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

Abstract

The invention provides a power system optimization control method and device based on power market transaction. The method comprises the following steps: acquiring real-time electricity price fluctuation data, load demand prediction data and dynamic topology connection data between power grid nodes; generating a simplified network structure adaptive to a transaction scene based on the data, and adjusting an equivalent impedance parameter of a key transmission path in real time; a node voltage amplitude and a phase angle are cooperatively regulated and controlled through a multi-stage regulation mechanism of the solid-state transformer, and a power distribution scheme is formed; monitoring operation data of the solid-state transformer in real time by utilizing an edge computing node, and generating a local control instruction by combining the current equivalent impedance parameter; and carrying out space-time mapping matching on the local instruction and the electricity price fluctuation data, generating a combined control strategy through collaborative priority ranking, and dynamically adjusting a power distribution scheme. According to the technical scheme provided by the invention, collaborative optimization of economical operation and physical transmission performance of the power system is realized.

Description

Technical Field

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

[0002] With the large-scale access of new energy and the frequent switching of power grid equipment, the topology structure of the power system shows high dynamic change characteristics (such as temporary removal of line faults and rapid start-stop of energy storage devices), resulting in the difficulty of traditional power control methods based on fixed topology models to meet the real-time operation requirements. In the power market trading environment, it is necessary to achieve two goals in the scenario of frequent topology changes: one is to quickly sense the change of the network structure and adaptively adjust the power distribution to ensure power supply stability; the other is to dynamically associate the market trading price signal with the power adjustment action to synchronously optimize the operation economy of the system.

[0003] The current mainstream solution adopts a dynamic optimization control method based on real-time data acquisition and prediction models. Through a centralized controller deployed at the master station, it periodically collects the power grid topology status and market trading data, establishes a short-term power flow prediction model, and generates generator output and energy storage adjustment commands based on the prediction results. This method partially adapts to the economic optimization requirements in the topology change scenario by online updating the topology parameter library and combining the market electricity price fluctuation trend to calculate the power adjustment amount of each node.

[0004] The existing solutions have the following defects: the centralized controller relies on periodic topology data acquisition and model reconstruction, and it is difficult to update the global model in time at the moment of equipment switching or failure, resulting in the mismatch between the power adjustment command and the actual topology status; there is no coupling mechanism between electricity price fluctuation and topology dynamic change, and only static electricity price weights are used to allocate power, so the economic optimization target cannot be dynamically adjusted at the moment of topology mutation; centralized optimization needs to process the data of all network nodes, and the calculation delay is significant in the scenario of high-frequency topology changes, making it difficult to meet the demand for second-level power adaptive adjustment. Summary of the Invention

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

[0006] In a first aspect, this application provides a power system optimization control method based on power market transactions, including: Obtain power market transaction data, where the power market transaction data includes electricity price fluctuation data and load demand prediction data, and synchronously collect the topology connection relationship data between multiple power grid nodes in the power grid; Generate a simplified network structure adapted to the power trading scenario based on the power market trading data and the topological connection relationship data, and adjust the equivalent impedance parameters of the key transmission paths in the simplified network structure; Based on the adjusted equivalent impedance parameters, synergistically regulate the voltage amplitude and phase angle of the grid nodes through the multi-level current conversion regulation mechanism of the solid-state transformer to form a power distribution scheme; During the execution of the power distribution scheme, monitor the operating state data of the solid-state transformer through the edge computing nodes deployed at the grid nodes, and generate local control instructions in combination with the adjusted equivalent impedance parameters; Perform spatio-temporal 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 distribution scheme to achieve dynamic optimal control of transmission loss and trading cost.

[0007] Optionally, the generating a simplified network structure adapted to the power trading scenario based on the power market trading data and the topological connection relationship data, and adjusting the equivalent impedance parameters of the key transmission paths in the simplified network structure includes: Associate the electricity price fluctuation data in the power market trading data with the node connection mode in the topological connection relationship data and extract correlation parameters, where the correlation parameters include the electricity price fluctuation amplitude and the topological connection density, and determine the power trading sensitive paths based on the electricity price fluctuation amplitude and the topological connection density; According to the distribution characteristics of the power trading sensitive paths, extract a topological sub-structure from the original power grid topology that includes at least two core nodes and more than three associated branches, and the initial impedance parameters of each branch in the topological sub-structure are allocated based on the historical trading load ratio of the corresponding nodes; Based on the topological sub-structure, merge the redundant connection branches with the same power transmission direction between the core nodes to generate a simplified network structure that retains the power trading sensitive paths, and the equivalent impedance parameters of the key transmission paths in the simplified network structure are corrected with directional weighting according to the electricity price fluctuation data; During the operation of the simplified network structure, continuously receive the load demand prediction data in the power market trading data, and perform reverse compensation adjustment on the equivalent impedance parameters according to the distribution ratio of the predicted load on the key transmission paths, so that the equivalent impedance parameters match the power flow direction in the trading scenario.

[0008] Optionally, the synergistically regulating the voltage amplitude and phase angle of the grid nodes 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 includes: Convert the adjusted equivalent impedance parameters into voltage regulation parameters that include the allowable voltage amplitude deviation range and phase angle difference range of each power grid node. The conversion process is based on the transmission capacity ratio of the key transmission path and the phase difference ratio between adjacent nodes for distribution; Generate a multi-level regulation mode according to the voltage regulation parameters and the electricity price fluctuation data. The multi-level regulation mode includes at least three regulation levels, and the triggering conditions of each regulation level are determined based on the electricity price fluctuation amplitude threshold and the fluctuation direction, where the fluctuation direction includes peak electricity price periods and off-peak electricity price periods; Under the multi-level regulation mode, based on the measured data of the real-time voltage amplitude and phase angle of the power grid node, match the deviation range in the voltage regulation parameters. When the measured data exceeds the deviation range, trigger the switching of adjacent regulation levels, and synchronously adjust the phase angle difference range during the switching process to maintain the consistency of the power transmission direction; Generate a power distribution plan that includes the power distribution priority and distribution ratio of each node according to the voltage amplitude and phase angle constraints corresponding to the switched regulation level. The distribution priority is positively correlated with the urgency of the load demand prediction data.

[0009] Optionally, during the execution of the power distribution plan, monitor the operation status data of the solid-state transformer through the edge computing nodes deployed at the power grid nodes, and generate local control instructions in combination with the adjusted equivalent impedance parameters, including: Real-time collect the operation status data of the solid-state transformer through the edge computing unit. The operation status data includes the instantaneous voltage fluctuation value at the output end, the instantaneous current phase difference value between adjacent nodes, and the temperature change rate of the power module inside the solid-state transformer; Correlate the instantaneous voltage fluctuation value at the output end in the operation status data with the adjusted equivalent impedance parameters. When the voltage fluctuation direction is opposite to the impedance threshold change direction, mark it as the first type of abnormal state; According to the distribution position of the first type of abnormal state, extract the phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topology connection relationship data, and generate a primary control instruction that includes the voltage compensation amount and the phase correction direction in combination with the instantaneous current phase difference value between adjacent nodes; Adjust the voltage compensation amount in the primary control instruction based on the temperature change rate. The adjustment method is that when the temperature change rate exceeds the preset safe interval, reduce the voltage compensation amount proportionally, and synchronously expand the allowable deviation angle of the phase correction direction to generate local control instructions.

[0010] Optionally, based on the topological sub-structure, redundant connection branches with the same power transmission direction as the core node are merged to generate a simplified network structure that retains the power trading 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: Identify all redundant branches connected to the core node in the topological sub-structure. The determination condition for the redundant branches is that there are two or more parallel connection branches in the same power transmission direction, and the historical transaction load differences of each branch are less than the set proportional threshold; Perform a merging process on the redundant branches, retain the branch with the highest historical transaction load as the key transmission path, and superimpose the transmission capacities of the remaining branches onto the key transmission path; According to the real-time electricity price fluctuation data, mark the power transmission direction corresponding to the peak electricity price period as the positive sensitive direction, and the opposite direction corresponding to the trough electricity price period as the reverse sensitive direction; Based on different sensitive directions, perform differential weighted adjustment on the equivalent impedance parameters of the key transmission paths to generate a simplified network structure that retains the power trading sensitive paths, where the path impedance value in the positive sensitive direction is lower than that in the reverse sensitive direction, and the impedance value is adjusted according to the amplitude of the real-time electricity price fluctuation.

[0011] Optionally, according to the distribution location of the first type of abnormal state, extract the 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, including: According to the distribution location of the first type of abnormal state, locate the abnormal area in the topological connection relationship data. The abnormal area includes at least two adjacent nodes and their connection paths; Extract the phase difference constraint range between each adjacent node in the abnormal area. The phase difference constraint range is determined based on the equivalent impedance parameters and historical operation data of the corresponding paths in the simplified network structure; Compare the instantaneous value of the current phase difference between the adjacent nodes with the phase difference constraint range. When the instantaneous value of the current phase difference between the adjacent nodes exceeds the phase difference constraint range, calculate the phase deviation amount and the deviation direction; Based on the phase deviation amount and the deviation direction, generate a primary control instruction including a voltage compensation amount and a phase correction direction. The magnitude of the voltage compensation amount is proportional to the phase deviation amount, and the phase correction direction is opposite to the deviation direction.

[0012] Optionally, the spatio-temporal mapping and matching of the local control instructions and the electricity price fluctuation data, the collaborative priority sorting of the local control instructions of multiple grid nodes according to the matching result, the generation of a joint control strategy, and the dynamic adjustment of the power distribution scheme to achieve the dynamic optimal control of transmission loss and transaction cost include: Extract the generation time of the local control instructions of each grid node and the corresponding node positions, and perform time window and regional division matching with the electricity price fluctuation data in the same time period to form a spatio-temporal correlation mapping table as the matching result; According to the matching result, mark the time periods and regions where the electricity price fluctuation data exceeds the preset threshold as high-sensitivity time period regions, and automatically raise the priority level of the local control instructions within the high-sensitivity time period regions; Perform cross-verification on the local control instructions of each grid node within the same time window. When it is detected that there is a conflict in the phase correction direction of the instructions of adjacent nodes, preferentially execute the local control instructions in the high-sensitivity time period regions; Generate a joint control strategy based on the preferential execution result, and dynamically adjust the power distribution ratio of each node in the power distribution scheme according to the joint control strategy, so as to increase the power transmission margin in the high-sensitivity time period regions and simultaneously reduce the transmission loss in the non-sensitive regions.

[0013] In a second aspect, the present application provides a power system optimization control device based on electricity market transactions, including: An acquisition module, configured to acquire electricity market transaction data, where the electricity market transaction data includes electricity price fluctuation data and load demand forecast data, and synchronously 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 electricity trading scenario according to the electricity market transaction data and the topological connection relationship data, and adjust the equivalent impedance parameters of key transmission paths in the simplified network structure; An adjustment module, configured to perform collaborative adjustment on the voltage amplitude and phase angle of grid nodes through a multi-level current conversion adjustment mechanism of a solid-state transformer based on the adjusted equivalent impedance parameters to form a power distribution scheme; A monitoring module, configured to monitor the operation state data of the solid-state transformer through edge computing nodes deployed at the grid nodes during the execution of the power distribution scheme, and generate local control instructions in combination with the adjusted equivalent impedance parameters; A generation module, configured to perform spatio-temporal mapping and matching of 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 result, generate a joint control strategy, and dynamically adjust the power distribution scheme to achieve the dynamic optimal control of transmission loss and transaction cost.

[0014] In a third aspect, an embodiment of the present application provides a computing device, including 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.

[0015] 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.

[0016] In the embodiment of the present application, power market transaction data is obtained, where the power market transaction data includes electricity price fluctuation data and load demand forecast data, and the topological connection relationship data between multiple power grid nodes in the power grid is synchronously collected; according to the power market transaction data and the topological connection relationship data, a simplified network structure adapted to the power trading 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 power grid nodes are coordinately adjusted through the multi-level current conversion adjustment mechanism of the solid-state transformer to form a power distribution plan; during the execution of the power distribution plan, the operation state data of the solid-state transformer is monitored by the edge computing nodes deployed at the power grid nodes, and local control instructions are generated in combination with the adjusted equivalent impedance parameters; the local control instructions are subjected to spatio-temporal mapping matching with the electricity price fluctuation data, and the local control instructions of multiple power grid nodes are coordinately prioritized according to the matching result, and a joint control strategy is generated and the power distribution plan is dynamically adjusted to achieve dynamic optimization control of transmission loss and transaction cost.

[0017] The technical solution of the present application has the following beneficial effects: By synchronously collecting electricity price fluctuations, load forecasts, and dynamic topology data, it ensures the real-time matching of market trading demands and the physical state of the power grid, providing full-dimensional input for subsequent dynamic optimization; generating a dynamically adapted simplified network structure based on the trading scenario, reducing the computational complexity under complex topologies, and at the same time realizing the dynamic adaptation of the transmission characteristics of key paths and market fluctuations through equivalent impedance parameter adjustment; using the multi-level adjustment mechanism of the solid-state transformer to quickly respond to changes in impedance parameters, and through the coordinated control of voltage amplitude and phase angle, ensuring that the economic objectives of the power distribution plan and the power grid stability are synchronously achieved; through edge computing nodes to monitor the device state in real time and fuse topology parameters, realizing the rapid generation and dynamic correction of local control instructions, and enhancing the local response ability to topology changes; based on spatio-temporal mapping matching and multi-instruction collaborative sorting, solving the contradiction between decentralized control and global optimization, dynamically balancing transmission loss and transaction cost, and achieving the optimal system-level economy.

[0018] Furthermore, by dynamically correlating electricity price fluctuation data with topological connection patterns, sensitive power trading paths are determined; a topological substructure containing core nodes and associated branches is extracted from the original power grid, and initial impedance parameters are allocated based on historical trading loads; redundant branches are merged to generate a simplified network structure that retains sensitive paths, and the impedance of key paths is directionally weighted and corrected according to real-time electricity price data; impedance parameters are dynamically compensated in combination with load forecasting data to ensure their matching with the power flow direction in the trading scenario. Through the dynamic identification of trading sensitive paths and the construction of a simplified network, the adaptation accuracy of the power grid model to market fluctuations is significantly improved; based on the dynamic adjustment mechanism of impedance parameters with electricity price directional weighting and load forecasting compensation, real-time collaborative optimization of the transmission capacity of key paths and the goal of trading economy is achieved, effectively reducing the risk of control mismatch caused by frequent topological changes.

[0019] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 The flowchart of the power system optimization control method based on power market trading provided by the present application is shown; Figure 2 The structural schematic diagram of the power system optimization control device based on power market trading provided by the present application is shown; Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions 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.

[0023] In some of the processes described in the specification, claims, and above-mentioned drawings of the present application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The operation numbers such as 101, 102, etc. are only used to distinguish different operations, and the numbers themselves do not represent any execution order. 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 such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0024] Researchers have found that existing power system optimization control methods generally have problems such as lag in economic optimization and mismatch of physical transmission characteristics in the scenario of frequent changes in power grid topology and dynamic coupling of power market transactions, and it is difficult to achieve real-time coordinated control of transmission losses and transaction costs. Based on this, a dynamic optimization control method for power systems based on power market transactions is provided. 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 the transmission parameters of the key path are dynamically adjusted; combined with the multi-level regulation of solid-state transformers and the generation of local instructions by edge computing nodes, an economically oriented power distribution scheme is formed; further, through space-time mapping matching and multi-node instruction collaborative sorting, the global power distribution strategy is dynamically optimized. This method can achieve dynamic balance control of transmission losses and market transaction costs in the scenario of rapid changes in power grid topology, and improve the economy and stability of power system operation.

[0025] The technical solution of the present application is applicable to the power adaptive regulation scenario under frequent changes in power grid topology.

[0026] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0027] Figure 1 The flowchart of the power system optimization control method based on power market transactions provided for the embodiments of the present application is as Figure 1 shown, and the method includes: 101. Obtain power market transaction data, where the power market transaction data includes electricity price fluctuation data and load demand forecast data, and synchronously collect topological connection relationship data between multiple power grid nodes in the power grid; In this step, the dynamic topological connection relation data refers to the real-time data of the physical connection status (such as line switching and equipment start / stop) between nodes in the power grid changing over time, including node connection patterns, transmission path availability, and real-time power flow information. The electricity price fluctuation data reflects the dynamic change trends of electricity prices in different time periods and regions in the power market, including peak electricity price periods, off-peak electricity price periods, and fluctuation amplitude characteristics.

[0028] In the embodiment of this application, through the multi-source data acquisition module deployed in the power grid dispatching center, the electricity price fluctuation data (such as time-of-use electricity price curves, regional electricity price difference matrices) released by the power trading platform and the future load demand distribution data (such as node-level load prediction values) generated by the load forecasting system are periodically acquired. At the same time, the wide-area measurement system (WMS) is used to capture the topological connection status between power grid nodes in real time (including line on / off status, equipment operation modes), and through the spatio-temporal correlation analysis algorithm, the electricity price data and topological data are aligned in time stamp and regionally mapped to form a heterogeneous data fusion framework. For example, using the event-driven data stream processing technology, real-time data updates are triggered for topological change events (such as line faults), and dynamic topological features (such as node connection density, power transmission direction distribution) are extracted through the sliding time window mechanism. The finally integrated topological connection relation data includes a triple of time-synchronized electricity price, load, and topology, which serves as the input for the subsequent optimization model.

[0029] Suppose the topological structure of a regional power grid mutates due to a line fault. At this time, in step 101, the real-time electricity price fluctuation data (such as the electricity price suddenly increasing from 0.5 yuan / kWh to 0.8 yuan / kWh), load demand prediction data (such as the load prediction value in the fault area decreasing by 20%), and dynamic topological connection relation data (such as the fault line being disconnected and the standby line being connected) in this area are collected. Through spatio-temporal alignment, the system identifies that the fault period overlaps with the electricity price peak period, triggering the subsequent optimization process.

[0030] 102. Generate a simplified network structure adapted to the power trading scenario according to the power market trading data and the topological connection relation data, and adjust the equivalent impedance parameters of the key transmission paths in the simplified network structure; In this step, the simplified network structure is an abstract network model extracted based on the original power grid topology and retaining the key transaction-sensitive paths, which is used to reduce the optimization calculation complexity.

[0031] In the embodiments of the present application, first, high-frequency trading nodes are screened according to power market trading data (such as nodes in the top 10% of hourly trading volume), and combined with topological connection relationship data, power grid nodes with frequent trading and direct electrical connection are obtained, and the power grid is independently structurally divided according to the power grid nodes; subsequently, each divided independent structure is merged into a single equivalent network structure as the simplified network structure; the line load changes in the simplified network structure are analyzed, the key transmission paths are identified according to the line load changes, and the equivalent impedance parameters of the key transmission paths are reversely calculated based on the actual operation data of the simplified network structure.

[0032] For example, continuing the above example, in step 102, first, high-frequency trading nodes in the top 10% of hourly trading volume are screened (such as load center node A and standby power node B), and it is confirmed that there is a direct electrical connection between these nodes in combination with topological connection relationship data; subsequently, node A, node B, and the 4 paths directly connected to them are divided into an independent structure and merged into a simplified network structure including 2 core nodes and 1 equivalent path. Analyzing the historical load data of this equivalent path, it is found that its peak load accounts for 35% of the whole network and is marked as the key transmission path; finally, according to the actual operation data of the simplified network structure (such as the real-time transmission current value and voltage loss value after a fault), the equivalent impedance parameter of the key path is reversely calculated to be 0.12Ω, which is 20% lower than the original value of 0.15Ω to adapt to the power transmission requirements during high electricity price periods. 103. Based on the adjusted equivalent impedance parameter, the voltage amplitude and phase angle of the power grid nodes are coordinately adjusted through the multi-level current conversion regulation mechanism of the solid-state transformer to form a power distribution plan; In this step, the multi-level current conversion regulation mechanism realizes the refined coordinated regulation of voltage amplitude and phase angle through the series-parallel combination of multi-level power modules inside the solid-state transformer.

[0033] In the embodiments of the present application, based on the equivalent impedance parameter output in step 102, a solid-state transformer regulation strategy based on model predictive control (MPC) is adopted. First, the equivalent impedance parameter is mapped to voltage regulation boundary conditions (such as the allowable voltage deviation range of ±5%), and the phase difference constraint between adjacent nodes is calculated through a dynamic phase coupling algorithm (such as the phase difference does not exceed 10°). The solid-state transformer adjusts the output voltage amplitude and phase angle through a multi-level current conversion module (such as an H-bridge cascade structure) according to the real-time voltage measurement data to make it meet the constraint conditions. At the same time, a distributed consensus algorithm is introduced to coordinate the adjustment actions of multiple nodes to ensure the economic objective of the global power distribution plan (such as preferentially meeting the power supply in high electricity price areas). Finally, a power distribution plan is generated, including the power injection priority and distribution ratio of each node.

[0034] For example, continuing with the previous example, in step 103, according to the adjusted equivalent impedance parameter (0.12 Ω), the allowable range of the node voltage in the fault area is set to 215 V - 225 V, and the phase difference constraint between adjacent nodes is 8°. The solid-state transformer boosts the output voltage from 210 V to 220 V through a cascaded converter module and adjusts the phase angle to reduce the phase difference to 6°, forming a power distribution scheme that preferentially supplies power to high electricity price areas.

[0035] 104. During the execution of the power distribution scheme, the edge computing nodes deployed at the grid nodes monitor the operating state data of the solid-state transformer, and generate local control instructions in combination with the adjusted equivalent impedance parameters; In this step, the local control instruction is a real-time adjustment instruction generated by the edge node for a specific grid node, including the voltage compensation amount and the phase correction direction.

[0036] In the embodiment of the present application, during the execution of the power distribution scheme, the edge computing nodes deployed at the grid nodes collect the operating state data of the solid-state transformer in real time (such as the instantaneous voltage fluctuation value at the output end and the internal module temperature change rate), and eliminate the noise interference through an adaptive filtering technique. Combining the equivalent impedance parameters updated in step 102, an anomaly detection model based on rule reasoning (such as marking as an anomaly when the voltage fluctuation direction is opposite to the impedance adjustment direction) is used to generate a preliminary control instruction. Further, through a dynamic weight allocation algorithm, the device safety parameters (such as the temperature change rate) and electrical parameters (such as the phase difference) are fused to compensate and correct the preliminary control instruction (such as reducing the voltage compensation amount at high temperatures). Finally, the local control instruction is output, including the real-time adjustment amount and direction of each node.

[0037] For example, continuing with the previous example, the edge computing node monitors that the temperature change rate of the solid-state transformer in the fault area exceeds the limit (0.5 °C / s). Combining the equivalent impedance parameter (0.12 Ω), a primary instruction is generated: voltage compensation +3 V, and the phase correction direction is counterclockwise. After safety compensation, the final local control instruction is adjusted to voltage compensation +2 V, and the phase correction angle is relaxed to 7°.

[0038] 105. Perform spatio-temporal mapping and 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 distribution scheme to achieve dynamic optimization control of transmission loss and transaction cost.

[0039] In this step, the joint control strategy is a global optimization scheme generated by fusing the local instructions of multiple nodes, which balances the transmission loss and the transaction cost.

[0040] In the embodiments of the present application, first, a spatio-temporal mapping match is performed between the local control instruction and the electricity price fluctuation data within 1 hour after the instruction is issued (for example, the instruction issued at 14:00 is associated with the electricity price data from 14:00 to 15:00); secondly, the spatio-temporal mapping match result is quantified into a matching degree, a priority score is calculated according to the matching degree, and the local control instructions of multiple power grid nodes are sorted according to the priority score; then, the top 30% of the high-priority control instructions are selected from the priority sorting result, and after the conflicts between the high-priority control instructions are processed by using a conflict resolution rule base, they are integrated into a joint control strategy; at the same time, the power distribution priorities of each node in the power distribution plan are 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.

[0041] For example, continuing with the above example, step 105 first performs a spatio-temporal mapping match between the local control instruction issued at 14:00 in the fault area and the electricity price fluctuation data (with an 18% increase) from 14:00 to 15:00, and calculates the matching degree score to be 0.92 (full score 1.0); secondly, a priority score is generated according to the matching degree score and the load emergency weight (0.92×0.7 + 0.3×0.3 = 0.83), and the instruction in the fault area is sorted as the highest priority; subsequently, the conflict resolution rule base is used to process the phase direction conflict with the instruction in the adjacent non-fault area, the instruction in the fault area is retained, and the phase constraint in the non-fault area is relaxed to ±8°; finally, the power distribution plan is adjusted according to the priority score through the dynamic resource allocation algorithm, so that the power ratio of the fault area is increased from 40% to 52%, and the transmission loss in the non-fault area is reduced by 8%, realizing the joint optimization control of transmission loss and transaction cost. Steps 101-105 dynamically fuse the electricity market data and the physical state of the power grid, construct a simplified network model adapted to the trading scenario, and based on the cooperative control mechanism of the solid-state transformer and edge computing, realize the rapid response of power distribution; further, through spatio-temporal mapping and multi-objective optimization, dynamically balance the transmission loss and transaction cost in the scenario of frequent topological changes, and improve the system economy and operation stability.

[0042] In order to improve the dynamic adaptation ability of the power grid to electricity market transactions in the scenario of frequent topological changes and solve the problem of mismatch between transmission characteristics and market fluctuations caused by traditional static models, a simplified network model driven by transaction-sensitive paths is constructed by coupling the characteristics of electricity price fluctuations and the law of topological dynamic evolution, and real-time optimization of equivalent impedance parameters is realized based on a multi-stage dynamic correction mechanism. In some embodiments, the generating a simplified network structure adapted to the electricity trading scenario according to the electricity market trading data and the topological connection relationship data, and adjusting the equivalent impedance parameters of the key transmission paths in the simplified network structure includes: 201. Correlate the electricity price fluctuation data in the power market trading data with the node connection patterns in the topological connection relationship data, and extract correlation parameters. The correlation parameters include the electricity price fluctuation amplitude and the topological connection density. Determine the sensitive paths for power trading based on the electricity price fluctuation amplitude and the topological connection density. In step 201, the electricity price fluctuation amplitude refers to the degree of intensity of electricity price changes in different time periods in the power market, including quantitative indicators such as peak volatility rate and valley value recovery rate. The topological connection density describes the physical connection strength between nodes in a specific area of the power grid, and is measured by the number of effective transmission paths per unit area or per unit number of nodes.

[0043] In the embodiment of this application, the correlation between the electricity price fluctuation data and the topological connection density is analyzed through the mutual information quantization algorithm. First, extract the time series characteristics of the electricity price fluctuation amplitude (such as fluctuation extreme points, change gradients) and the spatial distribution characteristics of the topological connection density (such as regional path redundancy, node degree centrality), and construct a spatio-temporal correlation matrix. Use the dynamic community discovery algorithm to identify the high-coupling regions of electricity price fluctuations and topological connections in the spatio-temporal correlation matrix (such as the high connection density region corresponding to the electricity price peak period), and screen out the sensitive paths for power trading based on the maximum information coefficient (MIC). Specifically, score the path sensitivity, and when the score exceeds the dynamic threshold (such as score = electricity price fluctuation amplitude × connection density weight), mark it as a sensitive path for power trading, and finally generate a sensitive path topological graph.

[0044] 202. According to the distribution characteristics of the sensitive paths for power trading, extract a topological substructure from the original power grid topology that includes at least two core nodes and more than three associated branches. The initial impedance parameters of each branch in the topological substructure are allocated based on the historical trading load ratio of the corresponding node. In step 202, the core node is a power grid node that undertakes the main power transmission task in the trading sensitive path, usually a load center or a power source access point. The historical trading load ratio refers to the percentage of the load borne by the node in the historical trading cycle in the total load of the entire network.

[0045] In the embodiments 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 trading load influence of each node is calculated (such as load ratio × path betweenness centrality), and the nodes ranked in the top 10% in terms of influence are selected as core nodes. Subsequently, an improved Prim algorithm is used to construct a minimum spanning tree, and a topological substructure including the core nodes and their associated branches is extracted. The initial impedance parameter allocation adopts the load weighting method: according to the sum of the historical trading load ratios of the nodes connected by each branch (such as the load ratio of node A is 30% + the load ratio of node B is 20% → the initial impedance of branch AB = base impedance × 50%), the base impedance value is dynamically adjusted (such as the base value is determined by the regional average transmission capacity), and finally the initial impedance parameters of the topological substructure and its branches are generated.

[0046] 203. Based on the topological substructure, redundant connection branches with the same power transmission direction as the core nodes are merged to generate a simplified network structure that retains the power trading 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. In step 203, the redundant connection branches are multiple parallel paths existing in the same power transmission direction, and their functions can be replaced by a single main path without affecting the transmission capacity. Directional weighted correction is a mechanism for differentially adjusting the equivalent impedance parameters according to the direction of electricity price fluctuation (such as electricity price increase or decrease).

[0047] In the embodiments of the present application, the dynamic graph pruning technology is used to merge the redundant connection branches. First, based on the power transmission direction clustering algorithm (such as the improved DBSCAN), the co-directional redundant branches are identified, and the branch with the largest transmission capacity is retained as the main path, and the capacities of the remaining branches are superimposed on the main path proportionally to generate a simplified network structure that retains the power trading sensitive paths. Among them, the equivalent impedance parameter correction adopts a non-linear directional weighting model: when the real-time electricity price fluctuation direction is positive (increase), a negative correction is applied to the impedance of the main path (such as impedance value = original value × (1 - electricity price increase rate × weight coefficient)) to improve the transmission capacity; when the reverse fluctuation occurs, a positive correction is applied (such as impedance value = original value × (1 + electricity price decrease rate × weight coefficient)) to limit the reverse power flow. The weight coefficient is dynamically calculated through the sigmoid function to ensure that the correction amplitude is non-linearly adapted to the electricity price fluctuation rate.

[0048] 204. During the operation of the simplified network structure, continuously receive the load demand prediction data in the power market trading data, and perform reverse compensation adjustment on the equivalent impedance parameters according to the distribution ratio of the predicted load on the key transmission paths, so that the equivalent impedance parameters match the power flow direction in the trading scenario.

[0049] In step 204, the reverse compensation adjustment is to perform a secondary adjustment on the corrected equivalent impedance parameters according to the load prediction data to offset the influence of the predicted load distribution deviation. The distribution ratio is the distribution ratio of the predicted load on each key transmission path, reflecting the expected characteristics of the future power flow direction.

[0050] In the embodiment of the present application, a sliding window prediction fusion mechanism is adopted to realize impedance parameter compensation. After continuously receiving the load demand prediction data, the load distribution trend is extracted through a sliding time window (for example, the load ratio of path A within the next 1 hour increases from 40% to 60%). Based on the trend analysis result, 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 this path (for example, impedance value = corrected value × (1 - load growth ratio × compensation coefficient)) to match the expected power flow direction. The compensation coefficient is adjusted through a dynamic feedback mechanism: when the deviation between the actual load distribution and the predicted value exceeds the threshold, the compensation coefficient is adaptively increased (for example, when the deviation increases by 5% each time, the coefficient is increased by 0.1), forming a closed-loop compensation control to keep the equivalent impedance parameter matched with the power flow direction in the trading scenario.

[0051] The following is a specific example: Suppose that due to a typhoon in a certain area, multiple wind turbines are disconnected from the grid (a scenario of frequent topological changes), and at the same time, there are significant fluctuations in the intraday electricity price in the power market (the peak value is 120% higher than the valley value). In step 201, the coupling relationship between the high electricity price fluctuations (peak value of 0.9 yuan / kWh) and the decrease in topological connection density (3 path breaks) in the typhoon-affected area is identified, and 2 paths connecting the remaining wind power clusters and the load center are marked as transaction-sensitive paths; in step 202, a sub-structure including a wind power access point (core node A), a load center (core node B), and 4 associated branches is extracted, and the initial impedance of the branches is allocated according to the historical load ratio (A: 35%, B: 45%) (0.18Ω, 0.15Ω, etc.); in step 203, after merging the redundant branches in the same direction, a simplified network structure is generated, and the impedance of the main path is negatively corrected according to the real-time electricity price increase (120%) (0.15Ω → 0.11Ω); in step 204, combined with the load prediction data (60% of the load will concentrate on node B within the next 1 hour), a secondary compensation is applied to the impedance of the main path (0.11Ω → 0.09Ω), and finally, the accurate matching between the equivalent impedance parameter and the power flow direction is achieved.

[0052] Steps 201 - 204 generate transaction-sensitive paths by dynamically associating electricity price fluctuations and topological evolution characteristics, and combine a multi-stage impedance correction mechanism (initial allocation → directional weighting → reverse compensation) to make the grid transmission characteristics adapt to the market trading requirements in real time; in the scenario of frequent topological changes, through redundant path merging and closed-loop parameter compensation, the transmission loss caused by model mismatch is significantly reduced, and at the same time, the power transmission efficiency during high electricity price periods is improved, realizing the coordinated control of economic optimization and physical operation stability.

[0053] In order to achieve dynamic and refined control of power distribution in scenarios where the power grid topology changes frequently, and to solve the problem of the conflict between voltage stability and economic objectives caused by the single adjustment mode of traditional methods, a power distribution mechanism driven by dynamic priority is constructed through deep coupling of multi-level adjustment modes with real-time electricity price fluctuations and load demands, ensuring that the system can respond quickly and maintain an optimal operating state when the topology mutates. In some embodiments, based on the adjusted equivalent impedance parameters, the multi-level current conversion adjustment mechanism of the solid-state transformer is used to coordinately adjust the voltage amplitude and phase angle of the power grid nodes to form a power distribution scheme, including: 301. Convert the adjusted equivalent impedance parameters into voltage adjustment parameters including the allowable voltage amplitude deviation range and phase angle difference range of each power grid node. The conversion process is based on the transmission capacity ratio of the key transmission path and the phase difference ratio between adjacent nodes for distribution; In step 301, the transmission capacity ratio refers to the percentage of the maximum power that the key transmission path can carry in the current power grid operating state in the total transmission capacity of the whole network. The phase difference ratio is the ratio of the actual phase angle difference between adjacent nodes to their allowable maximum phase angle difference, which is used to quantify the severity of the phase shift.

[0054] In the embodiments of the present application, a mapping model of dynamic impedance and voltage is used to convert the equivalent impedance parameters into voltage adjustment 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 allowable voltage amplitude deviation range of each node is determined through a weighted distribution algorithm (such as for every 10% increase in capacity ratio, the allowable deviation expands by ±0.5%). At the same time, according to the phase difference ratio between adjacent nodes (such as the phase difference between nodes B - C is 5°, and the maximum allowable value is 8° → ratio 62.5%), the fuzzy logic inference mechanism is used to dynamically adjust the phase angle difference range (such as when the ratio exceeds 70%, the allowable range is reduced). Finally, voltage adjustment parameters including the voltage amplitude deviation range (such as ±3%) and the phase angle difference range (such as ±7°) are generated.

[0055] 302. Generate a multi-level adjustment mode according to the voltage adjustment parameters and the electricity price fluctuation data. The multi-level adjustment mode includes at least three adjustment levels, and the triggering conditions of each adjustment level are determined based on the electricity price fluctuation amplitude threshold and the fluctuation direction. The fluctuation direction includes the peak electricity price period and the off-peak electricity price period; In step 302, the adjustment level is the control intensity level divided according to the electricity price fluctuation amplitude and direction, and different levels correspond to different voltage and phase constraint conditions. The fluctuation direction is a qualitative description of the electricity price change trend, including the peak electricity price period when the electricity price continues to rise and the off-peak electricity price period when it continues to fall.

[0056] In the embodiments of the present application, an event-driven hierarchical strategy is adopted to generate a multi-level regulation mode. First, based on the real-time electricity price fluctuation data, the fluctuation amplitude is calculated by the sliding window statistical method (for example, the electricity price increases by 15% within 1 hour), and the fluctuation direction is determined by using a trend direction identification algorithm (such as Hodrick-Prescott filtering). A three-level regulation mode is set: Basic regulation (fluctuation amplitude < 5%): Maintain the current voltage and phase constraints; Enhanced regulation (5% ≤ fluctuation amplitude < 10% and the direction is peak): Tighten the voltage deviation range (such as ±2%), and relax the phase difference range (such as ±9°) to improve the transmission capacity; Emergency regulation (fluctuation amplitude ≥ 10% or the direction is trough): Expand the voltage deviation range (such as ±4%), and strictly limit the phase difference (such as ±5°) to suppress reverse power. The trigger conditions are dynamically updated through a finite state machine model to ensure that the mode switching is synchronized with the market fluctuations.

[0057] 303. Under the multi-level regulation mode, based on the measured data of the real-time voltage amplitude and phase angle of the grid nodes, match the deviation range in the voltage regulation parameters. When the measured data exceeds the deviation range, trigger the switching of adjacent regulation levels, and synchronously adjust the phase angle difference range during the switching process to maintain the consistency of the power transmission direction; In step 303, the deviation range is the maximum interval that the voltage amplitude or phase angle is allowed to deviate from the standard value. The phase angle difference range is the allowable fluctuation interval of the phase angle difference between adjacent nodes, which is used to constrain the power transmission direction.

[0058] In the embodiments of the present application, the regulation level switching is realized through an adaptive threshold matching mechanism. After the voltage amplitude (such as the voltage of node D is 225V) and phase angle (such as the phase difference between node D and E is 6°) data of the grid nodes are collected in real time, the sliding mean filtering technology is adopted to eliminate instantaneous noise, and it is compared 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 continuously, trigger an event-triggered level switching protocol: if the voltage exceeds the limit, switch to a higher level (such as basic → enhanced); if the phase exceeds the limit, switch to a lower level (such as emergency → enhanced). During the switching process, the phase angle difference range between adjacent nodes is synchronously adjusted through a distributed consistency protocol (such as adjusted from ±7° to ±8°) to ensure that the power transmission direction does not reverse.

[0059] 304. According to the voltage amplitude and phase angle constraints corresponding to the switched regulation level, generate a power distribution plan including the power distribution priority and distribution ratio of each node, and the distribution priority is positively correlated with the urgency of the load demand prediction data.

[0060] In step 304, the assigned priority is the order of priority for node power injection, which is determined by the urgency of the load demand. The allocation ratio is the proportion obtained by each node in the total power allocation and is positively correlated with the priority.

[0061] In the embodiment of the present application, a power allocation scheme is generated using a dynamic priority queue. First, according to the urgency of the load demand prediction data (e.g., the load gap in area X reaches 30% in the next 1 hour), the urgency is quantified into node priority weights (e.g., the weight of the emergency area is increased by 50%) through the entropy weight method; based on the node priority weights, the priority coefficients for the power allocation of each node are set, and the priority coefficients and the voltage amplitude and phase angle constraints corresponding to the switched regulation levels are used as the input parameters of the multi-objective particle swarm optimization algorithm to dynamically solve the power allocation ratio of each node (e.g., the allocation ratio of high-priority nodes is increased from the baseline value of 25% to 40%). Finally, in the generated power allocation scheme including the power allocation priority and allocation ratio of each node, the nodes with high priority weights automatically obtain a larger power injection margin through the optimization algorithm, and at the same time, the voltage and phase deviation ranges are strictly limited at the algorithm constraint layer to achieve the safety regulation goal under the emergency level. The following is a specific example: Suppose that due to the sudden switching of large equipment in an industrial park, the power grid topology is instantaneously reconstructed (the line impedance changes by 30%), and at the same time, there is a peak-hour price fluctuation in the power market (the price increases by 18% within 1 hour). In step 301, the mutated equivalent impedance parameter (0.25 Ω → 0.18 Ω) is converted into a voltage regulation parameter, and the node voltage deviation range is set to ±4%, and the allowable phase difference between adjacent nodes is ±6°; in step 302, since the price fluctuation amplitude exceeds the threshold (18%), the emergency regulation mode is triggered, the voltage deviation is relaxed to ±5%, and the phase difference is tightened to ±4°; in step 303, it is monitored that the voltage of a certain node instantaneously exceeds the limit (+5.2%), and the enhanced regulation mode is switched, the voltage deviation is adjusted to ±3%, and the phase difference is simultaneously relaxed to ±7°; in step 304, in combination with the load prediction data (the emergency load gap in the industrial park is 25%), a power allocation scheme is generated, and the priority of the nodes in this area is increased to the highest, and the allocation ratio is increased from 35% to 50%.

[0062] Steps 301-304 achieve a refined balance of voltage stability and economic objectives through the dynamic adaptation of multi-level regulation modes and real-time electricity prices and load data; in scenarios with frequent topology changes, a priority-driven power allocation mechanism is used to quickly respond to local anomalies, ensure the power supply reliability of high-emergency load areas, and at the same time suppress the ineffective power loss in non-critical areas, improving the synergy of the overall system operation efficiency and market trading benefits.

[0063] In order to achieve fast response and safety optimization of device-level control in scenarios with frequent changes in power grid topology, and to solve the problem of lag in local anomaly handling caused by data transmission delay in traditional centralized control, this solution constructs a closed-loop control link driven by the operating state of the solid-state transformer through the dynamic monitoring and multi-parameter fusion mechanism of the edge computing unit, ensuring the coordinated improvement of the real-time performance of power regulation and device safety under abnormal conditions. In some embodiments, during the execution of the power distribution scheme, the operating state data of the solid-state transformer is monitored by the edge computing node deployed at the power grid node, and local control instructions are generated in combination with the adjusted equivalent impedance parameters, including: 401. Real-time collect the operating state data of the solid-state transformer through the edge computing unit, where the operating state data includes the instantaneous voltage fluctuation value at the output end, the instantaneous current phase difference value between adjacent nodes, and the temperature change rate of the power module inside the solid-state transformer; In step 401, the instantaneous voltage fluctuation value at the output end is the instantaneous deviation of the voltage at the output end of the solid-state transformer from the rated value within a very short time (such as millisecond level). The instantaneous current phase difference value between adjacent nodes is the instantaneous measured value of the phase angle difference of the current waveforms of adjacent power grid nodes at the same time point.

[0064] In the embodiments of the present application, a high-speed data acquisition module embedded in the edge computing unit is used to synchronously obtain multi-dimensional operating data of the solid-state transformer at a microsecond-level sampling frequency. The sliding window filtering technology is used to suppress the noise of the original voltage fluctuation value (such as removing instantaneous spikes above ±5%), and the instantaneous current phase difference value between adjacent nodes is accurately extracted through the phase-locked loop (PLL) algorithm. At the same time, a thermocouple array is used to monitor the temperature gradient distribution inside the power module of the solid-state transformer in real time, and the weighted average value of the temperature change rate of each module is calculated (such as the temperature change rate of module A is 0.3 °C / s, and that of module B is 0.5 °C / s → the overall change rate is 0.4 °C / s). Finally, the operating state data including the voltage fluctuation value, the instantaneous phase difference value, and the temperature change rate is generated.

[0065] 402. Correlate the instantaneous voltage fluctuation value at the output end in the operating state data with the adjusted equivalent impedance parameter. When the voltage fluctuation direction is opposite to the impedance threshold change direction, it is marked as the first type of abnormal state; In step 402, the impedance threshold change direction is the increasing or decreasing trend of the impedance value during the adjustment of the equivalent impedance parameter (such as 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 operating anomaly mark caused by the reverse conflict between the voltage fluctuation direction and the impedance adjustment direction.

[0066] In the embodiments of the present application, the fluctuation direction of the instantaneous voltage fluctuation value at the output end and the change direction of the impedance threshold of the adjusted equivalent impedance parameter are obtained, and a dynamic correlation analysis model is used to perform coupling analysis on the voltage fluctuation direction (such as voltage rise or fall) and the impedance threshold change direction (such as impedance decrease or increase). First, a correlation coefficient matrix of the voltage fluctuation direction vector (positive / negative) and the impedance change direction vector is established. When the correlation coefficient is lower than a preset critical value (such as -0.8), it is determined as an inverse conflict. For example, if the impedance threshold is decreasing (negative change) while the voltage fluctuation continues to rise (positive change), a first type of abnormal state mark is triggered, and the distribution position of the abnormal state is determined by mapping the node coordinates in the topological connection relationship data.

[0067] 403. According to the distribution position of the first type of abnormal state, extract the phase difference constraint range between at least two adjacent nodes associated with the distribution position 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; In step 403, the phase difference constraint range is the maximum interval of the allowable current phase angle difference between adjacent nodes in the power grid operation regulations. The phase correction direction is the change direction of the current phase angle that needs to be adjusted to eliminate the phase deviation (such as clockwise or counterclockwise).

[0068] In the embodiments of the present application, a primary control instruction is generated based on a fuzzy rule inference engine. First, extract the topological connection relationship data in the area where the first type of abnormal state is located, and obtain the phase difference constraint range of adjacent nodes (such as the allowable phase difference between nodes C-D is ±8°). Compare the instantaneous value of the real-time current phase difference (such as the measured phase difference between nodes C-D is 10°) with the constraint range, calculate the deviation amount (10° - 8° = +2°) and the deviation direction (positive overlimit). Determine the voltage compensation amount through a fuzzy membership function (such as compensating 0.5V of voltage for every 1° of overlimit of the deviation), and generate a phase correction direction in combination with the deviation direction (such as counterclockwise correction is required when the positive is overlimit). Finally, output a primary control instruction including a voltage compensation amount (+1V) and a phase correction direction (counterclockwise).

[0069] 404. Adjust the voltage compensation amount in the primary control instruction based on the temperature change rate. The adjustment method is that when the temperature change rate exceeds a preset safe interval, the voltage compensation amount is reduced proportionally, and the allowable deviation angle of the phase correction direction is synchronously expanded to generate a local control instruction.

[0070] In step 404, the preset safe interval is the allowable range of the 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 range of adjustment angle error that can be accepted when the phase correction direction is executed.

[0071] In the embodiments of the present application, a dynamic weight allocation algorithm is adopted to perform temperature compensation and correction on the primary control instruction. When the temperature change rate exceeds the upper limit of the safe 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 to +0.6V). At the same time, based on the device thermal inertia model, the allowable deviation angle of the phase correction direction is extended (such as the original ±1° → extended to ±3°) to avoid excessive adjustment caused by temperature limitations. The finally generated local control instruction includes the corrected voltage compensation amount, the extended deviation angle, and the phase correction direction, and is sent to the solid-state transformer execution module through the edge computing unit.

[0072] The following is a specific example: Suppose a main line of a substation trips (topological mutation), and after the standby line is connected, the voltage at the output end of the solid-state transformer fluctuates violently (instantaneous fluctuation value +8%), and at the same time, the temperature of the internal power module rises rapidly (change rate 0.6 °C / s). In step 401, the edge computing unit collects the voltage fluctuation value +8%, the instantaneous value of the phase difference of the current at the adjacent node 12° (exceeding the limit by 4°), and the temperature change rate 0.6 °C / s; the correlation analysis in step 402 finds that the positive voltage fluctuation conflicts with the negative impedance adjustment (the impedance of the standby line decreases), and marks this area as the first type of abnormal state; in step 403, the constraint range of the phase difference of the adjacent nodes in the fault area is extracted as ±7°, and a primary control instruction is generated: voltage compensation +2V, phase counterclockwise correction 5°; in step 404, due to the over-limit temperature change rate (0.6 °C / s), the adjusted instruction is voltage compensation +1.2V, and the allowable deviation angle is extended to ±4°, generating a local control instruction.

[0073] Steps 401-404 quickly identify and correct the abnormal operation of the solid-state transformer in the topological mutation scenario through the multi-source data real-time acquisition and dynamic compensation mechanism of the edge computing unit; combined with the instruction adjustment strategy with temperature safety constraints, accurate adjustment of voltage and phase is achieved on the premise of ensuring the reliability of the equipment, effectively improving the response speed and safety of local power control, and avoiding the risk of cascading failures caused by overload or overheating.

[0074] In order to improve the transmission efficiency and economic adaptability of transaction-sensitive paths in scenarios where the power grid topology changes frequently, and to solve the problem of inaccurate power distribution 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 the transmission capacity of key paths and market demand. In some embodiments, based on the topological substructure, redundant connection branches with the same power transmission direction as the core node are merged to generate a simplified network structure that retains the power transaction sensitive path. 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: 501. Identify all redundant branches connected to the core node in the topological substructure. The determination condition for the redundant branch is that there are two or more parallel-connected branches in the same power transmission direction, and the historical transaction load difference between each branch is less than the set proportional threshold. In step 501, the redundant branches are multiple parallel lines existing in the same power transmission direction, with overlapping functions and can be merged without affecting the overall transmission capacity. The historical transaction load difference refers to the difference in the load borne by different branches during the historical transaction period, quantified as a percentage.

[0075] In the embodiment of this application, an improved spectral clustering algorithm is used to cluster the power transmission directions of the topological substructure. First, extract the power flow direction characteristics of each branch (such as the power flow direction angle, transmission capacity), and calculate the direction consistency between branches through cosine similarity (for example, if the similarity > 95%, it is determined to be in the same direction). Combining the analysis of the historical transaction load difference (such as the load difference between branch A and B < 15%), when the number of branches in the same direction ≥ 2 and the load difference is less than the set threshold (such as 20%), it is marked as a redundant branch.

[0076] 502. Perform a merging process on the redundant branches, retain the branch with the highest historical transaction load as the key transmission path, and superimpose the transmission capacities of the remaining branches onto the key transmission path. In step 502, the key transmission path is the core power transmission channel formed after merging the redundant branches, carrying the superimposed comprehensive transmission capacity. The superimposition of transmission capacity is a capacity allocation mechanism that integrates the transmission capabilities of redundant branches into the key path proportionally.

[0077] In the embodiment of this application, the merging of redundant branches is realized based on the dynamic graph pruning technology. First, sort the redundant branches according to the historical transaction load (such as the load ratio of branch A is 30% and that of branch B is 25%), and select the branch with the highest load as the key transmission path. The transmission capacities of the remaining branches are integrated into the key transmission path through a weighted superposition algorithm (such as the capacity of branch B is superimposed according to the proportional coefficient of 0.8, and branch C is superimposed according to 0.5). The superimposed capacity value is processed by non-linear normalization (such as compressing the extreme value by the sigmoid function) to ensure that the capacity of the key path does not exceed the physical device limit.

[0078] 503. According to the real-time electricity price fluctuation data, mark the power transmission direction corresponding to the peak electricity price period as the positive sensitive direction, and the opposite direction corresponding to the low electricity price period as the reverse sensitive direction. In step 503, the positive sensitive direction is the power transmission direction that needs to be preferentially guaranteed during the peak electricity price period, 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 the low electricity price period, usually corresponding to the return of excess power.

[0079] In the embodiment of the present application, a sliding window statistical method is adopted to identify the trend of electricity price fluctuations. First, the real-time electricity price data is divided by time windows (such as 15 minutes), and the electricity price increase rate within each window is calculated (such as the increase rate in window 1 is 12%). Through a trend direction identification algorithm (such as the moving average crossover method), peak periods (the increase rate in three consecutive windows > 8%) and trough periods (the decrease rate in three consecutive windows > 5%) are determined. According to the period markers, the corresponding power transmission directions are marked: during peak periods, the power supply direction to the load center is marked as the positive sensitive direction, and during trough periods, the power return direction from the power supply side is marked as the negative sensitive direction.

[0080] 504. Differentially weighted adjustment is performed 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 of the power transaction, where the impedance value of the path in the positive sensitive direction is lower than that in the negative sensitive direction, and the impedance value is adjusted according to the fluctuation amplitude of the real-time electricity price.

[0081] In step 504, the differentially weighted adjustment applies different amplitudes of dynamic correction to the impedance of the key path according to the sensitive direction to adapt to the electricity price fluctuation requirements. The fluctuation amplitude of the real-time electricity price is the percentage change of the current period's electricity price relative to the reference value.

[0082] In the embodiment of the present application, a non-linear directional weighting model is adopted to adjust the impedance parameters. For the path in the positive sensitive direction, a negative correction coefficient is calculated based on the real-time electricity price increase rate (such as the increase rate is 15%) (such as the impedance value = original value × (1 - increase rate × 0.005)) to reduce the impedance and improve the transmission capacity. For the path in the negative sensitive direction, a positive correction coefficient is adopted (such as the impedance value = original value × (1 + decrease rate × 0.003)) to increase the impedance and suppress the reverse power flow. The correction coefficient is adjusted through a dynamic feedback mechanism: when the electricity price fluctuation rate exceeds the threshold (such as the change per minute > 0.5%), the weight of the correction coefficient is adaptively increased (such as for every 1% increase in the rate, the weight is increased by 0.1), and finally a simplified network structure with optimized sensitive directions is generated.

[0083] The following is a specific example: Suppose that due to the switching of multiple energy storage devices in a commercial area, the topology is frequently reconstructed, and the intraday electricity price shows fluctuations with a peak at noon (a 20% increase) and a valley at night (a 15% decrease). Step 501 identifies 3 co-directional redundant branches (with a historical load difference of 18%) connecting the energy storage cluster and the commercial load center, and marks them as redundant branches; Step 502 merges the redundant branches, retains Branch A with a load share of 35% as the key transmission path, and superimposes the capacities of Branch B (25%) and C (20%), increasing the total capacity to 160% of the original value; In Step 503, during the peak electricity price at noon, the energy storage discharging to the load center is marked as the positive sensitive direction, and during the valley at night, the surplus electricity of the load being sent back is marked as the reverse sensitive direction; In Step 504, the impedance of the key path is negatively corrected at noon (0.2Ω → 0.16Ω) and reversely corrected at night (0.2Ω → 0.23Ω) to form a simplified network structure that retains the sensitive path of the power transaction.

[0084] Steps 501 - 504 significantly improve the response efficiency of the key path to electricity price fluctuations through the dynamic merging of redundant branches and impedance correction driven by the sensitive direction; In the scenario of frequent topological changes, by adjusting the directional impedance, it suppresses the ineffective power flow, optimizes the transmission capacity during high electricity price periods, and at the same time reduces the redundant losses during low electricity price periods, achieving a double improvement in the economic operation of the power grid and the physical transmission performance.

[0085] In order to quickly locate and correct the phase mismatch problem caused by local anomalies in the scenario of frequent changes in the power grid topology, and solve the defect of inaccurate control commands caused by the delay in global model updates in traditional methods, through an abnormal area dynamic identification and phase difference closed-loop correction mechanism, a precise control link based on real-time operation data is constructed to ensure the real-time adaptability of power regulation actions to topological changes. In some embodiments, according to the distribution position of the first type of abnormal state, extracting the phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topological connection relationship data, and generating a primary control command including a voltage compensation amount and a phase correction direction by combining the instantaneous value of the current phase difference between the adjacent nodes, includes: 601. According to the distribution position of the first type of abnormal state, locate the abnormal area in the topological connection relationship data, and the abnormal area includes at least two adjacent nodes and their connection paths; In Step 601, the abnormal area is a local power grid range defined by the distribution position of the first type of abnormal state, including at least two adjacent nodes and their connection paths.

[0086] In the embodiments of the present application, an abnormal area detection model based on a graph neural network is adopted. First, a spatial overlay analysis is performed on the distribution positions (such as node coordinate sets) of the first type of abnormal states and the topological connection relationship data, and the adjacency features of abnormal nodes (such as adjacent node degrees, path betweenness) are extracted through a graph convolutional network. The influence coefficient of each node is calculated by weighted calculation using an attention mechanism. When the weighted sum of the influences between adjacent nodes exceeds the dynamic threshold, it is determined that the connection path forms an abnormal area, and the affected nodes and paths are marked.

[0087] 602. Extract the phase difference constraint range between adjacent nodes within the abnormal area, where the phase difference constraint range is determined based on the equivalent impedance parameters of the corresponding paths in the simplified network structure and historical operation data; In step 602, the phase difference constraint range is the limit interval of the current phase angle difference between adjacent nodes allowed by the grid safety regulations.

[0088] In the embodiments of the present application, the phase difference constraint range is dynamically determined through a fuzzy logic system. First, based on the equivalent impedance parameters of the corresponding paths in the simplified network structure (such as 0.15 Ω), the theoretical phase difference reference value is calculated (such as impedance value × transmission capacity coefficient = 8°). Combining the actual phase difference statistical distribution in the historical operation data (such as 95% of the data in the past 30 days is within ±9°), the membership function is used to fuse the theoretical value and the actual distribution to obtain the dynamic allowable range (such as ±8.5°). When a recent topological change event (such as equipment switching) is detected, the allowable range is temporarily expanded through a sliding window mechanism (such as ±9.2°) to generate the phase difference constraint range.

[0089] 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 the deviation direction; In step 603, the phase deviation direction is the deviation direction of the measured phase difference relative to the allowable range (positive overlimit or negative overlimit).

[0090] In the embodiments of the present application, an interval overlap detection algorithm is adopted for deviation analysis. The instantaneous value of the current phase difference between adjacent nodes is collected in real time (such as the phase difference between nodes A - B is 11°), and an overlap interval detection is performed with the phase difference constraint range output in step 602 (such as ±8.5°). When the instantaneous value exceeds the allowable range, first, the deviation amount is calculated, and the linear interpolation method is used to determine the exceeding ratio (such as 11° - 8.5° = 2.5° overlimit); then, the deviation direction is determined, and the sign function is used to judge the deviation direction (such as +2.5° is positive overlimit), which is used as the basis for generating the control instruction.

[0091] 604. Generate a primary control command including a voltage compensation amount and a phase correction direction based on the phase deviation amount and the deviation direction. The magnitude of the voltage compensation amount is proportional to the phase deviation amount, and the phase correction direction is opposite to the deviation direction.

[0092] 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.

[0093] In the embodiment of the present application, the primary control command is generated based on the control principle of proportional, integral and derivative (PID). The phase deviation amount is input into a PID controller (proportional coefficient Kp = 0.6, integral time Ti = 10 s) to calculate the voltage compensation amount (e.g., deviation +2.5° → compensation +1.5 V).

[0094] Determine the phase correction direction through an inverse mapping model: counterclockwise correction is required when the positive limit is exceeded, and clockwise correction is required when the negative limit is exceeded.

[0095] Finally, output a primary control command including the voltage compensation amount (+1.5 V) and the phase correction direction (counterclockwise), and send it to the edge computing unit for execution.

[0096] The following is a specific example: Suppose that due to strong winds, the transmission lines in many places of a mountainous area power grid dance (topological instantaneous change), resulting in continuous exceeding of the phase difference between nodes C and D. Step 601 detects the existence of a first type of abnormal state near node C and locates the abnormal area including nodes C, D and their connecting lines; step 602 extracts the phase difference constraint range of this path as ±7.8° (equivalent impedance 0.18 Ω, 95% of historical data distributed within ±8.3°); step 603 real-time monitors the instantaneous value of the phase difference as 9.6°, and calculates the deviation amount of +1.8° (the deviation direction is positive limit exceeding); the primary control command generated in step 604 is voltage compensation +1.08 V and phase counterclockwise correction.

[0097] Steps 601 - 604 can quickly generate accurate control commands in scenarios with frequent topological changes through the dynamic positioning of the abnormal area and the phase difference closed-loop correction mechanism; combined with the hybrid strategy of fuzzy logic and PID control, effectively suppress the power oscillation caused by phase mismatch, improve the response speed and control accuracy of local regulation, and ensure the synchronous achievement of the stable operation of the power grid under abnormal working conditions and the economic objectives of market transactions.

[0098] In order to achieve deep coordination between global control strategies and market transaction needs in scenarios where power grid topology changes frequently, and solve the problem of split optimization goals caused by command conflicts in traditional methods, a dynamic priority control system driven by electricity price sensitivity is constructed through spatiotemporal correlation mapping and multi-level command coordination mechanism to ensure that the power allocation efficiency in high-value time periods is maximized. In some embodiments, the local control instructions are matched with the electricity price fluctuation data in 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: 701. Extract the local control instruction generation time and the corresponding node position of each power grid node, match them with the electricity price fluctuation data of the same period by time window and area division, and form a time-space association mapping table as a matching result; In step 701, time window and area division matching is an operation of aligning the generation time of the local control instruction with the electricity price fluctuation data in time and space according to a fixed time period (such as 15 minutes) and power grid area (such as the load center area).

[0099] In the embodiment of the present application, the sliding window statistics method is used to segment the generation timestamp of the local control instructions (such as a window every 5 minutes), and the node location is mapped to the predefined power grid area grid (such as a 500m×500m grid) based on the geographic hash algorithm. Through the spatiotemporal alignment engine, the instruction generation location in each window is associated and matched with the electricity price fluctuation data of the corresponding time period (such as the electricity price increase of area A in window T is 10%). Finally, a spatiotemporal association mapping table is constructed to record the number of instructions, electricity price fluctuation amplitude and regional load characteristics of each spatiotemporal unit (time window×area).

[0100] 702. According to the matching result, 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 to a higher priority level; 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.

[0101] In the embodiment of the present application, highly sensitive time period areas are marked based on a dynamic threshold adjustment mechanism. First, the quantiles of the historical electricity price fluctuation data are calculated (such as the first 20% quantiles as the threshold benchmark). When the real-time electricity price fluctuation exceeds the benchmark value (such as 12%), the space-time unit is marked as a highly sensitive time period area. Through the weighted diffusion algorithm, the priority level of the highly sensitive time period area is increased according to the proportion of the electricity price increase (such as an increase of one level for every 5% increase), and the level of the adjacent area is associated at the same time (such as the level of the area within a diffusion radius of 5km is increased by 50%).

[0102] 703. Cross-verify the local control instructions of each power grid node within the same time window. When it is detected that there are conflicts in the phase correction directions of the instructions of adjacent nodes, preferentially execute the local control instructions in the high-sensitivity period area; In step 703, the conflict in the bit correction direction is the contradiction in the phase adjustment directions existing in the control instructions of adjacent nodes (for example, node A needs to be corrected counterclockwise, and node B needs to be corrected clockwise).

[0103] In the embodiment of the present application, a conflict detection algorithm in graph theory is used for instruction cross-verification. A directed graph model with power grid nodes as vertices and phase correction directions as edge attributes is constructed, and the direction conflicts between adjacent nodes are detected through traversing the adjacency matrix (for example, the edge A→B is counterclockwise, and the edge B→C is clockwise). When a conflict is detected, a priority adjudication is performed based on a multi-objective game theory model: the instructions in the high-sensitivity area retain the original direction, and the instructions in the non-sensitivity area are adjusted to the direction of the adjacent high-priority area. The adjudication result is synchronized to the relevant nodes through a distributed consistency protocol to ensure the global instruction direction consistency.

[0104] 704. Generate a joint control strategy based on the preferential execution result, and dynamically adjust the power distribution ratio of each node in the power distribution scheme according to the joint control strategy, so as to increase the power transmission margin in the high-sensitivity period area and synchronously reduce the transmission loss in the non-sensitivity area.

[0105] In step 704, the power transmission margin is the remaining power transmission capacity that can be increased by the key transmission path under the current operating state.

[0106] In the embodiment of the present application, an elastic resource allocation algorithm is used to generate a joint control strategy. Calculate the power distribution weight of each node according to the priority level (for example, high-sensitivity area weight = base value × priority level), and solve the constrained optimal allocation model through the Lagrange multiplier method (the objective function is to minimize the transmission loss and maximize the transmission margin). During the adjustment process, the power distribution ratio in the non-sensitivity area is compressed according to the weight decay coefficient (for example, it decays by 20% for each level decrease), and the released capacity is superimposed on the high-sensitivity area. Finally, a joint control strategy is generated and synchronously updated to the solid-state transformer execution module of each node.

[0107] The following is a specific example: Suppose that due to the sudden switching of a large data center, the topology of the power grid changes frequently at high frequencies, and at the same time, the fluctuation range during the peak electricity price period at noon reaches 18%. In step 701, 32 local instructions in the central area (grid G7) during the period from 12:00 to 12:15 at noon (window T) are matched with the electricity price data to form a spatio-temporal correlation mapping table as the matching result; in step 702, since the electricity price increase exceeds the threshold (18%), the G7 area is marked as a highly sensitive time period area, and the priority is raised to the highest level; in step 703, it is detected that there is a phase direction conflict (counterclockwise vs clockwise) in the adjacent nodes of the G7 area, and the counterclockwise instructions in the highly sensitive area are preferentially executed; in step 704: adjust the power distribution plan to generate a joint control strategy, the transmission margin of the G7 area is increased by 30%, and the loss of the surrounding non-sensitive areas is reduced by 15%.

[0108] Steps 701-704 accurately identify high-value control areas in scenarios with frequent topological changes through the spatio-temporal correlation mapping and dynamic priority mechanism; combined with the conflict resolution and flexible resource allocation strategies, they maximize the power transmission efficiency during highly sensitive time periods and synergistically suppress the ineffective losses in non-sensitive areas, significantly improving the economic performance of the power system operation and the adaptation accuracy of market trading benefits.

[0109] Figure 2 The following is a schematic structural diagram of an optimized control device for a power system based on power market trading provided by an embodiment of the present application, as Figure 2 shown. The system includes: An acquisition module 21, configured to acquire power market trading data, where the power market trading data includes electricity price fluctuation data and load demand prediction data, and synchronously collect topological connection relationship data between multiple power grid nodes in the power grid; An adjustment module 22, configured to generate a simplified network structure adapted to the power trading scenario according to the power market trading data and the topological connection relationship data, and adjust the equivalent impedance parameters of key transmission paths in the simplified network structure; An adjustment module 23, configured to perform coordinated adjustment on the voltage amplitude and phase angle of power grid nodes through a multi-level current conversion adjustment mechanism of a solid-state transformer based on the adjusted equivalent impedance parameters to form a power distribution plan; A monitoring module 24, configured to monitor the operation state data of the solid-state transformer through edge computing nodes deployed at the power grid nodes during the execution of the power distribution plan, and generate local control instructions in combination with the adjusted equivalent impedance parameters; A generation module 25, configured to perform spatio-temporal mapping matching on the local control instructions and the electricity price fluctuation data, perform coordinated priority sorting on the local control instructions of multiple power grid nodes according to the matching result, generate a joint control strategy, and dynamically adjust the power distribution plan to achieve dynamic optimal control of transmission loss and trading cost.

[0110] Figure 2 The described power system optimization control device based on power market transactions can execute Figure 1 the power system optimization control method based on power market transactions described in the illustrated embodiment. Its implementation principle and technical effects will not be elaborated further. For the power system optimization control device based on power market transactions in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to this method, and will not be elaborated here in detail.

[0111] In a possible design, Figure 2 the power system optimization control device based on power market transactions in the illustrated embodiment can be implemented as a computing device, such as Figure 3 as shown, this computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, where the one or more computer instructions are for the processing component 32 to call and execute.

[0112] The processing component 32 is used for the Figure 1 power system optimization control method based on power market transactions in the above

[0113] embodiment. Among them, 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 by 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 for executing the above method.

[0114] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile storage 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, a magnetic disk or an optical disc.

[0115] Of course, the computing device will necessarily also include other components, such as an input / output interface, a display component, a communication component, etc.

[0116] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0117] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0118] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server. The above processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0119] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 shown embodiment of the power system optimization control method based on electricity market trading.

[0120] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0121] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0122] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A power system optimization control method based on power market transactions, characterized in that: include: Acquire power market transaction data, the power market transaction data including power price fluctuation data and load demand forecast data, and simultaneously collect topological connection relationship data between multiple power grid nodes in the power grid; Generating a simplified network structure adapted to the power trading scenario according to 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 power grid node, and a local control instruction is generated in combination with the adjusted equivalent impedance parameter; 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 losses and transaction costs.

2. The method according to claim 1, characterized in that The generating a simplified network structure adapted to the power transaction scenario according to the power market transaction data and the topological connection relationship data, and adjusting the equivalent impedance parameters of the key transmission paths in the simplified network structure, includes: Associating the electricity price fluctuation data in the electricity market transaction data with the node connection mode in the topological connection relationship data and extracting correlation parameters, wherein the correlation parameters include the electricity price fluctuation amplitude and the topological connection density, and determining the electricity transaction sensitive path based on the electricity price fluctuation amplitude and the topological connection density; According to the distribution characteristics of the sensitive path of power trading, a topological substructure including at least two core nodes and more than three associated branches is extracted from the original power grid topology, and the initial impedance parameter of each branch in the topological substructure is allocated based on the historical trading 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, and the equivalent impedance parameters of the key transmission paths in the simplified network structure are directionally weighted 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 parameter is reversely compensated and adjusted according to the distribution ratio of the predicted load on the key transmission path, so that the equivalent impedance parameter keeps matching 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 adjustment mechanism of the solid-state transformer to form a power distribution scheme, including: Converting 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 allocated based on the transmission capacity ratio of the key transmission path and the phase difference ratio between adjacent nodes; Generate 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 the triggering condition of each regulation level is determined based on the electricity price fluctuation amplitude threshold and the fluctuation direction, wherein the fluctuation direction includes the electricity price peak period and the electricity price valley 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, and when the measurement data exceeds the deviation range, the switching of the adjacent regulation level is triggered, and the phase angle difference range is synchronously adjusted during the switching process 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, and the allocation priority is positively correlated with the urgency of the load demand forecast data.

4. The method according to claim 1, characterized in that: 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 power grid node, and a local control instruction is generated in combination with the adjusted equivalent impedance parameter, including: The edge computing unit is used to collect the operating status data of the solid-state transformer in real time, wherein the operating status data includes the instantaneous fluctuation value of the output voltage, the instantaneous value of the current phase difference between adjacent nodes, and the temperature change rate of the 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; According to the distribution position of the first type of abnormal state, extract the phase difference constraint range between at least two adjacent nodes associated with the distribution position of the first type of abnormal state 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; The voltage compensation amount in the primary control instruction is adjusted 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.

5. The method according to claim 2, characterized in that: The method of combining 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 as follows: there are 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; The redundant branches are merged, and the branch with the highest historical transaction load is retained as the key transmission path, and the transmission capacity of the remaining branches is added 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 positive 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 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.

6. The method according to claim 4, characterized in that The extracting, according to the distribution position of the first type of abnormal state, the phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topological connection relationship data, and combining the instantaneous value of the current phase difference between the adjacent nodes to generate a primary control instruction including a voltage compensation amount and a phase correction direction, includes: According to the distribution position of the first type of abnormal state, locate an abnormal area in the topological connection relationship data, wherein 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, wherein 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 amount and a phase correction direction is generated, wherein the voltage compensation amount is proportional to the phase deviation, and the phase correction direction is opposite to the deviation direction.

7. The method according to claim 1, characterized in that 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 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 position of each power grid node, match them with the electricity price fluctuation data of the same period by time window and area division, and form a time-space association mapping table as the matching result; According to the matching result, 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 to a higher 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 high-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.

8. 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, wherein the power market transaction data includes power price fluctuation data and load demand forecast data, and simultaneously acquires topological connection relationship data between multiple power grid nodes in the power grid; An adjustment module, used to generate a simplified network structure adapted to the power transaction scenario according to the power market transaction data and the topological connection relationship data, and adjust the equivalent impedance parameters of the 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 node through the multi-stage current conversion regulation mechanism of the solid-state transformer based on the adjusted equivalent impedance parameters to form a power distribution plan; A monitoring module, used for monitoring the operating status data of the solid-state transformer through an edge computing node deployed at the power grid node during the execution of the power allocation scheme, and generating a local control instruction in combination with the adjusted equivalent impedance parameter; 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 plan to achieve dynamic optimization control of transmission losses and transaction costs.

9. A computing device, characterized in that It comprises 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 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an electric power system optimization control method based on electric power market transactions as described in any one of claims 1 to 7 is implemented.

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