Low-voltage photovoltaic surplus electricity scheduling method and device for multi-voltage level network and computer equipment

Abstract

The application relates to a low-voltage photovoltaic surplus power scheduling method and device of a multi-voltage level network, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring carbon price signal data and electricity price signal data; constructing a signal matching boundary constraint condition based on the carbon price signal data and the electricity price signal data; selecting a target tie line from a plurality of tie lines and determining a scheduling timing adjustment period according to the signal matching boundary constraint condition; calculating the response time delay of a settlement subject corresponding to the target tie line, and allocating a responsibility proportion to the settlement subject based on the response time delay; taking the fitness degree meeting a preset cooperation condition as an optimization target, optimizing the power transmission amplitude of the target tie line according to the responsibility proportion, and obtaining a target power transmission amplitude; and generating a power scheduling instruction according to the scheduling timing adjustment period and the target power transmission amplitude. The method can schedule the low-voltage photovoltaic surplus power of the multi-voltage level network.

Description

Technical Field

[0001] This application relates to the field of power system dispatching and control technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for dispatching low-voltage photovoltaic surplus power in a multi-voltage level grid. Background Technology

[0002] With the current market-oriented reforms of the power system, the dual settlement mechanism has become the core framework for distributed photovoltaic (PV) power to participate in electricity spot trading. The large-scale integration of low-voltage PV power into multi-voltage grids also presents a real need for the coordinated realization of grid carbon reduction targets and full life-cycle economic benefits.

[0003] Currently, it is generally accepted that the higher the grid connection absorption rate of low-voltage photovoltaic surplus electricity, the greater the carbon reduction across the entire chain, and the higher the synergistic benefits throughout the entire life cycle. However, the existing multi-voltage-level grid scheduling of low-voltage photovoltaic surplus electricity does not consider the non-permanent technical contradiction that occurs when the timing of price signals is mismatched in the scenario of parallel time-of-use carbon price settlement and fixed grid connection price of low-voltage photovoltaic power. This leads to the inability to effectively link carbon price incentives and fixed electricity price settlement under the dual settlement mechanism. As a result, the carbon reduction target and the economic benefits throughout the entire life cycle of the multi-voltage-level grid cannot be synergistically implemented. This not only restricts the initiative of low-voltage photovoltaic users to participate in electricity market transactions, but also makes the scheduling and operation of the multi-voltage-level grid lack a unified synergistic logic, exacerbating the pressure on the upper-level grid's flexible resource scheduling.

[0004] Therefore, how to achieve the coordinated implementation of carbon reduction targets and full life-cycle economic benefits through low-voltage photovoltaic surplus power dispatch in multi-voltage grid structures is an urgent problem to be solved. Summary of the Invention

[0005] Based on this, it is necessary to address the aforementioned technical issues by providing a low-voltage photovoltaic surplus power dispatching method, device, computer equipment, computer-readable storage medium, and computer program product for a multi-voltage grid structure that can achieve the synergistic implementation of carbon reduction targets and full life-cycle economic benefits.

[0006] In a first aspect, this application provides a method for low-voltage photovoltaic surplus power dispatching in a multi-voltage-level grid structure, including:

[0007] Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels;

[0008] Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signals and electricity price signals;

[0009] Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in a multi-voltage level network, and the scheduling timing adjustment period of the target tie line is determined.

[0010] Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity;

[0011] Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0012] Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0013] In one embodiment, based on carbon price signal data and electricity price signal data, signal matching boundary constraints for carbon price signals and electricity price signals are constructed, including:

[0014] Time-series alignment processing is performed on carbon price signal data and electricity price signal data to obtain carbon price signal time series and electricity price signal time series;

[0015] Price fluctuation feature clustering was performed on the time series of carbon price signal and electricity price signal to obtain multiple price fluctuation clusters. Based on the cluster centers of each price fluctuation cluster, mismatched clusters were screened out from the multiple price fluctuation clusters.

[0016] Based on the number of mismatch clusters and price fluctuation clusters, the mismatch ratio of each time period is calculated. The time period with a mismatch ratio greater than the preset scenario activation threshold is taken as the target time period. The target time period is then divided into multiple scenario time periods.

[0017] For each scenario time period, based on the start and end times of the scenario time period, locate the target carbon price signal time series data segment and the target electricity price signal time series data segment in the carbon price signal time series and the electricity price signal time series.

[0018] Feature comparison is performed on the time series data segments of the target carbon price signal and the target electricity price signal to evaluate the signal matching degree of the carbon price signal and the electricity price signal in the scenario time period. Based on the signal matching degree, signal matching boundary constraints are constructed.

[0019] In one embodiment, a target tie line is selected from multiple tie lines in a multi-voltage level network based on signal matching boundary constraints, and the scheduling timing adjustment period of the target tie line is determined, including:

[0020] Acquire power flow direction monitoring data for multiple tie lines in a multi-voltage level network structure. The power flow direction monitoring data includes the power transmission direction and power transmission amplitude of each tie line.

[0021] Use the tie line whose power transmission amplitude satisfies the signal matching boundary constraint condition as the intermediate tie line;

[0022] Based on the surplus power of low-voltage photovoltaic power, target tie lines are selected from intermediate tie lines through the coordination mechanism between voltage levels and the power priority allocation rules, and the scheduling timing adjustment cycle of the target tie lines is determined.

[0023] In one embodiment, there are multiple settlement entities; based on the response latency of each settlement entity, a responsibility ratio is allocated to each settlement entity, including:

[0024] Based on the response delay, an initial liability ratio is allocated to each settlement entity;

[0025] For each settlement entity, determine whether the settlement entity's response latency exceeds the latency tolerance threshold;

[0026] If the response delay of a settlement entity exceeds the allowable delay threshold, the settlement entity will be designated as the entity with the excessive delay. Based on the response delays of other settlement entities, the initial responsibility ratio of the entity with the excessive delay will be allocated to the other settlement entities, which will be all settlement entities other than the entity with the excessive delay.

[0027] In one embodiment, based on the responsibility ratio of the settlement entity and with the fitness meeting preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of carbon reduction target evaluation indicators and life-cycle economic benefit evaluation indicators, including:

[0028] Based on the responsibility ratio of the settlement entity and the power transmission amplitude of the target interconnection line, weights are assigned to the carbon reduction target evaluation indicators and the life cycle economic benefit evaluation indicators.

[0029] Based on the weights, the carbon reduction target evaluation indicators and the full life cycle economic benefit evaluation indicators are weighted and summed to obtain the fitness.

[0030] Determine whether the fitness meets the preset coordination conditions. If the fitness does not meet the preset coordination conditions, then perform selection, crossover and mutation operations sequentially on the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain the intermediate power transmission amplitude. Replace the intermediate power transmission amplitude with the power transmission amplitude and return to continue execution according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line until the fitness meets the preset coordination conditions.

[0031] The power transmission amplitude corresponding to the fitness meeting the preset coordination conditions is taken as the target power transmission amplitude.

[0032] In one embodiment, the method further includes:

[0033] Acquire operational feedback data generated during the execution of power scheduling instructions. The operational feedback data includes the actual power transmission amplitude and actual power transmission time of each target tie line.

[0034] Based on operational feedback data, the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie line is calculated over multiple time periods.

[0035] Determine if the deviation value is greater than the preset deviation threshold. If the deviation value is greater than the preset deviation threshold, modify the signal matching boundary constraint to obtain the target signal matching boundary constraint. Replace the target signal matching boundary constraint with the signal matching boundary constraint and return to the step based on the signal matching boundary constraint to continue execution until the deviation value is less than the preset deviation threshold.

[0036] Secondly, this application also provides a low-voltage photovoltaic surplus power dispatching device with a multi-voltage level grid, comprising:

[0037] The acquisition module is used to acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with a multi-voltage level grid structure.

[0038] The module is used to construct the signal matching boundary constraints for carbon price signal and electricity price signal based on carbon price signal data and electricity price signal data;

[0039] The selection module is used to select a target tie line from multiple tie lines in a multi-voltage level network based on signal matching boundary constraints, and to determine the scheduling timing adjustment period of the target tie line.

[0040] The allocation module is used to calculate the response latency of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response latency of the settlement entity;

[0041] The optimization module is used to optimize the power transmission amplitude of the target tie line corresponding to the settlement entity based on the responsibility ratio of the settlement entity and with the fitness meeting the preset coordination conditions as the optimization objective. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0042] The generation module is used to generate power dispatch instructions based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude. The power dispatch instructions are used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0043] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0044] Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels;

[0045] Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signals and electricity price signals;

[0046] Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in a multi-voltage level network, and the scheduling timing adjustment period of the target tie line is determined.

[0047] Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity;

[0048] Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0049] Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0050] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0051] Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels;

[0052] Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signals and electricity price signals;

[0053] Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in a multi-voltage level network, and the scheduling timing adjustment period of the target tie line is determined.

[0054] Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity;

[0055] Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0056] Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0057] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0058] Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels;

[0059] Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signals and electricity price signals;

[0060] Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in a multi-voltage level network, and the scheduling timing adjustment period of the target tie line is determined.

[0061] Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity;

[0062] Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0063] Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0064] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for low-voltage photovoltaic surplus power dispatching in a multi-voltage-level grid system acquire carbon price signal data and low-voltage electricity price signal data from the upper-level grid within the low-voltage grid. Based on the carbon price signal data and electricity price signal data, signal matching boundary constraints for the carbon price signal and electricity price signal are constructed. According to the signal matching boundary constraints, a target tie line is selected from multiple tie lines in the multi-voltage-level grid, and the dispatch timing adjustment period of the target tie line is determined. In the process of constructing the signal matching boundary constraints for the carbon price signal and electricity price signal to obtain the dispatch timing adjustment period, a cross-voltage-level dispatch coordination logic centered on signal boundary parameters can be constructed, ensuring that the dispatching of low-voltage photovoltaic surplus power aligns with the operating rhythm of high- and low-voltage tie lines, providing a clear basis for the generation and issuance of dispatch instructions. The process involves calculating the response delay of the settlement entity corresponding to the target tie line, and assigning a responsibility ratio to the settlement entity based on this delay. According to the responsibility ratio of the settlement entity, and with the fitness requirement meeting preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life-cycle economic benefit evaluation index. Based on the scheduling timing adjustment cycle of the target tie line and the target power transmission amplitude, a power scheduling command is generated. This command is used to schedule surplus low-voltage photovoltaic power to other voltage level grids (excluding the low-voltage grid) within the multi-voltage level grid structure via the target tie line. Through this process, a linkage is established between the allocation of rights and responsibilities of the settlement entity and the impact of scheduling execution delays. The balance between carbon reduction targets and full life-cycle economic benefits is adapted to the coordination logic of grid operation, ultimately achieving the coordinated implementation of carbon reduction targets and full life-cycle economic benefits through the scheduling of surplus low-voltage photovoltaic power in a multi-voltage level grid structure. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is an application environment diagram of a low-voltage photovoltaic surplus power dispatching method for a multi-voltage-level grid structure in one embodiment;

[0067] Figure 2 This is a flowchart illustrating a low-voltage photovoltaic surplus power dispatching method for a multi-voltage-level grid structure in one embodiment.

[0068] Figure 3 This is a flowchart illustrating the steps for constructing signal matching boundary constraints in one embodiment;

[0069] Figure 4 This is a flowchart illustrating a low-voltage photovoltaic surplus power dispatching method for a multi-voltage level grid structure in another embodiment.

[0070] Figure 5 This is a structural block diagram of a low-voltage photovoltaic surplus power dispatching device with a multi-voltage level grid structure in one embodiment;

[0071] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0073] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0074] The low-voltage photovoltaic surplus power dispatching method for multi-voltage level grid structures provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Specifically, terminal 102 or server 104 executes a low-voltage photovoltaic surplus power dispatch method for a multi-voltage level grid. This method includes: acquiring carbon price signal data from the upper-level grid and electricity price signal data from the low-voltage grid in a multi-voltage level grid; constructing signal matching boundary constraints for the carbon price signal and electricity price signal based on the carbon price signal data and electricity price signal data; selecting a target tie line from multiple tie lines in the multi-voltage level grid according to the signal matching boundary constraints, and determining the dispatch timing adjustment period of the target tie line; calculating the response delay of the settlement entity corresponding to the target tie line, and based on the response delay of the settlement entity... The system allocates responsibility ratios to settlement entities based on time delays. Based on these responsibility ratios, and with the fitness level meeting preset coordination conditions as the optimization objective, it optimizes the power transmission amplitude of the target tie line corresponding to each settlement entity, obtaining the target power transmission amplitude of the target tie line. The fitness level is obtained by weighted summation of carbon reduction target evaluation indicators and life-cycle economic benefit evaluation indicators. Based on the scheduling timing adjustment cycle of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. This instruction is used to dispatch surplus low-voltage photovoltaic power to other voltage level grids (excluding the low-voltage grid) within the multi-voltage grid structure via the target tie line.

[0075] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0076] In one exemplary embodiment, such as Figure 2 As shown, a low-voltage photovoltaic surplus power dispatch method for multi-voltage level grids is provided, which is then applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 212. Wherein:

[0077] Step 202: Obtain carbon price signal data and low-voltage volt price signal data from the upper-level power grid in the low-voltage power grid with a multi-voltage level grid structure.

[0078] Among them, a multi-voltage level grid refers to a power network composed of multiple power grids with different voltage levels. Among them, a low-voltage power grid is a power network that uses low-voltage volts for power distribution.

[0079] For example, the upstream grid of the low-voltage grid is determined from the multi-voltage level grid structure, and carbon price signal data is collected from the carbon trading data interface of the upstream grid. The carbon price signal data is time-of-day carbon price signal data from the data release interface of the provincial carbon trading platform. The data release interface of the provincial carbon trading platform pushes carbon emission rights trading price information to the carbon trading data interface of the downstream grid at a period of fifteen minutes.

[0080] For example, carbon price signal data is collected from a smart metering terminal. The smart metering terminal is installed at the photovoltaic grid connection point in the low-voltage power grid. The electricity price signal data is the fixed feed-in tariff signal data collected from the low-voltage photovoltaic metering terminal.

[0081] Step 204: Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signal and electricity price signal.

[0082] The signal matching boundary constraints include the allowable range of peak-valley time offset and the range of fluctuation trend consistency. The allowable range of peak-valley time offset is determined by the time boundary parameter, and the range of fluctuation trend consistency is determined by the trend boundary parameter.

[0083] Optionally, time-series alignment, price fluctuation feature extraction, and clustering operations are sequentially performed on carbon price signal data and electricity price signal data to obtain multiple price fluctuation clusters. Based on the cluster centers of each price fluctuation cluster, at least one mismatched cluster is selected from the multiple price fluctuation clusters. Based on the multiple price fluctuation clusters and at least one mismatched cluster, trend boundary parameters and time boundary parameters are determined. Based on the trend boundary parameters and time boundary parameters, signal matching boundary constraints for carbon price signals and electricity price signals are constructed.

[0084] Step 206: Based on the signal matching boundary constraints, select the target tie line from among the multiple tie lines in the multi-voltage level network and determine the scheduling timing adjustment period of the target tie line.

[0085] The multi-voltage-level grid structure comprises power grids at multiple voltage levels, interconnected by multiple tie lines. The target tie line is selected from among these tie lines based on a pre-defined inter-voltage-level coordination mechanism and pre-defined power allocation priority rules, and is used for dispatching surplus low-voltage photovoltaic power. The dispatch timing adjustment period is used to define the time interval for issuing power dispatch commands.

[0086] For example, under the constraints of signal matching boundary conditions, a target tie line is selected from multiple tie lines in a multi-voltage level network through a coordination mechanism between voltage levels and a power priority allocation rule, and the scheduling timing adjustment period of the target tie line is determined.

[0087] Optionally, the dispatch interval is obtained by adjusting the scheduling time sequence of the target connection line, and finally a dispatch command dispatch frequency scheme is formed.

[0088] Step 208: Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity.

[0089] The response delay is the time difference between sending the instruction to the settlement entity and the response confirmation. The settlement entity and the target tie line are linked through the power grid topology, with each settlement entity corresponding to at least one target tie line.

[0090] For example, a scheduling instruction synchronization request is sent to the settlement entity corresponding to the target connection line through the settlement entity data interaction interface. The response confirmation time returned by each settlement entity is obtained, and the time difference between the time the instruction is sent and the response confirmation time is calculated. This time difference is used as the response delay. The settlement entity data interaction interface is deployed on the communication link between the power grid dispatch center and each settlement entity. After the scheduling instruction synchronization request is sent, the terminal equipment of each settlement entity receives the request and returns the response confirmation time.

[0091] Optionally, based on the mapping relationship between the response delay and liability ratio of each settlement entity, a liability ratio is assigned to each settlement entity to obtain the liability ratio allocation result of each settlement entity.

[0092] Step 210: Based on the responsibility ratio of the settlement entity, and with the fitness meeting the preset coordination conditions as the optimization objective, optimize the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0093] The fitness factor characterizes the degree to which the power allocation scheme corresponding to the settlement entity achieves a balance between carbon reduction targets and life-cycle economic benefits. The carbon reduction target evaluation index is the reduction in carbon emissions per unit of power transmission. The life-cycle economic benefit evaluation index is the difference between the revenue from surplus electricity fed into the grid and the equipment operation and maintenance costs. Preset coordination conditions include minimum thresholds for both the carbon reduction target evaluation index and the life-cycle economic benefit evaluation index.

[0094] For example, based on the responsibility ratio of the settlement entity and with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized through a genetic algorithm to obtain the target power transmission amplitude of the target tie line.

[0095] Step 212: Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, generate a power dispatch instruction. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0096] The power dispatch instructions include the target power transmission amplitude and start and end times of the target tie line, and there are multiple power dispatch instructions. Other voltage level grids refer to the grids at which dispatch should be performed.

[0097] For example, multiple power dispatch instructions are issued to the low-voltage grid of a multi-voltage-level grid structure to dispatch surplus low-voltage photovoltaic power to other voltage-level grids in the multi-voltage-level grid structure, excluding the low-voltage grid, via target interconnects.

[0098] In the aforementioned low-voltage photovoltaic surplus power dispatching method for multi-voltage level grids, carbon price signal data from the upper-level grid and electricity price signal data of the low-voltage grid are acquired within the multi-voltage level grid. Based on these data, signal matching boundary constraints for the carbon price and electricity price signals are constructed. According to these constraints, a target tie line is selected from multiple tie lines within the multi-voltage level grid, and the dispatch timing adjustment period for the target tie line is determined. Through the process of constructing the signal matching boundary constraints for the carbon price and electricity price signals to obtain the dispatch timing adjustment period, a cross-voltage level dispatch coordination logic centered on signal boundary parameters can be constructed. This ensures that the dispatching of low-voltage photovoltaic surplus power aligns with the operational rhythm of the high- and low-voltage tie lines, providing a clear basis for the generation and issuance of dispatch instructions. The process involves calculating the response delay of the settlement entity corresponding to the target tie line, and assigning a responsibility ratio to the settlement entity based on this delay. According to the responsibility ratio of the settlement entity, and with the fitness requirement meeting preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life-cycle economic benefit evaluation index. Based on the scheduling timing adjustment cycle of the target tie line and the target power transmission amplitude, a power scheduling command is generated. This command is used to schedule surplus low-voltage photovoltaic power to other voltage level grids (excluding the low-voltage grid) within the multi-voltage level grid structure via the target tie line. Through this process, a linkage is established between the allocation of rights and responsibilities of the settlement entity and the impact of scheduling execution delays. The balance between carbon reduction targets and full life-cycle economic benefits is adapted to the coordination logic of grid operation, ultimately achieving the coordinated implementation of carbon reduction targets and full life-cycle economic benefits through the scheduling of surplus low-voltage photovoltaic power in a multi-voltage level grid structure.

[0099] In one exemplary embodiment, such as Figure 3 As shown, based on carbon price signal data and electricity price signal data, signal matching boundary constraints for carbon price signals and electricity price signals are constructed, including steps 302 to 310. Wherein:

[0100] Step 302: Perform time-series alignment processing on the carbon price signal data and the electricity price signal data to obtain the carbon price signal time series and the electricity price signal time series.

[0101] The carbon price signal data is collected over a period of 15 minutes, while the electricity price signal data is collected over a period of minutes.

[0102] For example, the acquisition period of carbon price signal data is used as a baseline time window. For each baseline time window, the electricity price sampling points in all electricity price signal data within that baseline time window are obtained, and the arithmetic mean of each electricity price sampling point is calculated. This arithmetic mean is used as the standardized electricity price signal corresponding to that baseline time window. Based on the standardized electricity price signals and carbon price signal data, a carbon price signal time series and an electricity price signal time series are constructed.

[0103] Step 304: Perform price fluctuation feature clustering on the carbon price signal time series and the electricity price signal time series to obtain multiple price fluctuation clusters. Based on the cluster centers of each price fluctuation cluster, filter out mismatched clusters from the multiple price fluctuation clusters.

[0104] Among them, mismatch clusters refer to clusters where carbon price fluctuations and electricity price fluctuations exhibit significant asynchronous characteristics. The coordinates of the cluster centers include both the carbon price fluctuation amplitude component and the electricity price fluctuation amplitude component.

[0105] For example, price fluctuation features are extracted from the time series sequences of carbon price signals and electricity price signals to obtain the fluctuation amplitude features of the carbon price signals and electricity price signals. Specifically, the absolute value of the price difference between two adjacent sampling points of the carbon price signal is used as the fluctuation amplitude feature of the carbon price signal; the absolute value of the price difference between two adjacent sampling points of the electricity price signal is used as the fluctuation amplitude feature of the electricity price signal. The fluctuation amplitude features of the carbon price signal and the electricity price signal are used to characterize the price fluctuation intensity within two adjacent time periods.

[0106] Optionally, the fluctuation amplitude characteristics of carbon price signals and electricity price signals can be time-series paired to form a set of price fluctuation feature vectors.

[0107] Optionally, the K-means clustering algorithm can be used to cluster multiple price fluctuation feature vectors in the price fluctuation feature vector set to obtain multiple price fluctuation clusters.

[0108] For example, suppose the carbon price changes from 50 yuan per ton to 55 yuan per ton during a certain period. The fluctuation range characteristic of the carbon price signal during this period is 5 yuan. The carbon price fluctuation range characteristics of each time period are combined with the electricity price fluctuation range characteristics to form a price fluctuation feature vector. Multiple price fluctuation feature vectors form a price fluctuation feature vector set. Multiple initial cluster centers are randomly selected, and each price fluctuation feature vector is assigned to the price fluctuation cluster belonging to the nearest initial cluster center. The coordinates of the cluster centers of each price fluctuation cluster are then recalculated. The newly calculated cluster centers are replaced with the original initial cluster centers, and the iterative process of assigning each price fluctuation feature vector to the price fluctuation cluster belonging to the nearest initial cluster center continues until the coordinates of the cluster centers of each price fluctuation cluster no longer change, resulting in multiple price fluctuation clusters.

[0109] For example, the carbon price fluctuation amplitude component and the electricity price fluctuation amplitude component are extracted from the coordinates of each cluster center. The absolute value of the difference between the carbon price fluctuation amplitude component and the electricity price fluctuation amplitude component is calculated to obtain the deviation degree of the cluster center. Cluster centers whose deviation degree exceeds a preset time period matching threshold are identified as mismatched clusters.

[0110] Step 306: Based on the number of mismatch clusters and price fluctuation clusters, calculate the mismatch ratio for each time period, and take the time period with the mismatch ratio greater than the preset scenario activation threshold as the target time period. Then, perform scenario segmentation on each target time period to obtain multiple scenario time periods.

[0111] Each time period is fifteen minutes long. The mismatch percentage of each time period is used to characterize the overall deviation between the carbon price signal and the electricity price signal in terms of temporal characteristics within that time period. The target time period is the period in which there is a relatively obvious price signal temporal mismatch phenomenon, and it is also the period in which the price signal temporal mismatch control scenario under the dual settlement mechanism is activated. The scenario time period refers to the time period formed by merging multiple target time periods that simultaneously meet the conditions of continuous time periods and mismatch percentages of the same level.

[0112] For example, the number of mismatch clusters and price fluctuation clusters in each time period is counted, and the ratio between the number of mismatch clusters and the number of price fluctuation clusters in each time period is calculated to obtain the mismatch percentage in each time period.

[0113] Optionally, a scenario activation threshold can be preset based on power grid operation experience. The mismatch ratio is compared with the preset scenario activation threshold. If the mismatch ratio exceeds the scenario activation threshold, this time period is taken as the target time period.

[0114] For example, for every two adjacent target time periods, it is determined whether these two adjacent target time periods meet the continuity constraint condition and whether the mismatch ratio of these two adjacent target time periods meets the mismatch degree classification condition of belonging to the same level. Continuous time periods that simultaneously meet the above two constraints are merged into scene time periods. The above scene division process is performed on all time periods to obtain multiple scene time periods.

[0115] Among them, the time period continuity constraint is used to determine whether there is a temporal continuity relationship between two adjacent activated time periods; the mismatch degree classification condition divides each target time period into different levels according to the size of the mismatch ratio.

[0116] Step 308: For each scenario time period, based on the start and end times of the scenario time period, locate the target carbon price signal time series data segment and the target electricity price signal time series data segment in the carbon price signal time series and the electricity price signal time series.

[0117] For example, for each scenario time period, the start and end times of each scenario time period are obtained. Based on the start and end times of each scenario time period, the target carbon price signal time series data segment and the target electricity price signal time series data segment are located in the carbon price signal time series and the electricity price signal time series.

[0118] Step 310: Perform feature comparison on the time series data segments of the target carbon price signal and the target electricity price signal, evaluate the signal matching degree of the carbon price signal and the electricity price signal in the scenario time period, and construct signal matching boundary constraints based on the signal matching degree.

[0119] The signal matching degree includes the fluctuation trend consistency index and the peak-valley time offset index. The fluctuation trend consistency index is used to measure whether the carbon price signal and the electricity price signal change in the same direction; the peak-valley time offset index is used to measure the difference in the time when the carbon price signal and the electricity price signal reach the peak or valley value, characterizing the degree of synchronization between the carbon price signal and the electricity price signal at the time of extreme value occurrence.

[0120] For example, feature comparisons are performed on the time series data segments of the target carbon price signal and the target electricity price signal to evaluate the consistency index of the fluctuation trend of the carbon price signal and the peak-valley time offset index during the scenario time period.

[0121] For example, the time series data segments of the target carbon price signal and the target electricity price signal are compared to see the direction of change of the carbon price signal and the electricity price signal at the same time. If both signals show an upward trend or a downward trend at a certain time, it is determined that the fluctuation trend at that time is consistent. The percentage of times with consistent fluctuation trends within the applicable interval is counted to obtain the fluctuation trend consistency index.

[0122] For example, by identifying the times when the carbon price signal and the electricity price signal reach their respective peak or trough values, the time difference between the two times is calculated, and this time difference is used as an indicator of peak-trough time offset.

[0123] For example, based on the signal matching degree, the trend boundary parameters corresponding to the fluctuation trend consistency index and the time boundary parameters corresponding to the peak-valley time offset index are extracted. The trend boundary parameters and time boundary parameters are integrated to determine the boundary parameter information of signal matching. Among them, the trend boundary parameters are the critical values ​​of fluctuation trend consistency, and the time boundary parameters are the allowable range values ​​of peak-valley time offset.

[0124] In this embodiment, by performing time-series alignment, fluctuation clustering, and scenario segmentation on carbon price and electricity price signals, the time periods in which the two types of price signals are out of sync are accurately identified. Then, by combining the consistency of fluctuation trends and the peak-valley time offset to quantify the signal matching degree, a signal matching boundary constraint condition that conforms to the actual market fluctuation law is finally constructed. This solves the problem in traditional methods where the constraint condition does not conform to the actual market operation due to ignoring the time-series mismatch characteristics of carbon price and electricity price, thus causing the surplus power dispatch scheme to be unreasonable. This improves the accuracy of the constraint condition in the subsequent dispatch optimization process and provides a more accurate constraint basis for surplus power dispatch that takes into account both carbon reduction benefits and economic benefits.

[0125] In one embodiment, selecting a target tie line from multiple tie lines in a multi-voltage level grid based on signal matching boundary constraints and determining the scheduling timing adjustment period of the target tie line includes: acquiring power flow direction monitoring data of multiple tie lines in the multi-voltage level grid, the power flow direction monitoring data including the power transmission direction and power transmission amplitude of each tie line; designating tie lines whose power transmission amplitude satisfies the signal matching boundary constraints as intermediate tie lines; and, based on the surplus low-voltage photovoltaic power, selecting the target tie line from the intermediate tie lines through a voltage level coordination mechanism and power priority allocation rules, and determining the scheduling timing adjustment period of the target tie line.

[0126] The power flow monitoring data includes the power transmission direction and amplitude of the tie lines. The voltage level coordination mechanism determines the coordination order for transmitting surplus low-voltage photovoltaic power to the high-voltage grid. It stipulates that the low-voltage side should prioritize absorbing its own load before transmitting to the higher-level grid. The core logic is that surplus power generated by low-voltage photovoltaic power generation should be prioritized for absorption by loads within its own voltage level. Only when the load at its own level has been fully absorbed and there is still surplus power will cross-level transmission to the high-voltage grid be initiated. The power priority allocation rule is based on the capacity margin of each tie line, and surplus power is allocated to multiple tie lines for round-robin transmission according to priority.

[0127] For example, it is determined whether the trend of power transmission amplitude change is within the range of fluctuation trend consistency, and whether the times of power peak and trough occurrence are within the allowable range of time boundary parameters. If the power transmission amplitude satisfies the above signal matching boundary constraints, the corresponding tie line is taken as an intermediate tie line. Specifically, the trend of power transmission amplitude change is judged. If the power amplitude shows a monotonically increasing or decreasing trend in adjacent sampling times, and the slope of the change is within the numerical range specified by the trend boundary parameters, then the trend of change in that period is determined to satisfy the signal matching boundary constraints. The time when the power transmission amplitude reaches its peak or trough is extracted, and it is determined whether the difference between this time and the peak and trough times of the carbon price signal is within the allowable range specified by the time boundary parameters. If both judgments are satisfied, the corresponding tie line is taken as an intermediate tie line.

[0128] Optionally, power flow direction monitoring data for the tie lines can be obtained from power monitoring devices on the high- and low-voltage grid. These power monitoring devices are used to collect the power transmission direction and amplitude of each tie line.

[0129] For example, the ratio of low-voltage photovoltaic surplus power output is obtained based on the surplus power of low-voltage photovoltaic power. Based on this ratio and the coordination mechanism between voltage levels, the priority order for transmitting low-voltage photovoltaic surplus power to the upper-level grid is determined. The capacity margins of each tie line are then sorted from largest to smallest to obtain a priority ranking. Here, the capacity margin is the difference between the rated transmission capacity of the tie line and the current actual transmission power. A larger capacity margin indicates more ample transmission space for the tie line, and thus ranks higher in the priority order.

[0130] For example, based on the ratio of tie line capacity margin to low-voltage photovoltaic surplus power output, when the capacity margin of a single tie line cannot carry all the surplus power output at once, the surplus power is distributed to multiple tie lines for round-transmission according to priority, and the time interval between each round constitutes the scheduling timing adjustment cycle.

[0131] In this embodiment, by using the inter-voltage level coordination mechanism and power priority allocation rules, combined with signal matching boundary constraints to screen target tie lines, it is possible to achieve cross-voltage level dispatch of low-voltage photovoltaic surplus power while ensuring that the dispatch process matches the price signal under the dual settlement mechanism, avoiding dispatch revenue loss caused by price signal mismatch, and improving the overall economic efficiency of surplus power dispatch.

[0132] In one embodiment, there are multiple settlement entities; the responsibility ratio is allocated to the settlement entities based on their response latency, including: allocating an initial responsibility ratio to each settlement entity according to the response latency; for each settlement entity, determining whether the response latency of the settlement entity is greater than the latency tolerance threshold; if the response latency of the settlement entity is greater than the latency tolerance threshold, the settlement entity is designated as a latency-overdue entity, and the initial responsibility ratio of the latency-overdue entity is allocated to other settlement entities according to the response latency of other settlement entities, where other settlement entities are all settlement entities other than the latency-overdue entity.

[0133] Among them, settlement entities with shorter response times have stronger command response capabilities and undertake a larger proportion of power regulation tasks and settlement responsibilities during scheduling execution. The latency tolerance threshold is the scheduling timing adjustment cycle of the target tie line. Entities exceeding the latency limit are settlement entities that cannot complete the response confirmation of the current command before the next round of command issuance.

[0134] For example, since the settlement entity with shorter response latency bears a higher proportion of scheduling execution responsibility than other entities, an initial responsibility proportion is assigned to each settlement entity based on the mapping relationship between the response latency and the initial responsibility proportion of each settlement entity.

[0135] For example, it is determined whether the response latency of each settlement entity exceeds the latency tolerance threshold, and settlement entities whose response latency exceeds the latency tolerance threshold are identified as latency-overloaded entities. The initial liability ratio of latency-overloaded entities is redistributed among the other settlement entities, with entities having shorter response latency receiving a higher liability ratio increment during redistribution. Based on the initial liability ratio and liability ratio increment of each settlement entity, the liability ratio of each settlement entity is obtained.

[0136] Optionally, assuming the response delay of a settlement entity is half of the allowable delay threshold, the initial liability ratio is increased accordingly, with the specific increase determined based on the mapping relationship specified in the preset liability allocation ratio. Based on the mapping relationship between the response delay of each settlement entity and the initial liability ratio, the response delay is divided into multiple response delay intervals, each interval corresponding to a liability ratio value.

[0137] Optionally, by weighting response capabilities, the initial responsibility ratio of the entity with excessive delay is separated from the total responsibility. The initial responsibility ratio of the entity with excessive delay is then allocated differentially among other settlement entities according to the length of each response delay. Other settlement entities with shorter response delays receive a higher increase in their responsibility ratio. After the allocation is completed, the sum of the responsibility ratios of each settlement entity remains unchanged, resulting in the responsibility ratio allocation result for each settlement entity.

[0138] In this embodiment, by allocating settlement responsibility ratios based on response delay, the settlement entity can be forced to improve dispatch response speed, reduce dispatch execution deviations caused by untimely responses, ensure the timely and high-quality implementation of surplus power dispatch schemes, and also achieve the matching allocation of rights, responsibilities and benefits, ensuring the fairness of dispatch revenue distribution.

[0139] In one embodiment, based on the responsibility ratio of the settlement entity and with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the life cycle economic benefit evaluation index, including: assigning weights to the carbon reduction target evaluation index and the life cycle economic benefit evaluation index according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line; weighted summation of the carbon reduction target evaluation index and the life cycle economic benefit evaluation index based on the weights to obtain the fitness; determining whether the fitness meets the preset coordination conditions; if the fitness does not meet the preset coordination conditions, then performing selection, crossover, and mutation operations sequentially on the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain an intermediate power transmission amplitude, replacing the intermediate power transmission amplitude with the actual power transmission amplitude, and returning to continue execution based on the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line until the fitness meets the preset coordination conditions; and using the power transmission amplitude corresponding to the fitness meeting the preset coordination conditions as the target power transmission amplitude.

[0140] For example, based on the responsibility ratio of each settlement entity, the power transmission amplitude of the target tie line associated with the settlement entity with the higher responsibility ratio occupies a greater weight in the weighted power flow basic data, and the power transmission amplitude of the target tie line corresponding to the settlement entity is weighted.

[0141] Optionally, the product of surplus power fed into the grid and the carbon emission factor per unit of thermal power generation is calculated to obtain the carbon emissions avoided by replacing thermal power with surplus low-voltage photovoltaic power. Based on the carbon emissions avoided by replacing thermal power with surplus low-voltage photovoltaic power, the carbon emission reduction per unit of power transmission is calculated to obtain the carbon reduction target evaluation index.

[0142] Optionally, the product of the fixed feed-in tariff and the feed-in power for the current period is calculated to obtain the revenue from surplus electricity fed into the grid; the ratio between the total investment in the equipment and the expected operating years is calculated, and this ratio is allocated to each time period to obtain the equipment operation and maintenance cost for each time period; the difference between the immediate revenue from surplus electricity fed into the grid and the operation and maintenance cost of the photovoltaic equipment throughout its entire operating cycle is calculated to obtain the full life cycle economic benefit evaluation index.

[0143] For example, the power transmission amplitude of the target link is used as the decision variable. Each combination of power transmission amplitudes constitutes an individual, and multiple individuals form a population. The selection operation selects superior individuals from the current population based on fitness to enter the next generation. The crossover operation exchanges some gene segments between two parent individuals to produce offspring individuals. The mutation operation randomly changes the values ​​of certain genes in an individual with a certain probability. Through multiple rounds of iteration, the target power transmission amplitude whose fitness satisfies the preset cooperation conditions is obtained.

[0144] Optionally, based on the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line, the ratio of the current carbon price signal value to the electricity price signal value is determined. Weights are then assigned to the carbon reduction target evaluation index and the full life-cycle economic benefit evaluation index based on this ratio. Specifically, the weight of the carbon reduction target evaluation index is increased when the carbon price signal value is higher than the electricity price signal value; conversely, when the carbon price signal value is relatively high, indicating a stronger incentive for carbon emission reduction in the carbon trading market, the weight of the carbon reduction target evaluation index is increased, while the weight of the full life-cycle economic benefit evaluation index is correspondingly decreased.

[0145] Furthermore, when the ratio between the carbon price signal and the electricity price signal exceeds the preset weight switching threshold, the weight of the carbon reduction target evaluation indicator is set to a higher value; conversely, the weight of the life-cycle economic benefit evaluation indicator is set to a higher value, and the sum of the two weights remains constant.

[0146] In this embodiment, by combining carbon reduction targets with full life-cycle economic benefits to construct an adaptive optimization target, and dynamically adjusting the weights of the two types of indicators according to the signal ratio of carbon price and electricity price, it is possible to adapt to market signal changes under the dual settlement mechanism while taking into account carbon emission reduction incentives and economic benefits, thereby obtaining a surplus power dispatch scheme that is more in line with current market demands and low-carbon development goals, and improving the comprehensive benefits of low-voltage photovoltaic surplus power dispatch across voltage levels.

[0147] In one embodiment, the method further includes: acquiring operational feedback data generated during the execution of power scheduling instructions, the operational feedback data including the actual power transmission amplitude and actual power transmission time of each target tie-line; calculating the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie-line within multiple time periods based on the operational feedback data; determining whether the deviation value is greater than a preset deviation threshold; if the deviation value is greater than the preset deviation threshold, correcting the signal matching boundary constraint condition to obtain the target signal matching boundary constraint condition; replacing the target signal matching boundary constraint condition with the signal matching boundary constraint condition; and returning to the step based on the signal matching boundary constraint condition to continue execution until the deviation value is less than the preset deviation threshold.

[0148] The deviation value characterizes the degree of deviation between the execution process and the actual operation. The actual power transmission amplitude characterizes the true power transmission state of the target tie line at each moment.

[0149] Optionally, the operational feedback data is cleaned by removing outliers and missing time periods to obtain cleaned operational feedback data. Specifically, outliers and missing time periods are removed from the operational feedback data. Outliers include power values ​​exceeding the rated capacity of the tie line and negative power values. Missing time periods refer to the time intervals during which the power monitoring device failed to collect data normally. After removing these data, the cleaned operational feedback data is obtained.

[0150] Optionally, a preset deviation threshold can be set in advance based on the capacity margin and scheduling response capability of the target connection. When the absolute value of the deviation exceeds the preset deviation threshold, it indicates that there is a significant deviation between the actual operation and the plan for the current time period.

[0151] Furthermore, if the deviation value is greater than a preset deviation threshold, the mismatch ratio is adjusted based on the deviation value to correct the signal matching boundary constraints and obtain the target signal matching boundary constraints. Further, when the deviation value is positive, it indicates that the actual power transmission amplitude is higher than the target power transmission amplitude; in this case, the mismatch ratio for the corresponding time period is increased. When the deviation value is negative, it indicates that the actual power transmission amplitude is lower than the target power transmission amplitude; in this case, the mismatch ratio for the corresponding time period is decreased. Based on the adjusted mismatch ratio, the process returns to step 306, where the time period with the mismatch ratio greater than the preset scene activation threshold is used as the target time period, to correct the signal matching boundary constraints and obtain the target signal matching boundary constraints.

[0152] In this embodiment, by introducing operational feedback data to dynamically correct the signal matching boundary constraints, the deviation between the scheduling plan and the actual operation can be continuously reduced, the adaptability of the scheduling scheme to the actual operation scenario can be improved, the long-term stability and accuracy of low-voltage photovoltaic surplus power scheduling under multi-voltage level grid structure can be guaranteed, and the execution effect of low-voltage photovoltaic surplus power scheduling can be continuously optimized.

[0153] In one of the most specific embodiments, such as Figure 4 As shown, a low-voltage photovoltaic surplus power dispatching method for multi-voltage level grids according to this application includes the following steps:

[0154] Step 1: Obtain carbon price signal data and low-voltage electricity price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels.

[0155] Step 2: Perform time-series alignment processing on the carbon price signal data and the electricity price signal data to obtain the carbon price signal time series and the electricity price signal time series.

[0156] Step 3: Perform price fluctuation feature clustering on the carbon price signal time series and the electricity price signal time series to obtain multiple price fluctuation clusters. Based on the cluster centers of each price fluctuation cluster, filter out mismatch clusters from the multiple price fluctuation clusters.

[0157] Step 4: Based on the number of mismatch clusters and price fluctuation clusters, calculate the mismatch ratio for each time period. Select the time period with a mismatch ratio greater than the preset scenario activation threshold as the target time period. Perform scenario segmentation on each target time period to obtain multiple scenario time periods.

[0158] Step 5: Based on the start and end times of the scenario time period, locate the target carbon price signal time series data segment and the target electricity price signal time series data segment in the carbon price signal time series and the electricity price signal time series.

[0159] Step 6: Perform feature comparison on the time series data segments of the target carbon price signal and the target electricity price signal, evaluate the signal matching degree of the carbon price signal and the electricity price signal in the scenario time period, and construct signal matching boundary constraints based on the signal matching degree.

[0160] Step 7: Based on the signal matching boundary constraints, select the target tie line from among the multiple tie lines in the multi-voltage level network and determine the scheduling timing adjustment period of the target tie line.

[0161] Power flow direction monitoring data of multiple tie lines in a multi-voltage level grid is acquired. The power flow direction monitoring data includes the power transmission direction and power transmission amplitude of each tie line. Tie lines whose power transmission amplitude meets the signal matching boundary constraints are selected as intermediate tie lines. Based on the surplus power of low-voltage photovoltaic power, target tie lines are selected from the intermediate tie lines through the inter-voltage level coordination mechanism and power priority allocation rules, and the scheduling timing adjustment cycle of the target tie lines is determined.

[0162] Step 8: Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity.

[0163] Step 9: Based on the responsibility ratio of the settlement entity, the genetic algorithm is used to optimize the power transmission amplitude of the target tie line corresponding to the settlement entity with the fitness meeting the preset coordination conditions as the optimization objective, and the target power transmission amplitude of the target tie line is obtained. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0164] Step 10: Obtain the operational feedback data generated during the execution of power scheduling instructions.

[0165] Step 11: Based on the operational feedback data, calculate the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie line in multiple time periods.

[0166] Step 12: Determine if the deviation value is greater than a preset deviation threshold. If the deviation value is greater than the preset deviation threshold, modify the signal matching boundary constraint to obtain the target signal matching boundary constraint. Replace the target signal matching boundary constraint with the signal matching boundary constraint, and return to step 7 to continue execution until the deviation value is less than the preset deviation threshold.

[0167] If the deviation value exceeds a preset deviation threshold, the mismatch ratio is adjusted based on the deviation value to correct the signal matching boundary constraints, thus obtaining the target signal matching boundary constraints. Further, when the deviation value is positive, it indicates that the actual power transmission amplitude is higher than the target power transmission amplitude; in this case, the mismatch ratio for the corresponding time period is increased. When the deviation value is negative, it indicates that the actual power transmission amplitude is lower than the target power transmission amplitude; in this case, the mismatch ratio for the corresponding time period is decreased. Based on the adjusted mismatch ratio, the process returns to step 4 to continue correcting the signal matching boundary constraints, thus obtaining the target signal matching boundary constraints.

[0168] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0169] Based on the same inventive concept, this application also provides a low-voltage photovoltaic surplus power dispatching device for implementing the aforementioned low-voltage photovoltaic surplus power dispatching method for multi-voltage grid structures. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the low-voltage photovoltaic surplus power dispatching device for multi-voltage grid structures provided below can be found in the limitations of the low-voltage photovoltaic surplus power dispatching method for multi-voltage grid structures described above, and will not be repeated here.

[0170] In one exemplary embodiment, such as Figure 5As shown, a low-voltage photovoltaic surplus power dispatching device 500 with a multi-voltage level grid structure is provided, including: an acquisition module 502, a construction module 504, a selection module 506, an allocation module 508, an optimization module 510, and a generation module 512, wherein:

[0171] The acquisition module 502 is used to acquire carbon price signal data and low-voltage electricity price signal data from the upper-level power grid in a low-voltage power grid with a multi-voltage level grid structure.

[0172] Module 504 is used to construct signal matching boundary constraints for carbon price signals and electricity price signals based on carbon price signal data and electricity price signal data.

[0173] The selection module 506 is used to select a target tie line from multiple tie lines in a multi-voltage level network according to the signal matching boundary constraints, and to determine the scheduling timing adjustment period of the target tie line.

[0174] The allocation module 508 is used to calculate the response delay of the settlement entity corresponding to the target connection line, and to allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity.

[0175] The optimization module 510 is used to optimize the power transmission amplitude of the target tie line corresponding to the settlement entity based on the responsibility ratio of the settlement entity and with the fitness meeting the preset coordination conditions as the optimization objective. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0176] The generation module 512 is used to generate power dispatch instructions based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude. The power dispatch instructions are used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0177] In one embodiment, the construction module 504 is further configured to perform time-series alignment processing on carbon price signal data and electricity price signal data to obtain carbon price signal time-series sequences and electricity price signal time-series sequences; perform price fluctuation feature clustering processing on the carbon price signal time-series sequences and electricity price signal time-series sequences to obtain multiple price fluctuation clusters; based on the cluster centers of each price fluctuation cluster, filter out mismatch clusters from the multiple price fluctuation clusters; calculate the mismatch ratio of each time period based on the number of mismatch clusters and price fluctuation clusters; take the time period with the mismatch ratio greater than a preset scenario activation threshold as the target time period; perform scenario division processing on each target time period to obtain multiple scenario time periods; for each scenario time period, locate the target carbon price signal time-series data segment and the target electricity price signal time-series data segment in the carbon price signal time-series sequence and the electricity price signal time-series sequence based on the start and end times of the scenario time period; perform feature comparison on the target carbon price signal time-series data segment and the target electricity price signal time-series data segment to evaluate the signal matching degree of the carbon price signal and the electricity price signal in the scenario time period; and construct signal matching boundary constraints based on the signal matching degree.

[0178] In one embodiment, the selection module 506 is further configured to acquire power flow direction monitoring data of multiple interconnecting lines in a multi-voltage level grid, the power flow direction monitoring data including the power transmission direction and power transmission amplitude of each interconnecting line; select interconnecting lines whose power transmission amplitude satisfies the signal matching boundary constraint conditions as intermediate interconnecting lines; based on the surplus power of low-voltage photovoltaic power, select target interconnecting lines from the intermediate interconnecting lines through the inter-voltage level coordination mechanism and power priority allocation rules, and determine the scheduling timing adjustment cycle of the target interconnecting lines.

[0179] In one embodiment, the allocation module 508 is further configured to allocate an initial responsibility ratio to each settlement entity based on the response delay; for each settlement entity, determine whether the response delay of the settlement entity is greater than the delay tolerance threshold; if the response delay of the settlement entity is greater than the delay tolerance threshold, the settlement entity is designated as the delay-exceeding entity, and the initial responsibility ratio of the delay-exceeding entity is allocated to other settlement entities based on the response delay of other settlement entities, wherein the other settlement entities are all settlement entities other than the delay-exceeding entity.

[0180] In one embodiment, the optimization module 510 is further configured to assign weights to the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line; based on the weights, perform a weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index to obtain the fitness; determine whether the fitness meets the preset coordination conditions; if the fitness does not meet the preset coordination conditions, then sequentially perform selection operation, crossover operation and mutation operation on the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain an intermediate power transmission amplitude, replace the intermediate power transmission amplitude with the power transmission amplitude, and return to continue execution according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line until the fitness meets the preset coordination conditions; and use the power transmission amplitude corresponding to the fitness meeting the preset coordination conditions as the target power transmission amplitude.

[0181] In one embodiment, the low-voltage photovoltaic surplus power dispatching device 500 of the multi-voltage level grid further includes a correction module. The correction module is used to acquire the operation feedback data generated during the execution of power dispatching instructions. The operation feedback data includes the actual power transmission amplitude and actual power transmission time of each target tie line. Based on the operation feedback data, the module calculates the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie line in multiple time periods. The module determines whether the deviation value is greater than a preset deviation threshold. If the deviation value is greater than the preset deviation threshold, the module corrects the signal matching boundary constraint condition to obtain the target signal matching boundary constraint condition. The module replaces the target signal matching boundary constraint condition with the signal matching boundary constraint condition and returns to the step based on the signal matching boundary constraint condition to continue execution until the deviation value is less than the preset deviation threshold.

[0182] Each module in the aforementioned low-voltage photovoltaic surplus power dispatching device 500 with a multi-voltage grid structure can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0183] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores carbon price signal data and electricity price signal data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a low-voltage photovoltaic surplus power dispatch method for a multi-voltage level grid.

[0184] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0185] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0186] Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels;

[0187] Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signals and electricity price signals;

[0188] Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in a multi-voltage level network, and the scheduling timing adjustment period of the target tie line is determined.

[0189] Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity;

[0190] Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0191] Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0192] In one embodiment, when the processor executes the computer program, it further performs the following steps: performing time-series alignment processing on carbon price signal data and electricity price signal data to obtain carbon price signal time-series sequences and electricity price signal time-series sequences; performing price fluctuation feature clustering processing on the carbon price signal time-series sequences and electricity price signal time-series sequences to obtain multiple price fluctuation clusters; based on the cluster centers of each price fluctuation cluster, filtering out mismatch clusters from the multiple price fluctuation clusters; calculating the mismatch ratio of each time period based on the number of mismatch clusters and price fluctuation clusters; taking the time period with a mismatch ratio greater than a preset scenario activation threshold as the target time period; performing scenario division processing on each target time period to obtain multiple scenario time periods; for each scenario time period, locating the target carbon price signal time-series data segment and the target electricity price signal time-series data segment in the carbon price signal time-series sequence and the target electricity price signal time-series sequence based on the start and end times of the scenario time period; performing feature comparison on the target carbon price signal time-series data segment and the target electricity price signal time-series data segment; evaluating the signal matching degree of the carbon price signal and the electricity price signal in the scenario time period; and constructing signal matching boundary constraints based on the signal matching degree.

[0193] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring power flow direction monitoring data of multiple tie lines in a multi-voltage level grid, the power flow direction monitoring data including the power transmission direction and power transmission amplitude of each tie line; designating tie lines whose power transmission amplitude satisfies the signal matching boundary constraint as intermediate tie lines; and, based on the surplus power of low-voltage photovoltaic power, selecting target tie lines from the intermediate tie lines through the inter-voltage level coordination mechanism and power priority allocation rules, and determining the scheduling timing adjustment period of the target tie lines.

[0194] In one embodiment, when the processor executes the computer program, it further implements the following steps: assigning an initial responsibility ratio to each settlement entity based on the response latency; for each settlement entity, determining whether the response latency of the settlement entity is greater than the latency tolerance threshold; if the response latency of the settlement entity is greater than the latency tolerance threshold, designating the settlement entity as a latency-overdue entity, and allocating the initial responsibility ratio of the latency-overdue entity to other settlement entities based on the response latency of other settlement entities, wherein the other settlement entities are all settlement entities other than the latency-overdue entity.

[0195] In one embodiment, when the processor executes the computer program, it further performs the following steps: assigning weights to the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line; based on the weights, performing a weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index to obtain the fitness; determining whether the fitness meets the preset coordination conditions; if the fitness does not meet the preset coordination conditions, sequentially performing selection, crossover, and mutation operations on the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain an intermediate power transmission amplitude; replacing the intermediate power transmission amplitude with the power transmission amplitude; and returning to continue execution according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line until the fitness meets the preset coordination conditions; and using the power transmission amplitude corresponding to the fitness meeting the preset coordination conditions as the target power transmission amplitude.

[0196] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring operational feedback data generated during the execution of power scheduling instructions, the operational feedback data including the actual power transmission amplitude and actual power transmission time of each target tie-line; based on the operational feedback data, calculating the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie-line in multiple time periods; determining whether the deviation value is greater than a preset deviation threshold; if the deviation value is greater than the preset deviation threshold, correcting the signal matching boundary constraint condition to obtain the target signal matching boundary constraint condition; replacing the target signal matching boundary constraint condition with the signal matching boundary constraint condition; and returning to the step based on the signal matching boundary constraint condition to continue execution until the deviation value is less than the preset deviation threshold.

[0197] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0198] Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels;

[0199] Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signals and electricity price signals;

[0200] Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in a multi-voltage level network, and the scheduling timing adjustment period of the target tie line is determined.

[0201] Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity;

[0202] Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0203] Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0204] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing time-series alignment processing on carbon price signal data and electricity price signal data to obtain carbon price signal time-series sequences and electricity price signal time-series sequences; performing price fluctuation feature clustering processing on the carbon price signal time-series sequences and electricity price signal time-series sequences to obtain multiple price fluctuation clusters; based on the cluster centers of each price fluctuation cluster, filtering out mismatch clusters from the multiple price fluctuation clusters; calculating the mismatch ratio of each time period based on the number of mismatch clusters and price fluctuation clusters; taking the time period with a mismatch ratio greater than a preset scenario activation threshold as the target time period; performing scenario division processing on each target time period to obtain multiple scenario time periods; for each scenario time period, locating the target carbon price signal time-series data segment and the target electricity price signal time-series data segment in the carbon price signal time-series sequence and the target electricity price signal time-series sequence based on the start and end times of the scenario time period; performing feature comparison on the target carbon price signal time-series data segment and the target electricity price signal time-series data segment; evaluating the signal matching degree of the carbon price signal and the electricity price signal in the scenario time period; and constructing signal matching boundary constraints based on the signal matching degree.

[0205] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring power flow direction monitoring data of multiple tie lines in a multi-voltage level grid, the power flow direction monitoring data including the power transmission direction and power transmission amplitude of each tie line; designating tie lines whose power transmission amplitude satisfies the signal matching boundary constraint as intermediate tie lines; and, based on the surplus power of low-voltage photovoltaic power, selecting target tie lines from the intermediate tie lines through a voltage level coordination mechanism and power priority allocation rules, and determining the scheduling timing adjustment period of the target tie lines.

[0206] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: assigning an initial responsibility ratio to each settlement entity based on the response latency; for each settlement entity, determining whether the response latency of the settlement entity is greater than the latency tolerance threshold; if the response latency of the settlement entity is greater than the latency tolerance threshold, designating the settlement entity as a latency-overdue entity, and allocating the initial responsibility ratio of the latency-overdue entity to other settlement entities based on the response latency of other settlement entities, wherein the other settlement entities are all settlement entities other than the latency-overdue entity.

[0207] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: assigning weights to the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line; based on the weights, performing a weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index to obtain the fitness; determining whether the fitness meets the preset coordination conditions; if the fitness does not meet the preset coordination conditions, sequentially performing selection, crossover, and mutation operations on the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain an intermediate power transmission amplitude; replacing the intermediate power transmission amplitude with the power transmission amplitude; and returning to continue execution according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line until the fitness meets the preset coordination conditions; and using the power transmission amplitude corresponding to the fitness meeting the preset coordination conditions as the target power transmission amplitude.

[0208] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring operational feedback data generated during the execution of power scheduling instructions, the operational feedback data including the actual power transmission amplitude and actual power transmission time of each target tie line; based on the operational feedback data, calculating the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie line in multiple time periods; determining whether the deviation value is greater than a preset deviation threshold; if the deviation value is greater than the preset deviation threshold, correcting the signal matching boundary constraint condition to obtain the target signal matching boundary constraint condition; replacing the target signal matching boundary constraint condition with the signal matching boundary constraint condition; and returning to the step based on the signal matching boundary constraint condition to continue execution until the deviation value is less than the preset deviation threshold.

[0209] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0210] Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels;

[0211] Based on carbon price signal data and electricity price signal data, construct signal matching boundary constraints for carbon price signals and electricity price signals;

[0212] Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in a multi-voltage level network, and the scheduling timing adjustment period of the target tie line is determined.

[0213] Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate the responsibility ratio to the settlement entity based on the response delay of the settlement entity;

[0214] Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index.

[0215] Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power dispatch instruction is generated. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

[0216] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing time-series alignment processing on carbon price signal data and electricity price signal data to obtain carbon price signal time-series sequences and electricity price signal time-series sequences; performing price fluctuation feature clustering processing on the carbon price signal time-series sequences and electricity price signal time-series sequences to obtain multiple price fluctuation clusters; based on the cluster centers of each price fluctuation cluster, filtering out mismatch clusters from the multiple price fluctuation clusters; calculating the mismatch ratio of each time period based on the number of mismatch clusters and price fluctuation clusters; taking the time period with a mismatch ratio greater than a preset scenario activation threshold as the target time period; performing scenario division processing on each target time period to obtain multiple scenario time periods; for each scenario time period, locating the target carbon price signal time-series data segment and the target electricity price signal time-series data segment in the carbon price signal time-series sequence and the target electricity price signal time-series sequence based on the start and end times of the scenario time period; performing feature comparison on the target carbon price signal time-series data segment and the target electricity price signal time-series data segment; evaluating the signal matching degree of the carbon price signal and the electricity price signal in the scenario time period; and constructing signal matching boundary constraints based on the signal matching degree.

[0217] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring power flow direction monitoring data of multiple tie lines in a multi-voltage level grid, the power flow direction monitoring data including the power transmission direction and power transmission amplitude of each tie line; designating tie lines whose power transmission amplitude satisfies the signal matching boundary constraint as intermediate tie lines; and, based on the surplus power of low-voltage photovoltaic power, selecting target tie lines from the intermediate tie lines through a voltage level coordination mechanism and power priority allocation rules, and determining the scheduling timing adjustment period of the target tie lines.

[0218] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: assigning an initial responsibility ratio to each settlement entity based on the response latency; for each settlement entity, determining whether the response latency of the settlement entity is greater than the latency tolerance threshold; if the response latency of the settlement entity is greater than the latency tolerance threshold, designating the settlement entity as a latency-overdue entity, and allocating the initial responsibility ratio of the latency-overdue entity to other settlement entities based on the response latency of other settlement entities, wherein the other settlement entities are all settlement entities other than the latency-overdue entity.

[0219] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: assigning weights to the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line; based on the weights, performing a weighted summation of the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index to obtain the fitness; determining whether the fitness meets the preset coordination conditions; if the fitness does not meet the preset coordination conditions, sequentially performing selection, crossover, and mutation operations on the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain an intermediate power transmission amplitude; replacing the intermediate power transmission amplitude with the power transmission amplitude; and returning to continue execution according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line until the fitness meets the preset coordination conditions; and using the power transmission amplitude corresponding to the fitness meeting the preset coordination conditions as the target power transmission amplitude.

[0220] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring operational feedback data generated during the execution of power scheduling instructions, the operational feedback data including the actual power transmission amplitude and actual power transmission time of each target tie line; based on the operational feedback data, calculating the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie line in multiple time periods; determining whether the deviation value is greater than a preset deviation threshold; if the deviation value is greater than the preset deviation threshold, correcting the signal matching boundary constraint condition to obtain the target signal matching boundary constraint condition; replacing the target signal matching boundary constraint condition with the signal matching boundary constraint condition; and returning to the step based on the signal matching boundary constraint condition to continue execution until the deviation value is less than the preset deviation threshold.

[0221] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0222] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0223] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0224] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for dispatching low-voltage photovoltaic surplus power in a multi-voltage-level grid structure, characterized in that, The method includes: Acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with multiple voltage levels; Based on the carbon price signal data and the electricity price signal data, construct signal matching boundary constraints for the carbon price signal and the electricity price signal; Based on the signal matching boundary constraints, a target tie line is selected from multiple tie lines in the multi-voltage level network structure, and the scheduling timing adjustment period of the target tie line is determined. Calculate the response delay of the settlement entity corresponding to the target connection line, and allocate a responsibility ratio to the settlement entity based on the response delay of the settlement entity; Based on the responsibility ratio of the settlement entity, with the fitness meeting the preset coordination conditions as the optimization objective, the power transmission amplitude of the target tie line corresponding to the settlement entity is optimized to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of carbon reduction target evaluation indicators and life cycle economic benefit evaluation indicators. Based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude, a power scheduling instruction is generated. The power scheduling instruction is used to schedule the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage grid structure, excluding the low-voltage grid, through the target tie line.

2. The method according to claim 1, characterized in that, The step of constructing signal matching boundary constraints for carbon price signals and electricity price signals based on the carbon price signal data and the electricity price signal data includes: The carbon price signal data and the electricity price signal data are time-series aligned to obtain a carbon price signal time series and an electricity price signal time series. The carbon price signal time series and the electricity price signal time series are subjected to price fluctuation feature clustering processing to obtain multiple price fluctuation clusters. Based on the cluster centers of each price fluctuation cluster, mismatch clusters are screened out from the multiple price fluctuation clusters. Based on the number of mismatch clusters and price fluctuation clusters, the mismatch ratio of each time period is calculated. The time period in which the mismatch ratio is greater than the preset scenario activation threshold is taken as the target time period. The target time period is divided into scenarios to obtain multiple scenario time periods. For each scenario time period, based on the start and end times of the scenario time period, the target carbon price signal time series data segment and the target electricity price signal time series data segment are located in the carbon price signal time series and the electricity price signal time series. The time series data segments of the target carbon price signal and the target electricity price signal are compared by feature comparison. The signal matching degree of the carbon price signal and the electricity price signal in the scenario time period is evaluated. Based on the signal matching degree, signal matching boundary constraints are constructed.

3. The method according to claim 1, characterized in that, The step of selecting a target tie line from multiple tie lines in the multi-voltage level network structure according to the signal matching boundary constraints, and determining the scheduling timing adjustment period of the target tie line, includes: Acquire power flow direction monitoring data of multiple tie lines of the multi-voltage level network structure, wherein the power flow direction monitoring data includes the power transmission direction and power transmission amplitude of each tie line; The tie lines whose power transmission amplitude satisfies the signal matching boundary constraint are used as intermediate tie lines. Based on the surplus power of low-voltage photovoltaic power, a target tie line is selected from the intermediate tie lines through a coordination mechanism between voltage levels and a power priority allocation rule, and the scheduling timing adjustment cycle of the target tie line is determined.

4. The method according to claim 1, characterized in that, The number of settlement entities is multiple; the allocation of responsibility ratios to settlement entities based on their response latency includes: Based on the aforementioned response delay, an initial liability ratio is assigned to each settlement entity; For each settlement entity, determine whether the response delay of the settlement entity is greater than the delay tolerance threshold; If the response delay of the settlement entity exceeds the allowable delay threshold, the settlement entity is designated as the delay-overdue entity. Based on the response delays of other settlement entities, the initial responsibility ratio of the delay-overdue entity is allocated to the other settlement entities, which are all settlement entities other than the delay-overdue entity.

5. The method according to claim 1, characterized in that, The process involves optimizing the power transmission amplitude of the target tie line corresponding to the settlement entity based on the responsibility ratio of the settlement entity and with the fitness meeting preset coordination conditions as the optimization objective. The fitness is obtained by weighted summation of carbon reduction target evaluation indicators and life-cycle economic benefit evaluation indicators, including: Based on the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line, weights are assigned to the carbon reduction target evaluation index and the life cycle economic benefit evaluation index. Based on the weights, the carbon reduction target evaluation index and the full life cycle economic benefit evaluation index are weighted and summed to obtain the fitness. Determine whether the fitness meets the preset coordination conditions. If the fitness does not meet the preset coordination conditions, then perform selection, crossover and mutation operations sequentially on the power transmission amplitude of the target tie line corresponding to the settlement entity to obtain an intermediate power transmission amplitude. Replace the intermediate power transmission amplitude with the power transmission amplitude and return to continue execution according to the responsibility ratio of the settlement entity and the power transmission amplitude of the target tie line until the fitness meets the preset coordination conditions. The power transmission amplitude corresponding to the fitness condition meeting the preset coordination condition is taken as the target power transmission amplitude.

6. The method according to claim 1, characterized in that, The method further includes: Acquire operational feedback data generated during the execution of the power scheduling command, the operational feedback data including the actual power transmission amplitude and actual power transmission time of each target tie line; Based on the operational feedback data, the deviation between the actual power transmission amplitude and the target power transmission amplitude of each target tie line in multiple time periods is calculated; Determine whether the deviation value is greater than a preset deviation threshold. If the deviation value is greater than the preset deviation threshold, modify the signal matching boundary constraint to obtain the target signal matching boundary constraint. Replace the target signal matching boundary constraint with the signal matching boundary constraint and return to the step based on the signal matching boundary constraint to continue execution until the deviation value is less than the preset deviation threshold.

7. A low-voltage photovoltaic surplus power dispatching device with a multi-voltage level grid structure, characterized in that, The device includes: The acquisition module is used to acquire carbon price signal data and low-voltage volt price signal data from the upper-level power grid in a low-voltage power grid with a multi-voltage level grid structure. The construction module is used to construct signal matching boundary constraints for the carbon price signal and the electricity price signal based on the carbon price signal data and the electricity price signal data; The selection module is used to select a target tie line from multiple tie lines in the multi-voltage level network according to the signal matching boundary constraints, and to determine the scheduling timing adjustment period of the target tie line. The allocation module is used to calculate the response delay of the settlement entity corresponding to the target connection line, and allocate a responsibility ratio to the settlement entity based on the response delay of the settlement entity; The optimization module is used to optimize the power transmission amplitude of the target tie line corresponding to the settlement entity based on the responsibility ratio of the settlement entity and with the fitness meeting the preset coordination conditions as the optimization objective, so as to obtain the target power transmission amplitude of the target tie line. The fitness is obtained by weighted summation of carbon reduction target evaluation indicators and life cycle economic benefit evaluation indicators. The generation module is used to generate a power dispatch instruction based on the scheduling timing adjustment period of the target tie line and the target power transmission amplitude. The power dispatch instruction is used to dispatch the surplus low-voltage photovoltaic power to other voltage level grids in the multi-voltage level grid, excluding the low-voltage grid, through the target tie line.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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