Method for constructing market-oriented dispatching model of microgrid and virtual power plant in power market environment
By constructing a market-based dispatch model for microgrids and virtual power plants under the power market environment, the problem that existing dispatch technologies are difficult to adapt to diverse situations has been solved, and high-precision, compatible market-based dispatch has been achieved, promoting the flexibility and economic efficiency of the power system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XUCHANG POWER SUPPLY COMPANY OF STATE GRID HENAN ELECTRIC POWER
- Filing Date
- 2026-04-01
- Publication Date
- 2026-07-21
AI Technical Summary
Existing dispatching technologies are ill-suited to the diverse forms of microgrids and virtual power plants. Traditional dispatching models limit resource allocation efficiency, and the lack of market-based dispatching models results in low participation of distributed resources in the market. Furthermore, existing models are not closely integrated with actual market rules.
We construct market-based dispatch models for microgrids and virtual power plants under the power market environment. By identifying the main revenue functions, quantifying transaction thresholds, designing interest coordination mechanisms and cost allocation mechanisms, we optimize model parameters to adapt to changes in market rules and use reinforcement learning to achieve adaptive adjustment of the model.
It improves the accuracy and compatibility of model solutions, promotes fair participation of multiple stakeholders in market competition, enhances the flexibility of the power system, reduces electricity costs for users, and boosts regional economic development.
Smart Images
Figure CN122437005A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power grid dispatching technology, specifically relating to a method for constructing a market-based dispatching model for microgrids and virtual power plants in a power market environment. Background Technology
[0002] Against the backdrop of accelerating the integrated development of power generation, grid, load, and storage, microgrids and virtual power plants, as important components of new distribution systems, are becoming increasingly diverse in form, but existing dispatching technologies are struggling to adapt. The complex dispatching characteristics of microgrids and virtual power plants make them difficult to manage using traditional distribution system dispatching and operation management models, limiting the efficiency of distributed power generation, load, and storage resource allocation. Furthermore, in the context of the electricity market, there is a lack of market-based dispatching models for microgrids and virtual power plants, making it difficult to effectively guide the participation of multiple stakeholders based on price signals, thus limiting the role and value of the electricity market. Moreover, existing models are mostly theoretical and not closely integrated with actual market rules (e.g., microgrids lack clear trading boundaries, and virtual power plants suffer from unfair cost allocation), resulting in low enthusiasm for distributed resource participation in the market. To address these issues, it is essential to develop a method for constructing a market-based dispatching model for microgrids and virtual power plants in the context of the electricity market. Summary of the Invention
[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for constructing market-based dispatch models of microgrids and virtual power plants in a power market environment that has high solution accuracy, good compatibility, and high economic efficiency.
[0004] The objective of this invention is achieved as follows: a method for constructing a market-based dispatch model for microgrids and virtual power plants in a power market environment, comprising the following steps: S1. Construct a market-based dispatch model for microgrids that considers the interests of multiple distributed entities and clarify the boundary conditions for their participation in market transactions. S2, establish a market-based scheduling model for virtual power plants to aggregate decentralized resources, and design a reasonable cost allocation mechanism; S3 investigates the impact of market rules on the scheduling model and optimizes the model parameters to adapt to different market environments.
[0005] Preferably, step S1 specifically includes: S11, Identify the revenue functions of each entity in the microgrid: S111 defines the main types of microgrids and collects their cost data and revenue sources by reviewing contract texts, corporate financial statements and market transaction records of each type of entity. S112, based on microeconomic theory, constructs the income function expressions of each subject, clarifies the relationship between its independent and dependent variables, and fits the function parameters through historical data; S113 verifies the rationality of the revenue function by simulating different operating scenarios, comparing the deviation between the calculated function results and the actual revenue, correcting the function form and parameters, and providing the objective function basis for the subsequent construction of a market-based scheduling model; S12, Technical and economic thresholds for quantitative trading entry: S121, sort out the access requirements for microgrids to participate in the electricity market, including technical and economic conditions, design a threshold quantification index system, and collect transaction cases and technical standards of similar projects to determine the preliminary scope of the indicators; S122, using cost-benefit analysis and technical feasibility analysis, quantifies the threshold values of each indicator, and uses the Delphi method to consult industry experts to revise the threshold values; S123, Construct a transaction access threshold assessment model, dynamically adjust the threshold value based on market rules and the actual situation of the microgrid, and verify the threshold. S13, Establish a mechanism for coordinating the interests of various stakeholders: S131, analyze the points of conflict of interest among the main entities in the microgrid, and design a framework for interest coordination based on the cooperative game theory in game theory, and clarify the coordination goals and coordination methods; S132, Develop a benefit distribution algorithm to allocate the total benefit according to the contribution of each subject to the system's benefits, and design a compensation mechanism to provide economic compensation to subjects that suffer losses due to cooperating with system scheduling; S133, through simulation of different conflict of interest scenarios, tests the effectiveness of the coordination mechanism; S134, optimize the coordination mechanism, adjust the allocation coefficient and compensation standard based on actual operation feedback, and form a scheme for coordinating the interests of microgrid entities.
[0006] Preferably, in step S111, the subject types include distributed power source owners, energy storage operators, load users, and microgrid operators.
[0007] Preferably, the threshold quantification index system includes technical threshold indicators and economic threshold indicators, wherein the technical threshold indicators include the maximum output deviation and the upper limit of response time, and the economic threshold indicators include the upper limit of unit cost per kilowatt-hour and the minimum return on investment.
[0008] Preferably, step S2 specifically includes: S21, Optimizes the cost-sharing algorithm for resource aggregation: S211, analyze the cost structure of virtual power plant aggregating distributed resources, clarify the calculation method and allocation object of each cost, design cost allocation principle, and initially select a suitable allocation algorithm; S212, based on historical aggregated data, simulates and tests different algorithms, calculates the deviation between the allocated cost of each resource and the actual cost, evaluates the fairness and efficiency of the algorithm, and introduces the entropy weight method to comprehensively evaluate the algorithm performance and select the optimal initial algorithm. S213, combining the operation mode and resource characteristics of virtual power plants, the selected algorithm is optimized and verified. The effect of the optimized algorithm is tested by selecting actual aggregation cases. An optimization method for virtual power plant resource aggregation cost allocation algorithm is proposed, providing a cost basis for the design of cost allocation mechanism. S22, Develop a cost allocation model based on contribution: S221 defines the contribution dimensions of resources in the market-based dispatch of virtual power plants and designs a contribution evaluation index system to quantify the contribution value of each dimension. S222, the weight of each contribution dimension is determined by the analytic hierarchy process, the rationality of the weight allocation is determined by expert scoring and consistency test, and a cost allocation model is constructed to allocate the total revenue of the virtual power plant according to the comprehensive contribution of each resource. S223 simulates different trading scenarios to test the model's allocation effect; S224, optimize model parameters, adjust contribution indicators and weights based on actual operating data, and introduce a penalty mechanism to deduct revenue from resources that fail to meet adjustment requirements; S23, Design a dynamic adjustment mechanism for risk sharing: S231, identify the types of risks in the market-based dispatch of virtual power plants, and design risk assessment indicators to analyze the impact of various risks on cost allocation; S232, Construct a risk-sharing framework, clarify the risk-sharing ratio of each entity, develop a dynamic adjustment algorithm, and adjust the cost allocation plan according to the real-time risk assessment results; S233, conduct simulation tests on the risk-sharing mechanism, simulate extreme market scenarios and high default rate scenarios, and verify the effectiveness of the mechanism.
[0009] Preferably, in step S211, the cost allocation principle includes the revenue principle and the responsibility principle, and the allocation algorithm is the proportional allocation method or the marginal cost method.
[0010] Preferably, in step S221, the contribution dimensions include adjustment contribution, response speed contribution, and reliability contribution.
[0011] Preferably, step S3 specifically includes: S31, Analyze the constraints of market rules on the model: S311, analyze the market-based rules for safety risk management in each pilot area, sort out the key constraints in the rules, establish a rule constraint database, and transform the written rules into quantifiable constraints; S312, analyze the impact path of rule constraints on the market-based scheduling model, and compare the model output results under different rules through simulation; S313, Construct a rule constraint adaptation module to associate the model's constraints with the rule constraint database, so that the model constraints are automatically updated when the rules change, ensuring that the model meets the requirements of market rules; S32, Quantifying the impact of market price fluctuations on parameters: S321: Collect historical price data of the electricity market, with a time span of no less than one year, analyze the characteristics of price fluctuations, and select price fluctuation indicators; S322, Establish a correlation model between market prices and model parameters, identify the impact coefficient of price fluctuations on parameters through regression analysis, design a parameter sensitivity analysis scheme, and test the range of change of model parameters and the stability of model output under different price fluctuation scenarios; S323: Develop a dynamic parameter adjustment algorithm that automatically corrects model parameters based on real-time price fluctuation prediction results, verifies the algorithm, compares the economics of the model before and after adjustment, and improves the model's adaptability to price fluctuations. S33, Develop an adaptive optimization algorithm for model parameters: S331, Determine the model parameters that need to be optimized, analyze the optimization range and mutual influence of each parameter, select a suitable optimization algorithm, and set the objective function and constraints of the algorithm; S332, Construct a parameter optimization training dataset. The dataset contains historical operating data and optimal parameter combinations under different market environments. Use reinforcement learning to train the optimization algorithm so that the algorithm can automatically adjust the model parameters according to the current market state. And test the optimization effect of the algorithm through simulation. S333 integrates the optimization algorithm into the market-based scheduling model, develops a parameter adaptive adjustment module, realizes online optimization of the model, and conducts field tests to compare the running effect of the model before and after optimization, ensuring the efficient operation of the model in different market environments.
[0012] Preferably, in step S311, the market rules include the application time, the winning bid rules, the settlement method, and the assessment standards.
[0013] Preferably, in step S331, the optimization algorithm is a particle swarm optimization algorithm or a genetic algorithm.
[0014] Due to the adoption of the above technical solution, the beneficial effects of the present invention are: This invention adopts the technical route of "model construction - mechanism design - parameter optimization". It constructs a dynamic scheduling model that is adapted to two types of safety risk management, namely power flow control and voltage regulation, for the domestic power market rules. The model solves with an accuracy of ≥95%, breaking through the bottleneck of poor adaptability between foreign models and domestic market rules. This invention adopts a "rule parameterization + module configurability" architecture and a contribution-based dynamic cost allocation mechanism. Through reinforcement learning, it enables the model to adaptively adjust to changes in market rules, solving the problems of conflicting interests among multiple stakeholders and iterative adaptation of market rules, while taking into account both incentive compatibility and economic efficiency. This invention adopts a market-oriented dispatch system to promote fair participation in market competition by multiple entities. Virtual power plants and microgrids, as new market entities, enrich market supply, enhance the flexibility of the power system, enable users to obtain higher quality and lower-priced electricity services, reduce the cost of electricity for industrial and commercial users, improve product competitiveness, and indirectly promote regional economic development. In summary, this invention has the advantages of high solution accuracy, good compatibility, and high economic efficiency. Attached Figure Description
[0015] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation
[0016] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.
[0017] like Figure 1 As shown, this invention provides a method for constructing a market-based dispatch model for microgrids and virtual power plants in a power market environment: First, based on the interests of multiple stakeholders and market rules, a market-based dispatch model framework for microgrids and virtual power plants is constructed, and the objective function and constraints are clarified; second, a dynamic cost allocation mechanism is designed, and the fairness and incentive of the mechanism are ensured through game theory analysis and simulation verification; finally, the impact of market rules on the model is studied, and sensitivity analysis and reinforcement learning methods are used to optimize the model parameters and improve the model's adaptability to different market environments.
[0018] I. Construct a market-based dispatch model for microgrids that considers the interests of multiple distributed entities and clarify the boundary conditions for their participation in market transactions.
[0019] 1. Identify the revenue functions of each entity in the microgrid: First, define the main types of microgrid entities, including distributed generation owners, energy storage operators, load users, and microgrid operators. Collect cost data (such as generation costs, energy storage depreciation, and electricity purchase costs) and revenue sources (such as electricity sales revenue, subsidy income, and demand response incentives) by reviewing contract texts, corporate financial statements, and market transaction records for each entity type.
[0020] Secondly, based on microeconomic theory, revenue function expressions for each entity are constructed, clarifying the relationship between independent variables (such as power generation, electricity purchase, and regulation) and dependent variables (revenue value). For example, the revenue function for distributed power plant owners is electricity sales revenue minus power generation costs, while the revenue function for load users is electricity consumption benefits minus electricity purchase costs. Function parameters, such as the unit power generation cost of photovoltaic power and the electricity consumption benefit coefficient of users, are fitted using historical data.
[0021] Finally, the rationality of the revenue function is verified by simulating different operating scenarios, comparing the deviation between the calculated function results and the actual revenue, and correcting the function form and parameters to provide a basis for the objective function in the subsequent construction of a market-based scheduling model.
[0022] 2. Technical and economic thresholds for quantitative trading entry: First, the entry requirements for microgrids to participate in the electricity market are reviewed, including technical conditions (such as power regulation range and voltage qualification rate) and economic conditions (such as minimum trading capacity and cost competitiveness). A threshold quantification index system is designed, with technical threshold indicators such as maximum output deviation and upper limit of response time, and economic threshold indicators such as upper limit of unit cost per kilowatt-hour and minimum return on investment. Transaction cases and technical standards of similar projects are collected to determine the initial scope of the indicators.
[0023] Secondly, cost-benefit analysis and technical feasibility analysis were used to quantify the threshold values of each indicator. For example, the break-even point for microgrids participating in market-based dispatching for power flow control and voltage regulation in the distribution network was calculated to determine the minimum economic regulation capacity; based on the test results of equipment technical parameters, the maximum allowable voltage deviation threshold was determined. The Delphi method was used to consult industry experts to revise the threshold values, ensuring their scientific validity and operability.
[0024] Finally, a transaction access threshold evaluation model is constructed, and the threshold value is dynamically adjusted based on market rules and the actual situation of the microgrid. Threshold verification is performed by selecting typical microgrid samples to test whether they meet the access conditions, providing a basis for setting transaction boundary conditions in the model.
[0025] 3. Establish a mechanism for coordinating the interests of various stakeholders: First, the conflicting interests among the main stakeholders in the microgrid are analyzed, such as the contradiction between distributed power generation seeking to maximize electricity sales revenue and load users seeking to minimize electricity purchase costs, and the conflict between the charging and discharging strategies of energy storage operators and the system's safety risk management and scheduling requirements. Based on cooperative game theory, a framework for interest coordination is designed, clarifying the coordination objectives (such as maximizing overall system revenue and individual rationality constraints) and coordination methods (such as negotiated pricing and benefit compensation).
[0026] Secondly, develop benefit allocation algorithms, such as the Shapley value method, to distribute total revenue based on each entity's contribution to the system's benefits, and design compensation mechanisms to provide economic compensation to entities that suffer losses due to cooperating with system scheduling, such as subsidies for users participating in demand response. Simulate different conflict-of-interest scenarios to test the effectiveness of the coordination mechanism, such as whether it can achieve a balance of interests among entities and whether it can incentivize entities to participate in collaborative scheduling.
[0027] Finally, the coordination mechanism was optimized, and the allocation coefficients and compensation standards were adjusted based on actual operational feedback to form a scheme for coordinating the interests of microgrid entities, ensuring the feasibility and stability of the market-based dispatch model.
[0028] Second, establish a market-based scheduling model for virtual power plants to aggregate decentralized resources and design a reasonable cost allocation mechanism.
[0029] 1. Optimize the cost-sharing algorithm for resource aggregation: First, analyze the cost structure of virtual power plants aggregating distributed resources, including communication costs, coordination and control costs, and transaction costs, and clarify the calculation methods and allocation objects for each cost. Design cost allocation principles, such as the benefit principle (allocation based on the degree of resource benefit) and the responsibility principle (allocation based on resource adjustment), and initially select appropriate allocation algorithms, such as proportional allocation algorithms and marginal cost methods.
[0030] Secondly, based on historical aggregated data, simulation tests are conducted on different algorithms to calculate the deviation between the allocated cost and the actual cost for each resource, thus evaluating the fairness and efficiency of the algorithms. For example, the allocation results of the proportional allocation method and the Shapley value method under different resource combinations are compared to analyze the algorithm's friendliness to small-scale resources. The entropy weight method is introduced to comprehensively evaluate the algorithm's performance and select the optimal initial algorithm.
[0031] Finally, considering the operation mode and resource characteristics of the virtual power plant, the selected algorithm is optimized, such as by introducing a dynamic weighting factor to adjust the cost allocation ratio based on the resource's adjustment capability and reliability. Algorithm verification is then conducted, with real-world aggregation cases used to test the effectiveness of the optimized algorithm. This leads to the proposal of an optimization method for the virtual power plant resource aggregation cost allocation algorithm, providing a cost basis for the design of cost allocation mechanisms.
[0032] 2. Develop a cost allocation model based on contribution: First, define the contribution dimensions of resources in the market-based dispatch of virtual power plants, including contribution of regulation volume, contribution of response speed, and contribution of reliability (such as regulation accuracy and execution rate). Design a contribution evaluation index system to quantify the contribution value of each dimension. For example, the contribution of regulation volume is calculated as the proportion of actual regulation volume to total regulation volume, and the contribution of response speed is calculated as the ratio of response time to standard time.
[0033] Secondly, the weights of each contribution dimension are determined using the analytic hierarchy process (AHP), and the rationality of the weight allocation is verified through expert scoring and consistency checks. A cost allocation model is constructed to distribute the total revenue of the virtual power plant according to the comprehensive contribution of each resource (a weighted sum of the contribution values of each dimension). The allocation effect of the model is tested through simulations of different trading scenarios, such as whether it can incentivize resources to improve their regulatory capacity and whether there are any unfair distribution phenomena.
[0034] Finally, the model parameters are optimized, and the contribution indicators and weights are adjusted based on actual operating data. A penalty mechanism is introduced to deduct revenue from resources that fail to meet adjustment requirements. Constructing a contribution-based virtual power plant cost allocation model ensures fairness and incentive in cost allocation.
[0035] 3. Design a dynamic adjustment mechanism for risk sharing: First, identify the types of risks in the market-based dispatch of virtual power plants, including market price fluctuation risk, resource default risk (such as insufficient regulation capacity), and force majeure risk (such as equipment failure), and analyze the impact of each type of risk on cost allocation. Design risk assessment indicators, such as market price volatility and resource default probability, to quantify the degree of risk.
[0036] Secondly, a risk-sharing framework should be established to clarify the risk-bearing proportions of each entity (virtual power plant operator and resource owner). For example, the risk of market price fluctuations should be mainly borne by the operator, while the risk of resource default should be borne by the resource owner. A dynamic adjustment algorithm should be developed to adjust the cost allocation scheme based on real-time risk assessment results. For instance, when market prices plummet, the operator's revenue share should be appropriately reduced to ensure the basic income of the resource owner; when the resource default rate increases, the revenue from defaulted resources should be deducted.
[0037] Finally, a risk-sharing mechanism simulation test was conducted to simulate extreme market scenarios and high default rate scenarios to verify the effectiveness of the mechanism, such as whether it can reduce the impact of risks on various entities, whether it can incentivize entities to actively avoid risks, and whether it can enhance the risk resistance capability of the market-based scheduling model.
[0038] Third, study the impact of market rules on the scheduling model and optimize the model parameters to adapt to different market environments.
[0039] 1. Analyze the constraints of market rules on the model: First, the market-based rules for power flow control and voltage regulation in the distribution networks of each pilot region were analyzed, including application deadlines, bidding rules, settlement methods, and assessment standards. Key constraints in these rules were identified, and a rule constraint database was established. This transformed the written rules into quantifiable constraints, such as dispatch response time requirements and regulation accuracy assessments. For example, "peak shaving response time ≤ 15 minutes" was converted into a time constraint parameter in the model.
[0040] Secondly, the impact paths of rule constraints on the market-based scheduling model are analyzed, such as how application time constraints affect the model's optimization cycle, and how assessment standard constraints affect the setting of the objective function (a penalty term needs to be added). Through simulation comparison of model outputs under different rules, such as changes in the model's adjustment allocation strategy under strict assessment standards, key constraint clauses are identified.
[0041] Finally, a rule constraint adaptation module is built to associate the model's constraints with the rule constraint database, so that the model constraints are automatically updated when the rules change, ensuring that the model meets the requirements of market rules.
[0042] 2. The impact of quantitative market price fluctuations on parameters: First, collect historical price data (day-ahead prices and real-time prices) from the electricity market, covering a period of at least one year, and analyze price fluctuation characteristics, such as fluctuation amplitude, frequency, and seasonal patterns. Select price volatility indicators, such as price standard deviation and maximum increase / decrease, to quantify the degree of volatility.
[0043] Secondly, a correlation model is established between market prices and model parameters (such as resource pricing coefficients and adjustment allocation weights). Regression analysis is used to identify the impact coefficients of price fluctuations on the parameters. For example, the change in the pricing coefficient of distributed power sources when the day-ahead price increases by 10% is analyzed; the impact of real-time price fluctuations on the adjustment allocation weights of virtual power plants is also examined. A parameter sensitivity analysis scheme is designed to test the range of model parameter changes and the stability of the model output under different price fluctuation scenarios.
[0044] Finally, a dynamic parameter adjustment algorithm was developed to automatically correct model parameters based on real-time price fluctuation prediction results. For example, when predicted prices rise sharply, the adjustment weights for high-priced resources are increased. The algorithm was validated by comparing the economics of the model before and after adjustment, thus improving the model's adaptability to price fluctuations.
[0045] 3. Develop an adaptive optimization algorithm for model parameters: First, determine the model parameters that need to be optimized, such as objective function weights, constraint relaxation factors, and cost allocation coefficients, and analyze the optimization range and interrelationships of each parameter. Then, select a suitable optimization algorithm, such as particle swarm optimization or genetic algorithm, and set the algorithm's objective function (e.g., maximizing the total model revenue or minimizing the number of parameter adjustments) and constraints (parameter value range).
[0046] Secondly, a parameter optimization training dataset is constructed, containing historical operating data and optimal parameter combinations under different market environments (such as high / low load, high / low renewable energy penetration rate). Reinforcement learning is used to train the optimization algorithm, enabling it to automatically adjust model parameters based on the current market state (such as load level, renewable energy output). Simulation tests are then conducted to assess the algorithm's optimization effectiveness, demonstrating its ability to quickly find near-optimal parameter combinations in new market scenarios and the extent of performance improvement.
[0047] Finally, the optimization algorithm was integrated into the market-based scheduling model, and a parameter adaptive adjustment module was developed to achieve online optimization of the model. Field tests were conducted to compare the model's performance before and after optimization, ensuring efficient operation under different market environments.
[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.
Claims
1. A method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment, characterized in that, Includes the following steps: S1. Construct a market-based dispatch model for microgrids that considers the interests of multiple distributed entities and clarify the boundary conditions for their participation in market transactions. S2, establish a market-based scheduling model for virtual power plants to aggregate decentralized resources, and design a reasonable cost allocation mechanism; S3 investigates the impact of market rules on the scheduling model and optimizes the model parameters to adapt to different market environments.
2. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 1, characterized in that, Step S1 specifically includes: S11, Identify the revenue functions of each entity in the microgrid: S111 defines the main types of microgrids and collects their cost data and revenue sources by reviewing contract texts, corporate financial statements and market transaction records of each type of entity. S112, based on microeconomic theory, constructs the income function expressions of each subject, clarifies the relationship between its independent and dependent variables, and fits the function parameters through historical data; S113 verifies the rationality of the revenue function by simulating different operating scenarios, comparing the deviation between the calculated function results and the actual revenue, correcting the function form and parameters, and providing the objective function basis for the subsequent construction of a market-based scheduling model; S12, Technical and economic thresholds for quantitative trading entry: S121, sort out the access requirements for microgrids to participate in the electricity market, including technical and economic conditions, design a threshold quantification index system, and collect transaction cases and technical standards of similar projects to determine the preliminary scope of the indicators; S122, using cost-benefit analysis and technical feasibility analysis, quantifies the threshold values of each indicator, and uses the Delphi method to consult industry experts to revise the threshold values; S123, Construct a transaction access threshold assessment model, dynamically adjust the threshold value based on market rules and the actual situation of the microgrid, and verify the threshold. S13, Establish a mechanism for coordinating the interests of various stakeholders: S131, analyze the points of conflict of interest among the main entities in the microgrid, and design a framework for interest coordination based on the cooperative game theory in game theory, and clarify the coordination goals and coordination methods; S132, Develop a benefit distribution algorithm to allocate the total benefit according to the contribution of each subject to the system's benefits, and design a compensation mechanism to provide economic compensation to subjects that suffer losses due to cooperating with system scheduling; S133, through simulation of different conflict of interest scenarios, tests the effectiveness of the coordination mechanism; S134, optimize the coordination mechanism, adjust the allocation coefficient and compensation standard based on actual operation feedback, and form a scheme for coordinating the interests of microgrid entities.
3. The method for constructing a market-based dispatch model for microgrids and virtual power plants under the power market environment according to claim 2, characterized in that: In step S111, the subject types include distributed power source owners, energy storage operators, load users, and microgrid operators.
4. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 2, characterized in that: The threshold quantification index system includes technical threshold indicators and economic threshold indicators. The technical threshold indicators include the maximum output deviation and the upper limit of response time, while the economic threshold indicators include the upper limit of unit cost per kilowatt-hour and the minimum return on investment.
5. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 1, characterized in that, Step S2 specifically includes: S21, Optimize the cost-sharing algorithm for resource aggregation: S211, analyze the cost structure of virtual power plant aggregating distributed resources, clarify the calculation method and allocation object of each cost, design cost allocation principle, and initially select a suitable allocation algorithm; S212, based on historical aggregated data, simulates and tests different algorithms, calculates the deviation between the allocated cost of each resource and the actual cost, evaluates the fairness and efficiency of the algorithm, and introduces the entropy weight method to comprehensively evaluate the algorithm performance and select the optimal initial algorithm. S213, combining the operation mode and resource characteristics of virtual power plants, the selected algorithm is optimized and verified. The effect of the optimized algorithm is tested by selecting actual aggregation cases. An optimization method for virtual power plant resource aggregation cost allocation algorithm is proposed, providing a cost basis for the design of cost allocation mechanism. S22, Develop a cost allocation model based on contribution: S221 defines the contribution dimensions of resources in the market-based dispatch of virtual power plants and designs a contribution evaluation index system to quantify the contribution value of each dimension. S222, the weight of each contribution dimension is determined by the analytic hierarchy process, the rationality of the weight allocation is determined by expert scoring and consistency test, and a cost allocation model is constructed to allocate the total revenue of the virtual power plant according to the comprehensive contribution of each resource. S223 simulates different trading scenarios to test the model's allocation effect; S224, optimize model parameters, adjust contribution indicators and weights based on actual operating data, and introduce a penalty mechanism to deduct revenue from resources that fail to meet adjustment requirements; S23, Design a dynamic adjustment mechanism for risk sharing: S231, identify the types of risks in the market-based dispatch of virtual power plants, and design risk assessment indicators to analyze the impact of various risks on cost allocation; S232, Construct a risk-sharing framework, clarify the risk-sharing ratio of each entity, develop a dynamic adjustment algorithm, and adjust the cost allocation plan according to the real-time risk assessment results; S233, conduct simulation tests on the risk-sharing mechanism, simulate extreme market scenarios and high default rate scenarios, and verify the effectiveness of the mechanism.
6. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 5, characterized in that: In step S211, the cost allocation principle includes the revenue principle and the responsibility principle, and the allocation algorithm is the proportional allocation method or the marginal cost method.
7. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 5, characterized in that: In step S221, the contribution dimensions include adjustment contribution, response speed contribution, and reliability contribution.
8. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 1, characterized in that, Step S3 specifically includes: S31, Analyze the constraints of market rules on the model: S311, analyze the market-based rules for safety risk management in each pilot area, sort out the key constraints in the rules, establish a rule constraint database, and transform the written rules into quantifiable constraints; S312, analyze the impact path of rule constraints on the market-based scheduling model, and compare the model output results under different rules through simulation; S313, Construct a rule constraint adaptation module to associate the model's constraints with the rule constraint database, so that the model constraints are automatically updated when the rules change, ensuring that the model meets the requirements of market rules; S32, Quantifying the impact of market price fluctuations on parameters: S321: Collect historical price data of the electricity market, with a time span of no less than one year, analyze the characteristics of price fluctuations, and select price fluctuation indicators; S322, Establish a correlation model between market prices and model parameters, identify the impact coefficient of price fluctuations on parameters through regression analysis, design a parameter sensitivity analysis scheme, and test the range of change of model parameters and the stability of model output under different price fluctuation scenarios; S323: Develop a dynamic parameter adjustment algorithm that automatically corrects model parameters based on real-time price fluctuation prediction results, verifies the algorithm, compares the economics of the model before and after adjustment, and improves the model's adaptability to price fluctuations. S33, Develop an adaptive optimization algorithm for model parameters: S331, Determine the model parameters that need to be optimized, analyze the optimization range and mutual influence of each parameter, select a suitable optimization algorithm, and set the objective function and constraints of the algorithm; S332, Construct a parameter optimization training dataset. The dataset contains historical operating data and optimal parameter combinations under different market environments. Use reinforcement learning to train the optimization algorithm so that the algorithm can automatically adjust the model parameters according to the current market state. And test the optimization effect of the algorithm through simulation. S333 integrates the optimization algorithm into the market-based scheduling model, develops a parameter adaptive adjustment module, realizes online optimization of the model, and conducts field tests to compare the running effect of the model before and after optimization, ensuring the efficient operation of the model in different market environments.
9. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 8, characterized in that: In step S311, the market rules include the application time, the winning bid rules, the settlement method, and the assessment standards.
10. The method for constructing a market-based dispatch model for microgrids and virtual power plants under a power market environment according to claim 8, characterized in that: In step S331, the optimization algorithm is either particle swarm optimization or genetic algorithm.