A power distribution network power supply capacity tapping method for multi-type load reorganization

Abstract

The present application belongs to the technical field of power distribution network power supply capacity tapping, and provides a power distribution network power supply capacity tapping method for multi-type load reorganization, comprising: identifying high-risk areas in the power distribution network power supply area by analyzing the electricity use time concentration of the power distribution network power supply area; obtaining flexible loads and rigid loads of the high-risk areas in a historical period, marking the loads in different historical periods in the historical period, traversing and analyzing the marking results, extracting all to-be-analyzed period groups in the historical period, and analyzing the flexibility of the flexible loads in the historical periods in the to-be-analyzed period groups to output high-flexibility period groups in the to-be-analyzed period groups; and the present application builds a multi-period collaborative optimization model, combines mixed integer linear programming and a branch and bound method to solve an optimal transfer scheme, fully utilizes the residual capacity in low-load periods, and realizes collaborative optimization of dynamic demand response and power supply capacity.

Description

Technical Field

[0001] This invention belongs to the technical field of power supply capacity assessment in distribution networks, specifically a method for tapping the power supply capacity of distribution networks for multi-type load reorganization. Background Technology

[0002] With the increasing penetration of new energy sources and the deepening of power market reforms, distribution networks face challenges such as more complex load characteristics and greater difficulty in balancing supply and demand. Traditional distribution network power supply capacity assessments are mostly based on matching static peak load with equipment rated capacity, neglecting the temporal distribution characteristics of loads and the potential for flexible load regulation.

[0003] The existing technologies suffer from the following shortcomings: First, risk identification methods are crude, lacking quantitative analysis of the concentration of electricity consumption time, resulting in an inability to effectively distinguish between two different risk modes: "high total but dispersed" and "low total but concentrated," leading to insufficient precision in resource allocation. Second, load fluctuation monitoring capabilities are lagging behind. Existing technologies focus primarily on long-term load trends, neglecting the impact of short-term pulse fluctuations on the power grid, which can easily cause equipment overload or voltage flicker. Third, the potential for flexible load regulation is not fully explored. Traditional methods only distinguish between interruptible and non-interruptible loads, failing to establish detailed classification standards and quantitative systems for characteristic parameters of transferable and adjustable loads, resulting in "one-size-fits-all" regulation that wastes resources. Fourth, multi-period collaborative optimization capabilities are lacking. Existing strategies mostly adopt single-period static regulation, failing to consider the dynamic utilization of remaining capacity during low-load periods, and lacking cross-period load smoothing mechanisms, making it difficult to maximize resource utilization. Fifth, the priority ranking mechanism is imperfect. When dealing with multiple fluctuating periods, existing technologies typically use an average resource allocation method, failing to dynamically adjust priorities according to the severity of fluctuations, resulting in low regulation efficiency.

[0004] Therefore, this invention provides a method for tapping the potential of power supply capacity in distribution networks for multi-type load reorganization. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] By conducting a concentrated analysis of electricity consumption time in the power distribution network area, high-risk areas in the power distribution network area can be identified.

[0008] The system obtains the flexible and rigid loads of high-risk areas within a historical period, marks the loads at different historical periods within the historical period, and analyzes the marking results to determine whether there are pulse fluctuations within the historical period.

[0009] If pulse fluctuations exist, extract all the time period groups to be analyzed within the historical period, perform adjustability analysis on the flexible load of the historical time periods in the time period group to be analyzed, and output the time period group with high adjustability in the time period group to be analyzed.

[0010] By reorganizing and analyzing the loads during normal load periods, low load periods, and high load periods in the high-adjustability period group, a multi-period collaborative optimization model is constructed to transfer the flexible loads during high load periods to low load periods, thereby smoothing the load curves corresponding to the high-adjustability period group.

[0011] As a further aspect of the present invention, the specific process for identifying high-risk areas in the power distribution network area is as follows:

[0012] The power distribution network is divided into several power supply areas according to the transformer area, and the historical period is divided into several historical periods according to equal time intervals. By analyzing the power consumption of the power supply areas in the historical periods, the total power consumption of the power supply areas in the historical period is obtained. The power concentration index of the power supply areas in the historical period is calculated by the Herfindahl-Hirschman index formula.

[0013] If the risk level exceeds the electricity concentration index threshold, the corresponding power supply area will be designated as a high-risk area.

[0014] As a further aspect of the present invention: the process for obtaining the total power consumption of the electron-supplying area during the historical period is as follows:

[0015] The electricity consumption of the power supply area within a historical period is statistically analyzed and recorded as the electricity consumption of the historical period. The electricity consumption of all historical periods within the historical period is summed to obtain the total electricity consumption of the power supply area within the historical period.

[0016] As a further aspect of the present invention: the specific process for determining whether pulse fluctuations exist within the historical period is as follows:

[0017] By monitoring total load data through smart meters and combining it with algorithm classification processing, rigid and flexible loads are distinguished, and the rigid and flexible loads in high-risk areas within historical time periods are obtained. The loads are then summed to obtain the total load for each time period. By analyzing the total load for each time period, the time period group to be analyzed is determined.

[0018] The number of time period groups to be analyzed is counted. If the number of time period groups to be analyzed is greater than or equal to 1, it indicates that there is a pulse fluctuation phenomenon in the historical period; otherwise, it indicates that there is no pulse fluctuation phenomenon in the historical period.

[0019] As a further aspect of the present invention: the process for determining the time period group to be analyzed is as follows:

[0020] A first load warning value and a second load warning value are preset. The first load warning value is less than the second load warning value. The total load of each period is compared with the first load warning value and the second load warning value to determine the high load period, low load period, and normal load period.

[0021] All historical time period marking results within the historical period are integrated into a time period sequence according to time order. Starting from the first historical time period in the time period sequence, three adjacent historical time periods are extracted as a triplet. The historical time periods in the triplet are traversed. If the historical time period marking results in the triplet are normal load period, low load period, and high load period in sequence, the corresponding triplet is recorded as the time period group to be analyzed. The three historical time periods are moved in order, and three historical time periods are reselected as a triplet. The historical time periods in the triplet are traversed.

[0022] If the historical time period markers in the triplet are not, in order, normal load period, low load period, and high load period, then move one historical time period in order, reselect three historical time periods as a triplet, and iterate through the historical time periods in the triplet.

[0023] Output all time period groups to be analyzed until there are fewer than three remaining historical time periods in the time period series.

[0024] As a further aspect of the present invention: the process for determining the high-load period, low-load period, and normal-load period is as follows:

[0025] If the total load during a period is greater than or equal to the second load warning value, the corresponding historical period will be marked as a high load period.

[0026] If the total load for a given period is less than or equal to the first load warning value, the corresponding historical period will be marked as a low load period.

[0027] If the total load during a period is greater than the first load warning value and less than the second load warning value, the corresponding historical period will be marked as a normal load period.

[0028] As a further aspect of the present invention: the process for determining the high-adjustability time period group is as follows:

[0029] The flexible loads in the normal load period, low load period, and high load period of the time period to be analyzed are classified according to the classification criteria, and the characteristic parameters of different types of loads are obtained. The adjustability analysis of the flexible loads in the historical periods of the time period to be analyzed is performed to determine the adjustability index. If it is greater than or equal to the adjustability index threshold, the corresponding time period to be analyzed is recorded as the high adjustability time period group.

[0030] As a further aspect of the present invention: the process for determining the adjustability index is as follows:

[0031] Through the formula: Calculate the adjustability index SI of the time period group to be analyzed, where m is the number of flexible load types in the time period group to be analyzed, p is the adjustment capacity of the k-th type of flexible load, tr is the response time of the k-th type of flexible load, and td is the duration of the k-th type of flexible load.

[0032] As a further aspect of the present invention, the specific process for constructing the multi-time-period collaborative optimization model is as follows:

[0033] By analyzing the total load of the high-adjustability time period groups, the load fluctuation rate is determined. Combined with the rated capacity of the distribution network, the remaining capacity during low-load periods is determined. The load fluctuation rates of all high-adjustability time period groups are sorted from largest to smallest to obtain the load fluctuation rate sequence of high-adjustability time period groups. The high-adjustability time period group that ranks first in the load fluctuation rate sequence is extracted and denoted as the priority adjustment time period group. The rated capacity of the distribution network, the rigid load and flexible load of the normal load period, low load period, and high load period in the priority adjustment time period group are used as inputs. The objective function is to minimize the load fluctuation rate of the high-adjustability time period group. The constraints include: the amount of transferred load ≤ the remaining capacity during low load period and the amount of transferred load ≤ the transferable load during high load period. A multi-time period collaborative optimization model is constructed.

[0034] As a further aspect of the present invention: the process for determining the load fluctuation rate and the remaining capacity during low-load periods is as follows:

[0035] Substituting the total load of the normal load period, low load period, and high load period in the high-adjustability period group into the standard deviation formula, the load fluctuation rate of the high-adjustability period group is calculated.

[0036] Obtain the rated capacity of the distribution network, and then calculate the difference between the rated capacity and the total load during the low-load period to obtain the remaining capacity during the low-load period.

[0037] The beneficial effects of this invention are as follows:

[0038] This invention identifies high-risk areas within a power distribution network by analyzing the concentration of electricity consumption over time. It acquires the flexible and rigid loads of these high-risk areas over historical periods, marking loads at different historical time intervals. By analyzing the marking results, it determines whether pulse fluctuations exist within the historical period. The Herfindahl-Hirschman index quantifies the concentration of electricity consumption over time, providing data support for priority resource allocation. Through load type differentiation and pulse fluctuation detection, it accurately captures the characteristics of short-term, drastic load changes, assisting power departments in promptly initiating flexible load control or demand response strategies. This not only improves the granularity of risk identification but also achieves full-chain management from regional risk positioning to time-period fluctuation early warning, effectively reducing operational risks such as equipment overload and voltage instability, and ensuring the safe and efficient operation of the power distribution network.

[0039] This invention extracts all the time periods to be analyzed within a historical period, performs adjustability analysis on the flexible load of the historical periods within the time periods to be analyzed, and outputs the high-adjustability time period groups within the time periods to be analyzed. By recombining the loads of normal load periods, low load periods, and high load periods within the high-adjustability time period groups, a multi-time period collaborative optimization model is constructed to transfer the flexible load of high load periods to low load periods, thus smoothing the load curve corresponding to the high-adjustability time period groups. By accurately selecting high-adjustability time period groups with high adjustment potential and constructing a multi-time period collaborative optimization model, the demand of high load periods is transferred to low load periods, effectively smoothing the load curve and reducing volatility. Combining mixed-integer linear programming and the branch-and-bound method to solve for the optimal transfer scheme, the remaining capacity of low load periods is fully utilized, equipment overload is avoided, and the power supply stability and resource utilization of the distribution network are significantly improved, achieving collaborative optimization of dynamic demand response and power supply capacity. Attached Figure Description

[0040] The invention will now be further described with reference to the accompanying drawings.

[0041] Figure 1 This is a flowchart illustrating the steps of a method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization, according to an embodiment of the present invention.

[0042] Figure 2 This is a system block diagram of a power supply capacity tapping system for distribution networks oriented towards multi-type load reorganization, according to an embodiment of the present invention. Detailed Implementation

[0043] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0044] Example 1:

[0045] Please see Figure 1 As shown in the embodiment of the present invention, a method for tapping the potential of power supply capacity in a distribution network for multi-type load reorganization includes the following steps:

[0046] Step 1: Identify high-risk areas within the power distribution network by analyzing the concentration of electricity consumption time in the power supply area;

[0047] The power distribution network area is divided into several power supply areas according to the transformer substations, and the historical period is divided into several historical time periods according to equal time intervals, and then integrated into a historical time period sequence according to time order. , This represents the i-th historical time period, and n represents the total number of historical time periods;

[0048] Based on any power supply area, the electricity consumption of the power supply area within a historical period is statistically analyzed and recorded as the electricity consumption of the historical period. The electricity consumption of all historical periods within the historical period is summed to obtain the total electricity consumption of the power supply area within the historical period.

[0049] Based on the total electricity consumption of the power supply area during the historical period, using the Herfindahl-Hirschman exponent formula: The power concentration index of the power supply area during the historical period was calculated. In the formula, This represents the electricity consumption during the i-th historical time period. This represents the total electricity consumption of the electron-supplying region during the historical period.

[0050] It should be noted that, The higher the value, the more concentrated the electricity consumption is within a few specific time periods, because the proportion of electricity consumption during those few time periods is... When it is high, its square value This will significantly amplify this "concentration," leading to a sum Increase The smaller the value, the more dispersed the electricity consumption time distribution, because the proportion of electricity consumption in each time period is smaller at this time. Generally smaller, the squared value is even smaller, the sum The value is also smaller. By quantitatively distinguishing the power supply areas where the concentration of electricity consumption time is higher or lower than the critical value, the allocation of resources can be optimized, so that the power sector can take targeted measures (such as load regulation) for high-risk areas first, and reduce the risks of overload and voltage instability caused by excessive concentration of electricity consumption time.

[0051] In some embodiments, the power concentration index of the power supply area during a historical period is compared with a power concentration index threshold. The specific comparison process is as follows:

[0052] If the power consumption concentration index of the power supply area is greater than the power consumption concentration index threshold during the historical period, the corresponding power supply area will be recorded as a high-risk area.

[0053] If the power consumption concentration index of the power supply area is less than or equal to the power consumption concentration index threshold during the historical period, the corresponding power supply area will be recorded as a low-risk area.

[0054] Step 2: Obtain the flexible and rigid loads of high-risk areas within the historical cycle, mark the loads at different historical periods within the historical cycle, and determine whether there are pulse fluctuation phenomena within the historical cycle by traversing and analyzing the marking results.

[0055] By monitoring total load data through smart meters and combining it with algorithm classification processing, rigid and flexible loads are distinguished, the rigid and flexible loads in high-risk areas during historical periods are obtained, and the total load for the period is obtained by summing them up.

[0056] It should be noted that rigid loads refer to electricity demand that cannot be easily interrupted or adjusted, with fixed electricity consumption time and volume or minimal variation. Flexible loads refer to electricity demand that can be adjusted, interrupted, or transferred, with electricity consumption time and volume that can be adjusted according to grid demand or economic incentives.

[0057] A first load warning value and a second load warning value are preset, wherein both the first load warning value and the second load warning value are set by those skilled in the art based on historical experience, and the first load warning value is less than the second load warning value;

[0058] If the total load during a period is greater than or equal to the second load warning value, the corresponding historical period will be marked as a high load period.

[0059] If the total load for a given period is less than or equal to the first load warning value, the corresponding historical period will be marked as a low load period.

[0060] If the total load during a period is greater than the first load warning value and less than the second load warning value, then the corresponding historical period will be marked as a normal load period.

[0061] All historical time period marking results within the historical period are integrated into a time period sequence according to time order. Starting from the first historical time period in the time period sequence, three adjacent historical time periods are extracted as a triplet. The historical time periods in the triplet are traversed. If the historical time period marking results in the triplet are normal load period, low load period, and high load period in sequence, the corresponding triplet is recorded as the time period group to be analyzed. The three historical time periods are moved in order, and three historical time periods are reselected as a triplet. The historical time periods in the triplet are traversed.

[0062] If the historical time period markers in the triplet are not, in order, normal load period, low load period, and high load period, then move one historical time period in order, reselect three historical time periods as a triplet, and iterate through the historical time periods in the triplet.

[0063] Until there are fewer than three remaining historical time periods in the time series, output all the time period groups to be analyzed, count the number of time period groups to be analyzed, and if the number of time period groups to be analyzed is greater than or equal to 1, it indicates that there is a pulse fluctuation phenomenon within the historical period.

[0064] If the number of time period groups to be analyzed is less than 1, it means that there is no pulse fluctuation phenomenon in the historical cycle;

[0065] For example, assume the time period sequence is as follows (simplified to partial data): Historical period 1: Normal load period; Historical period 2: Low load period; Historical period 3: High load period; Historical period 4: Normal load period; Historical period 5: Low load period; Historical period 6: Low load period; Historical period 7: High load period; During the iteration, historical periods 1-3 are checked. If they meet the conditions of normal load period, low load period, and high load period, the corresponding historical periods 1-3 are recorded as the time period group to be analyzed; historical periods 4-6 do not meet the conditions; historical periods 5-7 do not meet the conditions.

[0066] The technical solution of this embodiment is as follows: By performing a concentrated analysis of electricity consumption time in the power distribution network supply area, high-risk areas in the power distribution network supply area are identified; flexible loads and rigid loads in the high-risk areas are obtained in the historical period, and the loads in different historical periods in the historical period are marked. By traversing and analyzing the marking results, it is determined whether there are pulse fluctuation phenomena in the historical period; this invention quantifies the concentration of electricity consumption time through the Herfindahl-Hirschman index, providing data support for priority resource allocation; by distinguishing load types and detecting pulse fluctuations, the characteristics of short-term drastic load changes are accurately captured, assisting the power sector in promptly initiating flexible load regulation or demand response strategies. This not only improves the granularity of risk identification, but also realizes the whole-chain management from regional risk positioning to time-period fluctuation early warning, effectively reducing operational risks such as equipment overload and voltage instability, and ensuring the safe and efficient operation of the power distribution network.

[0067] Example 2:

[0068] Please see Figure 1 As shown in the embodiment of the present invention, a method for tapping the potential of power supply capacity in a distribution network for multi-type load reorganization further includes the following steps:

[0069] Step 3: If pulse fluctuations exist, extract all the time period groups to be analyzed within the historical period, perform adjustability analysis on the flexible load of the historical time periods in the time period group to be analyzed, and output the time period group with high adjustability in the time period group to be analyzed.

[0070] Based on any group of time periods to be analyzed, the flexible loads of the normal load period, low load period and high load period in the group of time periods to be analyzed are classified according to the classification criteria, and the characteristic parameters of different types of loads are obtained.

[0071] Classification criteria:

[0072] Interruptible loads: such as auxiliary equipment in industrial production lines and non-continuous production processes (which must ensure that interruption does not affect core production).

[0073] Transferable loads: such as electric vehicle charging and cold storage equipment (must support the shifting of power consumption time);

[0074] Adjustable loads: such as air conditioning temperature setting and lighting brightness adjustment (must support continuous power adjustment);

[0075] Characteristic parameters:

[0076] Adjustable capacity: The maximum power range that can be adjusted for flexible loads (e.g., power can be reduced by 30% for air conditioning loads).

[0077] Response time: The time from the issuance of the command to the actual adjustment of the flexible load (e.g., electric vehicle charging piles need to respond within ≤5 minutes).

[0078] Duration: The longest time a flexible load can maintain its regulated state (e.g., an industrial interruptible load can be maintained for 2 hours).

[0079] Adjustability analysis is performed on the flexible load of historical periods in the analysis period group. Specifically:

[0080] Through the formula: Calculate the adjustability index SI of the time period group to be analyzed, where m is the number of flexible load types in the time period group to be analyzed, p is the adjustment capacity of the k-th type of flexible load, tr is the response time of the k-th type of flexible load, and td is the duration of the k-th type of flexible load.

[0081] In some embodiments, the adjustability index is compared with an adjustability index threshold. The specific comparison process is as follows:

[0082] If the adjustability index is greater than or equal to the adjustability index threshold, the corresponding time period group to be analyzed is recorded as the high adjustability time period group.

[0083] If the adjustability index is less than the adjustability index threshold, the corresponding time period group to be analyzed will be recorded as the low adjustability time period group.

[0084] Step 4: By reorganizing and analyzing the loads during normal load periods, low load periods, and high load periods in the high-adjustability period group, a multi-period collaborative optimization model is constructed to transfer the flexible loads during high load periods to low load periods, thereby smoothing the load curves corresponding to the high-adjustability period group.

[0085] Substituting the total load of the normal load period, low load period, and high load period in the high-adjustability period group into the standard deviation formula, the load fluctuation rate of the high-adjustability period group is calculated.

[0086] Obtain the rated capacity of the distribution network, and then calculate the difference between the rated capacity of the distribution network and the total load during the low-load period to obtain the remaining capacity during the low-load period.

[0087] The load volatility of all adjustable high-period groups is sorted from largest to smallest to obtain a load volatility sequence of adjustable high-period groups. The adjustable high-period group that ranks first in the load volatility sequence is extracted and denoted as the priority adjustment period group. The rated capacity of the distribution network, the rigid load and flexible load of the normal load period, low load period, and high load period in the priority adjustment period group are used as inputs. The objective function is to minimize the load volatility of the adjustable high-period groups. The constraints include: the amount of transferred load ≤ the remaining capacity of the low load period and the amount of transferred load ≤ the transferable load of the high load period. A multi-period collaborative optimization model is constructed. The optimization model is solved by mixed integer linear programming. The optimal solution is found by branch and bound method, and the amount of transferred load is output.

[0088] Based on the amount of transferred load, the flexible load in the high-load period of the subsequent priority adjustment period group is transferred to the low-load period, and the load fluctuation rate of the priority adjustment period group is reassessed. By reducing the load fluctuation rate of the priority adjustment period group, the potential of the power supply capacity of the distribution network is tapped, thereby improving the power supply capacity of the distribution network.

[0089] By performing load reorganization analysis on the normal load period, low load period, and high load period within the adjustable high-load period group, focusing only on the load during these periods, the purpose of transferring the flexible load from the high-load period to the low-load period within the time-series adjustable high-load period group is:

[0090] Function 1: To balance the load during low-load and high-load periods, reduce the peak-to-valley difference between low-load and high-load periods, and release the power supply potential during high-load periods;

[0091] Function 2: Reduce electricity consumption during high-load periods, lower the risk of equipment overload during high-load periods, avoid power outages caused by sudden load changes, and improve the safety margin of the distribution network;

[0092] Thirdly, a smooth load curve can delay the demand for distribution network expansion, avoid excessive investment caused by peak loads, and improve equipment utilization during low-load periods by staggering electricity use, thus extending equipment life.

[0093] The technical solution of this embodiment is as follows: If pulse fluctuations exist, all time periods to be analyzed within the historical period are extracted. Adjustability analysis is performed on the flexible load of the historical time periods within the time periods to be analyzed, and the high-adjustability time period group within the time periods to be analyzed is output. By recombining the loads of normal load periods, low load periods, and high load periods within the high-adjustability time period group, a multi-time period collaborative optimization model is constructed. The flexible load of high load periods is transferred to low load periods, achieving smoothing of the load curve corresponding to the high-adjustability time period group. This invention accurately selects high-adjustability time period groups with high adjustment potential and constructs a multi-time period collaborative optimization model to transfer demand from high load periods to low load periods, effectively smoothing the load curve and reducing volatility. By combining mixed-integer linear programming and the branch-and-bound method to solve for the optimal transfer scheme, the remaining capacity during low load periods is fully utilized, equipment overload is avoided, and the power supply stability and resource utilization of the distribution network are significantly improved, achieving collaborative optimization of dynamic demand response and power supply capacity.

[0094] Example 3:

[0095] Please see Figure 2 As shown in the figure, the power supply capacity tapping system for distribution networks oriented towards multi-type load reorganization according to an embodiment of the present invention includes the following modules:

[0096] Electricity Consumption Concentration Analysis Module: By performing electricity consumption time concentration analysis on the power supply area of ​​the distribution network, high-risk areas in the power supply area of ​​the distribution network are identified;

[0097] Short-term fluctuation judgment module: acquires the flexible load and rigid load of high-risk areas in the historical cycle, marks the load of different historical periods in the historical cycle, and judges whether there is pulse fluctuation phenomenon in the historical cycle by traversing and analyzing the marking results.

[0098] Adjustability Analysis Module: If pulse fluctuations exist, extract all time periods to be analyzed within the historical period, perform adjustability analysis on the flexible load of the historical time periods in the time period to be analyzed, and output the time period group with high adjustability in the time period to be analyzed.

[0099] Smoothing module: By reorganizing and analyzing the load during normal load periods, low load periods, and high load periods in the high-adjustability period group, a multi-period collaborative optimization model is constructed to transfer the flexible load during high load periods to low load periods, thereby smoothing the load curve corresponding to the high-adjustability period group.

[0100] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for tapping the potential of power supply capacity in a distribution network for multi-type load reorganization, characterized in that: include: By conducting a concentrated analysis of electricity consumption time in the power distribution network area, high-risk areas in the power distribution network area can be identified. The specific process for identifying high-risk areas within the power distribution network is as follows: The power distribution network is divided into several power supply areas according to the transformer area, and the historical period is divided into several historical periods according to equal time intervals. By analyzing the power consumption of the power supply areas in the historical periods, the total power consumption of the power supply areas in the historical period is obtained. The power concentration index of the power supply areas in the historical period is calculated by the Herfindahl-Hirschman index formula. If the risk level exceeds the electricity concentration index threshold, the corresponding electricity supply area will be designated as a high-risk area. The system obtains the flexible and rigid loads of high-risk areas within a historical period, marks the loads at different historical periods within the historical period, and analyzes the marking results to determine whether there are pulse fluctuations within the historical period. If pulse fluctuations exist, extract all the time period groups to be analyzed within the historical period, perform adjustability analysis on the flexible load of the historical time periods in the time period group to be analyzed, and output the time period group with high adjustability in the time period group to be analyzed. By reorganizing and analyzing the loads during normal load periods, low load periods, and high load periods in the high-adjustability period group, a multi-period collaborative optimization model is constructed to transfer the flexible loads during high load periods to low load periods, thereby smoothing the load curves corresponding to the high-adjustability period group.

2. The method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization as described in claim 1, characterized in that: The process for obtaining the total electricity consumption of the electron-supplying area during the historical period is as follows: The electricity consumption of the power supply area within a historical period is statistically analyzed and recorded as the electricity consumption of the historical period. The electricity consumption of all historical periods within the historical period is summed to obtain the total electricity consumption of the power supply area within the historical period.

3. The method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization as described in claim 1, characterized in that: The specific process for determining whether pulse fluctuations exist within the historical period is as follows: By monitoring total load data through smart meters and combining it with algorithm classification processing, rigid and flexible loads are distinguished, and the rigid and flexible loads in high-risk areas within historical time periods are obtained. The loads are then summed to obtain the total load for each time period. By analyzing the total load for each time period, the time period group to be analyzed is determined. The number of time period groups to be analyzed is counted. If the number of time period groups to be analyzed is greater than or equal to 1, it indicates that there is a pulse fluctuation phenomenon in the historical period; otherwise, it indicates that there is no pulse fluctuation phenomenon in the historical period.

4. The method for tapping the potential of power supply capacity in a distribution network for multi-type load reorganization as described in claim 3, characterized in that: The process for determining the time period group to be analyzed is as follows: A first load warning value and a second load warning value are preset. The first load warning value is less than the second load warning value. The total load of each period is compared with the first load warning value and the second load warning value to determine the high load period, low load period, and normal load period. All historical time period marking results within the historical period are integrated into a time period sequence according to time order. Starting from the first historical time period in the time period sequence, three adjacent historical time periods are extracted as a triplet. The historical time periods in the triplet are traversed. If the historical time period marking results in the triplet are normal load period, low load period, and high load period in sequence, the corresponding triplet is recorded as the time period group to be analyzed. The three historical time periods are moved in order, and three historical time periods are reselected as a triplet. The historical time periods in the triplet are traversed. If the historical time period markers in the triplet are not, in order, normal load period, low load period, and high load period, then move one historical time period in order, reselect three historical time periods as a triplet, and iterate through the historical time periods in the triplet. Output all time period groups to be analyzed until there are fewer than three remaining historical time periods in the time period series.

5. The method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization as described in claim 4, characterized in that: The process for determining the high-load period, low-load period, and normal-load period is as follows: If the total load during a period is greater than or equal to the second load warning value, the corresponding historical period will be marked as a high load period. If the total load for a given period is less than or equal to the first load warning value, the corresponding historical period will be marked as a low load period. If the total load during a period is greater than the first load warning value and less than the second load warning value, the corresponding historical period will be marked as a normal load period.

6. The method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization as described in claim 5, characterized in that: The process for determining the high-adjustability time period group is as follows: The flexible loads in the normal load period, low load period, and high load period of the time period to be analyzed are classified according to the classification criteria, and the characteristic parameters of different types of loads are obtained. The adjustability analysis of the flexible loads in the historical periods of the time period to be analyzed is performed to determine the adjustability index. If it is greater than or equal to the adjustability index threshold, the corresponding time period to be analyzed is recorded as the high adjustability time period group.

7. A method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization, as described in claim 6, is characterized in that: The process for determining the adjustability index is as follows: Through the formula: Calculate the adjustability index SI of the time period group to be analyzed, where m is the number of flexible load types in the time period group to be analyzed, p is the adjustment capacity of the k-th type of flexible load, tr is the response time of the k-th type of flexible load, and td is the duration of the k-th type of flexible load.

8. A method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization, as described in claim 6, is characterized in that: The specific process for constructing the multi-time-period collaborative optimization model is as follows: By analyzing the total load of the high-adjustability time period groups, the load fluctuation rate is determined. Combined with the rated capacity of the distribution network, the remaining capacity during low-load periods is determined. The load fluctuation rates of all high-adjustability time period groups are sorted from largest to smallest to obtain the load fluctuation rate sequence of high-adjustability time period groups. The high-adjustability time period group that ranks first in the load fluctuation rate sequence is extracted and denoted as the priority adjustment time period group. The rated capacity of the distribution network, the rigid load and flexible load of the normal load period, low load period, and high load period in the priority adjustment time period group are used as inputs. The objective function is to minimize the load fluctuation rate of the high-adjustability time period group. The constraints include: the amount of transferred load ≤ the remaining capacity during low load period and the amount of transferred load ≤ the transferable load during high load period. A multi-time period collaborative optimization model is constructed.

9. A method for tapping the potential of power supply capacity in a distribution network oriented towards multi-type load reorganization, as described in claim 8, is characterized in that: The process for determining the load fluctuation rate and the remaining capacity during low-load periods is as follows: Substituting the total load of the normal load period, low load period, and high load period in the high-adjustability period group into the standard deviation formula, the load fluctuation rate of the high-adjustability period group is calculated. Obtain the rated capacity of the distribution network, and then calculate the difference between the rated capacity and the total load during the low-load period to obtain the remaining capacity during the low-load period.

Images