Virtual power plant optimal scheduling method based on carbon-green certificate transaction

Abstract

The invention discloses a virtual power plant optimal scheduling method based on carbon-green certificate transaction, and relates to the technical field of electric power, and the method comprises the steps: S1, building a carbon-green certificate joint transaction model according to a stepped carbon transaction mechanism and a green certificate transaction mechanism; s2, establishing a virtual power plant low-carbon model according to the P2G-CCS coupling model, the hydrogen fuel cell operation model and the hydrogen-doped gas unit model, and establishing constraint conditions in combination with system energy demand response; s3, determining an optimization target according to the carbon-green certificate combined transaction model and the virtual power plant low-carbon model, and constructing a virtual power plant optimization scheduling model; s4, a CPLEX solver is adopted to process the virtual power plant optimization scheduling model based on optimization constraints, and an optimization scheduling result is obtained; the energy utilization efficiency and the energy consumption efficiency in the operation process of the virtual power plant are improved, meanwhile, low-carbon operation of the virtual power plant is considered, and the problem that in conventional P2G-CCS coupling operation, the virtual power plant cannot give consideration to the energy utilization rate, the energy consumption efficiency and the low-carbon operation at the same time is solved.

Description

Technical Field

[0001] This application relates to the field of power technology, and specifically to an optimized scheduling method for a virtual power plant based on carbon-green certificate trading. Background Art

[0002] A virtual power plant (VPP) is an important technical means to integrate new energy resources and achieve peak shaving and valley filling. Achieving the green and low-carbon operation of a virtual power plant is of great significance for the low-carbon transformation of the energy system in the modern power industry.

[0003] To achieve the low-carbon economic operation of a virtual power plant, it is necessary to use low-carbon technologies to achieve the low-carbonization of each output unit. In the prior art, the carbon capture and storage technology (CCS) and other units such as organic Rankine cycle and three-dimensional balance of electric vehicle aggregators are coupled to achieve the low-carbonization of the operation of the virtual power plant. At the same time, to increase the proportion of renewable energy in the system, fuel cells and gas turbines are introduced to jointly achieve combined heat and power generation. By considering the impact of the carbon trading and green power certificate mechanisms on the system, the carbon emission reduction capacity of the virtual power plant is explored to maximize the operation benefits. However, none of these technologies have considered the utilization path of the hydrogen generated by power-to-hydrogen (P2H) during the coupling of CCS and power-to-gas (P2G), and have not fully considered the impact of the combined carbon-green certificate trading on the operation of the virtual power plant, resulting in low hydrogen utilization rate and low energy consumption efficiency.

[0004] Patent "An Optimal Scheduling Method for a Virtual Power Plant with CCS-P2G-EMC Coupling", Publication Number: CN118551949A, Publication Date: August 27, 2024, discloses: constructing a collaborative framework for a virtual power plant with CCS-P2G-EMC coupling, capturing carbon dioxide generated by coal-fired units through the CCS system for sequestration, and the carbon dioxide generated by gas turbines being converted into solid carbon by the EMC device through electrolysis to achieve two-time fixation and utilization of carbon dioxide; constructing a virtual power plant system and its corresponding system model, including a coal-fired unit with CCS and a P2G model, an EMC and CHP model, an energy storage system model, and a carbon emission model; constructing an optimal scheduling model for the virtual power plant; constructing the constraint conditions for the optimal scheduling model, including electric power balance constraint, thermal power balance constraint, and natural gas balance constraint; effectively reducing the total operating cost of the VPP and reducing carbon emissions. The main idea of this invention is to introduce an EMC device into the virtual power plant to capture and convert the carbon dioxide generated by gas turbines into carbon nanotubes, significantly reducing the operating cost and carbon emissions of the virtual power plant; mainly focusing on the fixation and utilization of carbon dioxide to reduce carbon emissions but unable to improve the overall operating performance of the system, such as not solving the impact on the operating flexibility, stability, and system production capacity during the carbon capture process, with poor applicability and efficiency. Summary of the Invention

[0005] This application addresses the problem that the prior art fails to solve the available conversion of resources in the carbon sequestration technology and the power-to-gas coupling process, as well as the energy efficiency impact caused by carbon trading, resulting in low operating performance of the virtual power plant; and proposes an optimal scheduling method for a virtual power plant based on carbon-green certificate trading. By establishing a carbon-green certificate joint trading model according to the stepped carbon trading mechanism and the green certificate trading mechanism, establishing constraint conditions based on the P2G-CCS coupling model, the hydrogen fuel cell operation model, and the hydrogen-blended gas turbine model combined with the system energy demand response, establishing an optimal scheduling model for the virtual power plant by combining the above models and using the CPLEX solver to solve the optimal scheduling model based on the constraint conditions to obtain the optimal scheduling result of the virtual power plant; comprehensively considering the hydrogen utilization rate during the power-to-hydrogen process and the impact of the carbon-green certificate joint trading on the operation of the virtual power plant, improving the energy utilization efficiency and energy consumption efficiency during the operation of the virtual power plant, and taking into account the low-carbon operation of the virtual power plant.

[0006] To solve the above technical problems, the technical solution adopted in this application is: an optimal scheduling method for a virtual power plant based on carbon-green certificate trading, including the following steps: S1. Establish a carbon-green certificate joint trading model according to the stepped carbon trading mechanism and the green certificate trading mechanism; S2. Establish a low-carbon model for the virtual power plant based on the P2G-CCS coupling model, the hydrogen fuel cell operation model, and the hydrogen-blended gas turbine unit model, and establish constraint conditions in combination with the system energy demand response; S3. Determine the optimization objective according to the carbon-green certificate joint trading model and the low-carbon model of the virtual power plant, and construct an optimized dispatching model for the virtual power plant; S4. Use the CPLEX solver to process the optimized dispatching model of the virtual power plant based on the optimization constraints to obtain the optimized dispatching result.

[0007] In this solution, by establishing a carbon-green certificate joint trading model, the impact of carbon-green certificate joint trading on the operation of the virtual power plant is fully considered, effectively increasing the proportion of renewable energy power generation, restricting the carbon emissions of the virtual power plant, and providing new ideas for the low-carbon operation of the virtual power plant and the improvement of the renewable energy consumption efficiency through the carbon-green certificate joint trading method; by adopting the P2G-CCS coupling operation mode, the power-to-gas technology is decomposed into two processes of power-to-hydrogen and methanation, and by adding hydrogen fuel cell equipment, the renewable energy power is increased, the system's dependence on traditional coal-fired power generation is reduced, and through carbon capture technology, the inflow of carbon dioxide into the atmosphere is effectively reduced. It can also be traded through the green certificate market to increase the revenue of the virtual power plant. Then, an organic Rankine cycle system is introduced to break the traditional "electricity determined by heat" mode, improve the flexibility of system operation, and improve the energy utilization rate. By determining the optimization objective through the carbon-green certificate joint trading model and the low-carbon model of the virtual power plant, an optimized dispatching model of the virtual power plant is constructed while considering both carbon-green certificate joint trading and P2G-CCS coupling, taking into account both the economy and low-carbon nature of the virtual power plant operation; using the CPLEX solver to solve the model, the non-linear model is transformed into a linear model, and the optimized dispatching result of the virtual power plant can be quickly calculated under various complex types of non-linear constraint conditions, improving the model solving rate and further improving the optimized dispatching efficiency of the virtual power plant, thereby improving the operation performance of all aspects of the virtual power plant.

[0008] Preferably, the S1 includes: Calculate the actual carbon emission quota for the virtual power plant to participate in carbon trading based on the initial carbon emission quota of the virtual power plant and the actual carbon emissions of the system; Adopt a stepped carbon trading mechanism to establish a carbon trading model for calculating carbon trading costs according to the actual carbon emissions and the actual carbon emission quota; Establish a green certificate model for calculating trading revenue according to the green power certificate data and the energy equipment output data during the virtual power plant transaction; Combine the carbon trading model and the green certificate model to obtain a carbon-green certificate joint trading model.

[0009] Preferably, calculating the actual carbon emission quota for the virtual power plant to participate in carbon trading based on the initial carbon emission quota of the virtual power plant and the actual carbon emissions of the system includes: Calculating the initial carbon emission quota based on the electric power of a gas turbine unit in one cycle and the corresponding carbon emission right allocation amount, the heating power and the corresponding carbon emission right allocation amount, and combining the electric power of a coal-fired unit in one cycle and the corresponding carbon emission right allocation amount; Obtaining the actual carbon emissions of the system by subtracting the carbon emissions from the emission, capture, and sequestration during the P2G-CCS coupled operation from the total carbon emissions of the virtual power plant; Calculating the actual carbon emission quota for the virtual power plant to actually participate in carbon market trading based on the initial carbon emission quota and the actual carbon emissions of the system.

[0010] Preferably, the carbon-green certificate joint trading model is expressed as follows: In the formula, is the carbon trading cost of the virtual power plant at time; is the basic price of carbon trading; L is the interval length of the difference between the given carbon emissions and the carbon quota; σ is the compensation coefficient for carbon emissions less than the carbon quota; θ is the increase in the carbon trading price when carbon emissions exceed the carbon quota; H gre is the number of green power certificates participating in the transaction in the virtual power plant system; F gre is the income of the green certificate of the virtual power plant system; P i is the output situation of the i-th renewable energy device in the system; t′ is the duration of the device participating in the scheduling; c gre is the price of the green power certificate; is the number of renewable energy devices in the virtual power plant.

[0011] Preferably, the S2 includes: Constructing a P2G-CCS coupling model based on the coupled operation mode of power-to-gas decomposition, carbon capture, and carbon sequestration during the operation of the virtual power plant; constructing a hydrogen fuel cell operation model based on the operation parameters of the hydrogen fuel cell, gas turbine, and waste heat boiler in the organic Rankine cycle mechanism of the virtual power plant; Constructing a hydrogen-doped gas turbine unit model based on the hydrogen-doping characteristics during the operation of the gas turbine and the operation parameters of the gas boiler; Forming a low-carbon model of the virtual power plant according to the P2G-CCS coupling model, the hydrogen fuel cell operation model, and the hydrogen-doped gas turbine unit model; Establishing the constraint conditions for the operation optimization of the virtual power plant according to the low-carbon model of the virtual power plant in combination with the load change characteristics during the system's energy consumption period.

[0012] Preferably, the load change characteristics during the system's energy consumption period are expressed as follows: In the formula, is the amount of transferable load of the k-th type of load participating in demand response at time t; is the amount of transferable load of the k-th type of load at time t; is the amount of load of the k-th type of load participating in demand response at time t; δ′ k and δ″ k respectively represent whether the state of the k-th type of load at time t is transferred in or out; the value of k is e, g, h (e is the electrical load, g is the gas load, and h is the heat load).

[0013] Preferably, the constraint conditions at least include wind power constraint, gas turbine power limit constraint and ramping constraint, gas boiler power limit constraint and ramping constraint, coal-fired unit power limit constraint and ramping constraint, CHP operation constraint, hydrogen fuel cell power limit constraint and ramping constraint, carbon capture constraint, demand response constraint, energy storage constraint, and power balance constraint.

[0014] Preferably, the S3 includes: Obtaining the green certificate trading revenue, carbon trading and carbon sequestration cost sum according to the carbon-green certificate joint trading model; Obtaining the energy purchase cost, wind curtailment cost, coal-fired unit start-stop cost, and coal consumption cost according to the virtual power plant low-carbon model; aiming at minimizing the operation cost of the virtual power plant, establishing an optimization objective function according to the green certificate trading revenue, carbon trading and carbon sequestration cost sum, the energy purchase cost, wind curtailment cost, coal-fired unit start-stop cost, and coal consumption cost to obtain the virtual power plant optimal scheduling model.

[0015] Preferably, the virtual power plant optimal scheduling model is expressed as follows: In the formula, f is the total cost of the virtual power plant operation; f buy is the energy purchase cost, representing the sum of the gas purchase and hydrogen purchase costs; is the sum of the carbon trading and carbon sequestration costs; f DR is the compensation cost of demand response; f cur is the wind curtailment cost; f th is the sum of the coal-fired unit start-stop and coal consumption costs; f gre is the green certificate trading revenue.

[0016] Preferably, the S4 includes: Converting the virtual power plant optimal scheduling model into a mixed integer linear model through piecewise linearization; Based on the above constraints, the CPLEX solver is used to solve the mixed-integer linear model, and the scheduling optimization results of the virtual power plant under the condition of minimum operating cost are output.

[0017] Advantages of this application: 1. Under the carbon trading and green certificate trading mechanisms, considering the coupled operation of power-to-gas and carbon capture, power-to-gas is decomposed into two stages: power-to-hydrogen and methanation. The produced hydrogen is used as the fuel of the hydrogen energy conversion equipment. At the same time, the joint output of the HFC equipment and the gas turbine is increased to achieve combined heat and power generation. By adopting the combined mechanism of carbon trading and green certificate trading, the actual carbon emissions in the system are effectively restricted, the operating cost of the system is reduced, and the low-carbon transformation process of the VPP is accelerated. Compared with the traditional carbon trading, the ladder-type carbon trading adopted significantly reduces the total operating cost of the system. 2. By adopting the P2G-CCS coupling mechanism and subdividing power-to-gas into the processes of power-to-hydrogen and methanation, the hydrogen generated in the process is mixed with natural gas in a fixed ratio, which improves the energy utilization rate of the system. And adding a carbon capture device effectively reduces the total amount of carbon dioxide flowing into the atmosphere, realizes carbon cycling, and effectively improves the low-carbon economic benefits of the virtual power plant. 3. Considering the combined carbon-green certificate trading and P2G-CCS coupling, adding a hydrogen fuel cell and a gas turbine unit together to form combined heat and power generation, and jointly outputting to the organic Rankine cycle to achieve heat determined by electricity, which improves the operating flexibility of the system, increases the system production capacity, and enhances the operating stability of the system. Description of the drawings

[0018] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objectives and advantages of this application will become more obvious. The drawings are only for the purpose of showing the preferred embodiments and are not considered as a limitation of this application. Moreover, throughout the drawings, the same reference symbols are used to represent the same components.

[0019] Figure 1 It is a flowchart of an optimization scheduling method for a virtual power plant based on carbon-green certificate trading according to an embodiment of this application.

[0020] Figure 2 It is a system structure diagram of the virtual power plant according to a specific embodiment of this application.

[0021] Figure 3 It is an initial load data diagram of the virtual power plant according to a specific embodiment of this application.

[0022] Figure 4 It is a comparison diagram of the total system cost and hydrogen purchase cost under different hydrogen blending ratios according to a specific embodiment of this application.

[0023] Figure 5 It is a comparison diagram of the hydrogen purchase cost and carbon emissions under different hydrogen prices according to a specific embodiment of this application.

[0024] Figure 6 This is a schematic diagram of the total system operation cost at different hydrogen valences in a specific embodiment of the present application.

[0025] Figure 7 This is a comparison chart of the total cost of system operation under different green certificate prices and carbon base prices in a specific embodiment of the present application.

[0026] Figure 8 This is a comparison chart of the system carbon emissions and operation costs under different compensation coefficients in a specific embodiment of the present application. Specific Embodiments

[0027] To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only the best embodiments of the present application, which are only used to explain the present application and do not limit the protection scope of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0028] Some of the nouns or terms that appear in the process of describing the embodiments of the present application are applicable to the following explanations: Green Certificate Trading Mechanism (GCT): It refers to the issuance of certificates to non-fossil energy power generators, gradually transitioning the subsidy method for renewable energy power generation from direct government subsidies to a market-based subsidy form. This mechanism effectively increases the proportion of renewable energy power generation.

[0029] P2G-CCS: A coupled and coordinated operation mode of power-to-gas technology and carbon capture and carbon sequestration technology.

[0030] Organic Rankine Cycle (ORC): It refers to an ideal cycle process using water vapor as the working fluid, mainly including isentropic compression, isobaric heating, isentropic expansion, and an isobaric condensation process. It is used for the power cycle of steam devices.

[0031] Embodiment 1: As Figure 1 shown, an optimized scheduling method for a virtual power plant based on carbon-green certificate trading includes the following steps: S1. Establish a carbon-green certificate joint trading model according to the stepped carbon trading mechanism and the green certificate trading mechanism.

[0032] Specifically, S1 includes: Calculate the actual carbon emission quota for the virtual power plant to participate in carbon trading based on the initial carbon emission quota of the virtual power plant and the actual carbon emissions of the system; Adopt the stepped carbon trading mechanism and establish a carbon trading model for calculating carbon trading costs according to the actual carbon emissions and the actual carbon emission quota; Establish a green certificate model for calculating trading revenue based on the green power certificate data and energy equipment output data during the virtual power plant transaction; Combine the carbon trading model and the green certificate model to obtain a carbon-green certificate combined trading model.

[0033] Specifically, calculating the actual carbon emission quota for the virtual power plant to participate in carbon trading based on the initial carbon emission quota of the virtual power plant and the actual carbon emissions of the system includes: Calculate the initial carbon emission quota according to the electric power of a gas turbine unit in one cycle and the corresponding carbon emission right allocation amount, the heating power and the corresponding carbon emission right allocation amount, combined with the electric power of a coal-fired unit in one cycle and the corresponding carbon emission right allocation amount; Obtain the actual carbon emissions of the system by subtracting the carbon emissions from the emission, capture and sequestration during the P2G-CCS combined operation from the total carbon emissions of the virtual power plant; Calculate the actual carbon emission quota for the virtual power plant to actually participate in carbon market trading based on the initial carbon emission quota and the actual carbon emissions of the system.

[0034] Specifically, the objects for allocating the actual carbon emission quota for the virtual power plant (hereinafter referred to as VPP) to participate in carbon trading are the gas turbine units for combined heat and power generation and the coal-fired units for thermal power generation. The initial carbon emission quota is calculated through the operating parameters of these units. The calculation formula is as follows: In the formula, E CSED is the initial amount of carbon emission rights for the entire VPP; are the carbon emission right allocation amounts for the power supply power and heating power of the gas turbine unit in one cycle respectively; is the carbon emission right allocation amount for the power supply power of the coal-fired unit in one cycle; P e (t), H h (t) are the electric power and thermal power of the gas turbine unit respectively; P th (t) is the electric power of the coal-fired unit; T is a scheduling cycle of the model.

[0035] Furthermore, considering that P2G-CCS will capture and sequester some CO2 and some CO2 will flow into the atmosphere, the actual carbon emissions of the VPP in this application are the total emissions of the system minus the carbon dioxide amounts processed in these two parts. The total carbon emissions of the VPP are calculated as follows: In the formula, λ1 and λ2 are the carbon emission intensities corresponding to consuming unit natural gas and the carbon emission intensity of the coal-fired unit respectively; is the power corresponding to the natural gas consumed by the gas boiler in a scheduling cycle; It is the power corresponding to the natural gas consumed by the combined heat and power gas turbine within a scheduling period.

[0036] Furthermore, after calculating the initial carbon emission allowance of the VPP and the actual carbon emissions of the system, calculate the carbon emission allowance that the VPP actually participates in the carbon market transaction: In the formula, E air is the net CO2 emissions of the VPP system; E1 is the amount of CO2 discharged into the atmosphere at time t; E2 is the amount of carbon dioxide not captured during the carbon capture process at time t; E J is the amount of CO2 absorbed by CCS at time t; γ1 is the carbon capture efficiency.

[0037] Specifically, the carbon-green certificate joint trading model is expressed as follows: In the formula, is the carbon trading cost of the virtual power plant at time is the basic price of carbon trading; L is the interval length of the difference between the given carbon emissions and the carbon quota; σ is the compensation coefficient when the carbon emissions are less than the carbon quota; θ is the increase in the carbon trading price when the carbon emissions exceed the carbon quota; H gre is the number of green power certificates participating in the transaction in the virtual power plant system; F gre is the income of the green certificate of the virtual power plant system; P i is the output of the i-th renewable energy device in the system; t' is the duration of the device participating in the scheduling; c gre is the price of the green power certificate; is the number of renewable energy devices in the virtual power plant.

[0038] In this embodiment, a stepped carbon trading mechanism is adopted. Compared with the traditional carbon trading model, it can better constrain carbon emissions and calculate carbon trading costs. The stepped carbon trading model gives a fixed interval for the difference between the actual carbon emissions and the carbon emission quota. If the difference exceeds the given interval range, then the quota needs to be purchased and the carbon trading cost increases; if the actual emissions are less than the given quota, the unused quota can be sold to obtain the income of carbon emission reduction. According to the coordinated operation of the carbon trading mechanism and the green power certificate mechanism, the carbon emissions of the virtual power plant are effectively constrained.

[0039] S2. Build a low-carbon model of the virtual power plant according to the P2G-CCS coupling model, the hydrogen fuel cell operation model, and the hydrogen-doped gas turbine unit model Specifically, the S2 includes: Construct a P2G-CCS coupling model based on the coupling operation mode of power-to-gas decomposition, carbon capture, and carbon sequestration during the operation of a virtual power plant; construct a hydrogen fuel cell operation model based on the operation parameters of hydrogen fuel cells, gas turbines, and waste heat boilers in the virtual power plant under the organic Rankine cycle mechanism; Construct a hydrogen-blended gas unit model based on the hydrogen blending characteristics during the operation of a gas turbine and the operation parameters of a gas boiler; Form a low-carbon model of the virtual power plant according to the P2G-CCS coupling model, the hydrogen fuel cell operation model, and the hydrogen-blended gas unit model; Establish the constraint conditions for the operation optimization of the virtual power plant according to the low-carbon model of the virtual power plant in combination with the load change characteristics during the system's energy consumption period.

[0040] In some embodiments, as Figure 2 shown, the virtual power plant coordinates multiple small-scale power generation units and loads, conducts centralized dispatching and optimization management, reduces carbon emissions, promotes the generation of green certificates, expands the revenue sources of the virtual power plant while promoting the development of renewable energy. In this embodiment, first, the power-to-gas technology is decomposed into two processes: power-to-hydrogen and methanation, and hydrogen fuel cell equipment is added to increase the amount of renewable energy electricity, reduce the system's dependence on traditional coal-fired power generation units, and effectively reduce the inflow of carbon dioxide into the atmosphere through carbon capture technology. It can also be traded through the green certificate market to increase the revenue of the virtual power plant. Then, an organic Rankine cycle system is introduced to break the traditional "heat determines power" mode and improve the flexibility of system operation. Finally, an energy storage device is used to store the unused energy, thereby improving the energy utilization rate.

[0041] It can be understood that the P2G technology can be decomposed into two processes including electrolytic hydrogen production (P2H) and methanation. In the P2H process, a part of the produced hydrogen can be used as the raw material for gas turbines and gas boilers; methanation converts CO2 into natural gas, which realizes carbon cycling, effectively reduces carbon emissions, and the addition of hydrogen to the fuel of gas turbines and gas boilers can also reduce carbon dioxide emissions to a certain extent.

[0042] Specifically, the volume of CO2 required for the methanation process is equal to the volume of CH4 generated. The specific calculation formula is as follows: In the formula, is the electric power input to P2G at time t; is the power of the natural gas output by P2G at time t; η P2G is the conversion efficiency of P2G; V Gas (t) are the volumes of CO2 absorbed and natural gas generated by P2G at time t, respectively; H g is the calorific value of natural gas; is the mass of CO₂ absorbed by P2G at time t; is the CO₂ density.

[0043] Specifically, the CCS technology includes two processes: carbon capture and carbon sequestration. During the operation of the virtual power plant, CO₂ is generated during the operation of gas turbines, gas boilers, and coal-fired units. Part of the carbon dioxide is discharged into the atmosphere along with the flue gas during the generation process, and another part of the carbon dioxide is captured by the CCS technology. However, the capture efficiency cannot reach 100%, so a small amount of carbon dioxide will also be discharged into the atmosphere during the capture process. The captured CO₂ can be sequestered as a raw material for methanation, thus realizing the coupled operation of P2G-CCS.

[0044] The P2G-CCS coupling model is as follows: In the formula, H CCS (t) is the total power of CCS in the t period; is the operating energy consumption of CCS in the t period; is the fixed energy consumption of CCS, which can be regarded as a constant; H G (t) is the power generation power of the coal-fired or gas-fired unit in the t period; e G is the carbon emission intensity per unit of electric power generated; γ1 is the carbon capture efficiency of CCS, and the value in this article is 90%; λ GE is the electric power consumed per unit of CO₂ captured; E CCT (t) is the capture amount of CO₂ in the t period; is the upper limit of the operating power of CCS.

[0045] In some embodiments, during the operation of the VPP system, a solid oxide fuel cell (SOFC) is adopted. This type of cell can operate stably in a high-temperature environment and generate waste heat while generating electricity using hydrogen energy. Therefore, waste heat can be collected using a waste heat boiler (WHB) while the hydrogen fuel cell (HFC) device generates electricity, effectively improving the energy utilization efficiency. At the same time, the waste heat generated during gas turbine power generation can also be collected and utilized to achieve combined heat and power generation.

[0046] Furthermore, this embodiment also introduces an organic Rankine cycle (ORC) combined with combined heat and power generation to construct a hydrogen fuel cell operation model. ORC can generate electricity using waste heat and can cooperate with gas turbines, hydrogen fuel cells, and waste heat boilers to improve the flexible response of the supply side. The hydrogen fuel cell operation model is as follows: In the formula, are the electric power and thermal power output at time t respectively; P HFC is the hydrogen power input to the HFC at time t; They are the power generation efficiency and heat generation efficiency of the hydrogen fuel cell respectively.

[0047] Furthermore, the combination of a hydrogen fuel cell, a gas turbine, a waste heat boiler, and an organic Rankine cycle realizes combined heat and power (CHP). The calculation formula of the CHP model is as follows: In the formula, They are the thermal powers of the hydrogen fuel cell, the gas turbine, the waste heat boiler, and the organic Rankine cycle respectively; is the thermal power output from the gas turbine to the CHP; P′ WHB , P″ WHB are the thermal powers input and output by the waste heat boiler respectively; P′ ORC , P″ ORC are the thermal power input and the electric power output of the organic Rankine cycle respectively; μ WHB , μ WHB are the conversion efficiencies of the waste heat boiler and the organic Rankine cycle system respectively; is the electric power output by the combined heat and power; is the thermal power output by the combined heat and power.

[0048] In this embodiment, part of the thermal power generated by the hydrogen fuel cell and the gas turbine is collected and utilized by the waste heat boiler to generate a heat load, and the other part of the waste heat is utilized by the ORC to produce an electric load, realizing a flexible response on the supply side.

[0049] In some embodiments, the hydrogen-blended gas unit model mainly includes a hydrogen-blended gas turbine model and a hydrogen-blended gas boiler model; the hydrogen generated during the P2H process with natural gas is mixed within a certain proportion range and supplied as fuel to the gas unit. The hydrogen-blended gas turbine model is as follows: In the formula, R GT is the hydrogen blending ratio of the gas turbine; is the volume of hydrogen and natural gas for the gas turbine operation at time t; They are the powers of H2 and CH4 of the gas turbine within time t respectively; They are the calorific values of H2 and CH4 respectively; They are the thermal power and electric power output by the gas turbine at time t respectively; λ GT.H , λ GT.E are the thermal conversion efficiency and electric conversion efficiency of the gas turbine respectively.

[0050] The hydrogen-blended gas boiler model is as follows: Wherein, R GB is the hydrogen blending ratio of the gas boiler; is the volume of hydrogen and natural gas in the gas boiler at time t; are the powers of H2 and CH4 in the gas boiler within time t, respectively; is the thermal power output by the gas turbine within time t; λ GB.H are the thermal conversion efficiencies of the gas turbine, respectively.

[0051] Specifically, the load change characteristics during the energy consumption period of the system are expressed as follows: Wherein, is the load transferable amount of the k-th type of load after participating in demand response at time t; is the load transferable amount of the k-th type of load at time t; is the load amount of the k-th type of load participating in demand response at time t; δ′ k , δ″ k respectively represent whether the state of the k-th type of load at time t is transferred in or out; the value of k is e, g, h (e is the electric load, g is the gas load, h is the heat load).

[0052] In this embodiment, it is considered that during the peak energy consumption period of the system, some loads can be transferred to the low energy consumption period, and multiple loads can be converted with each other in the same period, improving the flexibility of demand response and the stability of the system.

[0053] Specifically, the constraint conditions at least include wind power constraint, gas turbine power limit constraint and ramp constraint, gas boiler power limit constraint and ramp constraint, coal-fired unit power limit constraint and ramp constraint, CHP operation constraint, hydrogen fuel cell power limit constraint and ramp constraint, carbon capture constraint, demand response constraint, energy storage constraint, and power balance constraint.

[0054] Type and establish constraint conditions in combination with the system energy demand response.

[0055] Specifically, the calculation formulas of each constraint condition are as follows: Wind power constraint: Wherein, P WT is the predicted wind power at time t; P GD is the power supply of wind power at time t.

[0056] Gas turbine power limit constraint and ramp constraint: Wherein, is the total output of the gas turbine unit at time t; are the lower and upper limits of the electrical output of GT at time t, respectively; are the lower and upper limits of the heat output of GT at time t, respectively; ΔP GT.D 、ΔP GT.U are the lower and upper limits of the ramp power of GT, respectively.

[0057] Power limit constraint and ramp constraint of gas-fired boiler: In the formula, are the lower and upper limits of the heat output of GB in the period from t to t-1, respectively; ΔP GB.D 、ΔP GB.U are the lower and upper limits of the ramp power of GB, respectively.

[0058] Power limit constraint and ramp constraint of coal-fired unit: In the formula, is the heat output of the coal-fired unit at time t; P th.min 、P th.max are the lower and upper limits of the heat output of the coal-fired unit in the period from t to t-1, respectively; ΔP th.D 、ΔP th.U are the lower and upper limits of the ramp power of the coal-fired unit, respectively.

[0059] CHP operation constraint: In the formula, P′ WHB.min 、P′ WHB.max are the lower and upper limits of the heat output of the waste heat boiler at time t, respectively; P′ ORC.min 、P′ ORC.max are the lower and upper limits of the heat output of ORC in period t, respectively; ΔP′ WHB.D ΔP′ WHB.U are the lower and upper limits of the ramp power of the waste heat boiler, respectively; ΔP′ ORC.D 、ΔP′ ORC.U are the lower and upper limits of the ramp power of ORC, respectively.

[0060] Power limit constraint and ramp constraint of hydrogen fuel cell: In the formula, P HFC.min 、P HFC.max are the lower and upper limits of the heat output of the coal-fired unit at time t, respectively; ΔP HFC.D 、ΔP HFC.U are the lower and upper limits of the ramp power of the coal-fired unit, respectively.

[0061] Carbon capture constraint: 0 ≤ H CCS (t) ≤ H ccs.max (21) Wherein, H ccs.max is the upper limit of the power for the operation of the carbon capture equipment.

[0062] Demand response constraint: Wherein, represents the upper and lower limits of the load value of the k-th type of load participating in demand response at time t.

[0063] Energy storage constraint: Wherein, is the net input power of the energy storage at time t; are the charging power and discharging power at time t, respectively; are 0-1 variables, representing the charging efficiency and discharging efficiency respectively; is the load state of the energy storage at time t; are the maximum charging power and maximum discharging power at time t, respectively; are the upper and lower limits of the energy storage capacity at time t, respectively; s ∈ {E, H, G, H2} represents the set of electrical, thermal, natural gas, and hydrogen energy storages.

[0064] In some examples, the parameters of the energy storage device are shown in Table 1: Table 1 Parameters of the Energy Storage Device

[0065] In some other examples, the parameters of each device in the virtual power plant system are shown in Table 2: Table 2 Device Parameters

[0066] Power balance constraint: Wherein, is the power of hydrogen generated during the power-to-gas process at time t; represents the power of hydrogen consumed during the methanation process.

[0067] In this embodiment, by establishing wind power constraints, the problem that the output of wind turbines changes frequently due to the uncertainty of wind speed is overcome, which helps the virtual power plant balance the output of wind power and other distributed energy sources and achieve optimal allocation of resources. By the gas turbine power limit constraint and ramp constraint, the stable operation of the unit and the safety of the power grid are ensured. By the gas boiler power limit constraint and ramp constraint, the stable and efficient operation of the gas boiler is guaranteed. By the coal-fired unit power limit constraint and ramp constraint, the safety operation requirements of the coal-fired unit are ensured, which helps the coal-fired unit operate in an efficient and stable state and reduces the impact on the environment at the same time. By the CHP operation constraint, the coordinated and optimized operation of the heat and power systems is realized to improve the energy utilization efficiency. By the hydrogen fuel cell power limit constraint and ramp constraint, the output change of the hydrogen fuel cell is smoothed and the impact on the power grid is reduced. By the carbon capture constraint, the carbon emissions of the virtual power plant are reduced and the recycling of carbon is realized. By the demand response constraint and power balance constraint, the electricity consumption behavior on the load side is regulated to ensure that the electricity produced by the virtual power plant at each moment is consistent with the demand at the load end, achieve the balance of power supply and demand, and improve the stability and economy of the power grid. By the energy storage constraint, the output fluctuation of renewable energy is smoothed, and the stability and reliability of the power grid are further improved.

[0068] S3. Determine the optimization objective according to the carbon-green certificate joint trading model and the virtual power plant low-carbon model, and construct a virtual power plant optimal scheduling model.

[0069] Specifically, the S3 includes: Obtain the green certificate trading revenue, carbon trading and carbon sequestration costs according to the carbon-green certificate joint trading model; Obtain the energy purchase cost, wind curtailment cost, start-stop cost of coal-fired units and coal consumption cost according to the virtual power plant low-carbon model; with the goal of minimizing the operation cost of the virtual power plant, establish an optimization objective function according to the green certificate trading revenue, carbon trading and carbon sequestration costs, the energy purchase cost, wind curtailment cost, start-stop cost of coal-fired units and coal consumption cost to obtain the virtual power plant optimal scheduling model.

[0070] Specifically, the virtual power plant optimal scheduling model is expressed as follows: In the formula, f is the total cost of the VPP; f buy is the energy purchase cost, representing the sum of the gas purchase cost and the hydrogen purchase cost; is the sum of the carbon trading and carbon sequestration costs; f DR is the compensation cost of demand response; f cur is the wind curtailment cost; f th is the sum of the start-stop and coal consumption costs of coal-fired units; f gre is the green certificate trading revenue.

[0071] Specifically, the purchase energy cost includes the hydrogen purchase cost and the gas purchase cost, and the calculation formula is as follows: In the formula, c H and c g are the unit prices of hydrogen and natural gas respectively; are the costs corresponding to the VPP system purchasing hydrogen and natural gas at time t respectively, and T is a scheduling period.

[0072] Carbon trading and carbon sequestration costs: In the formula, is the carbon trading cost; is the unit carbon sequestration cost.

[0073] Wind curtailment cost: In the formula, P cur (t) is the wind curtailment power at time t; λ cur is the penalty cost per unit of wind curtailment power.

[0074] Start-up and shut-down costs and coal consumption costs of coal-fired units: In the formula, f′ th and f″ th are the start-up and shut-down costs and coal consumption costs of the coal-fired unit respectively; C th is the start-up cost coefficient; b t represents the operating state of the coal-fired unit at time t; c th1 and c th2 and c th3 are the coal consumption cost coefficients.

[0075] In this embodiment, by simultaneously considering the carbon-green certificate joint trading and the P2G-CCS coupling, an optimal scheduling model of the virtual power plant is constructed, realizing both the economy and low carbon of the virtual power plant operation.

[0076] S4. Use the CPLEX solver to process the optimal scheduling model of the virtual power plant based on the above optimization constraints, and obtain the optimal scheduling result.

[0077] Specifically, the S4 includes: Transform the optimal scheduling model of the virtual power plant into a mixed-integer linear model through piecewise linearization; Based on the above constraint conditions, use the CPLEX solver to solve the mixed-integer linear model, and output the scheduling optimization result of the virtual power plant under the condition of the minimum operating cost.

[0078] In some embodiments, the virtual power plant optimization scheduling model is a mixed-integer non-linear model. After using piecewise linearization to transform the established model into a mixed-integer linear model, the CPLEX solver can be called through MATLAB+YALMIP to solve the model, so as to improve the model calculation efficiency. The specific decomposition method is as follows: Linearization of piecewise linear function: To limit the values of x and b to a single interval, introduce binary variable y i (i = 1, 2, 3) and continuous variable z i (i = 1, 2, 3). If the continuous variable z = y1y2 satisfies the following conditions: Then (31) and y1y2 are equivalent. Therefore, (30) is transformed into (31), and the two are equivalent, thus transforming the mixed-integer non-linear model into a linear model: As a specific example analysis, in this embodiment, the virtual power plant operation system includes a wind farm, a gas boiler, a gas turbine, a coal-fired unit, a hydrogen fuel cell device, a waste heat boiler, a P2G device, a CCS device, and electricity storage, heat storage, hydrogen storage, and gas storage devices. The initial source-load data is as Figure 3 shown.

[0079] Furthermore, to verify the economy and low carbon of the P2G-CCS coupling model, three scenarios are set for comparative verification. In Scenario 1, CCS is not considered, and the emitted carbon dioxide is directly discharged into the atmosphere; in Scenario 2, methanation is not considered, and the produced hydrogen is used as fuel for the hydrogen energy equipment; in Scenario 3, P2G-CCS coupling is considered. In all three scenarios, carbon-green certificate joint trading and a fixed hydrogen blending ratio of 10% are adopted. The scheduling optimization results are shown in Table 3. In Scenario 1, CCS is not considered, and the carbon emission is as high as 2719.093 tons, which is directly discharged into the atmosphere, and the carbon trading cost is positive, indicating that the emissions have exceeded the given carbon quota and a large amount of quota needs to be purchased, and the total cost is much higher than that of Scenario 2 and Scenario 3 using CCS technology; in Scenario 2, the methanation process is not considered, and the hydrogen produced by the power-to-gas technology is all used as fuel for the hydrogen energy conversion equipment. Compared with Scenario 1, the carbon emission in Scenario 2 has decreased by up to 88.57%, and the carbon trading cost is negative, indicating that there are unused carbon quotas that can be sold to achieve carbon revenue after carbon reduction and reduce the total system operation cost; in Scenario 3, P2G-CCS coupling is considered, which not only reduces the system's carbon dioxide emissions but also utilizes the methanation process to achieve carbon circulation within the VPP system. The system's energy purchase cost is greatly reduced, that is, the system operation is made low-carbon and the economic requirements of the system operation are also met; Table 3 Scheduling optimization of Scenario 1, Scenario 2, Scenario 3 and Scenario 4 Table 4 Scheduling cost and HCP equipment output of Scenario 5 and Scenario 6 Table 5 Scheduling optimization of Scenario 5, Scenario 7 and Scenario 8

[0080] Furthermore, the operation benefits of hydrogen blending in the gas turbine are analyzed. Based on Scenario 3, Scenario 4 and Scenario 5 are set up. In the operation system of Scenario 4, the fuel of the gas turbine does not contain hydrogen. In Scenario 5, the hydrogen blending ratio of the system is adjusted to 20%. The results of the dispatching operation are shown in Table 3 and Table 5. In Scenario 4, the fuels of the gas turbine and the gas boiler do not contain hydrogen. Compared with Scenario 3, the carbon emissions increase by 18%, which is not conducive to the low-carbon development of the VPP system and the operation total cost and the energy purchase cost increase. In Scenario 5, the proportion of hydrogen blending in the gas turbine is increased. The carbon emissions are reduced by 5.542 tons compared with Scenario 3, and the energy purchase cost is reduced by 72,760 yuan.

[0081] The influence of the hydrogen blending ratio of the gas turbine on the low-carbon and economic performance of the VPP system. The hydrogen blending ratios of the system are set to 2%, 6%, 10%, 14% and 18% respectively. The system carbon emissions and the hydrogen purchase cost are as Figure 4 shown. With the increase of the hydrogen blending ratio, the output of the gas turbine increases, and the cost of purchasing hydrogen energy also increases. However, the required load of the system is certain. The system carbon emissions decrease, and the carbon quota that the system can sell increases. Therefore, the operation total cost decreases. Therefore, increasing the proportion of hydrogen in the gas of the system gas turbine can increase the proportion of renewable energy in the system and increase the benefits of VPP operation. However, in order to ensure the stable operation of the system, the hydrogen blending ratio needs to be controlled within 20%. Therefore, the hydrogen blending ratio can be controlled between 18% and 19%, which can not only maintain the stable operation of the system, but also meet the economic and low-carbon requirements of the system.

[0082] Furthermore, an analysis was conducted on the impact of adding HFC equipment and hydrogen price on the system operation benefits. In Scenario 5, hydrogen energy conversion equipment was added, and a combined heat and power generation system was formed by HFC and GT. In Scenario 6, HFC was not set, and only GT was considered to form a combined heat and power generation system. The results of the system optimal dispatch are shown in Table 4. Compared with Scenario 6, the total cost and curtailment cost in Scenario 5 both decreased, the carbon emissions decreased by 1.167 tons, and the total output of HCP increased by 22.909 MW·h. This proves that the newly added HFC equipment and gas turbine in the system jointly output to the ORC. Besides breaking the traditional heat-determined power constraint, it realizes power-to-heat to improve the flexibility of system operation, and at the same time can ensure the total cost and carbon emissions, and enhance the production capacity of the system.

[0083] The hydrogen price set in Scenario 3 was 1.5 yuan per standard cubic meter. Now the hydrogen price is adjusted to analyze the impact of hydrogen price on the operation cost and carbon emissions of the system. The operation results are as Figure 5 and Figure 6 shown: As the hydrogen price rises, both the hydrogen purchase cost and the total cost of the system show an upward trend. However, the carbon emissions of the system are the lowest when the hydrogen price is 1.6, and when the hydrogen price is greater than 1.6 yuan per standard cubic meter, the carbon emissions gradually increase and no low-carbon benefits can be generated. Therefore, when the hydrogen price is between 1.5 and 1.6 yuan per standard cubic meter, it meets the low-carbon and economic requirements of system operation. If the hydrogen price is too high, it will violate the low-carbon and economic requirements of the virtual power plant system operation.

[0084] Furthermore, an analysis was conducted on the impact of carbon trading and green certificate trading mechanisms. Based on Scenario 5, Scenario 7 and Scenario 8 were set. In Scenario 7, carbon trading and green certificate trading were not considered in the VPP system operation. In Scenario 8, only stepped carbon trading was considered during the VPP operation. The dispatching optimization results of Scenario 7 and Scenario 8 are shown in Table 5. The total operation cost of the system under the Scenario 7 model is 1.10535 million yuan more than that of Scenario 5, and the carbon emissions are 10.839 tons more than those of Scenario 5. Without considering policy constraints and only relying on low-carbon technologies to constrain the carbon emissions of the system, to a certain extent, the inflow of carbon dioxide into the atmosphere can be controlled, but the benefits of carbon trading and green certificate trading are lost, and the operation cost of the system increases significantly. Scenario 8 only considers stepped carbon trading, and compared with Scenario 7, there is a profit from selling unused carbon quotas, so the total cost and carbon emissions are both lower than those of Scenario 7. However, the green certificate trading mechanism is not considered, so the system operation cost cannot be further reduced, and without the constraint of the green certificate trading mechanism, the carbon emissions also increase compared with Scenario 5.

[0085] As Figure 7 shown, the impact of carbon trading price and green certificate trading price on the total system operation cost. The increase in carbon trading price and the rise of green certificate price can both increase the system's revenue and reduce the system operation cost, while meeting the economic requirements of virtual power plant system operation while constraining carbon emissions.

[0086] Furthermore, based on Scenario 5, the system operation under stepped carbon trading and traditional carbon trading is discussed. Under both carbon trading methods, the total system operation cost decreases with the increase of the carbon base price. However, under the same carbon base price, the total system operation cost of traditional carbon trading is higher than that of stepped carbon trading. Moreover, under the same compensation coefficient, the traditional carbon trading mode is more sensitive to the change of the carbon base price, with larger carbon emission fluctuations. When the carbon base price reaches 140 yuan, the carbon emissions tend to be stable. In contrast, the carbon emissions of stepped carbon trading are stable, the risk-bearing capacity of the system increases, and the overall stability of the system operation is improved.

[0087] It can be understood that when the carbon emissions are less than the actual carbon quota in stepped carbon trading, the carbon quota can be sold to obtain income. At this time, the compensation coefficient will become an important influencing factor participating in carbon trading. When the actual carbon emissions decrease and the unused carbon quota increases, the price of stepped carbon trading also continuously rises. As Figure 8 shown, under different compensation coefficients for four carbon base prices, the actual carbon emissions and operation costs of the system. With the increase of the compensation coefficient, the system tries to reduce the amount of carbon dioxide emitted into the atmosphere to obtain more carbon trading income. Moreover, the higher the carbon base price, the lower the sensitivity of the system to the compensation coefficient. When the carbon base price reaches 250 yuan, the influence of the compensation coefficient is negligible, and the carbon emissions of the system tend to be stable.

[0088] Beneficial effects of the embodiment: By establishing a carbon-green certificate joint trading model, the impact of carbon-green certificate joint trading on the operation of virtual power plants is fully considered, effectively increasing the proportion of renewable energy generation, restricting the carbon emissions of virtual power plants, and providing new ideas for the low-carbon operation of virtual power plants and the improvement of renewable energy consumption efficiency; By adopting the P2G-CCS coupled operation mode, the power-to-gas technology is decomposed into two processes: power-to-hydrogen and methanation, and hydrogen fuel cell equipment is added to increase the amount of renewable energy electricity, reduce the system's dependence on traditional coal-fired power generation, and effectively reduce the inflow of carbon dioxide into the atmosphere through carbon capture technology. It can also be traded through the green certificate market to increase the revenue of virtual power plants. Then, an organic Rankine cycle system is introduced to break the traditional "electricity determined by heat" mode, improve the flexibility of system operation, and improve the energy utilization rate. By determining the optimization objectives through the carbon-green certificate joint trading model and the virtual power plant low-carbon model, an optimization scheduling model of the virtual power plant is constructed while considering both carbon-green certificate joint trading and P2G-CCS coupling, taking into account both the economy and low-carbon characteristics of virtual power plant operation; The CPLEX solver is used to solve the model, converting the non-linear model into a linear model, and quickly calculating the model under various complex types of non-linear constraint conditions to obtain the optimization scheduling results of the virtual power plant, improving the model solving rate and further improving the optimization scheduling efficiency of the virtual power plant, thereby improving the operation performance of the virtual power plant in all aspects. It solves the problem of low operation performance of virtual power plants caused by the inability of existing technologies to solve the available conversion of resources in the carbon sequestration technology and power-to-gas coupling process, as well as the energy efficiency impact caused by carbon trading, significantly improving the flexibility of virtual power plant operation, increasing system production capacity, and enhancing the stability of system operation.

[0089] The above specific implementation manners are the preferred implementation manners of this application, and do not limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation manner. Any equivalent changes made according to the shape, structure, and method of this application are within the protection scope of this application.

Claims

1. A virtual power plant optimization scheduling method based on carbon-green certificate trading, characterized by: The steps include: S1. Establish a carbon-green certificate joint trading model based on the ladder carbon trading mechanism and green certificate trading mechanism; S2. Build a virtual power plant low-carbon model based on the P2G-CCS coupling model, hydrogen fuel cell operation model, and hydrogen-blended gas unit model, and establish constraints in combination with the system energy demand response; S3. Determine the optimization target according to the carbon-green certificate joint trading model and the virtual power plant low-carbon model, and build a virtual power plant optimization scheduling model; S4. Using a CPLEX solver to process the virtual power plant optimization scheduling model based on the optimization constraints to obtain an optimization scheduling result.

2. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 1 is characterized by: The S1 includes: Calculate the actual carbon emission quota of the virtual power plant participating in carbon trading based on the initial carbon emission quota of the virtual power plant and the actual carbon emissions of the system; Adopting a tiered carbon trading mechanism, and establishing a carbon trading model for calculating carbon trading costs according to the actual carbon emissions and the actual carbon emission quota; A green certificate model for calculating transaction revenue is established based on the green power certificate data and energy equipment output data during virtual power plant transactions; The carbon trading model and the green certificate model are combined to obtain a carbon-green certificate joint trading model.

3. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 2 is characterized in that: The actual carbon emission quota of the virtual power plant participating in carbon trading is calculated based on the initial carbon emission quota of the virtual power plant and the actual carbon emissions of the system, including: The initial carbon emission quota is calculated based on the electric power of the gas-fired unit for one cycle and the corresponding carbon emission right allocation, the heating power and the corresponding carbon emission right allocation, combined with the electric power of the coal-fired unit for one cycle and the corresponding carbon emission right allocation; The actual carbon emissions of the system are obtained by subtracting the carbon emissions of emissions, capture and sealing during the operation of P2G-CCS coupling from the total carbon emissions of the virtual power plant; The actual carbon emission quota of the virtual power plant that actually participates in carbon market transactions is calculated based on the initial carbon emission quota and the actual carbon emissions of the system.

4. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 2 is characterized in that: The carbon-green certificate joint trading model is as follows: In the formula, f co2 for Carbon trading costs of virtual power plants at all times; is the basic price of carbon trading; L is the interval length of the difference between a given carbon emission and carbon quota; σ is the compensation coefficient when carbon emissions are less than carbon quota; θ is the increase in carbon trading price when carbon emissions exceed carbon quota; H gre The number of green electricity certificates participating in transactions in the virtual power plant system; gre is the income of green certificate of virtual power plant system; P i is the output of the i-th renewable energy device in the system; t′ is the duration of the device participating in the scheduling; c gre is the price of green electricity certificate; is the number of renewable energy devices in the virtual power plant.

5. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 1 is characterized in that: The S2 includes: A P2G-CCS coupling model is constructed based on the coupled operation mode of power-to-gas decomposition, carbon capture and storage in the virtual power plant. A hydrogen fuel cell operation model is constructed based on the operating parameters of hydrogen fuel cells, gas turbines and waste heat boilers in the virtual power plant under the organic Rankine cycle mechanism. A hydrogen-blended gas unit model is constructed based on the hydrogen-blended characteristics of the gas turbine during operation and the operating parameters of the gas boiler; A virtual power plant low-carbon model is obtained according to the P2G-CCS coupling model, the hydrogen fuel cell operation model and the hydrogen-blended gas unit model; According to the virtual power plant low-carbon model and the load variation characteristics of the system energy consumption period, constraint conditions for optimizing the operation of the virtual power plant are established.

6. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 5 is characterized in that: The load variation characteristics of the system energy consumption period are expressed as follows: In the formula, is the load amount that can be transferred after the kth load participates in demand response at time t; is the load amount that can be transferred for the kth load at time t; is the load amount of the kth load participating in demand response at time t; δ k ′、δ k ″ respectively represent whether the state of the k-th load at time t is transferred in or out; the value of k is e, g, h, where e is the electrical load, g is the gas load, and h is the thermal load.

7. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 5 is characterized in that: The constraints include at least wind power constraints, gas unit power limit constraints and ramp constraints, gas boiler power limit constraints and ramp constraints, coal-fired unit power limit constraints and ramp constraints, CHP operation constraints, hydrogen fuel cell power limit constraints and ramp constraints, carbon capture constraints, demand response constraints, energy storage constraints and power balance constraints.

8. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 5 is characterized in that: The S3 includes: According to the carbon-green certificate joint trading model, the green certificate trading income, carbon trading and carbon storage costs and are obtained; According to the virtual power plant low-carbon model, the energy purchase cost, wind abandonment cost, coal-fired unit start-up and shutdown cost, and coal consumption cost are obtained; With the goal of minimizing the operating costs of virtual power plants, an optimization objective function is established based on the green certificate trading income, carbon trading and carbon storage costs, the energy purchase cost, wind abandonment cost, coal-fired unit start-up and shutdown cost, and coal consumption cost to obtain an optimal scheduling model for virtual power plants.

9. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 8 is characterized in that: The virtual power plant optimization scheduling model is expressed as follows: minf=f buy +f co2 +f cur +f th -f gre ; Where f is the total cost of operating the virtual power plant; buy is the energy purchase cost, representing the sum of the gas purchase cost and the hydrogen purchase cost; is the sum of carbon trading and carbon sequestration costs; f DR is the compensation cost for demand response; cur is the wind curtailment cost; f th is the sum of the start-up and shutdown costs of coal-fired units and coal consumption costs; f gre The proceeds from green certificate trading.

10. The virtual power plant optimization scheduling method based on carbon-green certificate trading according to claim 1 is characterized by: The S4 includes: The virtual power plant optimization scheduling model is converted into a mixed integer linear model through piecewise linearization; Based on the constraints, the CPLEX solver is used to solve the mixed integer linear model and output the scheduling optimization result of the virtual power plant under the condition of minimum operating cost.

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