CN120150131B

CN120150131B - Power system optimization control method and device based on power market transaction

Info

Publication number: CN120150131B
Application number: CN202510595763.3A
Authority: CN
Inventors: 马兰; 翟冬峰; 刘炎; 赵振东; 冯柯
Original assignee: Luoyang New Energy Technology Development Group Co ltd
Current assignee: Luoyang New Energy Technology Development Group Co ltd
Priority date: 2025-05-09
Filing date: 2025-05-09
Publication date: 2025-09-23
Anticipated expiration: 2045-05-09
Also published as: CN120150131A

Abstract

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

Description

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

Technical Field

The application relates to the technical field of power system optimization control, in particular to a power system optimization control method and device based on power market transaction.

Background

With the large-scale access of new energy and the frequent switching of power grid equipment, the topological structure of the power system presents high dynamic change characteristics (such as temporary line fault removal and rapid start-stop of energy storage equipment), so that the traditional power control method based on the fixed topological model is difficult to adapt to real-time operation requirements. In the power market trading environment, the dual aims of quickly sensing network structure change and adaptively adjusting power distribution to ensure power supply stability and synchronously optimizing system operation economy are realized under the scene of frequent topology change.

The current mainstream scheme adopts a dynamic optimization control method based on a real-time data acquisition and prediction model, periodically acquires the topological state of a power grid and market transaction data through a centralized controller arranged at a master station, establishes a short-term power flow prediction model, and generates a generator output and energy storage regulation instruction based on a prediction result. According to the method, the topology parameter library is updated on line, and the power adjustment quantity of each node is calculated by combining with the market price fluctuation trend, so that the method is partially suitable for the economic optimization requirement under the topology change scene.

The existing scheme has the defects that the centralized controller relies on periodic topology data acquisition and model reconstruction, a global model is difficult to update in time at the moment of equipment switching or failure, so that a power regulation command is mismatched with an actual topology state, a coupling mechanism of power price fluctuation and topology dynamic change is not established, power is distributed only by static power price weight, an economic optimization target cannot be dynamically regulated when the topology is suddenly changed, centralized optimization needs to process node data of the whole network, calculation delay is obvious under a topology high-frequency change scene, and second-level power self-adaptive regulation requirements are difficult to meet.

Disclosure of Invention

The application provides an electric power system optimization control method and device based on electric power market transaction, which are used for solving the problems of insufficient operation economy and physical transmission performance of an electric power system in the prior art.

In a first aspect, the present application provides a power system optimization control method based on power market trading, including:

acquiring electric power market transaction data, wherein the electric power market transaction data comprises electricity price fluctuation data and load demand prediction data, and synchronously acquiring topological connection relation data among a plurality of grid nodes in a power grid;

generating a simplified network structure adapting to the electric power trade scene according to the electric power market trade data and the topological connection relation data, and adjusting equivalent impedance parameters of a key transmission path in the simplified network structure;

Based on the adjusted equivalent impedance parameters, the voltage amplitude and the phase angle of the power grid node are cooperatively adjusted through a multistage variable current adjustment mechanism of the solid-state transformer, so that a power distribution scheme is formed;

in the execution process of the power distribution scheme, monitoring the running state data of the solid-state transformer through edge computing nodes deployed on the power grid nodes, and generating a local control instruction by combining the adjusted equivalent impedance parameters;

And carrying out space-time mapping matching on the local control instructions and the electricity price fluctuation data, carrying out collaborative priority ordering on the local control instructions of a plurality of grid nodes according to a matching result, generating a joint control strategy, dynamically adjusting the power distribution scheme, and realizing dynamic optimization control of transmission loss and transaction cost.

Optionally, the generating a simplified network structure adapted to a power trade scenario according to the power market trade data and the topology connection relation data, and adjusting equivalent impedance parameters of a key transmission path in the simplified network structure includes:

Correlating the electricity price fluctuation data in the electricity market transaction data with the node connection modes in the topological connection relation data and extracting correlation parameters, wherein the correlation parameters comprise electricity price fluctuation amplitude and topological connection density, and determining an electricity transaction sensitive path based on the electricity price fluctuation amplitude and the topological connection density;

Extracting a topological substructure comprising at least two core nodes and more than three associated branches from an original power grid topology according to the distribution characteristics of the power transaction sensitive paths, wherein initial impedance parameters of all branches in the topological substructure are distributed based on historical transaction load proportions of corresponding nodes;

Based on the topological substructure, combining redundant connection branches with the same power transmission direction as the core nodes to generate a simplified network structure for reserving the power transaction sensitive path, wherein equivalent impedance parameters of a key transmission path in the simplified network structure are subjected to directional weighted correction according to the electricity price fluctuation data;

And continuously receiving load demand prediction data in the electric power market transaction data in the operation process of the simplified network structure, and performing reverse compensation adjustment on the equivalent impedance parameter according to the distribution proportion of the predicted load on the key transmission path so as to keep the equivalent impedance parameter matched with the power flow direction in the transaction scene.

Optionally, the adjusting the voltage amplitude and the phase angle of the grid node based on the adjusted equivalent impedance parameter through a multistage variable current adjustment mechanism of the solid-state transformer, to form a power distribution scheme includes:

Converting the adjusted equivalent impedance parameters into voltage regulation parameters comprising a voltage amplitude deviation range and a phase angle difference range allowed by each power grid node, and distributing the conversion process based on the transmission capacity proportion of the key transmission path and the phase difference proportion between adjacent nodes;

Generating a multi-level regulation mode according to the voltage regulation parameters and the electricity price fluctuation data, wherein the multi-level regulation mode comprises at least three regulation levels, the triggering condition of each regulation level is determined based on an electricity price fluctuation amplitude threshold value and a fluctuation direction, and the fluctuation direction comprises an electricity price peak period and an electricity price valley period;

in the multi-stage regulation mode, based on the measurement data of the real-time voltage amplitude and the phase angle of the power grid node, matching the deviation range in the voltage regulation parameters, triggering the switching of adjacent regulation levels when the measurement data exceeds the deviation range, and synchronously regulating the phase angle difference range in the switching process to keep the consistency of the power transmission direction;

and generating a power distribution scheme comprising power distribution priorities and distribution proportions of all nodes according to voltage amplitude and phase angle constraints corresponding to the switched regulation levels, wherein the distribution priorities are positively related to the emergency degree of the load demand prediction data.

Optionally, during the execution of the power distribution scheme, monitoring, by an edge computing node disposed at the grid node, operation state data of the solid-state transformer, and generating a local control instruction in combination with the adjusted equivalent impedance parameter, including:

Collecting operation state data of the solid-state transformer in real time through the edge computing unit, wherein the operation state data comprise an output end voltage instantaneous fluctuation value, an adjacent node current phase difference instantaneous value and a temperature change rate of a power module inside the solid-state transformer;

Correlating the instantaneous fluctuation value of the voltage at the output end in the running state data with the adjusted equivalent impedance parameter, and marking the running state data as a first type of abnormal state when the fluctuation direction of the voltage is opposite to the change direction of the impedance threshold value;

According to the distribution position of the first abnormal state, extracting a phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topological connection relation data, and generating a primary control instruction comprising a voltage compensation quantity and a phase correction direction by combining the current phase difference instantaneous value between the adjacent nodes;

and adjusting the voltage compensation quantity in the primary control instruction based on the temperature change rate in a way that when the temperature change rate exceeds a preset safety interval, the voltage compensation quantity is reduced proportionally, and the allowable deviation angle of the phase correction direction is synchronously expanded to generate a local control instruction.

Optionally, based on the topology substructure, combining redundant connection branches with the same power transmission direction as the core nodes to generate a simplified network structure for retaining the power transaction sensitive path, where the equivalent impedance parameters of the key transmission path in the simplified network structure are subjected to directional weighted correction according to the electricity price fluctuation data, and the method includes:

Identifying all redundant branches connected with a core node in the topological substructure, wherein the judgment condition of the redundant branches is that two or more parallel connection branches exist in the same power transmission direction, and the historical transaction load difference of each branch is smaller than a set proportion threshold;

combining the redundant branches, reserving the branch with the highest historical transaction load as a key transmission path, and overlapping the transmission capacity of the other branches to the key transmission path;

Marking the power transmission direction corresponding to the electricity price peak period as a forward sensitive direction and the reverse direction corresponding to the electricity price valley period as a reverse sensitive direction according to the real-time electricity price fluctuation data;

and carrying out differential weighting adjustment on equivalent impedance parameters of the key transmission path based on different sensitive directions to generate a simplified network structure for retaining the power transaction sensitive path, 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 along with the fluctuation amplitude of the real-time electricity price.

Optionally, the extracting a phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topological connection relationship data according to the distribution position of the first type of abnormal state, and generating a primary control instruction including a voltage compensation amount and a phase correction direction by combining the current phase difference instantaneous value between the adjacent nodes, including:

Positioning an abnormal region in the topological connection relation data according to the distribution position of the first type of abnormal state, wherein the abnormal region comprises at least two adjacent nodes and connection paths thereof;

extracting phase difference constraint ranges among adjacent nodes in the abnormal region, wherein the phase difference constraint ranges are determined based on equivalent impedance parameters and historical operation data of corresponding paths in the simplified network structure;

comparing the current phase difference instantaneous value between the adjacent nodes with the phase difference constraint range, and calculating a phase deviation value and a deviation direction when the current phase difference instantaneous value between the adjacent nodes exceeds the phase difference constraint range;

based on the phase deviation amount and the deviation direction, a primary control command including a voltage compensation amount, the magnitude of which is proportional to the phase deviation amount, and a phase correction direction, the phase correction direction being opposite to the deviation direction, is generated.

Optionally, the performing space-time mapping matching on the local control instruction and the electricity price fluctuation data, performing collaborative priority ordering on the local control instructions of the multiple grid nodes according to a matching result, generating a joint control strategy, and dynamically adjusting the power distribution scheme to realize dynamic optimization control of transmission loss and transaction cost, including:

Extracting local control instruction generation time and corresponding node positions of each power grid node, and carrying out time window and region division matching with the power price fluctuation data of the same period of time to form a space-time association mapping table as a matching result;

Marking a time period and a region of the electricity price fluctuation data exceeding a preset threshold value as a high-sensitivity time period region according to the matching result, wherein a local control instruction in the high-sensitivity time period region automatically increases the priority level;

Cross-verifying local control instructions of all power grid nodes in the same time window, and when detecting that the instructions of adjacent nodes have phase correction direction conflict, preferentially executing the local control instructions of the high-sensitivity time period region;

generating a joint control strategy based on a priority execution result, dynamically adjusting the power distribution proportion of each node in the power distribution scheme according to the joint control strategy, increasing the power transmission margin of the high-sensitivity period region, and synchronously reducing the transmission loss of the non-sensitive region.

In a second aspect, the present application provides an electric power system optimization control device based on electric power market trade, comprising:

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring electric power market transaction data, the electric power market transaction data comprise electricity price fluctuation data and load demand prediction data, and topology connection relation data among a plurality of grid nodes in a power grid are synchronously acquired;

The adjustment module is used for generating a simplified network structure adapting to the power trade scene according to the power market trade data and the topological connection relation data, and adjusting equivalent impedance parameters of a key transmission path in the simplified network structure;

the adjusting module is used for carrying out cooperative adjustment on the voltage amplitude and the phase angle of the power grid node through a multistage variable current adjusting mechanism of the solid-state transformer based on the adjusted equivalent impedance parameter to form a power distribution scheme;

The monitoring module is used for monitoring the running state data of the solid-state transformer through edge computing nodes deployed on the power grid nodes in the execution process of the power distribution scheme and generating a local control instruction by combining the adjusted equivalent impedance parameters;

And the generation module is used for carrying out space-time mapping matching on the local control instructions and the electricity price fluctuation data, carrying out collaborative priority ordering on the local control instructions of the plurality of power grid nodes according to a matching result, generating a joint control strategy, dynamically adjusting the power distribution scheme and realizing dynamic optimization control of transmission loss and transaction cost.

In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component, where the storage component stores one or more computer instructions, and the one or more computer instructions are used to be invoked and executed by the processing component to implement the power system optimization control method based on the power market transaction according to the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, where the computer program when executed by a computer implements the power system optimization control method based on the power market transaction according to the first aspect.

The embodiment of the application acquires electric power market transaction data, wherein the electric power market transaction data comprises electric price fluctuation data and load demand prediction data, synchronously acquires topological connection relation data among a plurality of grid nodes in a power grid, generates a simplified network structure adapting to an electric power transaction scene according to the electric power market transaction data and the topological connection relation data, adjusts equivalent impedance parameters of a key transmission path in the simplified network structure, cooperatively adjusts voltage amplitude and phase angle of the grid nodes through a multistage variable flow adjustment mechanism of a solid-state transformer based on the adjusted equivalent impedance parameters to form a power distribution scheme, monitors running state data of the solid-state transformer through edge calculation nodes deployed at the grid nodes in the execution process of the power distribution scheme, combines the adjusted equivalent impedance parameters to generate local control instructions, performs space-time mapping matching on the local control instructions and the electric price fluctuation data, performs cooperative priority ordering on the local control instructions of the plurality of grid nodes according to a matching result, generates a joint control strategy and dynamically adjusts the power distribution scheme to realize dynamic optimization control of transmission loss and transaction cost.

The technical scheme of the application has the following beneficial effects:

The method comprises the steps of synchronously collecting power price fluctuation, load prediction and dynamic topology data, guaranteeing real-time matching of market transaction requirements and physical states of a power grid, providing full-dimensional input for subsequent dynamic optimization, generating a dynamic adaptive simplified network structure based on transaction scenes, reducing calculation complexity under complex topology, simultaneously realizing dynamic adaptation of transmission characteristics of a critical path and the market fluctuation through equivalent impedance parameter adjustment, utilizing a multistage adjustment mechanism of a solid-state transformer to quickly respond to impedance parameter change, ensuring that an economic target of a power distribution scheme is synchronously achieved with the stability of the power grid through cooperative control of voltage amplitude and phase angle, monitoring equipment states in real time through edge computing nodes and fusing topology parameters, realizing quick generation and dynamic correction of local control instructions, improving localized response capability to topology change, solving contradiction between distributed control and global optimization based on time-space mapping matching and multi-instruction cooperative sequencing, dynamically balancing transmission loss and transaction cost, and realizing system-level economic optimization.

The method comprises the steps of determining a power transaction sensitive path by dynamically associating power price fluctuation data with a topological connection mode, extracting a topological substructure comprising core nodes and associated branches from an original power grid, distributing initial impedance parameters based on historical transaction loads, merging redundant branches to generate a simplified network structure for reserving the sensitive path, carrying out directional weighted correction on the impedance of the critical path according to real-time power price data, and dynamically compensating the impedance parameters by combining load prediction data to ensure that the impedance parameters are matched with power flow directions in a transaction scene. The dynamic adjustment mechanism of impedance parameters based on electricity price directivity weighting and load prediction compensation realizes real-time collaborative optimization of key path transmission capacity and transaction economy targets, and effectively reduces control mismatch risks caused by frequent topology changes.

These and other aspects of the application will be more readily apparent from the following description of the embodiments.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 shows a flow chart of the power system optimization control method based on power market transactions provided by the application;

fig. 2 shows a schematic structural diagram of an optimization control device of a power system based on power market trade;

FIG. 3 illustrates a schematic diagram of a computing device provided by the present application.

Detailed Description

In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.

In some of the flows described in the specification and claims of the present application and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

Researchers find that the problems of economic optimization lag, physical transmission characteristic mismatch and the like commonly exist in the conventional power system optimization control method under the scene of frequent change of power grid topology and dynamic coupling of power market transaction, and real-time cooperative regulation and control of transmission loss and transaction cost are difficult to realize. Based on the method, the dynamic optimization control method of the electric power system based on electric power market trading is provided, specifically, a simplified network structure of trading scene adaptation is generated and key path transmission parameters are dynamically adjusted by fusing real-time electricity price fluctuation data, load prediction data and dynamic topological connection relation, an economy-oriented power distribution scheme is formed by combining multistage adjustment of a solid-state transformer and localized instruction generation of edge computing nodes, and a global power distribution strategy is dynamically optimized by further collaborative sequencing of space-time mapping matching and multi-node instructions. According to the method, dynamic balance control of transmission loss and market transaction cost can be realized under the condition of rapid change of the power grid topology, and the running economy and stability of the power system are improved.

The technical scheme of the application can be applied to the power self-adaptive regulation scene under the frequent change of the power grid topology.

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

Fig. 1 is a flowchart of a power system optimization control method based on power market transaction according to an embodiment of the present application, as shown in fig. 1, the method includes:

101. Acquiring electric power market transaction data, wherein the electric power market transaction data comprises electricity price fluctuation data and load demand prediction data, and synchronously acquiring topological connection relation data among a plurality of grid nodes in a power grid;

In this step, the dynamic topology connection relationship data refers to real-time data of the physical connection state (such as line switching and equipment start-stop) between nodes in the power grid, which changes with time, and includes node connection mode, transmission path availability and real-time power flow direction information. The electricity price fluctuation data reflects the dynamic change trend of the electricity price in different time periods and areas in the electric power market, and comprises peak electricity price time periods, off-peak electricity price time periods and fluctuation amplitude characteristics.

In the embodiment of the application, the multi-source data acquisition module deployed in the power grid dispatching center periodically acquires the power price fluctuation data (such as a time-of-use power price curve and a regional power price difference matrix) issued by the power trading platform and the future load demand distribution data (such as a node level load forecast value) generated by the load forecast system. Meanwhile, a wide area measurement system (WMS) is utilized to capture the topological connection state (comprising the line on-off state and the equipment operation mode) among the grid nodes in real time, and the electricity price data and the topological data are subjected to time stamp alignment and area mapping through a space-time correlation analysis algorithm to form a heterogeneous data fusion frame. For example, event-driven data stream processing technology is adopted to trigger real-time data update for topology change events (such as line faults), and dynamic topology features (such as node connection density and power transmission direction distribution) are extracted through a sliding time window mechanism. And finally integrating the topological connection relation data, wherein the data comprise time-synchronous electricity price, load and topology triples which are used as the input of a follow-up optimization model.

Assuming that the power grid in a certain area has a sudden topological structure change due to a line fault, step 101 collects real-time power price fluctuation data (e.g. power price suddenly increases from 0.5 yuan/kWh to 0.8 yuan/kWh), load demand prediction data (e.g. load prediction value of the fault area decreases by 20%), and dynamic topological connection relation data (e.g. fault line disconnection and standby line connection). Through space-time alignment, the system recognizes that the fault period overlaps with the electricity price peak period, and triggers the subsequent optimization flow.

102. Generating a simplified network structure adapting to the electric power trade scene according to the electric power market trade data and the topological connection relation data, and adjusting equivalent impedance parameters of a key transmission path in the simplified network structure;

In this step, the simplified network structure is an abstract network model extracted based on the original grid topology and retaining the critical transaction sensitive paths, and is used for reducing the optimization computation complexity.

In the embodiment of the application, high-frequency transaction nodes (such as nodes with the front 10% of transaction amount per hour) are screened according to electric market transaction data, a power grid node which is frequently transacted and is electrically and directly communicated is obtained by combining topological connection relation data, an independent structure is divided according to the power grid node, each divided independent structure is combined into a single equivalent network structure to serve as a simplified network structure, line load change in the simplified network structure is analyzed, a key transmission path is identified according to the line load change, and equivalent impedance parameters of the key transmission path are reversely calculated according to actual operation data of the simplified network structure.

For example, continuing the above example, step 102 first screens out the high frequency transaction nodes (e.g., load center node A, standby power node B) with the top 10% of the transaction amount ranking per hour, confirms that there is a direct electrical connection between these nodes in combination with the topology connection relationship data, then divides the node A, B and its directly connected 4 paths into independent structures, and merges into a simplified network structure comprising 2 core nodes and 1 equivalent path. And finally, reversely calculating the equivalent impedance parameter of the critical path to be 0.12 omega, which is reduced by 20 percent compared with the original value of 0.15 omega according to the actual operation data (such as real-time transmission current value and voltage loss value after failure) of the simplified network structure so as to adapt to the power transmission requirement of the high-electricity-price period. 103. Based on the adjusted equivalent impedance parameters, the voltage amplitude and the phase angle of the power grid node are cooperatively adjusted through a multistage variable current adjustment mechanism of the solid-state transformer, so that a power distribution scheme is formed;

in the step, the multistage variable current regulation mechanism is a series-parallel combination of multistage power modules in the solid-state transformer, so that the fine cooperative regulation and control of the voltage amplitude and the phase angle are realized.

In the embodiment of the 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, the equivalent impedance parameter is mapped into a voltage regulation boundary condition (such as an allowable voltage deviation range + -5%), and a phase difference constraint (such as a phase difference not exceeding 10 °) between adjacent nodes is calculated through a dynamic phase coupling algorithm. And the solid-state transformer adjusts the amplitude and the phase angle of the output voltage through a multistage converter module (such as an H-bridge cascade structure) according to the real-time voltage measurement data, so that the solid-state transformer meets the constraint condition. Meanwhile, a distributed consistency algorithm is introduced to coordinate multi-node regulation actions, so that an economic goal of a global power distribution scheme (such as preferentially meeting the power supply of a high-electricity-price area) is ensured. And finally, generating a power distribution scheme which comprises the power injection priority and the distribution proportion of each node.

For example, continuing the above example, step 103 sets the fault region node voltage allowable range to 215V-225V and the adjacent node phase difference constraint to 8 ° according to the adjusted equivalent impedance parameter (0.12Ω). The solid-state transformer increases the output voltage from 210V to 220V through the cascade converter module, and adjusts the phase angle to reduce the phase difference to 6 degrees, so that a power distribution scheme for supplying power to the high-electricity-price area preferentially is formed.

104. In the execution process of the power distribution scheme, monitoring the running state data of the solid-state transformer through edge computing nodes deployed on the power grid nodes, and generating a local control instruction by combining the adjusted equivalent impedance parameters;

in this step, the local control command is a real-time adjustment command for a specific grid node generated by the edge node, including the voltage compensation amount and the phase correction direction.

In the embodiment of the application, in the execution process of a power distribution scheme, an edge computing node arranged on a power grid node acquires running state data (such as an output end voltage instantaneous fluctuation value and an internal module temperature change rate) of the solid-state transformer in real time, and noise interference is removed through an adaptive filtering technology. And (3) combining the equivalent impedance parameters updated in the step (102), and adopting an abnormality detection model (such as an abnormality marked when the voltage fluctuation direction is opposite to the impedance adjustment direction) based on rule reasoning to generate a preliminary control instruction. Further, by means of a dynamic weight distribution algorithm, the equipment safety parameters (such as a temperature change rate) and the electrical parameters (such as a phase difference) are fused, and compensation correction (such as voltage compensation amount reduction at high temperature) is performed on the preliminary control command. And finally outputting a local control instruction which comprises the real-time adjustment quantity and direction of each node.

For example, continuing the above 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 ℃ per second), and generates a primary instruction, namely voltage compensation +3V and the phase correction direction is anticlockwise by combining the equivalent impedance parameter (0.12Ω). After safety compensation, the final local control instruction is adjusted to be voltage compensation +2V, and the phase correction angle is widened to 7 degrees.

105. And carrying out space-time mapping matching on the local control instructions and the electricity price fluctuation data, carrying out collaborative priority ordering on the local control instructions of a plurality of grid nodes according to a matching result, generating a joint control strategy, dynamically adjusting the power distribution scheme, and realizing dynamic optimization control of transmission loss and transaction cost.

In the step, the joint control strategy is a global optimization scheme generated by fusing multi-node local instructions, and the transmission loss and the transaction cost are balanced.

In the embodiment of the application, firstly, space-time mapping matching is carried out on the local control instructions and electricity price fluctuation data within 1 hour after the instructions are issued (for example, 14:00-15:00 electricity price data are associated with the instructions issued by 14:00), secondly, the space-time mapping matching result is quantized into matching degree, priority scores are calculated according to the matching degree, the local control instructions of a plurality of grid nodes are prioritized according to the priority scores, then, the first 30% of high-priority control instructions are selected from the priority ranking result, conflict among the high-priority control instructions is processed by adopting a conflict resolution rule base and then integrated into a joint control strategy, meanwhile, the power distribution priority of each node in a power distribution scheme is adjusted according to the priority scores through a dynamic resource distribution algorithm, and the transmission loss and the transaction cost in the implementation process of the joint control strategy are optimized.

For example, continuing the above example, step 105 firstly performs space-time mapping matching on the local control instruction issued by the fault area 14:00 and the electricity price fluctuation data (amplitude 18%) of 14:00-15:00 time period, calculates the matching degree score as 0.92 (full score of 1.0), secondly generates the priority score (0.92×0.7+0.3×0.3=0.83) according to the matching degree score and the load emergency degree weight, sorts the fault area instruction into the highest priority, then adopts a conflict resolution rule base to process the phase direction conflict with the adjacent non-fault area instruction, reserves the fault area instruction and releases the phase constraint of the non-fault area to +/-8 degrees, and finally adjusts the power distribution scheme according to the priority score through a dynamic resource distribution algorithm, so that the power ratio of the fault area is increased from 40% to 52%, the transmission loss of the non-fault area is reduced by 8%, and the joint optimization control of the transmission loss and the transaction cost is realized. 101-105, constructing a simplified network model of transaction scene adaptation by dynamically fusing electric power market data and power grid physical states, realizing quick response of power distribution based on a cooperative control mechanism of solid-state transformers and edge calculation, and further dynamically balancing transmission loss and transaction cost under a scene of frequent topological change by space-time mapping and multi-objective optimization, thereby improving system economy and operation stability.

In order to improve the dynamic adaptation capability of the power grid to the electric power market transaction under the scene of frequent topological change, the problem of mismatch between transmission characteristics and market fluctuation caused by a static model in the traditional method is solved, a simplified network model driven by a transaction sensitive path is constructed by coupling electricity price fluctuation characteristics and a topological dynamic evolution rule, 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 a power trade scenario according to the power market trade data and the topology connection relation data, and adjusting equivalent impedance parameters of a key transmission path in the simplified network structure includes:

201. Correlating the electricity price fluctuation data in the electricity market transaction data with the node connection modes in the topological connection relation data and extracting correlation parameters, wherein the correlation parameters comprise electricity price fluctuation amplitude and topological connection density, and determining an electricity transaction sensitive path based on the electricity price fluctuation amplitude and the topological connection density;

In step 201, the price fluctuation range refers to the intensity of the electricity price change at different time periods in the electricity market, and includes quantitative indexes such as peak fluctuation rate, valley recovery rate and the like. The topology connection density is a measure describing the physical connection strength between nodes in a specific area in the power grid and is measured by the number of effective transmission paths per unit area or per unit number of nodes.

In the embodiment of the application, the relevance of electricity price fluctuation data and topology connection density is analyzed through a mutual information quantization algorithm. Firstly, extracting time sequence characteristics (such as fluctuation extreme points and gradient) of electricity price fluctuation amplitude and spatial distribution characteristics (such as regional path redundancy and node degree centrality) of topological connection density, and constructing a space-time correlation matrix. And identifying a high coupling area (such as an area with high connection density corresponding to the peak value period of the electricity price) of the electricity price fluctuation and the topological connection in the space-time correlation matrix by adopting a dynamic community discovery algorithm, and screening out a power transaction sensitive path based on a Maximum Information Coefficient (MIC). Specifically, the path sensitivity score is marked as a power transaction sensitive path when the score exceeds a dynamic threshold (such as score = electricity price fluctuation amplitude x connection density weight), and a sensitive path topology map is finally generated.

202. Extracting a topological substructure comprising at least two core nodes and more than three associated branches from an original power grid topology according to the distribution characteristics of the power transaction sensitive paths, wherein initial impedance parameters of all branches in the topological substructure are distributed based on historical transaction load proportions of corresponding nodes;

in step 202, the core node is a grid node, typically a load center or a power access point, that assumes the primary power transfer task in the transaction-sensitive path. The historical transaction load proportion refers to the percentage of the total load of the whole network which is borne by the node in the historical transaction period.

In the embodiment of the application, the core node is identified from the original power grid topology based on a k-shell decomposition algorithm in complex network analysis. Firstly, calculating the transaction load influence (such as load proportion multiplied by path betweenness centrality) of each node, and selecting the node with the influence ranking of 10% as a core node. The modified Prim algorithm is then used to build a minimum spanning tree, extracting the topology substructure containing the core nodes and their associated branches. The initial impedance parameter distribution adopts a load weighting method, and a reference impedance value (such as a reference value is determined by regional average transmission capacity) is dynamically adjusted according to the sum of historical transaction load proportions of the connecting nodes of each branch (such as the load ratio of a node A is 30% + the load ratio of a node B is 20% → the initial impedance of a branch AB=reference impedance multiplied by 50%), so that the topological substructure and the initial impedance parameters of each branch thereof are finally generated.

203. Based on the topological substructure, combining redundant connection branches with the same power transmission direction as the core nodes to generate a simplified network structure for reserving the power transaction sensitive path, wherein equivalent impedance parameters of a key transmission path in the simplified network structure are subjected to directional weighted correction according to the electricity price fluctuation data;

In step 203, the redundant connection branches are multiple parallel paths that exist in the same power transfer direction, whose function can be replaced by a single main path without affecting the transfer capability. The directional weighted correction is a mechanism for differentially adjusting the equivalent impedance parameters according to the fluctuation direction of electricity price (such as rising or falling of electricity price).

In the embodiment of the application, a dynamic graph pruning technology is adopted to merge redundant connection branches. Firstly, identifying the same-direction redundant branches based on a power transmission direction clustering algorithm (such as an improved DBSCAN), reserving the branch with the largest transmission capacity as a main path, and proportionally overlapping the capacities of the other branches to the main path to generate a simplified network structure for reserving the power transaction sensitive path. The equivalent impedance parameter correction adopts a nonlinear directivity weighting model, wherein when the real-time electricity price fluctuation direction is positive (rising), negative correction (such as impedance value=original value x (1-electricity price fluctuation amplitude x weight coefficient) is applied to the main path impedance to improve transmission capacity, and when the real-time electricity price fluctuation direction is reverse fluctuation, positive correction (such as impedance value=original value x (1+electricity price fall amplitude x weight coefficient) is applied to limit reverse power flow) is applied to the main path impedance, and the weight coefficient is dynamically calculated through a sigmoid function to ensure nonlinear adaptation of correction amplitude and electricity price fluctuation rate.

204. And continuously receiving load demand prediction data in the electric power market transaction data in the operation process of the simplified network structure, and performing reverse compensation adjustment on the equivalent impedance parameter according to the distribution proportion of the predicted load on the key transmission path so as to keep the equivalent impedance parameter matched with the power flow direction in the transaction scene.

In step 204, the inverse compensation adjustment is performed by performing a secondary adjustment on the corrected equivalent impedance parameter according to the load prediction data to offset the influence of the predicted load distribution deviation. The distribution ratio is a ratio of the distribution of the predicted load on each critical transmission path, reflecting the expected characteristics of the future power flow direction.

In the embodiment of the application, the impedance parameter compensation is realized by adopting a sliding window prediction fusion mechanism. After continuously receiving the load demand prediction data, a load distribution trend (such as the load ratio of path A increases from 40% to 60% in the future 1 hour) is extracted through a sliding time window. Based on the trend analysis result, the compensation quantity of the equivalent impedance parameter is calculated, namely negative compensation (such as impedance value=correction value x (1-load increase proportion x compensation coefficient) is applied to the impedance of a certain path when the predicted load is concentrated to match the expected power flow direction, and the compensation coefficient is adjusted through a dynamic feedback mechanism, namely when the deviation between the actual load distribution and the predicted value exceeds a threshold value, the compensation coefficient is adaptively increased (such as the deviation is increased by 5 percent and the coefficient is increased by 0.1) to form closed-loop compensation control, so that the equivalent impedance parameter is kept matched with the power flow direction in a transaction scene.

The following is a specific example:

Assume that a certain area causes the grid-connected disconnection of a plurality of wind turbines due to typhoons (a scene of frequent topological change), and meanwhile, the electricity price in the power market fluctuates severely (the peak value rises by 120% compared with the valley value). Step 201, recognizing the coupling relation between high electricity price fluctuation (peak value 0.9 yuan/kWh) and topology connection density reduction (path breakage 3), marking 2 paths connecting the rest wind power clusters and the load center as transaction sensitive paths, step 202, extracting a substructure comprising a wind power access point (core node A), the load center (core node B) and 4 associated branches, distributing branch initial impedance (0.18Ω, 0.15Ω and the like) according to historical load proportion (A: 35%, B: 45%), step 203, merging the same-direction redundant branches to generate a simplified network structure, carrying out negative correction (0.15Ω -0.11Ω) on the main path impedance according to real-time electricity price fluctuation (120%), step 204, combining load prediction data (load concentrated 60% in the future 1 hour) and applying secondary compensation (0.11Ω -0.09 Ω) on the main path impedance, and finally realizing the accurate matching of equivalent impedance parameters and power flow direction.

The steps 201-204 generate a transaction sensitive path through dynamically associating power price fluctuation and topology evolution characteristics, combine a multi-stage impedance correction mechanism (initial allocation, directional weighting and reverse compensation) to enable the transmission characteristics of the power grid to be adapted to market transaction requirements in real time, and under the scene of frequent topology change, through redundancy path combination and closed loop parameter compensation, the transmission loss caused by model mismatch is obviously reduced, meanwhile, the power transmission efficiency of a power price increasing period is improved, and the cooperative control of economic optimization and physical operation stability is realized.

In order to realize dynamic refined control of power distribution under the scene of frequent change of power grid topology, the problem of conflict between voltage stability and economic targets caused by single regulation mode in the traditional method is solved, a power distribution mechanism driven by dynamic priority is constructed through deep coupling of a multistage regulation mode, real-time electricity price fluctuation and load demands, and the system is ensured to quickly respond and maintain an optimal running state when the topology is suddenly changed. In some embodiments, the adjusting the voltage amplitude and the phase angle of the grid node based on the adjusted equivalent impedance parameter through a multistage variable current adjustment mechanism of the solid-state transformer forms a power distribution scheme, including:

301. converting the adjusted equivalent impedance parameters into voltage regulation parameters comprising a voltage amplitude deviation range and a phase angle difference range allowed by each power grid node, and distributing the conversion process based on the transmission capacity proportion of the key transmission path and the phase difference proportion between adjacent nodes;

In step 301, the transmission capacity ratio refers to the percentage of the total transmission capacity of the whole network that is the maximum power that the critical transmission path can carry in the current grid operation state. The phase difference ratio is the ratio of the actual phase angle difference between adjacent nodes to its allowable maximum phase angle difference for quantifying the severity of the phase shift.

In the embodiment of the application, the equivalent impedance parameter is converted into the voltage regulation parameter by adopting a mapping model of dynamic impedance and voltage. The voltage amplitude deviation range of each node is determined by a weighted distribution algorithm based on the real-time transmission capacity proportion of the key transmission path (such as the capacity proportion of the path A is 30 percent), and the allowable deviation is enlarged by +/-0.5 percent (such as the capacity proportion is increased by 10 percent). Meanwhile, according to the phase difference ratio between adjacent nodes (for example, the phase difference between the nodes B and C is 5 degrees, the maximum allowable value is 8 degrees to the ratio of 62.5 percent), the phase angle difference range is dynamically adjusted by using a fuzzy logic reasoning mechanism (for example, the allowable range is narrowed when the ratio exceeds 70 percent). Finally, voltage regulation parameters including a voltage amplitude deviation range (such as +/-3%) and a phase angle difference range (such as +/-7 DEG) are generated.

302. Generating a multi-level regulation mode according to the voltage regulation parameters and the electricity price fluctuation data, wherein the multi-level regulation mode comprises at least three regulation levels, the triggering condition of each regulation level is determined based on an electricity price fluctuation amplitude threshold value and a fluctuation direction, and the fluctuation direction comprises an electricity price peak period and an electricity price valley period;

In step 302, the adjustment level is a regulation intensity level divided according to the amplitude and direction of the power price fluctuation, and different levels correspond to different voltage and phase constraints. The fluctuation direction is a qualitative description of the change trend of the electricity price, and comprises a peak period in which the electricity price continuously rises and a valley period in which the electricity price continuously falls.

In the embodiment of the application, a multi-level adjustment mode is generated by adopting an event-driven grading strategy. Firstly, calculating fluctuation amplitude (such as 15% of power price rising in 1 hour) by a sliding window statistical method based on real-time power price fluctuation data, and determining fluctuation direction by using a trend direction identification algorithm (such as Hodrick-Prescott filtering). Setting a three-stage regulation mode:

Basic regulation (fluctuation amplitude < 5%) maintains the current voltage and phase constraint;

the voltage deviation range (such as +/-2%) is tightened, and the phase difference range (such as +/-9 ℃) is widened to improve the transmission capability;

Emergency regulation (fluctuation amplitude is more than or equal to 10% or the direction is valley) expands the voltage deviation range (such as +/-4%), and severely limits the phase difference (such as +/-5 DEG) to inhibit reverse power. The triggering condition is dynamically updated through the finite state machine model, so that the mode switching is ensured to be synchronous with market fluctuation.

303. In the multi-stage regulation mode, based on the measurement data of the real-time voltage amplitude and the phase angle of the power grid node, matching the deviation range in the voltage regulation parameters, triggering the switching of adjacent regulation levels when the measurement data exceeds the deviation range, and synchronously regulating the phase angle difference range in the switching process to keep the consistency of the power transmission direction;

In step 303, the deviation range is the maximum interval in which the voltage amplitude or phase angle is allowed to deviate from the standard value. The phase angle difference range is an allowable fluctuation interval of phase angle difference between adjacent nodes for restricting the power transmission direction.

In the embodiment of the application, the adjustment level switching is realized through a self-adaptive threshold matching mechanism. After the voltage amplitude (such as node D voltage 225V) and phase angle (such as node D-E phase difference 6 degrees) data of the grid nodes are collected in real time, instantaneous noise is eliminated by adopting a sliding average filtering technology, and the instantaneous noise is compared with the deviation range under the current regulation level (such as the enhanced regulation requirement voltage deviation plus or minus 2 percent, the actual measurement deviation plus 2.5 percent). When the data is overrun three times in succession, the level switching protocol based on event triggering is triggered, namely, switching to a higher level (such as basic-enhancing) if the voltage is overrun, and switching to a lower level (such as emergency-enhancing) if the phase is overrun. In the switching process, the phase angle difference range of adjacent nodes is synchronously adjusted (for example, the phase angle difference range is adjusted from +/-7 degrees to +/-8 degrees) through a distributed consistency protocol, so that the power transmission direction is ensured not to be reversed.

304. And generating a power distribution scheme comprising power distribution priorities and distribution proportions of all nodes according to voltage amplitude and phase angle constraints corresponding to the switched regulation levels, wherein the distribution priorities are positively related to the emergency degree of the load demand prediction data.

In step 304, the allocation priority is the priority of node power injection, and is determined by the emergency degree of load demand. The allocation proportion is the duty cycle obtained by each node in the total power allocation and is positively correlated with the priority.

In the embodiment of the application, a dynamic priority queue is adopted to generate a power distribution scheme. Firstly predicting the emergency degree of data (for example, the load gap of an area X for 1 hour reaches 30%) according to the load demand, measuring the emergency course into node priority weight (for example, the emergency area weight is increased by 50%) by an entropy weight method, setting priority coefficient of power distribution of each node based on the node priority weight, restricting voltage amplitude and phase angle corresponding to the priority coefficient and the switched regulation level as input parameters of a multi-target particle swarm optimization algorithm, dynamically solving the power distribution proportion of each node (for example, the high priority node distribution proportion is increased from a reference value of 25% to 40%), and finally generating a power distribution scheme comprising the power distribution priority and the distribution proportion of each node, wherein the node with the high priority weight automatically obtains larger power injection margin by an optimization algorithm, and meanwhile, strictly limiting the voltage and phase deviation range at an algorithm constraint layer to realize the safety regulation and control target under the emergency level. The following is a specific example:

Suppose that an industrial area causes instantaneous reconstruction of a power grid topology (line impedance suddenly changes by 30%) due to sudden switching of large-scale equipment, and meanwhile, electricity price peak period fluctuation occurs in an electric power market (rising by 18% within 1 hour). Step 301 converts the abrupt change equivalent impedance parameter (0.25Ω→0.18Ω) into a voltage regulation parameter, sets the node voltage deviation range to ±4%, and allows the adjacent node phase difference to be ±6°, in step 302, triggers an emergency regulation mode to relax the voltage deviation to ±5%, and tightens the phase difference to ±4°, step 303 monitors that the voltage of a certain node is instantaneously overrun (+5.2%) and switches to an enhancement regulation mode, adjusts the voltage deviation to ±3%, and synchronously relaxes the phase difference to ±7°, and step 304 combines the load prediction data (industrial area load emergency gap 25%) to generate a power distribution scheme, wherein the node priority of the area is increased to the highest, and the distribution proportion is increased from 35% to 50%.

The steps 301-304 realize the fine balance of voltage stability and economic targets through the dynamic adaptation of a multilevel regulation mode and real-time electricity price and load data, quickly respond to local abnormality through a priority-driven power distribution mechanism under the scene of frequent topological changes, ensure the power supply reliability of a high emergency load area, simultaneously inhibit the invalid power loss of a non-critical area, and promote the cooperation of the overall operation efficiency of the system and market trading benefits.

In order to realize quick response and safety optimization of equipment-level control under a scene of frequent change of a power grid topology and solve the problem of local exception handling hysteresis caused by data transmission delay in the traditional centralized control, the scheme constructs a closed-loop control link driven by the running state of a solid-state transformer through a dynamic monitoring and multi-parameter fusion mechanism of an edge computing unit, and ensures the real-time performance of power regulation and the cooperative improvement of equipment safety under abnormal working conditions. In some embodiments, during the execution of the power distribution scheme, monitoring, by an edge computing node disposed at the grid node, the operation state data of the solid-state transformer, and generating a local control instruction in combination with the adjusted equivalent impedance parameter, including:

401. Collecting operation state data of the solid-state transformer in real time through the edge computing unit, wherein the operation state data comprise an output end voltage instantaneous fluctuation value, an adjacent node current phase difference instantaneous value and a temperature change rate of a power module inside the solid-state transformer;

In step 401, the output voltage transient fluctuation value is the transient deviation amount by which the solid state transformer output voltage deviates from the nominal value in a very short time (e.g., milliseconds). The instantaneous value of the current phase difference between adjacent nodes is an instantaneous measurement of the phase angle difference of the current waveforms of adjacent grid nodes at the same point in time.

In the embodiment of the application, the multidimensional operation data of the solid-state transformer is synchronously acquired by the embedded high-speed data acquisition module of the edge calculation unit at the microsecond sampling frequency. Noise suppression (such as removing transient peaks of + -5%) is carried out on the original voltage fluctuation value by adopting a sliding window filtering technology, and the current phase difference transient values between adjacent nodes are accurately extracted by a phase-locked loop (PLL) algorithm. Meanwhile, the temperature gradient distribution of the power module inside the solid-state transformer is monitored in real time by utilizing the thermocouple array, and the weighted average value of the temperature change rate of each module is calculated (for example, the temperature change rate of the module A is 0.3 ℃ per second, the temperature change rate of the module B is 0.5 ℃ per second, and the overall change rate is 0.4 ℃ per second). And finally generating operation state data comprising the voltage fluctuation value, the phase difference instantaneous value and the temperature change rate.

402. Correlating the instantaneous fluctuation value of the voltage at the output end in the running state data with the adjusted equivalent impedance parameter, and marking the running state data as a first type of abnormal state when the fluctuation direction of the voltage is opposite to the change direction of the impedance threshold value;

in step 402, the impedance threshold change direction is the increasing or decreasing trend of the impedance value (e.g. the impedance decreases to a negative change and the impedance increases to a positive change) during the adjustment of the equivalent impedance parameter. The first type of abnormal state is an operation abnormality flag caused by a reverse collision of the voltage fluctuation direction and the impedance adjustment direction.

In the embodiment of the application, the fluctuation direction of the instantaneous fluctuation value of the voltage at the output end and the impedance threshold change direction of the adjusted equivalent impedance parameter are obtained, and the dynamic association analysis model is adopted to carry out coupling analysis on the voltage fluctuation direction (such as voltage rising or falling) and the impedance threshold change direction (such as impedance falling or rising). Firstly, a correlation coefficient matrix of a voltage fluctuation direction vector (positive/negative direction) and an impedance change direction vector is established, and when the correlation coefficient is lower than a preset critical value (such as-0.8), the inverse collision is judged. For example, if the impedance threshold is decreasing (changing in the negative direction) and the voltage fluctuation continuously increases (changing in the positive direction), the first type of abnormal state flag is triggered, and the distribution position of the abnormal state is determined by node coordinate mapping in the topological connection relation data.

403. According to the distribution position of the first abnormal state, extracting a phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topological connection relation data, and generating a primary control instruction comprising a voltage compensation quantity and a phase correction direction by combining the current phase difference instantaneous value between the adjacent nodes;

In step 403, the phase difference constraint range is the maximum interval of current phase angle differences between adjacent nodes allowed in the grid operation code. The phase correction direction is the direction (e.g. clockwise or anticlockwise) of the current phase angle change to be adjusted to eliminate the phase deviation.

In the embodiment of the application, a primary control instruction is generated based on a fuzzy rule reasoning engine. Firstly, topological connection relation data of an area where the first type of abnormal state is located is extracted, and a phase difference constraint range of adjacent nodes (for example, a phase difference allowable by C-D of the nodes is +/-8 degrees) is obtained. And comparing the real-time current phase difference instantaneous value (such as the actual measured phase difference of 10 DEG of the node C-D) with the constraint range, and calculating the deviation amount (10 DEG-8 DEG= +2 DEG) and the deviation direction (forward overrun). And determining voltage compensation quantity (such as 0.5V of compensation voltage of 1 degree when the deviation exceeds the limit) through a fuzzy membership function, and generating a phase correction direction (such as anticlockwise correction when the forward direction exceeds the limit) by combining the deviation direction. The final output contains the voltage compensation amount (+1v) and the primary control instruction of the phase correction direction (counterclockwise).

404. And adjusting the voltage compensation quantity in the primary control instruction based on the temperature change rate in a way that when the temperature change rate exceeds a preset safety interval, the voltage compensation quantity is reduced proportionally, and the allowable deviation angle of the phase correction direction is synchronously expanded to generate a local control instruction.

In step 404, the preset safety interval is an allowable range of the temperature change rate of the solid state transformer power module, and beyond this range, the risk of overheating the device may be raised. The allowable deviation angle is an acceptable adjustment angle error range when the phase correction direction is performed.

In the embodiment of the application, a dynamic weight distribution algorithm is adopted to carry out temperature compensation correction on the primary control instruction. When the temperature change rate exceeds the upper limit of the safety interval (such as 0.4 ℃ per second), the voltage compensation quantity (such as original compensation +1V-regulated +0.6V) is scaled down by an exponential decay function. Meanwhile, an allowable deviation angle (such as original + -1 DEG-expansion to + -3 DEG) of the phase correction direction is expanded based on a thermal inertia model of the equipment, so that excessive adjustment caused by temperature limitation is avoided. The finally generated local control instruction comprises the corrected voltage compensation quantity, the expansion deviation angle and the phase correction direction, and the corrected voltage compensation quantity, the expansion deviation angle and the phase correction direction are transmitted to the solid-state transformer execution module through the edge calculation unit.

The following is a specific example:

Assuming that one main line of a certain transformer substation trips (topology mutation), the voltage of the output end of the solid-state transformer fluctuates severely (instantaneous fluctuation value +8%) after the standby line is connected, and meanwhile, the temperature of the internal power module rises rapidly (the change rate is 0.6 ℃ per second). The method comprises the steps of collecting a voltage fluctuation value of +8% (exceeding 4 ℃) and an adjacent node current phase difference instantaneous value of 12 DEG and a temperature change rate of 0.6 DEG/s through an edge calculation unit, marking the area as a first type of abnormal state according to the conflict of positive voltage fluctuation and negative impedance adjustment (standby line impedance reduction) found by correlation analysis of the step 402, extracting a phase difference constraint range of adjacent nodes of the fault area of +7 DEG to generate a primary control instruction, namely voltage compensation +2V and phase anticlockwise correction of 5 DEG, and generating a local control instruction in the step 404 according to the fact that the temperature change rate exceeds the limit (0.6 DEG/s), wherein the adjustment instruction is voltage compensation +1.2V, and the allowable deviation angle is expanded to +4 DEG.

The steps 401-404 are used for rapidly identifying and correcting the abnormal operation of the solid-state transformer under the topology abrupt change scene through a multi-source data real-time acquisition and dynamic compensation mechanism of the edge calculation unit, and combining with an instruction adjustment strategy of temperature safety constraint, the accurate adjustment of voltage and phase is realized on the premise of ensuring the reliability of equipment, the response speed and safety of local power control are effectively improved, and the cascade fault risk caused by overload or overheat is avoided.

In order to improve the transmission efficiency and economical adaptation capability of a transaction sensitive path under the scene of frequent change of the power grid topology, the problem of power distribution misalignment caused by redundant path dispersion in the traditional method is solved, a simplified network model driven by electricity price fluctuation is constructed through a dynamic redundancy combination and directional impedance correction mechanism, and real-time accurate matching of the transmission capability of a key path and market demands is realized. In some embodiments, based on the topology substructure, combining redundant connection branches with the same power transmission direction as the core nodes to generate a simplified network structure for retaining the power transaction sensitive path, where the equivalent impedance parameters of the critical transmission path in the simplified network structure are subjected to directional weighted correction according to the electricity price fluctuation data, and the method includes:

501. Identifying all redundant branches connected with a core node in the topological substructure, wherein the judgment condition of the redundant branches is that two or more parallel connection branches exist in the same power transmission direction, and the historical transaction load difference of each branch is smaller than a set proportion threshold;

In step 501, the redundant branches are multiple parallel lines that exist in the same power transfer direction, whose functions overlap and can be combined without affecting the overall transfer capability. The historical transaction load difference refers to the load amount difference born by different branches in the historical transaction period and is quantified by percentage.

In the embodiment of the application, an improved spectral clustering algorithm is adopted to cluster the power transmission direction of the topological sub-structure. Firstly, extracting the power flow direction characteristics (such as a tidal flow direction angle and transmission capacity) of each branch, and calculating the direction consistency (such as similarity >95% and judging the direction to be the same) among the branches through cosine similarity. And when the number of the equidirectional branches is more than or equal to 2 and the load difference is smaller than a set threshold (such as 20%), marking the branches as redundant branches according to historical transaction load difference analysis (such as load difference of branches A and B is less than 15%).

502. Combining the redundant branches, reserving the branch with the highest historical transaction load as a key transmission path, and overlapping the transmission capacity of the other branches to the key transmission path;

In step 502, the critical transmission path is a core power transmission channel formed after combining the redundant branches, and carries the integrated transmission capacity after superposition. Transmission capacity superposition is a capacity allocation mechanism that integrates the transmission capacity of redundant branches proportionally to the critical path.

In the embodiment of the application, redundant branch merging is realized based on a dynamic graph pruning technology. The redundant branches are first ordered according to historical transaction loads (such as 30% for branch A and 25% for branch B), and the highest load branch is selected as the critical transmission path. The transmission capacities of the other branches are integrated to a key transmission path through a weighted superposition algorithm (for example, the capacities of the branches B are superposed according to a scale factor of 0.8, and the capacities of the branches C are superposed according to 0.5), and the superposed capacity values are processed through nonlinear normalization (for example, the compressed extremum of a sigmoid function) to ensure that the capacities of the key transmission path do not exceed the limit of physical equipment.

503. Marking the power transmission direction corresponding to the electricity price peak period as a forward sensitive direction and the reverse direction corresponding to the electricity price valley period as a reverse sensitive direction according to the real-time electricity price fluctuation data;

In step 503, the forward direction is the power transmission direction in which the electricity price is guaranteed preferentially during the peak period, and generally corresponds to the power supply requirement of the load center. The reverse sensitivity direction is the reverse flow direction of the power to be suppressed in the electricity price valley period, and generally corresponds to the return of the surplus power.

In the embodiment of the application, a sliding window statistical method is adopted to identify the fluctuation trend of the electricity price. Firstly, real-time electricity price data is divided according to time windows (such as 15 minutes), and the fluctuation of the electricity price in each window (such as 12% of the fluctuation of window 1) is calculated. The peak period (3 windows in succession rise > 8%) and the valley period (3 windows in succession fall > 5%) are determined by a trend direction recognition algorithm (such as a moving average crossover method). And according to the corresponding power transmission direction of the time mark, marking the power supply direction of the load center as a forward sensitive direction in the peak time and marking the power supply side return direction as a reverse sensitive direction in the valley time.

504. And carrying out differential weighting adjustment on equivalent impedance parameters of the key transmission path based on different sensitive directions to generate a simplified network structure for retaining the power transaction sensitive path, 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 along with the fluctuation amplitude of the real-time electricity price.

In step 504, the differential weighting adjustment is to apply dynamic corrections of different magnitudes to the critical path impedance according to the sensitivity direction to adapt to the price fluctuation demand. The real-time electricity price fluctuation amplitude is the percentage change amount of the current time period electricity price relative to the reference value.

In the embodiment of the application, the nonlinear directivity weighting model is adopted to adjust the impedance parameters. For the positive sensitive direction path, a negative correction coefficient (e.g., impedance value=original value× (1-amplitude×0.005)) is calculated based on the real-time power price amplitude (e.g., amplitude 15%), and the impedance is reduced to improve the transmission capability. For the reverse sensitive direction path, a forward correction coefficient (such as impedance value=original value× (1+drop width×0.003)) is adopted, and impedance is increased to suppress reverse power flow. And the correction coefficient is adjusted through a dynamic feedback mechanism, when the fluctuation rate of the electricity price exceeds a threshold value (such as change of >0.5 percent per minute), the weight of the correction coefficient is increased in a self-adaptive mode (such as the rate exceeds 1 percent, and the weight is increased by 0.1 percent), and finally the simplified network structure with optimized sensitivity direction is generated.

The following is a specific example:

Suppose that a business area has heavy topology caused by switching of a plurality of energy storage devices, and meanwhile, the daily electricity price presents the fluctuation of an afternoon peak value (rising by 20%) and a night valley value (falling by 15%). Step 501 identifies 3 equidirectional redundant branches (18% of historical load difference) connecting an energy storage cluster and a commercial load center and marks the branches as redundant branches, step 502 merges the redundant branches, reserves branch A with the load ratio of 35% as a key transmission path, stacks branch B (25%) and C (20%) capacities, and improves the total capacity to 160% of an original value, in step 503, the midnight electricity price peak marks energy storage discharge to the load center as a positive sensitive direction, and the night valley marks load residual electricity return as a reverse sensitive direction, in step 504, the midnight carries out negative correction (0.2Ω -0.16Ω) on the key path impedance, and the midnight reverse correction (0.2Ω -0.23Ω) to form a simplified network structure for reserving the power transaction sensitive path.

In the scene of frequent topology change, the reactive power flow is restrained by directional impedance adjustment, the transmission capacity of a high electricity price period is optimized, the redundancy loss of a low electricity price period is reduced, and the dual promotion of the running economy and the physical transmission performance of a power grid is realized.

In order to quickly locate and correct the phase mismatch problem caused by local abnormality in the frequent change scene of the power grid topology, the problem of control instruction misalignment caused by global model update delay in the traditional method is solved, an accurate control link based on real-time operation data is constructed through an abnormal region dynamic identification and phase difference closed loop correction mechanism, and real-time adaptability of power regulation actions and topology change is ensured. In some embodiments, the extracting a phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topological connection relationship data according to the distribution position of the first type of abnormal state, and generating a primary control instruction including a voltage compensation amount and a phase correction direction by combining the current phase difference instantaneous value between the adjacent nodes, includes:

601. Positioning an abnormal region in the topological connection relation data according to the distribution position of the first type of abnormal state, wherein the abnormal region comprises at least two adjacent nodes and connection paths thereof;

In step 601, an anomaly area is a local grid range defined by a first type of anomaly distribution location, including at least two neighboring nodes and their connection paths.

In the embodiment of the application, an abnormal region detection model based on a graph neural network is adopted. Firstly, carrying out space superposition analysis on the distribution positions (such as node coordinate sets) of the first type of abnormal states and topological connection relation data, and extracting adjacent features (such as adjacent node degree and path betweenness) of abnormal nodes through a graph convolution network. And calculating the influence coefficient of each node by using the attention mechanism weighting, judging that the connecting path forms an abnormal area when the influence weighted sum between adjacent nodes exceeds a dynamic threshold value, and marking the affected nodes and paths.

602. Extracting phase difference constraint ranges among adjacent nodes in the abnormal region, wherein the phase difference constraint ranges are determined based on equivalent impedance parameters and historical operation data of corresponding paths in the simplified network structure;

in step 602, the phase difference constraint range is a limit interval of the current phase angle difference between adjacent nodes allowed by the grid safety regulations.

In the embodiment of the application, the phase difference constraint range is dynamically determined through a fuzzy logic system. First, a theoretical phase difference reference value (e.g., impedance value×transmission capacity coefficient=8°) is calculated based on an equivalent impedance parameter (e.g., 0.15Ω) of a corresponding path in the simplified network structure. Combining actual phase difference statistical distribution in historical operation data (for example, 95% data in the past 30 days is within +/-9 degrees), and adopting membership function to fuse theoretical value and actual distribution to obtain dynamic allowable range (for example, +/-8.5 degrees). When a recent topology change event (such as device switching) is detected, a permissible range (such as +/-9.2 degrees) is temporarily expanded through a sliding window mechanism, and a phase difference constraint range is generated.

603. Comparing the current phase difference instantaneous value between the adjacent nodes with the phase difference constraint range, and calculating a phase deviation value and a deviation direction when the current phase difference instantaneous value between the adjacent nodes exceeds the phase difference constraint range;

in step 603, the phase deviation direction is the deviation direction (positive overrun or negative overrun) of the measured phase difference from the allowable range.

In the embodiment of the application, a section overlapping detection algorithm is adopted for deviation analysis. And collecting current phase difference instantaneous values (such as 11 DEG of phase difference of nodes A-B) between adjacent nodes in real time, and detecting an overlapping section with a phase difference constraint range (such as + -8.5 DEG) output by the step 602. When the instantaneous value exceeds the allowable range, firstly, calculating the deviation amount, adopting a linear interpolation method to determine the excess ratio (such as 11-8.5 degrees and 2.5 degrees overrun), then, judging the deviation direction through a sign function (such as +2.5 degrees and positive overrun), and taking the deviation direction as the generation basis of the control instruction.

604. Based on the phase deviation amount and the deviation direction, a primary control command including a voltage compensation amount, the magnitude of which is proportional to the phase deviation amount, and a phase correction direction, the phase correction direction being opposite to the deviation direction, is generated.

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

In the embodiment of the application, the primary control instruction is generated based on the control principle of proportion, integral and derivative (PID). The phase deviation amount is input to a PID controller (proportionality coefficient kp=0.6, integration time ti=10s), and a voltage compensation amount (e.g., deviation+2.5° →compensation+1.5v) is calculated.

And determining the phase correction direction through a reverse mapping model, wherein the phase correction direction is required to be corrected anticlockwise when the positive direction exceeds the limit, and is corrected clockwise when the negative direction exceeds the limit.

The final output contains the voltage compensation amount (+1.5v) and the primary control instruction of the phase correction direction (counterclockwise), and is issued and executed by the edge calculation unit.

The following is a specific example:

and supposing that a power grid in a mountain area causes a plurality of transmission lines to swing (topological instantaneous change) due to strong wind, the phase difference between the nodes C-D is caused to continuously overrun. The method comprises the steps of detecting a first abnormal state near a node C, positioning an abnormal region containing the node C, D and a connecting line thereof, extracting a path phase difference constraint range of +/-7.8 degrees (equivalent impedance of 0.18 omega and 95% distribution of historical data of +/-8.3 degrees) in a step 602, monitoring a phase difference instantaneous value of 9.6 degrees in real time in a step 603, calculating a deviation amount of +1.8 degrees (the deviation direction is positive overrun), and correcting the phase anticlockwise in a primary control instruction generated in the step 604, wherein the voltage compensation is +1.08V.

And 601-604, by means of a dynamic positioning and phase difference closed loop correction mechanism of an abnormal region, rapidly generating an accurate control instruction under a topology frequent change scene, and combining a mixed strategy of fuzzy logic and PID control, effectively inhibiting power oscillation caused by phase mismatch, improving response speed and control precision of local regulation, and ensuring stable operation of a power grid under abnormal working conditions and synchronous achievement of market trading economical targets.

In order to realize deep coordination of a global control strategy and market trading demands under a power grid topology frequent change scene, the problem of optimizing target splitting caused by instruction conflict in the traditional method is solved, a dynamic priority control system of electricity price sensitive driving is constructed through space-time association mapping and a multi-level instruction coordination mechanism, and the power distribution efficiency maximization of a high-value period is ensured. In some embodiments, the performing space-time mapping matching between the local control command and the electricity price fluctuation data, performing collaborative priority ordering on the local control commands of the plurality of grid nodes according to a matching result, generating a joint control strategy, and dynamically adjusting the power distribution scheme to realize dynamic optimization control of transmission loss and transaction cost, including:

701. Extracting local control instruction generation time and corresponding node positions of each power grid node, and carrying out time window and region division matching with the power price fluctuation data of the same period of time to form a space-time association mapping table as a matching result;

In step 701, the time window and area division matching is an operation of performing space-time alignment of the generation time of the local control command and the electricity price fluctuation data according to a fixed period (e.g., 15 minutes) and a power grid area (e.g., a load center area).

In the embodiment of the application, a sliding window statistical method is adopted to segment the generation time stamp of the local control instruction (for example, a window is formed every 5 minutes), and meanwhile, the node position is mapped to a predefined grid region grid (for example, a 500m×500m grid) based on a geographic hash algorithm. And carrying out association matching on the instruction generation position in each window and the power price fluctuation data of the corresponding time period through a space-time alignment engine (for example, the power price fluctuation of the area A in the window T is 10%). Finally, a space-time correlation mapping table is constructed, and the instruction number, the electricity price fluctuation range and the area load characteristics of each space-time unit (time window multiplied by area) are recorded.

702. Marking a time period and a region of the electricity price fluctuation data exceeding a preset threshold value as a high-sensitivity time period region according to the matching result, wherein a local control instruction in the high-sensitivity time period region automatically increases the 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 priority is.

In the embodiment of the application, the high sensitivity period area is marked based on a dynamic threshold adjustment mechanism. Firstly, calculating the score of historical power price fluctuation data (for example, the score value of the first 20% is used as a threshold value benchmark), and marking the space-time unit as a high-sensitivity period area when the real-time power price fluctuation amplitude exceeds a benchmark value (for example, 12%). The priority level of the area with high sensitivity period is increased according to the power price fluctuation ratio (for example, the fluctuation exceeds 5% by one level) through a weight diffusion algorithm, and the level of the adjacent area is associated (for example, the level of the area within 5km of diffusion radius is increased by 50%).

703. Cross-verifying local control instructions of all power grid nodes in the same time window, and when detecting that the instructions of adjacent nodes have phase correction direction conflict, preferentially executing the local control instructions of the high-sensitivity time period region;

in step 703, the bit correction direction conflict is a phase adjustment direction conflict (e.g., node a needs to be corrected counterclockwise and node B needs to be corrected clockwise) existing in the neighboring node control command.

In the embodiment of the application, the conflict detection algorithm in graph theory is adopted for instruction cross-validation. And constructing a directed graph model with the grid nodes as vertexes and the phase correction direction as edge attributes, and detecting the direction conflict between adjacent nodes through the traversal of an adjacency matrix (if the edge A-B is anticlockwise and the edge B-C is clockwise). When the conflict is detected, priority arbitration is carried out based on the multi-target game theory model, namely, the high-sensitive area instruction keeps the original direction, and the non-sensitive area instruction is adjusted to be in the direction of the adjacent high-priority area. The arbitration result is synchronized to the relevant node through the distributed consistency protocol, so that the consistency of the global instruction direction is ensured.

704. Generating a joint control strategy based on a priority execution result, dynamically adjusting the power distribution proportion of each node in the power distribution scheme according to the joint control strategy, increasing the power transmission margin of the high-sensitivity period region, and synchronously reducing the transmission loss of the non-sensitive region.

In step 704, the power transfer margin is a power transfer capability margin that the critical transmission path may increase in the current operating state.

In the embodiment of the application, the joint control strategy is generated by adopting an elastic resource allocation algorithm. And calculating the power distribution weight (such as high sensitive area weight=basic value multiplied by priority level) of each node according to the priority level, and solving an optimal distribution model with constraint by a Lagrangian multiplier method (the objective function is transmission loss minimization and transmission margin maximization). In the adjustment process, the power distribution proportion of the non-sensitive area is compressed according to the weight attenuation coefficient (such as 20% of attenuation of each stage), and the released capacity is superposed to the high-sensitive area. And finally generating a joint control strategy and synchronously updating the joint control strategy to the solid-state transformer execution modules of all the nodes.

The following is a specific example:

The power grid is assumed to have high-frequency topology change caused by burst switching of a large data center, and meanwhile, the fluctuation amplitude of the peak period of the midday electricity price reaches 18%. Step 701, matching 32 local instructions in a central area (grid G7) of a midday 12:00-12:15 period (window T) with electricity price data to form a space-time association mapping table as a matching result, step 702, marking the G7 area as a high-sensitivity period area due to the fact that the electricity price fluctuation exceeds a threshold value (18%), improving the priority to the highest level, step 703, detecting that phase direction conflict exists between adjacent nodes of the G7 area (anticlockwise vs clockwise), preferentially executing the anticlockwise instructions of the high-sensitivity area, and step 704, adjusting a power distribution scheme to generate a joint control strategy, improving the transmission margin of the G7 area by 30% and reducing the loss of a peripheral non-sensitive area by 15%.

Steps 701-704 accurately identify a high-value control area under a scene of frequent topological change through space-time association mapping and a dynamic priority mechanism, and combine conflict resolution and an elastic resource allocation strategy to realize the maximization of the power transmission efficiency in a high-sensitivity period and the cooperative suppression of the invalid loss in a non-sensitive area, thereby remarkably improving the operation economy of the power system and the adaptation precision of market trading benefits.

Fig. 2 is a schematic structural diagram of an electric power system optimization control device based on electric power market transaction according to an embodiment of the present application, as shown in fig. 2, the system includes:

an obtaining module 21, configured to obtain electric power market transaction data, where the electric power market transaction data includes electricity price fluctuation data and load demand prediction data, and collect topology connection relationship data between a plurality of grid nodes in a power grid synchronously;

The adjustment module 22 is configured to generate a simplified network structure adapted to a power trade scenario according to the power market trade data and the topology connection relationship data, and adjust equivalent impedance parameters of a key transmission path in the simplified network structure;

The adjusting module 23 is configured to cooperatively adjust a voltage amplitude and a phase angle of a power grid node through a multi-stage variable current adjusting mechanism of the solid-state transformer based on the adjusted equivalent impedance parameter, so as to form a power distribution scheme;

The monitoring module 24 is configured to monitor, through an edge computing node disposed at the grid node, operation state data of the solid-state transformer during execution of the power distribution scheme, and generate a local control instruction in combination with the adjusted equivalent impedance parameter;

the generating module 25 is configured to perform space-time mapping matching on the local control instruction and the electricity price fluctuation data, perform collaborative priority ordering on the local control instructions of the plurality of grid nodes according to a matching result, generate a joint control policy, and dynamically adjust the power allocation scheme, so as to realize dynamic optimization control of transmission loss and transaction cost.

The power system optimization control device based on the power market transaction described in fig. 2 may execute the power system optimization control method based on the power market transaction described in the embodiment shown in fig. 1, and its implementation principle and technical effects are not repeated. The specific manner in which the individual modules, units, and operations of the power system optimization control device based on the electric market transaction in the above embodiment have been described in detail in the embodiments related to the method, will not be described in detail here.

In one possible design, the power market transaction based power system optimization control device of the embodiment of fig. 2 may be implemented as a computing device, as shown in fig. 3, which may include a storage component 31 and a processing component 32;

the storage component 31 stores one or more computer instructions for execution by the processing component 32.

The processing component 32 is used in the power market transaction based power system optimization control method of the embodiment described above with respect to fig. 1.

Wherein the processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps of the methods described above. Of course, the processing component may also be implemented as one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements for executing the methods described above.

The storage component 31 is configured to store various types of data to support operations at the terminal. The memory component may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

Of course, the computing device may necessarily include other components as well, such as input/output interfaces, display components, communication components, and the like.

The input/output interface provides an interface between the processing component and a peripheral interface module, which may be an output device, an input device, etc.

The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, and the like.

The computing device may be a physical device or an elastic computing host provided by the cloud computing platform, and at this time, the computing device may be a cloud server, and the processing component, the storage component, and the like may be a base server resource rented or purchased from the cloud computing platform.

The embodiment of the application also provides a computer storage medium which stores a computer program, and the computer program can realize the power system optimization control method based on the power market transaction of the embodiment shown in the figure 1 when being executed by a computer.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same, and although the present application has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present application.

Claims

1. An electric power system optimization control method based on electric power market transaction is characterized by comprising the following steps:

Performing space-time mapping matching on the local control instructions and the electricity price fluctuation data, performing collaborative priority ordering on the local control instructions of a plurality of grid nodes according to matching results, generating a joint control strategy, dynamically adjusting the power distribution scheme, and realizing dynamic optimization control of transmission loss and transaction cost;

during the execution of the power distribution scheme, monitoring the operation state data of the solid-state transformer through an edge computing node deployed at the power grid node, and generating a local control instruction by combining the adjusted equivalent impedance parameter, wherein the method comprises the following steps:

Collecting operation state data of the solid-state transformer in real time through the edge computing node, wherein the operation state data comprise an output end voltage instantaneous fluctuation value, an adjacent node current phase difference instantaneous value and a temperature change rate of a power module inside the solid-state transformer;

extracting phase difference constraint ranges between at least two adjacent nodes associated with the distribution positions of the first type of abnormal states in the topological connection relation data according to the distribution positions of the first type of abnormal states, and generating a primary control instruction comprising a voltage compensation quantity and a phase correction direction by combining current phase difference instantaneous values between the adjacent nodes;

2. The method of claim 1, wherein generating a simplified network structure adapted to a power trading scenario from the power market trading data and the topological connection relationship data, and adjusting equivalent impedance parameters of critical transmission paths in the simplified network structure, comprises:

3. The method according to claim 1, wherein the co-adjusting the voltage amplitude and the phase angle of the grid node by a multi-stage variable current adjustment mechanism of the solid state transformer based on the adjusted equivalent impedance parameter forms a power distribution scheme, comprising:

4. The method of claim 2, wherein the merging redundant connection branches in the same direction as power transfer between core nodes based on the topology substructure generates a reduced network structure that retains the power transaction sensitive path, comprising:

5. The method according to claim 1, wherein the extracting, according to the distribution position of the first type of abnormal state, a phase difference constraint range between at least two adjacent nodes associated with the distribution position in the topological connection relation data, and generating a primary control instruction including a voltage compensation amount and a phase correction direction in combination with the instantaneous value of the current phase difference between the adjacent nodes, includes:

6. The method of claim 1, wherein the performing space-time mapping matching between the local control command and the electricity price fluctuation data, performing collaborative prioritization on the local control commands of the plurality of grid nodes according to a matching result, generating a joint control strategy, and dynamically adjusting the power distribution scheme to realize dynamic optimization control of transmission loss and transaction cost, comprises:

7. An electric power system optimization control device based on electric power market trade, characterized by comprising:

The generation module is used for carrying out space-time mapping matching on the local control instructions and the electricity price fluctuation data, carrying out collaborative priority ordering on the local control instructions of a plurality of power grid nodes according to a matching result, generating a joint control strategy, dynamically adjusting the power distribution scheme and realizing dynamic optimization control of transmission loss and transaction cost;

8. The computing device is characterized by comprising a processing component and a storage component, wherein the storage component stores one or more computer instructions, and the one or more computer instructions are used for being invoked and executed by the processing component to realize the power system optimization control method based on the power market transaction according to any one of claims 1-6.

9. A computer storage medium storing a computer program which, when executed by a computer, implements a power system optimization control method based on electric power market transactions according to any one of claims 1 to 6.