Integrated transaction system for smart microgrid based on multi-energy complementation

Abstract

This invention relates to the field of smart microgrid technology and discloses an integrated smart microgrid generation and sales trading system based on multi-energy complementarity. The system includes modules for energy trading control, multi-source complementarity optimization, and dynamic trading matching. The multi-source complementarity optimization module generates initial trading strategies at the day-ahead, intraday, and real-time stages; the load forecasting module collects electricity load, adjustable response capability, and market price data; the risk assessment module generates quantitative risk level indicators by combining historical default records and grid operating status; the energy trading control module generates optimized trading instructions through a multi-objective collaborative decision-making algorithm; the dynamic trading matching module adjusts supply and demand matching rules and provides feedback on execution status; the system also includes fault tolerance correction and trading strategy verification modules, which can improve the efficiency and risk management capabilities of microgrid energy trading and are suitable for integrated smart microgrid generation and sales trading.

Description

Technical Field

[0001] This invention relates to the field of smart microgrid technology, specifically to an integrated trading system for smart microgrid issuance based on multi-energy complementarity. Background Technology

[0002] As the global energy structure transitions towards cleaner and lower-carbon energy sources, smart microgrids, as a crucial carrier integrating distributed energy resources (such as solar, wind, and energy storage systems) with user loads, have become a research hotspot in the energy sector due to their efficient operation and market-based trading. Traditional microgrid trading systems face problems such as insufficient multi-energy coordination, delayed market response, and weak risk management, making it difficult to meet the demands of modern energy systems for security, economy, and flexibility.

[0003] From the perspective of multi-energy complementarity, the output of distributed energy in existing technologies is intermittent and uncertain. For example, solar energy is affected by weather, and wind energy is constrained by wind speed fluctuations. The output characteristics of different energy types vary significantly. Traditional systems lack effective multi-source optimization mechanisms and cannot dynamically generate reasonable trading strategies based on energy type and output forecast data, resulting in low energy utilization and frequent power curtailment. For instance, during periods of high wind and solar power generation, failure to adjust the charging and discharging priorities of energy storage and cross-grid trading parameters in a timely manner can easily lead to energy waste or local grid overload.

[0004] In terms of transaction matching and load forecasting, traditional systems rely on a single method for load forecasting, depending solely on historical electricity consumption data, without fully considering the adjustable load response capabilities of users and the impact of market price fluctuations. This makes transaction matching rules rigid and unable to dynamically adjust the supply and demand relationship in real time. When market prices fluctuate sharply, user electricity consumption behavior may change significantly, and traditional systems struggle to respond quickly, leading to increased transaction execution deviations and impacting the economic efficiency of the microgrid.

[0005] Risk assessment and fault tolerance mechanisms are another weakness of traditional systems. Existing risk assessments are mostly based on simple historical data statistics, lacking in-depth analysis combined with the grid's operational status. For example, they fail to integrate real-time operating parameters such as frequency deviation, node voltage exceedances, and line load rates with historical default records, making it difficult to accurately quantify the level of transaction risk. Furthermore, when anomalies occur during transaction execution, traditional systems lack effective backtracking and adjustment mechanisms, failing to quickly locate the cause of the anomaly and correct the strategy, potentially leading to escalation of the fault and affecting the safe and stable operation of the microgrid.

[0006] Furthermore, with the diversification of energy trading nodes in microgrids, different nodes may adopt different communication protocols and data formats. The poor protocol compatibility of traditional systems leads to poor data exchange, increasing the difficulty and cost of system integration. For example, some distributed energy devices use proprietary communication protocols that are incompatible with the microgrid central control system, requiring manual protocol conversion, which reduces the system's real-time performance and reliability.

[0007] In terms of strategy verification and optimization, traditional systems lack scientific verification mechanisms, making it impossible to effectively evaluate and optimize generated trading strategies. The robustness of these trading strategies is insufficient, and they may face various uncertainties in actual operation, such as sudden weather changes and market policy adjustments, which can easily lead to strategy failure and affect the stable operation of the microgrid. Summary of the Invention

[0008] The purpose of this invention is to provide an integrated trading system for smart microgrid issuance based on multi-energy complementarity, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: an integrated trading system for intelligent microgrid issuance based on multi-energy complementarity, the system comprising:

[0010] The system includes an energy trading control module, a multi-source complementary optimization module, a dynamic trading matching module, a load forecasting module, and a risk assessment module.

[0011] The multi-source complementary optimization module is used to generate an initial trading strategy based on the preset energy type and output prediction data. The initial trading strategy includes the distributed energy output allocation ratio, energy storage charging and discharging priority, and cross-grid trading boundary parameters.

[0012] The load forecasting module collects user electricity load curves, adjustable load response capability data, and market price fluctuation data in real time, and sends the data to the energy trading main control module.

[0013] The risk assessment module generates a quantitative indicator of risk level by analyzing historical transaction default records and real-time power grid operation status.

[0014] The energy trading master control module receives the initial trading strategy, load forecast data and risk level quantitative indicators, and generates optimized trading instructions through a multi-objective collaborative decision-making algorithm.

[0015] The dynamic transaction matching module adjusts the energy supply and demand matching rules according to the optimized transaction instructions and provides real-time feedback on the transaction execution status to the energy transaction main control module.

[0016] The system also includes a fault tolerance correction module. If the deviation between the transaction execution status reported by the dynamic transaction matching module and the optimized transaction instruction exceeds a preset threshold, an abnormal transaction signal is generated, and the strategy backtracking adjustment mechanism is triggered through the energy trading master control module.

[0017] Preferably, the multi-source complementary optimization module is implemented as follows:

[0018] Based on energy output forecast data, the power generation types are prioritized and associated with the corresponding energy output constraint sets;

[0019] Trading strategies are divided into phases based on time scales, including the day-ahead planning phase, the intraday rolling phase, and the real-time adjustment phase.

[0020] During the current planning phase, the set of output constraints will be matched with the base load curve using fuzzy logic to generate the initial output allocation ratio.

[0021] During the intraday rolling phase, the cross-network transaction boundary parameters are dynamically adjusted based on market price fluctuation data.

[0022] During the real-time adjustment phase, the priority of energy storage charging and discharging is updated through a sliding window mechanism until the transaction cutoff threshold is reached.

[0023] Preferably, the specific analysis process of the risk assessment module includes:

[0024] Collect data on the frequency of default types, duration of defaults, and amount of compensation from historical transaction default records;

[0025] A power grid operation status assessment index system was constructed using the analytic hierarchy process, and risk weight coefficients were generated.

[0026] Real-time power grid operation status is obtained through a data acquisition and monitoring system, from which parameters such as frequency deviation range, number of node voltage over-limits, and line load rate are extracted.

[0027] By correlating the risk weighting coefficient with the line load rate parameter, a quantitative risk level index is generated, which includes the transmission security coefficient, market volatility tolerance, and default probability index.

[0028] Preferably, the specific steps of the multi-objective collaborative decision-making algorithm are as follows:

[0029] The initial trading strategy parameters, load forecast data, and risk level quantitative indicators are respectively converted into decision input vectors;

[0030] The correlation features of each vector are extracted using a fuzzy clustering algorithm, and the feature conflict weights are calculated.

[0031] Based on the weights, conflict resolution is performed on the associated features to generate a collaborative decision-making feature set;

[0032] The collaborative decision-making feature set is mapped to the trading strategy space to output optimized trading instructions, including power allocation codes, energy storage scheduling sequences, and trading price fluctuation ranges.

[0033] Preferably, the strategy backtracking adjustment mechanism is implemented as follows:

[0034] When an abnormal trading signal is generated, the energy trading main control module initiates the abnormal source tracing and analysis process to filter abnormal data nodes according to the time series.

[0035] For output allocation deviations, an output compensation model is used to adjust the ramp rate limit of distributed energy sources;

[0036] For energy storage scheduling deviations, the charging and discharging priority queue is reconstructed through a state backtracking mechanism;

[0037] For deviations in trading prices, the boundary values ​​of the price fluctuation range are recalculated using a market equilibrium model.

[0038] Preferably, the system further includes a trading strategy verification module, which is implemented as follows:

[0039] Collect and optimize the deviation data between trading instructions and actual trading status to generate a set of trading conflict features;

[0040] A conflict probability distribution model was constructed using Monte Carlo simulation to verify the robustness of the multi-objective collaborative decision-making algorithm.

[0041] If the probability of conflict is lower than the preset threshold after N consecutive simulations, then the boundary parameters in the set of output constraint conditions are updated.

[0042] Preferably, the design of the conflict probability distribution model includes:

[0043] The conflicts of power allocation, energy storage scheduling, and price fluctuation are quantified into the first conflict factor, the second conflict factor, and the third conflict factor, respectively.

[0044] Based on the default probability index in the risk level quantification indicator, a dynamic correction coefficient is applied to the third conflict factor.

[0045] A strategy verification report is generated by fitting the distribution pattern of conflict factors using a probability density function.

[0046] Preferably, the system further includes a protocol compatibility adaptation module, the operation of which is as follows:

[0047] Scan the connected energy trading nodes to obtain the communication protocol types, data update frequency, and security authentication levels supported by the nodes;

[0048] Conduct a compatibility assessment of node parameters with the required parameters for optimizing trading instructions;

[0049] If the compatibility assessment result is lower than the preset threshold, a protocol conversion instruction is generated to convert the data format into a standard communication protocol.

[0050] If the compatibility assessment result is higher than the preset threshold, a protocol optimization instruction is generated to activate the node's data compression and transmission function.

[0051] Preferably, the specific method for the compatibility assessment is as follows:

[0052] The matching degree between the communication protocol type and the system requirement protocol is checked, and the number of missing protocol fields is calculated.

[0053] Calculate the absolute value of the difference between the data update frequency and the demand frequency during the real-time adjustment phase;

[0054] Compare the security certification level with the minimum requirement level of the risk assessment module;

[0055] The above calculation results are combined into a compatibility score using a weighted summation formula.

[0056] Preferably, the system further includes a supply and demand balance adjustment module, which is implemented in the following ways:

[0057] Real-time monitoring of the dynamic difference between energy supply and demand, and correlation optimization of the supply and demand balance threshold of trading orders;

[0058] If the dynamic difference exceeds the preset range of the supply and demand balance threshold, the adjustment priority strategy will be activated:

[0059] Prioritize adjusting the output priority of distributed energy resources, then modify the charging and discharging rate of energy storage, and finally update the cross-grid transaction parameters.

[0060] If the dynamic difference is lower than the preset range of the supply and demand balance threshold, the excess energy consumption model will be activated, and the remaining energy will be allocated to the trading node with the highest safety factor in the risk assessment module.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] The multi-source complementary optimization module achieves refined management of distributed energy output by prioritizing power generation types and dynamically generating initial trading strategies based on time scales (day-ahead planning, intraday rolling, and real-time adjustment phases). In the day-ahead planning phase, fuzzy logic is used to match output constraints with the base load curve, enabling early optimization of energy allocation and reducing power curtailment. In the intraday rolling phase, cross-grid trading parameters are adjusted based on market price fluctuations, capturing trading opportunities arising from these fluctuations and improving the microgrid's economic returns. In the real-time adjustment phase, a sliding window mechanism updates the charging and discharging priorities of energy storage, enabling rapid response to real-time output fluctuations, maintaining the microgrid's power balance, and improving energy utilization.

[0063] The load forecasting module collects user electricity load curves, adjustable load response capabilities, and market price fluctuation data in real time and transmits them to the main control module, enabling the system to dynamically perceive user demand and market changes. Combined with a multi-objective collaborative decision-making algorithm, the load forecasting data is integrated with initial trading strategies and risk indicators to generate optimized trading instructions that include output allocation codes and energy storage scheduling sequences. This achieves precise matching of supply and demand, reduces trading deviations caused by inaccurate load forecasting, and improves user satisfaction with electricity usage.

[0064] The risk assessment module constructs a power grid operation status assessment index system using the analytic hierarchy process (AHP). Combining historical default records and real-time operating parameters (frequency deviation, number of node voltage overruns, line load rate, etc.), it generates quantitative risk level indicators including transmission safety coefficients and market volatility tolerance, providing a scientific basis for optimizing trading strategies. This enables the system to identify high-risk transactions in advance, take targeted measures to reduce the probability of default, and ensure the safe and stable operation of the microgrid.

[0065] The dynamic transaction matching module adjusts the supply and demand matching rules based on optimized transaction instructions and provides real-time feedback on the execution status. Combined with the strategy backtracking adjustment mechanism of the fault tolerance correction module (such as using compensation models, state backtracking, and market equilibrium models to correct deviations in power output, energy storage, and price respectively), it can quickly locate problems and initiate correction processes when transactions are abnormal, shortening the anomaly handling time, reducing economic losses, and improving the system's fault tolerance and reliability.

[0066] The trading strategy verification module constructs a Monte Carlo simulation conflict probability distribution model by collecting deviation data. This model can verify the robustness of the algorithm and dynamically update the output constraints, enabling the system to optimize the strategy through continuous iteration, improve its ability to cope with uncertainties, and ensure the effectiveness of the trading strategy in complex scenarios.

[0067] The protocol compatibility and adaptation module performs compatibility assessments by scanning node parameters and generates protocol conversion or optimization instructions for different situations. This solves the problem of inconsistent communication protocols among multiple nodes, reduces the difficulty of system integration, improves the real-time performance and reliability of data interaction, and enhances the scalability and compatibility of the system.

[0068] The supply and demand balance adjustment module monitors the supply and demand difference in real time and initiates a tiered adjustment strategy, prioritizing the adjustment of distributed energy output and energy storage rate, and finally adjusting cross-grid trading parameters. At the same time, it activates the excess energy consumption model, which can effectively maintain the supply and demand balance of the microgrid, reduce energy waste, and improve the system's operating efficiency and economy. Attached Figure Description

[0069] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent microgrid issuance and trading system based on multi-energy complementarity as described in this invention.

[0070] Figure 2 This is a schematic diagram of the working principle of the multi-source complementary optimization module;

[0071] Figure 3 The flowchart is for a multi-objective collaborative decision-making algorithm;

[0072] Figure 4 This is a flowchart of the strategy backtracking and adjustment mechanism. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Please see Figures 1-4 This invention relates to an integrated energy trading system for smart microgrids based on multi-energy complementarity. The system includes: an energy trading control module, a multi-source complementarity optimization module, a dynamic trading matching module, a load forecasting module, a risk assessment module, and a fault-tolerant correction module. The implementation of the system is described in detail below, along with its specific structure and functions:

[0075] The core architecture of the system is based on a multi-module collaborative working mode.

[0076] The multi-source complementary optimization module generates an initial trading strategy based on preset energy types and output forecast data. This strategy includes the distributed energy output allocation ratio, energy storage charging and discharging priority, and cross-grid trading boundary parameters.

[0077] The load forecasting module collects user electricity load curves, adjustable load response capacity data, and market price fluctuation data in real time, and sends the above data to the energy trading main control module to provide real-time load information for system decision-making.

[0078] The risk assessment module analyzes historical transaction default records and real-time power grid operation status to generate quantitative risk level indicators, including transmission security coefficient, market volatility tolerance, and default probability index, providing an assessment basis for the security of trading strategies.

[0079] The energy trading master control module serves as the decision-making center of the system. It receives initial trading strategies, load forecast data, and risk level quantitative indicators, and generates optimized trading instructions through a multi-objective collaborative decision-making algorithm.

[0080] The dynamic trading matching module adjusts the energy supply and demand matching rules according to the optimized trading instructions and provides real-time feedback on the trading execution status to the energy trading main control module, forming a closed-loop control.

[0081] As the system's anomaly handling unit, the fault tolerance correction module generates a trading anomaly signal if the deviation between the trading execution status reported by the dynamic trading matching module and the optimized trading instruction exceeds a preset threshold. This signal is then triggered by the energy trading master control module to establish a strategy backtracking and adjustment mechanism, ensuring the stability of the trading process.

[0082] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0083] Example 1:

[0084] The system's multi-source complementary optimization module achieves optimal energy allocation through a phased strategy. The module first prioritizes power generation types based on energy output forecast data. This process comprehensively considers factors such as energy type characteristics, supply stability, and environmental attributes. For example, renewable energy sources like solar and wind power, due to their clean and sustainable characteristics, are typically given a higher priority than traditional thermal power. While determining priorities, the module associates a set of corresponding output constraints for each energy source. These constraints cover various key factors affecting energy output. For example, solar energy's output constraints are mainly related to sunlight intensity and weather conditions; for wind power, wind speed and stability are the main constraints; and for thermal power, output constraints may involve fuel supply and equipment operating status.

[0085] The module divides the trading strategy into a day-ahead planning phase, an intraday rolling phase, and a real-time adjustment phase based on a time scale. Each phase has different objectives and priorities, and the orderly connection between them enables dynamic optimization of the energy trading strategy.

[0086] During the daytime planning phase, the core task of the module is to perform fuzzy logic matching between the set of output constraints and the base load curve. The base load curve is derived from statistical analysis of historical user electricity consumption data, reflecting the general trend of user electricity load changes over time. The fuzzy logic matching process is a complex data analysis process. The module analyzes the correlation between historical load data and energy output, considering various possible influencing factors, such as seasonal variations, differences between weekdays and weekends, and peak and off-peak electricity consumption at different times. Through this matching, the module can generate an initial output allocation ratio, which aims to allocate the output of various energy sources as rationally as possible while meeting users' basic electricity needs. For example, analysis reveals that during peak daytime hours, solar irradiance is higher, and solar energy output is stronger. Therefore, the module will determine an initial allocation ratio with a higher proportion of photovoltaic output during this period to fully utilize renewable energy and reduce dependence on traditional energy sources.

[0087] Entering the intraday rolling phase, the module needs to dynamically adjust the cross-grid transaction boundary parameters based on market price fluctuation data. Market price fluctuations are a crucial factor affecting energy trading costs and benefits. The module acquires market price information in real time, analyzing price trends and fluctuation amplitudes. When real-time market electricity prices rise, to reduce electricity purchase costs, the module adjusts the electricity purchase boundary parameters with the main grid. For example, it lowers the upper limit of electricity purchases from the main grid and prioritizes the use of local distributed energy and energy storage systems for power supply. Conversely, when market electricity prices are low, it may appropriately increase the proportion of electricity purchased from the main grid to reserve a certain amount of energy. In addition, the module also considers the impact of other factors on market prices, such as changes in energy supply and demand and policy adjustments, to ensure that the adjustment of cross-grid transaction boundary parameters can adapt to dynamic market changes and maximize economic benefits.

[0088] During the real-time adjustment phase, the module updates the energy storage charging and discharging priorities using a sliding window mechanism. The sliding window mechanism is a dynamic data processing method that continuously acquires the latest load fluctuation data and energy output data at regular time intervals. Based on this latest data, the module assesses the current energy supply and demand situation in real time and adjusts the charging and discharging sequence of the energy storage accordingly. For example, when a sudden load increase is detected, the module prioritizes adjusting the energy storage discharging priority, discharging only the portion of the energy storage with sufficient charge and in good condition to quickly respond to load demands and avoid power shortages. Conversely, when there is an energy surplus, the module prioritizes charging the energy storage to store excess energy and improve energy utilization efficiency. This process continues until the transaction cutoff threshold is reached, ensuring that the energy storage system can always play its optimal regulatory role according to actual needs throughout the entire transaction process.

[0089] Throughout the multi-source complementary optimization module's operation, each stage is closely interconnected. The results of the previous stage provide the foundation and basis for the next, while the subsequent stage adjusts and optimizes the strategies of the previous stage based on new information. Through this phased strategy, the module can fully consider various influencing factors at different time scales, achieving multi-source complementary optimal allocation of energy, improving the energy utilization efficiency and trading rationality of smart microgrids, ensuring the stability and reliability of energy supply, and reducing transaction costs and risks.

[0090] Example 2:

[0091] The risk assessment module generates quantitative indicators of risk level through multi-dimensional data collection and analysis. Its specific implementation covers multiple stages such as data collection, indicator system construction, parameter extraction and correlation analysis. Each stage is interconnected and progressive, working together to achieve a comprehensive assessment of transaction risks.

[0092] The module first collects data on historical transaction default records. This data includes the frequency of default types, duration of defaults, and compensation amounts. The frequency of default types needs to be detailed down to each specific default scenario, such as power outages, price deviations from agreements, and substandard energy quality. The number of occurrences for each type within a given time period is recorded to reflect the probability of occurrence and the stability of the trading environment. The duration of defaults needs to be precisely recorded, detailing the time from occurrence to resolution for each default event. This data reflects the severity of the impact of the default on the trading system and users. For example, prolonged power outages can cause significant losses to users' production and operations, thereby affecting the reputation and trading order of the entire microgrid. Compensation amount data is directly related to the economic consequences of defaults. By statistically analyzing the compensation amounts for different default events, the scale of economic risk brought about by the default can be assessed, providing an important basis for subsequent risk weight calculations.

[0093] After collecting historical transaction default data, the module constructs a power grid operation status assessment index system using the Analytic Hierarchy Process (AHP). The application of AHP requires first clarifying the assessment objective: to comprehensively and accurately assess the risk impact of power grid operation status on energy trading. Then, the assessment objective is decomposed into multiple levels of indicators. For example, the first level can be set as an overall assessment of the power grid operation status, while the second level can be subdivided into specific assessment indicators such as frequency deviation range, number of node voltage overruns, and line load factor. Each secondary indicator can be further refined into more specific sub-indicators; for example, the frequency deviation range can consider allowable deviation values ​​and deviation durations over different time periods. During the construction of the index system, industry experts need to score the importance of each indicator, and the weight coefficients of each indicator are calculated using mathematical methods to form risk weight coefficients. This process must fully consider professional knowledge and practical experience in power grid operation to ensure that the weight coefficients reasonably reflect the importance of each indicator in the risk assessment.

[0094] The acquisition of real-time power grid operation status data relies on a Supervisory Control and Data Acquisition (SCADA) system. This system collects various parameters of power grid operation in real time through sensors and monitoring equipment distributed at various nodes of the power grid. This module extracts parameters such as frequency deviation range, number of node voltage overruns, and line load rate from the SCADA system. Monitoring the frequency deviation range is used to assess the frequency stability of the power grid. Power grid frequency is one of the important indicators of power quality; excessive frequency deviation can affect the normal operation of power equipment and even lead to power grid failures. Statistics on the number of node voltage overruns reflect the stability of voltage at each node in the power grid; voltage overruns may lead to equipment damage or a decline in power quality for users. The line load rate parameter is used to measure the load condition of power grid lines. Excessively high line load rates may cause line overheating, tripping, and other faults, affecting the security of energy transmission.

[0095] After acquiring the risk weighting coefficients and real-time power grid operating status parameters, the module performs a correlation analysis to generate a quantitative risk level index. During the correlation analysis, parameters such as frequency deviation range, number of node voltage overruns, and line load rate are first multiplied by the risk weighting coefficients to obtain the risk contribution value corresponding to each parameter. For example, if the risk weighting coefficient for the line load rate parameter is high, and real-time monitoring shows that the load rate of a certain line is close to its rated value, the risk contribution value corresponding to that parameter will increase significantly. Then, the risk contribution values ​​of each parameter are comprehensively calculated, and a quantitative risk level index including transmission security factor, market volatility tolerance, and default probability index is generated through a specific mathematical model (such as weighted summation).

[0096] The transmission security factor primarily reflects the risk status of the power grid transmission process. Its calculation is based on transmission-related parameters such as line load factor and the number of times node voltage exceeds limits, along with corresponding risk weighting coefficients. It aims to assess the likelihood of transaction interruptions or delays caused by power grid faults during energy transmission. Market volatility tolerance combines market price fluctuation data with power grid operating status parameters to analyze the power grid's ability to withstand market price fluctuations. For example, large frequency deviations in the power grid can lead to instability in energy production and supply, resulting in drastic market price fluctuations. This indicator can be used to measure the system's ability to maintain normal trading under market volatility. The default probability index is an indicator generated by integrating historical transaction default records and real-time power grid operating status parameters. By analyzing the distribution patterns of power grid operating status parameters in historical default data, a correlation model between the default probability and current power grid state parameters is established to predict the likelihood of default under the current trading environment.

[0097] Throughout the operation of the risk assessment module, data accuracy and real-time performance are crucial. The collection of historical transaction default records must ensure data integrity and authenticity to avoid missing important default events. The acquisition of real-time grid operating status parameters must be achieved through a reliable monitoring system to ensure that the data promptly reflects the actual operating conditions of the grid. The application of the Analytic Hierarchy Process (AHP) must strictly adhere to mathematical logic and professional judgment to ensure the rationality of risk weighting coefficients. The correlation analysis process must employ scientific methods and models to ensure that the quantitative indicators of risk levels accurately reflect the risk situation faced by the system, providing a reliable decision-making basis for the energy trading control module, thereby guaranteeing the safe and stable operation of the integrated smart microgrid issuance and sales trading system.

[0098] Example 3:

[0099] The multi-objective collaborative decision-making algorithm optimizes trading strategies through vector transformation, feature extraction, and conflict resolution. Its specific implementation involves core steps such as standardized processing of decision inputs, feature correlation analysis, conflict resolution strategies, and strategy space mapping. Each step forms a complete decision chain through data processing and logical operations.

[0100] The algorithm first performs a decision input vector transformation operation. Initial trading strategy parameters, load forecast data, and risk level quantification indicators need to be converted from their original data form into computer-recognizable numerical vectors. For the initial trading strategy parameters, discrete data such as distributed energy output allocation ratios, energy storage charging and discharging priorities, and cross-grid transaction boundary parameters need to be normalized. For example, the output allocation ratio is converted into a value within the 0-1 range, the energy storage charging and discharging priority is mapped to an integer sequence through ordinal encoding, and the cross-grid transaction boundary parameters are converted into corresponding numerical range vectors based on their physical meaning. Load forecast data includes user electricity load curves, adjustable load response capacity data, and market price fluctuation data. The time series data of the load curves needs to be converted into equally spaced sampled numerical vectors. Adjustable load response capacity is converted into a multi-dimensional vector through quantification indicators (such as maximum adjustable load and response time threshold). Market price fluctuation data is extracted according to time windows to form feature vectors by extracting statistical features such as mean and variance. The transmission safety coefficient, market volatility tolerance, and default probability index in the risk level quantification indicators can be directly incorporated as numerical components into the decision input vector, ultimately forming a set of decision input vectors containing multi-dimensional data.

[0101] After vector transformation, the algorithm extracts the correlation features of each vector using fuzzy clustering. Fuzzy clustering can handle the fuzziness and uncertainty between data, making it suitable for feature mining of multi-source heterogeneous data. Specifically, it first calculates the similarity matrix between each decision input vector. Similarity can be measured using methods such as Euclidean distance and cosine similarity to assess the closeness of different vectors in the feature space. Then, a fuzzy membership function is used to assign the probability of each data point belonging to different clusters, forming a fuzzy cluster partition. During clustering, the algorithm automatically identifies features with similar trends or correlations in each vector. For example, peak load periods in the load forecast vector and peak renewable energy output periods in the initial trading strategy vector may form a correlation cluster, reflecting the matching relationship between energy supply and demand in the time dimension; the default probability index in the risk level quantification vector and market price fluctuation characteristics in the load forecast vector may cluster together, reflecting the potential impact of market fluctuations on trading risk. By iteratively optimizing the cluster centers and membership matrix, a stable clustering result is finally obtained. Each cluster corresponds to a set of strongly correlated feature combinations. The algorithm further calculates the importance weight of each feature in the cluster, i.e. the feature conflict weight. This weight reflects the potential conflict degree of different features in collaborative decision-making. Features with higher weights need to be processed first in subsequent conflict resolution.

[0102] Feature conflict resolution is a crucial step in multi-objective collaborative decision-making. Its purpose is to establish a coordination mechanism among multi-dimensional features to prevent decision failures caused by contradictions between features. The algorithm sorts related features according to their conflict weights, prioritizing feature conflicts with higher weights. For features with direct conflicts (such as increasing the proportion of distributed energy output potentially lowering the priority of energy storage charging), a rule-based conflict resolution strategy is adopted. For example, a "renewable energy priority consumption" principle is preset to maintain the dominant position of renewable energy output features when conflicts occur, while compensating by adjusting secondary features such as energy storage charging and discharging rates. For feature conflicts with indirect impacts (such as the correlation between market price fluctuations and transmission security coefficients), the weight allocation method in multi-objective optimization algorithms is used. Based on the risk weight coefficients output by the risk assessment module, different resolution weights are assigned to market price features and transmission security features, generating collaborative feature values ​​through linear combination or nonlinear transformation. During the resolution process, the algorithm monitors the changing trends of feature vectors in real time and dynamically adjusts the resolution strategy to ensure that the generated collaborative decision feature set retains the effective information of the original features to the greatest extent possible, while eliminating decision interference caused by conflicts.

[0103] After resolving feature conflicts, the algorithm maps the collaborative decision-making feature set to the trading strategy space, achieving a non-linear transformation from the feature space to the strategy space. The trading strategy space is a multi-dimensional space, with dimensions corresponding to key parameters of the trading strategy, including output allocation coding, energy storage scheduling sequences, and trading price fluctuation ranges. Output allocation coding is a binary or decimal representation of the output ratio of distributed energy resources. Through coding mapping, continuous output ratios can be converted into discrete control commands, facilitating direct execution by the energy trading master control module. The energy storage scheduling sequence is a time-series command generated based on charging and discharging priorities, including the start time, duration, and power of each energy storage unit's charging and discharging. The trading price fluctuation range is determined based on the market equilibrium model and risk tolerance, including parameters such as price upper and lower limits and fluctuation step size. The mapping process is achieved by establishing a feature-strategy mapping model. This model can employ machine learning algorithms such as neural networks and support vector machines, training the model using historical trading data to learn the mapping relationship between the feature set and the optimal trading strategy. When the collaborative decision-making feature set is input, the model outputs the corresponding optimized trading command. This command, after format conversion, is sent to the dynamic trading matching module to drive adjustments to the energy supply and demand matching rules.

[0104] Throughout the operation of the multi-objective collaborative decision-making algorithm, the accuracy of data preprocessing directly affects the effectiveness of feature extraction. Noise removal and missing value imputation of the original data are necessary to ensure the reliability of the decision input vector. The parameter settings of the fuzzy clustering algorithm (such as the number of clusters and the type of membership function) need to be optimized according to the data characteristics to avoid misjudgment of features due to inappropriate parameter selection. Feature conflict resolution strategies need to be combined with the actual operating rules and business requirements of the system to ensure that the resolved feature set conforms to the physical constraints and market rules of energy trading. The mapping model of the trading strategy space needs to be updated with training data regularly to adapt to changes in the energy market environment and the microgrid's operating status, ensuring the timeliness and effectiveness of optimized trading instructions. Through the organic combination of the above steps, the multi-objective collaborative decision-making algorithm achieves deep fusion and intelligent decision-making of multi-source data, providing scientific and efficient strategy support for the integrated issuance and trading of smart microgrids.

[0105] Example 4:

[0106] The strategy backtracking and adjustment mechanism handles transaction anomalies through anomaly tracing and module-based adjustments. Its specific implementation covers core processes such as anomaly signal triggering, source analysis, type-based deviation handling, and strategy updates. Each process is interconnected, forming a complete anomaly handling closed loop.

[0107] When the deviation between the transaction execution status reported by the dynamic transaction matching module and the optimized transaction instruction exceeds a preset threshold, the fault tolerance correction module generates a transaction anomaly signal and sends it to the energy trading master control module, triggering a strategy backtracking and adjustment mechanism. The energy trading master control module first initiates an anomaly tracing and analysis process, which uses time series data to screen and investigate key data nodes during transaction execution. Specifically, the system retrieves output data from each module in chronological order, including initial transaction strategy parameters generated by the multi-source complementary optimization module, real-time data collected by the load forecasting module, risk level quantification indicators output by the risk assessment module, optimized transaction instructions generated by the multi-objective collaborative decision-making algorithm, and execution status data reported by the dynamic transaction matching module. By comparing and analyzing the continuity and consistency of these data over time, the system identifies the abnormal data nodes causing the deviation, such as significant differences between the output allocation data and the optimized instruction at a certain moment, or disordered execution of the energy storage scheduling sequence.

[0108] The system employs a modular adjustment strategy to address different types of deviations:

[0109] ① Output Allocation Deviation Handling: When a deviation is detected between the actual output of distributed energy and the output allocation code in the optimized trading instruction, the system calls the output compensation model to adjust the ramp-up rate limit of the distributed energy. The ramp-up rate limit refers to the maximum rate of output change of energy equipment per unit time, and its setting must consider the physical characteristics and operational safety of the equipment. For example, the output of photovoltaic equipment is intermittent due to the influence of sunlight intensity, and its ramp-up rate limit is usually low; while the ramp-up rate limit of thermal power equipment is relatively high. When handling deviations, the system first calculates the difference between the actual output and the target output and the duration of the deviation. If the difference exceeds the allowable range and the duration is long, the system will gradually adjust the ramp-up rate limit according to the output compensation model. Specifically, for insufficient output, the upper limit of the ramp-up rate is appropriately increased within the safe range of the equipment to encourage the energy equipment to accelerate its output increase; for excessive output, the upper limit of the ramp-up rate is reduced to decrease energy output and avoid impacting the power grid. During the adjustment process, the system monitors the operating status parameters of the equipment in real time (such as temperature and pressure) to ensure that the adjustment operation does not affect the safe and stable operation of the equipment.

[0110] ② Energy Storage Scheduling Deviation Handling: If the actual execution order or power of energy storage charging and discharging is inconsistent with the energy storage scheduling sequence in the optimized trading instruction, the system reconstructs the charging and discharging priority queue through a state backtracking mechanism. The state backtracking mechanism performs reverse analysis of the charging and discharging process of energy storage units based on the historical operating data and current state information of the energy storage system. First, the system retrieves the state data of the energy storage units at abnormal times, including remaining power, number of charging and discharging cycles, and health status index, to assess the current availability of each energy storage unit. Then, based on real-time load forecast data and energy supply and demand conditions, the charging and discharging priority of each energy storage unit is recalculated. For example, when a sudden increase in load causes disorder in the energy storage discharging sequence, the system prioritizes energy storage units with sufficient remaining power, good health status, and fast response speed to the front of the discharging queue to quickly meet load demand; when there is an energy surplus requiring energy storage charging, energy storage units with lower remaining power and higher charging efficiency are prioritized for charging. The reconstructed charging and discharging priority queue is sent to the energy storage system for execution through the dynamic trading matching module. Simultaneously, the system records this deviation event and its handling process, providing a reference for subsequent optimization of energy storage scheduling strategies.

[0111] ③ Handling Price Deviations: When actual transaction price fluctuations exceed the price fluctuation range set in the optimized transaction instructions, the system recalculates the boundary values ​​of the price fluctuation range using a market equilibrium model. The market equilibrium model is based on supply and demand theory, comprehensively considering key variables affecting prices such as energy supply, user demand, market competition, and policy factors. The system first collects real-time energy supply and demand data, including distributed energy output, energy storage charging and discharging power, user electricity load, and cross-grid transaction volume, to calculate the current market supply and demand balance. If supply exceeds demand, it indicates downward pressure on market prices. The system lowers the lower limit of the price fluctuation range according to the market equilibrium model to stimulate user consumption or increase cross-grid sales. If supply is less than demand, it indicates a potential price increase. The system correspondingly raises the upper limit of the price fluctuation range and balances market supply and demand by adjusting energy supply and demand matching rules (such as prioritizing the use of higher-priced energy). When recalculating the price boundary values, the system also considers the market volatility tolerance index output by the risk assessment module to ensure that the new price fluctuation range is within the system's acceptable risk range, avoiding transaction defaults or market disorder caused by drastic price fluctuations.

[0112] After anomaly handling is completed, the system verifies and records the adjusted trading strategy. The verification process compares the adjusted execution status with the optimized trading instructions to check if the deviation has been reduced to within a preset threshold. If the expected effect is not achieved, the anomaly tracing process is restarted until the deviation is effectively controlled. Simultaneously, the system stores relevant data about this anomaly (such as anomaly type, occurrence time, impact scope, and handling measures) in a historical database, providing data support for subsequent updates to risk weight coefficients in the risk assessment module and optimization of multi-objective collaborative decision-making algorithms. This strategy backtracking and adjustment mechanism, through this refined anomaly handling approach, ensures that the integrated smart microgrid issuance and trading system can promptly detect and correct trading deviations in the face of complex and ever-changing operating environments, maintaining the stability and reliability of the trading process and protecting the legitimate rights and interests of both energy supply and demand parties.

[0113] Example 5:

[0114] This embodiment covers a trading strategy verification module, a protocol compatibility adaptation module, and a supply and demand balance adjustment module. Each module achieves trading strategy optimization, device protocol adaptation, and dynamic energy supply and demand balance through data collection, analysis and processing, and strategy execution. The specific implementation method is as follows:

[0115] The trading strategy verification module generates a set of trading conflict features, including power allocation conflicts, energy storage scheduling conflicts, and price fluctuation conflicts, by collecting deviation data between optimized trading orders and actual trading states. The module uses Monte Carlo simulation to construct a conflict probability distribution model, quantifying the three types of conflicts into quantifiable indicators.

[0116] First Conflict Factor (CF1): Reflects the degree of mismatch between distributed energy output allocation and actual load demand, such as the proportion of actual photovoltaic output being lower than the planned value;

[0117] The second conflict factor (CF2) measures the deviation between the energy storage charging and discharging sequence or power and the scheduling sequence, such as the load response lag caused by the delay in the energy storage discharge start-up time.

[0118] The third conflict factor (CF3) represents the deviation between the fluctuation of the transaction price and the preset range of the optimization instruction, such as the extent to which the real-time electricity price exceeds the price ceiling.

[0119] The module applies a dynamic correction coefficient (K) to the third conflict factor based on the Default Probability Index (DPI) output by the risk assessment module. This coefficient, ranging from 0.8 to 1.2, adjusts the weighting of market price volatility risk on the conflict assessment. A strategy verification report is generated by fitting the distribution patterns of the three conflict factors using a probability density function. The report includes conflict frequency, impact level, and trend analysis. If the conflict probability falls below a preset threshold (e.g., 5%) after N consecutive simulations, the current trading strategy is deemed robust enough. The module then updates the boundary parameters of the output constraint set in the multi-source complementary optimization module, such as adjusting the minimum operating threshold for wind power output or the upper limit of energy storage charging and discharging power.

[0120] The protocol compatibility and adaptation module scans energy trading nodes (such as distributed power sources, energy storage devices, and user terminals) connected to the smart microgrid to obtain parameters such as the communication protocol types supported by the nodes, data update frequencies, and security authentication levels. The module then performs a compatibility assessment between the node parameters and the required parameters for optimized trading instructions. This assessment process includes three core steps:

[0121] ① Protocol matching degree detection: The node communication protocol type is compared with the system requirement protocol (such as Modbus, MQTT), the number of missing protocol fields is calculated, and the protocol matching degree (PM) is generated. The value range is 0-1, and the higher the value, the better the protocol compatibility.

[0122] ② Data frequency consistency calculation: Computation node data update frequency (F n The absolute value of the difference between the frequency (in seconds, units: times / second) and the demand frequency (Fᵣ, units: times / second) during the real-time adjustment phase of the system is normalized to obtain the frequency consistency index. The closer this indicator is to 1, the better the data update frequency matches the system requirements.

[0123] ③ Security Level Comparison: Compare the node's security authentication level (S) n The safety level compliance (e.g., ISO 27001 certification level) is compared with the minimum requirement level (Sᵣ) set by the risk assessment module to generate a safety level compliance rating. If S n If Sᵣ is greater than or equal to Sᵣ, then the degree of conformity is 1; otherwise, it is 1. .

[0124] By integrating the above three indicators using a weighted summation formula, a compatibility score (CS) is generated, as follows:

[0125]

[0126] in, The weighting coefficients for each parameter ( These represent the importance of protocol compatibility, data frequency consistency, and security level comparison in compatibility assessment, respectively. For example, if the system has high requirements for communication stability, it can be set... , , .

[0127] If the compatibility score is lower than a preset threshold (e.g., 0.6), the module generates a protocol conversion instruction, which converts the node data format into a standard communication protocol supported by the system (e.g., JSON format) through middleware; if the score is higher than the threshold, a protocol optimization instruction is generated, which activates the node's data compression and transmission function to reduce the amount of data transmitted and thus reduce network load.

[0128] The supply and demand balance adjustment module monitors the total energy supply in real time. ) and total demand ( The dynamic difference of ) ), and correlate with the preset supply and demand balance threshold in the optimized trading instructions ( (Unit: kW·h) to realize the judgment and regulation of energy supply and demand status.

[0129] when When this occurs, it indicates that the energy supply and demand imbalance exceeds the allowable range, and the module will activate adjustment strategies according to the following priorities:

[0130] a. Adjust the output priority of distributed energy: Based on the power generation type priority set by the multi-source complementary optimization module, prioritize increasing the output ratio of renewable energy (such as photovoltaic and wind power). If it is still impossible to balance supply and demand, reduce the output of high-carbon energy (such as coal-fired power generation) or start the backup power supply.

[0131] b. Modify the energy storage charging and discharging rate: If there is an energy shortage, increase the energy storage discharge power to quickly replenish the supply; if there is an energy surplus, increase the energy storage charging power to store the excess energy. The adjustment range of the charging and discharging rate must be within the safe operating range of the energy storage equipment (e.g., not exceeding 120% of the rated power).

[0132] c. Update cross-network transaction parameters: Conduct power interaction with the main grid or adjacent microgrids, increase the amount of electricity purchased when there is an energy shortage, and increase the amount of electricity sold when there is a surplus. The adjustment of transaction parameters needs to take into account market price fluctuations and the transmission security factor of the risk assessment module.

[0133] when When this condition is met, it indicates a basic balance between energy supply and demand. The module then activates the surplus energy consumption model, allocating the surplus energy (if ΔE>0) to the trading node with the highest safety factor in the risk assessment module. The safety factor comprehensively considers the node's historical default record, grid connection reliability, and user credit rating. For example, priority is given to industrial users with adjustable load capacity and no default record. Through remote instructions issued by smart meters, these nodes are guided to increase non-critical load electricity consumption (such as charging energy storage devices), thereby achieving local consumption of surplus energy.

[0134] During the adjustment process, the module records the type, magnitude, and timestamp of the adjustment actions in real time, forming a supply and demand balance adjustment log. This provides data support for subsequent optimization of trading strategies and multi-objective collaborative decision-making algorithms. Simultaneously, the module maintains real-time communication with the dynamic trading matching module, ensuring that the execution status of adjustment instructions is fed back to the energy trading main control module, forming a closed-loop control system.

[0135] The trading strategy verification module provides the basis for strategy optimization for the protocol compatibility and adaptation module and the supply-demand balance adjustment module. For example, through conflict analysis, it can identify data delays caused by protocol incompatibility in certain devices, thereby triggering a protocol conversion mechanism. The protocol compatibility and adaptation module ensures accurate data transmission from each node, avoiding delays in supply-demand balance adjustment due to communication failures. The execution results of the supply-demand balance adjustment module are then fed back to the trading strategy verification module as actual trading status data, forming a cyclical optimization link of "verification-adaptation-adjustment-re-verification". Through the collaborative work of these three modules, the system achieves fully automated management of the entire process from strategy verification and protocol adaptation to supply-demand adjustment, improving the trading efficiency and stability of smart microgrids in multi-energy complementary scenarios.

[0136] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0137] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-energy complementary-based intelligent microgrid integrated transaction system, characterized in that, It includes an energy trading control module, a multi-source complementary optimization module, a dynamic trading matching module, a load forecasting module, and a risk assessment module; The multi-source complementary optimization module is used to generate an initial trading strategy based on the preset energy type and output prediction data. The initial trading strategy includes the distributed energy output allocation ratio, energy storage charging and discharging priority, and cross-grid trading boundary parameters. The load forecasting module collects user electricity load curves, adjustable load response capability data, and market price fluctuation data in real time, and sends the data to the energy trading main control module. The risk assessment module generates a quantitative indicator of risk level by analyzing historical transaction default records and real-time power grid operation status. The energy trading master control module receives the initial trading strategy, load forecast data and risk level quantitative indicators, and generates optimized trading instructions through a multi-objective collaborative decision-making algorithm. The dynamic transaction matching module adjusts the energy supply and demand matching rules according to the optimized transaction instructions and provides real-time feedback on the transaction execution status to the energy transaction main control module. The system also includes a fault tolerance correction module. If the deviation between the transaction execution status fed back by the dynamic transaction matching module and the optimized transaction instruction exceeds a preset threshold, a transaction anomaly signal is generated, and the strategy backtracking adjustment mechanism is triggered through the energy trading master control module. The specific analysis process of the risk assessment module includes: Collect data on the frequency of default types, duration of defaults, and amount of compensation from historical transaction default records; A power grid operation status assessment index system was constructed using the analytic hierarchy process, and risk weight coefficients were generated. Real-time power grid operation status is obtained through a data acquisition and monitoring system, from which parameters such as frequency deviation range, number of node voltage over-limits, and line load rate are extracted. By correlating the risk weighting coefficient with the line load rate parameter, a quantitative risk level index is generated, which includes the transmission security coefficient, market volatility tolerance, and default probability index.

2. The multi-energy complementary based smart microgrid integrated transaction system of claim 1, wherein, The specific implementation method of the multi-source complementary optimization module is as follows: Based on energy output forecast data, power generation types are prioritized and associated with a set of corresponding energy output constraints. Trading strategies are divided into phases based on time scales, including the day-ahead planning phase, the intraday rolling phase, and the real-time adjustment phase. During the current planning phase, the set of output constraints will be matched with the base load curve using fuzzy logic to generate the initial output allocation ratio. During the intraday rolling phase, the cross-network transaction boundary parameters are dynamically adjusted based on market price fluctuation data. During the real-time adjustment phase, the priority of energy storage charging and discharging is updated through a sliding window mechanism until the transaction cutoff threshold is reached. 3.The multi-energy complementary based smart microgrid integrated transaction system of claim 1, wherein, The specific steps of the multi-objective collaborative decision-making algorithm are as follows: The initial trading strategy parameters, load forecast data, and risk level quantitative indicators are respectively converted into decision input vectors; The correlation features of each vector are extracted using a fuzzy clustering algorithm, and the feature conflict weights are calculated. Based on the weights, conflict resolution is performed on the associated features to generate a collaborative decision-making feature set; The collaborative decision-making feature set is mapped to the trading strategy space to output optimized trading instructions, including power allocation codes, energy storage scheduling sequences, and trading price fluctuation ranges.

4. The multi-energy complementary based smart microgrid integrated transaction system of claim 3, wherein, The strategy backtracking adjustment mechanism is implemented as follows: When an abnormal trading signal is generated, the energy trading main control module initiates the abnormal source tracing and analysis process to filter abnormal data nodes according to the time series. For output allocation deviations, an output compensation model is used to adjust the ramp rate limit of distributed energy sources; For energy storage scheduling deviations, the charging and discharging priority queue is reconstructed through a state backtracking mechanism; For deviations in trading prices, the boundary values ​​of the price fluctuation range are recalculated using a market equilibrium model.

5. The multi-energy complementary based smart microgrid integrated transaction system for sale according to claim 2, wherein, The system also includes a trading strategy verification module, which is implemented as follows: Collect and optimize the deviation data between trading instructions and actual trading status to generate a set of trading conflict features; A conflict probability distribution model was constructed using Monte Carlo simulation to verify the robustness of the multi-objective collaborative decision-making algorithm. If the probability of conflict is lower than the preset threshold after N consecutive simulations, then the boundary parameters in the set of output constraint conditions are updated. 6.The multi-energy complementary based smart microgrid integrated transaction system of claim 5, wherein, The design of the conflict probability distribution model includes: The conflicts of power allocation, energy storage scheduling, and price fluctuation are quantified into the first conflict factor, the second conflict factor, and the third conflict factor, respectively. Based on the default probability index in the risk level quantification indicator, a dynamic correction coefficient is applied to the third conflict factor. A strategy verification report is generated by fitting the distribution pattern of conflict factors using a probability density function.

7. The multi-energy complementary based smart microgrid integrated transaction system for sale according to claim 1, wherein, The system also includes a protocol compatibility adaptation module, the operation of which is as follows: Scan the connected energy trading nodes to obtain the communication protocol types, data update frequency, and security authentication levels supported by the nodes; Conduct a compatibility assessment of node parameters with the required parameters for optimizing trading instructions; If the compatibility assessment result is lower than the preset threshold, a protocol conversion instruction is generated to convert the data format into a standard communication protocol. If the compatibility assessment result is higher than the preset threshold, a protocol optimization instruction is generated to activate the node's data compression and transmission function. 8.The multi-energy complementary based smart microgrid integrated transaction system of claim 7, wherein, The specific method for compatibility assessment is as follows: The matching degree between the communication protocol type and the system requirement protocol is checked, and the number of missing protocol fields is calculated. Calculate the absolute value of the difference between the data update frequency and the demand frequency during the real-time adjustment phase; Compare the security certification level with the minimum requirement level of the risk assessment module; The above calculation results are combined into a compatibility score using a weighted summation formula.

9. The multi-energy complementary based smart microgrid retail integration transaction system according to claim 1, wherein, The system also includes a supply and demand balance adjustment module, which is implemented in the following ways: Real-time monitoring of the dynamic difference between energy supply and demand, and correlation optimization of the supply and demand balance threshold of trading orders; If the dynamic difference exceeds the preset range of the supply and demand balance threshold, the adjustment priority strategy will be activated: Prioritize adjusting the output priority of distributed energy resources, then modify the charging and discharging rate of energy storage, and finally update the cross-grid transaction parameters. If the dynamic difference is lower than the preset range of the supply and demand balance threshold, the excess energy consumption model will be activated, and the remaining energy will be allocated to the trading node with the highest safety factor in the risk assessment module.

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