Power system optimization control method and device based on power market transactions

Abstract

The present application provides a power system optimization control method and device based on power market transactions. Among them, real-time electricity price fluctuation data, load demand forecast data and dynamic topology connection data between grid nodes are obtained; based on the above data, a simplified network structure adapted to the transaction scenario is generated, and the equivalent impedance parameters of the key transmission path are adjusted in real time; the node voltage amplitude and phase angle are coordinated and regulated by the multi-level regulation mechanism of the solid-state transformer to form a power distribution scheme; the edge computing node is used to monitor the solid-state transformer operation data in real time, and local control instructions are generated in combination with the current equivalent impedance parameters; the local instructions are matched with the electricity price fluctuation data in time and space, and a joint control strategy is generated through collaborative priority sorting to dynamically adjust the power distribution scheme. The technical solution provided by the present application realizes the coordinated optimization of the economic operation and physical transmission performance of the power system.

Description

Technical Field

[0001] The present application relates to the technical field of power system optimization control, and in particular to a power system optimization control method and device based on power market transactions. Background Art

[0002] With the large-scale integration of renewable energy and the frequent switching of grid equipment, power system topologies are experiencing highly dynamic changes (e.g., temporary disconnection of line faults and rapid startup and shutdown of energy storage devices). This makes traditional power control methods based on fixed topology models difficult to adapt to real-time operational demands. In power market trading environments, two objectives must be achieved in scenarios with frequent topological changes: first, rapid detection of network structure changes and adaptive adjustment of power distribution to ensure power supply stability; second, dynamic correlation of market trading price signals with power regulation actions to simultaneously optimize system economics.

[0003] The current mainstream solution utilizes a dynamic optimization control method based on real-time data acquisition and prediction models. A centralized controller deployed at the master station periodically collects grid topology status and market transaction data, establishes a short-term power flow prediction model, and generates generator output and energy storage adjustment instructions based on the prediction results. This method calculates power adjustments for each node by online updating of the topology parameter library and incorporating market electricity price fluctuations, partially adapting to the economic optimization needs of topology-changing scenarios.

[0004] Existing solutions have the following defects: the centralized controller relies on periodic topology data collection and model reconstruction, and it is difficult to update the global model in time at the moment of equipment switching or failure, resulting in a mismatch between power regulation instructions and the actual topology state; there is no coupling mechanism between electricity price fluctuations and dynamic topology changes, and power is only allocated based on static electricity price weights, which makes it impossible to dynamically adjust the economic optimization target when the topology suddenly changes; centralized optimization needs to process data from all nodes in the entire network, and the calculation delay is significant in scenarios with high-frequency topology changes, making it difficult to meet the needs of second-level power adaptive regulation. Summary of the Invention

[0005] The present application provides a power system optimization control method and device based on power market transactions to solve the problems of insufficient power system operation economy and physical transmission performance in the prior art.

[0006] In a first aspect, the present application provides a power system optimization control method based on power market transactions, comprising:

[0007] Acquiring power market transaction data, including power price fluctuation data and load demand forecast data, and simultaneously collecting topological connection relationship data between multiple grid nodes in the power grid;

[0008] generating a simplified network structure adapted to the power trading scenario based on the power market transaction data and the topological connection relationship data, and adjusting equivalent impedance parameters of key transmission paths in the simplified network structure;

[0009] Based on the adjusted equivalent impedance parameters, the voltage amplitude and phase angle of the grid nodes are coordinated and adjusted through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution plan;

[0010] During the execution of the power allocation scheme, the operating status data of the solid-state transformer is monitored by an edge computing node deployed at the grid node, and a local control instruction is generated in combination with the adjusted equivalent impedance parameter;

[0011] The local control instructions are matched with the electricity price fluctuation data in time and space, and the local control instructions of multiple power grid nodes are collaboratively prioritized according to the matching results. A joint control strategy is generated and the power allocation scheme is dynamically adjusted to achieve dynamic optimization control of transmission loss and transaction costs.

[0012] Optionally, generating a simplified network structure adapted to a power trading scenario based on the power market transaction data and the topological connection relationship data, and adjusting equivalent impedance parameters of key transmission paths in the simplified network structure, includes:

[0013] Associating electricity price fluctuation data in the electricity market transaction data with node connection patterns in the topological connection relationship data and extracting correlation parameters, the correlation parameters including the electricity price fluctuation amplitude and the topological connection density, and determining electricity transaction sensitive paths based on the electricity price fluctuation amplitude and the topological connection density;

[0014] Extracting a topology substructure comprising at least two core nodes and three or more associated branches from the original power grid topology based on the distribution characteristics of the power transaction sensitive paths, wherein the initial impedance parameter of each branch in the topology substructure is allocated based on the historical transaction load ratio of the corresponding node;

[0015] Based on the topological substructure, redundant connection branches with the same power transmission direction as that between core nodes are merged to generate a simplified network structure that retains the sensitive path of power trading. The equivalent impedance parameters of the key transmission paths in the simplified network structure are directionally weighted and modified according to the electricity price fluctuation data.

[0016] During the operation of the simplified network structure, the load demand forecast data in the power market transaction data is continuously received, and the equivalent impedance parameters are reversely compensated and adjusted according to the distribution ratio of the predicted load on the key transmission path, so that the equivalent impedance parameters are kept matched with the power flow direction in the transaction scenario.

[0017] Optionally, based on the adjusted equivalent impedance parameters, the voltage amplitude and phase angle of the grid node are coordinated adjusted by a multi-stage current conversion regulation mechanism of a solid-state transformer to form a power distribution scheme, including:

[0018] Converting the adjusted equivalent impedance parameters into voltage regulation parameters including the voltage amplitude deviation range and phase angle difference range allowed for each grid node, wherein the conversion process is distributed based on the transmission capacity ratio of the critical transmission path and the phase difference ratio between adjacent nodes;

[0019] generating a multi-level regulation mode according to the voltage regulation parameter and the electricity price fluctuation data, wherein the multi-level regulation mode includes at least three regulation levels, and a trigger condition of each regulation level is determined based on an electricity price fluctuation amplitude threshold and a fluctuation direction, wherein the fluctuation direction includes a peak electricity price period and a valley electricity price period;

[0020] In the multi-level regulation mode, based on the real-time voltage amplitude and phase angle measurement data of the grid node, the deviation range in the voltage regulation parameter is matched. When the measurement data exceeds the deviation range, the switching of the adjacent regulation level is triggered. During the switching process, the phase angle difference range is synchronously adjusted to maintain the consistency of the power transmission direction;

[0021] According to the voltage amplitude and phase angle constraints corresponding to the switched regulation level, a power allocation scheme including power allocation priority and allocation ratio of each node is generated, wherein the allocation priority is positively correlated with the urgency of the load demand forecast data.

[0022] Optionally, during the execution of the power allocation scheme, the operating status data of the solid-state transformer is monitored by an edge computing node deployed at the grid node, and a local control instruction is generated in combination with the adjusted equivalent impedance parameter, including:

[0023] The edge computing unit collects operating status data of the solid-state transformer in real time, wherein the operating status data includes an instantaneous fluctuation value of the output voltage, an instantaneous value of the current phase difference between adjacent nodes, and a temperature change rate of a power module inside the solid-state transformer;

[0024] Associating the instantaneous fluctuation value of the output terminal voltage in the operating status data with the adjusted equivalent impedance parameter, and marking it as a first type of abnormal state when the voltage fluctuation direction is opposite to the impedance threshold change direction;

[0025] Extracting, based on the distribution location of the first type of abnormal state, a phase difference constraint range between at least two adjacent nodes associated with the distribution location in the topological connection relationship data, and generating a primary control instruction including a voltage compensation amount and a phase correction direction in combination with an instantaneous value of the current phase difference between the adjacent nodes;

[0026] The voltage compensation amount in the primary control instruction is adjusted based on the temperature change rate. The adjustment method is that when the temperature change rate exceeds a preset safety interval, the voltage compensation amount is proportionally reduced, and the allowable deviation angle of the phase correction direction is simultaneously expanded to generate a local control instruction.

[0027] Optionally, based on the topological substructure, redundant connection branches having the same power transmission direction as that between core nodes are merged to generate a simplified network structure retaining the sensitive path of power trading, and equivalent impedance parameters of key transmission paths in the simplified network structure are directionally weighted modified according to the electricity price fluctuation data, including:

[0028] Identify all redundant branches connected to the core node in the topological substructure, where the redundant branches are determined to be two or more branches connected in parallel in the same power transmission direction, and the difference in historical transaction loads between the branches is less than a set ratio threshold;

[0029] Merge the redundant branches, retain the branch with the highest historical transaction load as the key transmission path, and add the transmission capacity of the remaining branches to the key transmission path;

[0030] According to the real-time electricity price fluctuation data, the power transmission direction corresponding to the peak electricity price period is marked as the forward sensitive direction, and the opposite direction corresponding to the low electricity price period is marked as the reverse sensitive direction;

[0031] Based on different sensitive directions, the equivalent impedance parameters of the key transmission path are differentially weighted adjusted to generate a simplified network structure that retains the sensitive path of power trading, wherein the path impedance value in the forward sensitive direction is lower than that in the reverse sensitive direction, and the impedance value is adjusted with the fluctuation amplitude of the real-time electricity price.

[0032] Optionally, the extracting, based on the distribution location of the first type of abnormal state, a phase difference constraint range between at least two adjacent nodes associated with the distribution location in the topological connection relationship data, and generating a primary control instruction including a voltage compensation amount and a phase correction direction in combination with the instantaneous value of the current phase difference between the adjacent nodes includes:

[0033] Locating an abnormal area in the topological connection relationship data according to the distribution position of the first type of abnormal state, where the abnormal area includes at least two adjacent nodes and their connection paths;

[0034] Extracting a phase difference constraint range between adjacent nodes in the abnormal area, where the phase difference constraint range is determined based on an equivalent impedance parameter of a corresponding path in the simplified network structure and historical operation data;

[0035] Comparing the instantaneous value of the current phase difference between the adjacent nodes with the phase difference constraint range, and when the instantaneous value of the current phase difference between the adjacent nodes exceeds the phase difference constraint range, calculating the phase deviation amount and the deviation direction;

[0036] Based on the phase deviation and the deviation direction, a primary control instruction including a voltage compensation and a phase correction direction is generated. The voltage compensation is proportional to the phase deviation, and the phase correction direction is opposite to the deviation direction.

[0037] Optionally, performing spatiotemporal mapping matching on the local control instructions and the electricity price fluctuation data, collaboratively prioritizing the local control instructions of multiple grid nodes according to the matching results, generating a joint control strategy, and dynamically adjusting the power allocation scheme to achieve dynamic optimization control of transmission loss and transaction costs, includes:

[0038] Extract the local control instruction generation time and corresponding node location of each grid node, match them with the electricity price fluctuation data of the same period by time window and region division, and form a spatiotemporal correlation mapping table as the matching result;

[0039] According to the matching results, the time period and area where the electricity price fluctuation data exceeds the preset threshold are marked as a high-sensitivity time period area, and the local control instructions in the high-sensitivity time period area are automatically upgraded in priority level;

[0040] Cross-verify the local control instructions of each grid node in the same time window, and when it is detected that there is a phase correction direction conflict between the instructions of adjacent nodes, give priority to executing the local control instructions of the highly sensitive time period area;

[0041] A joint control strategy is generated based on the priority execution result, and the power allocation ratio of each node in the power allocation scheme is dynamically adjusted according to the joint control strategy, so that the power transmission margin in the highly sensitive period area is increased and the transmission loss in the non-sensitive area is simultaneously reduced.

[0042] In a second aspect, the present application provides a power system optimization control device based on power market transactions, comprising:

[0043] An acquisition module is used to acquire power market transaction data, including power price fluctuation data and load demand forecast data, and simultaneously collect topological connection relationship data between multiple grid nodes in the power grid;

[0044] an adjustment module, configured to generate a simplified network structure adapted to the power transaction scenario based on the power market transaction data and the topological connection relationship data, and adjust equivalent impedance parameters of key transmission paths in the simplified network structure;

[0045] A regulation module is used to coordinately adjust the voltage amplitude and phase angle of the grid nodes based on the adjusted equivalent impedance parameters through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution plan;

[0046] A monitoring module, configured to monitor the operating status data of the solid-state transformer through an edge computing node deployed at the grid node during the execution of the power allocation scheme, and generate a local control instruction based on the adjusted equivalent impedance parameter;

[0047] A generation module is used to perform spatiotemporal mapping matching between the local control instructions and the electricity price fluctuation data, collaboratively prioritize the local control instructions of multiple power grid nodes according to the matching results, generate a joint control strategy and dynamically adjust the power allocation scheme to achieve dynamic optimization control of transmission losses and transaction costs.

[0048] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the power system optimization control method based on power market transactions as described in the first aspect above.

[0049] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements the power system optimization control method based on power market transactions as described in the first aspect.

[0050] In an embodiment of the present application, power market transaction data is obtained, wherein the power market transaction data includes electricity price fluctuation data and load demand forecast data, and topological connection relationship data between multiple grid nodes in the power grid are simultaneously collected; based on the power market transaction data and the topological connection relationship data, a simplified network structure adapted to the power transaction scenario is generated, and the equivalent impedance parameters of the key transmission paths in the simplified network structure are adjusted; based on the adjusted equivalent impedance parameters, the voltage amplitude and phase angle of the grid node are collaboratively adjusted through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution scheme; during the execution of the power distribution scheme, the operating status data of the solid-state transformer is monitored by an edge computing node deployed at the grid node, and local control instructions are generated in combination with the adjusted equivalent impedance parameters; the local control instructions are matched with the electricity price fluctuation data in time and space, and the local control instructions of multiple grid nodes are collaboratively prioritized according to the matching results to generate a joint control strategy and dynamically adjust the power distribution scheme to achieve dynamic optimization control of transmission loss and transaction cost.

[0051] The technical solution of this application has the following beneficial effects:

[0052] By synchronously collecting electricity price fluctuations, load forecasts and dynamic topology data, we ensure real-time matching between market transaction demand and the physical state of the power grid, providing full-dimensional input for subsequent dynamic optimization; based on transaction scenarios, we generate a dynamically adaptive simplified network structure to reduce the computational complexity under complex topologies, and at the same time, through the adjustment of equivalent impedance parameters, we achieve dynamic adaptation of the transmission characteristics of key paths to market fluctuations; we utilize the multi-level adjustment mechanism of solid-state transformers to quickly respond to changes in impedance parameters, and through the coordinated control of voltage amplitude and phase angle, we ensure that the economic goals of the power distribution scheme and the stability of the power grid are achieved simultaneously; through edge computing nodes, we monitor the equipment status in real time and integrate topology parameters to achieve rapid generation and dynamic correction of local control instructions, and improve the localized response capability to topology changes; based on spatiotemporal mapping matching and multi-instruction collaborative sorting, we resolve the contradiction between decentralized control and global optimization, dynamically balance transmission losses and transaction costs, and achieve system-level economic optimization.

[0053] Furthermore, by dynamically associating electricity price fluctuation data with topological connection patterns, sensitive paths for power transactions are identified. A topological substructure containing core nodes and associated branches is extracted from the original power grid, and initial impedance parameters are assigned based on historical transaction loads. Redundant branches are merged to generate a simplified network structure that retains sensitive paths, and directionally weighted corrections are made to critical path impedances based on real-time electricity price data. Impedance parameters are dynamically compensated based on load forecast data to ensure they match the power flow in the transaction scenario. Through the dynamic identification of transaction-sensitive paths and the simplified network construction, the grid model's adaptability to market fluctuations is significantly improved. A dynamic impedance parameter adjustment mechanism based on price directionality weighting and load forecast compensation enables real-time coordinated optimization of critical path transmission capacity and transaction economic objectives, effectively reducing the risk of control mismatch caused by frequent topological changes.

[0054] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 A flow chart of a power system optimization control method based on power market transactions provided by the present application is shown;

[0057] Figure 2 The schematic diagram of the structure of the power system optimization control device based on power market transaction provided by the present application is shown;

[0058] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0060] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0061] Researchers have found that existing power system optimization and control methods generally suffer from problems such as economic optimization lag and physical transmission characteristic mismatch in scenarios where grid topology frequently changes and power market transactions are dynamically coupled, making it difficult to achieve real-time coordinated regulation of transmission losses and transaction costs. Based on this, a dynamic optimization and control method for power systems based on power market transactions is proposed. Specifically, by integrating real-time electricity price fluctuation data, load forecast data, and dynamic topological connection relationships, a simplified network structure adapted to the transaction scenario is generated and key path transmission parameters are dynamically adjusted. Combined with the multi-level regulation of solid-state transformers and the localized instruction generation of edge computing nodes, an economic-oriented power allocation scheme is formed. Furthermore, through spatiotemporal mapping matching and multi-node instruction collaborative sorting, the global power allocation strategy is dynamically optimized. This method can achieve dynamic balanced control of transmission losses and market transaction costs in scenarios where grid topology changes rapidly, thereby improving the economy and stability of power system operation.

[0062] The technical solution of the present application is applicable to power adaptive regulation scenarios under frequent changes in power grid topology.

[0063] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0064] Figure 1 The present invention provides a flowchart of a power system optimization control method based on power market transactions, such as Figure 1 As shown, the method includes:

[0065] 101. Acquire power market transaction data, including power price fluctuation data and load demand forecast data, and simultaneously collect topological connection relationship data between multiple grid nodes in the power grid;

[0066] In this step, dynamic topology connection data refers to real-time data on the physical connection status between nodes in the power grid (such as line switching and equipment startup and shutdown) over time. This includes node connection patterns, transmission path availability, and real-time power flow information. Electricity price fluctuation data reflects the dynamic trends in electricity prices across different time periods and regions in the power market, including peak and off-peak price periods, and fluctuation characteristics.

[0067] In an embodiment of the present application, a multi-source data acquisition module deployed in a power grid dispatching center periodically collects electricity price fluctuation data (such as time-of-use electricity price curves, regional electricity price difference matrices) released by the power trading platform and future load demand distribution data (such as node-level load forecast values) generated by the load forecasting system. At the same time, a wide area measurement system (WMS) is used to capture the topological connection status between power grid nodes (including line on / off status, equipment operation mode) in real time, and through a spatiotemporal correlation analysis algorithm, the electricity price data and topology data are timestamp aligned and regionally mapped to form a heterogeneous data fusion framework. For example, an event-driven data stream processing technology is used to trigger real-time data updates for topology change events (such as line failures), and a sliding time window mechanism is used to extract dynamic topological features (such as node connection density and power transmission direction distribution). The final integrated topological connection relationship data includes a time-synchronized triple of electricity price, load, and topology, which serves as the input of the subsequent optimization model.

[0068] Suppose a regional power grid experiences a sudden change in topology due to a line fault. Step 101 collects real-time price fluctuation data (e.g., a sudden increase from 0.5 yuan / kWh to 0.8 yuan / kWh), load demand forecast data (e.g., a 20% drop in the predicted load in the faulty area), and dynamic topological connectivity data (e.g., disconnection of the faulty line and access to a backup line). Through spatiotemporal alignment, the system identifies that the fault period overlaps with peak electricity price periods, triggering subsequent optimization processes.

[0069] 102. Generate a simplified network structure adapted to the power transaction scenario based on the power market transaction data and the topological connection relationship data, and adjust equivalent impedance parameters of key transmission paths in the simplified network structure;

[0070] In this step, the network structure is simplified into an abstract network model extracted based on the original power grid topology and retaining key transaction sensitive paths, in order to reduce the optimization calculation complexity.

[0071] In an embodiment of the present application, high-frequency trading nodes (for example, the top 10% of nodes in terms of hourly trading volume) are first screened based on power market trading data, and combined with topological connection relationship data, grid nodes with frequent transactions and direct electrical connections are obtained, and the grid is divided into independent structures based on the grid nodes; each of the divided independent structures is then merged into a single equivalent network structure as a simplified network structure; the line load changes in the simplified network structure are analyzed, the key transmission paths are identified based on the line load changes, and the equivalent impedance parameters of the key transmission paths are reversely calculated based on the actual operating data of the simplified network structure.

[0072] For example, continuing with the above example, step 102 first screens out the top 10% of high-frequency trading nodes (such as load center node A and backup power supply node B) in terms of hourly trading volume, and confirms the existence of direct electrical connections between these nodes in combination with the topological connection relationship data; then, nodes A, B and the four directly connected paths are divided into independent structures and merged into a simplified network structure containing two core nodes and one equivalent path. Analysis of the historical load data of the equivalent path revealed that its peak load accounted for 35% of the entire network, marking it as a critical transmission path; finally, based on the actual operating data of the simplified network structure (such as the real-time transmission current value and voltage loss value after the fault), the equivalent impedance parameter of the critical path is reversely calculated to be 0.12Ω, which is 20% lower than the original value of 0.15Ω, in order to adapt to the power transmission demand during high electricity price periods. 103. Based on the adjusted equivalent impedance parameters, the voltage amplitude and phase angle of the grid nodes are coordinated and adjusted through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution plan;

[0073] In this step, the multi-stage current conversion regulation mechanism is to achieve fine coordinated control of voltage amplitude and phase angle through the series-parallel combination of multi-stage power modules inside the solid-state transformer.

[0074] In an embodiment of the present application, based on the equivalent impedance parameters output in step 102, a solid-state transformer regulation strategy based on model predictive control (MPC) is adopted. First, the equivalent impedance parameters are mapped to voltage regulation boundary conditions (such as the allowable voltage deviation range of ±5%), and the phase difference constraints between adjacent nodes are calculated through a dynamic phase coupling algorithm (such as a phase difference of no more than 10°). Based on real-time voltage measurement data, the solid-state transformer adjusts the output voltage amplitude and phase angle through a multi-stage conversion module (such as an H-bridge cascade structure) to meet the constraints. At the same time, a distributed consistency algorithm is introduced to coordinate the adjustment actions of multiple nodes to ensure the economic goals of the global power allocation plan (such as giving priority to power supply to high-price areas). Finally, a power allocation plan is generated, which includes the power injection priority and allocation ratio of each node.

[0075] For example, continuing with the previous example, step 103 sets the permitted voltage range for nodes in the fault area to 215V-225V based on the adjusted equivalent impedance parameter (0.12Ω), and constrains the phase difference between adjacent nodes to 8°. The solid-state transformer boosts the output voltage from 210V to 220V through cascaded converter modules, and adjusts the phase angle to reduce the phase difference to 6°, forming a power allocation plan that prioritizes power supply to areas with high electricity prices.

[0076] 104. During the execution of the power allocation scheme, monitor the operating status data of the solid-state transformer by an edge computing node deployed at the grid node, and generate a local control instruction in combination with the adjusted equivalent impedance parameter;

[0077] In this step, the local control instruction is a real-time adjustment instruction generated by the edge node for a specific grid node, which includes the voltage compensation amount and phase correction direction.

[0078] In an embodiment of the present application, during the execution of the power distribution scheme, the edge computing node deployed at the grid node collects the operating status data of the solid-state transformer in real time (such as the instantaneous fluctuation value of the output voltage and the temperature change rate of the internal module), and eliminates noise interference through adaptive filtering technology. Combined with the equivalent impedance parameters updated in step 102, an abnormality detection model based on rule reasoning is adopted (such as when the voltage fluctuation direction is opposite to the impedance adjustment direction, it is marked as abnormal) to generate preliminary control instructions. Furthermore, through a dynamic weight allocation algorithm, the equipment safety parameters (such as the temperature change rate) and the electrical parameters (such as the phase difference) are integrated to compensate and correct the preliminary control instructions (such as reducing the voltage compensation amount at high temperatures). Finally, a local control instruction is output, which includes the real-time adjustment amount and direction of each node.

[0079] For example, continuing with the previous example, the edge computing node detected an excessive temperature change rate (0.5°C / s) in the solid-state transformer in the fault area. Combined with the equivalent impedance parameter (0.12Ω), it generated a primary command: voltage compensation +3V, with a counterclockwise phase correction. After safety compensation, the final local control command was adjusted to voltage compensation +2V, with the phase correction angle relaxed to 7°.

[0080] 105. Perform spatiotemporal mapping matching on the local control instructions and the electricity price fluctuation data, perform collaborative priority sorting on the local control instructions of multiple grid nodes according to the matching results, generate a joint control strategy and dynamically adjust the power allocation scheme to achieve dynamic optimization control of transmission loss and transaction cost.

[0081] In this step, the joint control strategy is a global optimization solution generated by fusing local instructions from multiple nodes to balance transmission loss and transaction cost.

[0082] In an embodiment of the present application, a spatiotemporal mapping is first performed between the local control instruction and the electricity price fluctuation data within 1 hour after the instruction is issued (e.g., the instruction issued at 14:00 is associated with the electricity price data from 14:00 to 15:00); secondly, the spatiotemporal mapping matching result is quantified as a matching degree, a priority score is calculated based on the matching degree, and the local control instructions of multiple power grid nodes are prioritized according to the priority score; then, the top 30% high-priority control instructions are selected from the priority sorting results, and conflicts between the high-priority control instructions are processed using a conflict resolution rule base, and then integrated into a joint control strategy; at the same time, the power allocation priority of each node in the power allocation scheme is adjusted according to the priority score through a dynamic resource allocation algorithm, so as to optimize the transmission loss and transaction cost during the implementation of the joint control strategy.

[0083] For example, continuing with the previous example, step 105 first performs spatiotemporal mapping matching on the local control instructions issued at 14:00 in the fault area with the electricity price fluctuation data (an increase of 18%) during the 14:00-15:00 period, and calculates a matching score of 0.92 (out of a maximum score of 1.0). Secondly, a priority score (0.92×0.7+0.3×0.3=0.83) is generated based on the matching score and the load urgency weight, and the instructions in the fault area are ranked as the highest priority. Subsequently, a conflict resolution rule base is used to resolve the phase direction conflict with the instructions in the adjacent non-fault area, retaining the instructions in the fault area and relaxing the phase constraint in the non-fault area to ±8°. Finally, the power allocation plan is adjusted according to the priority score through the dynamic resource allocation algorithm, so that the power share in the fault area is increased from 40% to 52%, and the transmission loss in the non-fault area is reduced by 8%, thereby achieving joint optimization control of transmission loss and transaction cost. Steps 101-105 dynamically integrate electricity market data and the physical state of the power grid to build a simplified network model adapted to the transaction scenario, and achieve rapid response in power distribution based on the collaborative control mechanism of solid-state transformers and edge computing. Furthermore, through spatiotemporal mapping and multi-objective optimization, transmission losses and transaction costs are dynamically balanced in scenarios with frequent topological changes, thereby improving the system's economy and operational stability.

[0084] In order to improve the power grid's dynamic adaptability to power market transactions in scenarios with frequent topological changes and address the mismatch between transmission characteristics and market fluctuations caused by static models in traditional methods, a simplified network model driven by transaction-sensitive paths is constructed by coupling electricity price fluctuation characteristics with the dynamic evolution of topology, and real-time optimization of equivalent impedance parameters is achieved based on a multi-stage dynamic correction mechanism. In some embodiments, generating a simplified network structure adapted to power trading scenarios based on the power market transaction data and the topological connection relationship data, and adjusting the equivalent impedance parameters of key transmission paths in the simplified network structure, includes:

[0085] 201. Correlating electricity price fluctuation data in the electricity market transaction data with node connection patterns in the topological connection relationship data and extracting correlation parameters, wherein the correlation parameters include electricity price fluctuation amplitude and topological connection density, and determining electricity transaction sensitive paths based on the electricity price fluctuation amplitude and topological connection density;

[0086] In step 201, price fluctuation refers to the severity of price changes in the power market over different time periods, including quantitative indicators such as peak volatility and valley recovery rate. Topological connection density describes the strength of physical connections between nodes within a specific area of ​​the power grid, measured by the number of effective transmission paths per unit area or number of nodes.

[0087] In an embodiment of the present application, the correlation between electricity price fluctuation data and topological connection density is analyzed by a mutual information quantification algorithm. First, the time series characteristics of the electricity price fluctuation amplitude (such as fluctuation extreme points, change gradient) and the spatial distribution characteristics of the topological connection density (such as regional path redundancy and node degree centrality) are extracted to construct a spatiotemporal correlation matrix. A dynamic community discovery algorithm is used to identify high-coupling areas between electricity price fluctuations and topological connections in the spatiotemporal correlation matrix (such as high connection density areas corresponding to peak electricity price periods), and sensitive power trading paths are screened out based on the maximum information coefficient (MIC). Specifically, the path sensitivity is scored, and when the score exceeds a dynamic threshold (such as score = electricity price fluctuation amplitude × connection density weight), it is marked as a sensitive power trading path, and finally a sensitive path topology map is generated.

[0088] 202. Extracting a topology substructure comprising at least two core nodes and at least three associated branches from the original power grid topology based on the distribution characteristics of the power transaction sensitive paths, wherein the initial impedance parameter of each branch in the topology substructure is allocated based on the historical transaction load ratio of the corresponding node;

[0089] In step 202, a core node is a grid node that carries the primary power transmission task along a transaction-sensitive path, typically a load center or power access point. The historical transaction load ratio refers to the percentage of the node's load during a historical transaction cycle relative to the total network load.

[0090] In an embodiment of the present application, based on the k-shell decomposition algorithm in complex network analysis, core nodes are identified from the original power grid topology. First, the transaction load influence of each node is calculated (such as load proportion × path betweenness centrality), and the nodes with the top 10% influence are selected as core nodes. Subsequently, the improved Prim algorithm is used to construct a minimum spanning tree to extract the topological substructure containing the core nodes and their associated branches. The initial impedance parameter allocation adopts the load weighting method: according to the sum of the historical transaction load proportions of the nodes connected to each branch (such as node A load proportion 30% + node B proportion 20% → branch AB initial impedance = benchmark impedance × 50%), the benchmark impedance value is dynamically adjusted (such as the benchmark value is determined by the average transmission capacity of the region), and finally the topological substructure and the initial impedance parameters of each branch are generated.

[0091] 203. Based on the topological substructure, redundant connection branches with the same power transmission direction as that between core nodes are merged to generate a simplified network structure that retains the sensitive path of power trading. The equivalent impedance parameters of the key transmission paths in the simplified network structure are directionally weighted and modified according to the electricity price fluctuation data.

[0092] In step 203, redundant connection branches are multiple parallel paths in the same power transmission direction, whose functions can be replaced by a single primary path without affecting transmission capacity. Directional weighted correction is a mechanism for differentially adjusting equivalent impedance parameters based on the direction of electricity price fluctuations (e.g., rising or falling electricity prices).

[0093] In an embodiment of the present application, a dynamic graph pruning technology is used to merge redundant connection branches. First, based on a power transmission direction clustering algorithm (such as an improved DBSCAN), redundant branches in the same direction are identified, and the branch with the largest transmission capacity is retained as the main path. The capacities of the remaining branches are proportionally superimposed on the main path to generate a simplified network structure that retains the sensitive path of the power transaction. Among them, the equivalent impedance parameter correction adopts a nonlinear directional weighted model: when the real-time electricity price fluctuation direction is positive (increasing), a negative correction is applied to the main path impedance (such as impedance value = original value × (1-electricity price increase × weight coefficient) to improve the transmission capacity; when the fluctuation is in the opposite direction, a positive correction is applied (such as impedance value = original value × (1 + electricity price decrease × weight coefficient) to limit the reverse power flow. The weight coefficient is dynamically calculated by the sigmoid function to ensure that the correction amplitude is nonlinearly adapted to the electricity price fluctuation rate.

[0094] 204. During the operation of the simplified network structure, the load demand forecast data in the power market transaction data is continuously received, and the equivalent impedance parameters are reversely compensated and adjusted according to the distribution ratio of the predicted load on the key transmission path, so that the equivalent impedance parameters are kept matched with the power flow direction in the transaction scenario.

[0095] In step 204, reverse compensation adjustment is a secondary adjustment of the corrected equivalent impedance parameters based on the load forecast data to offset the impact of the predicted load distribution deviation. The distribution ratio is the proportion of the predicted load on each key transmission path, reflecting the expected characteristics of future power flow.

[0096] In an embodiment of the present application, a sliding window prediction fusion mechanism is used to implement impedance parameter compensation. After continuously receiving load demand forecast data, the load distribution trend is extracted through a sliding time window (such as the load proportion of path A increases from 40% to 60% in the next hour). Based on the trend analysis results, the compensation amount of the equivalent impedance parameter is calculated: when the predicted load is concentrated on a certain path, a negative compensation is applied to the impedance of the path (such as impedance value = correction value × (1-load growth ratio × compensation coefficient) to match the expected power flow. The compensation coefficient is adjusted through a dynamic feedback mechanism: when the deviation between the actual load distribution and the predicted value exceeds a threshold, the compensation coefficient is adaptively increased (such as the coefficient increases by 0.1 for every 5% increase in the deviation), forming a closed-loop compensation control to keep the equivalent impedance parameter matched with the power flow in the transaction scenario.

[0097] Here's a specific example:

[0098] Assume that a typhoon causes multiple wind turbines in a region to go offline (a scenario with frequent topology changes), and at the same time, the power market experiences drastic intraday price fluctuations (peak prices increase by 120% compared to valley prices). Step 201 identifies the coupling relationship between high electricity price fluctuations (peak 0.9 yuan / kWh) in the typhoon-affected area and a decrease in topological connectivity density (three broken paths). Two paths connecting the remaining wind farm clusters and the load center are marked as transaction-sensitive paths. Step 202 extracts a substructure consisting of the wind farm access point (core node A), the load center (core node B), and four associated branches. Initial impedances (0.18Ω, 0.15Ω, etc.) are assigned to each branch based on historical load proportions (A: 35%, B: 45%). Step 203 merges redundant branches in the same direction to generate a simplified network structure. Based on the real-time electricity price increase (120%), a negative correction is applied to the main path impedance (0.15Ω → 0.11Ω). Step 204, combined with load forecast data (60% load concentration on node B in the next hour), a secondary compensation is applied to the main path impedance (0.11Ω → 0.09Ω), ultimately achieving a precise match between the equivalent impedance parameters and power flow.

[0099] Steps 201-204 generate transaction-sensitive paths by dynamically correlating electricity price fluctuations with topology evolution characteristics, and combine this with a multi-stage impedance correction mechanism (initial allocation → directional weighting → reverse compensation) to adapt the grid transmission characteristics to market transaction needs in real time. In scenarios where topology changes frequently, redundant path merging and closed-loop parameter compensation are used to significantly reduce transmission losses caused by model mismatch, while improving power transmission efficiency during periods of high electricity prices, achieving coordinated control of economic optimization and physical operation stability.

[0100] In order to achieve dynamic and refined control of power distribution in scenarios where the grid topology frequently changes, and to resolve the conflict between voltage stability and economic efficiency caused by the single regulation mode in traditional methods, a dynamic priority-driven power distribution mechanism is constructed by deeply coupling the multi-level regulation mode with real-time electricity price fluctuations and load demand, ensuring that the system responds quickly and maintains the optimal operating state when the topology suddenly changes. In some embodiments, the voltage amplitude and phase angle of the grid node are coordinated and adjusted through the multi-level current conversion regulation mechanism of the solid-state transformer based on the adjusted equivalent impedance parameters to form a power distribution scheme, including:

[0101] 301. Convert the adjusted equivalent impedance parameter into a voltage regulation parameter including a voltage amplitude deviation range and a phase angle difference range allowed for each grid node, wherein the conversion process is performed based on a transmission capacity ratio of the key transmission path and a phase difference ratio between adjacent nodes;

[0102] In step 301, the transmission capacity ratio refers to the percentage of the maximum power that the critical transmission path can carry under the current grid operating conditions as a percentage of the total transmission capacity of the entire network. The phase difference ratio is the ratio of the actual phase angle difference between adjacent nodes to the maximum allowable phase angle difference, which is used to quantify the severity of phase offset.

[0103] In the embodiment of the present application, a dynamic impedance and voltage mapping model is used to convert the equivalent impedance parameters into voltage regulation parameters. First, based on the real-time transmission capacity ratio of the key transmission path (such as the capacity ratio of path A is 30%), the voltage amplitude deviation range of each node is determined by a weighted allocation algorithm (such as the allowable deviation is expanded by ±0.5% for every 10% increase in capacity ratio). At the same time, according to the phase difference ratio between adjacent nodes (such as the phase difference of node BC is 5°, the maximum allowable value is 8°→ratio 62.5%), the fuzzy logic reasoning mechanism is used to dynamically adjust the phase angle difference range (such as narrowing the allowable range when the ratio exceeds 70%). Finally, a voltage regulation parameter including a voltage amplitude deviation range (such as ±3%) and a phase angle difference range (such as ±7°) is generated.

[0104] 302. Generate a multi-level regulation mode based on the voltage regulation parameter and the electricity price fluctuation data, wherein the multi-level regulation mode includes at least three regulation levels, and a trigger condition for each regulation level is determined based on an electricity price fluctuation amplitude threshold and a fluctuation direction, wherein the fluctuation direction includes a peak electricity price period and a valley electricity price period;

[0105] In step 302, the regulation level is a classification of control intensity based on the magnitude and direction of electricity price fluctuations. Different levels correspond to different voltage and phase constraints. The fluctuation direction qualitatively describes the trend of electricity price changes, including peak periods of continuous price increases and trough periods of continuous price decreases.

[0106] In this embodiment, an event-driven hierarchical strategy is used to generate a multi-level regulation mode. First, based on real-time electricity price fluctuation data, the fluctuation amplitude is calculated using a sliding window statistical method (e.g., a 15% increase in electricity prices within 1 hour), and the fluctuation direction is determined using a trend direction identification algorithm (e.g., Hodrick-Prescott filtering). A three-level regulation mode is set:

[0107] Basic regulation (fluctuation <5%): maintain current voltage and phase constraints;

[0108] Enhanced regulation (5% ≤ fluctuation amplitude < 10% and in the direction of peak): Tighten the voltage deviation range (e.g., ±2%) and relax the phase difference range (e.g., ±9°) to improve transmission capacity;

[0109] Emergency regulation (fluctuation amplitude ≥ 10% or in the valley direction): Expand the voltage deviation range (e.g., ±4%) and strictly limit the phase difference (e.g., ±5°) to suppress reverse power. Trigger conditions are dynamically updated through a finite state machine model to ensure that mode switching is synchronized with market fluctuations.

[0110] 303. In the multi-level regulation mode, based on the real-time voltage amplitude and phase angle measurement data of the grid node, matching the deviation range in the voltage regulation parameter; when the measurement data exceeds the deviation range, triggering the switching of the adjacent regulation level; during the switching process, synchronously adjusting the phase angle difference range to maintain the consistency of the power transmission direction;

[0111] In step 303, the deviation range is the maximum range within which the voltage amplitude or phase angle is allowed to deviate from the standard value. The phase angle difference range is the allowable fluctuation range of the phase angle difference between adjacent nodes, which is used to constrain the power transmission direction.

[0112] In an embodiment of the present application, the regulation level switching is achieved through an adaptive threshold matching mechanism. After real-time collection of the voltage amplitude (such as the voltage of node D is 225V) and phase angle (such as the phase difference of node DE is 6°) data of the power grid nodes, the sliding mean filtering technology is used to eliminate instantaneous noise and compare it with the deviation range under the current regulation level (such as enhanced regulation requires a voltage deviation of ±2%, and the measured deviation is +2.5%). When the data exceeds the limit three times in a row, the event-triggered level switching protocol is triggered: if the voltage exceeds the limit, it switches to a higher level (such as basic → enhanced); if the phase exceeds the limit, it switches to a lower level (such as emergency → enhanced). During the switching process, the phase angle difference range of adjacent nodes is synchronously adjusted through the distributed consistency protocol (such as from ±7° to ±8°) to ensure that the power transmission direction is not reversed.

[0113] 304. Generate a power allocation plan including power allocation priorities and allocation ratios for each node based on the voltage amplitude and phase angle constraints corresponding to the switched regulation level, wherein the allocation priority is positively correlated with the urgency of the load demand forecast data.

[0114] In step 304, the allocation priority is the order in which power is injected into the nodes, which is determined by the urgency of the load demand. The allocation ratio is the proportion of the total power allocated to each node, which is positively correlated with the priority.

[0115] In an embodiment of the present application, a dynamic priority queue is used to generate a power allocation plan. First, according to the urgency of the load demand forecast data (such as the load gap of area X in the next hour will reach 30%), the urgency is quantified into a node priority weight through the entropy weight method (such as the weight of the emergency area is increased by 50%); based on the node priority weight, the priority coefficient of the power allocation of each node is set, and the voltage amplitude and phase angle constraints corresponding to the priority coefficient and the adjustment level after switching are used as input parameters of the multi-objective particle swarm optimization algorithm to dynamically solve the power allocation ratio of each node (such as the allocation ratio of high-priority nodes is increased from the baseline value of 25% to 40%). Finally, a power allocation plan containing the power allocation priority and allocation ratio of each node is generated. Nodes with high priority weights automatically obtain a larger power injection margin through the optimization algorithm. At the same time, the voltage and phase deviation ranges are strictly limited in the algorithm constraint layer to achieve the safety regulation goal under the emergency level. The following is a specific example:

[0116] Suppose that the sudden switching on and off of large equipment in an industrial zone causes a transient reconfiguration of the grid topology (a 30% sudden change in line impedance), and that electricity prices in the power market fluctuate during peak hours (an 18% increase within one hour). Step 301 converts the mutated equivalent impedance parameter (0.25Ω → 0.18Ω) into voltage regulation parameters, setting the node voltage deviation range to ±4% and the phase difference between adjacent nodes to ±6°. In step 302, because the price fluctuation exceeds the threshold (18%), emergency regulation mode is triggered, relaxing the voltage deviation to ±5% and tightening the phase difference to ±4°. Step 303 detects a transient voltage overshoot at a node (+5.2%), switching to enhanced regulation mode, adjusting the voltage deviation to ±3% and simultaneously relaxing the phase difference to ±7°. Step 304, combined with load forecast data (an urgent 25% load shortfall in the industrial zone), generates a power allocation plan, raising the priority of the node in this zone to the highest, and increasing the allocation ratio from 35% to 50%.

[0117] Steps 301-304 achieve a refined balance between voltage stability and economic goals through dynamic adaptation of multi-level regulation modes and real-time electricity prices and load data. In scenarios where topology changes frequently, a priority-driven power allocation mechanism quickly responds to local anomalies, ensuring power supply reliability in high-emergency load areas while suppressing ineffective power losses in non-critical areas, thereby improving the synergy between the overall system operating efficiency and market transaction benefits.

[0118] In order to achieve rapid response and security optimization of device-level control in scenarios where the power grid topology frequently changes, and to solve the problem of delayed local exception processing caused by data transmission delays in traditional centralized control, this solution uses the dynamic monitoring and multi-parameter fusion mechanism of the edge computing unit to build a closed-loop control link driven by the operating status of the solid-state transformer, ensuring the coordinated improvement of the real-time power regulation and equipment safety under abnormal working conditions. In some embodiments, during the execution of the power allocation scheme, the operating status data of the solid-state transformer is monitored by the edge computing node deployed at the grid node, and local control instructions are generated in combination with the adjusted equivalent impedance parameters, including:

[0119] 401. Collecting operating status data of the solid-state transformer in real time through the edge computing unit, the operating status data including an instantaneous fluctuation value of the output terminal voltage, an instantaneous value of the current phase difference between adjacent nodes, and a temperature change rate of a power module inside the solid-state transformer;

[0120] In step 401, the instantaneous output voltage fluctuation value is the instantaneous deviation of the solid-state transformer output voltage from the rated value within a very short period of time (e.g., milliseconds). The instantaneous phase difference between adjacent nodes is the instantaneous measured value of the phase angle difference between the current waveforms of adjacent grid nodes at the same point in time.

[0121] In an embodiment of the present application, a high-speed data acquisition module embedded in an edge computing unit is used to synchronously acquire multi-dimensional operating data of the solid-state transformer at a microsecond sampling frequency. A sliding window filtering technique is used to suppress noise from the original voltage fluctuation value (e.g., eliminating instantaneous spikes exceeding ±5%), and a phase-locked loop (PLL) algorithm is used to accurately extract the instantaneous value of the current phase difference between adjacent nodes. Simultaneously, a thermocouple array is used to monitor the temperature gradient distribution of the power modules within the solid-state transformer in real time, calculating the weighted average of the temperature change rate of each module (e.g., the temperature change rate of module A is 0.3°C / s, and that of module B is 0.5°C / s, which translates to an overall change rate of 0.4°C / s). Ultimately, operating status data is generated, including voltage fluctuation values, instantaneous phase difference values, and temperature change rates.

[0122] 402. Correlate the instantaneous fluctuation value of the output terminal voltage in the operating status data with the adjusted equivalent impedance parameter. When the voltage fluctuation direction is opposite to the impedance threshold change direction, mark it as a first type abnormal state.

[0123] In step 402, the impedance threshold change direction is the increase or decrease trend of the impedance value during the equivalent impedance parameter adjustment process (e.g., a decrease in impedance is a negative change, and an increase in impedance is a positive change). The first type of abnormal state is an operational abnormality flag caused by the opposite conflict between the voltage fluctuation direction and the impedance adjustment direction.

[0124] In this embodiment of the present application, the fluctuation direction of the instantaneous voltage fluctuation value at the output terminal and the change direction of the impedance threshold of the adjusted equivalent impedance parameter are obtained. A dynamic correlation analysis model is used to couple the voltage fluctuation direction (e.g., voltage increase or decrease) with the impedance threshold change direction (e.g., impedance decrease or increase). First, a correlation coefficient matrix is ​​established between the voltage fluctuation direction vector (positive / negative) and the impedance change direction vector. When the correlation coefficient is lower than a preset critical value (e.g., -0.8), a reverse conflict is determined. For example, if the impedance threshold is decreasing (negative change) while the voltage fluctuation continues to increase (positive change), a first-class abnormal state flag is triggered. The distribution location of the abnormal state is determined by mapping the node coordinates in the topological connection relationship data.

[0125] 403. Extract, based on the distribution location of the first type of abnormal state, a phase difference constraint range between at least two adjacent nodes associated with the distribution location in the topological connection relationship data, and generate a primary control instruction including a voltage compensation amount and a phase correction direction in combination with the instantaneous value of the current phase difference between the adjacent nodes;

[0126] In step 403, the phase difference constraint range is the maximum range of current phase angle differences between adjacent nodes allowed in the grid operation regulations. The phase correction direction is the direction of current phase angle change (e.g., clockwise or counterclockwise) required to eliminate phase deviation.

[0127] In an embodiment of the present application, a primary control instruction is generated based on a fuzzy rule inference engine. First, the topological connection relationship data of the area where the first type of abnormal state is located is extracted to obtain the phase difference constraint range of adjacent nodes (such as the allowed phase difference of node CD is ±8°). The instantaneous value of the real-time current phase difference (such as the measured phase difference of node CD is 10°) is compared with the constraint range, and the deviation amount (10°-8°=+2°) and the deviation direction (forward overrun) are calculated. The voltage compensation amount is determined by the fuzzy membership function (such as the compensation voltage is 0.5V for every 1° deviation overrun), and the phase correction direction is generated in combination with the deviation direction (such as counterclockwise correction is required when the forward overrun is exceeded). The final output includes a primary control instruction of the voltage compensation amount (+1V) and the phase correction direction (counterclockwise).

[0128] 404. Adjust the voltage compensation amount in the primary control instruction based on the temperature change rate. When the temperature change rate exceeds a preset safety interval, the voltage compensation amount is proportionally reduced, and the allowable deviation angle of the phase correction direction is simultaneously expanded to generate a local control instruction.

[0129] In step 404, the preset safety interval is the allowable range of temperature change rate of the solid-state transformer power module. Exceeding this range may cause the risk of equipment overheating. The allowable deviation angle is the acceptable adjustment angle error range when executing the phase correction direction.

[0130] In an embodiment of the present application, a dynamic weight allocation algorithm is used to perform temperature compensation correction on the primary control instructions. When the temperature change rate exceeds the upper limit of the safety range (such as 0.4°C / s), the voltage compensation amount is proportionally reduced through an exponential decay function (such as the original compensation +1V → adjusted +0.6V). At the same time, based on the device thermal inertia model, the allowable deviation angle of the phase correction direction is expanded (such as the original ±1° → expanded to ±3°) to avoid over-adjustment due to temperature limitations. The final generated local control instruction contains the corrected voltage compensation amount, the expanded deviation angle, and the phase correction direction, and is sent to the solid-state transformer execution module through the edge computing unit.

[0131] Here's a specific example:

[0132] Suppose a main line in a substation trips (a topological mutation). After the backup line is connected, the output voltage of the solid-state transformer fluctuates dramatically (instantaneous fluctuation value +8%), and the temperature of the internal power module rises rapidly (rate of change 0.6°C / s). Step 401, using the edge computing unit, collects data on the voltage fluctuation value of +8%, the instantaneous phase difference between adjacent nodes of 12° (exceeding the limit by 4°), and the temperature change rate of 0.6°C / s. Correlation analysis in step 402 reveals a conflict between the positive voltage fluctuation and the negative impedance adjustment (resulting in a reduced impedance on the backup line), marking the area as a Class I abnormal state. Step 403 extracts the phase difference constraint range of ±7° between adjacent nodes in the fault area and generates a primary control command: voltage compensation +2V and phase correction 5° counterclockwise. In step 404, due to the excessive temperature change rate (0.6°C / s), the adjustment command is set to +1.2V, with the deviation angle allowed to extend to ±4°, generating a local control command.

[0133] Steps 401-404 use the edge computing unit's real-time multi-source data collection and dynamic compensation mechanism to quickly identify and correct abnormal operation of the solid-state transformer in topology mutation scenarios. Combined with the instruction adjustment strategy based on temperature safety constraints, precise voltage and phase adjustment is achieved while ensuring equipment reliability, effectively improving the response speed and safety of local power control and avoiding the risk of cascading failures caused by overload or overheating.

[0134] In order to improve the transmission efficiency and economic adaptability of transaction-sensitive paths in scenarios where the power grid topology frequently changes, and to address the power allocation mismatch problem caused by the dispersion of redundant paths in traditional methods, a simplified network model driven by electricity price fluctuations is constructed through a dynamic redundancy merging and directional impedance correction mechanism to achieve real-time and accurate matching of critical path transmission capacity with market demand. In some embodiments, based on the topological substructure, redundant connection branches with the same power transmission direction as between core nodes are merged to generate a simplified network structure that retains the power transaction-sensitive paths. The equivalent impedance parameters of the key transmission paths in the simplified network structure are directionally weighted and corrected according to the electricity price fluctuation data, including:

[0135] 501. Identify all redundant branches connected to the core node in the topology substructure. The redundant branches are determined to be two or more branches connected in parallel in the same power transmission direction, and the difference in historical transaction loads of the branches is less than a set ratio threshold.

[0136] In step 501, redundant branches are multiple parallel lines in the same power transmission direction, with overlapping functions that can be merged without affecting overall transmission capacity. Historical transaction load differences refer to the differences in load borne by different branches over the historical transaction period, quantified as a percentage.

[0137] In this embodiment, an improved spectral clustering algorithm is used to cluster the power transmission directions of topological substructures. First, the power flow characteristics of each branch (such as the power flow angle and transmission capacity) are extracted. The directional consistency between branches is calculated using cosine similarity (e.g., similarity > 95% is considered to be in the same direction). Combined with historical transaction load difference analysis (e.g., the load difference between branches A and B is <15%), branches with the same direction are marked as redundant when the number of branches is ≥ 2 and the load difference is less than a set threshold (e.g., 20%).

[0138] 502. Merge the redundant branches, retain the branch with the highest historical transaction load as the key transmission path, and add the transmission capacity of the remaining branches to the key transmission path;

[0139] In step 502, the critical transmission path is the core power transmission channel formed by merging redundant branches, carrying the combined transmission capacity. Transmission capacity superposition is a capacity allocation mechanism that proportionally integrates the transmission capacity of redundant branches into the critical path.

[0140] In this embodiment, redundant branches are merged using dynamic graph pruning technology. First, redundant branches are sorted by historical transaction load (e.g., branch A has a load of 30% and branch B has a load of 25%). The branch with the highest load is selected as the critical transmission path. The transmission capacity of the remaining branches is then integrated into the critical transmission path using a weighted superposition algorithm (e.g., branch B's capacity is superimposed by a scaling factor of 0.8, and branch C's by 0.5). The superimposed capacity values ​​are then subjected to nonlinear normalization (e.g., using a sigmoid function to compress extreme values) to ensure that the critical path capacity does not exceed the physical limits of the equipment.

[0141] 503. Based on the real-time electricity price fluctuation data, the power transmission direction corresponding to the peak electricity price period is marked as the forward sensitive direction, and the reverse direction corresponding to the low electricity price period is marked as the reverse sensitive direction;

[0142] In step 503, the forward sensitive direction is the power transmission direction that needs to be prioritized during peak electricity price periods, usually corresponding to the power supply demand of the load center. The reverse sensitive direction is the power reverse flow direction that needs to be suppressed during low electricity price periods, usually corresponding to the return of excess power.

[0143] In an embodiment of the present application, a sliding window statistical method is used to identify electricity price fluctuation trends. First, the real-time electricity price data is divided into time windows (such as 15 minutes), and the electricity price increase in each window is calculated (such as a 12% increase in window 1). The peak period (increase of 3 consecutive windows > 8%) and the valley period (decrease of 3 consecutive windows > 5%) are determined through a trend direction identification algorithm (such as a moving average crossover method). The power transmission direction corresponding to the time period is marked: the peak period marks the load center power supply direction as the forward sensitive direction, and the valley period marks the power supply side return direction as the reverse sensitive direction.

[0144] 504. Perform differentiated weighted adjustments on the equivalent impedance parameters of the key transmission path based on different sensitive directions to generate a simplified network structure that retains the sensitive path for power trading, wherein the path impedance value in the forward sensitive direction is lower than that in the reverse sensitive direction, and the impedance value is adjusted with the fluctuation amplitude of the real-time electricity price.

[0145] In step 504, the differential weighted adjustment is to dynamically modify the critical path impedance by varying magnitudes based on the sensitive direction to adapt to the demand for electricity price fluctuations. The real-time electricity price fluctuation magnitude is the percentage change in the electricity price relative to the baseline value during the current period.

[0146] In the embodiment of the present application, a nonlinear directional weighted model is used to adjust the impedance parameters. For the forward sensitive direction path, a negative correction coefficient (such as impedance value = original value × (1-increase × 0.005)) is calculated based on the real-time electricity price increase (such as a 15% increase), and the impedance is reduced to improve the transmission capacity. For the reverse sensitive direction path, a positive correction coefficient (such as impedance value = original value × (1 + decrease × 0.003)) is used to increase the impedance to suppress the reverse flow. The correction coefficient is adjusted through a dynamic feedback mechanism: when the electricity price fluctuation rate exceeds a threshold (such as a change of >0.5% per minute), the correction coefficient weight is adaptively increased (such as the weight is increased by 0.1 for every 1% increase in the rate), and finally a simplified network structure optimized for the sensitive direction is generated.

[0147] Here's a specific example:

[0148] Suppose a commercial district experiences frequent topology reconfigurations due to the switching on and off of multiple energy storage devices. Simultaneously, daily electricity prices fluctuate between midday peaks (a 20% increase) and nighttime valleys (a 15% decrease). Step 501 identifies three redundant branches (with an 18% historical load difference) connecting the energy storage cluster and the commercial load center and marks them as redundant branches. Step 502 merges the redundant branches, retaining branch A, which accounts for 35% of the load, as the critical transmission path. The capacity of branches B (25%) and C (20%) is added, increasing the total capacity to 160% of the original value. In step 503, the midday peak electricity price marks the energy storage discharge to the load center as the positive sensitive direction, while the nighttime valley marks the return of excess load power as the negative sensitive direction. In step 504, the critical path impedance is negatively modified during midday (0.2Ω→0.16Ω) and negatively modified at night (0.2Ω→0.23Ω), forming a simplified network structure that retains the sensitive paths for power trading.

[0149] Steps 501-504 significantly improve the response efficiency of the critical path to electricity price fluctuations through dynamic merging of redundant branches and impedance correction driven by sensitive directions. In scenarios where topology frequently changes, directional impedance adjustment suppresses the flow of reactive power, optimizes transmission capacity during high electricity price periods, and reduces redundancy losses during low electricity price periods, achieving a dual improvement in grid operation economy and physical transmission performance.

[0150] In order to quickly locate and correct phase mismatch problems caused by local anomalies in scenarios where the power grid topology frequently changes, and to address the control instruction inaccuracy defects caused by global model update delays in traditional methods, a precise control link based on real-time operating data is constructed through dynamic identification of abnormal areas and a closed-loop correction mechanism for phase differences, ensuring real-time adaptability of power regulation actions to topology changes. In some embodiments, based on the distribution location of the first type of abnormal state, the phase difference constraint range between at least two adjacent nodes associated with the distribution location in the topological connection relationship data is extracted, and combined with the instantaneous value of the current phase difference between the adjacent nodes, a primary control instruction containing a voltage compensation amount and a phase correction direction is generated, including:

[0151] 601. Locate an abnormal area in the topological connection relationship data according to the distribution position of the first type of abnormal state, where the abnormal area includes at least two adjacent nodes and their connection paths.

[0152] In step 601, the abnormal area is a local power grid range defined by the distribution location of the first type of abnormal state, including at least two adjacent nodes and their connection paths.

[0153] In the embodiments of the present application, an abnormal region detection model based on a graph neural network is employed. First, the distribution locations of the first type of abnormal state (e.g., node coordinate sets) are spatially overlaid with the topological connectivity data. Adjacency features of abnormal nodes (e.g., adjacent node degrees and path betweenness) are extracted using a graph convolutional network. The influence coefficient of each node is weighted and calculated using an attention mechanism. When the weighted sum of the influences between adjacent nodes exceeds a dynamic threshold, the connection path is determined to constitute an abnormal region, and the affected nodes and paths are annotated.

[0154] 602. Extract a phase difference constraint range between adjacent nodes in the abnormal area, where the phase difference constraint range is determined based on an equivalent impedance parameter of a corresponding path in the simplified network structure and historical operation data;

[0155] In step 602 , the phase difference constraint range is the limit range of the current phase angle difference between adjacent nodes allowed by grid safety regulations.

[0156] In an embodiment of the present application, the phase difference constraint range is dynamically determined by a fuzzy logic system. First, based on the equivalent impedance parameter of the corresponding path in the simplified network structure (such as 0.15Ω), the theoretical phase difference reference value is calculated (such as impedance value × transmission capacity coefficient = 8°). Combined with the actual phase difference statistical distribution in the historical operation data (such as 95% of the data in the past 30 days are within ±9°), the membership function is used to fuse the theoretical value and the actual distribution to obtain a dynamic allowable range (such as ±8.5°). When a recent topology change event is detected (such as equipment switching), the sliding window mechanism is used to temporarily expand the allowable range (such as ±9.2°) to generate a phase difference constraint range.

[0157] 603. Compare the instantaneous value of the current phase difference between adjacent nodes with the phase difference constraint range. When the instantaneous value of the current phase difference between adjacent nodes exceeds the phase difference constraint range, calculate the phase deviation amount and deviation direction.

[0158] In step 603 , the phase deviation direction is the offset direction of the measured phase difference relative to the allowable range (positive excess or negative excess).

[0159] In this embodiment, an interval overlap detection algorithm is used for deviation analysis. The instantaneous current phase difference between adjacent nodes (e.g., a phase difference of 11° between nodes A and B) is collected in real time and then overlapped with the phase difference constraint range (e.g., ±8.5°) output in step 602 is detected. If the instantaneous value exceeds the allowable range, the deviation is first calculated, and the excess ratio is determined using linear interpolation (e.g., 11° - 8.5° = 2.5°). The deviation direction is then determined using a sign function (e.g., +2.5° indicates a positive excess), which serves as the basis for generating control instructions.

[0160] 604. Generate a primary control instruction including a voltage compensation amount and a phase correction direction based on the phase deviation amount and the deviation direction, wherein the voltage compensation amount is proportional to the phase deviation amount, and the phase correction direction is opposite to the deviation direction.

[0161] In step 604 , the phase correction direction is the direction (clockwise or counterclockwise) in which the current phase angle needs to be adjusted to eliminate the phase deviation.

[0162] In this embodiment, the primary control command is generated based on the proportional, integral, and differential (PID) control principle. The phase deviation is input into the PID controller (proportional coefficient Kp = 0.6, integral time Ti = 10s), and the voltage compensation is calculated (e.g., a deviation of +2.5° leads to a compensation of +1.5V).

[0163] The phase correction direction is determined by the reverse mapping model: when the positive direction exceeds the limit, counterclockwise correction is required, and when the negative direction exceeds the limit, clockwise correction is required.

[0164] The final output includes primary control instructions for voltage compensation (+1.5V) and phase correction direction (counterclockwise), and is sent down for execution through the edge computing unit.

[0165] Here's a specific example:

[0166] Suppose a power grid in a mountainous area experiences galloping of multiple transmission lines (a transient topological change), causing the phase difference between nodes CD to continuously exceed the limit. Step 601 detects a Class I abnormality near node C and locates the abnormal area encompassing nodes C and D and their connecting lines. Step 602 extracts the phase difference constraint for this path as ±7.8° (equivalent impedance 0.18Ω, 95% distribution of historical data ±8.3°). Step 603 monitors the instantaneous phase difference in real time, finding a value of 9.6°. The deviation is calculated as +1.8° (the deviation is in the positive direction). Step 604 generates a primary control command: voltage compensation +1.08V and phase correction counterclockwise.

[0167] Steps 601-604 use the dynamic positioning of abnormal areas and the closed-loop correction mechanism of phase difference to quickly generate precise control instructions in scenarios with frequent topology changes. By combining a hybrid strategy of fuzzy logic and PID control, they effectively suppress power oscillations caused by phase mismatch, improve the response speed and control accuracy of local regulation, and ensure the stable operation of the power grid under abnormal conditions and the simultaneous achievement of market transaction economic goals.

[0168] In order to achieve deep coordination between global control strategies and market transaction demands in scenarios where power grid topology frequently changes, and to resolve the problem of split optimization objectives caused by command conflicts in traditional methods, a dynamic priority control system driven by electricity price sensitivity is constructed through spatiotemporal correlation mapping and a multi-level command coordination mechanism to ensure maximum power allocation efficiency during high-value periods. In some embodiments, the local control instructions are matched with the electricity price fluctuation data through spatiotemporal mapping, and the local control instructions of multiple power grid nodes are prioritized according to the matching results. A joint control strategy is generated and the power allocation scheme is dynamically adjusted to achieve dynamic optimization control of transmission losses and transaction costs, including:

[0169] 701. Extract the local control instruction generation time and corresponding node location of each grid node, match them with the electricity price fluctuation data of the same period by time window and region division, and form a spatiotemporal correlation mapping table as the matching result;

[0170] In step 701, time window and area division matching is an operation of aligning the generation time of local control instructions with electricity price fluctuation data in time and space according to fixed time periods (such as 15 minutes) and power grid areas (such as load center areas).

[0171] In an embodiment of the present application, a sliding window statistical method is used to segment the generation timestamps of local control instructions (e.g., one window every 5 minutes), and the node locations are mapped to a predefined grid area grid (e.g., a 500m×500m grid) based on a geographic hashing algorithm. Through a spatiotemporal alignment engine, the instruction generation locations within each window are correlated and matched with the electricity price fluctuation data of the corresponding time period (e.g., the electricity price of region A in window T increases by 10%). Finally, a spatiotemporal correlation mapping table is constructed to record the number of instructions, the amplitude of electricity price fluctuations, and regional load characteristics for each spatiotemporal unit (time window × region).

[0172] 702. Based on the matching result, the time period and area where the electricity price fluctuation data exceeds a preset threshold are marked as a highly sensitive time period and area, and the local control instructions in the highly sensitive time period and area are automatically upgraded in priority level;

[0173] In step 702 , the priority level is the execution priority of the local control instruction in the global optimization, and the higher the level, the higher the execution priority.

[0174] In an embodiment of the present application, a highly sensitive time period area is marked based on a dynamic threshold adjustment mechanism. First, the quantiles of the historical electricity price fluctuation data are calculated (e.g., the top 20% quantiles are the threshold benchmark). When the real-time electricity price fluctuation exceeds the benchmark value (e.g., 12%), the spatiotemporal unit is marked as a highly sensitive time period area. Through a weighted diffusion algorithm, the priority level of the highly sensitive time period area is increased according to the proportion of the electricity price increase (e.g., one level is increased for every 5% increase), and the levels of adjacent areas are associated at the same time (e.g., the level of the area within a diffusion radius of 5km is increased by 50%).

[0175] 703. Cross-validate the local control instructions of the grid nodes in the same time window. When it is detected that the instructions of adjacent nodes have a phase correction direction conflict, preferentially execute the local control instructions of the highly sensitive time period area.

[0176] In step 703 , the phase correction direction conflict is the conflict in the phase adjustment direction in the control instructions of adjacent nodes (eg, node A needs to be corrected counterclockwise, while node B needs to be corrected clockwise).

[0177] In an embodiment of the present application, a conflict detection algorithm in graph theory is used to perform cross-verification of instructions. A directed graph model is constructed with grid nodes as vertices and phase correction directions as edge attributes, and direction conflicts between adjacent nodes are detected by traversing the adjacency matrix (such as edge A→B is counterclockwise, and edge B→C is clockwise). When a conflict is detected, a priority decision is made based on a multi-objective game theory model: instructions in highly sensitive areas retain their original directions, and instructions in non-sensitive areas are adjusted to the directions of adjacent high-priority areas. The decision results are synchronized to relevant nodes through a distributed consistency protocol to ensure global instruction direction consistency.

[0178] 704. Generate a joint control strategy based on the priority execution result, and dynamically adjust the power allocation ratio of each node in the power allocation scheme according to the joint control strategy, so as to increase the power transmission margin in the highly sensitive period area and simultaneously reduce the transmission loss in the non-sensitive area.

[0179] In step 704 , the power transmission margin is the amount of power transmission capability that can be increased on the critical transmission path under the current operating state.

[0180] In this embodiment of the present application, a flexible resource allocation algorithm is used to generate a joint control strategy. The power allocation weight for each node is calculated based on the priority level (e.g., the weight of a highly sensitive area = base value × priority level). The constrained optimal allocation model (with the objective function of minimizing transmission loss and maximizing transmission margin) is solved using the Lagrange multiplier method. During the adjustment process, the power allocation ratio for non-sensitive areas is compressed according to the weight attenuation coefficient (e.g., 20% attenuation for each level reduction), and the released capacity is added to the highly sensitive areas. Finally, a joint control strategy is generated and synchronously updated to the solid-state transformer execution module at each node.

[0181] Here's a specific example:

[0182] Assume that the power grid experiences frequent topological changes due to the sudden switching on and off of a large data center, and that electricity prices fluctuate by 18% during the peak lunchtime period. Step 701 matches 32 local instructions in the central area (grid G7) between 12:00 PM and 12:15 PM (window T) with electricity price data, forming a spatiotemporal correlation mapping table as the matching result. Step 702 marks G7 as a highly sensitive area due to the price increase exceeding a threshold (18%), raising its priority to the highest level. Step 703 detects a phase direction conflict (counterclockwise vs. clockwise) between adjacent nodes in G7, prioritizing the counterclockwise instructions in the highly sensitive area. Step 704 adjusts the power allocation scheme to generate a joint control strategy, increasing the transmission margin in G7 by 30% and reducing losses in surrounding non-sensitive areas by 15%.

[0183] Steps 701-704 use spatiotemporal correlation mapping and a dynamic priority mechanism to accurately identify high-value control areas in scenarios with frequent topology changes. Combined with conflict resolution and flexible resource allocation strategies, they maximize power transmission efficiency during highly sensitive periods and collaboratively suppress ineffective losses in non-sensitive areas, significantly improving the economic efficiency of power system operation and the adaptation accuracy of market transaction returns.

[0184] Figure 2 The present invention provides a schematic diagram of a power system optimization control device based on power market transactions, as shown in FIG. Figure 2 As shown, the system includes:

[0185] An acquisition module 21 is configured to acquire power market transaction data, including power price fluctuation data and load demand forecast data, and simultaneously acquire topological connection relationship data between multiple power grid nodes in the power grid;

[0186] An adjustment module 22 is configured to generate a simplified network structure adapted to the power transaction scenario based on the power market transaction data and the topological connection relationship data, and to adjust equivalent impedance parameters of key transmission paths in the simplified network structure;

[0187] The regulation module 23 is configured to coordinately adjust the voltage amplitude and phase angle of the grid node based on the adjusted equivalent impedance parameters through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution plan;

[0188] A monitoring module 24 is configured to monitor the operating status data of the solid-state transformer through an edge computing node deployed at the grid node during the execution of the power allocation scheme, and generate a local control instruction based on the adjusted equivalent impedance parameter;

[0189] The generation module 25 is used to perform spatiotemporal mapping matching between the local control instructions and the electricity price fluctuation data, perform collaborative priority sorting on the local control instructions of multiple grid nodes according to the matching results, generate a joint control strategy and dynamically adjust the power allocation scheme to achieve dynamic optimization control of transmission loss and transaction costs.

[0190] Figure 2 The power system optimization control device based on power market transactions can perform Figure 1 The implementation principles and technical effects of the power system optimization and control method based on power market transactions described in the illustrated embodiment will not be elaborated on here. The specific manner in which the various modules and units of the power system optimization and control device based on power market transactions in the above-mentioned embodiment perform their operations has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0191] In one possible design, Figure 2 The power system optimization control device based on power market transactions of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0192] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0193] The processing component 32 is used for the above Figure 1 The embodiment provides a power system optimization control method based on power market transactions.

[0194] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0195] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0196] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0197] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0198] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0199] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0200] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a power system optimization control method based on power market transactions.

[0201] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0202] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0203] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A power system optimization control method based on power market transactions, characterized in that: include: Acquiring power market transaction data, including power price fluctuation data and load demand forecast data, and simultaneously collecting topological connection relationship data between multiple grid nodes in the power grid; generating a simplified network structure adapted to the power trading scenario based on the power market transaction data and the topological connection relationship data, and adjusting equivalent impedance parameters of key transmission paths in the simplified network structure; Based on the adjusted equivalent impedance parameters, the voltage amplitude and phase angle of the grid nodes are coordinated and adjusted through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution plan; During the execution of the power allocation scheme, the operating status data of the solid-state transformer is monitored by an edge computing node deployed at the grid node, and a local control instruction is generated in combination with the adjusted equivalent impedance parameter; Performing spatiotemporal mapping matching between the local control instructions and the electricity price fluctuation data, collaboratively prioritizing the local control instructions of multiple grid nodes based on the matching results, generating a joint control strategy, and dynamically adjusting the power allocation plan to achieve dynamic optimization control of transmission losses and transaction costs; During the execution of the power allocation scheme, the operating status data of the solid-state transformer is monitored by an edge computing node deployed at the grid node, and a local control instruction is generated in combination with the adjusted equivalent impedance parameter, including: The edge computing node collects operating status data of the solid-state transformer in real time, wherein the operating status data includes an instantaneous fluctuation value of the output voltage, an instantaneous value of the current phase difference between adjacent nodes, and a temperature change rate of a power module inside the solid-state transformer; Associating the instantaneous fluctuation value of the output terminal voltage in the operating status data with the adjusted equivalent impedance parameter, and marking it as a first type of abnormal state when the voltage fluctuation direction is opposite to the impedance threshold change direction; Extracting, based on the distribution location of the first type of abnormal state, a phase difference constraint range between at least two adjacent nodes associated with the distribution location of the first type of abnormal state in the topological connection relationship data, and generating a primary control instruction including a voltage compensation amount and a phase correction direction in combination with an instantaneous value of the current phase difference between the adjacent nodes; The voltage compensation amount in the primary control instruction is adjusted based on the temperature change rate. The adjustment method is that when the temperature change rate exceeds a preset safety interval, the voltage compensation amount is proportionally reduced, and the allowable deviation angle of the phase correction direction is simultaneously expanded to generate a local control instruction.

2. The method according to claim 1, characterized in that Generating a simplified network structure adapted to the power transaction scenario based on the power market transaction data and the topological connection relationship data, and adjusting equivalent impedance parameters of key transmission paths in the simplified network structure, includes: Associating electricity price fluctuation data in the electricity market transaction data with node connection patterns in the topological connection relationship data and extracting correlation parameters, the correlation parameters including the electricity price fluctuation amplitude and the topological connection density, and determining electricity transaction sensitive paths based on the electricity price fluctuation amplitude and the topological connection density; Extracting a topology substructure comprising at least two core nodes and three or more associated branches from the original power grid topology based on the distribution characteristics of the power transaction sensitive paths, wherein the initial impedance parameter of each branch in the topology substructure is allocated based on the historical transaction load ratio of the corresponding node; Based on the topological substructure, redundant connection branches with the same power transmission direction as that between core nodes are merged to generate a simplified network structure that retains the sensitive path of power trading. The equivalent impedance parameters of the key transmission paths in the simplified network structure are directionally weighted and modified according to the electricity price fluctuation data. During the operation of the simplified network structure, the load demand forecast data in the power market transaction data is continuously received, and the equivalent impedance parameters are reversely compensated and adjusted according to the distribution ratio of the predicted load on the key transmission path, so that the equivalent impedance parameters are kept matched with the power flow direction in the transaction scenario.

3. The method according to claim 1, characterized in that The voltage amplitude and phase angle of the grid node are coordinated adjusted based on the adjusted equivalent impedance parameters through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution scheme, including: Converting the adjusted equivalent impedance parameters into voltage regulation parameters including the voltage amplitude deviation range and phase angle difference range allowed for each grid node, wherein the conversion process is distributed based on the transmission capacity ratio of the critical transmission path and the phase difference ratio between adjacent nodes; generating a multi-level regulation mode according to the voltage regulation parameter and the electricity price fluctuation data, wherein the multi-level regulation mode includes at least three regulation levels, and a trigger condition of each regulation level is determined based on an electricity price fluctuation amplitude threshold and a fluctuation direction, wherein the fluctuation direction includes a peak electricity price period and a valley electricity price period; In the multi-level regulation mode, based on the real-time voltage amplitude and phase angle measurement data of the grid node, the deviation range in the voltage regulation parameter is matched. When the measurement data exceeds the deviation range, the switching of the adjacent regulation level is triggered. During the switching process, the phase angle difference range is synchronously adjusted to maintain the consistency of the power transmission direction; According to the voltage amplitude and phase angle constraints corresponding to the switched regulation level, a power allocation scheme including power allocation priority and allocation ratio of each node is generated, wherein the allocation priority is positively correlated with the urgency of the load demand forecast data.

4. The method according to claim 2, characterized in that The method of merging redundant connection branches with the same power transmission direction between core nodes based on the topological substructure to generate a simplified network structure that retains the sensitive path of power trading includes: Identify all redundant branches connected to the core node in the topological substructure, where the redundant branches are determined to be two or more branches connected in parallel in the same power transmission direction, and the difference in historical transaction loads between the branches is less than a set ratio threshold; Merge the redundant branches, retain the branch with the highest historical transaction load as the key transmission path, and add the transmission capacity of the remaining branches to the key transmission path; According to the real-time electricity price fluctuation data, the power transmission direction corresponding to the peak electricity price period is marked as the forward sensitive direction, and the opposite direction corresponding to the low electricity price period is marked as the reverse sensitive direction; Based on different sensitive directions, the equivalent impedance parameters of the key transmission path are differentially weighted adjusted to generate a simplified network structure that retains the sensitive path of power trading, wherein the path impedance value in the forward sensitive direction is lower than that in the reverse sensitive direction, and the impedance value is adjusted with the fluctuation amplitude of the real-time electricity price.

5. The method according to claim 1, wherein The method further comprises extracting a phase difference constraint range between at least two adjacent nodes associated with the distribution location of the first type of abnormal state from the topological connection relationship data, and generating a primary control instruction including a voltage compensation amount and a phase correction direction in combination with an instantaneous value of the current phase difference between the adjacent nodes, including: Locating an abnormal area in the topological connection relationship data according to the distribution position of the first type of abnormal state, where the abnormal area includes at least two adjacent nodes and their connection paths; Extracting a phase difference constraint range between adjacent nodes in the abnormal area, where the phase difference constraint range is determined based on an equivalent impedance parameter of a corresponding path in the simplified network structure and historical operation data; Comparing the instantaneous value of the current phase difference between the adjacent nodes with the phase difference constraint range, and when the instantaneous value of the current phase difference between the adjacent nodes exceeds the phase difference constraint range, calculating the phase deviation amount and the deviation direction; Based on the phase deviation and the deviation direction, a primary control instruction including a voltage compensation and a phase correction direction is generated. The voltage compensation is proportional to the phase deviation, and the phase correction direction is opposite to the deviation direction.

6. The method according to claim 1, wherein The local control instructions are matched with the electricity price fluctuation data in a spatiotemporal mapping manner, the local control instructions of multiple grid nodes are collaboratively prioritized according to the matching results, a joint control strategy is generated, and the power allocation scheme is dynamically adjusted to achieve dynamic optimization control of transmission loss and transaction cost, including: Extract the local control instruction generation time and corresponding node location of each grid node, match them with the electricity price fluctuation data of the same period by time window and region division, and form a spatiotemporal correlation mapping table as the matching result; According to the matching results, the time period and area where the electricity price fluctuation data exceeds the preset threshold are marked as a high-sensitivity time period area, and the local control instructions in the high-sensitivity time period area are automatically upgraded in priority level; Cross-verify the local control instructions of each grid node in the same time window, and when it is detected that there is a phase correction direction conflict between the instructions of adjacent nodes, give priority to executing the local control instructions of the highly sensitive time period area; A joint control strategy is generated based on the priority execution result, and the power allocation ratio of each node in the power allocation scheme is dynamically adjusted according to the joint control strategy, so that the power transmission margin in the highly sensitive period area is increased and the transmission loss in the non-sensitive area is simultaneously reduced.

7. A power system optimization control device based on power market transactions, characterized in that: include: An acquisition module is used to acquire power market transaction data, including power price fluctuation data and load demand forecast data, and simultaneously collect topological connection relationship data between multiple grid nodes in the power grid; an adjustment module, configured to generate a simplified network structure adapted to the power transaction scenario based on the power market transaction data and the topological connection relationship data, and adjust equivalent impedance parameters of key transmission paths in the simplified network structure; A regulation module is used to coordinately adjust the voltage amplitude and phase angle of the grid nodes based on the adjusted equivalent impedance parameters through the multi-stage current conversion regulation mechanism of the solid-state transformer to form a power distribution plan; A monitoring module, configured to monitor the operating status data of the solid-state transformer through an edge computing node deployed at the grid node during the execution of the power allocation scheme, and generate a local control instruction based on the adjusted equivalent impedance parameter; a generation module for performing spatiotemporal mapping matching between the local control instructions and the electricity price fluctuation data, collaboratively prioritizing the local control instructions of multiple grid nodes based on the matching results, generating a joint control strategy, and dynamically adjusting the power allocation scheme to achieve dynamic optimization control of transmission losses and transaction costs; During the execution of the power allocation scheme, the operating status data of the solid-state transformer is monitored by an edge computing node deployed at the grid node, and a local control instruction is generated in combination with the adjusted equivalent impedance parameter, including: The edge computing node collects operating status data of the solid-state transformer in real time, wherein the operating status data includes an instantaneous fluctuation value of the output voltage, an instantaneous value of the current phase difference between adjacent nodes, and a temperature change rate of a power module inside the solid-state transformer; Associating the instantaneous fluctuation value of the output terminal voltage in the operating status data with the adjusted equivalent impedance parameter, and marking it as a first type of abnormal state when the voltage fluctuation direction is opposite to the impedance threshold change direction; Extracting, based on the distribution location of the first type of abnormal state, a phase difference constraint range between at least two adjacent nodes associated with the distribution location of the first type of abnormal state in the topological connection relationship data, and generating a primary control instruction including a voltage compensation amount and a phase correction direction in combination with an instantaneous value of the current phase difference between the adjacent nodes; The voltage compensation amount in the primary control instruction is adjusted based on the temperature change rate. The adjustment method is that when the temperature change rate exceeds a preset safety interval, the voltage compensation amount is proportionally reduced, and the allowable deviation angle of the phase correction direction is simultaneously expanded to generate a local control instruction.

8. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a power system optimization control method based on power market transactions as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the power system optimization control method based on power market transactions according to any one of claims 1 to 6 is implemented.

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