A regional virtual power plant collaborative operation and dispatching system and method based on double-layer game

Abstract

This invention discloses a regional virtual power plant collaborative operation and dispatching system based on a two-level game theory approach. The system constructs an operation system framework including VPP operators, load aggregators, and energy aggregators. It obtains a dispatching dataset through load, generation, and electricity price information and establishes a two-level game model. On the load side, an optimal electricity demand curve is obtained using a time-of-use (TOU) pricing strategy through a demand response optimization model. On the energy side, a collaborative generation plan is obtained using a resource cooperation game model based on a TOU pricing strategy. The TOU pricing strategy is updated based on the optimal electricity demand curve and the collaborative generation plan. The updated TOU pricing strategy is then used to obtain an updated optimal electricity demand curve and an updated collaborative generation plan, until both meet a preset equilibrium condition, resulting in the optimal collaborative operation and dispatching outcome. This invention improves the renewable energy absorption capacity and grid operation stability, and enhances the operational efficiency and economic benefits of the virtual power plant.

Description

Technical Field

[0001] This invention relates to the technical field of virtual power plant resource scheduling optimization, specifically to a regional virtual power plant collaborative operation scheduling system and method based on two-level game theory. Background Technology

[0002] Distributed photovoltaic and wind power, among other clean energy sources, are widely used in regional scenarios, becoming an important supplement to the power system. However, these resources suffer from problems such as dispersed distribution, heterogeneous types, large load fluctuations, and poor adjustability, resulting in insufficient market trading and grid adaptation capabilities. While virtual power plants can aggregate distributed energy, loads, and energy storage for optimized scheduling, existing scheduling strategies are mostly limited to single-resource optimization or static pricing scheduling, lacking dynamic interaction and collaborative optimization among multiple heterogeneous resources. The price transmission mechanism between the grid and virtual power plants is imperfect, leading to low efficiency in supply and demand regulation and energy trading, and difficulty in coping with the uncertainties brought about by fluctuations in renewable energy output. At the same time, existing related research mostly adopts single game models, failing to integrate multiple realistic factors such as grid interaction, user participation willingness, and carbon emission constraints, making it unable to adapt to the complex operation and scheduling scenarios of regional virtual power plants. The core challenges of coordinating the interests of multiple stakeholders, efficient consumption of renewable energy, and stable grid operation have not been effectively solved. Summary of the Invention

[0003] The purpose of this invention is to provide a regional virtual power plant collaborative operation and scheduling system and method based on two-layer game theory. This invention improves the renewable energy absorption capacity and grid operation stability, and enhances the operating efficiency and economic benefits of virtual power plants.

[0004] To achieve this objective, the present invention designs a regional virtual power plant collaborative operation and scheduling system based on a two-layer game theory approach, which includes: The system framework building module is used to build a regional virtual power plant collaborative operation system framework that includes VPP operators, load aggregators, and energy aggregators; The data acquisition and processing module is used to collect load information of load-side resources managed by load aggregators, power generation information of energy-side resources managed by energy aggregators, and electricity price information of VPP operators in the framework of regional virtual power plant collaborative operation system. The load information, power generation information and electricity price information are preprocessed to obtain the preprocessed scheduling dataset. The two-layer game model construction module is used to build a two-layer game model based on the interaction relationship between VPP operators, load aggregators and energy aggregators in the regional virtual power plant collaborative operation system framework, as well as the preprocessed scheduling dataset. The load-side response optimization module is used to establish a demand response optimization model for load aggregators. Through the demand response optimization model, the VPP operator's time-of-use pricing strategy in the two-level game model is used to optimize the demand response of various types of loads and obtain the optimal power demand curve. The energy-side collaborative optimization module is used to establish an energy-side resource cooperation game model for energy aggregators. Through the energy-side resource cooperation game model, the time-of-use pricing strategy of VPP operators in the two-level game model is used to optimize the collaborative output of various energy resources and obtain a collaborative power generation plan. The collaborative operation and dispatch module is used to update the time-of-use pricing strategy of the VPP operator based on the optimal power demand curve and the collaborative generation plan. The load aggregator and the energy aggregator use the updated time-of-use pricing strategy of the VPP operator to obtain the updated optimal power demand curve and the updated collaborative generation plan, respectively, until the updated collaborative generation plan, the updated optimal power demand curve and the updated time-of-use pricing strategy of the VPP operator all meet the preset equilibrium conditions, and the optimal collaborative operation and dispatch result is obtained.

[0005] Preferably, the specific content of the VPP operator in the regional virtual power plant collaborative operation system framework includes: According to VPP operators during the time period Price of electricity purchased from thermal power units VPP operators during the time period Price of electricity purchased from wind power VPP operators during the time period Price of electricity purchased from solar power and VPP operators during the time period The price of electricity sold to the load aggregator The objective revenue function that maximizes the revenue of VPP operators is obtained. The expression is: in, n Indicates the total number of scheduling periods. Indicates VPP operator during the time period Revenue from selling electricity to load aggregators, Indicates VPP operator during the time period The cost of purchasing electricity from energy aggregators Indicates VPP operator during the time period The costs or benefits of interacting with the power grid; VPP operators during the time period Revenue from selling electricity to load aggregators The expression is: in, Indicates the load aggregator during the time period Total electricity demand; VPP operators during the time period Cost of purchasing electricity from energy aggregators The expression is: in, Indicates the thermal power unit during the time period Power generation output, Indicates wind power during the time period Power generation output, Indicates the time period of photovoltaics The power generation output; VPP operators during the time period Costs or benefits of interacting with the power grid The expression is: in, , Indicates energy aggregators during the period Total power generation output; Indicates VPP operator during the time period The price of selling electricity to the grid. Indicates VPP operator during the time period The price at which electricity is purchased from the power grid.

[0006] Preferably, the specific content of the load aggregator in the regional virtual power plant collaborative operation system framework includes: According to the load aggregator in the time period Total electricity demand and load aggregators during the time period Electricity prices after demand response Establish a price elasticity matrix M Linear relationship model: in, Indicates the load aggregator during the time period The change in load, Indicates the load aggregator during the time period The original base load, Indicates time period The change in electricity price Indicates the period before demand response. The electricity purchase price, M Represents the price elasticity matrix; in, This indicates the change in electricity price during time period 1 relative to time period 2. n The impact of load at that time Indicates the time period as nThe impact of electricity price changes during time period 1 on load. Indicates the time period as n Electricity price changes over time periods n The impact of load at that time.

[0007] Preferably, the specific content of the energy aggregator in the regional virtual power plant collaborative operation system framework includes: According to energy aggregators, thermal power units during the time period Power generation Wind power during the period Power generation Photovoltaics during time period Power generation Calculate the revenue of thermal power units, wind power revenue, and photovoltaic revenue; The revenue expression for thermal power units is: in, Indicates the thermal power unit during the time period Power generation output, This indicates the revenue of thermal power units. This represents the fuel cost coefficient. Indicates the cost of carbon emissions trading; The revenue expression for wind power is: in, This indicates the revenue from wind power. Represents a set of scenes. Representing a scene The probability, Indicates wind power during the time period Power generation output, Wind power depreciation residual value income is discounted to the dispatch period. The value after that, This is the converted value for wind power operation and maintenance costs. Penalty costs for wind power deviations; The revenue expression for photovoltaics is: in, Indicates the revenue from photovoltaics. Indicates the time period of photovoltaics Power generation output, The residual value of photovoltaic depreciation is discounted to the dispatch period. The value after that, This is the converted value of photovoltaic operation and maintenance costs. Penalty costs for photovoltaic deviations.

[0008] Preferably, the specific method for obtaining the preprocessed scheduling dataset is as follows: The system collects load information of load-side resources managed by load aggregators in the framework of the regional virtual power plant collaborative operation system, including the collection of basic electricity consumption curves, adjustable capacity, response time constraints, and user comfort constraints of charging piles, residential and industrial loads. The system collects power generation information of energy-side resources managed by energy aggregators in the framework of the regional virtual power plant collaborative operation system, including collecting predicted output information of wind power and photovoltaic power, generating multiple typical scenarios and their corresponding probabilities, and collecting the operating cost coefficient, ramp rate, output upper and lower limits and carbon emission coefficient of thermal power units. Time-of-use electricity pricing for VPP operators connected to the power grid within the framework of a regional virtual power plant collaborative operation system. Time-of-use electricity pricing The collected data will be distributed according to the scheduled time period. The data is aligned and organized to form a preprocessed scheduling dataset that includes load, new energy forecasts, and market prices.

[0009] Preferably, a demand response optimization model for load aggregators is established. This model is then used in a two-level game model to solve for the demand response optimization of various load types, obtaining the optimal electricity demand curve. The specific process is as follows: The adjustable load resources in the preprocessed scheduling dataset are classified and aggregated to form a load-side resource set; Collect the basic power consumption curve, adjustable capacity parameters, and user constraints for each type of load within the target scheduling cycle; The time period is obtained by weighting flexible load reduction indicators and economic efficiency indicators. User satisfaction The expression is: in, As a flexible load reduction indicator, The weighting coefficient for the flexible load reduction index. As an indicator of economic effectiveness, The weighting coefficients for economic efficiency indicators. For time period User satisfaction; Flexible load reduction indicators The calculation formula is: in, For time period The original base load, For time period The amount of load that can be shifted or reduced through demand response; Economic efficiency indicators The calculation formula is: in, The electricity purchase price before demand response. The electricity price sold by load aggregators after demand response; And the electricity sales price of load aggregators after demand response Satisfy the following expression: in, Indicates time period The amount of electricity price adjustment; Set the satisfaction constraints that must be met: ; in, The minimum satisfaction threshold; According to the price elasticity matrix M Based on the linear relationship model, the total electricity demand of the load aggregator in time period t after implementing demand response is calculated. for: Obtain the revenue function of the load aggregator The expression is: in, The weighting coefficients representing economic returns. The weighting coefficients representing user satisfaction. n Indicates the total number of scheduling periods. Indicates the load aggregator during the time period Electricity prices after demand response Indicates the load aggregator during the time period Total electricity demand Indicates VPP operator during the time period The price at which electricity is sold to load aggregators. Indicates time period User satisfaction; Revenue function of load aggregator The following constraints need to be met: Electricity sales price upper and lower limits constraints: ; in, For time period The upper limit of electricity sales price, For time period The lower limit of electricity sales price; Post-response load upper and lower limit constraints: ; in, This is the upper bound of the load after demand response. This is the lower bound of the load after demand response; Satisfaction constraints: ; Thus, a demand response optimization model for load aggregators is constructed; Based on the time-of-use pricing strategy of VPP operators in the aforementioned two-layer game model, in the time period The price of electricity sold to the load aggregator The load aggregator maximizes the revenue function With the goal of optimizing the demand response of various loads through a demand response optimization model, the optimal electricity demand curve is obtained.

[0010] Preferably, an energy aggregator's energy-side resource cooperation game model is established. This model, using the time-of-use pricing strategy of VPP operators in a two-layer game model, optimizes the coordinated output of various energy resources to obtain the coordinated power generation plan. The specific process is as follows: This alliance comprises thermal power units, wind power units, and photovoltaic units that participate in collaborative optimization and competitive negotiation within energy aggregators. ,in, For thermal power units, For wind power, For photovoltaics; The participating parties form sub-alliances. , , ; thermal power units The profit expression is: in, Indicates the thermal power unit during the time period Power generation output, This indicates the revenue of thermal power units. This represents the fuel cost coefficient. Indicates the cost of carbon emissions trading; wind power The profit expression is: in, This indicates the revenue from wind power. Represents a set of scenes. Representing a scene The probability, Indicates wind power during the time period Power generation output, Wind power depreciation residual value income is discounted to the dispatch period. The value after that, This is the converted value for wind power operation and maintenance costs. Penalty costs for wind power deviations; Photovoltaics The profit expression is: in, Indicates the revenue from photovoltaics. Indicates the time period of photovoltaics Power generation output, The residual value of photovoltaic depreciation is discounted to the dispatch period. The value after that, This is the converted value of photovoltaic operation and maintenance costs. Penalty costs for photovoltaic deviations; Energy aggregators maximize alliances Taking the total profit as the objective, the objective function is: + + This leads to the construction of a game model for energy aggregators' cooperation on energy-side resources; Based on the time-of-use pricing strategy of VPP operators in the aforementioned two-layer game model, in the time period Price of electricity purchased from thermal power units During the period Price of electricity purchased from wind power and during the time period Price of electricity purchased from solar power With the goal of maximizing total revenue, a collaborative power generation plan that maximizes the total revenue of the alliance is obtained by using an energy-side resource cooperation game model to optimize the coordinated output of thermal power units, wind power, and photovoltaic power. , , ).

[0011] Preferably, the specific process for obtaining the optimal collaborative operation scheduling result is as follows: VPP operators based on the optimal electricity demand curve and the aforementioned collaborative power generation plan ( , , ), and power balance constraints Calculate VPP operator during time period Interaction power with the power grid ; in, Indicates the load aggregator during the time period Total electricity demand Indicates the thermal power unit during the time period Power generation output, Indicates wind power during the time period Power generation output, Indicates the time period of photovoltaics The power generation output; This allows VPP operators to obtain information during specific time periods. Costs or benefits of interacting with the power grid Substitute into the target revenue function of the VPP operator To maximize the objective return function To achieve this, update the time-of-use pricing strategy of the VPP operator, publish the updated time-of-use pricing strategy to the load aggregator and energy aggregator, and iteratively solve the problem: in, Iteration j Time-of-use pricing strategy for VPP operators after +1 Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from thermal power units Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from wind power Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from solar power Iteration j The VPP operator after +1 time in the time period The price at which electricity is sold to load aggregators; Iteration j Electricity sales strategy of load aggregators after +1 Iteration j The load aggregator after +1 time is in the time period Electricity prices after demand response; Iteration j The collaborative power generation plan of energy aggregators after +1 time, Iteration j +1 times the thermal power units in the energy aggregator during the time period Power generation output, Iteration j Wind power in the energy aggregator after +1 time period Power generation output, Iteration j After +1, photovoltaics in the energy aggregator during the time period The power generation output; When the iterated collaborative generation plan, optimal power demand curve, and time-of-use pricing strategy all meet the preset equilibrium conditions, that is, when the strategy changes of the collaborative generation plan, optimal power demand curve, and time-of-use pricing strategy are all less than the preset threshold in two adjacent iterations, the optimal collaborative operation and scheduling result is obtained.

[0012] A regional virtual power plant collaborative operation and scheduling method based on two-level game theory includes the following steps: Construct a framework for a regional virtual power plant collaborative operation system that includes VPP operators, load aggregators, and energy aggregators; The system collects load information of load-side resources managed by load aggregators, power generation information of energy-side resources managed by energy aggregators, and electricity price information of VPP operators within the framework of the regional virtual power plant collaborative operation system. The load information, power generation information, and electricity price information are preprocessed to obtain a preprocessed scheduling dataset. Based on the interaction relationships among VPP operators, load aggregators, and energy aggregators in the regional virtual power plant collaborative operation system framework, and the preprocessed scheduling dataset, a two-layer game model is established. Establish a demand response optimization model for load aggregators. Using the demand response optimization model, the time-of-use pricing strategy of VPP operators in the two-level game model is used to solve the demand response optimization problem for various types of loads and obtain the optimal power demand curve. Establish an energy-side resource cooperation game model for energy aggregators. Through this model, utilize the time-of-use pricing strategy of VPP operators in a two-layer game model to optimize the collaborative output of various energy resources and obtain a collaborative power generation plan. The VPP operator's time-of-use pricing strategy is updated based on the optimal electricity demand curve and the collaborative generation plan. The load aggregator and the energy aggregator use the updated VPP operator's time-of-use pricing strategy to obtain the updated optimal electricity demand curve and the updated collaborative generation plan, respectively, until the updated collaborative generation plan, the updated optimal electricity demand curve, and the updated VPP operator's time-of-use pricing strategy all meet the preset equilibrium conditions, thus obtaining the optimal collaborative operation and dispatch result.

[0013] A computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method.

[0014] The beneficial effects of this invention are: This invention addresses the scheduling coordination and conflict of interest issues among multiple stakeholders in a regional virtual power plant by constructing a two-layer game model with VPP operators as leaders and load aggregators and energy aggregators as followers. It leverages the VPP operators' time-of-use pricing strategy to open up interaction channels between the load and energy sides, fully tapping the adjustment potential of distributed resources and enhancing flexibility in responding to electricity market supply and demand fluctuations. Furthermore, by combining price elasticity matrices and user satisfaction constraints on the load side, this invention optimizes demand response, smoothing load peak-valley differences, reducing electricity costs, ensuring a better user experience, and increasing user participation in demand response. Finally, by forming a cooperative game alliance among thermal power, wind power, and photovoltaic power on the energy side, this invention achieves coordinated output from multiple energy sources, maximizing the overall revenue of energy aggregators, improving the renewable energy absorption capacity of thermal power, wind power, and photovoltaic power, mitigating the impact of renewable energy output fluctuations on grid dispatch, and enhancing grid operational stability. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the structure of the present invention; Figure 2 This is a flowchart of the present invention; Figure 3 A schematic diagram of initial load data provided for an embodiment of the present invention; Figure 4 This invention provides a wind power output diagram for 10 scenarios. Figure 5 This invention provides a photovoltaic power output diagram for farmers under 10 different scenarios. Figure 6 A schematic diagram illustrating the demand response effect provided in an embodiment of the present invention; Figure 7 This invention provides a power output result for wind power, rural photovoltaic power, and thermal power units in an embodiment of the invention. Figure 8 This invention provides a revenue result for wind power, rural photovoltaic power, and thermal power units. Figure 9 This is a pollutant emission calculation result provided in an embodiment of the present invention; Figure 10 An example of an electricity trading result provided in an embodiment of the present invention; Figure 11 This is a virtual power plant revenue settlement result provided in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 A regional virtual power plant collaborative operation and scheduling system based on two-level game theory, such as Figure 1As shown, it includes: The system framework building module is used to build a regional virtual power plant collaborative operation system framework that includes VPP operators, load aggregators and energy aggregators. This design enables orderly interaction and collaborative scheduling among multiple entities by building a multi-entity collaborative operation infrastructure. The data acquisition and processing module is used to collect load information of load-side resources managed by load aggregators, power generation information of energy-side resources managed by energy aggregators, and electricity price information of VPP operators in the framework of regional virtual power plant collaborative operation system. The load information, power generation information, and electricity price information are preprocessed to obtain a preprocessed scheduling dataset. This design can eliminate data redundancy and timing errors by preprocessing multi-source data of load, power generation, and electricity price, thereby improving the accuracy and reliability of subsequent scheduling calculations. The two-layer game model construction module is used to establish a two-layer game model with the VPP operator as the leader and the load aggregator and energy aggregator as the followers in the regional virtual power plant collaborative operation system framework, based on the interaction relationship between VPP operators, load aggregators and energy aggregators in the regional virtual power plant collaborative operation system framework and the preprocessed scheduling dataset. This design can accurately simulate the decision-making logic of VPP operators leading and load aggregators and energy aggregators following by establishing a two-layer game model, which can solve the multi-objective optimization conflict problem and realize the synergistic coupling of pricing decision and scheduling decision. The load-side response optimization module is used to establish a demand response optimization model for load aggregators. Through the demand response optimization model, the VPP operator's time-of-use pricing strategy in the two-layer game model (the time-of-use pricing strategy uses the electricity price settlement result of the previous target scheduling cycle as the initial electricity price strategy) is used to optimize the demand response of various loads and obtain the optimal electricity demand curve. This design guides the response of various loads through the time-of-use pricing strategy and optimizes the electricity consumption curve by combining the correlation between electricity price and load. It can smooth out load peaks and valleys, reduce electricity costs, and thus obtain the optimal electricity demand curve, achieving flexible load-side regulation. The energy-side collaborative optimization module is used to establish an energy-side resource cooperation game model for energy aggregators. Through this model, the time-of-use pricing strategy of VPP operators in a two-layer game model is used to optimize the collaborative output of various energy resources (including wind power, photovoltaic, and thermal power units), resulting in a collaborative power generation plan (which is used for subsequent time-of-use pricing strategy updates and supply-demand balance calculations). This design, through the energy-side resource cooperation game model, allows thermal power, wind power, and photovoltaic to overcome the limitations of independent decision-making through cooperative game theory, achieve complementary collaborative output, maximize the overall energy-side benefits, and improve the capacity for renewable energy absorption, thereby enhancing the operational efficiency of the energy side. The collaborative operation and dispatch module updates the time-of-use pricing strategy of the VPP operator based on the optimal electricity demand curve and the collaborative generation plan. The load aggregator and the energy aggregator use the updated time-of-use pricing strategy of the VPP operator to obtain the updated optimal electricity demand curve and the updated collaborative generation plan, respectively, until the updated collaborative generation plan, the updated optimal electricity demand curve, and the updated time-of-use pricing strategy of the VPP operator all meet the preset equilibrium conditions, thus obtaining the optimal collaborative operation and dispatch result. This design, by updating the time-of-use pricing strategy, the optimal electricity demand curve, and the collaborative generation plan to the preset equilibrium conditions, enables the strategies of each entity to gradually converge to the game equilibrium point, ensuring the stability and optimality of the dispatch result, and enabling the obtained optimal collaborative operation and dispatch result to achieve supply and demand balance and optimal benefits for multiple entities.

[0017] For the two-level game model in a regional virtual power plant collaborative operation system framework, where VPP operators are the leaders and load aggregators and energy aggregators are the followers, some optimized technical solutions include: The leader refers to the entity that first determines the upper-level decision variables in the two-level game model during the game decision-making process, and guides the load aggregator and energy aggregator to make subsequent response decisions based on the upper-level decision variables (i.e., time-of-use pricing strategy) made and announced by the leader (VPP operator) in the two-level game model, and then makes its own optimization decisions under the time-of-use pricing strategy (the entities are the load aggregator and energy aggregator). VPP operators, acting as the upper-level coordinating entity, establish a power purchase and sale interaction interface with the power grid and publish time-of-use pricing strategies to load aggregators and energy aggregators. Load aggregators, acting as the lower-level load-side response entities, establish connections with flexible load resources within the region and formulate load-side demand response results based on the time-of-use pricing strategies. Energy aggregators, acting as the lower-level energy-side response entities, establish connections with distributed power sources within the region and formulate energy-side supply response results based on the time-of-use pricing strategies. The VPP operators update their time-of-use pricing strategies based on the load-side demand response results and the energy-side supply response results, thus forming a two-tiered game-theoretic collaborative operation mechanism of upper-level pricing and lower-level response.

[0018] In the above technical solution, the specific content of the VPP operator in the regional virtual power plant collaborative operation system framework includes: According to VPP operators during the time period Price of electricity purchased from thermal power units VPP operators during the time period Price of electricity purchased from wind power VPP operators during the time period Price of electricity purchased from solar power and VPP operators during the time period The price of electricity sold to the load aggregator The objective revenue function that maximizes the revenue of VPP operators is obtained. The expression is: in, n Indicates the total number of scheduling periods. Indicates VPP operator during the time period Revenue from selling electricity to load aggregators, Indicates VPP operator during the time period The cost of purchasing electricity from energy aggregators Indicates VPP operator during the time period The costs of interacting with the power grid (buying electricity) or the revenue from selling electricity; VPP operators during the time period Revenue from selling electricity to load aggregators The expression is: in, Indicates the load aggregator during the time period Total electricity demand; VPP operators during the time period Cost of purchasing electricity from energy aggregators The expression is: in, Indicates the thermal power unit during the time period Power generation output, Indicates wind power during the time period Power generation output, Indicates the time period of photovoltaics The power generation output; VPP operators during the time period The cost of (purchasing) electricity or the revenue of (selling) electricity in relation to the power grid The expression is: in, , Indicates energy aggregators during the period Total power generation output; Indicates VPP operator during the time period The price of selling electricity to the grid. Indicates VPP operator during the time period The price at which electricity is purchased from the power grid; At the same time, VPP operators need to meet price constraints and power balance constraints: in, Indicates VPP operator during the time period Interaction power with the power grid; VPP operators, as leaders, through adjustments , , and Under the premise of ensuring power balance, we can guide the demand response on the load side and the coordinated power generation on the energy side, thereby maximizing the overall market benefits. The above design calculates electricity sales revenue, electricity purchase cost, and grid interaction cost, ensuring that VPP operators' pricing decisions align with market rules and economic logic, guaranteeing the rationality and feasibility of pricing, and providing an economic basis for the formulation of time-of-use pricing strategies.

[0019] In the above technical solution, the specific content of the load aggregator in the framework of the regional virtual power plant collaborative operation system includes: According to the load aggregator in the time period Total electricity demand and load aggregators during the time period Electricity prices after demand response Establish a price elasticity matrix M Linear relationship model: in, Indicates the load aggregator during the time period The change in load, Indicates the load aggregator during the time period The original base load, Indicates time period The change in electricity price Indicates the period before demand response. The electricity purchase price, M Represents the price elasticity matrix; in, This indicates the change in electricity price during time period 1 relative to time period 2. n The impact of load at that time Indicates the time period as n The impact of electricity price changes during time period 1 on load; The price elasticity coefficient represents the time period. n Electricity price changes over time periods n The above design reflects the self-elasticity and cross-elasticity of electricity prices through a price elasticity matrix, quantifies the linkage between electricity prices and load, makes load regulation more in line with actual electricity consumption characteristics, can characterize the impact of electricity price changes on load at different times, and improves the accuracy of demand response.

[0020] In the above technical solution, the specific content of the energy aggregator in the framework of the regional virtual power plant collaborative operation system includes: According to energy aggregators, thermal power units during the time period Power generation Wind power during the period Power generation Photovoltaics during time period Power generation Calculate the revenue of thermal power units, wind power revenue, and photovoltaic revenue; The revenue expression for thermal power units is: in, Indicates the thermal power unit during the time period Power generation output, This indicates the revenue of thermal power units. This represents the fuel cost coefficient. Indicates the cost of carbon emissions trading; The revenue expression for wind power is: in, This indicates the revenue from wind power. Represents a set of scenes. Representing a scene The probability, Indicates wind power during the time period Power generation output, Wind power depreciation residual value income is discounted to the dispatch period. The value after that, This is the converted value for wind power operation and maintenance costs. Penalty costs for wind power deviations; The revenue expression for photovoltaics is: in, Indicates the revenue from photovoltaics. Indicates the time period of photovoltaics Power generation output, The residual value of photovoltaic depreciation is discounted to the dispatch period. The value after that, This is the converted value of photovoltaic operation and maintenance costs. The design penalizes photovoltaic deviations by calculating the actual operating costs of various energy sources, making energy output decisions more in line with real-world scenarios and balancing power generation revenue with environmental costs.

[0021] The specific method for obtaining the preprocessed scheduling dataset in the above technical solution is as follows: The system collects load information of load-side resources managed by load aggregators in the framework of the regional virtual power plant collaborative operation system, including the collection of basic electricity consumption curves, adjustable capacity, response time constraints, and user comfort constraints of charging piles, residential and industrial loads. The system collects power generation information of energy-side resources managed by energy aggregators in the framework of the regional virtual power plant collaborative operation system, including collecting predicted output information of wind power and photovoltaic power, generating multiple typical scenarios and their corresponding probabilities, and collecting the operating cost coefficient, ramp rate, output upper and lower limits and carbon emission coefficient of thermal power units. Time-of-use electricity pricing for VPP operators connected to the power grid within the framework of a regional virtual power plant collaborative operation system. Time-of-use electricity pricing The collected data will be distributed according to the scheduled time period. Alignment and organization are performed to form a preprocessed scheduling dataset that includes load, renewable energy forecasts, and market prices. The above design, by standardizing the data collection scope and eliminating the time-series deviations of load, power generation, and energy information through unified data organization rules, can ensure the time-series alignment and standardization of multi-source data and improve data availability.

[0022] In the above technical solution, the demand response optimization model of the load aggregator is established. The specific process of using this model and the time-of-use pricing strategy of the VPP operator in a two-level game model to optimize the demand response for various types of loads and obtain the optimal electricity demand curve is as follows: The adjustable load resources in the preprocessed scheduling dataset are classified and aggregated to form a load-side resource set; Collect the basic power consumption curve (i.e., the original load data before demand response) of each type of load within the target scheduling period (the target scheduling period can be set to 24 hours), adjustable capacity parameters (including transferable load capacity and load reduction capacity), and user constraints (including the time period constraints during which the load is allowed to respond, and user comfort constraints that reflect the comfort of power consumption). The time period is obtained by weighting flexible load reduction indicators and economic efficiency indicators. User satisfaction The expression is: in, As a flexible load reduction indicator, The weighting coefficient for the flexible load reduction index. As an indicator of economic effectiveness, The weighting coefficients for economic efficiency indicators. For time period User satisfaction; Flexible load reduction indicators The calculation formula is: in, For time period The original base load, For time period The amount of load that can be shifted or reduced through demand response; Economic efficiency indicators The calculation formula is: in, The electricity purchase price before demand response. The electricity price sold by load aggregators after demand response; And the electricity sales price of load aggregators after demand response Satisfy the following expression: in, Indicates time period The amount of electricity price adjustment; Set the satisfaction constraints that must be met: ; in, The minimum satisfaction threshold; According to the price elasticity matrix M Based on the linear relationship model, the total electricity demand of the load aggregator in time period t after implementing demand response is calculated. for: Obtain the revenue function of the load aggregator The expression is: in, The weighting coefficients representing economic returns. The weighting coefficients representing user satisfaction. n Indicates the total number of scheduling periods. Indicates the load aggregator during the time period Electricity prices after demand response Indicates the load aggregator during the time period Total electricity demand Indicates VPP operator during the time period The price at which electricity is sold to load aggregators. Indicates time period User satisfaction; Revenue function of load aggregator The following constraints need to be met: Electricity sales price upper and lower limits constraints: ; in, For time period The upper limit of electricity sales price, For time period The lower limit of electricity sales price; Post-response load upper and lower limit constraints: ; in, This is the upper bound of the load after demand response. This is the lower bound of the load after demand response; Satisfaction constraints: ; Thus, a demand response optimization model for load aggregators is constructed; Based on the time-of-use pricing strategy of VPP operators in the aforementioned two-layer game model, in the time period The price of electricity sold to the load aggregator The load aggregator maximizes the revenue function With the goal of optimizing the demand response of various loads through a demand response optimization model, the optimal power demand curve is obtained. The above design, by constructing a load-side demand response model and adding user satisfaction constraints, combines flexible load reduction indicators, economic effectiveness indicators and user satisfaction, which can balance the economic benefits of load regulation with user experience, enhance user participation, and avoid excessive regulation affecting user electricity consumption.

[0023] In the above technical solution, an energy-side resource cooperation game model is established for energy aggregators. This model, using the time-of-use pricing strategy of VPP operators in a two-layer game model, optimizes the coordinated output of various energy resources to obtain the coordinated power generation plan. The specific process is as follows: This alliance comprises thermal power units, wind power units, and photovoltaic units that participate in collaborative optimization and competitive negotiation within energy aggregators. ,in, For thermal power units, For wind power, For photovoltaics; The participating parties form sub-alliances. , , ; thermal power units The profit expression is: in, Indicates the thermal power unit during the time period Power generation output, This indicates the revenue of thermal power units. This represents the fuel cost coefficient. Indicates the cost of carbon emissions trading; Carbon emission trading costs The calculation formula is: in, For carbon emission rights price, For carbon emissions, This refers to the upper limit of the permitted carbon emissions allocated within the target scheduling period; For transaction fees; wind power The profit expression is: in, This indicates the revenue from wind power. Represents a set of scenes. Representing a scene The probability, Indicates wind power during the time period Power generation output, Wind power depreciation residual value income is discounted to the dispatch period. The value after that, This is the converted value for wind power operation and maintenance costs. Penalty costs for wind power deviations; Photovoltaics The profit expression is: in, Indicates the revenue from photovoltaics. Indicates the time period of photovoltaics Power generation output, The residual value of photovoltaic depreciation is discounted to the dispatch period. The value after that, This is the converted value of photovoltaic operation and maintenance costs. Penalty costs for photovoltaic deviations; Energy aggregators maximize alliances Taking the total profit as the objective, the objective function is: + + This leads to the construction of a game model for energy aggregators' cooperation on energy-side resources; Based on the time-of-use pricing strategy of VPP operators in the aforementioned two-layer game model, in the time period Price of electricity purchased from thermal power units During the period Price of electricity purchased from wind power and during the time period Price of electricity purchased from solar power With the goal of maximizing total revenue, a collaborative power generation plan that maximizes the total revenue of the alliance is obtained by using an energy-side resource cooperation game model to optimize the coordinated output of thermal power units, wind power, and photovoltaic power. , , The above design forms a cooperative alliance between thermal power, wind power, and photovoltaic power, constructs an energy-side resource cooperation game model, optimizes output with the goal of maximizing overall benefits, leverages the complementary advantages of multiple energy sources, can maximize the collaborative benefits of multiple energy entities, and improve the overall energy dispatch.

[0024] The specific process for obtaining the optimal collaborative operation scheduling result in the above technical solution is as follows: VPP operators based on the optimal electricity demand curve and the aforementioned collaborative power generation plan ( , , ), and power balance constraints Calculate VPP operator during time period Interaction power with the power grid ; in, Indicates the load aggregator during the time period Total electricity demand Indicates the thermal power unit during the time period Power generation output, Indicates wind power during the time period Power generation output, Indicates the time period of photovoltaics The power generation output; This allows VPP operators to obtain information during specific time periods. Costs or benefits of interacting with the power grid Substitute into the target revenue function of the VPP operator To maximize the objective return function To achieve this, update the time-of-use pricing strategy of the VPP operator, publish the updated time-of-use pricing strategy to the load aggregator and energy aggregator, and iteratively solve the problem: in, Iteration j Time-of-use pricing strategy for VPP operators after +1 Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from thermal power units Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from wind power Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from solar power Iteration j The VPP operator after +1 time in the time period The price at which electricity is sold to load aggregators; Iteration j Electricity sales strategy of load aggregators after +1 Iteration j The load aggregator after +1 time is in the time period Electricity prices after demand response; Iteration j The collaborative power generation plan of energy aggregators after +1 time, Iteration j +1 times the thermal power units in the energy aggregator during the time period Power generation output, Iteration j Wind power in the energy aggregator after +1 time period Power generation output, Iteration j After +1, photovoltaics in the energy aggregator during the time period The power generation output; When the iterated collaborative generation plan, optimal power demand curve, and time-of-use pricing strategy all meet the preset equilibrium condition, that is, when the strategy changes of the collaborative generation plan, optimal power demand curve, and time-of-use pricing strategy are all less than the preset threshold in two adjacent iterations, the optimal collaborative operation scheduling result is obtained. The above design, by using the strategy change being less than the preset threshold as the preset equilibrium condition, can ensure that the scheduling result converges to the optimal stable state, thereby improving the practicality and reliability of the scheduling scheme.

[0025] Example 2 A regional virtual power plant collaborative operation and scheduling method based on two-level game theory, such as Figure 2As shown, an operational system framework including VPP operators, load aggregators, and energy aggregators is constructed. A scheduling dataset is obtained through load, generation, and electricity price information, and a two-layer game model is established. On the load side, the optimal electricity demand curve is obtained using a time-of-use pricing strategy through a demand response optimization model. On the energy side, a coordinated generation plan is obtained using a time-of-use pricing strategy through an energy-side resource cooperation game model. The time-of-use pricing strategy is updated based on the optimal electricity demand curve and the coordinated generation plan. The updated optimal electricity demand curve and the updated coordinated generation plan are obtained using the updated time-of-use pricing strategy until both meet the preset equilibrium conditions, resulting in the optimal coordinated operation and scheduling result.

[0026] The specific methods for collaborative operation and scheduling of regional virtual power plants include the following steps: Construct a framework for a regional virtual power plant collaborative operation system that includes VPP operators, load aggregators, and energy aggregators; The system collects load information of load-side resources managed by load aggregators, power generation information of energy-side resources managed by energy aggregators, and electricity price information of VPP operators within the framework of the regional virtual power plant collaborative operation system. The load information, power generation information, and electricity price information are preprocessed to obtain a preprocessed scheduling dataset. Based on the interaction relationships among VPP operators, load aggregators, and energy aggregators in the regional virtual power plant collaborative operation system framework, and the preprocessed scheduling dataset, a two-layer game model is established. Establish a demand response optimization model for load aggregators. Using the demand response optimization model, the time-of-use pricing strategy of VPP operators in the two-level game model is used to solve the demand response optimization problem for various types of loads and obtain the optimal power demand curve. Establish an energy-side resource cooperation game model for energy aggregators. Through this model, utilize the time-of-use pricing strategy of VPP operators in a two-layer game model to optimize the collaborative output of various energy resources and obtain a collaborative power generation plan. The VPP operator's time-of-use pricing strategy is updated based on the optimal electricity demand curve and the collaborative generation plan. The load aggregator and the energy aggregator use the updated VPP operator's time-of-use pricing strategy to obtain the updated optimal electricity demand curve and the updated collaborative generation plan, respectively, until the updated collaborative generation plan, the updated optimal electricity demand curve, and the updated VPP operator's time-of-use pricing strategy all meet the preset equilibrium conditions, thus obtaining the optimal collaborative operation and dispatch result.

[0027] Example 3 Taking a county-level virtual power plant as an example, the framework for a collaborative operation system for a county-level virtual power plant is constructed as follows: Load aggregators are responsible for the unified management of adjustable load resources such as charging piles, livestock loads, residential loads, and industrial loads within the county. After receiving the electricity sales price published by the VPP operator, the load aggregator adjusts various transferable and reduceable loads through the demand response optimization model to form an optimized optimal electricity demand curve, which is then fed back to the VPP operator.

[0028] Energy aggregators are responsible for coordinating the power generation resources of wind power, rural photovoltaic power, and thermal power units. Based on the electricity purchase prices for wind power, rural photovoltaic power, and thermal power units published by VPP operators, they optimize the power generation plan to obtain a coordinated power generation plan.

[0029] Through the county-level virtual power plant collaborative operation system framework, VPP operators can ensure efficient resource utilization while guaranteeing electricity demand, and achieve flexibility and stability in resource dispatching in the face of price fluctuations and changes in load demand.

[0030] Collect load-side and energy-side resource information: Collect basic electricity consumption curves for various types of loads, including residential loads, industrial loads, livestock loads, and charging pile loads. These basic electricity consumption curves reflect the electricity demand of different load types within the target scheduling cycle. Figure 3 As shown in the figure, this embodiment of the invention provides an initial load data diagram, which shows a typical intraday load fluctuation curve, reflecting the peak and trough of electricity consumption at different times, as well as the load-side regulation capability.

[0031] For the acquisition of new energy resources, the focus is on the power generation forecast information for wind power and photovoltaics. The output of wind and photovoltaic power is affected by weather and natural conditions, therefore, advance forecasting is necessary, and dispatch strategies should be adjusted based on the forecast results. For example... Figure 4 As shown in the figure, this embodiment of the invention provides a schematic diagram of wind power output under 10 scenarios, illustrating the changes in wind power output under different wind speeds and weather conditions; as Figure 5 As shown in the figure, this embodiment of the invention provides a schematic diagram of the photovoltaic power output of farmers under 10 scenarios, which shows the changes in the output power of farmers' photovoltaics under different wind speeds and weather conditions. Through predictive analysis of multiple scenarios, the fluctuation pattern of wind power can be grasped more accurately, and a basis for scheduling optimization can be provided.

[0032] Based on the constructed framework of the county-level virtual power plant collaborative operation system and the obtained load-side and energy-side basic data, a two-layer game model is established with VPP operators as leaders and load aggregators and energy aggregators as followers. The inner game is used to determine the optimal electricity demand on the load side and the optimal power generation output on the energy side, while the outer game is used to determine the electricity purchase price and electricity sales price between VPP operators and load aggregators and energy aggregators.

[0033] The inner game belongs to the quantitative decision-making layer, which is used to determine the physical quantity decision results of each participant under the given electricity price signal. This includes the electricity demand curve of the load aggregator and the power generation curve of the energy aggregator. The load aggregator executes demand response according to the electricity sales price given by the VPP operator, and the energy aggregator coordinates the power generation plans of wind power, rural photovoltaic and thermal power units according to the electricity purchase price given by the VPP operator, thereby forming the optimal electricity demand curve on the load side and the coordinated power generation plan on the energy side, respectively.

[0034] After obtaining the optimal electricity demand curve on the load side and the coordinated power generation plan on the energy side from the inner-layer game, the optimal settlement price for each internal participant is determined through the outer-layer game. Then, the time-of-use pricing strategy is used to influence the decisions of load aggregators and energy aggregators in the next round of the inner-layer game, thus forming a two-layer interactive mechanism.

[0035] In the iterative process of the two-layer game, when the strategies of the VPP operator, energy aggregator, and load aggregator all satisfy the preset equilibrium conditions in two adjacent iterations, the outer game is considered to have reached equilibrium, and the optimal scheduling result of the county-level virtual power plant within the target scheduling period (24 hours) is output. The result is as follows: Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 As shown, four comparative operating conditions are set up, where PV represents photovoltaic power, WT represents wind power, TG represents small gas turbine power, and Grid represents grid power purchase and sales. Case I is the baseline operating condition where both the load side and the energy side make independent decisions and do not consider user satisfaction or coordinated dispatch. Case II is the operating condition where only the load side participates in demand response optimization and the energy side does not perform coordinated generation optimization. Case II-1 is the operating condition where only the load side participates in demand response optimization and the energy side does not perform coordinated generation optimization, without considering user satisfaction. Case II-2 is the operating condition where only the load side participates in demand response optimization and the energy side does not perform coordinated generation optimization, considering user satisfaction. Case III is the operating condition where only the energy side performs coordinated generation optimization and the load side does not perform demand response optimization. Case IV is the joint optimization operating condition that simultaneously considers load-side demand response, energy-side coordinated generation, and VPP operator pricing decisions. Figure 6-11 A comparative analysis was conducted based on the four operating conditions (Case I, Case II, Case III, and Case IV) to characterize the differences in load response, power generation plans, electricity purchase and sale revenue, and overall operational performance of county-level virtual power plants under different dispatch strategies.

[0036] Example 4 A computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 2.

[0037] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0038] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0039] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0040] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0041] 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

[0042] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A regional virtual power plant collaborative operation and scheduling system based on a two-layer game theory approach, characterized in that, It includes: The system framework building module is used to build a regional virtual power plant collaborative operation system framework that includes VPP operators, load aggregators, and energy aggregators; The data acquisition and processing module is used to collect load information of load-side resources managed by load aggregators, power generation information of energy-side resources managed by energy aggregators, and electricity price information of VPP operators in the framework of regional virtual power plant collaborative operation system. The load information, power generation information and electricity price information are preprocessed to obtain the preprocessed scheduling dataset. The two-layer game model construction module is used to build a two-layer game model based on the interaction relationship between VPP operators, load aggregators and energy aggregators in the regional virtual power plant collaborative operation system framework, as well as the preprocessed scheduling dataset. The load-side response optimization module is used to establish a demand response optimization model for load aggregators. Through the demand response optimization model, the VPP operator's time-of-use pricing strategy in the two-level game model is used to optimize the demand response of various types of loads and obtain the optimal power demand curve. The energy-side collaborative optimization module is used to establish an energy-side resource cooperation game model for energy aggregators. Through the energy-side resource cooperation game model, the time-of-use pricing strategy of VPP operators in the two-level game model is used to optimize the collaborative output of various energy resources and obtain a collaborative power generation plan. The collaborative operation and dispatch module is used to update the time-of-use pricing strategy of the VPP operator based on the optimal power demand curve and the collaborative generation plan. The load aggregator and the energy aggregator use the updated time-of-use pricing strategy of the VPP operator to obtain the updated optimal power demand curve and the updated collaborative generation plan, respectively, until the updated collaborative generation plan, the updated optimal power demand curve and the updated time-of-use pricing strategy of the VPP operator all meet the preset equilibrium conditions, and the optimal collaborative operation and dispatch result is obtained.

2. The regional virtual power plant collaborative operation and scheduling system based on two-layer game theory according to claim 1, characterized in that: The specific content of the VPP operator in the aforementioned regional virtual power plant collaborative operation system framework includes: According to VPP operators during the time period Price of electricity purchased from thermal power units VPP operators during the time period Price of electricity purchased from wind power VPP operators during the time period Price of electricity purchased from solar power and VPP operators during the time period The price of electricity sold to the load aggregator The objective revenue function that maximizes the revenue of VPP operators is obtained. The expression is: in, n Indicates the total number of scheduling periods. Indicates VPP operator during the time period Revenue from selling electricity to load aggregators, Indicates VPP operator during the time period The cost of purchasing electricity from energy aggregators Indicates VPP operator during the time period The costs or benefits of interacting with the power grid; VPP operators during the time period Revenue from selling electricity to load aggregators The expression is: in, Indicates the load aggregator during the time period Total electricity demand; VPP operators during the time period Cost of purchasing electricity from energy aggregators The expression is: in, Indicates the thermal power unit during the time period Power generation output, Indicates wind power during the time period Power generation output, Indicates the time period of photovoltaics The power generation output; VPP operators during the time period Costs or benefits of interacting with the power grid The expression is: in, , Indicates that energy aggregators are in the period Total power generation output; Indicates VPP operator during the time period The price of selling electricity to the grid. Indicates VPP operator during the time period The price at which electricity is purchased from the power grid.

3. The regional virtual power plant collaborative operation and scheduling system based on two-layer game theory according to claim 1, characterized in that: The specific content of the load aggregator in the framework of the regional virtual power plant collaborative operation system includes: According to the load aggregator in the time period Total electricity demand and load aggregators during the time period Electricity prices after demand response Establish a price elasticity matrix M Linear relationship model: in, Indicates the load aggregator during the time period The change in load, Indicates the load aggregator during the time period The original base load, Indicates time period The change in electricity price Indicates the period before demand response. The electricity purchase price, M Represents the price elasticity matrix; in, This indicates the change in electricity price during time period 1 relative to time period 2. n The impact of load at that time Indicates the time period as n The impact of electricity price changes during time period 1 on load. Indicates the time period as n Electricity price changes over time periods n The impact of load at that time.

4. The regional virtual power plant collaborative operation and scheduling system based on two-layer game theory according to claim 1, characterized in that: The specific content of the energy aggregator in the framework of the regional virtual power plant collaborative operation system includes: According to energy aggregators, thermal power units during the time period Power generation Wind power during the period Power generation Photovoltaics during time period Power generation Calculate the revenue of thermal power units, wind power revenue, and photovoltaic revenue; The benefit expression for thermal power units is: in, Indicates the thermal power unit during the time period Power generation output, This indicates the revenue of thermal power units. This represents the fuel cost coefficient. Indicates the cost of carbon emissions trading; The revenue expression for wind power is: in, This indicates the revenue from wind power. Represents a set of scenes. Representing a scene The probability, Indicates wind power during the time period Power generation output, Wind power depreciation residual value income is discounted to the dispatch period. The value after that, This is the converted value for wind power operation and maintenance costs. Penalty costs for wind power deviations; The revenue expression for photovoltaics is: in, Indicates the revenue from photovoltaics. Indicates the time period of photovoltaics Power generation output, The residual value of photovoltaic depreciation is discounted to the dispatch period. The value after that, This is the converted value of photovoltaic operation and maintenance costs. Penalty costs for photovoltaic deviations.

5. The regional virtual power plant collaborative operation and scheduling system based on two-layer game theory according to claim 1, characterized in that: The specific method for obtaining the preprocessed scheduling dataset is as follows: The system collects load information of load-side resources managed by load aggregators in the framework of the regional virtual power plant collaborative operation system, including the collection of basic electricity consumption curves, adjustable capacity, response time constraints, and user comfort constraints of charging piles, residential and industrial loads. The system collects power generation information of energy-side resources managed by energy aggregators in the framework of the regional virtual power plant collaborative operation system, including collecting predicted output information of wind power and photovoltaic power, generating multiple typical scenarios and their corresponding probabilities, and collecting the operating cost coefficient, ramp rate, output upper and lower limits and carbon emission coefficient of thermal power units. Time-of-use electricity pricing for VPP operators connected to the power grid within the framework of a regional virtual power plant collaborative operation system. Time-of-use electricity pricing The collected data will be distributed according to the scheduled time period. The data is aligned and organized to form a preprocessed scheduling dataset that includes load, new energy forecasts, and market prices.

6. The regional virtual power plant collaborative operation and scheduling system based on two-layer game theory according to claim 3, characterized in that: The specific process of establishing a demand response optimization model for load aggregators, and then using the time-of-use pricing strategy of VPP operators in a two-level game model to optimize the demand response for various types of loads and obtain the optimal electricity demand curve is as follows: The adjustable load resources in the preprocessed scheduling dataset are classified and aggregated to form a load-side resource set; Collect the basic power consumption curve, adjustable capacity parameters, and user constraints for each type of load within the target scheduling cycle; The time period is obtained by weighting flexible load reduction indicators and economic efficiency indicators. User satisfaction The expression is: in, As a flexible load reduction indicator, The weighting coefficient for the flexible load reduction index. As an indicator of economic efficiency, The weighting coefficients for economic efficiency indicators. For time period User satisfaction; Flexible load reduction indicators The calculation formula is: in, For time period The original base load, For time period The amount of load that can be shifted or reduced through demand response; Economic efficiency indicators The calculation formula is: in, The electricity purchase price before demand response. The electricity price sold by load aggregators after demand response; And the electricity sales price of load aggregators after demand response Satisfy the following expression: in, Indicates time period The amount of electricity price adjustment; Set the following satisfaction constraints that must be met: ; in, The minimum satisfaction threshold; According to the price elasticity matrix M Based on the linear relationship model, the total electricity demand of the load aggregator in time period t after implementing demand response is calculated. for: Obtain the revenue function of the load aggregator The expression is: in, The weighting coefficients representing economic returns. The weighting coefficients representing user satisfaction. n Indicates the total number of scheduling periods. Indicates the load aggregator during the time period Electricity prices after demand response Indicates the load aggregator during the time period Total electricity demand Indicates VPP operator during the time period The price at which electricity is sold to load aggregators. Indicates time period User satisfaction; Revenue function of load aggregator The following constraints need to be met: Electricity sales price upper and lower limits constraints: ; in, For time period The upper limit of electricity sales price, For time period The lower limit of electricity sales price; Post-response load upper and lower limit constraints: ; in, This is the upper bound of the load after demand response. This is the lower bound of the load after demand response; Satisfaction constraints: ; Thus, a demand response optimization model for load aggregators is constructed; Based on the time-of-use pricing strategy of VPP operators in the aforementioned two-layer game model, in the time period The price of electricity sold to the load aggregator The load aggregator maximizes the revenue function With the goal of optimizing the demand response of various loads through a demand response optimization model, the optimal electricity demand curve is obtained.

7. The regional virtual power plant collaborative operation and scheduling system based on two-layer game theory according to claim 4, characterized in that: A game theory model for energy aggregators' energy-side resource cooperation is established. This model, using the time-of-use pricing strategy of VPP operators in a two-layer game theory model, optimizes the coordinated output of various energy resources to obtain the coordinated power generation plan. The specific process is as follows: This alliance comprises thermal power units, wind power units, and photovoltaic units that participate in collaborative optimization and competitive negotiation within energy aggregators. ,in, For thermal power units, For wind power, For photovoltaics; The participating parties form sub-alliances. , , ; thermal power units The profit expression is: in, Indicates the thermal power unit during the time period Power generation output, This indicates the revenue of thermal power units. This represents the fuel cost coefficient. Indicates the cost of carbon emissions trading; wind power The profit expression is: in, This indicates the revenue from wind power. Represents a set of scenes. Representing a scene The probability, Indicates wind power during the time period Power generation output, Wind power depreciation residual value income is discounted to the dispatch period. The value after that, This is the converted value for wind power operation and maintenance costs. Penalty costs for wind power deviations; Photovoltaics The profit expression is: in, Indicates the revenue from photovoltaics. Indicates the time period of photovoltaics Power generation output, The residual value of photovoltaic depreciation is discounted to the dispatch period. The value after that, This is the converted value of photovoltaic operation and maintenance costs. Penalty costs for photovoltaic deviations; Energy aggregators maximize alliances Taking the total profit as the objective, the objective function is: + + This leads to the construction of a game model for energy aggregators' cooperation on energy-side resources; Based on the time-of-use pricing strategy of VPP operators in the aforementioned two-layer game model, in the time period Price of electricity purchased from thermal power units During the period Price of electricity purchased from wind power and during the time period Price of electricity purchased from solar power With the goal of maximizing total revenue, a collaborative power generation plan that maximizes the total revenue of the alliance is obtained by using an energy-side resource cooperation game model to optimize the coordinated output of thermal power units, wind power, and photovoltaic power. , , ).

8. The regional virtual power plant collaborative operation and scheduling system based on two-layer game theory according to claim 6 or 7, characterized in that: The specific process for obtaining the optimal collaborative operation scheduling result is as follows: VPP operators based on the optimal electricity demand curve and the aforementioned collaborative power generation plan ( , , ), and power balance constraints Calculate VPP operator during time period Interaction power with the power grid ; in, Indicates the load aggregator during the time period Total electricity demand Indicates the thermal power unit during the time period Power generation output, Indicates wind power during the time period Power generation output, Indicates the time period of photovoltaics The power generation output; This allows VPP operators to obtain information during specific time periods. Costs or benefits of interacting with the power grid Substitute into the target revenue function of the VPP operator To maximize the objective return function To achieve this, update the time-of-use pricing strategy of the VPP operator, publish the updated time-of-use pricing strategy to the load aggregator and energy aggregator, and iteratively solve the problem: in, Iteration j Time-of-use pricing strategy for VPP operators after +1 Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from thermal power units Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from wind power Iteration j The VPP operator after +1 time in the time period The price of electricity purchased from solar power Iteration j The VPP operator after +1 time in the time period The price at which electricity is sold to load aggregators; Iteration j Electricity sales strategy of load aggregators after +1 Iteration j The load aggregator after +1 time is in the time period Electricity prices after demand response; Iteration j The collaborative power generation plan of energy aggregators after +1 time, Iteration j +1 times the thermal power units in the energy aggregator during the time period Power generation output, Iteration j Wind power in the energy aggregator after +1 time period Power generation output, Iteration j After +1, photovoltaics in the energy aggregator during the time period The power generation output; When the iterated collaborative generation plan, optimal power demand curve, and time-of-use pricing strategy all meet the preset equilibrium conditions, that is, when the strategy changes of the collaborative generation plan, optimal power demand curve, and time-of-use pricing strategy are all less than the preset threshold in two adjacent iterations, the optimal collaborative operation and scheduling result is obtained.

9. A regional virtual power plant collaborative operation and scheduling method based on two-level game theory, characterized in that, It includes the following steps: Construct a framework for a regional virtual power plant collaborative operation system that includes VPP operators, load aggregators, and energy aggregators; The system collects load information of load-side resources managed by load aggregators, power generation information of energy-side resources managed by energy aggregators, and electricity price information of VPP operators within the framework of the regional virtual power plant collaborative operation system. The load information, power generation information, and electricity price information are preprocessed to obtain a preprocessed scheduling dataset. Based on the interaction relationships among VPP operators, load aggregators, and energy aggregators in the regional virtual power plant collaborative operation system framework, and the preprocessed scheduling dataset, a two-layer game model is established. Establish a demand response optimization model for load aggregators. Using the demand response optimization model, the time-of-use pricing strategy of VPP operators in the two-level game model is used to solve the demand response optimization problem for various types of loads and obtain the optimal power demand curve. Establish an energy-side resource cooperation game model for energy aggregators. Through this model, utilize the time-of-use pricing strategy of VPP operators in a two-layer game model to optimize the collaborative output of various energy resources and obtain a collaborative power generation plan. The VPP operator's time-of-use pricing strategy is updated based on the optimal electricity demand curve and the collaborative generation plan. The load aggregator and the energy aggregator use the updated VPP operator's time-of-use pricing strategy to obtain the updated optimal electricity demand curve and the updated collaborative generation plan, respectively, until the updated collaborative generation plan, the updated optimal electricity demand curve, and the updated VPP operator's time-of-use pricing strategy all meet the preset equilibrium conditions, thus obtaining the optimal collaborative operation and dispatch result.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 9.

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