate and analyze the first scheduling feature in the first data group based on the scheduling evaluation instruction to obtain the first scheduling fitness.

[0070] The fitness adjustment module 40 is used to implement step S400: adjust the first scheduling fitness with the first scheduling feedback coefficient corresponding to the first type as the weight to obtain the first effective scheduling fitness.

[0071] The target scheduling scheme acquisition module 50 is used to implement step S500: if the first effective scheduling fitness reaches the predetermined fitness threshold, use the first scheduling scheme in the first data group as the target day-ahead scheduling scheme for the target regional distribution network.

[0072] Furthermore, the predetermined flexible load types include shiftable load types, interruptible load types, and translatable load types.

[0073] Furthermore, the scheduling evaluation instruction issuing module 20 includes: The historical load feature acquisition unit is used to acquire the historical load features of the target regional distribution network; the historical power generation feature acquisition unit is used to acquire the historical power generation features of the target regional distribution network; the historical power grid feature acquisition unit is used to acquire the historical power grid features of the target regional distribution network; the historical distribution network feature construction unit is used to construct the historical distribution network features based on the historical load features, the historical power generation features, and the historical power grid features; wherein, the historical load features include historical charge and discharge power and historical state of charge, the historical power generation features include historical ramp rate and historical landslide rate, and the historical power grid features include historical power flow, historical voltage, and historical current.

[0074] Furthermore, the scheduling evaluation instruction issuing module 20 further includes: The scheduling re-evaluation instruction issuing unit is used to issue a scheduling re-evaluation instruction if the first distribution network feature does not meet the predetermined distribution network constraints; the iterative repetition unit is used to obtain the second data group in the first database based on the scheduling re-evaluation instruction, and use the second data group as the first data group to iteratively repeat steps S200 to S500 to obtain the target day-ahead scheduling scheme.

[0075] Furthermore, the evaluation and analysis module 30 includes: A traversal matching unit for reading scheduling evaluation metrics and traversing and matching the scheduling evaluation metrics in the first scheduling feature to obtain a first scheduling metric parameter; a weighted calculation unit for performing coefficient of variation weighted calculation on the normalized first scheduling metric parameter to obtain the first scheduling fitness; wherein the scheduling evaluation metrics at least include scheduling operation cost, scheduling network loss, scheduling load utilization rate, and scheduling power generation consumption capacity.

[0076] Furthermore, the fitness adjustment module 40 includes: A first predetermined scheduling factor set reading unit for reading the first predetermined scheduling factor set of the first type, the first predetermined scheduling factor set having an identifier of a first predetermined weight allocation; a scheduling monitoring unit for performing scheduling monitoring on the target area distribution network based on the first predetermined scheduling factor set to obtain a first scheduling factor data set; a weighting unit for weighting the first scheduling factor data set in combination with the first predetermined weight allocation to obtain the first scheduling feedback coefficient.

[0077] Furthermore, the fitness adjustment module 40 includes: An independent variable acquisition unit for taking the first scheduling feature as an independent variable; a dependent variable acquisition unit for taking the first scheduling fitness as a dependent variable; a correlation analysis unit for performing many-to-one correlation analysis on the independent variable and the dependent variable to obtain a correlation analysis result; a first predetermined weight allocation acquisition unit for analyzing the correlation analysis result and constructing the first predetermined scheduling factor set, and obtaining the first predetermined weight allocation corresponding to the first predetermined scheduling factor set.

[0078] Furthermore, the target scheduling scheme acquisition module 50 includes: A dynamic monitoring unit for performing dynamic monitoring on the target area distribution network to obtain real-time distribution network characteristics; a real-time distribution network constraint acquisition unit for replacing the historical distribution network characteristics with the real-time distribution network characteristics according to a rolling adjustment response mechanism to obtain real-time distribution network constraints; an iterative repetition unit for iteratively repeating steps S200 to S500 with the real-time distribution network constraints as the predetermined distribution network constraints, and taking the first scheduling scheme as the target intraday scheduling scheme of the target area distribution network.

[0079] Through the foregoing detailed description of the regional distribution network scheduling method based on flexible load optimization in this specification, those skilled in the art can clearly know the regional distribution network scheduling system based on flexible load optimization in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for related parts, reference may be made to the description in the method part.

[0080] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A regional distribution network dispatching method based on flexible load optimization, characterized in that: The method comprises: Step S100: extracting a first type from predetermined flexible load types, and traversing the first type in a scheduling database to obtain a first database, wherein the first database includes a first data group; Step S200: If the first distribution network characteristic in the first data group meets the predetermined distribution network constraint, a dispatch evaluation instruction is issued, wherein the predetermined distribution network constraint refers to a conditional constraint obtained by analyzing the historical distribution network characteristics of the distribution network in the target area; Step S300: evaluating and analyzing the first scheduling feature in the first data group based on the scheduling evaluation instruction to obtain a first scheduling fitness; Step S400: adjusting the first scheduling fitness by taking the first scheduling feedback coefficient corresponding to the first type as a weight to obtain a first effective scheduling fitness; Step S500: If the first effective scheduling fitness reaches a predetermined fitness threshold, the first scheduling scheme in the first data group is used as the target day-ahead scheduling scheme for the target regional distribution network.

2. The regional distribution network dispatching method based on flexible load optimization as claimed in claim 1, characterized in that: The predetermined flexible load types include a transferable load type, an interruptible load type and a translatable load type.

3. The regional distribution network dispatching method based on flexible load optimization as claimed in claim 1, characterized in that: Step S200 includes: Obtaining historical load characteristics of the target area distribution network; Obtaining historical power generation characteristics of the target area distribution network; Acquire historical power grid characteristics of the target area distribution network; Establishing the historical distribution network characteristics based on the historical load characteristics, the historical power generation characteristics and the historical power grid characteristics; The historical load characteristics include historical charging and discharging power and historical state of charge, the historical power generation characteristics include historical climbing rate and historical landslide rate, and the historical power grid characteristics include historical current, historical voltage and historical current.

4. The regional distribution network dispatching method based on flexible load optimization as claimed in claim 1, characterized in that: Step S200 further includes: If the first distribution network characteristic does not meet the predetermined distribution network constraint, issuing a dispatch re-evaluation instruction; A second data group in the first database is acquired based on the scheduling re-evaluation instruction, and the second data group is used as the first data group, and steps S200 to S500 are iteratively repeated to obtain the target day-ahead scheduling plan.

5. The regional distribution network dispatching method based on flexible load optimization as claimed in claim 1, characterized in that: Step S300 further includes: Reading a scheduling evaluation index, and traversing and matching the scheduling evaluation index in the first scheduling feature to obtain a first scheduling index parameter; Performing weighted calculation of the coefficient of variation on the normalized first scheduling indicator parameter to obtain the first scheduling fitness; Among them, the dispatching evaluation indicators at least include dispatching operation cost, dispatching network loss, dispatching load utilization rate and dispatching power generation absorption capacity.

6. The regional distribution network dispatching method based on flexible load optimization as claimed in claim 1, characterized in that: Step S400 includes: Reading a first predetermined scheduling factor set of the first type, the first predetermined scheduling factor set having an identifier of a first predetermined weight allocation; Performing dispatch monitoring on the target area distribution network based on the first predetermined dispatch factor set to obtain a first dispatch factor data set; The first scheduling factor data set is weighted in combination with the first predetermined weight allocation to obtain the first scheduling feedback coefficient.

7. The regional distribution network dispatching method based on flexible load optimization as claimed in claim 6, characterized in that: Reading a first predetermined scheduling factor set of the first type, wherein the first predetermined scheduling factor set has an identifier of a first predetermined weight allocation, including: Taking the first scheduling characteristic as an independent variable; Taking the first scheduling fitness as a dependent variable; Performing a many-to-one correlation analysis on the independent variable and the dependent variable to obtain a correlation analysis result; Analyze the correlation analysis result and form the first predetermined scheduling factor set, and obtain the first predetermined weight allocation corresponding to the first predetermined scheduling factor set.

8. The regional distribution network dispatching method based on flexible load optimization as claimed in claim 1, characterized in that: After step S500, the method further includes: Dynamically monitor the distribution network in the target area to obtain real-time distribution network characteristics; According to a rolling adjustment response mechanism, the real-time distribution network characteristics are used to replace the historical distribution network characteristics to obtain real-time distribution network constraints; The steps S200 to S500 are iteratively repeated using the real-time distribution network constraint as the predetermined distribution network constraint, and the first dispatching plan is used as the target intra-day dispatching plan for the target regional distribution network.

9. Regional distribution network dispatching system based on flexible load optimization, characterized in that: The system for implementing the regional distribution network dispatching method based on flexible load optimization according to any one of claims 1 to 8 comprises: A first database acquisition module, configured to implement step S100: extracting a first type of predetermined flexible load types, and traversing the first type in a scheduling database to obtain a first database, wherein the first database includes a first data group; A dispatch evaluation instruction issuing module is used to implement step S200: if the first distribution network characteristic in the first data group meets the predetermined distribution network constraint, issue a dispatch evaluation instruction, wherein the predetermined distribution network constraint refers to a conditional constraint obtained by analyzing the historical distribution network characteristics of the distribution network in the target area; An evaluation and analysis module, configured to implement step S300: evaluating and analyzing the first scheduling feature in the first data group based on the scheduling evaluation instruction to obtain a first scheduling fitness; A fitness adjustment module, used to implement step S400: adjusting the first scheduling fitness by taking the first scheduling feedback coefficient corresponding to the first type as a weight to obtain a first effective scheduling fitness; The target scheduling scheme acquisition module is used to implement step S500: if the first effective scheduling fitness reaches a predetermined fitness threshold, the first scheduling scheme in the first data group is used as the target day-ahead scheduling scheme of the target regional distribution network.

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