Regional Distribution Network Dispatch Method and System Based on Flexible Load Optimization

Abstract

This invention provides a regional distribution network dispatching method and system based on flexible load optimization, relating to the field of distribution network dispatching technology. The method includes: extracting a first type from predetermined flexible load types and traversing a dispatching database to obtain a first database; if the first distribution network characteristics meet predetermined distribution network constraints, issuing a dispatching evaluation command, whereby the predetermined distribution network constraints are conditional constraints obtained by analyzing the historical distribution network characteristics of the target regional distribution network; evaluating and analyzing the first dispatching characteristics to obtain a first dispatching fitness; adjusting the first dispatching feedback coefficient as a weight to obtain a first effective dispatching fitness; and if a predetermined fitness threshold is reached, using the first dispatching scheme as the target day-ahead dispatching scheme. This invention solves the technical problem that existing distribution network dispatching strategies are mainly based on historical data or fixed patterns, lacking a flexible adjustment mechanism for real-time load changes, thus affecting the stability and reliability of the power grid.

Description

Technical Field

[0001] This invention relates to the field of distribution network dispatching technology, specifically to a regional distribution network dispatching method and system based on flexible load optimization. Background Technology

[0002] In traditional distribution network dispatching, load is typically considered a fixed demand, and dispatching strategies are mainly based on historical data or fixed patterns, lacking flexible adjustment mechanisms for real-time load changes. Load fluctuations, especially during high-demand periods, can lead to overloading or imbalances in the grid, increasing the risk of overload and faults. Furthermore, existing dispatching evaluation methods generally use fixed indicators, lacking specific assessments for different load types and dispatching characteristics. For example, some load types may be easier to dispatch and adjust, while others may have higher priority. The lack of identification and handling of these differences leads to inaccurate dispatching fitness assessments, ultimately affecting the overall grid dispatching optimization effect. Summary of the Invention

[0003] This application provides a regional distribution network dispatching method and system based on flexible load optimization, aiming to solve the technical problem that existing distribution network dispatching strategies are mainly based on historical data or fixed patterns, lacking a flexible adjustment mechanism for real-time load changes, thus affecting the stability and reliability of the power grid.

[0004] The first aspect disclosed in this application provides a regional distribution network dispatching method based on flexible load optimization. The method includes: step S100: extracting a first type from a predetermined flexible load type and traversing the first type in a dispatching database to obtain a first database, wherein the first database includes a first data group; step S200: if a first distribution network characteristic in the first data group meets a predetermined distribution network constraint, issuing a dispatching evaluation instruction, wherein the predetermined distribution network constraint refers to the conditional constraint obtained by analyzing the historical distribution network characteristics of the target regional distribution network; step S300: evaluating and analyzing a first dispatching characteristic in the first data group based on the dispatching evaluation instruction to obtain a first dispatching fitness; step S400: adjusting the first dispatching fitness with a first dispatching feedback coefficient corresponding to the first type as a weight to obtain a first effective dispatching fitness; step S500: if the first effective dispatching fitness reaches a predetermined fitness threshold, using the first dispatching scheme in the first data group as the target day-ahead dispatching scheme for the target regional distribution network.

[0005] The second aspect of this application discloses a regional distribution network dispatching system based on flexible load optimization. The system is used in the aforementioned regional distribution network dispatching method based on flexible load optimization. The system includes: a first database acquisition module, configured to perform step S100: extracting a first type from predetermined flexible load types and traversing the first type in a dispatching database to obtain a first database, wherein the first database includes a first data group; and a dispatching evaluation instruction issuance module, configured to perform step S200: if a first distribution network characteristic in the first data group conforms to predetermined distribution network constraints, issuing a dispatching evaluation instruction, wherein the predetermined distribution network constraints refer to the analysis target area. The system includes: a constraint condition derived from the historical distribution network characteristics; an evaluation and analysis module for implementing step S300: evaluating and analyzing the first scheduling characteristics in the first data group based on the scheduling evaluation instruction to obtain a first scheduling fitness; a fitness adjustment module for implementing step S400: adjusting 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; and a target scheduling scheme acquisition module for implementing step S500: if the first effective scheduling fitness reaches a predetermined fitness threshold, using 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:

[0007] By extracting the first type from the predetermined flexible load types, the dispatching scheme can be flexibly adjusted according to different types of load demands. Traversing the dispatching database to obtain the first database for the first type allows for the formulation of optimal dispatching strategies for different load characteristics, improving load allocation flexibility. Analyzing distribution network characteristics based on historical data of the target area ensures that the dispatching scheme meets the constraints of power grid operation. By determining whether it meets the predetermined distribution network constraints, problems such as power grid overload and voltage fluctuations can be effectively avoided, improving the feasibility of the dispatching scheme and the stability of power grid operation. For dispatching schemes that meet the constraints, dispatching evaluation instructions are issued to promote further optimization of the dispatching scheme. Through the evaluation and analysis of dispatching characteristics, the first dispatching fitness is obtained. The evaluation process can comprehensively reflect the effectiveness of the dispatching scheme and identify which dispatching schemes are more suitable for the power grid. To further improve resource utilization and grid dispatch efficiency, the dispatch fitness is adjusted by combining dispatch feedback coefficients to optimize the dispatch scheme. Through weighted adjustment of feedback coefficients, the dispatch fitness can be optimized in a targeted manner according to the characteristics of different types of loads. Feedback adjustment can improve the dispatch scheme's responsiveness to changes in grid operating status, improve the scheme's accuracy and adaptability, and ensure better grid operation requirements in practical applications. When the adjusted dispatch fitness reaches a predetermined threshold, the target day-ahead dispatch scheme is finally selected. This scheme is based on the latest data and evaluation results to ensure stable grid operation during the dispatch period. Through the iterative process, the dispatch scheme not only meets the constraints of historical data but also flexibly responds to real-time changes, ensuring the rational allocation of grid load and power generation resources, reducing operating costs, and improving energy utilization efficiency.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the regional distribution network dispatching method based on flexible load optimization provided in the embodiments of this application.

[0010] Figure 2 This is a schematic diagram of the regional distribution network dispatching system based on flexible load optimization provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached figures: First database acquisition module 10, scheduling evaluation instruction issuance module 20, evaluation and analysis module 30, fitness adjustment module 40, target scheduling scheme acquisition module 50. Detailed Implementation

[0012] This application provides a regional distribution network dispatching method and system based on flexible load optimization, which solves the technical problem that existing distribution network dispatching strategies are mainly based on historical data or fixed patterns and lack a flexible adjustment mechanism for real-time load changes, thus affecting the stability and reliability of the power grid.

[0013] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0014] Example 1, as Figure 1 As shown in the figure, this application provides a regional distribution network dispatching method based on flexible load optimization, the method comprising:

[0015] 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, wherein the first database includes a first data group.

[0016] Pre-defined flexible load types are load types that can be flexibly adjusted according to demand. These include transferable load types, interruptible load types, and shiftable load types. Pre-defined flexible load types are typically defined during the system design phase, based on specific needs and scheduling objectives. Randomly selecting the first type from the pre-defined flexible load types as the analysis object facilitates subsequent full-type traversal analysis of the pre-defined flexible load types.

[0017] The first type of load is traversed in the scheduling database, which contains multiple different load data, scheduling parameters, historical operation data, etc. The traversal process is based on the first type of load. For the first type of load, all data records related to this type are traversed, including load data, power generation data, grid data and other related feature data, to ensure that all relevant data can be extracted for subsequent analysis.

[0018] During the traversal, all datasets that meet the conditions are merged into a new database, which is the first database. This database contains the first data group related to the first type extracted. The first data group is a specific set of data extracted from the scheduling database, and each data group represents a set of related data.

[0019] Step S200: If the first distribution network characteristics in the first data group meet the predetermined distribution network constraints, issue a dispatch evaluation instruction, wherein the predetermined distribution network constraints refer to the conditional constraints obtained by analyzing the historical distribution network characteristics of the target area distribution network.

[0020] 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 status, current, voltage, power flow, and power distribution of the distribution network. The distribution network feature can reflect information such as the operating status of the distribution network, load demand, and the balance between power generation and consumption.

[0021] Pre-defined distribution network constraints are conditions derived from analyzing the historical characteristics of the distribution network in the target area. These constraints are pre-set and aim to ensure that the distribution network can maintain a stable, reliable, and safe state during operation. Pre-defined constraints include voltage range limits, load distribution limits, current limits, power balance, etc. These constraints can be extracted from historical data, such as analyzing the historical voltage, current, power, and load characteristics of the distribution network to obtain the historical operation patterns and boundary conditions of the distribution network.

[0022] The first distribution network characteristics in the first data group are compared with the predetermined distribution network constraints. Specifically, by comparing the actual operating characteristics of the current distribution network with the typical operating characteristics of the historical distribution network, it is determined whether the constraints exceed the preset range. If the characteristic value exceeds the predetermined range, such as excessively high or low voltage or excessive current, it does not meet the constraints; otherwise, it meets the constraints.

[0023] If the characteristics of the first distribution network in the first data group meet the predetermined distribution network constraints, a dispatch evaluation instruction is issued, indicating that the evaluation and optimization of the dispatch scheme can continue. The purpose of this instruction is to guide the process into the next dispatch analysis stage, to further evaluate, optimize, or adjust the dispatch scheme, so as to ensure that the operation of the distribution network meets both technical specifications and actual needs.

[0024] Step S300: Based on the scheduling evaluation instruction, evaluate and analyze the first scheduling feature in the first data group to obtain the first scheduling fitness.

[0025] The first dispatching characteristic refers to the specific parameters and data related to dispatching operations. For example, power generation plans include power generation plans for different time periods, such as generator start-up and shutdown times and power generation; load dispatching plans are specifically the dispatching strategies for loads, including load transfer and shifting; grid status refers to the grid status at different points in time, such as voltage, current, and power distribution; and dispatching optimization parameters are specifically the optimization objectives set according to demand and conditions, such as dispatching costs and network losses.

[0026] Based on the dispatch evaluation instructions, the first dispatch characteristic is evaluated and analyzed. Specifically, the completion rate of dispatch objectives is analyzed, such as whether load demand can be met on time and whether power supply and demand can be balanced; operational efficiency is analyzed to determine whether dispatch minimizes operating costs, such as fuel consumption and power transmission losses; system stability is analyzed to check whether the power grid maintains a stable operating state during dispatch and whether it meets the grid stability requirements such as voltage and current; and resource optimization is analyzed to determine whether operations such as generator start-up and shutdown and load transfer achieve the goal of maximizing resource utilization.

[0027] After evaluation and analysis, the first scheduling fitness is calculated. Fitness is a comprehensive evaluation value, usually obtained by weighted summation, used to measure the merits and feasibility of the scheduling scheme. This process quantifies the merits of the scheduling scheme, providing data support for subsequent optimization and adjustment. The scheduling fitness reflects whether the scheduling scheme can efficiently and safely meet the needs of the distribution network and provides a basis for subsequent optimization and adjustment.

[0028] Step S400: Adjust the first scheduling fitness using the first scheduling feedback coefficient corresponding to the first type as the weight to obtain the first effective scheduling fitness.

[0029] The first type has a corresponding first dispatch feedback coefficient, which reflects the importance and influence of the load type in dispatch optimization. The feedback coefficients of different load types may be different. The feedback coefficient is obtained based on actual operating conditions or historical data. It represents the adaptability of the load type in distribution network dispatch or the flexibility of dispatch adjustment. For example, some load types may be easier to adjust, so their feedback coefficients are larger.

[0030] Using the first scheduling feedback coefficient as a weight to adjust the first scheduling fitness means that the scheduling fitness will be affected by the load type. For example, if the feedback coefficient of a certain load type is large, it means that the load type is more important in the current scheduling, and its corresponding scheduling fitness will be given a greater weight, thus affecting the final scheduling evaluation result.

[0031] The adjusted fitness value is the first effective scheduling fitness, which more accurately reflects the effectiveness of the scheduling scheme, especially considering the flexibility of load types and the effect of scheduling adjustments. This effective fitness value will be used as the standard for subsequent judgments on whether the scheduling objectives have been achieved.

[0032] Step S500: If the first effective scheduling fitness reaches a predetermined fitness threshold, the first scheduling scheme in the first data group is taken as the target day-ahead scheduling scheme of the target area distribution network.

[0033] A fitness threshold is pre-set, based on system requirements, scheduling objectives, and historical data. This threshold represents the minimum requirement for a scheduling scheme to reach an acceptable level. The first effective scheduling fitness is compared to the pre-set fitness threshold. If the first effective scheduling fitness is greater than or equal to the fitness threshold, 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 set is taken as the target day-ahead scheduling scheme for the target area distribution network. This target day-ahead scheduling scheme refers to the scheduling scheme for the next day determined based on the current scheduling evaluation and optimization results. It represents parameters such as load allocation, generation scheduling, and network operation status of the distribution network in the coming day.

[0034] Furthermore, if the first effective dispatch fitness does not reach the predetermined threshold, the dispatch scheme needs to be further optimized or adjusted. For example, it may trigger a return to the previous steps for adjustment, or re-evaluate different data sets. Only when the dispatch scheme reaches the fitness threshold after multiple adjustments and optimizations will it be finally selected as the target day-ahead dispatch scheme, thereby ensuring that the distribution network can efficiently and stably carry out load distribution and power generation dispatch in the future operation.

[0035] Furthermore, the predetermined flexible load types include transferable load types, interruptible load types, and shiftable load types.

[0036] The types of pre-defined flexible loads include transferable loads, interruptible loads, and shiftable loads. These three types of loads have different characteristics and applicable scenarios. In distribution network dispatching, they can effectively improve the flexibility and stability of the power grid.

[0037] Transferable loads refer to loads that can be flexibly transferred between different time periods without causing significant impact on the entire system. For example, some electrical equipment (such as air conditioners and heaters) can be transferred from peak electricity consumption periods to off-peak periods, or their use can be delayed without affecting normal operation. This is suitable for reducing peak loads, such as postponing part of the load of industrial production or the electricity consumption of commercial facilities to periods when the grid is less overloaded.

[0038] Interruptible loads are loads that can be interrupted for a short period of time when the power system is under stress. Such loads can be temporarily disconnected 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's security. They are used to reduce power demand for a short period of time when the power grid is underpowered or overloaded, and are suitable for power grid load management and emergency backup power supply.

[0039] Shiftable loads refer to loads that can be optimally dispatched by adjusting their distribution without changing the total energy consumption. They differ from transferable loads because their usage time does not need to be completely transferred. Instead, loads can be shifted between different areas or equipment. They are suitable for situations where it is necessary to balance the loads of multiple areas or equipment, such as shifting the loads of commercial or industrial facilities between different areas to avoid overloading the power grid in some areas.

[0040] Furthermore, step S200 includes:

[0041] The historical load characteristics of the target area distribution network are obtained; the historical generation characteristics of the target area distribution network are obtained; the historical grid characteristics of the target area distribution network are obtained; the historical distribution network characteristics are constructed based on the historical load characteristics, the historical generation characteristics, and the historical grid characteristics; wherein, the historical load characteristics include historical charging and discharging power and historical state of charge, the historical generation characteristics include historical ramp rate and historical slippage rate, and the historical grid characteristics include historical power flow, historical voltage, and historical current.

[0042] Historical load characteristics refer to historical data related to the load demand of the distribution network, reflecting the load fluctuations and changes in load demand of the power grid in different time periods. Historical load characteristics include historical charging and discharging power and historical state of charge (SOC). Historical charging and discharging power refers to the charging and discharging power of the power grid or energy storage system in a historical time period. By observing the changes in charging and discharging power, we can understand how the power grid balances power supply and demand in different time periods and how it performs energy storage and release operations. Historical SOC refers to the charging status of energy storage devices. During power grid dispatching, SOC reflects the remaining power of energy storage devices and their contribution to power grid load balance. Historical SOC data can help the system determine the historical charging and discharging behavior of energy storage devices and predict their impact on future dispatching.

[0043] Historical power generation characteristics refer to historical data related to the operation and power generation capacity of generator sets in the power grid. Historical power generation characteristics include historical ramp rate and historical slip rate. The historical ramp rate refers to the rate at which the power of a generator set increases from low to high when starting up or when the load changes. A high ramp rate means that the generator can respond quickly to changes in load demand and is suitable for rapidly changing load environments. The historical slip rate refers to the rate at which the power of a generator set decreases from high to low when the load decreases. A low slip rate means that the generator set can reduce its output power more smoothly and avoid grid fluctuations.

[0044] Historical power grid characteristics refer to the operational status characteristics of the power grid, reflecting the actual operation of the distribution network at different historical moments. These characteristics include historical power flow, historical voltage, and historical current. Historical power flow refers to the path and magnitude of power flow in the distribution network. Power flow characteristics include the direction and magnitude of power flow, as well as the distribution of power from one area to another. Historical power flow data helps analyze the transmission of power grid load and identify potential bottlenecks and overload areas. Historical voltage reflects the historical fluctuations in grid voltage, including whether the voltage was within a safe range at different times. Excessively high or low voltage can damage grid equipment or reduce efficiency; therefore, voltage characteristic data is crucial for assessing grid stability. Historical current reflects the current magnitude in various parts of the distribution network, especially the current in critical lines and equipment. Current characteristics help determine whether the grid is overloaded and whether there are potential current limiting problems.

[0045] By integrating historical load characteristics, historical power generation characteristics, and historical grid characteristics, historical distribution network characteristics are constructed. By separately acquiring the historical load characteristics, historical power generation characteristics, and historical grid characteristics of the target area's distribution network, data support is provided for subsequent dispatch assessment. This historical data not only helps analyze the operating status of the distribution network but also provides a predictive basis for future dispatch optimization, enabling the distribution network to adjust dispatch strategies according to actual operating conditions, thereby improving the stability and efficiency of the power grid.

[0046] Furthermore, step S200 also includes:

[0047] If the characteristics of the first distribution network do not meet the predetermined distribution network constraints, a scheduling reassessment instruction is issued; based on the scheduling reassessment instruction, a second data group is obtained from the first database, and the second data group is used as the first data group. Steps S200 to S500 are iteratively repeated to obtain the target day-ahead scheduling scheme.

[0048] If the characteristics of the first distribution network do not meet the predetermined distribution network constraints, it means that the operating conditions of the power grid have exceeded the preset safety or operating range. In this case, a dispatch reassessment instruction is issued, prompting that the current dispatch scheme needs to be re-evaluated or adjusted.

[0049] According to the dispatch reassessment instruction, a new dataset is extracted from the first database as the second data group. The second data group contains different load data, generation data, or grid data. The purpose of extracting this new data group is to recalculate and adjust it according to the grid constraints in order to meet the predetermined grid constraints.

[0050] Using the second data set as input, the process from steps S200 to S500 is restarted. This includes determining whether the new data set meets the grid constraints, performing a dispatch evaluation, calculating a new dispatch fitness, weighting and adjusting the dispatch fitness to obtain an effective dispatch fitness, and finally determining whether a predetermined fitness threshold has been reached. If the conditions are met, the target-date dispatch plan is determined. This process iterates continuously until the dispatch plan meets the predetermined grid constraints and fitness threshold. Through iteration, the dispatch plan can be continuously optimized to ensure that the dispatch result satisfies both the grid's security constraints and efficiently allocates resources. Finally, after multiple iterations and adjustments, a dispatch plan that meets the grid constraints and fitness requirements is selected and used as the target-date dispatch plan, ready for execution of distribution network operation dispatch on the target date.

[0051] Furthermore, step S300 also includes:

[0052] Read the scheduling evaluation index and iterate and match the scheduling evaluation index in the first scheduling feature to obtain the first scheduling index parameter; perform a coefficient of variation weighted calculation on the normalized first scheduling index parameter to obtain the first scheduling fitness; wherein, the scheduling evaluation index includes at least scheduling operation cost, scheduling network loss, scheduling load utilization rate and scheduling power generation absorption capacity.

[0053] The dispatch evaluation indicators are key parameters used to measure the performance of a dispatch scheme. They reflect the effectiveness of the scheme across multiple dimensions, including at least dispatch operation costs, dispatch network losses, dispatch load utilization, and dispatch generation absorption capacity. Dispatch operation costs reflect the cost-effectiveness of the dispatch process and typically include generation costs, fuel costs, and equipment usage costs. Dispatch schemes with lower operating costs are preferred. Dispatch network losses refer to the energy loss in the power grid during power transmission, usually determined by factors such as current and voltage losses and line impedance. Lower network losses indicate a more efficient dispatch scheme. Dispatch load utilization measures the efficiency of load resource utilization, usually calculated as the ratio between actual load and maximum load. Higher load utilization indicates a more reasonable power grid load allocation. Dispatch generation absorption capacity reflects the ability to absorb power resources, i.e., whether generating units can fully absorb power generation when demand is insufficient. Dispatch schemes with strong absorption capacity can better balance generation and demand.

[0054] The scheduling evaluation indicators are iterated and matched in the first scheduling features to find the parts related to the scheduling evaluation indicators. 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. The scheduling evaluation indicators are matched with the first scheduling features to obtain specific first scheduling indicator parameters. These scheduling indicator parameters are values ​​obtained by analyzing the scheduling features and reflect the performance of each evaluation indicator in the current scheduling scheme.

[0055] Since different scheduling evaluation indicators may have different dimensions, such as operating costs being in monetary units and load utilization being in percentages, it is necessary to normalize these indicators. The purpose of normalization is to transform data with different units and dimensions into a comparable standard form, which facilitates comprehensive analysis and weighted calculation. The normalization method is to adjust the data through a normalization formula so that all indicator parameters are within the same scale range, such as between 0 and 1, thereby eliminating the influence of dimensions.

[0056] The coefficient of variation (CVA) is an indicator that describes the degree of dispersion of data. It reflects the relative fluctuation of data. For scheduling indicators, the CVA is usually used to measure the uncertainty and volatility of scheduling results. The normalized scheduling indicator parameters are weighted according to their CVA. That is, indicators with larger CVA are given higher weights because they may have a greater impact on scheduling. Conversely, indicators with lower volatility have lower weights. After normalization and weighted calculation, the first scheduling fitness is obtained. This fitness comprehensively reflects the overall performance of the scheduling scheme across multiple evaluation dimensions. The higher the fitness value, the more optimized the scheduling scheme is and the better it meets the system's requirements.

[0057] Furthermore, step S400 includes:

[0058] Read the first predetermined scheduling factor set of the first type, the first predetermined scheduling factor set having an identifier of the first predetermined weight allocation; perform scheduling monitoring on the target area distribution network based on the first predetermined scheduling factor set to obtain the first scheduling factor dataset; combine the first predetermined weight allocation to weight the first scheduling factor dataset to obtain the first scheduling feedback coefficient.

[0059] Read the first predetermined scheduling factor set of the first type. The scheduling factor set refers to a set of key factors used to reflect scheduling performance during the scheduling process. For example, scheduling factors include various factors such as load in the system, generation plan, and grid stability. These factors directly affect the effectiveness and optimization degree of the scheduling results. Each load type will have specific scheduling factors, and these factor sets are related to specific types of loads.

[0060] The first predetermined scheduling factor set has an identifier for the first predetermined weight allocation. The weight allocation refers to the degree of importance given to each factor when evaluating the scheduling factors. Different scheduling factors may have different impacts on the final scheduling scheme. Therefore, different weights are assigned to each factor. The weight allocation identifier records the information of these factor weights and is used to guide the subsequent weighted calculation.

[0061] Based on the first predetermined scheduling factor set, scheduling monitoring is carried out in the distribution network of the target area to obtain the first scheduling factor dataset. This dataset contains real-time data related to the scheduling factors, such as the load status of the power grid, the operating status of generators, and the voltage and current status of the power grid. The first scheduling factor dataset contains relevant data for multiple time periods, which helps to grasp the operation of the distribution network in real time so as to adjust the scheduling scheme as needed.

[0062] According to the first predetermined weight allocation identifier, the data in the first scheduling factor dataset are weighted. The purpose of the weighting calculation is to adjust the influence of different scheduling factors on the final result based on their relative importance. The resulting first scheduling feedback coefficient is the weighted result of the scheduling factors, reflecting the degree of contribution of each scheduling factor to the overall scheduling scheme. The scheduling feedback coefficient is usually adjusted according to the performance of each factor in the scheduling process (such as load demand, power generation absorption capacity, etc.) to ensure that the scheduling scheme is more in line with the optimization goal of the system.

[0063] Furthermore, reading the first predetermined scheduling factor set of the first type, wherein the first predetermined scheduling factor set has an identifier for a first predetermined weight allocation, includes:

[0064] The first scheduling feature is used as the independent variable; the first scheduling fitness is used as the dependent variable; a many-to-one correlation analysis is performed on the independent variable and the dependent variable to obtain the correlation analysis results; the correlation analysis results are analyzed and the first predetermined scheduling factor set is constructed, and the first predetermined weight allocation corresponding to the first predetermined scheduling factor set is obtained.

[0065] The first dispatch characteristics include information such as the load of the distribution network, the generation plan, and the grid status. These characteristics are key input data in the dispatch scheme and will have a direct impact on the final dispatch effect. By using the first dispatch characteristics as independent variables, we can predict how the dispatch fitness will change.

[0066] The first scheduling fitness is the standard for evaluating the quality of a scheduling scheme. It is the result obtained by weighting multiple scheduling evaluation indicators. The first scheduling fitness is used as the dependent variable, that is, the result that we want to explain or predict. It is determined by the first scheduling characteristics.

[0067] A many-to-one correlation analysis is performed on the independent and dependent variables, which simultaneously considers the relationship between multiple independent variables (first scheduling characteristics) and one dependent variable (first scheduling fitness). Many-to-one analysis helps to reveal which scheduling characteristics have a greater impact on fitness and the strength of the relationship between them. Correlation analysis uses statistical methods, such as Pearson correlation coefficient and regression analysis, to measure the linear or nonlinear relationship between the independent and dependent variables and outputs the correlation analysis results. It describes the degree of correlation between each scheduling characteristic (independent variable) and scheduling fitness (dependent variable). These results provide a basis for subsequent selection of scheduling factors and weight allocation.

[0068] Based on the correlation analysis results, scheduling features highly correlated with scheduling fitness are selected as the first predetermined set of scheduling factors. These factors are key to scheduling optimization and directly affect the quality of scheduling results. After constructing the factor set, a weight is assigned to each factor to represent its importance to the scheduling result. By analyzing the correlation, it can be determined which factors contribute more to scheduling fitness and are given higher weights, while those that contribute less are assigned lower weights, ensuring that the weight of each factor truly reflects its importance in scheduling decisions.

[0069] Furthermore, after step S500, the following steps are also included:

[0070] The target area distribution network is dynamically monitored to obtain real-time distribution network characteristics; according to the 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 real-time distribution network constraints are used as the predetermined distribution network constraints to iteratively repeat steps S200 to S500, and the first scheduling scheme is used as the target intraday scheduling scheme for the target area distribution network.

[0071] Real-time dynamic monitoring of the distribution network in the target area is conducted to acquire current grid operation data, including various operating parameters such as load, generation status, voltage, current, and power flow. Dynamic monitoring is a real-time data acquisition process that reflects the specific operating status of the distribution network at the current moment, ensuring that dispatching plans can respond to possible changes in grid operation. Through dynamic monitoring, real-time distribution network characteristics are obtained, including the current load distribution, generator status, and energy storage device charging and discharging status, reflecting the immediate operating condition of the distribution network and providing the latest information for subsequent dispatching adjustments.

[0072] The rolling adjustment response mechanism is a dynamic and real-time adjustment strategy. Its function is to adjust the original dispatching scheme and distribution network constraints based on real-time data. This mechanism enables rapid response to changes in the power grid and flexible adjustment of load, generation, and dispatching schemes. For example, if the grid load suddenly increases or generation problems occur during certain periods, the rolling adjustment mechanism will adjust the dispatching scheme accordingly based on real-time data to avoid grid overload or instability. By replacing the historical distribution network characteristics built on historical data with real-time distribution network characteristics, the dispatching scheme not only relies on historical data but also fully considers the current actual operation of the power grid, ensuring that the dispatching scheme can respond in real-time to dynamic changes in the power grid.

[0073] After obtaining the real-time distribution network constraints, they are input as new constraints into the scheduling process. Steps S200 to S500, including scheduling evaluation, optimization, and weighting, are re-executed. This process is iterative, ensuring that each scheduling is adjusted based on the latest distribution network state and constraints. This iterative process means that the distribution network scheduling scheme can be flexibly adjusted in real-time changes, ensuring the stable and efficient operation of the power grid. After one or more iterations, the optimal scheduling scheme is selected as the target intraday scheduling scheme. This scheme is based on the comprehensive optimization result of real-time constraints and scheduling requirements, aiming to achieve the smooth operation of the distribution network.

[0074] In summary, the regional distribution network dispatching method based on flexible load optimization provided in this application has the following technical effects:

[0075] By extracting the first type from the predetermined flexible load types, the dispatching scheme can be flexibly adjusted according to different types of load demands. Traversing the dispatching database to obtain the first database for the first type allows for the formulation of optimal dispatching strategies for different load characteristics, improving load allocation flexibility. Analyzing distribution network characteristics based on historical data of the target area ensures that the dispatching scheme meets the constraints of power grid operation. By determining whether it meets the predetermined distribution network constraints, problems such as power grid overload and voltage fluctuations can be effectively avoided, improving the feasibility of the dispatching scheme and the stability of power grid operation. For dispatching schemes that meet the constraints, dispatching evaluation instructions are issued to promote further optimization of the dispatching scheme. Through the evaluation and analysis of dispatching characteristics, the first dispatching fitness is obtained. The evaluation process can comprehensively reflect the effectiveness of the dispatching scheme and identify which dispatching schemes are more suitable for the power grid. To further improve resource utilization and grid dispatch efficiency, the dispatch fitness is adjusted by combining dispatch feedback coefficients to optimize the dispatch scheme. Through weighted adjustment of feedback coefficients, the dispatch fitness can be optimized in a targeted manner according to the characteristics of different types of loads. Feedback adjustment can improve the dispatch scheme's responsiveness to changes in grid operating status, improve the scheme's accuracy and adaptability, and ensure better grid operation requirements in practical applications. When the adjusted dispatch fitness reaches a predetermined threshold, the target day-ahead dispatch scheme is finally selected. This scheme is based on the latest data and evaluation results to ensure stable grid operation during the dispatch period. Through the iterative process, the dispatch scheme not only meets the constraints of historical data but also flexibly responds to real-time changes, ensuring the rational allocation of grid load and power generation resources, reducing operating costs, and improving energy utilization efficiency.

[0076] Example 2, based on the same inventive concept as the regional distribution network dispatching method based on flexible load optimization in the aforementioned examples, such as... Figure 2 As shown in the figure, this application provides a regional distribution network dispatching system based on flexible load optimization, the system comprising:

[0077] The first database acquisition module 10 is used to implement step S100: extracting the first type from the predetermined flexible load types and traversing the first type in the scheduling database to obtain the first database, wherein the first database includes a first data group.

[0078] 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, a scheduling evaluation instruction is issued, wherein the predetermined distribution network constraints refer to the condition constraints obtained by analyzing the historical distribution network characteristics of the target area distribution network.

[0079] 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.

[0080] 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.

[0081] The target scheduling scheme acquisition module 50 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 area distribution network.

[0082] Furthermore, the predetermined flexible load types include transferable load types, interruptible load types, and shiftable load types.

[0083] Furthermore, the scheduling evaluation instruction issuing module 20 includes:

[0084] The system includes a historical load characteristic acquisition unit for acquiring historical load characteristics of the target area distribution network; a historical power generation characteristic acquisition unit for acquiring historical power generation characteristics of the target area distribution network; a historical power grid characteristic acquisition unit for acquiring historical power grid characteristics of the target area distribution network; and a historical distribution network characteristic assembly unit for assembling 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 ramp rate and historical slippage rate; and the historical power grid characteristics include historical power flow, historical voltage, and historical current.

[0085] Furthermore, the scheduling evaluation instruction issuing module 20 also includes:

[0086] The scheduling reassessment instruction issuing unit is used to issue a scheduling reassessment instruction if the first distribution network characteristics do not conform to the predetermined distribution network constraints; the iterative repetition unit is used to obtain a second data group in the first database based on the scheduling reassessment 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.

[0087] Furthermore, the evaluation and analysis module 30 includes:

[0088] The traversal matching unit is used to read the scheduling evaluation index and traverse and match the scheduling evaluation index in the first scheduling feature to obtain the first scheduling index parameter; the weighted calculation unit is used to perform a coefficient of variation weighted calculation on the normalized first scheduling index parameter to obtain the first scheduling fitness; wherein, the scheduling evaluation index includes at least scheduling operation cost, scheduling network loss, scheduling load utilization rate and scheduling power generation absorption capacity.

[0089] Furthermore, the fitness adjustment module 40 includes:

[0090] The first predetermined scheduling factor set reading unit is used to read the first predetermined scheduling factor set of the first type, wherein the first predetermined scheduling factor set has an identifier of the first predetermined weight allocation; the scheduling monitoring unit is used to perform scheduling monitoring on the target area distribution network based on the first predetermined scheduling factor set to obtain the first scheduling factor dataset; the weighting unit is used to weight the first scheduling factor dataset in combination with the first predetermined weight allocation to obtain the first scheduling feedback coefficient.

[0091] Furthermore, the fitness adjustment module 40 includes:

[0092] The independent variable acquisition unit is used to take the first scheduling feature as the independent variable; the dependent variable acquisition unit is used to take the first scheduling fitness as the dependent variable; the correlation analysis unit is used to perform a many-to-one correlation analysis on the independent variable and the dependent variable to obtain the correlation analysis result; the first predetermined weight allocation acquisition unit is used to analyze the correlation analysis result and construct the first predetermined scheduling factor set, and obtain the first predetermined weight allocation corresponding to the first predetermined scheduling factor set.

[0093] Furthermore, the target scheduling scheme acquisition module 50 includes:

[0094] A dynamic monitoring unit is used to dynamically monitor the distribution network in the target area to obtain real-time distribution network characteristics; a real-time distribution network constraint acquisition unit is used to replace the historical distribution network characteristics with the real-time distribution network characteristics according to the rolling adjustment response mechanism to obtain real-time distribution network constraints; an iterative repetition unit is used to iteratively repeat steps S200 to S500 with the real-time distribution network constraints as the predetermined distribution network constraints, and to use the first scheduling scheme as the target intraday scheduling scheme for the distribution network in the target area.

[0095] Through the foregoing detailed description of the regional distribution network dispatching method based on flexible load optimization, those skilled in the art can clearly understand the regional distribution network dispatching 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 relevant parts can be referred to the method section.

[0096] The above description of the disclosed embodiments enables those skilled in the art to make or use this 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 this application. Therefore, this application is not 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 includes: 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, wherein the first database includes a first data group; Step S200: If the first distribution network characteristics in the first data group meet the predetermined distribution network constraints, issue a dispatch evaluation instruction, wherein the predetermined distribution network constraints refer to the conditional constraints obtained by analyzing the historical distribution network characteristics of the target area distribution network. Step S300: Based on the scheduling evaluation instruction, evaluate and analyze the first scheduling feature in the first data group to obtain the first scheduling fitness; Step S400: Read the first predetermined scheduling factor set of the first type, wherein the first predetermined scheduling factor set has an identifier of the first predetermined weight allocation; perform scheduling monitoring on the distribution network of the target area based on the first predetermined scheduling factor set to obtain the first scheduling factor dataset; weight the first scheduling factor dataset in combination with the first predetermined weight allocation to obtain the first scheduling feedback coefficient; 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; Step S500: If the first effective scheduling fitness reaches a predetermined fitness threshold, the first scheduling scheme in the first data group is taken as the target day-ahead scheduling scheme of the target area distribution network. If the first distribution network characteristics do not meet the predetermined distribution network constraints, a dispatch reassessment instruction is issued. Based on the scheduling re-estimation instruction, the second data group in the first database is obtained, and the second data group is used as the first data group. Steps S200 to S500 are iteratively repeated to obtain the target day-ahead scheduling scheme. Step S300 also includes: Read the scheduling evaluation index and iterate and match the scheduling evaluation index in the first scheduling feature to obtain the first scheduling index parameter; The first scheduling fitness is obtained by performing a weighted calculation of the coefficient of variation on the normalized first scheduling index parameters. The scheduling evaluation indicators include at least scheduling operation costs, scheduling network losses, scheduling load utilization, and scheduling power generation absorption capacity. Read the first predetermined scheduling factor set of the first type, wherein the first predetermined scheduling factor set has an identifier for a first predetermined weight allocation, including: Use the first scheduling feature as the independent variable; Use the first scheduling fitness as the dependent variable; A many-to-one correlation analysis was performed on the independent variable and the dependent variable to obtain the correlation analysis results. Analyze the correlation analysis results and construct the first predetermined scheduling factor set, and obtain the first predetermined weight allocation corresponding to the first predetermined scheduling factor set.

2. The regional distribution network dispatching method based on flexible load optimization as described in claim 1, characterized in that, The predetermined flexible load types include transferable load types, interruptible load types, and lateral load types.

3. The regional distribution network dispatching method based on flexible load optimization as described in claim 1, characterized in that, Step S200 includes: Obtain the historical load characteristics of the distribution network in the target area; Obtain the historical power generation characteristics of the distribution network in the target area; Obtain the historical power grid characteristics of the target area distribution network; The historical distribution network characteristics are constructed 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 ramp rate and historical landslide rate; and the historical power grid characteristics include historical power flow, historical voltage, and historical current.

4. The regional distribution network dispatching method based on flexible load optimization as described in claim 1, characterized in that, Following step S500, the method further includes: Dynamic monitoring of the distribution network in the target area is performed to obtain real-time distribution network characteristics; According to the rolling adjustment response mechanism, the real-time distribution network characteristics are replaced with the historical distribution network characteristics to obtain the real-time distribution network constraints; The real-time distribution network constraints are used as the predetermined distribution network constraints to iteratively repeat steps S200 to S500, and the first scheduling scheme is used as the target intraday scheduling scheme for the target area distribution network.

5. A regional distribution network dispatching system based on flexible load optimization, characterized in that, The system is used to implement the regional distribution network dispatching method based on flexible load optimization as described in any one of claims 1-4, the system comprising: The first database acquisition module is used to implement step S100: extracting the first type from the predetermined flexible load types and traversing the first type in the scheduling database to obtain the first database, wherein the first database includes a first data group; The scheduling evaluation instruction issuing module is used to implement step S200: if the first distribution network characteristics in the first data group meet the predetermined distribution network constraints, a scheduling evaluation instruction is issued, wherein the predetermined distribution network constraints refer to the conditional constraints obtained by analyzing the historical distribution network characteristics of the target area distribution network. The evaluation and analysis module 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. The fitness adjustment module 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; 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 area distribution network.

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