Power distribution network optimization method adaptive to high-capacity load transfer of power distribution network

Abstract

The invention belongs to the technical field of power distribution network optimization, and discloses a power distribution network optimization method adaptive to high-capacity load transfer of a power distribution network. The optimization method of the power distribution network comprises the following specific steps: step 1, data intelligent analysis and feature recognition; step 2, multi-dimensional feature matching and strategy generation; step 3, space intelligent transfer optimization; step 4, time intelligent transfer optimization; according to the method, the intelligent management level of the power distribution network can be remarkably improved, and accurate prediction and intelligent scheduling of loads are realized; the load pressure of the regional power distribution network is effectively relieved, the load rate is balanced, the peak-valley difference can be remarkably reduced, and the overall operation efficiency and stability of the power grid are improved; in conclusion, the method has remarkable advantages and benefits in the aspects of improving the operation efficiency of the power grid, guaranteeing the reliability of power supply, promoting optimal configuration of resources, promoting energy transformation and the like.

Description

Technical Field

[0001] The invention belongs to the technical field of distribution network optimization, and in particular relates to a distribution network optimization method adapted to large-capacity load transfer in the distribution network. Background Art

[0002] Traditional methods mainly rely on manual experience and limited historical data to predict and dispatch loads. This static and lagging management method is difficult to accurately capture the real-time changes and potential trends of loads, which makes it difficult to cope with sudden load peaks or troughs, and easily causes instability or even failure of power grid operation. Secondly, the existing method lacks a cross-regional coordination mechanism, and each regional power grid often operates independently, making it difficult to achieve optimal allocation and sharing of power resources. This isolated operation mode not only limits the competition and development of the power market, but also leads to waste of power resources and increased transaction costs. In today's increasingly tight energy and increasingly prominent environmental problems, this inefficient resource allocation method is obviously unable to meet the needs of sustainable development.

[0003] Furthermore, the traditional approach’s neglect of demand-side response is also an issue that cannot be ignored. As an important part of the power grid, the flexibility and responsiveness of the demand side are of great significance for balancing the power grid load and improving operational efficiency. However, existing methods often only focus on supply-side regulation, while ignoring the potential of the demand side, resulting in a single load regulation method and poor results. This not only exacerbates the peak-to-valley difference problem, but also limits the power grid’s ability to accept renewable energy, hindering the process of energy transformation.

[0004] In summary, the drawbacks of the existing large-capacity load management methods of distribution networks mainly include delayed prediction and scheduling, insufficient cross-regional coordination, and neglected demand-side response. These problems not only affect the operating efficiency and stability of the power grid, but also restrict the advancement of energy transformation and green development. Therefore, improvements and innovations are proposed. Summary of the invention

[0005] The object of the present invention is to provide a distribution network optimization method that is adaptable to large-capacity load transfer in the distribution network, so as to solve the problems raised in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a distribution network optimization method adapted to large-capacity load transfer in the distribution network, the specific steps of the distribution network optimization method are as follows:

[0007] Step 1: Intelligent data analysis and feature identification: Use big data and artificial intelligence technology to deeply mine the load fluctuation data of the regional distribution network, combine the real-time load data of the adjacent regional distribution network, intelligently identify the load fluctuation pattern of the regional distribution network and the complementary load characteristics between adjacent regions, and form an accurate load characteristic map;

[0008] Step 2: Multi-dimensional feature matching and strategy generation: Based on the load characteristic map obtained in step 1, a multi-dimensional feature matching algorithm is used to determine whether the load characteristics of the distribution network meet the preset spatial transfer optimization conditions or time transfer optimization conditions. According to the matching results, the corresponding optimization strategy set is automatically generated;

[0009] Step 3: Spatial intelligent transfer optimization: For areas that meet the spatial transfer optimization conditions, an intelligent planning optimization method based on reinforcement learning is used to automatically adjust the switch configuration in the substation and network structure to achieve intelligent switching and balanced distribution of loads. At the same time, the Internet of Things technology is combined to monitor the network status in real time and dynamically adjust the operation mode to ensure the optimal N-1 security and load rate.

[0010] Step 4: Time intelligent transfer optimization: For areas that only require time transfer optimization, innovative demand-side response and energy storage system collaborative optimization mechanism are introduced; future load trends are predicted through intelligent algorithms, combined with user electricity consumption behavior analysis, and personalized energy storage charging and discharging plans are formulated, and new loads such as electric vehicle charging stations and heat and cold storage systems are linked to achieve smooth load transfer in the time dimension, reduce peak-to-valley differences, and improve the overall efficiency of the power grid;

[0011] Step 5: Cross-regional collaborative optimization: Based on steps 3 and 4, a cross-regional collaborative optimization strategy is further introduced to utilize the complementary characteristics of the distribution networks in adjacent regions, and to achieve load balancing and optimal resource allocation in a larger range through cross-regional power trading and shared energy storage.

[0012] Preferably, the data intelligent analysis and feature identification process also includes the prediction and evaluation of the impact of extreme weather and special events on holidays on load fluctuations, so as to improve the accuracy and adaptability of the optimization strategy.

[0013] Preferably, the load characteristic specific steps in step 1 include:

[0014] The first step is data collection: collect load data from meters, substations, and power plants in the power system, and determine the time range of the data according to analysis requirements, including active power and reactive power;

[0015] The second step is data preprocessing: remove outliers, missing values, and data points that do not meet the requirements; standardize and normalize the data for subsequent analysis; ensure that all data points are aligned on the time axis for time series analysis;

[0016] Third, feature extraction: calculate the basic statistics of the load data, such as the mean, standard deviation, maximum, minimum, and peak-to-valley difference; extract the periodicity and trend characteristics of the load data over time; analyze the load correlation between different regions or different users;

[0017] Step 4: Pattern recognition: group the load data according to similarity and identify different load patterns; use machine learning algorithms to classify the load data and distinguish different types of loads; time series analysis: use time series models to predict and analyze the trends of load data;

[0018] Step 5: Generate a spectrum: Draw a curve chart of the load change over time based on the above data to intuitively display the load fluctuation; use color depth to represent the size and distribution of the load, and form a heat map to display the spatial distribution characteristics of the load; extract the main components of the load data through principal component analysis, and draw a spectrum to display the main fluctuation characteristics of the load.

[0019] Preferably, the demand-side response and energy storage system collaborative optimization mechanism also includes personalized modeling and prediction of user electricity consumption behavior, guiding users to actively participate in load regulation through incentive mechanisms, thereby improving the flexibility and response speed of the user side.

[0020] Preferably, the cross-regional collaborative optimization strategy also includes real-time monitoring and forecasting of electricity market prices, taking economic benefits as a guide to achieve optimal decision-making for cross-regional electricity transactions and promote effective allocation and utilization of resources.

[0021] Preferably, the multi-dimensional feature matching algorithm formula in step 2 is specifically:

[0022]

[0023] Among them, x n Indicates the nth characteristic value of the current load characteristic, y n It represents the nth feature value of the preset optimization condition, and a is the total number of features.

[0024] Preferably, the intelligent algorithm in step 4 is specifically a time series prediction algorithm, which includes an autoregressive integrated moving average model and an exponential smoothing algorithm, specifically:

[0025] Autoregressive integrated moving average model: φ(A)(y n -c)=θ(A)∈ t

[0026] Among them, φ(A) and θ(A) are polynomials about the backshift operator A, y t is the value of the time series at time n, c is a constant term, ∈ n is the error term.

[0027] Exponential smoothing algorithm: S i =αx i +(1-α)S i-1

[0028] Among them, S i is the smoothed value at time i, x i is the actual observation value at time i, and α is the smoothing parameter (0<α<1).

[0029] Preferably, the introduction of the cross-region collaborative optimization strategy in step 5 involves a mathematical model of the optimization problem, specifically:

[0030] Mathematical model of optimization problem:

[0031] in,

[0032] · is the electricity generation cost of region i in period t;

[0033] · is the inter-regional electricity transaction cost of region i in period t;

[0034] · is the power transmission loss cost of region i in period t;

[0035] · is the possible income of region i in period t;

[0036] T is the total number of time periods.

[0037] Preferably, the constraints in the mathematical model of the optimization problem are:

[0038] Electricity supply and demand balance:

[0039] in,

[0040] is the amount of power transferred from region j to region i in time period t;

[0041] is the power generation of region i in period t;

[0042] and are the energy storage discharge and charge in region i in time period t, respectively;

[0043] D i,t is the electricity demand of region i in period t.

[0044] Energy storage system constraints:

[0045] Among them, E i,t is the energy storage capacity of region i at the end of period t;

[0046] ηcha and ηdis are the charging and discharging efficiencies of the energy storage system, respectively;

[0047] Δt is the time interval;

[0048] E i,min and E i,max They are the minimum and maximum limits of energy storage capacity respectively.

[0049] The beneficial effects of the present invention are as follows:

[0050] By integrating cutting-edge technologies such as big data, artificial intelligence, and the Internet of Things, the present invention can accurately capture and predict subtle changes in load, providing a solid foundation for the intelligent dispatching of power grids;

[0051] Specifically, this method first analyzes the load characteristics of the distribution network through intelligent analysis and feature recognition, reveals its internal laws and trends, and provides a scientific basis for the subsequent optimization strategy formulation. Subsequently, the introduction of multi-dimensional feature matching and strategy generation mechanism makes the formulation of optimization strategies more flexible and diverse, and can be tailored according to the actual conditions of different regions to ensure the maximization of optimization effects.

[0052] In terms of spatial intelligent transfer optimization, this method demonstrates a strong spatial scheduling capability. Through intelligent planning optimization and real-time monitoring of the Internet of Things, it realizes intelligent switching and balanced distribution of loads between regions, effectively alleviating the load pressure in local areas and improving the load balance of the entire power grid. In terms of time intelligent transfer optimization, it cleverly uses the demand-side response and energy storage system collaborative optimization mechanism to guide users to actively participate in load regulation, realize smooth transfer of loads in the time dimension, reduce peak-to-valley differences, and improve the overall operation efficiency of the power grid.

[0053] The method proposed in this invention also breaks through regional restrictions and realizes cross-regional collaborative optimization. Through real-time monitoring and prediction of the power market, as well as innovative means such as cross-regional power trading and shared energy storage, it not only promotes the optimal allocation and utilization of power resources, but also reduces transaction costs and enhances the economy of the power grid. This cross-regional collaborative operation mode undoubtedly opens up a broader space for the future development of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a simplified flow chart of the main steps of the distribution network optimization method of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] like Figure 1 As shown, an embodiment of the present invention provides a distribution network optimization method adapted to large-capacity load transfer in the distribution network, and the specific steps of the distribution network optimization method are as follows:

[0057] Step 1: Intelligent data analysis and feature identification: Use big data and artificial intelligence technology to deeply mine the load fluctuation data of the regional distribution network, combine the real-time load data of the adjacent regional distribution network, intelligently identify the load fluctuation pattern of the regional distribution network and the complementary load characteristics between adjacent regions, and form an accurate load characteristic map;

[0058] The core of this step is to deeply mine the massive load fluctuation data of the regional distribution network and integrate the real-time load information of adjacent areas to form a comprehensive and accurate load characteristic map. We use advanced big data processing technology and artificial intelligence algorithms, such as deep learning and cluster analysis, to analyze load data in multiple dimensions and at multiple levels. In particular, we introduce time series analysis technology to capture the cyclical and seasonal characteristics of load fluctuations, and combine extreme weather prediction models and holiday impact assessment mechanisms to conduct forward-looking analysis of load fluctuations under special events, thereby greatly improving the accuracy and forward-looking nature of the optimization strategy.

[0059] Step 2: Multi-dimensional feature matching and strategy generation: Based on the load characteristic map obtained in step 1, a multi-dimensional feature matching algorithm is used to determine whether the load characteristics of the distribution network meet the preset spatial transfer optimization conditions (such as high load fluctuation and strong complementarity) or time transfer optimization conditions (such as single high load fluctuation). According to the matching results, the corresponding optimization strategy set is automatically generated;

[0060] Based on the load characteristic map constructed in step 1, we designed an efficient multi-dimensional feature matching algorithm that can comprehensively consider the spatial distribution, temporal characteristics, fluctuation intensity and complementary characteristics of the load in adjacent areas to achieve accurate feature matching. Once the match is successful, the system will automatically trigger the strategy generation engine, which will quickly generate a variety of optimization strategy sets based on preset optimization goals and constraints, such as load rate balance, N-1 safety, etc. These strategy sets not only cover a variety of optimization methods such as spatial transfer and temporal transfer, but also ensure that each strategy can achieve optimal load distribution and maximize economic benefits under the premise of ensuring safe and stable operation of the power grid through algorithm optimization.

[0061] Step 3: Spatial intelligent transfer optimization: For areas that meet the spatial transfer optimization conditions, an intelligent planning optimization method based on reinforcement learning is used to automatically adjust the switch configuration in the substation and network structure to achieve intelligent switching and balanced distribution of loads. At the same time, the Internet of Things technology is combined to monitor the network status in real time and dynamically adjust the operation mode to ensure the optimal N-1 security and load rate.

[0062] For areas that meet the spatial transfer optimization conditions, we use an intelligent planning optimization method based on reinforcement learning. This method can simulate and learn various complex scenarios in power grid operation, and find the optimal load shedding and balanced distribution plan through continuous trial and error and optimization. In actual operation, we combine the reinforcement learning algorithm with the physical model of the power grid, and realize intelligent load transfer by adjusting the switch configuration of the substation and optimizing the network structure. At the same time, we use the Internet of Things technology to build a real-time monitoring system to monitor the operation status of the power grid in an all-round and all-weather manner, ensuring that potential safety hazards can be discovered and handled in a timely manner during the load transfer process, and ensuring the N-1 security of the power grid.

[0063] Step 4: Time intelligent transfer optimization: For areas that only require time transfer optimization, innovative demand-side response and energy storage system collaborative optimization mechanism are introduced; future load trends are predicted through intelligent algorithms, combined with user electricity consumption behavior analysis, and personalized energy storage charging and discharging plans are formulated, and new loads such as electric vehicle charging stations and heat and cold storage systems are linked to achieve smooth load transfer in the time dimension, reduce peak-to-valley differences, and improve the overall efficiency of the power grid;

[0064] In terms of time intelligent transfer optimization, we innovatively introduced a collaborative optimization mechanism of demand-side response and energy storage system. Through intelligent algorithms to accurately predict future load trends, and combined with user electricity consumption behavior analysis, we can tailor personalized energy storage charging and discharging plans for each user. At the same time, we actively link new loads such as electric vehicle charging stations and heat and cold storage systems to achieve smooth transfer of loads in the time dimension through intelligent scheduling. This collaborative optimization mechanism can not only effectively reduce the peak-to-valley difference and improve the overall efficiency of the power grid, but also guide users to actively participate in load regulation through incentive mechanisms, thereby improving the flexibility and response speed of the user side.

[0065] Step 5: Cross-regional collaborative optimization: Based on steps 3 and 4, a cross-regional collaborative optimization strategy is further introduced to utilize the complementary characteristics of the distribution networks in adjacent regions, and to achieve load balancing and optimal resource allocation in a larger range through cross-regional power trading and shared energy storage.

[0066] Based on steps 3 and 4, we further proposed a cross-regional collaborative optimization strategy. This strategy makes full use of the complementary characteristics of the distribution networks in adjacent regions, and achieves load balancing and resource optimization configuration in a larger range through cross-regional power trading and shared energy storage. We have established an efficient power market trading mechanism, using big data analysis technology to monitor and predict power market prices in real time, ensuring that the latest market price information can be fully utilized in trading decisions to maximize economic benefits. At the same time, we strengthen the construction of information sharing and collaboration mechanisms between cross-regional power grids, and improve the safety and reliability of the entire power grid by jointly responding to emergencies and collaboratively planning power grid development.

[0067] The process of intelligent data analysis and feature recognition also includes the prediction and evaluation of the impact of extreme weather and special events on holidays on load fluctuations, so as to improve the accuracy and adaptability of the optimization strategy.

[0068] In terms of special event prediction and assessment, we have introduced advanced weather forecasting models and holiday consumption behavior analysis technology, combining historical data and real-time information to accurately predict and assess load fluctuations during extreme weather and holidays;

[0069] The specific steps of load characteristics in step 1 include:

[0070] The first step is data collection: collect load data from meters, substations, and power plants in the power system, and determine the time range of the data according to analysis requirements, including active power and reactive power;

[0071] The second step is data preprocessing: remove outliers, missing values, and data points that do not meet the requirements; standardize and normalize the data for subsequent analysis; ensure that all data points are aligned on the time axis for time series analysis;

[0072] Third, feature extraction: calculate the basic statistics of the load data, such as the mean, standard deviation, maximum, minimum, and peak-to-valley difference; extract the periodicity and trend characteristics of the load data over time; analyze the load correlation between different regions or different users;

[0073] Step 4: Pattern recognition: group the load data according to similarity and identify different load patterns; use machine learning algorithms to classify the load data and distinguish different types of loads; time series analysis: use time series models to predict and analyze the trends of load data;

[0074] Step 5: Generate a spectrum: Draw a curve chart of the load change over time based on the above data to intuitively display the load fluctuation; use color depth to represent the size and distribution of the load, and form a heat map to display the spatial distribution characteristics of the load; extract the main components of the load data through principal component analysis, and draw a spectrum to display the main fluctuation characteristics of the load.

[0075] Among them, the coordinated optimization mechanism of demand-side response and energy storage system also includes personalized modeling and prediction of user electricity consumption behavior, guiding users to actively participate in load regulation through incentive mechanisms, and improving the flexibility and response speed of the user side.

[0076] In the process of personalized modeling and prediction, we collect and analyze the breadth and depth of user electricity usage data, which is constantly expanding, including but not limited to electricity usage habits, device preferences, electricity price sensitivity and other information. Through advanced machine learning algorithms, we can achieve more accurate electricity usage behavior prediction and personalized energy storage charging and discharging plan formulation.

[0077] Among them, the cross-regional collaborative optimization strategy also includes real-time monitoring and forecasting of electricity market prices, guided by economic benefits, to achieve optimal decision-making for cross-regional electricity transactions and promote the effective allocation and utilization of resources.

[0078] In terms of real-time monitoring and forecasting of power market prices, we have used big data analysis technology to build a power market monitoring network covering multiple adjacent regions to achieve high-frequency and high-precision market price forecasts, providing strong support for cross-regional power transactions. At the same time, we have strengthened the construction of communication and cooperation mechanisms between cross-regional power grids to jointly respond to challenges and opportunities in power grid development.

[0079] The multi-dimensional feature matching algorithm formula in step 2 is as follows:

[0080]

[0081] Among them, x n Indicates the nth characteristic value of the current load characteristic, y n It represents the nth feature value of the preset optimization condition, and a is the total number of features.

[0082] Among them, the intelligent algorithm in step 4 is specifically a time series prediction algorithm, which includes an autoregressive integrated moving average model and an exponential smoothing algorithm, specifically:

[0083] Autoregressive integrated moving average model: φ(A)(y n -c)=θ(A)∈ t

[0084] Among them, φ(A) and θ(A) are polynomials about the backshift operator A, y tis the value of the time series at time n, c is a constant term, ∈ n is the error term.

[0085] Exponential smoothing algorithm: S i =αx i +(1-α)S i-1

[0086] Among them, S i is the smoothed value at time i, x i is the actual observation value at time i, and α is the smoothing parameter (0<α<1).

[0087] Among them, the introduction of cross-regional collaborative optimization strategy in step 5 involves the mathematical model of the optimization problem, specifically:

[0088] Mathematical model of optimization problem:

[0089] in,

[0090] · is the electricity generation cost of region i in period t;

[0091] · is the inter-regional electricity transaction cost of region i in period t;

[0092] · is the power transmission loss cost of region i in period t;

[0093] · is the possible income of region i in period t;

[0094] T is the total number of time periods.

[0095] Among them, the constraints in the mathematical model of the optimization problem are:

[0096] Electricity supply and demand balance:

[0097] in,

[0098] is the amount of power transferred from region j to region i in time period t;

[0099] is the power generation of region i in period t;

[0100] and are the energy storage discharge and charge in region i in time period t, respectively;

[0101] D i,t is the electricity demand of region i in period t.

[0102] Energy storage system constraints:

[0103] Among them, E i,t is the energy storage capacity of region i at the end of period t;

[0104] ηcha and ηdis are the charging and discharging efficiencies of the energy storage system, respectively;

[0105] Δt is the time interval;

[0106] E i,min and E i,max They are the minimum and maximum limits of energy storage capacity respectively.

[0107] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0108] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distribution network optimization method adapted to large-capacity load transfer in a distribution network, characterized in that: The specific steps of the distribution network optimization method are as follows: Step 1: Intelligent data analysis and feature identification: Use big data and artificial intelligence technology to deeply mine the load fluctuation data of the regional distribution network, combine the real-time load data of the adjacent regional distribution network, intelligently identify the load fluctuation pattern of the regional distribution network and the complementary load characteristics between adjacent regions, and form an accurate load characteristic map; Step 2: Multi-dimensional feature matching and strategy generation: Based on the load characteristic map obtained in step 1, a multi-dimensional feature matching algorithm is used to determine whether the load characteristics of the distribution network meet the preset spatial transfer optimization conditions or time transfer optimization conditions; According to the matching results, the corresponding optimization strategy set is automatically generated; Step 3: Spatial intelligent transfer optimization: For areas that meet the spatial transfer optimization conditions, an intelligent planning optimization method based on reinforcement learning is used to automatically adjust the switch configuration in the substation and network structure to achieve intelligent switching and balanced distribution of loads. At the same time, the Internet of Things technology is combined to monitor the network status in real time and dynamically adjust the operation mode to ensure the optimal N-1 security and load rate. Step 4: Time intelligent transfer optimization: For areas that only require time transfer optimization, innovative demand-side response and energy storage system collaborative optimization mechanism are introduced; future load trends are predicted through intelligent algorithms, combined with user electricity consumption behavior analysis, and personalized energy storage charging and discharging plans are formulated, and electric vehicle charging stations and new loads of heat and cold storage systems are linked to achieve smooth load transfer in the time dimension, reduce peak-to-valley differences, and improve the overall efficiency of the power grid; Step 5: Cross-regional collaborative optimization: Based on steps 3 and 4, a cross-regional collaborative optimization strategy is further introduced to utilize the complementary characteristics of the distribution networks in adjacent regions, and to achieve load balancing and optimal resource allocation in a larger range through cross-regional power trading and shared energy storage.

2. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 1, characterized in that: The data intelligent analysis and feature identification process also includes the prediction and evaluation of the impact of extreme weather and special events on holidays on load fluctuations, so as to improve the accuracy and adaptability of the optimization strategy.

3. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 1, characterized in that: The load characteristics in step 1 specifically include: The first step is data collection: collect load data from meters, substations, and power plants in the power system, and determine the time range of the data according to analysis requirements, including active power and reactive power; The second step is data preprocessing: remove outliers, missing values, and data points that do not meet the requirements; standardize and normalize the data for subsequent analysis; ensure that all data points are aligned on the time axis for time series analysis; Third, feature extraction: calculate the basic statistics of the load data, such as the mean, standard deviation, maximum value, minimum value, and peak-to-valley difference; extract the periodicity and trend characteristics of the load data changing over time; Analyze the load correlation between different areas or different users; Step 4: Pattern recognition: group the load data according to similarity and identify different load patterns; use machine learning algorithms to classify the load data and distinguish different types of loads; time series analysis: use time series models to predict and analyze the trends of load data; Step 5: Generate a spectrum: Draw a curve chart of the load change over time based on the above data to intuitively display the load fluctuation; use color depth to represent the size and distribution of the load, and form a heat map to display the spatial distribution characteristics of the load; extract the main components of the load data through principal component analysis, and draw a spectrum to display the main fluctuation characteristics of the load.

4. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 1, characterized in that: The demand-side response and energy storage system collaborative optimization mechanism also includes personalized modeling and prediction of user electricity consumption behavior, guiding users to actively participate in load regulation through incentive mechanisms, thereby improving the flexibility and response speed of the user side.

5. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 1, characterized in that: The cross-regional collaborative optimization strategy also includes real-time monitoring and forecasting of electricity market prices, which is guided by economic benefits to achieve optimal decision-making for cross-regional electricity transactions and promote the effective allocation and utilization of resources.

6. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 1, characterized in that: The multi-dimensional feature matching algorithm formula in step 2 is specifically: Among them, x n Indicates the nth characteristic value of the current load characteristic, y n It represents the nth feature value of the preset optimization condition, and a is the total number of features.

7. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 1, characterized in that: The intelligent algorithm in step 4 is specifically a time series prediction algorithm, which includes an autoregressive integrated moving average model and an exponential smoothing algorithm, specifically: Autoregressive integrated moving average model: φ(A)(y n -c)=θ(A)∈ t Among them, φ(A) and θ(A) are polynomials about the backshift operator A, y t is the value of the time series at time n, c is a constant term, ∈ n is the error term. Exponential smoothing algorithm: S i =αx i +(1-α)S i-1 Among them, S i is the smoothed value at time i, x i is the actual observation value at time i, and α is the smoothing parameter (0<α<1).

8. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 1, characterized in that: The introduction of the cross-regional collaborative optimization strategy in step 5 involves a mathematical model of the optimization problem, specifically: Mathematical model of optimization problem: in, · is the electricity generation cost of region i in period t; · is the inter-regional electricity transaction cost of region i in period t; · is the power transmission loss cost of region i in period t; · is the possible income of region i in period t; T is the total number of time periods.

9. A distribution network optimization method adapted to large-capacity load transfer in distribution network according to claim 8, characterized in that: The constraints in the mathematical model of the optimization problem are: Electricity supply and demand balance: in, is the amount of power transferred from region j to region i in time period t; is the power generation of region i in period t; and are the storage capacity of region i in period t. Capacity to discharge and charge; D i,t is the electricity demand of region i in period t. Energy storage system constraints: Among them, E i,t is the energy storage capacity of region i at the end of period t; ηcha and ηdis are the charging and discharging efficiencies of the energy storage system, respectively; Δt is the time interval; E i,min and E i,max They are the minimum and maximum limits of energy storage capacity respectively.

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