Virtual power plant optimization method considering multivariate uncertainty under cooperation of power market and carbon market

Abstract

According to the virtual power plant optimization method considering multivariate uncertainty under power-carbon market collaboration, a single virtual power plant carbon mechanism model is established, a plurality of virtual power plant operation optimization models are established, a plurality of virtual power plant power-carbon collaborative transaction models are established and solved, and finally the above steps are carried out for solving. According to the method, the influence of electricity price uncertainty and new energy output uncertainty of the electricity market on virtual power plant optimization scheduling can be effectively overcome, and the stable operation capability and the economic benefit of the system are improved; according to the invention, an electricity-carbon market collaborative decision-making mechanism is constructed, and economic low-carbon operation of the virtual power plant can be ensured while electricity-carbon balance is realized; according to the method, an electric energy sharing contribution degree and carbon quota sharing contribution degree model is constructed, profit distribution among virtual power plant alliance members is realized, and the fairness and enthusiasm of the members participating in cooperative transaction are improved; according to the invention, the ADMM algorithm is improved, the penalty factor is dynamically adjusted, the ADMM convergence speed is improved, and the influence of the initial selection of the penalty parameter on the algorithm performance is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, in particular to a multi-virtual power plant cooperation game optimization method considering multiple uncertainties based on an electricity-carbon market coordination mechanism, which solves the problems of market electricity price, new energy output uncertainty and fair distribution of benefits after each subject participates in cooperation when multiple virtual power plants face in electricity-carbon joint transaction, and improves the algorithm performance of the solving model. BACKGROUND

[0002] At present, with the rapid development of China's power system, the large-scale access of high-proportion renewable energy and flexible resources such as distributed energy storage and electric vehicles, the mismatch between the temporal and spatial characteristics of load demand and power output faced by the power system is becoming more and more serious. Therefore, by using advanced communication technology and control technology, distributed resources such as distributed power, controllable load and energy storage are effectively aggregated and optimized, and virtual power plants are born to participate in the operation of the power grid and the transaction of the electricity market.

[0003] The so-called virtual power plant is a management platform that intelligently aggregates and optimally dispatches dispersed distributed energy (such as solar energy, wind energy, energy storage, controllable load, etc.) through advanced information communication technology and software systems. Virtual power plant is not a real power plant, but a distributed power management system that participates in the operation and dispatch of the power grid, mainly composed of power generation system, energy storage equipment and communication system. The core of virtual power plant is "communication" and "aggregation", which can participate in the power market and power grid operation as a special power plant, provide peak shaving, frequency modulation, backup and other regulation services, thereby enhancing the power supply capacity and promoting new energy consumption.

[0004] With the further development of virtual power plants and power systems, there are often multiple virtual power plants under the same power grid to form a virtual power plant alliance. Through point-to-point (Peer to Peer, P2P) energy transactions between members, the alliance realizes the optimal utilization of resources and reduces the dependence on the upper power grid.

[0005] In order to overcome the limitations of traditional single subject optimization method, the existing technology generally uses game theory to analyze the complex interaction in P2P energy transactions, for example: 1. In "Coordinated Planning of Distribution Network-Multi-Integrated Energy System Based on Master-Slave Game Theory" [J]. Electric Power Automation Equipment, 2022, 42(6):45-52, a multi-microgrid collaborative optimization model was constructed, and the Shapley method considering marginal contribution was used to allocate cooperative benefits. However, the Shapley method cannot ensure the maximization of overall benefits in application. On this basis, in "Analysis of Virtual Power Plant Game Model Based on Improved Shapley Algorithm" [J]. Electrical Measurement & Instrumentation, 2022, 59(12):41-47, a multi-microgrid cooperative game model was innovatively established based on Nash negotiation theory. This model integrates the personal preferences and actual contributions of the participants, thereby realizing a more reasonable and scientific allocation of cooperative benefits. 2. In order to further improve the low-carbon operation level of multiple virtual power plant alliances, the paper "Low-carbon Economic Dispatch of Electric-Gas Integrated Energy System Participating in Multi-Coupled Market" [J]. Smart Power, 2023, 51(5):8-14 proposes a low-carbon economic operation strategy for virtual power plants by incorporating carbon trading costs into the optimized dispatch of virtual power plants. 3. In "A communication-efficient coalition graph game-based framework for electricity and carbon trading in networked energy hubs" [J]. Applied Energy, 2023, 329: 120221, the differences between the topologies of electricity and carbon quotas are considered, and a two-stage optimization method based on a coalition graph game framework is proposed. The first stage solves the electricity trading optimization problem, striving to maximize economic benefits while ensuring the balance of electricity supply and demand; the second stage solves the carbon trading optimization problem, aiming to promote the achievement of carbon emission reduction targets through a reasonable carbon quota allocation and trading mechanism.

[0006] 4. In the paper "Optimization Scheduling of Multi-Microgrid Cooperative Game Theory Considering Multiple Uncertainties and Electricity-Carbon Coupled Trading" [J]. Journal of Electrical Engineering, 2024, 39(09):2635-2651, the cooperative operation of multi-microgrid systems and electricity-carbon coupled trading under uncertain environment are studied, which effectively addresses the uncertainty on the source side. 5. In the paper "Optimization Strategy for Multi-Microgrid Power Cooperative Operation Considering Electricity Price Uncertainty and Game Fraud Behavior" [J]. Power System Technology, 2022, 46(11):4217-4230, an optimization strategy for multi-microgrid power cooperative operation considering electricity price uncertainty and game fraud behavior is proposed. The proposed strategy can effectively improve the operating efficiency of each entity and its ability to cope with uncertain risks.

[0007] However, analysis revealed that technologies 1-3 above are all studied under deterministic scenarios and do not consider the impact of uncertainties during system operation. These uncertainties refer to factors such as the power generation capacity of new energy sources and fluctuations in electricity market prices that may exist during the actual operation of virtual power plants. In severe cases, these uncertainties may threaten the safe and stable operation of the main body. Technologies 4-5 above lack consideration of the multiple uncertainties and unfair distribution of cooperative benefits faced by multiple virtual power plants in cooperative operation, thus weakening the operational stability of virtual power plants and their enthusiasm for participating in P2P transactions.

[0008] In summary, existing research has several problems in considering collaborative optimization, multiple uncertainties, and the distribution of cooperative benefits, which are summarized as follows: 1) Under current technology, there are uncertainties in electricity market prices and renewable energy output, which have a certain impact on the cooperative operation of multiple virtual power plant alliances; 2) Under the current technology, most research on carbon emissions is still at the stage of determining carbon emissions based on electricity, and prioritizing electricity before carbon emissions, without considering the synergy between electricity and carbon emissions, that is, the synergy between electricity and carbon emissions in decision-making in the electricity market and carbon quota trading market. 3) Under the current technology, research on multi-entity collaborative operation does not consider the issue of fair distribution of benefits based on the principle of the size of contribution after each entity participates in the cooperation, which reduces the enthusiasm of members to participate in the cooperation; 4) The Alternating Direction Method of Multipliers (ADMM) algorithm used in the existing technology has a slow convergence speed, and the initial selection of the penalty parameter has a significant impact on the algorithm performance. Summary of the Invention

[0009] In summary, to address the impacts of electricity market price uncertainty and renewable energy output uncertainty on virtual power plant optimal dispatch in existing technologies / research, the lack of coordination in electricity carbon market decision-making, low participation enthusiasm of members in cooperative transactions due to poor fairness in benefit distribution, and the slow convergence speed of the existing ADMM algorithm, where the initial selection of penalty parameters has a significant impact on algorithm performance, this invention provides a virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market, which can effectively solve the above problems.

[0010] The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market of the present invention is described in detail below: 1) Establish a carbon mechanism model for a single virtual power plant: This step constructs a virtual power plant operation framework model for a single virtual power plant, including wind turbines, photovoltaic units, combined heat and power units, gas boilers, electric energy storage, and thermal energy storage equipment. 2) Establish multiple virtual power plant operation optimization models: This step models the market electricity price uncertainty of multiple virtual power plants and the uncertainty of wind and solar power, and establishes model constraints; 3) Establish and solve multiple virtual power plant carbon trading models: The virtual power plant carbon trading model described in this step is specifically a cooperative game model for multiple virtual power plant carbon trading based on asymmetric Nash negotiation, and the ADMM algorithm is used to solve multiple virtual power plant carbon trading models in a distributed manner. 4) Solve steps 1) to 3) and determine whether the convergence condition has been met before directly outputting the electricity carbon trading volume.

[0011] According to the present invention, the virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market is characterized in that, in the operation of the virtual power plant in the single virtual power plant carbon mechanism model in step 1), the carbon dioxide emissions in the virtual power plant mainly come from the combined heat and power unit and the gas boiler, and the two are combined to form a single virtual power plant carbon emission model and a single virtual power plant carbon quota model.

[0012] The virtual power plant optimization method considering multiple uncertainties under the coordination of the electric carbon market according to the present invention is characterized in that the single virtual power plant carbon emission model is specifically as follows: 1) Cogeneration unit model: This combined heat and power (CHP) unit is the main energy supply equipment in the virtual power plant. It produces electricity and heat by burning natural gas, while also generating CO2. The relationships between the CHP unit's power generation, calorific value, natural gas consumption, and CO2 emissions are as follows:

[0013] Since the current power system is still dominated by thermal power generation, it can be assumed that when the virtual power plant purchases electricity through the upper-level power grid, this electricity comes from the electricity generated by the coal-fired units burning fossil fuels in the thermal power plant.

[0014] The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market according to the present invention is characterized in that the single virtual power plant carbon quota model is specifically as follows: Using the baseline method as the initial carbon emission allowance allocation method, wind power and photovoltaic units are allocated initial carbon allowances based on equivalent consumption. The carbon emission allowance model for virtual power plants is as follows:

[0015] The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market according to the present invention is characterized in that the market electricity price uncertainty modeling in step 2) is specifically as follows: , That is, equation (5) is transformed into:

[0016]

[0017] The uncertainties arising from the grid connection of wind and solar power as new energy sources impact the scheduling and operation of multiple virtual power plant alliances. This is because, in virtual power plants containing distributed renewable energy, the power output of wind and solar power within the virtual power plant becomes a random variable due to factors such as local climate and environment. To ensure the reliability of the system scheduling strategy, this step employs a chance constraint method to address the uncertainty of wind and solar power output within the virtual power plant.

[0018] To overcome the impact of electricity market price uncertainty and renewable energy output uncertainty on the optimal scheduling of virtual power plants, this invention constructs corresponding objective function models and wind and solar uncertainty models. For the electricity market price uncertainty problem, robust optimization is widely used because it only requires knowledge of the confidence intervals of uncertain variables without needing to know the probability distribution function. This invention constructs a min-max objective function based on robust optimization, considering electricity price uncertainty, to ensure the reliability of decision-making in response to price risks by providing the worst-case scenario for electricity prices. For the wind and solar uncertainty problem, this invention employs a chance constraint method to address the uncertainty of wind turbine and photovoltaic output. By considering the probability distribution of such deviations, the system can maintain a certain degree of flexibility and robustness when facing uncertainties in wind and solar output, reducing operational risks caused by extreme uncertainties.

[0019] The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity-carbon market according to the present invention is characterized in that the model constraints in step 2) are divided into: establishing an energy storage device model, establishing demand response constraints, virtual power plant operation balance constraints, and power interaction constraints between the virtual power plant and the upper-level grid, and finally establishing an operation model for each virtual power plant considering electricity-carbon sharing, as detailed below:

[0020] The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market according to the present invention is characterized in that, in step 3), the cooperative game model of multiple virtual power plants' electricity carbon collaborative trading based on asymmetric Nash negotiation specifically involves constructing multiple virtual power plants' electricity carbon collaborative trading models, whereby each virtual power plant trades electricity and carbon quotas through P2P energy trading, forming multiple virtual power plant cooperative alliances to achieve energy sharing and coordination complementarity on a larger scale. The modeling of this cooperative game model of multiple virtual power plants' electricity carbon collaborative trading based on asymmetric Nash negotiation is as follows: , Decomposing equation (37) above yields the distributed optimal operation model for each virtual power plant: , 1.4) Calculate the original residual and the dual residual as follows: , 1.6) The number of update iterations is k = k + 1; 1.7) Determine the convergence status of the algorithm:

[0021] The solution process for the asymmetric Nash negotiation revenue allocation subproblem is similar to that for the cost minimization subproblem of a multi-virtual power plant alliance.

[0022] To address the issue of coordinated decision-making in the electricity carbon market, carbon trading is incorporated into the objective function of optimized scheduling. In the electricity market, virtual power plants (VPS) trade electricity with the upstream grid to generate revenue and then use their generating units to meet user demand. Correspondingly, some generating units output power, causing CO2 emissions and consuming carbon allowances. VPS can enter the carbon market to buy and sell allowances when they have a surplus or deficit. In the mechanism of coordinated decision-making in the electricity carbon market, VPS need to coordinate their decisions in the electricity market and the carbon allowance trading market. First, VPS must consider their own electricity market trading contracts and carbon emission compliance requirements when arranging unit output; second, VPS can engage in arbitrage based on multiple market price signals to maximize their own profits.

[0023] Virtual power plants form alliances and engage in carbon trading among their members, achieving efficient resource utilization and reducing dependence on upstream markets. A symmetric Nash negotiation model is constructed to address the profit distribution problem arising from cooperation among multiple virtual power plants. In this model, all participants have equal bargaining power in profit distribution. However, each virtual power plant's behavior and contribution in carbon trading differ. To measure the impact of shared carbon trading volume among virtual power plants on profit distribution and to achieve a fairer profit allocation, thereby improving the fairness and enthusiasm of members participating in cooperative trading, this invention employs an asymmetric Nash negotiation model. Separate contribution models for electricity sharing and carbon quota sharing are constructed. Under carbon trading collaboration, decisions are mutually influential, manifesting as joint decision-making. To ensure equal decision-making status in both markets, the two models are weighted equally.

[0024] The Advanced Parameter Model (ADMM) algorithm is an important method for solving separable, large-scale, convex optimization problems. ADMM penalizes parameters during the iteration process. The penalty parameter remains constant. To improve the convergence speed of ADMM and reduce the impact of the initial choice of penalty parameter on algorithm performance, potentially different penalty parameters can be used in each iteration, dynamically adjusting the value of the penalty factor. Therefore, an improved ADMM algorithm, the Variable Penalty Parameter ADMM, is adopted to solve the model.

[0025] The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market according to the present invention is characterized in that, in step 4), after solving steps 1) to 3), determining whether the convergence condition has been met, the electricity carbon trading volume is directly output, specifically as follows: 1) Collect the computational parameters required for resource modeling and input them into the model; 2) Among them, the model reference formula (1) for cogeneration unit, the model reference formula (2) for gas boiler, the model reference formula (3) for carbon emission, the model reference formula (4) for carbon quota, the model reference formula (5) to (10) for virtual power plant independent operation considering market electricity price uncertainty, the model reference formula (11) to (15) for wind and solar uncertainty, the model reference formula (16) for energy storage equipment, the model reference formula (17) for flexible load, the model reference formula (18) to (20) for other constraints, the model reference formula (21) to (24) for each virtual power plant operation considering electricity-carbon sharing, and the model reference formula (27) for cooperative game based on Nash negotiation. 3) Based on equation (38), write the objective function and constraints for subproblem 1 - minimizing the cost of multiple virtual power plant alliances, and make a decision on the carbon trading volume of virtual power plants; 4) Solve the model of subproblem 1 using the ADMM algorithm with variable penalty parameters; 5) Determine if the convergence condition has been met. If yes, output the electricity carbon trading volume directly; otherwise, continue solving. 6) Substitute the result of 5) into subproblem 2 - maximizing the distribution of revenue based on asymmetric bargaining, and write the objective function and constraints of subproblem 2 according to equation (47) to make a decision on the carbon trading price of the virtual power plant; 7) Solve the model of subproblem 2 using the ADMM algorithm with variable penalty parameters; 8) Determine if the convergence condition has been met. If yes, output the electricity carbon trading volume directly. If no, continue solving.

[0026] The virtual power plant optimization method considering multiple uncertainties under the collaborative approach of the electricity carbon market of the present invention has achieved the following beneficial effects: 1. The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market of the present invention can effectively overcome the impact of electricity market price uncertainty and new energy output uncertainty on the optimal scheduling of virtual power plants, and improve the system's stable operation capability and economic benefits; 2. The virtual power plant optimization method under the coordination of the electricity carbon market of the present invention, which considers multiple uncertainties, constructs a collaborative decision-making mechanism for the electricity carbon market, which can achieve electricity carbon balance while ensuring the economical and low-carbon operation of the virtual power plant; 3. The virtual power plant optimization method under the collaborative electricity carbon market of the present invention, which considers multiple uncertainties, constructs a model of electricity sharing contribution and carbon quota sharing contribution, realizes a fairer profit distribution among virtual power plant alliance members, and improves the fairness and enthusiasm of members to participate in cooperative transactions; 4. The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market of the present invention further improves the ADMM algorithm. By dynamically adjusting the penalty factor, the convergence speed of ADMM is improved, and the impact of the initial selection of the penalty parameter on the algorithm performance is reduced. Attached Figure Description

[0027] Fig. 1 This is a diagram of the operating architecture of multiple virtual power plants under the electric carbon market collaboration mechanism of the present invention, which considers multiple uncertainties in the virtual power plant optimization method under the electric carbon market collaboration. Fig. 2 This is a flowchart illustrating step 4) of the virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market of the present invention. Detailed Implementation

[0028] The following description, in conjunction with the accompanying drawings and embodiments, further describes the technical means, creative features, objectives, and effects of the virtual power plant optimization method under the coordination of the electricity carbon market of the present invention, which considers multiple uncertainties.

[0029] Example

[0030] like Figs. 1-2 As shown, the optimization method for virtual power plants considering multiple uncertainties under the coordination of the electricity carbon market is as follows: 1) Establishing a carbon mechanism model for a single virtual power plant: This step constructs a virtual power plant operation framework model for a single virtual power plant, including wind turbines, photovoltaic units, combined heat and power units, gas boilers, electric energy storage, and thermal energy storage equipment. 2) Establish multiple virtual power plant operation optimization models: This step models the market electricity price uncertainty of multiple virtual power plants and the uncertainty of wind and solar power, and establishes model constraints; 3) Establish and solve multiple virtual power plant carbon trading models: The virtual power plant carbon trading model described in this step is specifically a cooperative game model for multiple virtual power plant carbon trading based on asymmetric Nash negotiation, and the ADMM algorithm is used to solve multiple virtual power plant carbon trading models in a distributed manner. 4) Solve steps 1) to 3) and determine whether the convergence condition has been met before directly outputting the electricity carbon trading volume.

[0031] In the operation of the virtual power plant in the single virtual power plant carbon mechanism model of step 1), the carbon dioxide emissions in the virtual power plant mainly come from the combined heat and power unit and the gas boiler. The two are combined to form the single virtual power plant carbon emission model and the single virtual power plant carbon quota model.

[0032] The carbon emission model for a single virtual power plant is as follows: 1) Cogeneration unit model: This combined heat and power (CHP) unit is the main energy supply equipment in the virtual power plant. It produces electricity and heat by burning natural gas, while also generating CO2. The relationships between the CHP unit's power generation, calorific value, natural gas consumption, and CO2 emissions are as follows:

[0033] The carbon quota model for a single virtual power plant is as follows: Using the baseline method as the initial carbon emission allowance allocation method, wind power and photovoltaic units are allocated initial carbon allowances based on equivalent consumption. The carbon emission allowance model for virtual power plants is as follows:

[0034] Step 2) involves modeling the uncertainty of market electricity prices, as follows: , That is, equation (5) is transformed into:

[0035] Step 2) involves the following model constraints: establishing an energy storage device model, establishing demand response constraints, virtual power plant operation balance constraints, and power interaction constraints between the virtual power plant and the upper-level grid, and finally establishing operation models for each virtual power plant considering electricity-carbon sharing, as detailed below:

[0036] Step 3) describes a cooperative game model for collaborative carbon trading among multiple virtual power plants based on asymmetric Nash negotiations. Specifically, it involves constructing a collaborative carbon trading model for multiple virtual power plants. Each virtual power plant trades electricity and carbon quotas through P2P energy trading, forming a cooperative alliance to achieve energy sharing and coordination on a larger scale. The modeling of this collaborative game model for collaborative carbon trading among multiple virtual power plants based on asymmetric Nash negotiations is as follows:

[0037] Step 3) involves using the ADMM algorithm to solve the distributed problem for multiple virtual power plant carbon trading models. Specifically: Considering that the subproblem of minimizing the cost of multiple virtual power plant alliances and the subproblem of maximizing revenue distribution based on asymmetric bargaining have separable convex functions and constraints, the ADMM algorithm is used for distributed solution. ADMM penalizes parameters during the iteration process. The penalty parameter remains constant. To improve the convergence speed of ADMM and reduce the impact of the initial choice of the penalty parameter on the algorithm's performance, a potentially different penalty parameter can be used in each iteration. By dynamically adjusting the value of the penalty factor, the ADMM algorithm with variable penalty parameters is used to solve the two subproblems, as follows: , Decomposing equation (37) above yields the distributed optimal operation model for each virtual power plant: , 1.4) Calculate the original residual and the dual residual as follows: , 1.6) The number of update iterations is k = k + 1; 1.7) Determine the convergence status of the algorithm:

[0038] The solution process for the asymmetric Nash negotiation revenue allocation subproblem is similar to that for the cost minimization subproblem of a multi-virtual power plant alliance.

[0039] like Fig. 2 As shown, step 4) involves solving steps 1) to 3), determining whether the convergence condition has been met, and then directly outputting the electricity carbon trading volume. Specifically: 1) Collect the computational parameters required for resource modeling and input them into the model; 2) Among them, the model reference formula (1) for cogeneration unit, the model reference formula (2) for gas boiler, the model reference formula (3) for carbon emission, the model reference formula (4) for carbon quota, the model reference formula (5) to (10) for virtual power plant independent operation considering market electricity price uncertainty, the model reference formula (11) to (15) for wind and solar uncertainty, the model reference formula (16) for energy storage equipment, the model reference formula (17) for flexible load, the model reference formula (18) to (20) for other constraints, the model reference formula (21) to (24) for each virtual power plant operation considering electricity-carbon sharing, and the model reference formula (27) for cooperative game based on Nash negotiation. 3) Based on equation (38), write the objective function and constraints for subproblem 1 - minimizing the cost of multiple virtual power plant alliances, and make a decision on the carbon trading volume of virtual power plants; 4) Solve the model of subproblem 1 using the ADMM algorithm with variable penalty parameters; 5) Determine if the convergence condition has been met. If yes, output the electricity carbon trading volume directly. If no, continue solving.

[0040] 6) Substitute the result of 5) into subproblem 2 - maximizing the distribution of revenue based on asymmetric bargaining, and write the objective function and constraints of subproblem 2 according to equation (47) to make a decision on the carbon trading price of the virtual power plant; 7) Solve the model of subproblem 2 using the ADMM algorithm with variable penalty parameters; 8) Determine if the convergence condition has been met. If yes, output the electricity carbon trading volume directly. If no, continue solving.

[0041] The virtual power plant optimization method considering multiple uncertainties under the collaborative approach of the electricity carbon market of this invention can effectively overcome the impact of electricity market price uncertainty and renewable energy output uncertainty on the optimal scheduling of virtual power plants, thereby improving the system's stable operation capability and economic benefits. This invention constructs an electricity carbon market collaborative decision-making mechanism, achieving electricity carbon balance while ensuring the economical and low-carbon operation of virtual power plants. This invention constructs models for electricity sharing contribution and carbon quota sharing contribution, realizing a fairer profit distribution among virtual power plant alliance members and improving the fairness and enthusiasm of members participating in cooperative transactions. This invention further improves the ADMM algorithm by dynamically adjusting the penalty factor, thereby increasing the convergence speed of ADMM and reducing the impact of the initial selection of penalty parameters on algorithm performance.

[0042] Those skilled in the art should recognize that the above embodiments are merely illustrative of this application and are not intended to limit this application. Any variations or modifications to the above embodiments that fall within the scope of the essential spirit of this application will fall within the scope of the claims of this application.

Claims

1. A virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market, which is described in detail below: 1) Establish a carbon mechanism model for a single virtual power plant: This step constructs a virtual power plant operation framework model for a single virtual power plant, including wind turbines, photovoltaic units, combined heat and power units, gas boilers, electric energy storage, and thermal energy storage equipment. 2) Establish multiple virtual power plant operation optimization models: This step models the market electricity price uncertainty of multiple virtual power plants and the uncertainty of wind and solar power, and establishes model constraints; 3) Establish and solve multiple virtual power plant carbon trading models: The virtual power plant carbon trading model described in this step is specifically a cooperative game model for multiple virtual power plant carbon trading based on asymmetric Nash negotiation, and the ADMM algorithm is used to solve multiple virtual power plant carbon trading models in a distributed manner. 4) Solve steps 1) to 3) and determine whether the convergence condition has been met before directly outputting the electricity carbon trading volume.

2. The virtual power plant optimization method considering multiple uncertainties under the collaborative approach of the electricity carbon market as described in claim 1, characterized in that, In the operation of the virtual power plant in the single virtual power plant carbon mechanism model of step 1), the carbon dioxide emissions in the virtual power plant mainly come from the combined heat and power unit and the gas boiler. The two are combined to form the single virtual power plant carbon emission model and the single virtual power plant carbon quota model.

3. The virtual power plant optimization method considering multiple uncertainties under the collaborative approach of the electricity carbon market as described in claim 2, characterized in that, The carbon emission model for a single virtual power plant is as follows: 1) Cogeneration unit model: This combined heat and power (CHP) unit is the main energy supply equipment in the virtual power plant. It produces electricity and heat by burning natural gas, while also generating CO2. The relationships between the power generation, calorific value, and natural gas consumption of combined heat and power (CHP) units, as well as their CO2 emissions, are as follows: ， 4. The virtual power plant optimization method considering multiple uncertainties under the coordination of the electricity carbon market as described in claim 2, characterized in that, The carbon quota model for a single virtual power plant is as follows: Using the baseline method as the initial carbon emission allowance allocation method, wind power and photovoltaic units are allocated initial carbon allowances based on equivalent consumption. The carbon emission allowance model for virtual power plants is as follows: 。 5. The virtual power plant optimization method considering multiple uncertainties under the collaborative approach of the electricity carbon market as described in claim 1, characterized in that, The market electricity price uncertainty modeling in step 2) is as follows: ， Decomposing equation (37) above yields the distributed optimal operation model for each virtual power plant: ， 1.6) The number of update iterations is k = k + 1; 1.7) Determine the convergence status of the algorithm:

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