Cooperative transaction method, system and device for multiple virtual power plants and readable medium

Abstract

The invention relates to a multi-virtual power plant collaborative transaction method, system and device and a readable medium, and the method comprises the steps: constructing a multi-energy coupling model containing a gas turbine, an energy storage device, biomass power generation and a new energy vehicle, and generating a multi-source data set to quantify the carbon emission cost and charge and discharge constraints; a wind and light output joint probability distribution model is generated based on an F-vine Copula distribution function, and a severe scene set is screened through random sampling and scene closeness evaluation; designing a renewable energy source-load correlation coefficient and a green energy consumption compensation mechanism, and correcting a virtual power plant scheduling objective function; establishing a double-layer dynamic transaction mechanism, and iteratively optimizing an upper-layer dynamic electricity price and a lower-layer multi-source transaction electricity quantity; a multi-stage robust optimization algorithm is adopted to process the uncertainty of wind, light and electric vehicles, and a transaction strategy among multiple virtual power plants in a severe scene is generated, so that the problems of low-carbon excitation deficiency, insufficient multi-source uncertainty coping and the like are solved, and unification of economy, low-carbon property and robustness of collaborative scheduling transaction of the multiple virtual power plants is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative transactions of virtual power plants, and in particular to a collaborative transaction method, system, device and readable medium for multiple virtual power plants. Background Art

[0002] With the rapid development of the global economy, electric energy has become a vital energy source due to its clean and efficient nature. To improve energy efficiency and integrate regional resources, virtual power plants (VPPs) have become a key research topic. Research into the optimal scheduling of VPPs can improve energy efficiency, achieve system economics, and achieve low-carbon operation. VPPs enable clustered aggregation and optimized control of distributed resources such as distributed power sources, energy storage, and controllable loads, allowing them to participate in grid scheduling and electricity market transactions as a whole. By integrating and managing distributed power sources, loads, and energy storage facilities within a park, they can improve comprehensive energy utilization efficiency and promote the local consumption of renewable energy. In recent years, extensive research has been conducted on the optimized operation of VPPs.

[0003] Existing research on VPPs optimizes scheduling for greater economic returns and value, solving for optimal economic returns through objective functions and operational constraints. Furthermore, research on optimal strategies for interaction between VPPs and distribution networks can enhance the flexibility of the VPP itself. Other studies have considered shared energy storage, enabling flexible energy management through storage regulation. However, VPP management limited to a single region cannot effectively integrate regional resources. Therefore, research on multi-region virtual power plants (MVPPs) has shown that multi-regional research can better improve energy efficiency. Due to the global growth of electric vehicles, optimizing VPP scheduling for EV integration has also become a research area.

[0004] Regarding renewable energy uncertainty, existing research addresses the uncertainty of wind turbine and photovoltaic output by generating a basic set of wind and solar scenarios using Latin hypercube sampling, then employing a modified K-means algorithm to reduce these scenarios and establish a stochastic optimization model. Building on this, research has also addressed the uncertainty of VPP reserves and other types of uncertainty, proposing optimal scheduling solutions under uncertainties such as market prices, renewable energy generation, and reserve deployment. Furthermore, recent research has proposed robust optimization scheduling methods for MVPPs, addressing the uncertain parameters of different stakeholder behaviors. Regarding solution methods, data-driven and risk-constrained approaches are more common. Decision-making theory, however, offers a more promising application for multiple uncertainties.

[0005] Regarding virtual power plant (VPP) market trading mechanisms, research has focused on electricity pricing mechanisms. This research employs multi-agent uncertainty methods and deep learning approaches in conjunction with power markets to achieve optimal pricing strategies. Building on this, some studies consider MVPP aggregation risks and explore how VPPs can manage financial risk through electricity trading across different markets. To further explore low-carbon economic scheduling of VPPs from a market perspective, some studies have proposed VPP optimization scheduling methods that consider carbon emission costs, building on this with flexible carbon emission optimization methods. Low-carbon scheduling is also being pursued by integrating carbon capture equipment. Furthermore, research has been conducted on VPP optimization scheduling related to carbon green certificates. Furthermore, research has addressed potential privacy and security issues in traditional centralized scheduling processes and proposed a decentralized VPP market trading mechanism based on blockchain technology, ensuring both economic efficiency and transaction security. Other research has explored a two-way market trading method, considering the bidirectional flow of electricity and natural gas. By developing different scenarios, optimization models that consider dual energy markets are being developed to maximize VPP profits.

[0006] According to the aforementioned research, conventional virtual power plant scheduling in existing technologies focuses primarily on optimizing the economic efficiency of a single region, lacking the coordinated quantification of cross-regional carbon trading costs and green energy absorption capacity. On the one hand, the strong randomness of wind and solar renewable energy leads to output forecast deviations, and existing scenario generation methods struggle to accurately capture the impact of extreme scenarios on trading strategies, resulting in insufficient robustness in scheduling schemes. On the other hand, the green energy absorption incentive mechanism and the spatiotemporal matching of load demand have yet to be effectively linked, leading to a disconnect between compensation incentives and low-carbon goals. Furthermore, the dynamic electricity price interaction and transaction volume feedback mechanisms between multiple virtual power plants lack iterative optimization capabilities, making it difficult to balance economic benefits with system stability. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a collaborative trading method, system, equipment and readable medium for multiple virtual power plants to achieve the coordinated quantification of carbon metering and green energy consumption rewards.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: A collaborative transaction method for multiple virtual power plants, characterized by comprising the following steps: S1: constructing a low-carbon operation framework of a virtual power plant containing a multi-energy coupling model based on the acquired multi-source historical data, and processing the multi-source raw data based on the low-carbon operation framework of the virtual power plant to generate a multi-energy coupling data set; S2: Perform joint probability modeling and sampling sorting on the acquired historical data sets of wind turbine and photovoltaic output, evaluate the historical scenario sets of the historical data sets of wind turbine and photovoltaic output, and generate a typical scenario set containing a severe scenario set and an ideal scenario set; S3: Calculating the renewable energy-load correlation coefficient and the compensation amount for the multi-energy coupling data set and the typical scenario set to generate a green energy consumption compensation amount; S4: establishing a multi-agent transaction mechanism based on the multi-energy coupling data set, the typical scenario set, and the green energy consumption compensation amount, and utilizing the multi-agent transaction mechanism to iteratively optimize the purchase and sale electricity price from the upper layer and the multi-source transaction current from the lower layer, thereby generating a final optimized dynamic purchase and sale electricity price and transaction current feedback signal; S5: Based on a multi-level robust optimization solution algorithm, robust scheduling processing is performed on the typical scenario set, the transaction current feedback signal and the charging and discharging power distribution set of new energy vehicles in the multi-energy coupling data set to generate cross-regional transaction strategies and equipment output plans under harsh scenarios. The cross-regional transaction strategies are used to guide the coordinated scheduling of multiple virtual power plants.

[0009] In order to solve the above technical problems, other technical solutions adopted by the present invention are: A collaborative trading system for multiple virtual power plants, comprising: A virtual power plant framework construction module is used to construct a low-carbon operation framework of the virtual power plant containing a multi-energy coupling model based on the acquired multi-source historical data, and to process the multi-source raw data based on the low-carbon operation framework of the virtual power plant to generate a multi-energy coupling data set; The multi-source historical data includes gas turbine historical operating data, energy storage device charge and discharge records, biomass fuel parameters, and new energy vehicle charging logs; the multi-energy coupling data set includes gas turbine output power, energy storage device charge and discharge constraint relationships, biomass power generation equipment power generation and carbon emission reduction, and new energy vehicle charge and discharge power distribution sets; The severe-ideal scenario generation module is used to perform joint probability modeling and sampling sorting on the acquired historical data sets of wind turbine and photovoltaic output, evaluate the historical scenario sets of the historical data sets of wind turbine and photovoltaic output, and generate a typical scenario set containing a severe scenario set and an ideal scenario set; An energy-load association module, configured to calculate the renewable energy-load association coefficient and the compensation amount for the multi-energy coupling data set and the typical scenario set, and generate a green energy consumption compensation amount; a trading mechanism construction module, configured to process the multi-energy coupling data set, the typical scenario set, and the green energy consumption compensation amount, establish a multi-agent trading mechanism, gradually iteratively optimize the purchase and sale electricity price from the upper layer and the multi-source transaction current from the lower layer, and generate a final optimized dynamic purchase and sale electricity price and transaction current feedback signal; The robust optimization solution module is used to perform robust scheduling processing on the typical scenario set, the transaction current feedback signal and the charging and discharging power distribution set of new energy vehicles in the multi-energy coupling data set based on a multi-level robust optimization solution algorithm, and generate cross-regional transaction strategies and equipment output plans under harsh scenarios. The cross-regional transaction strategies are used to guide the coordinated scheduling of multiple virtual power plants.

[0010] In order to solve the above technical problems, other technical solutions adopted by the present invention are: A device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the collaborative trading method of multiple virtual power plants that combines carbon metering with green energy consumption incentives.

[0011] In order to solve the above technical problems, other technical solutions adopted by the present invention are: A storage medium having a computer program stored thereon, which, when executed by a processor, implements a collaborative trading method for multiple virtual power plants that combines carbon metering with green energy consumption incentives.

[0012] The beneficial effects of the present invention are as follows: the present invention provides a collaborative trading method, system, device and readable medium for multiple virtual power plants, which constructs a low-carbon operation framework for virtual power plants containing a multi-energy coupling model and processes multi-source data, comprehensively integrates multi-source information such as gas turbines, energy storage, biomass and new energy vehicles, generates a multi-energy coupling data set, and lays a solid data foundation for subsequent optimization processing. Joint probability modeling, sampling sorting and scenario evaluation are performed on historical data of wind turbine and photovoltaic output to effectively generate a typical scenario set containing severe and ideal scenarios, fully consider the uncertainty of renewable energy output, and improve the robustness of the scheduling scheme. The renewable energy-load correlation coefficient and the green energy consumption compensation amount are calculated to generate low-carbon incentive parameters, establish a green energy consumption reward mechanism that matches the load demand in time and space, and enhance the correlation between compensation incentives and low-carbon goals. Based on the multi-energy coupling data set, typical scenario set and low-carbon incentive parameters, an MVPP multi-agent trading mechanism is constructed to achieve iterative optimization of dynamic electricity price interaction and transaction power feedback, balancing economic benefits and system stability. Finally, a multi-level robust optimization solution algorithm is used for robust scheduling processing to generate cross-regional trading strategies and equipment output plans under harsh scenarios, effectively guiding the coordinated scheduling of multiple virtual power plants, significantly improving the flexibility, reliability and environmental friendliness of the power system in the context of vehicle-grid integration and renewable energy uncertainty, effectively solving the problems existing in traditional virtual power plant scheduling, and realizing the coordinated quantitative optimization of cross-regional carbon trading costs and green energy absorption capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of a multi-virtual power plant collaborative trading method combining carbon metering and green energy consumption incentives provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of the MVPP multi-agent transaction mechanism provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of the vehicle-network integration uncertainty risk management model provided by an embodiment of the present invention; Figure 4 A schematic diagram of a process for generating green energy consumption compensation amounts according to an embodiment of the present invention; Figure 5 A schematic diagram of the structure of a multi-virtual power plant collaborative trading system combining carbon metering and green energy consumption incentives, provided in another embodiment of the present invention; Description of labels: 600. A multi-virtual power plant collaborative trading system combining carbon measurement and green energy consumption incentives; 610. VPP framework construction module; 620. Severe-ideal scenario generation module; 630. Energy-compliance correlation module; 640. Trading mechanism construction module; 650. Robust optimization solution module. DETAILED DESCRIPTION

[0014] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0015] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate preferred embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0017] In one embodiment, Figure 1 As shown, a multi-virtual power plant collaborative trading method combining carbon metering and green energy consumption incentives is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps: S1: Based on the acquired multi-source historical data, a virtual power plant (VPP) low-carbon operation framework containing a multi-energy coupling model is constructed, and the multi-source original data is processed based on the virtual power plant (VPP) low-carbon operation framework to generate a multi-energy coupling data set.

[0018] Specifically, multi-source historical data includes gas turbine historical operation data, energy storage equipment charging and discharging records, biomass fuel parameters and new energy vehicle charging logs; multi-energy coupling data sets include gas turbine output power, energy storage equipment charging and discharging constraint relationships, biomass power generation equipment power generation and carbon emission reduction, and new energy vehicle charging and discharging power distribution sets.

[0019] Specifically, the system collects multi-source historical data generated by various types of equipment and business processes related to virtual power plants, specifically covering historical operating data of gas turbines, including detailed parameters such as their output power, fuel consumption, operating time, number of starts and stops at different times and under different operating conditions; energy storage equipment charging and discharging records, such as the charging power, discharge power, charging and discharging time, state of charge (SOC) change curve of battery energy storage, and related charging and discharging data of supercapacitor energy storage; biomass fuel parameters, involving fuel type, calorific value, supply frequency, unit energy consumption, etc.; new energy vehicle charging logs, including information such as the charging power, charging time, and charging frequency of new energy vehicles connected to charging piles in different time periods.

[0020] After completing data collection, the system builds a low-carbon operating framework based on the acquired multi-source historical data, which comprehensively reflects the mutual conversion, transmission, and utilization of various energy sources within the virtual power plant. This framework integrates the operating characteristics of different energy units, such as gas turbines, energy storage equipment, biomass power generation equipment, and new energy vehicles. By establishing corresponding mathematical models to describe the energy flow and interrelationships between these energy units, such as the gas turbine's electric power and thermal power conversion efficiency model, the energy storage equipment's residual power relationship model, the biomass power generation equipment's power generation calculation model, and the interaction model between the charging and discharging power of new energy vehicles and the grid load, it forms a multi-energy coupling model, providing the infrastructure support for subsequent data processing and analysis.

[0021] Specifically, the system cleans source historical data, removing outliers, erroneous data, and duplicates to ensure data accuracy and reliability. The cleaned data is then converted and integrated based on the physical relationships and energy conversion laws of each energy unit in the multi-energy coupling model. For example, the system calculates the output power of a gas turbine based on its operating data and determines its power output range based on relevant constraints. It analyzes the charge and discharge records of energy storage devices to identify their charge and discharge constraints, including upper and lower limits for charging power, and a reasonable range for state of charge (SOC). It calculates the power generation capacity and corresponding carbon emission reductions of biomass power generation equipment based on biomass fuel parameters. This carbon emission reduction is calculated based on standards and formulas related to carbon emission reductions resulting from replacing traditional fossil fuels with biomass. Furthermore, it collects and analyzes new energy vehicle charging logs to generate a charge and discharge power distribution set, reflecting the distribution of new energy vehicle charging power over different time periods. Ultimately, this processed data is integrated into a multi-energy coupling data set, providing a foundation for subsequent analysis and optimization.

[0022] S2: Perform joint probability modeling and sampling sorting on the acquired historical data sets of wind turbine and photovoltaic output, evaluate and process the historical scenario sets of the historical data sets of wind turbine and photovoltaic output, and generate a typical scenario set containing a severe scenario set and an ideal scenario set.

[0023] Specifically, the system collects long-term historical output data from wind farms and photovoltaic power plants. This data includes wind turbine power generation data at different wind speeds, seasons, and time periods, as well as photovoltaic panel power generation data under varying light intensities, temperatures, and seasons. This ensures that the data covers a sufficient range of operating conditions and meteorological variations to fully reflect the stochastic and volatile nature of wind turbine and photovoltaic output. Furthermore, the system employs an appropriate joint probability distribution model to model the historical wind and photovoltaic output data sets. Considering the potential correlation between wind and photovoltaic output, a multivariate probability distribution model that can reflect this relationship, such as a joint probability distribution model based on a Copula function, is selected. Statistical analysis methods are used to determine the model parameters, ensuring that they accurately describe the joint probability characteristics of wind and photovoltaic output. Based on this, Monte Carlo sampling and other methods are used to sample the joint probability distribution model, generating a large number of sample wind and photovoltaic output scenarios. These sample scenarios are then ranked according to specific criteria, such as output size and probability of occurrence, to facilitate subsequent scenario evaluation and screening.

[0024] Finally, the system comprehensively evaluates the historical scenario set formed by the historical datasets of wind turbine and photovoltaic output. First, the system combines historical meteorological data with power system operation data to analyze the meteorological conditions and grid load characteristics corresponding to each scenario in the historical scenario set, such as wind speed, sunlight intensity, and temperature. This analysis identifies scenarios that are considered adverse and ideal. For example, extreme low temperatures limiting wind turbine output and continuous rainy weather causing a significant drop in photovoltaic output are adverse scenarios, while stable wind speeds, sufficient sunlight, and stable grid load demand are ideal scenarios. Furthermore, based on a set of evaluation indicators, such as the frequency of occurrence and the degree of impact on the virtual power plant's operating costs and reliability, the system quantitatively evaluates and screens the various scenarios in the historical scenario set. Finally, representative adverse and ideal scenarios are selected from the historical scenario set to form a representative scenario set containing both adverse and ideal scenarios. This representative scenario set covers the typical characteristics of wind turbine and photovoltaic output to a certain extent, providing basic scenario data support for subsequent virtual power plant scheduling optimization and trading strategy formulation.

[0025] S3: Calculate the renewable energy-load correlation coefficient and compensation amount for the multi-energy coupling data set and typical scenario set to generate the green energy consumption compensation amount.

[0026] Specifically, the system takes a multi-energy coupling data set and a set of typical scenarios as input to calculate the renewable energy-load correlation coefficient. By analyzing the temporal and spatial correlation between renewable energy output and load demand, the system uses methods such as the Pearson correlation coefficient to calculate the correlation coefficient and quantify the degree of coupling between the two. Simultaneously, based on green energy consumption incentive policies and market mechanisms, the system calculates the green energy consumption compensation amount, taking into account factors such as renewable energy power generation, load consumption, and carbon emission reduction benefits. This compensation amount is intended to incentivize virtual power plants to increase renewable energy consumption and achieve low-carbon operation goals. After these calculations, the system generates a green energy consumption compensation amount that includes the renewable energy-load correlation coefficient, providing key data support for the subsequent establishment of trading mechanisms.

[0027] S4: Based on the multi-energy coupling data set, typical scenario set and low-carbon incentive parameters, an MVPP multi-agent trading mechanism is established to gradually iteratively optimize the purchase and sale electricity prices from the upper layer and the multi-source trading currents from the lower layer, and generate the final optimized dynamic purchase and sale electricity prices and trading current feedback signals.

[0028] Specifically, this mechanism involves multiple entities, including distributed energy resources within the virtual power plant (VPP), such as gas turbines, energy storage equipment, biomass power generation equipment, and new energy vehicles, as well as external power grids and market participants. By constructing a multi-objective optimization model encompassing economic efficiency, low carbon efficiency, and system stability, with electricity purchase and sales prices and multi-source transaction currents as optimization variables, an iterative optimization algorithm is employed to gradually optimize the upper-level electricity purchase and sales prices and the lower-level multi-source transaction currents. During the optimization process, the trading strategy is continuously adjusted, taking into account factors such as device characteristics in the multi-energy coupling data set, uncertainties in typical scenarios, and correlation coefficients and compensation amounts in low-carbon incentive parameters, to achieve a balance between economic benefits and system stability for the VPP. Ultimately, optimized dynamic electricity purchase and sales prices and transaction current feedback signals are generated, providing a basis for real-time trading decisions for the VPP.

[0029] S5: Based on a multi-level robust optimization solution algorithm, robust scheduling processing is performed on the charging and discharging power distribution set of new energy vehicles in the typical scenario set, trading current feedback signal and multi-energy coupling data set to generate cross-regional trading strategies and equipment output plans under harsh scenarios. The cross-regional trading strategy is used to guide the coordinated scheduling of multiple virtual power plants.

[0030] Specifically, if Figure 2As shown, the multi-level robust optimization algorithm can effectively address multiple uncertainties in the system, including fluctuations in renewable energy output, changes in load demand, and uncertainties in the charging and discharging behavior of new energy vehicles. During the robust scheduling process, system operation is optimized for harsh scenarios, generating cross-regional trading strategies and equipment output plans. These cross-regional trading strategies guide the coordinated scheduling of multiple virtual power plants, enabling optimized allocation of energy resources across a wider range and improving system flexibility and reliability. Furthermore, the equipment output plan specifies the output arrangements for each distributed energy resource in different scenarios, ensuring that virtual power plants can maintain stable participation in grid scheduling and market transactions while meeting low-carbon operation goals.

[0031] In summary, the present invention provides a multi-virtual power plant collaborative trading method that combines carbon metering with green energy consumption incentives. By constructing a low-carbon operation framework for virtual power plants containing a multi-energy coupling model and processing multi-source data, it comprehensively integrates multi-source information such as gas turbines, energy storage, biomass and new energy vehicles to generate a multi-energy coupling data set, laying a solid data foundation for subsequent optimization processing. Joint probability modeling, sampling sorting and scenario evaluation are performed on historical data of wind turbine and photovoltaic output to effectively generate a typical scenario set containing severe and ideal scenarios, fully considering the uncertainty of renewable energy output and improving the robustness of the scheduling scheme. The renewable energy-load correlation coefficient and the green energy consumption compensation amount are calculated to generate low-carbon incentive parameters, establish a green energy consumption incentive mechanism that matches the load demand in time and space, and enhance the correlation between compensation incentives and low-carbon goals. Based on the multi-energy coupling data set, typical scenario set and low-carbon incentive parameters, an MVPP multi-subject trading mechanism is constructed to achieve iterative optimization of dynamic electricity price interaction and transaction power feedback, balancing economic benefits and system stability. Finally, a multi-level robust optimization solution algorithm is used for robust scheduling processing to generate cross-regional trading strategies and equipment output plans under harsh scenarios, effectively guiding the coordinated scheduling of multiple virtual power plants, significantly improving the flexibility, reliability and environmental friendliness of the power system in the context of vehicle-grid integration and renewable energy uncertainty, effectively solving the problems existing in traditional virtual power plant scheduling, and realizing the coordinated quantitative optimization of cross-regional carbon trading costs and green energy absorption capacity.

[0032] In one embodiment, a virtual power plant (VPP) low-carbon operation framework includes an electric power and thermal power conversion efficiency model, a surplus power relationship model, and a power generation power calculation model. Based on the virtual power plant (VPP) low-carbon operation framework, multi-source raw data is processed to generate a multi-energy coupling data set, including: S11: Calculating the gas turbine historical operating data of the multi-source historical data based on the electric power and thermal power conversion efficiency model, constraining the upper and lower limits of the operating power and the ramp rate, and generating the gas turbine output power.

[0033] Specifically, a model for converting electrical power to thermal power was established based on the characteristics and operating principles of gas turbines. This model comprehensively considers factors such as the thermal efficiency, power generation efficiency, and fuel calorific value of the gas turbine. Using experimental data or manufacturer-provided performance curves, it determines the conversion relationship between the electrical power and thermal power of the gas turbine under different operating conditions. The output power of a gas turbine (GT) can be expressed as follows: ; in: denote the electrical power and thermal power of the gas turbine respectively; The points represent the conversion efficiency of the gas turbine; represents the gas energy input to the gas turbine at time t.

[0034] The operating power constraints and ramp rate constraints of the gas turbine and gas boiler can be described as follows: ; ; in, 、 They represent the lower and upper limits of the gas turbine operating power at time t, represents the actual operating power of the gas turbine at time t, 、 Respectively represent the lower and upper limits of the gas turbine ramp rate, represents the actual operating power of the gas turbine at time t-1.

[0035] The gas turbine's historical operating data, including fuel consumption, operating hours, inlet temperature, and pressure, from multiple sources, is substituted into the established conversion efficiency model to calculate the gas turbine's output power at different times. The output power calculated by the model more accurately reflects the gas turbine's power generation capacity under actual operating conditions and takes into account the impact of varying operating conditions on output power.

[0036] Optimally, the system sets maximum and minimum allowable output power to ensure that the gas turbine does not experience equipment damage or unstable operation due to excessively high or low power during operation. The system also considers the gas turbine's ramp rate limit—the rate of change of output power per unit time. By analyzing historical operating data, a reasonable ramp rate range is determined and constrained when calculating output power. This prevents problems such as excessive equipment stress or grid frequency fluctuations caused by overly rapid gas turbine power adjustments. Ultimately, gas turbine output power data is generated that meets the upper and lower operating power limits and ramp rate constraints.

[0037] S12: Based on the residual power relationship model, the charge and discharge status flags are defined for the charge and discharge records of the energy storage device from multi-source historical data, the charge and discharge power is updated, the charge and discharge power range and the energy storage capacity are constrained, and the charge and discharge constraint relationship of the energy storage device is output.

[0038] Specifically, for the electric energy storage device battery (ES), during operation, the charge / discharge (thermal) state indicator is introduced for the energy storage device. Indicates heat storage symbol. Indicates the heat release sign, which limits it from charging and releasing heat at the same time, so the following equation is available: ; ; in, Indicates the remaining power of the battery. Indicates the power at the end of the previous state, 、 Respectively represent the charge and discharge coefficients of the battery, 、 Represent the charging and discharging power of the battery respectively. The operation constraints and ramp constraints of the energy storage device can be described as follows: ; ; ; in, 、 Respectively represent the lower and upper limits of the battery charging power, and Respectively represent the lower limit and upper limit of battery discharge power. 、 They represent the lower and upper limits of the ramp rate when the battery is charging and discharging. When the energy storage device is in operation, its capacity should also be constrained: ; in: 、 Respectively represent the upper and lower limits of the energy storage device capacity, Represents the capacity of the energy storage device. After the above processing, the charge and discharge constraints of the energy storage device are output, clarifying the power and capacity limitations of the energy storage device under different operating conditions, and providing detailed characteristics of the energy storage device for subsequent optimization and scheduling.

[0039] S13: Calculate the biomass fuel parameters of multi-source historical data based on the power generation calculation model, optimize the biomass power generation power and carbon emission reduction, and constrain the blending ratio to generate the power generation power and carbon emission reduction of the biomass power generation equipment.

[0040] Specifically, the utilization process of biomass considers recycling natural resources such as straw and generating electricity through reasonable co-combustion.

[0041] ; ; in, represents the biomass power generation capacity, Indicates power generation efficiency, represents the fuel conversion efficiency, is the net calorific value, represents the mass of biomass fuel, T represents the power generation time interval, Indicates the total calorific value of biomass fuel, represents the moisture content of biomass fuel, and h represents the ash content. The operation of biomass power generation equipment should also meet the following operating constraints: ; ; in, 、 They represent the lower and upper limits of the power generation capacity of the biomass power generation equipment at time t, represents the actual power generation of the biomass power generation equipment at time t, 、 Respectively, they represent the lower and upper limits of the ramp power of the biomass power generation equipment at time t. Taking into account the operating requirements of the biomass power generation equipment and the stability of the fuel supply, the blending ratio is constrained. The blending ratio refers to the mixing ratio of biomass fuel and other auxiliary fuels. A reasonable blending ratio can ensure the stable operation and combustion efficiency of the power generation equipment while meeting environmental protection requirements. Based on the equipment's design parameters and actual operating experience, the allowable blending ratio range is determined, and corresponding constraints are imposed when calculating power generation and carbon emission reduction. This ensures that the biomass power generation equipment operates under the conditions that meet the blending ratio restrictions, generating power generation and carbon emission reduction data that meet the requirements of the biomass power generation equipment.

[0042] S14: Based on the cluster analysis method, cluster analysis is performed on the new energy vehicle charging logs and the uncertainty set boundary is constructed to generate the charging and discharging power distribution set of the new energy vehicles.

[0043] Specifically, new energy vehicles are different from renewable energy sources such as wind turbines and photovoltaics in that their output is uncertain. The interaction process of electric vehicles is relatively more complex and is significantly affected by subjective human factors. Therefore, their uncertainty set space is difficult to characterize. This embodiment performs cluster analysis on new energy vehicles based on the discrete scenario distributed robust optimization method, considering the probability distribution of different types of new energy vehicles, as described below: ; ; in, and Respectively represent the charging and discharging amount of new energy vehicles, 、 Both represent the number of new energy vehicles. Define the summation range, Participate in the construction of the calculation relationship of the total amount of new energy vehicles, Indicates the number of new energy vehicle types, Represents the proportion of different types of electric vehicles; in order to describe the error between the distribution set of new energy vehicles and the actual distribution, the uncertainty space it constitutes is described as follows: ; in, represents the uncertainty set of new energy vehicles, represents the uncertainty distribution vector, which represents the proportion of each type of new energy vehicles, represents the empirical distribution in historical data, and After the above processing, the charge and discharge power distribution set of new energy vehicles is generated, which reflects the charge and discharge power distribution of different types of new energy vehicles in different time periods, providing basic data for subsequent vehicle-grid integration analysis and optimized scheduling.

[0044] S15: Integrate data on gas turbine output power, charge and discharge constraint relationships, power generation power, carbon emission reduction, and charge and discharge power distribution sets to generate a multi-energy coupling data set.

[0045] Specifically, the system integrates the data obtained for gas turbine output power, charge and discharge constraints of energy storage devices, power generation and carbon emission reductions of biomass power generation equipment, and charge and discharge power distribution of new energy vehicles. By integrating data from these different types of energy devices, a comprehensive multi-energy coupling data set is constructed. This data set encompasses the key operating parameters and characteristics of various energy devices within the virtual power plant, accurately reflecting the multi-energy coupling relationships and overall operating status within the virtual power plant, providing a complete data foundation for subsequent optimized scheduling and coordinated trading.

[0046] In one embodiment, the present invention provides a multi-virtual power plant collaborative trading method combining carbon metering and green energy consumption incentives, wherein S2 specifically includes the following steps: S21: Based on the F-vine Copula distribution function, the acquired historical data set of wind turbine and photovoltaic output is subjected to joint probability modeling processing to generate a joint probability distribution model of wind and solar output.

[0047] Specifically, due to the uncertainty in the access process of renewable energy and electric vehicles, which poses operational risks to the energy network, this embodiment constructs a vehicle-grid integration uncertainty risk management model, the principle diagram of which is shown in the figure below. Figure 3 Traditional uncertainty models often lack scenario generation for wind and solar correlation models. This system uses a multivariate kernel density function to construct probability distribution functions for wind turbine and photovoltaic power generation uncertainty scenarios. This inverse transform yields sampled wind turbine and photovoltaic output for each time period, ultimately generating a typical daily curve that accounts for the stochastic nature of wind and solar power generation. This data is derived from a year of measured wind turbine and photovoltaic output data from a location in southeastern China. This data is used to further determine wind and solar power output uncertainty, further ensuring the reliability of the wind and solar power uncertainty model.

[0048] Based on the uncertainty risk management model of vehicle-grid integration, the system performs joint probability modeling on the historical data sets of wind turbine and photovoltaic output to generate a joint probability distribution model of wind and solar output. Preferably, the system can use Sklar theorem when constructing the joint probability distribution model, which assumes that the random variable The joint distribution function of , and its marginal distribution function is , then there exists a Copula function , such that: ; This embodiment selects the F-vine Copula function that can take into account both positive and negative correlation characteristics. The F-vine Copula is a Copula model based on the vine structure. It decomposes the multidimensional Copula function into the product of a series of binary Copula functions to construct the F-vine kernel density function. The kernel density estimation method is used to determine the cumulative distribution function of wind and solar power output. The sample size is a random variable of n. The sample points are , its probability density function is , its kernel density estimate: ; in, Indicates the window width, Represents the kernel function, and then integrates it to obtain the cumulative distribution function of wind and solar power output as follows: ; Then convert the cumulative distribution of wind and solar power into uniform distribution , we have the following equation ; in, is a monotonically increasing function, It represents the probability that the value of the wind power cumulative distribution function F(P) is less than or equal to ξ under the probability measure Q, Indicates that under the probability measure Q, the random variable P takes a value less than or equal to The probability of Indicates that the inverse function After conversion, under the original cumulative distribution of wind power, the value is less than or equal to The probability of . The maximum likelihood method of distribution is used to solve the unknown parameters. The likelihood function of the wind and solar random variable sample is () can be described as: ; in, and are the unknown parameters in the marginal distribution, 、 represents the input sample data, and represents the cumulative distribution function associated with the wind and solar random variable, and Represents and The corresponding probability density function, θ, represents the unknown parameter in the Copula function. The system maximizes the likelihood function L(θ) to obtain an estimated value for parameter θ. After the above steps, the system constructs a joint probability distribution model for wind turbine and photovoltaic output covering 24 time periods of the day, laying the foundation for subsequent uncertainty analysis and scenario generation. The unknown parameters can then be calculated using the inverse function. From this, the expression of the joint probability distribution model for each time period in this article can be obtained as: ; in, represents the joint probability distribution function of wind turbine and photovoltaic output during period t, represents the Fvine Copula distribution function, and They represent the marginal distribution functions of wind turbine and photovoltaic output in period t, represents the set of joint probability distribution functions of wind turbine and photovoltaic output in each of the 24 time periods of a day, and They represent the uniformly distributed variables obtained by transforming the wind turbine and photovoltaic output in period t through their respective marginal distribution functions.

[0049] S22: Based on the joint probability distribution model, the historical data sets of wind turbine and photovoltaic output are randomly sampled, and the sampled output values ​​are calculated by the inverse function sampling method to generate a preliminary scenario set.

[0050] Specifically, the system generates a series of uniformly distributed random numbers and , these random numbers correspond to the uniformly distributed variables of wind turbine and photovoltaic output in period t. Subsequently, the system converts the uniformly distributed random numbers into the actual values ​​of wind turbine and photovoltaic output through the inverse function sampling method. The specific conversion formula is as follows: ; ; The system repeats this process, generating multiple sample points and ultimately forming a preliminary set of scenarios. Each scenario fully incorporates wind turbine and PV output data for 24 periods of the day. This approach enables the system to generate a large number of preliminary scenarios that fully reflect the uncertainty of wind turbine and PV output, providing a rich and detailed sample base for subsequent scenario evaluation and reduction efforts.

[0051] S23: Evaluate and process the preliminary scene set, calculate the scene closeness by approximating the ideal solution sorting method, and generate a typical scene set containing a bad scene set and an ideal scene set.

[0052] Specifically, the system uses the principle of the Top-of-Shortest-Solutions (TOPSIS) method to calculate the relative closeness of the wind and solar power output value based on the Euclidean distance between the ideal and non-ideal wind and solar power scenario solutions. The larger the value, the closer it is to the ideal output, thereby obtaining the evaluation of the corresponding adverse and ideal scenarios. For the n-dimensional wind and solar power output data obtained, there are m evaluation indicators for one day, and then a wind and solar power output evaluation matrix is ​​constructed. as follows: ; Construct it into a standardized decision matrix form ,in as follows: ; in, is the mth element in the nth column of the wind and solar power output evaluation matrix V, is the minimum value of the nth column in the wind and solar power output evaluation matrix V, The maximum value of the nth column in the wind and solar power output evaluation matrix V is calculated, and then its scene closeness value is calculated , the calculation formula of scene closeness is: ; ; ; in, Indicates the scene proximity, and Respectively represent the distance between the output data and the ideal scenario solution and the bad scenario solution, and represent the ideal scenario solution and the bad scenario solution respectively, Indicates the value of the jth indicator in the i-th scenario.

[0053] In one embodiment, Figure 4 As shown, S3 of a multi-virtual power plant collaborative trading method combining carbon metering and green energy consumption incentives provided by the present invention specifically includes the following steps: S31: Calculate the renewable energy-load correlation coefficient based on the renewable energy output and load demand data in the multi-energy coupling data set.

[0054] Specifically, the renewable energy-load correlation coefficient is obtained by the following steps: S311: Based on the renewable energy output and load demand data in the multi-energy coupling data set, the covariance of renewable energy and load is calculated using a covariance calculation formula to generate a covariance index of the uncertainty output of renewable energy and the regional load.

[0055] Specifically, the calculation process for the covariance index between renewable energy and regional VPP is as follows: ; in, represents the covariance index, represents the renewable energy consumption power of the ith region in period t, represents the load demand of the ith area in period t, 、 Representing the mean of renewable energy output and load, respectively; through this calculation, the system accurately captures the dynamic relationship between renewable energy output and load demand, providing critical data support for subsequent analysis and decision-making. In practice, the system processes large amounts of data from different regions and time periods to ensure that the covariance index fully reflects the complex interactions between renewable energy and load, providing a reliable basis for optimized scheduling and operational management of the system.

[0056] S312: Complementarity calculation of renewable energy output and load demand data is performed based on a standard deviation calculation method to generate a standard deviation index including the standard deviation of renewable energy output and the standard deviation of load demand.

[0057] Specifically, the calculation formula of the standard deviation indicator is: ; ; in, , Represents the standard deviation of load demand. Using this metric, the system can gain a deeper understanding of the distribution characteristics and fluctuations of renewable energy output and load demand data. This is crucial for assessing system stability and developing appropriate control strategies. By calculating the standard deviation of renewable energy output and load demand data, the system can better identify abnormal fluctuations in the data, prepare for responses, and ensure safe and stable system operation.

[0058] S313: Calculate the correlation degree of the covariance index and the standard deviation index based on the correlation coefficient calculation formula to generate a renewable energy-load correlation coefficient.

[0059] The calculation formula of renewable energy-load correlation coefficient is: ; in, represents the renewable energy-load correlation coefficient, represents the covariance index between the uncertainty of renewable power output and regional load, Indicates the standard deviation of wind turbine and photovoltaic renewable energy consumption in the current period, Represents the standard deviation of load demand. The system analyzes a large amount of historical data to calculate the renewable energy-load correlation coefficient. This data includes wind turbine and photovoltaic output data, as well as regional load demand data. By deeply analyzing this data, the system can identify potential relationships between renewable energy output and load demand. For example, during certain periods, wind turbine output may be highly correlated with peak industrial load, while during other periods, photovoltaic output may align with peak residential electricity demand. By calculating these correlations, the system provides key insights for subsequent optimized scheduling.

[0060] S32: Generate green energy consumption compensation amount based on renewable energy-load correlation coefficient and the consumption power in typical scenarios.

[0061] Specifically, based on the renewable energy-load correlation coefficient, it is used as a measurement indicator for regional VPP wind and solar power consumption, and a certain compensation is given for the consumption of renewable energy. At this time, the calculation formula for the green energy consumption compensation amount is: ; in, Indicates the amount of compensation for green energy consumption, Indicates the compensation standard parameter, represents the absorption power of renewable energy in the i-th region during period t, and T represents the time period. The system quantifies the economic value of renewable energy absorption by integrating the renewable energy-load correlation coefficient and the absorption power. The calculation of the compensation amount fully takes into account the uncertainty of renewable energy output and the volatility of load demand, aiming to encourage virtual power plants to give priority to absorbing renewable energy while meeting load demand, thereby achieving low-carbon operation goals. Specifically, within each scheduling cycle, the system dynamically calculates the compensation amount based on the actual absorption of renewable energy and the correlation coefficient, and incorporates it into the economic benefit model of the virtual power plant. This not only improves the utilization rate of renewable energy, but also reduces carbon emissions, achieving dual optimization of economy and low carbon.

[0062] The system fully accounts for regional and time-of-day variations in green energy consumption compensation calculations. The compensation standard parameter, φ, can be adjusted based on policy requirements or market conditions to accommodate varying incentive needs. This allows the system to flexibly guide virtual power plant operational strategies, enabling them to actively incorporate renewable energy and reduce reliance on traditional fossil fuels, driven by economic benefits. Ultimately, this data-driven optimization approach enables efficient coordinated scheduling of multiple virtual power plants, enhancing the flexibility, reliability, and environmental friendliness of the overall energy system.

[0063] In one embodiment, the present invention provides a multi-virtual power plant collaborative trading method combining carbon metering and green energy consumption incentives, wherein S4 specifically includes the following steps: S41: Based on the dynamic pricing model, the grid electricity price and supply-demand gap in the multi-energy coupling data set are processed, and the upper and lower limits of electricity prices and supply-demand balance conditions are constrained by the upper-level aggregator to generate the initial dynamic purchase and sale electricity price.

[0064] Specifically, the system processes the grid electricity price and supply-demand gap in the multi-energy coupling data set based on the dynamic pricing model. In this process, the system generates the initial dynamic purchase and sale electricity price by constraining the upper and lower limits of electricity prices and the supply and demand balance conditions through the upper-level aggregator. Among them, the aggregation operator process publishes the price of each time period. , mainly focusing on the interaction process between the power market and VPP, the objective function can be described as follows: ; in, is the initial dynamic electricity purchase and sale price, and They represent the electricity selling price and electricity purchasing price of the current market power grid respectively. and They represent the electricity selling price and electricity purchasing price determined by the aggregator, and It is the electricity sold and purchased by the aggregator to the electricity market, and is the amount of electricity purchased and sold by the i-th entity in the interaction between the aggregator and the APP. The supply and demand balance constraints and electricity price constraints satisfied are as follows: ; ; in, It represents the total amount of electricity purchased or sold by the aggregator from the electricity market at time t.

[0065] S42: Based on the optimization scheduling algorithm, the typical scenario set, green energy consumption compensation amount and the initial dynamic purchase and sale electricity price are processed, and the gas turbine output, energy storage charging and discharging plan, biomass power generation power and cross-regional transaction power are optimized through the lower-level virtual power plant to generate a multi-source transaction current feedback signal.

[0066] Specifically, after generating the initial dynamic electricity purchase and sales price, the system further processes the typical scenario set, green energy consumption compensation amount, and the initial dynamic electricity purchase and sales price based on the optimization scheduling algorithm. The underlying virtual power plant optimizes gas turbine output, energy storage charging and discharging plans, biomass power generation, and cross-regional trading power to generate a multi-source trading current feedback signal. Its objective function can be described as follows: ;

[0067] in, denote the interaction cost with the aggregator, , , represents the cost of gas turbines, energy storage, and flexible interruption loads, represents the cost of interaction between VPP and new energy vehicles, Represents the carbon emission cost. During the optimization process, the system must meet the operating constraints of the gas turbine, energy storage equipment, biomass power generation equipment, and new energy vehicles. At the same time, it must also meet the supply and demand balance conditions: ; ; in, is the power of the ith subject interacting with the electricity market at time t, is the output power of the i-th gas turbine equipment at time t, is the power of the i-th energy storage device at time t, is the charging and discharging power of the i-th new energy vehicle at time t, is the output power of the i-th biomass power generation equipment at time t, is the power consumption of the i-th load at time t, representing the power consumption of the load at that moment.

[0068] By optimizing the scheduling algorithm, the system generates multi-source transaction current feedback signals, including gas turbine output, energy storage charging and discharging schedules, biomass power generation, and cross-regional transaction power. These feedback signals reflect the optimal operating strategy of the virtual power plant under different operating conditions.

[0069] S43: Based on the iterative correction mechanism, the multi-source transaction current feedback signal and the supply-demand gap are processed, and the matching relationship between the purchase and sale electricity price and the transaction electricity volume is dynamically adjusted to generate the final optimized dynamic purchase and sale electricity price and the converged transaction current feedback signal.

[0070] Specifically, during the iterative correction process, the system repeatedly adjusts the purchase and sale prices and the amount of electricity traded until convergence conditions are met. Specifically, the system determines convergence by comparing the difference between the purchase and sale prices and the amount of electricity traded between the previous and current rounds. If the difference is less than a set threshold, convergence is considered; otherwise, iterative adjustments continue. Through this iterative correction mechanism, the system can dynamically adjust the purchase and sale prices and the amount of electricity traded, gradually optimizing the operating strategies of aggregators and virtual power plants while maintaining supply and demand balance and operational constraints, ultimately achieving economically efficient operation and low-carbon goals for the multi-virtual power plant system.

[0071] In one embodiment, the present invention provides a multi-virtual power plant collaborative trading method combining carbon metering and green energy consumption incentives, wherein S5 specifically includes the following steps: S51: Based on the multi-level robust optimization algorithm, the typical scenario set, transaction current feedback signal and charging and discharging power distribution set of new energy vehicles are processed. By dividing the uncertainty problem of VPP renewable energy vehicle network into main problem and sub-problems, the optimal scheduling strategy of known scenarios and the search boundary of harsh scenarios are solved respectively.

[0072] Specifically, in response to the uncertainty of wind and solar power, the multi-level robust optimization in this embodiment integrates the historical data of wind and solar power uncertainty variables to obtain different sample distributions. Fuzzy sets are constructed for different distribution parameters of the samples. The scenario method is used to reduce the wind and solar power output under different probabilities. The uncertainty of electric vehicles is integrated using a polyhedral uncertainty set. The compact form of the multi-level robust optimization solution algorithm in this paper is established as follows: ; ; in, , and They represent the electricity selling price and electricity purchasing price determined by the aggregator, is the initial dynamic electricity purchase and sale price, represents the power allocation of the i-th device, It represents the parameter space determined according to the characteristics of wind and solar uncertainty, relevant physical laws, and empirical data. It represents the feasible domain determined by factors such as the equipment operation limitations, grid constraints, and safety regulations of the virtual power plant. The above problem can be divided into a multi-level robust optimization problem. Among them, the uncertainty problem of renewable energy vehicle grid for each virtual power plant proposed in this paper can be divided into a main problem and a sub-problem. The main problem obtains the optimal solution when the source and load output are known, provides a lower bound for the model, and passes the result to the sub-problem. The sub-problem provides an upper bound for the model by finding the worst scenario in the uncertainty set, and passes the corresponding scenario output to the main function.

[0073] The system decomposes the uncertainty problem of the VPP renewable energy vehicle-to-grid system, focusing on solving the optimal scheduling strategy for known scenarios. During this process, the system comprehensively considers multiple factors, including renewable energy output, energy storage device status, and the operating characteristics of traditional generators, striving to minimize operating costs and maximize economic efficiency while meeting grid operational constraints. By establishing a precise mathematical model, the system transforms scheduling strategy optimization into a complex multi-variable, multi-constrained optimization problem, which it solves using advanced optimization algorithms. Meanwhile, a sub-problem focuses on determining the search boundary for adverse scenarios. The system simulates various extreme conditions and analyzes their impact on virtual power plant operations to determine the boundary of possible adverse scenarios. This boundary not only covers extreme fluctuations in renewable energy output, but also encompasses scenarios that could pose system operational risks, such as sudden changes in load demand and large-scale simultaneous charging of new energy vehicles. This provides comprehensive and rigorous boundary conditions for subsequent optimal scheduling. This process fully considers factors such as renewable energy output volatility, load demand uncertainty, and the randomness of new energy vehicle charging and discharging behavior, aiming to build a solid defense for the virtual power plant against various uncertainties.

[0074] S52: Based on the optimal scheduling strategy, the sub-problems of the uncertainty problem are processed and converted into a single-layer optimization model through the dual transformation method. Based on the search boundary of harsh scenarios, the transaction constraints under extreme scenarios are generated through convex relaxation technology.

[0075] Specifically, the system uses a dual transformation method to transform complex subproblems into a single-layer optimization model that is easy to solve. The advantage of this method is that it can simplify the structure of the problem, allowing problems that were originally difficult to solve directly to be solved efficiently. At the same time, the system uses convex relaxation technology to generate transaction constraints under extreme scenarios based on the previously determined harsh scenario search boundary. The application of convex relaxation technology can ensure the mathematical solvability and stability of the generated constraints, ensuring that the system can still meet the basic requirements of power grid operation, such as power balance and voltage stability, in extremely harsh scenarios. These transaction constraints not only involve power transactions between virtual power plants and external power grids, but also include the coordination and allocation of different energy resources within the virtual power plant, providing clear boundary conditions for subsequent optimization and scheduling.

[0076] S53: Based on the optimal scheduling strategy and transaction constraints, an alternating iterative solution process is performed. By updating the parameter boundaries of the main problem and sub-problems of the uncertainty problem, a cross-regional trading strategy and equipment output plan under harsh scenarios are generated. The cross-regional trading strategy is used to guide the coordinated scheduling of multiple virtual power plants.

[0077] Specifically, if Figure 3 As shown in the figure, the system uses an alternating iterative solution approach to further optimize the main problem and subproblems of the uncertainty problem. During each iteration, the system continuously updates the parameter boundaries of the main problem and subproblems, ensuring that the main problem better reflects the uncertainties in actual operation, while the subproblems more accurately capture the system characteristics under harsh scenarios. In this way, the system gradually approaches the optimal solution, ultimately generating a cross-regional trading strategy and equipment output plan suitable for harsh scenarios. The cross-regional trading strategy is primarily used to guide the coordinated scheduling of multiple virtual power plants. By rationally arranging power transactions between virtual power plants in different regions, it optimizes resource allocation and improves the overall system's operational efficiency and economic efficiency. The equipment output plan specifies the output arrangements and adjustment requirements for various power generation and energy storage devices within the virtual power plant during different time periods, ensuring that the virtual power plant can maximize renewable energy consumption and maintain system stability while meeting grid operational constraints.

[0078] In summary, this proposed method combines carbon metering with green energy consumption incentives to establish a multi-virtual power plant (VPP) collaborative trading model that incorporates renewable energy sources such as wind and solar power, as well as a biomass power plant model, through a carbon trading mechanism to ensure low carbon performance. To address the uncertainties of wind turbines and photovoltaics, a modified kernel distribution function is used for risk management, and the TOPSIS method is used to evaluate energy output under normal and severe weather conditions to address a variety of practical operating scenarios. Furthermore, considering vehicle-grid integration, a model is established that incorporates the dynamic interaction of new energy vehicles, with particular attention to the uncertainties in the charging and discharging processes between EVs and VPPs, and a corresponding interaction model is constructed. Finally, a multi-VPP trading mechanism is established that incorporates a dynamic electricity pricing mechanism. In addition to ensuring economic efficiency, the proposed algorithm compensates for renewable energy by establishing a correlation coefficient between renewable energy and energy consumption behavior, thus ensuring low carbon performance. Robust optimal scheduling of multiple VPPs is achieved under the context of vehicle-grid integration and renewable energy uncertainties, and simulations verify its effectiveness in improving power system flexibility, reliability, and environmental friendliness. Further research will be conducted to consider the deep correlation between renewable energy and load, as well as the long- and short-timescale characteristics.

[0079] Preferably, if Figure 5 As shown, the present invention provides a multi-virtual power plant collaborative trading system 600 that combines carbon metering with green energy consumption incentives. The system is configured with the following modules: A VPP framework construction module 610 is configured to construct a virtual power plant (VPP) low-carbon operation framework containing a multi-energy coupling model based on the acquired multi-source historical data, and to process the multi-source raw data based on the virtual power plant (VPP) low-carbon operation framework to generate a multi-energy coupling data set; The multi-source historical data includes historical gas turbine operation data, energy storage device charge and discharge records, biomass fuel parameters, and new energy vehicle charging logs. The multi-energy coupling data set includes gas turbine output power, energy storage device charge and discharge constraints, biomass power generation equipment power generation and carbon emission reduction, and new energy vehicle charge and discharge power distribution sets. The severe-ideal scenario generation module 620 is used to perform joint probability modeling and sampling sorting on the acquired historical data sets of wind turbine and photovoltaic output, evaluate the historical scenario sets of the historical data sets of wind turbine and photovoltaic output, and generate a typical scenario set containing a severe scenario set and an ideal scenario set; Energy-load correlation module 630, used to calculate renewable energy-load correlation coefficient and compensation amount for multi-energy coupling data set and typical scenario set, and generate green energy consumption compensation amount; The trading mechanism construction module 640 is used to process the multi-energy coupling data set, typical scenario set, and green energy consumption compensation amount to establish an MVPP multi-agent trading mechanism, gradually iteratively optimize the purchase and sale electricity price from the upper layer and the multi-source transaction current from the lower layer, and generate the final optimized dynamic purchase and sale electricity price and transaction current feedback signal; The robust optimization solution module 650 is used to perform robust scheduling processing on the charging and discharging power distribution set of new energy vehicles in the typical scenario set, transaction current feedback signal and multi-energy coupling data set based on the multi-level robust optimization solution algorithm, and generate cross-regional trading strategies and equipment output plans under harsh scenarios. The cross-regional trading strategies are used to guide the coordinated scheduling of multiple virtual power plants.

[0080] In summary, the present invention provides a multi-virtual power plant collaborative trading system that combines carbon metering and green energy consumption incentives. By constructing a low-carbon operation framework for virtual power plants containing a multi-energy coupling model and processing multi-source data, it comprehensively integrates multi-source information such as gas turbines, energy storage, biomass and new energy vehicles to generate a multi-energy coupling data set, laying a solid data foundation for subsequent optimization processing. Joint probability modeling, sampling sorting and scenario evaluation are performed on historical data of wind turbine and photovoltaic output to effectively generate a typical scenario set containing severe and ideal scenarios, fully considering the uncertainty of renewable energy output and improving the robustness of the scheduling scheme. The renewable energy-load correlation coefficient and the green energy consumption compensation amount are calculated to generate low-carbon incentive parameters, establish a green energy consumption incentive mechanism that matches the load demand in time and space, and enhance the correlation between compensation incentives and low-carbon goals. Based on the multi-energy coupling data set, typical scenario set and low-carbon incentive parameters, an MVPP multi-subject trading mechanism is constructed to achieve iterative optimization of dynamic electricity price interaction and transaction power feedback, balancing economic benefits and system stability. Finally, a multi-level robust optimization solution algorithm is used for robust scheduling processing to generate cross-regional trading strategies and equipment output plans under harsh scenarios, effectively guiding the coordinated scheduling of multiple virtual power plants, significantly improving the flexibility, reliability and environmental friendliness of the power system in the context of vehicle-grid integration and renewable energy uncertainty, effectively solving the problems existing in traditional virtual power plant scheduling, and realizing the coordinated quantitative optimization of cross-regional carbon trading costs and green energy absorption capacity.

[0081] Preferably, the VPP framework building module 610 is configured with the following units: The gas turbine output power generation unit is used to calculate the gas turbine historical operating data from multiple sources based on the electric power and thermal power conversion efficiency model, constrain its operating power upper and lower limits and ramp rate, and generate the gas turbine output power; An energy storage device charge and discharge constraint relationship generation unit is used to define a charge and discharge status flag for the energy storage device charge and discharge records of multi-source historical data based on a residual power relationship model, update the charge and discharge power, constrain the charge and discharge power range and energy storage capacity, and output the charge and discharge constraint relationship of the energy storage device; The biomass power generation equipment data generation unit is used to calculate the biomass fuel parameters of multi-source historical data based on the power generation calculation model, optimize the biomass power generation and carbon emission reduction, and constrain the blending ratio to generate the power generation and carbon emission reduction of the biomass power generation equipment; A new energy vehicle charging and discharging power distribution set generation unit is used to perform cluster analysis on the new energy vehicle charging logs based on the cluster analysis method and construct the uncertainty set boundary to generate the new energy vehicle charging and discharging power distribution set; The multi-energy coupling data set generation unit is used to integrate data on gas turbine output power, charge and discharge constraint relationships, power generation power, carbon emission reduction, and charge and discharge power distribution sets to generate a multi-energy coupling data set.

[0082] Preferably, the worst-ideal scenario generation module 620 is configured with the following units: A joint probability distribution model generation unit is used to perform joint probability modeling processing on the acquired wind turbine and photovoltaic output historical data sets based on the F-vine Copula distribution function to generate a joint probability distribution model of wind and photovoltaic output; A preliminary scenario set generation unit is used to randomly sample the historical data sets of wind turbine and photovoltaic output based on the joint probability distribution model, calculate the sampled output values ​​through the inverse function sampling method, and generate a preliminary scenario set; The typical scene set generation unit is used to evaluate and process the preliminary scene set, calculate the scene closeness by approximating the ideal solution sorting method, and generate a typical scene set containing a bad scene set and an ideal scene set.

[0083] Preferably, the energy-load association module 630 is configured with the following units: A correlation coefficient calculation unit, configured to calculate a renewable energy-load correlation coefficient based on renewable energy output and load demand data in a multi-energy coupling data set; The compensation amount generating unit is used to generate the green energy consumption compensation amount based on the renewable energy-load correlation coefficient and the consumption power in the typical scenario.

[0084] Preferably, the correlation coefficient calculation unit includes a covariance index generation subunit, a standard deviation index generation subunit, and a correlation coefficient generation subunit. The covariance index generation subunit is used to calculate the covariance of renewable energy and load based on the renewable energy output and load demand data in the multi-energy coupling data set using a covariance calculation formula, and generate the uncertainty output of renewable energy and the covariance index of regional load; the standard deviation index generation subunit is used to calculate the complementarity of renewable energy output and load demand data based on the standard deviation calculation method, and generate a standard deviation index containing the standard deviation of renewable energy output and the standard deviation of load demand; the correlation coefficient generation subunit is used to calculate the correlation between the covariance index and the standard deviation index based on the correlation coefficient calculation formula, and generate the renewable energy-load correlation coefficient.

[0085] Preferably, the transaction mechanism building module 640 is configured with the following units: The initial dynamic purchase and sale electricity price generation unit is used to process the grid electricity price and supply and demand gap in the multi-energy coupling data set based on the dynamic pricing model, and generate the initial dynamic purchase and sale electricity price by constraining the upper and lower limits of electricity prices and the supply and demand balance conditions of the upper-level aggregator; A multi-source transaction current feedback signal generation unit is used to process a typical scenario set, green energy consumption compensation amounts, and initial dynamic purchase and sales electricity prices based on an optimized scheduling algorithm. It generates multi-source transaction current feedback signals by optimizing gas turbine output, energy storage charging and discharging plans, biomass power generation, and cross-regional transaction power through the underlying virtual power plant. The final optimization result generation unit is used to process the multi-source transaction current feedback signal and the supply-demand gap based on the iterative correction mechanism, and generate the final optimized dynamic purchase and sale electricity price and converged transaction current feedback signal by dynamically adjusting the matching relationship between the purchase and sale electricity price and the transaction electricity volume.

[0086] Preferably, the robust optimization solution module 650 is configured with the following units: The problem decomposition and solution unit is used to process a set of typical scenarios, transaction current feedback signals, and the charging and discharging power distribution set of new energy vehicles based on a multi-level robust optimization algorithm. By dividing the uncertainty problem of the VPP renewable energy vehicle network into a main problem and sub-problems, it solves the optimal scheduling strategy for known scenarios and the search boundary for harsh scenarios respectively; The constraint generation unit is used to process the sub-problems of the uncertainty problem based on the optimal scheduling strategy, transform it into a single-layer optimization model through the dual transformation method, and generate transaction constraints under extreme scenarios through convex relaxation technology based on the harsh scenario search boundary; The strategy and plan generation unit is used to perform alternating iterative solution processing based on the optimal scheduling strategy and transaction constraints. By updating the parameter boundaries of the main problem and sub-problems of the uncertainty problem, it generates cross-regional trading strategies and equipment output plans under harsh scenarios. The cross-regional trading strategy is used to guide the coordinated scheduling of multiple virtual power plants.

[0087] In one embodiment, the present application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned multi-virtual power plant collaborative trading method combining carbon metering and green energy consumption incentives.

[0088] In one embodiment, the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the multi-virtual power plant collaborative trading method combining carbon metering with green energy consumption incentives is implemented.

[0089] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0090] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0091] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A collaborative trading method for multiple virtual power plants, characterized in that: The following steps are involved: S1: constructing a low-carbon operation framework of a virtual power plant containing a multi-energy coupling model based on the acquired multi-source historical data, and processing the multi-source raw data based on the low-carbon operation framework of the virtual power plant to generate a multi-energy coupling data set; S2: Perform joint probability modeling and sampling sorting on the acquired historical data sets of wind turbine and photovoltaic output, evaluate the historical scenario sets of the historical data sets of wind turbine and photovoltaic output, and generate a typical scenario set containing a severe scenario set and an ideal scenario set; S3: Calculating the renewable energy-load correlation coefficient and the compensation amount for the multi-energy coupling data set and the typical scenario set to generate a green energy consumption compensation amount; S4: establishing a multi-agent transaction mechanism based on the multi-energy coupling data set, the typical scenario set, and the green energy consumption compensation amount, and utilizing the multi-agent transaction mechanism to iteratively optimize the purchase and sale electricity price from the upper layer and the multi-source transaction current from the lower layer, thereby generating a final optimized dynamic purchase and sale electricity price and transaction current feedback signal; S5: Based on a multi-level robust optimization solution algorithm, robust scheduling processing is performed on the typical scenario set, the transaction current feedback signal and the charging and discharging power distribution set of new energy vehicles in the multi-energy coupling data set to generate cross-regional transaction strategies and equipment output plans under harsh scenarios. The cross-regional transaction strategies are used to guide the coordinated scheduling of multiple virtual power plants.

2. The collaborative transaction method of multiple virtual power plants according to claim 1, characterized in that: The low-carbon operation framework of the virtual power plant includes an electric power and thermal power conversion efficiency model, a surplus power relationship model, and a power generation power calculation model; The low-carbon operation framework based on the virtual power plant processes the multi-source raw data to generate a multi-energy coupling data set, including: S11: Calculating the gas turbine historical operating data of the multi-source historical data based on the electric power and thermal power conversion efficiency model, and generating the gas turbine output power with the upper and lower limits of the gas turbine operating power and the ramp rate as constraints; S12: defining a charge and discharge status flag for the energy storage device charge and discharge records of the multi-source historical data based on the residual power relationship model, updating the charge and discharge power, and outputting a charge and discharge constraint relationship for the energy storage device based on the charge and discharge power range and energy storage capacity as constraints; S13: Calculating the biomass fuel parameters of the multi-source historical data based on the power generation calculation model, updating the biomass power generation power and carbon emission reduction, and generating the power generation power and carbon emission reduction of the biomass power generation equipment based on the blending ratio as a constraint; S14: Based on the cluster analysis method, cluster analysis is performed on the new energy vehicle charging logs and the uncertainty set boundary is constructed to generate the charging and discharging power distribution set of the new energy vehicle; S15: Integrate data on the gas turbine output power, the charge-discharge constraint relationship, the power generation power and carbon emission reduction, and the charge-discharge power distribution set to generate a multi-energy coupling data set.

3. The collaborative transaction method of multiple virtual power plants according to claim 1, characterized in that: The S2 includes: S21: Perform joint probability modeling on the acquired historical data sets of wind turbine and photovoltaic output based on the F-vine Copula distribution function to generate a joint probability distribution model of wind and solar output. The expression of the joint probability distribution model is: ； in, express t The joint probability distribution function of wind turbine and photovoltaic output in each period, represents the Fvine Copula distribution function, and Respectively t The marginal distribution function of wind turbine and photovoltaic output in each period, represents the set of joint probability distribution functions of wind turbine and photovoltaic output in each of the 24 time periods of a day, and Respectively indicate t The wind turbine and photovoltaic outputs in each period are converted into uniformly distributed variables through their respective marginal distribution functions; S22: performing random sampling processing on the historical data sets of wind turbine and photovoltaic output based on the joint probability distribution model, calculating the sampled output values ​​by an inverse function sampling method, and generating a preliminary scenario set; S23: Evaluate the preliminary scene set, calculate the scene closeness by approximating the ideal solution sorting method, and generate a typical scene set containing a bad scene set and an ideal scene set. The calculation formula of the scene closeness is: ； ； ； in, Indicates the scene proximity, and Respectively represent the distance between the output data and the ideal scenario solution and the bad scenario solution, and represent the ideal scenario solution and the bad scenario solution respectively, Indicates the i In the scene j The value of an indicator.

4. The collaborative transaction method of multiple virtual power plants according to claim 1, characterized in that: The S3 includes: S31: Calculate the renewable energy-load correlation coefficient based on the renewable energy output and load demand data in the multi-energy coupling data set. The calculation formula of the renewable energy-load correlation coefficient is: ； in, represents the renewable energy-load correlation coefficient, represents the covariance index between the uncertainty of renewable power output and regional load, Indicates the standard deviation of wind turbine and photovoltaic renewable energy consumption in the current period, represents the standard deviation of load demand; S32: Based on the renewable energy-load correlation coefficient and the absorption power in the typical scenario, a green energy absorption compensation amount is generated. The calculation formula of the green energy absorption compensation amount is: ； in, Indicates the amount of compensation for green energy consumption, Indicates the compensation standard parameter, represents the renewable energy consumption power of the ith region in period t, T Indicates a time period.

5. The collaborative transaction method of multiple virtual power plants according to claim 4, characterized in that: The S31 includes: S311: Based on the renewable energy output and load demand data in the multi-energy coupling data set, the covariance between renewable energy and load is calculated using a covariance calculation formula to generate a covariance index of the uncertainty output of renewable energy and regional load. The calculation formula of the covariance index is: ； in, represents the covariance index, Indicates t Time period i The renewable energy consumption capacity of a region, Indicates that the representative t Time period i The load demand of the region, 、 denote the mean values ​​of renewable energy output and load respectively; S312: Complementarity calculation is performed on the renewable energy output and load demand data based on a standard deviation calculation method to generate a standard deviation index containing the renewable energy output standard deviation and the load demand standard deviation. The calculation formula of the standard deviation index is: ； ； in, , represents the standard deviation of load demand; S313: Calculate the correlation between the covariance index and the standard deviation index based on the correlation coefficient calculation formula to generate a renewable energy-load correlation coefficient.

6. The collaborative transaction method of multiple virtual power plants according to claim 1, characterized in that: The S4 includes: S41: Processing the grid electricity price and the supply-demand gap in the multi-energy coupling data set based on a dynamic pricing model, and generating an initial dynamic purchase and sale electricity price by constraining the upper and lower limits of electricity prices and supply-demand balance conditions through upper-layer aggregators; S42: Processing the typical scenario set, the green energy consumption compensation amount, and the initial dynamic purchase and sale electricity price based on an optimization scheduling algorithm, optimizing gas turbine output, energy storage charging and discharging plans, biomass power generation power, and cross-regional transaction power through a lower-level virtual power plant, and generating a multi-source transaction current feedback signal; S43: Based on the iterative correction mechanism, the multi-source transaction current feedback signal and the supply-demand gap are processed, and the matching relationship between the purchase and sale electricity price and the transaction electricity volume is dynamically adjusted to generate the final optimized dynamic purchase and sale electricity price and the converged transaction current feedback signal.

7. A method for collaborative trading of multiple virtual power plants according to any one of claims 1 to 6, characterized in that: The S5 includes: S51: Processing the typical scenario set, the transaction current feedback signal, and the charging and discharging power distribution set of new energy vehicles based on a multi-level robust optimization algorithm, and solving the optimal scheduling strategy for known scenarios and the search boundary for harsh scenarios by dividing the uncertainty problem of the VPP renewable energy vehicle network into a main problem and sub-problems; S52: Based on the optimal scheduling strategy, the sub-problems of the uncertainty problem are processed, and the sub-problems are converted into a single-layer optimization model through a dual transformation method. Based on the harsh scenario search boundary, the transaction constraints under the extreme scenario are generated through a convex relaxation technique; S53: Based on the optimal scheduling strategy and the transaction constraints, an alternating iterative solution process is performed, and a cross-regional trading strategy and equipment output plan under severe scenarios are generated by updating the parameter boundaries of the main problem and sub-problems of the uncertainty problem. The cross-regional trading strategy is used to guide the coordinated scheduling of multiple virtual power plants.

8. A collaborative trading system for multiple virtual power plants, characterized in that: The system comprises: A virtual power plant framework construction module is used to construct a low-carbon operation framework of the virtual power plant containing a multi-energy coupling model based on the acquired multi-source historical data, and to process the multi-source raw data based on the low-carbon operation framework of the virtual power plant to generate a multi-energy coupling data set; The multi-source historical data includes gas turbine historical operating data, energy storage device charge and discharge records, biomass fuel parameters, and new energy vehicle charging logs; the multi-energy coupling data set includes gas turbine output power, energy storage device charge and discharge constraint relationships, biomass power generation equipment power generation and carbon emission reduction, and new energy vehicle charge and discharge power distribution sets; The severe-ideal scenario generation module is used to perform joint probability modeling and sampling sorting on the acquired historical data sets of wind turbine and photovoltaic output, evaluate the historical scenario sets of the historical data sets of wind turbine and photovoltaic output, and generate a typical scenario set containing a severe scenario set and an ideal scenario set; An energy-load association module, configured to calculate the renewable energy-load association coefficient and the compensation amount for the multi-energy coupling data set and the typical scenario set, and generate a green energy consumption compensation amount; a trading mechanism construction module, configured to process the multi-energy coupling data set, the typical scenario set, and the green energy consumption compensation amount, establish a multi-agent trading mechanism, gradually iteratively optimize the purchase and sale electricity price from the upper layer and the multi-source transaction current from the lower layer, and generate a final optimized dynamic purchase and sale electricity price and transaction current feedback signal; The robust optimization solution module is used to perform robust scheduling processing on the typical scenario set, the transaction current feedback signal and the charging and discharging power distribution set of new energy vehicles in the multi-energy coupling data set based on a multi-level robust optimization solution algorithm, and generate cross-regional transaction strategies and equipment output plans under harsh scenarios. The cross-regional transaction strategies are used to guide the coordinated scheduling of multiple virtual power plants.

9. A device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of a collaborative trading method for multiple virtual power plants that combines carbon metering with green energy consumption incentives according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a collaborative trading method for multiple virtual power plants combining carbon metering and green energy consumption incentives according to any one of claims 1 to 7 are implemented.

Images