A multi-objective optimal scheduling method and system for post-disaster restoration of power grids

Abstract

The present application provides a multi-objective optimal scheduling method and system for post-disaster power grid restoration, which relates to the technical field of power grid optimal scheduling. The scheduling load constraint amount of the distribution load on the energy consumption side of the power grid is marked according to the scanning result and the load response degree of the distribution generator set in the scheduling instruction; the collaborative scheduling index on the energy consumption side of the power grid during post-disaster power grid restoration is determined; based on the load disturbance information and the load restoration efficiency of the power grid lines after the distribution network is damaged by disasters at all levels, the power balance constraints corresponding to the load nodes of multiple stations during post-disaster power grid restoration are verified to obtain an electricity consumption verification sequence; based on the collaborative scheduling index and the electricity consumption verification sequence, the automatic scheduling order of the energy consumption side of the power grid during post-disaster power grid restoration is controlled, and the load on the energy consumption side of the power grid is optimized. The present application can accurately scan each load node during the post-disaster restoration process of a large-scale power grid, so as to improve the restoration efficiency and reduce the restoration delay to ensure the stability of the power grid.

Description

Technical Field

[0001] This application relates to the technical field of power grid optimal scheduling. More specifically, this application relates to a multi-objective optimal scheduling method and system for power grid post-disaster restoration. Background Art

[0002] Power grid optimal scheduling refers to the process of reasonably allocating power resources in the power grid during grid operation or post-disaster restoration through optimization algorithms and scheduling strategies to maximize power supply stability, economy, reliability, and environmental benefits. In the context of power grid post-disaster restoration, optimal scheduling is particularly important as it involves comprehensively scheduling power supply and load distribution in the power grid based on multiple objectives such as load demand, equipment status, restoration capabilities, and priorities at each node of the power grid.

[0003] However, existing multi-objective optimal scheduling methods for power grid post-disaster restoration lack accurate load scanning and resilience assessment mechanisms when dealing with large-scale power grid restoration, and are unable to effectively handle complex multi-site, multi-objective scheduling tasks. This results in inflexible load distribution and priority scheduling during the power grid restoration process, with a lag in response, leading to an overly long post-disaster restoration time, uneven power supply restoration, and high power grid operation risks, thus affecting the overall stability and restoration efficiency of the power grid. Therefore, how to accurately scan each load node during large-scale power grid post-disaster restoration to improve restoration efficiency and reduce restoration delay to ensure power grid stability is a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides a multi-objective optimal scheduling method and system for power grid post-disaster restoration, which can accurately scan each load node during large-scale power grid post-disaster restoration to improve restoration efficiency and reduce restoration delay to ensure power grid stability.

[0005] In a first aspect, this application provides a multi-objective optimal scheduling method for power grid post-disaster restoration. The scheduling method includes the following steps:

[0006] Respond to the scheduling instruction for power grid post-disaster restoration issued by the large-scale power grid security guarantee center, scan the distribution load on the energy consumption side of the large-scale power grid, and mark the scheduling load constraint amount of the distribution load on the energy consumption side of the power grid according to the scanning result and the load response degree of the distribution generator set in the scheduling instruction;

[0007] Collect the capacity configuration characteristics corresponding to multiple sites during power grid post-disaster restoration, and determine the coordinated scheduling index on the energy consumption side of the power grid during power grid post-disaster restoration based on the load response degree in the capacity configuration characteristics and the energy dispatch load levels of multiple sites during power grid post-disaster restoration;

[0008] Control the load configuration quantity of energy distribution during the power grid energy supply dispatching, extract the load disturbance information of the energy load of multiple energy - using sides when responding to energy distribution, and verify the power balance constraints corresponding to the load nodes of multiple stations during the post - disaster recovery of the power grid based on the load disturbance information and the load recovery efficiency of the power grid lines after the power grid is damaged by disasters at all levels, so as to obtain an electricity consumption verification sequence;

[0009] Based on the collaborative dispatching index and the electricity consumption verification sequence, control the automatic dispatching order of the energy - using side of the power grid during the post - disaster recovery of the power grid, and optimize the load of the energy - using side of the power grid.

[0010] In this embodiment, the collaborative dispatching index of the energy - using side of the power grid during the post - disaster recovery of the power grid determined based on the load responsiveness in the capacity configuration characteristics and the energy dispatching load levels of multiple stations during the post - disaster recovery of the power grid specifically includes:

[0011] Extract the load responsiveness from the capacity configuration characteristics;

[0012] Determine the energy discrete quantity of multiple stations during the post - disaster recovery of the power grid according to the load responsiveness;

[0013] Determine the energy allocation information of the energy - using side of the power grid during the post - disaster recovery of the power grid;

[0014] Determine the collaborative dispatching index of the energy - using side of the power grid during the post - disaster recovery of the power grid according to the energy discrete quantity and the energy allocation information.

[0015] In this embodiment, extracting the load disturbance information of the energy load of multiple energy - using sides when responding to energy distribution specifically includes:

[0016] Determine the load priority of each energy - using side when performing energy distribution;

[0017] Determine the load aggregation quantity of the energy - using side of the power grid according to all load priorities;

[0018] Determine the load disturbance information of the energy load of the energy - using side when responding to energy distribution according to the load aggregation quantity.

[0019] In this embodiment, verifying the power balance constraints corresponding to the load nodes of multiple stations during the post - disaster recovery of the power grid based on the load disturbance information and the load recovery efficiency of the power grid lines after the power grid is damaged by disasters at all levels, and obtaining the electricity consumption verification sequence specifically includes:

[0020] Determine the load recovery efficiency of the power grid lines after the power grid is damaged by disasters at all levels;

[0021] Determine the load nodes of multiple stations during the post - disaster recovery of the power grid;

[0022] Determine the power balance amounts corresponding to the load nodes of multiple sites during the post-disaster restoration of the power grid according to the load disturbance information;

[0023] Calibrate the power consumption on the energy-using side according to the load restoration efficiency, the load nodes, and the power balance amounts to obtain an electricity calibration sequence.

[0024] In this embodiment, controlling the automatic dispatching order of the energy-using side of the power grid during the post-disaster restoration of the power grid based on the collaborative dispatching index and the electricity consumption calibration sequence specifically includes:

[0025] Determine the resilience index of the energy-using side of the power grid during the post-disaster restoration of the power grid according to the collaborative dispatching index;

[0026] Determine the dispatching response characteristics of the energy-using side of the power grid during the post-disaster restoration of the power grid according to the electricity consumption calibration sequence;

[0027] Determine the load dispatching index of the energy-using side of the power grid during the post-disaster restoration of the power grid according to the resilience index and the dispatching response characteristics, and sort according to the load dispatching index, so as to obtain the automatic dispatching order of the energy-using side of the power grid during the post-disaster restoration of the power grid.

[0028] In this embodiment, the load disturbance information refers to the load changes and fluctuations caused by energy distribution, load adjustment, and the execution of response instructions during the power grid dispatching process.

[0029] In this embodiment, the electricity consumption calibration sequence refers to the calibration sequence of the electricity consumption of each node during the power grid restoration.

[0030] In this embodiment, the load allocation amount refers to the specific power supply amount allocated to meet the power demands of each site or region during the power grid dispatching process.

[0031] In this embodiment, the capacity allocation characteristics refer to the set of key parameters related to the power supply capacity of each site in the power grid during post-disaster restoration or daily operation.

[0032] In a second aspect, the present application provides a multi-objective optimal dispatching system for post-disaster restoration of a power grid, which is used to execute a multi-objective optimal dispatching method for post-disaster restoration of a power grid. The dispatching system includes:

[0033] A load marking module, which is used to respond to the dispatching instruction for post-disaster restoration of the power grid issued by the large-scale power grid security guarantee center, scan the distribution load on the energy-using side of the large-scale power grid, and mark the dispatching load constraint amount of the distribution load on the energy-using side of the power grid according to the scan result and the load response degree of the distribution generator set in the dispatching instruction;

[0034] A collaborative scheduling module, configured to collect capacity configuration features corresponding to multiple sites during the post-disaster restoration of the power grid, and determine the collaborative scheduling index on the energy consumption side of the power grid during the post-disaster restoration of the power grid based on the load responsiveness in the capacity configuration features and the energy dispatch load levels of multiple sites during the post-disaster restoration of the power grid;

[0035] A power verification module, configured to control the load configuration amount of energy distribution during the power supply dispatch of the power grid, extract the load disturbance information of the energy load when multiple energy consumption sides respond to energy distribution, and verify the power balance constraints corresponding to the load nodes of multiple sites during the post-disaster restoration of the power grid based on the load disturbance information and the load restoration efficiency of the power grid lines after the power distribution network is damaged by disasters at various levels, so as to obtain an electricity consumption verification sequence;

[0036] An automatic scheduling module, configured to control the automatic scheduling sequence of the energy consumption side of the power grid during the post-disaster restoration of the power grid based on the collaborative scheduling index and the electricity consumption verification sequence, and optimize the load of the energy consumption side of the power grid.

[0037] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0038] By responding to the scheduling instructions for the post-disaster restoration of the power grid issued by the large-scale power grid security protection center, scanning the distribution load of the energy consumption side of the large-scale power grid, and marking the scheduling load constraint amount of the distribution load of the energy consumption side of the power grid according to the scanning result and the load responsiveness of the distribution generator set in the scheduling instructions; collecting the capacity configuration features corresponding to multiple sites during the post-disaster restoration of the power grid, and determining the collaborative scheduling index on the energy consumption side of the power grid during the post-disaster restoration of the power grid based on the load responsiveness in the capacity configuration features and the energy dispatch load levels of multiple sites during the post-disaster restoration of the power grid; controlling the load configuration amount of energy distribution during the power supply dispatch of the power grid, extracting the load disturbance information of the energy load when multiple energy consumption sides respond to energy distribution, and verifying the power balance constraints corresponding to the load nodes of multiple sites during the post-disaster restoration of the power grid based on the load disturbance information and the load restoration efficiency of the power grid lines after the power distribution network is damaged by disasters at various levels, so as to obtain an electricity consumption verification sequence; controlling the automatic scheduling sequence of the energy consumption side of the power grid during the post-disaster restoration of the power grid based on the collaborative scheduling index and the electricity consumption verification sequence, and optimizing the load of the energy consumption side of the power grid.

[0039] It can be seen that in this application, first of all, by responding to the power grid post-disaster restoration scheduling instruction and scanning the distribution load on the energy consumption side of the power grid, the restoration requirements and scheduling constraints of each load can be accurately identified, ensuring the rationality and efficiency of load scheduling during the power grid restoration process, optimizing the allocation of power resources, and reducing the possible load overloading or imbalance during the restoration process; collecting the capacity configuration characteristics of multiple sites and combining the load response degree and energy scheduling load level can more accurately evaluate the overall coordinated scheduling ability of the power grid during post-disaster restoration, ensuring that the power grid restoration in each region can be coordinated. Controlling the load configuration quantity of energy distribution during power grid energy supply scheduling and extracting load disturbance information can identify the load change trend of each energy consumption side in real time. By analyzing the load disturbance information, the power grid load scheduling strategy can be optimized, improving the real-time performance and stability of power grid restoration and avoiding power grid fluctuations caused by excessive load changes. By verifying the power balance constraint based on the load disturbance information and the restoration efficiency of the power grid line, it can ensure the balance between the power supply and demand of each load node, reducing the unstable factors during the power grid restoration process. Combining the coordinated scheduling index and the power consumption verification sequence, the control of the automatic scheduling order can dynamically adjust the power supply restoration order of each load node, thereby optimizing the load distribution on the energy consumption side of the power grid. It improves the flexibility and efficiency of power grid restoration, ensures the priority restoration of important nodes during the power restoration process, and at the same time avoids system resource waste and unnecessary delays during the restoration process.

[0040] In summary, the technical solution adopted in this application can accurately scan each load node during the large-scale power grid post-disaster restoration process to improve the restoration efficiency and reduce the restoration time delay to ensure the power grid stability. Brief Description of the Drawings

[0041] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0042] Figure 1 is a flowchart of the multi-objective optimization scheduling method for power grid post-disaster restoration provided by this application;

[0043] Figure 2 is an exemplary flowchart for determining the power consumption verification sequence provided by this application;

[0044] Figure 3 is an exemplary flowchart for controlling the automatic scheduling order of the power grid energy consumption side during power grid post-disaster restoration provided by this application;

[0045] Figure 4 is a module structure diagram of the multi-objective optimization scheduling system for power grid post-disaster restoration provided by this application. Detailed Embodiments

[0046] In order to make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with embodiments and the accompanying drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual R & D and use stage.

[0047] Embodiment 1. To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the specification drawings and specific implementation manners. Refer to Figure 1 As shown, this figure is an exemplary flowchart of a multi-objective optimal scheduling method for post-disaster power grid restoration according to this embodiment of the present application. The scheduling method includes the following steps:

[0048] In step S1, in response to the scheduling instruction for post-disaster power grid restoration issued by the large-scale power grid security guarantee center, the distribution load on the power consumption side of the large-scale power grid is scanned, and the scheduling load constraint amount of the distribution load on the power consumption side of the power grid is marked according to the scanning result and the load response degree of the distribution generator set in the scheduling instruction.

[0049] Specifically, through smart meters, distribution monitoring terminals, and distributed energy management systems deployed on each power consumption side (such as residential areas, industrial parks, and commercial areas), key power data including power, current, voltage, etc. are collected in real time. The device transmits the collected data to the central dispatching control system of the power grid to form a load distribution map of the power consumption side. The dispatching system analyzes the load adjustment ability of each power consumption side in combination with the load response degree of the distribution generator set mentioned in the scheduling instruction. The load response degree is an important indicator reflecting the adjustment flexibility, and the load adjustment range and adjustment speed need to be evaluated during calculation. By setting constraint thresholds, the distribution loads on each power consumption side are marked with different scheduling load constraint amounts, including the maximum allowable load, the minimum allowable load, and the flexible adjustment interval. The actual operating conditions of the post-disaster power grid equipment, such as the repair progress of the lines and the availability of distribution facilities, also need to be combined during this process to ensure that the marking of the constraint amount conforms to the actual capabilities of the current power grid. Finally, the system generates a load constraint report, and the scheduling load constraint amount is read from this load constraint report, which will not be elaborated here.

[0050] It should be noted that in this application, the dispatching instruction for post-disaster restoration of the power grid refers to the directive operation plan issued by the power grid security assurance center after the power grid suffers from natural disasters or emergencies to quickly restore the operation stability and power supply capacity of the power grid; the distribution load on the energy consumption side of the power grid in a large-scale power grid refers to the total power demand consumed by various electrical equipment, users or systems distributed on the power consumption side in the distribution network. This includes the load demands of industrial, commercial, and residential users; the load response degree is an indicator of the flexibility and ability of the energy consumption side of the power grid to adjust its power demand after receiving the dispatching instruction; the dispatching load constraint amount refers to the dispatching range and limiting conditions set for the distribution load on the energy consumption side during the power grid dispatching process to ensure the stable operation of the system and the balance between supply and demand. It includes the maximum allowable value, the minimum allowable value, and the flexible adjustment range of the load, which are usually determined based on the actual operation capacity of the power grid equipment, the user load response degree, and the dispatching requirements. The dispatching load constraint amount is used to guide the load distribution and regulation decisions, prevent power grid operation risks caused by overloading or improper regulation of the load, and ensure the power supply safety and efficiency during post-disaster restoration or special working conditions.

[0051] In step S2, collect the capacity configuration characteristics corresponding to multiple stations during the post-disaster restoration of the power grid, and determine the coordinated dispatching index of the energy consumption side of the power grid during the post-disaster restoration of the power grid based on the load response degree in the capacity configuration characteristics and the energy dispatching load levels of multiple stations during the post-disaster restoration of the power grid.

[0052] Specifically, the collection of the capacity configuration characteristics corresponding to multiple stations during the post-disaster restoration of the power grid can be implemented in the following ways: The capacity configuration data of the stations can be collected in real time through the monitoring system of the stations (such as the SCADA system), including information such as the rated capacity, standby capacity, equipment load rate, and distributed energy access capacity of the transformers. At the same time, combined with the status monitoring system of on-site equipment, obtain the equipment integrity and availability data of each station after the disaster, such as the line damage situation and the operation status of substation equipment. Use the data communication network to upload the collected capacity configuration characteristics to the central dispatching control platform. In the platform, classify, summarize, and clean the data of each station to form a capacity characteristic report, that is, obtain the capacity configuration characteristics.

[0053] It should be noted that in this application, the capacity configuration characteristics refer to the set of key parameters related to the power supply capacity of each station in the power grid during post-disaster restoration or daily operation.

[0054] In this embodiment, the coordinated dispatching index of the energy consumption side of the power grid during the post-disaster restoration of the power grid can be determined based on the load response degree in the capacity configuration characteristics and the energy dispatching load levels of multiple stations during the post-disaster restoration of the power grid in the following ways, that is:

[0055] Extract the load response degree from the capacity configuration characteristics;

[0056] Determine the energy discreteness of multiple sites during the post-disaster restoration of the power grid according to the load responsiveness;

[0057] Determine the energy allocation information on the energy consumption side of the power grid during the post-disaster restoration of the power grid;

[0058] Determine the coordinated scheduling index on the energy consumption side of the power grid during the post-disaster restoration of the power grid according to the energy discreteness and the energy allocation information.

[0059] In specific implementation, first, obtain the power grid load data from the real-time monitoring systems (such as SCADA systems and smart meters) of each site, record the actual load demands (such as power and energy consumption) and the maximum adjustable load of each site, compare the load response situations in different time periods, and evaluate the adjustment ability of the load during the post-disaster restoration. The load responsiveness = ΔP / Δt, where ΔP represents the load adjustment amount (the increase or decrease amount of the load), and Δt is the time required for load adjustment. This calculation can output a load responsiveness value for each site, reflecting the flexibility and efficiency of load adjustment after the site receives the scheduling instruction. Then, determine the actual load demand and the theoretical load demand (such as the normal load during the post-disaster restoration) of each site. Assume that the load demand of each site is P 需求 , and the maximum adjustable load is P 可调 , and the energy discreteness represents the difference between the demand of each site and its adjustment ability, which can be calculated by the following formula: where, P 需求，i is the load demand of site i, and P 可调，i is the load adjustment ability of site i. The value of this is usually between 0 and 1. 0 indicates no difference, and 1 indicates that the demand is completely mismatched or cannot be adjusted. Finally, an optimization algorithm (such as linear programming or genetic algorithm) can be used for energy allocation. According to the energy discreteness of the site, determine the amount of energy that needs to be supplemented. The specific calculation method is E 调配，i = energy discreteness i ×P 需求，i , where this calculation represents the amount of energy that site i needs to adjust to balance its actual load demand and adjustment ability; optimize the energy allocation process through the linear programming algorithm to ensure the overall power grid supply-demand balance. Assume that the total scheduling ability of the power grid is P 总调度 , then the objective function can be set, Through the constraint conditions, ensure that each site will not exceed its maximum adjustment range during the allocation process. Finally, through the method of weighted summation, comprehensively consider the load responsiveness and the energy discreteness to form the coordinated scheduling index. The formula is: coordinated scheduling index i = load responsiveness i × energy discreteness i. For all sites, calculate the total coordinated scheduling index,

[0060] It should be noted that in this application, the energy discrete quantity refers to the degree of difference between the energy demand and its regulation ability of each site in the power grid; the energy allocation information refers to the energy distribution plan formulated according to the energy demand, load response ability and available resources of each site during the power grid dispatching process to ensure the balance between system supply and demand and stable operation; the load response degree refers to the index of the ability of the power grid's energy consumption side (such as each site, user or device) to adjust its power demand when receiving the power grid dispatching instruction; the energy dispatching load level refers to the load regulation level or priority set according to the load demand of different sites or regions and the operation state of the power grid during power grid dispatching; the coordinated dispatching index refers to the index used to measure and optimize the coordination and cooperation ability between multiple sites or regions during the power grid dispatching process.

[0061] In step S3, control the load configuration quantity of energy distribution during the power grid energy supply dispatching, extract the load disturbance information of the energy load of multiple energy consumption sides when responding to the energy distribution, and verify the power balance constraints corresponding to the load nodes of multiple sites during the post-disaster recovery of the power grid based on the load disturbance information and the load recovery efficiency of the power grid lines after being damaged by disasters at all levels to obtain the power consumption verification sequence.

[0062] When specifically implemented, controlling the load configuration quantity of energy distribution during the power grid energy supply dispatching can be specifically achieved by the following method: First, determine the power demand of each site by collecting the real-time load data of each site in the power grid. Then, consider the load response ability of each site, that is, the load range that can be adjusted after receiving the dispatching instruction. Sites with stronger load response ability can quickly adjust the load, while sites with weaker response ability require more energy support. Next, the dispatching system calculates the load configuration quantity of each site according to the total power supply ability of the power grid. Through an optimization algorithm (such as linear programming), the dispatching system ensures that the load demands of each site are met while avoiding power supply overload. Priority is given to those sites with higher load demands or crucial for power grid recovery to ensure that key areas obtain sufficient power supply. Among them, the dispatching system will also monitor the operation state of the power grid in real time and dynamically adjust the load configuration quantity to ensure the stable operation of the power grid. Finally, through precise load configuration, ensure that the load demands during the post-disaster recovery or peak period of the power grid are effectively managed and met.

[0063] It should be noted that in this application, the load configuration quantity refers to the specific power supply quantity allocated to meet the power demands of each site or region during the power grid dispatching process. The load configuration quantity is reasonably allocated according to factors such as the load demand, response ability, priority of each site and the power supply ability of the power grid.

[0064] In an optional embodiment of the present application, load optimization on the energy consumption side of the power grid includes optimizing power grid resources. For example, the power grid resources of the power grid can be optimized according to a pre-configured resource allocation optimization model. Among them, the resource allocation optimization model is constructed based on a comprehensive loss index and an emergency resource information set, and the comprehensive loss index is determined according to a partition risk assessment model, power grid load data, and power grid load data.

[0065] In an optional embodiment of the present application, the construction process of the partition risk assessment model includes:

[0066] Determine the key statistical features in each preset time period. Among them, the key statistical features may include statistical features such as the variance, average value, difference between the maximum and minimum values of each power historical parameter value in this time period, the health status of each power grid device, and the statistical features of each environmental parameter in this time period, as well as the quantization values of all geographical information features. The quantization values of the geographical information features are, for example, information such as altitude, slope, land use type, terrain classification index, and digital elevation.

[0067] Use the Bayesian network model as the partition risk assessment model, and the partition risk assessment model takes the key statistical features of each time period as input;

[0068] Use the structure learning algorithm to construct the structure of the Bayesian network to represent the conditional dependence relationship between key statistical features, and manually add known physical or engineering constraints;

[0069] The partition risk assessment model outputs a pre-estimated value of the probability of a failure occurring in this time period according to the key statistical features of each time period; by using the maximum likelihood estimation or Bayesian estimation method for each time period, the parameters of each conditional probability table in the Bayesian network are iteratively optimized;

[0070] In an optional embodiment of the present application, the resource demand ratio Bi is calculated according to the following formula:

[0071]

[0072] Among them, i represents the numbering mark of each power grid partition; Ri represents the estimated resource demand mark in the i-th power grid partition, and the estimated resource demand Ri = a1×Li + a2×Ni + a3×Ri; where a1, a2, and a3 are all preset proportionality coefficients; where Li represents the real-time power load mark in the i-th power grid partition; Ni represents the number mark of the key electrical facilities in the i-th power grid partition; FPi represents the failure probability mark of the i-th power grid partition estimated by the partition risk assessment model; Bi represents the resource demand ratio mark in the i-th power grid partition.

[0073] The power grid loss index Among them, both a4 and a5 are preset proportionality coefficients, and Ltotal is the total load of all power grid partitions.

[0074] In an optional embodiment of the present application, taking the minimization of the objective optimization function M as the optimization goal and the first constraint condition and the second constraint condition as the constraint condition set, a resource allocation optimization model is constructed:

[0075] The expression of the objective optimization function M of the resource allocation optimization model is: M = ∑ i (Si×(1 - xi)+Pi×xi); where Pi is the cost to be borne for dispatching emergency resources to the i-th power grid partition; xi represents the allocation ratio variable set for each power grid partition; the total amount of emergency resources is 1.

[0076] In this embodiment, extracting the load disturbance information of the energy load when multiple energy - using sides respond to energy allocation can be specifically implemented by the following steps:

[0077] Determine the load priority of each energy - using side when performing energy allocation;

[0078] Determine the load aggregation amount of the energy - using side of the power grid according to all load priorities;

[0079] Determine the load disturbance information of the energy load when the energy - using side responds to energy allocation according to the load aggregation amount.

[0080] In specific implementation, first, it is necessary to determine the load priority according to the load demand, importance, and restoration priority of each energy - consuming side in the power grid, including: load demand quantity. A larger load demand may have a higher priority, especially in the initial stage of power grid restoration, with key power supply to large - load areas. Restoration importance. Some key facilities have a higher priority during post - disaster restoration. Response ability. Energy - consuming sides with strong load response ability may have a higher priority in scheduling because they can quickly adjust the load and relieve the power grid pressure. Then, classify each energy - consuming side according to the priority and calculate the total load quantity required by each type of energy - consuming side within a specific time period. The aggregated load quantity is the result of weighted summation of the load demands of each energy - consuming side, ensuring that the power grid can reasonably allocate energy and avoid over - concentration of load. Finally, merge the load demands of each energy - consuming side through the aggregated load quantity and determine the load adjustment range that each energy - consuming side can accept according to these aggregated load quantities. The load adjustment range refers to the range within which the power grid can adjust the load of each energy - consuming side according to the scheduling instruction. For example, if the aggregated load quantity of a certain site is large, the load of this site can be adjusted within a certain range, and the load can be reduced to respond to the demand fluctuation of the power grid. Collect the real - time load data, load response ability, and scheduling instructions of each site. Then re - evaluate the load adjustment range of each site, simulate the possible fluctuations during the load change process, and consider factors such as response lag and load concentration, that is, obtain the load disturbance information of the energy load when the energy - consuming side responds to energy distribution.

[0081] It should be noted that in this application, the load priority refers to the priority order of load adjustment determined according to the load demand, importance, and contribution to power grid stability or restoration of each energy - consuming side during the power grid scheduling process; the aggregated load quantity refers to the total load quantity obtained by merging or summarizing the load demands of multiple energy - consuming sides according to certain rules during the power grid scheduling process; the load disturbance information refers to the load changes and fluctuations caused by energy distribution, load adjustment, and execution of response instructions during the power grid scheduling process.

[0082] Preferably, in this embodiment, refer to Figure 2 As shown, this figure is an exemplary flowchart for determining the power - consumption verification sequence in the embodiment of this application. In this embodiment, based on the load disturbance information and the load restoration efficiency of the power - grid lines after the power grid is damaged by disasters at all levels, the power - balance constraints corresponding to the load nodes of multiple sites during the post - disaster restoration of the power grid are verified, and the power - consumption verification sequence can be specifically implemented by the following steps:

[0083] First, in step S31, determine the load restoration efficiency of the power - grid lines after the power grid is damaged by disasters at all levels;

[0084] Secondly, in step S32, determine the load nodes of multiple sites during the post - disaster restoration of the power grid;

[0085] Then, in step S33, according to the load disturbance information, determine the power balance amounts corresponding to the load nodes of multiple sites during the post-disaster restoration of the power grid.

[0086] Finally, in step S34, calibrate the power consumption on the energy-using side according to the load restoration efficiency, the load nodes, and the power balance amounts to obtain an electricity calibration sequence.

[0087] In specific implementation, first, it is necessary to evaluate the line damage situation during the post-disaster restoration of the distribution network. Different levels of disasters may cause different degrees of line damage, which in turn affects the load restoration ability of the power grid lines. According to the level of the disaster and the degree of line damage, the dispatching system determines the load restoration efficiency of each line. Next, during the post-disaster restoration process, the load nodes of the power grid refer to the key areas or sites that need to be powered in the power grid. The load demands and restoration status of these sites directly affect the efficiency and stability of the power grid restoration. Through post-disaster assessment, the dispatching system determines the sites that need to be restored first and uses them as load nodes. The power supply to these load nodes will be allocated according to the priority to ensure that the key areas are powered on as soon as possible. Then, after determining the load nodes, the dispatching system needs to calculate the power balance amount of each load node according to the load disturbance information. The power balance amount reflects the difference between the power supply demand and the actual supply of the load nodes in the power grid. The load disturbance information provides the load fluctuation situation when each energy-using side responds to energy allocation. The dispatching system calculates the actual power demand of the load nodes through this information and combines the load restoration efficiency of the post-disaster lines to evaluate the power balance state of each load node in the power grid. Finally, based on the load restoration efficiency, the demands of the load nodes, and the power balance amounts, the dispatching system calibrates the power consumption on the energy-using side. By adjusting the power supply to each load node of the power grid, ensure that the power consumption demands of each site and the power supply capacity of the power grid are balanced during the post-disaster restoration process of the power grid, thereby avoiding overloading or insufficient power supply. The calibrated power consumption will form an electricity calibration sequence.

[0088] It should be noted that in this application, the load restoration efficiency refers to the efficiency and degree of restoring the load-carrying capacity and power supply capacity of the power grid lines or the distribution network after damage at all levels of disasters during the post-disaster restoration of the power grid; the load nodes refer to specific locations or areas that need to be powered in the power grid; the power balance amount refers to the difference between the power demand and the actual supply in a specific area or node during the operation of the power grid. A positive value indicates that the actual power supply capacity of this area or node exceeds the load demand, and there is surplus power. A negative value indicates that the power supply capacity of this area or node is insufficient to meet the load demand, and there is a power gap; the electricity calibration sequence refers to the calibration sequence of the power consumption of each node during the power grid restoration.

[0089] In step S4, based on the collaborative scheduling metrics and the power consumption verification sequence, control the automatic scheduling order of the power consumption side of the power grid during the post-disaster recovery of the power grid, and optimize the load of the power consumption side of the power grid.

[0090] Preferably, in this embodiment, refer to Figure 3 As shown, this figure is an exemplary flowchart for controlling the automatic scheduling order of the power consumption side of the power grid during the post-disaster recovery of the power grid. In this embodiment, the specific steps for controlling the automatic scheduling order of the power consumption side of the power grid based on the collaborative scheduling metrics and the power consumption verification sequence can be implemented as follows:

[0091] First, in step S41, determine the resilience index of the power consumption side of the power grid during the post-disaster recovery of the power grid according to the collaborative scheduling metrics;

[0092] Then, in step S42, determine the scheduling response characteristics of the power consumption side of the power grid during the post-disaster recovery of the power grid according to the power consumption verification sequence;

[0093] Finally, in step S43, determine the load scheduling index of the power consumption side of the power grid during the post-disaster recovery of the power grid according to the resilience index and the scheduling response characteristics, and sort according to the load scheduling index, so as to obtain the automatic scheduling order of the power consumption side of the power grid during the post-disaster recovery of the power grid.

[0094] In specific implementation, first, the power grid dispatching system needs to evaluate the power grid's post-disaster recovery ability according to the collaborative dispatching indicators. The collaborative dispatching indicators comprehensively consider the response capabilities of each energy consumption side of the power grid during the recovery process, including the load recovery rate, the load adjustment range and coordination of each node, etc. By analyzing these indicators, the resilience indicators of each energy consumption side can be determined. The resilience indicators reflect the rapid recovery ability and power distribution ability of each energy consumption side during post-disaster recovery. For example, for areas carrying critical loads (such as hospitals, communication sites, etc.), their resilience indicators are usually relatively high, while for ordinary residential areas, their resilience may be relatively low. Through the evaluation of resilience, the power grid dispatching system can determine which areas or nodes should be given priority to restore power supply to ensure the stability and recovery efficiency of the power grid. Then, the power consumption verification sequence reflects the actual power consumption situation of each load node during the power distribution and dispatching process in the post-disaster recovery process. The dispatching system evaluates the dispatching response characteristics of each load node by analyzing the power consumption verification sequence, that is, the response characteristics of each load node after receiving the dispatching instruction. These response characteristics include the speed of load change, the sensitivity of response, the lag during the adjustment process, etc. For example, some load nodes may immediately adjust the load after receiving the recovery instruction, while other nodes may have a response lag or a smaller load adjustment range. Finally, the power grid dispatching system combines the resilience indicators and dispatching response characteristics obtained in the previous two steps, comprehensively analyzes the performance of each load node during post-disaster recovery, and then determines the load dispatching indicators during the power grid's post-disaster recovery. Nodes with stronger resilience and faster response will be given higher priorities, while nodes with weaker resilience and response lag will be arranged in the subsequent recovery plan. And the dispatching system sorts each load node according to these load dispatching indicators. The sorted result is the automatic dispatching order during the power grid's post-disaster recovery.

[0095] It should be noted that in this application, the resilience indicator refers to the ability indicator that measures the ability of each energy consumption side or load node of the power grid to quickly recover the load-carrying capacity during the power grid's post-disaster recovery process; the dispatching response characteristic refers to the response behavior and characteristics of the load node to the dispatching command after the energy consumption side of the power grid receives the power grid dispatching instruction; the automatic dispatching order of the energy consumption side of the power grid during the power grid's post-disaster recovery refers to the priority order of restoring power supply to each load node automatically generated during the power grid's post-disaster recovery process based on factors such as the resilience, dispatching response characteristics and priorities of each load node. This order determines which areas or nodes should be given priority to restore power supply during post-disaster recovery and which can be restored later to ensure the stability and recovery efficiency of the power grid.

[0096] In addition, it should be noted that in this application, for load optimization on the energy consumption side of the power grid, first, the dispatching system needs to collect real-time power consumption data of each load node in the power grid, including load demand, load response characteristics, resilience indicators, etc. These data are collected through sensors and smart meters and analyzed in real time to evaluate the power consumption of each node. According to the collected data, the dispatching system formulates adjustment strategies based on the load priority, recovery ability, and response characteristics of each load node. For load nodes to be restored first, their load-carrying capacity will be optimized by increasing the power supply or adjusting the dispatching order. On the basis of ensuring the stability of the power grid, the system reasonably distributes the load of each node in the power grid through an optimization algorithm. The adjusted load optimization plan will be reflected in the power grid dispatching system in real time to ensure that while meeting the recovery requirements, load overload or deficiency is avoided. After the implementation of the load optimization plan, the system will continuously monitor the changes in the power grid load and make dynamic adjustments according to the actual situation to continuously optimize the load distribution on the energy consumption side of the power grid.

[0097] Thus, in this application, first, by responding to the post-disaster recovery dispatching instructions of the power grid and scanning the distribution load on the energy consumption side of the power grid, the recovery requirements and dispatching constraints of each load can be accurately identified, ensuring the rationality and efficiency of load dispatching during the power grid recovery process, optimizing the allocation of power resources, and reducing the possible load overload or imbalance during the recovery process. By collecting the capacity configuration characteristics of multiple sites and combining the load response degree and energy dispatching load level, the overall coordinated dispatching ability of the power grid during post-disaster recovery can be more accurately evaluated, ensuring that the power grid recovery in each region can be coordinated. Controlling the load configuration quantity of energy distribution during power grid energy supply dispatching and extracting load disturbance information can identify the load change trend of each energy consumption side in real time. Through the analysis of load disturbance information, the power grid load dispatching strategy can be optimized, improving the real-time performance and stability of power grid recovery and avoiding power grid fluctuations caused by excessive load changes. By verifying the power balance constraint based on load disturbance information and the recovery efficiency of power grid lines, it can ensure the balance between power supply and demand of each load node, reducing unstable factors during the power grid recovery process. Combining coordinated dispatching indicators and power consumption verification sequences, the control of the automatic dispatching order can dynamically adjust the power supply recovery order of each load node, thereby optimizing the load distribution on the energy consumption side of the power grid. It improves the flexibility and efficiency of power grid recovery, ensures the priority recovery of important nodes during the power recovery process, and at the same time avoids system resource waste and unnecessary delays during the recovery process.

[0098] In summary, the technical solution adopted in this application can accurately scan each load node during the post-disaster recovery process of a large-scale power grid to improve the recovery efficiency and reduce the recovery time delay to ensure the stability of the power grid.

[0099] Embodiment 2, this application provides a multi-objective optimal dispatching system for power grid post-disaster recovery, referring toFigure 4 As shown, the figure is a schematic diagram of the scheduling system according to this embodiment of the present application. The scheduling system includes:

[0100] A load marking module 100, configured to respond to the scheduling instruction for post-disaster power grid restoration issued by the large-scale power grid security guarantee center, scan the distribution load on the power consumption side of the large-scale power grid, and mark the scheduling load constraint amount of the distribution load on the power consumption side of the power grid according to the scanning result and the load response degree of the distribution generator set in the scheduling instruction;

[0101] A collaborative scheduling module 200, configured to collect the capacity configuration characteristics corresponding to multiple stations during post-disaster power grid restoration, and determine the collaborative scheduling index on the power consumption side of the power grid during post-disaster power grid restoration based on the load response degree in the capacity configuration characteristics and the energy scheduling load levels of multiple stations during post-disaster power grid restoration;

[0102] A power verification module 300, configured to control the load configuration amount of energy distribution during power grid energy supply scheduling, extract the load disturbance information of the energy load on multiple power consumption sides when responding to energy distribution, and verify the power balance constraint corresponding to the load nodes of multiple stations during post-disaster power grid restoration based on the load disturbance information and the load restoration efficiency of the power grid lines after the power distribution network is damaged at various levels to obtain an electricity consumption verification sequence;

[0103] An automatic scheduling module 400, configured to control the automatic scheduling order on the power consumption side of the power grid during post-disaster power grid restoration based on the collaborative scheduling index and the electricity consumption verification sequence, and optimize the load on the power consumption side of the power grid.

[0104] The above specific implementation manners further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-objective optimal scheduling method for post-disaster restoration of power grids, characterized in that, The scheduling method includes the following steps: Respond to the scheduling instruction for post-disaster power grid restoration issued by the large-scale power grid security guarantee center, scan the distribution load on the power consumption side of the large-scale power grid, and mark the scheduling load constraint amount of the distribution load on the power consumption side of the power grid according to the scan result and the load response degree of the distribution generator set in the scheduling instruction; Collect the capacity configuration characteristics corresponding to multiple sites during post-disaster power grid restoration, and determine the collaborative scheduling index on the power consumption side of the power grid during post-disaster power grid restoration based on the load response degree in the capacity configuration characteristics and the energy dispatch load levels of multiple sites during post-disaster power grid restoration. Specifically, it includes: extracting the load response degree from the capacity configuration characteristics; determining the energy discrete amount of multiple sites during post-disaster power grid restoration according to the load response degree; determining the energy allocation information on the power consumption side of the power grid during post-disaster power grid restoration; and determining the collaborative scheduling index on the power consumption side of the power grid during post-disaster power grid restoration according to the energy discrete amount and the energy allocation information; Control the load configuration amount of energy allocation during power grid energy supply scheduling, extract the load disturbance information of the energy load on multiple power consumption sides when responding to energy allocation, and verify the power balance constraint corresponding to the load nodes of multiple sites during post-disaster power grid restoration based on the load disturbance information and the load restoration efficiency of the power grid lines after the power grid is damaged at all levels to obtain the electricity verification sequence. Specifically, it includes: determining the load restoration efficiency of the power grid lines after the power grid is damaged at all levels; determining the load nodes of multiple sites during post-disaster power grid restoration; determining the power balance amount corresponding to the load nodes of multiple sites during post-disaster power grid restoration according to the load disturbance information; and calibrating the electricity consumption of the power consumption side according to the load restoration efficiency, the load nodes, and the power balance amount to obtain the electricity verification sequence; Control the automatic scheduling order of the power consumption side of the power grid during post-disaster power grid restoration based on the collaborative scheduling index and the electricity verification sequence. Specifically, it includes: determining the resilience index of the power consumption side of the power grid during post-disaster power grid restoration according to the collaborative scheduling index; determining the scheduling response characteristics of the power consumption side of the power grid during post-disaster power grid restoration according to the electricity verification sequence; determining the load scheduling index of the power consumption side of the power grid during post-disaster power grid restoration according to the resilience index and the scheduling response characteristics, and sorting according to the load scheduling index to obtain the automatic scheduling order of the power consumption side of the power grid during post-disaster power grid restoration; And perform load optimization on the power consumption side of the power grid.

2. The multi-objective optimal scheduling method for power grid post-disaster restoration according to claim 1, characterized in that Specifically, extracting the load disturbance information of the energy load on multiple power consumption sides when responding to energy allocation includes: Determining the load priority of each power consumption side when performing energy allocation; Determining the load aggregation amount of the power consumption side of the power grid according to all load priorities; Determining the load disturbance information of the energy load on the power consumption side when responding to energy allocation according to the load aggregation amount.

3. The multi-objective optimal scheduling method for power grid post-disaster restoration according to claim 1, characterized in that The load disturbance information refers to the load changes and fluctuations caused by energy allocation, load adjustment, and the execution of response instructions during power grid scheduling.

4. The multi-objective optimal scheduling method for power grid post-disaster restoration according to claim 1, characterized in that, The electricity verification sequence refers to the calibration sequence of the electricity consumption of each node during power grid restoration.

5. The multi-objective optimal scheduling method for power grid post-disaster recovery according to claim 1, characterized in that, The load allocation quantity refers to the specific power supply quantity allocated to meet the power demands of each site or region during the power grid dispatching process.

6. The multi-objective optimal scheduling method for power grid post-disaster restoration according to claim 1, characterized in that, The capacity configuration feature refers to the set of key parameters related to the power supply capacity of each site in the power grid during post-disaster recovery or daily operation.

7. A multi-objective optimal scheduling system for post-disaster restoration of power grid, which is used to execute a multi-objective optimal scheduling method for post-disaster restoration of power grid according to any one of claims 1 to 6, characterized in that The dispatching system includes: A load marking module, which is used to respond to the dispatching instruction for the post-disaster recovery of the power grid issued by the large-scale power grid security guarantee center, scan the distribution load on the energy consumption side of the large-scale power grid, and mark the dispatching load constraint quantity of the distribution load on the energy consumption side of the power grid according to the scanning result and the load response degree of the distribution generator set in the dispatching instruction; A collaborative dispatching module, which is used to collect the capacity configuration features corresponding to multiple sites during the post-disaster recovery of the power grid, and determine the collaborative dispatching index on the energy consumption side of the power grid during the post-disaster recovery of the power grid based on the load response degree in the capacity configuration features and the energy dispatching load levels of multiple sites during the post-disaster recovery of the power grid; A power verification module, which is used to control the load allocation quantity of energy distribution during the power supply dispatching of the power grid, extract the load disturbance information of the energy load when multiple energy consumption sides respond to energy distribution, and verify the power balance constraint corresponding to the load nodes of multiple sites during the post-disaster recovery of the power grid based on the load disturbance information and the load recovery efficiency of the power grid lines after the power grid is damaged by disasters at all levels, so as to obtain the power consumption verification sequence; An automatic dispatching module, which is used to control the automatic dispatching order of the energy consumption side of the power grid during the post-disaster recovery of the power grid based on the collaborative dispatching index and the power consumption verification sequence, and optimize the load on the energy consumption side of the power grid.

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