Virtual power plant carbon-green transaction optimization scheduling method considering green power and carbon emission coupling relationship

Abstract

The invention belongs to the technical field of power systems, and particularly relates to a virtual power plant carbon-green transaction optimal scheduling method considering a green power and carbon emission coupling relationship, which comprises the following steps: S1, by taking the lowest total cost as an objective function, constructing an optimal scheduling model for a virtual power plant to participate in carbon transaction and green certificate transaction; the optimal scheduling model comprises a calculation model or a calculation formula of carbon transaction cost, green certificate transaction cost and operation cost of each device; s2, solving the optimal scheduling model according to actual parameters of a virtual power plant to be subjected to optimal scheduling to obtain an optimal scheduling scheme of the virtual power plant participating in carbon transaction and green certificate transaction; and S3, optimizing the operation scheduling of the virtual power plant by using the optimized scheduling scheme obtained in the S2. The method can ensure that the operation scheduling of the virtual power plant comprehensively complies with and optimizes the complex operation mechanism of the power market while considering the carbon transaction cost and the green certificate transaction cost, and realizes accurate regulation and control.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a virtual power plant carbon-green transaction optimization scheduling method that takes into account the green electricity and carbon emission coupling relationship. Background Art

[0002] With the rapid development of renewable energy and the increasing proportion of distributed power sources connected to the grid, the operational safety and economy of traditional power systems are facing unprecedented challenges. The high penetration rate of clean energy and the access to diversified loads have made the operation and dispatch of the power grid more complicated, and put forward higher requirements for the optimization of dispatch strategies. In this context, virtual power plants (VPPs) have emerged as a new type of coordinated control and energy management technology for distributed power sources. It integrates decentralized distributed power sources efficiently through advanced control technology, and realizes centralized management and optimized dispatch of distributed energy.

[0003] Driven by the "dual carbon" goals, flexible resources such as distributed renewable energy, energy storage, and demand response have developed rapidly. By integrating these resources, virtual power plants not only improve the power generation efficiency of distributed energy, but also better meet the current demand for renewable energy consumption. However, with the entry of social capital into the electricity market, different virtual power plants belong to different investors, forming a multi-agent game pattern. Under this pattern, the composition of the flexible resources of virtual power plants varies according to the preferences of investors, making the operation and dispatch of virtual power plants more complex and changeable.

[0004] In addition, with the rapid development of the market-based trading system, a trading system with multiple forms, multiple types of subjects and detailed rules has been formed. In order to improve the market mechanism, the carbon emission trading (CET) mechanism and the green certificate trading (GCT) mechanism have been established, and the renewable energy quota system and the carbon trading quota system have been introduced. Although the establishment of these mechanisms has provided more opportunities for virtual power plants to participate in market transactions, it has also increased the complexity of the operation and dispatch of virtual power plants.

[0005] However, existing virtual power plant operation optimization methods often ignore the impact of carbon trading and green certificate trading on the operating costs of virtual power plants, resulting in the inability of the optimization scheduling scheme to fully reflect the role of the market mechanism. At the same time, due to the complex and changeable composition of the flexibility resources of virtual power plants, traditional optimization scheduling methods are difficult to adapt to such changes, resulting in unsatisfactory optimization results. In addition, with the deepening of power market reform, competition in the power market will become more intense. Virtual power plants need to grasp market dynamics more accurately and participate in market transactions at the lowest operating cost in order to have an advantage in the competition.

[0006] Therefore, how to ensure that the operation and dispatch of virtual power plants fully conform to and optimize the complex operation mechanism of the power market while taking into account the transaction costs of carbon trading and green certificates, and achieve precise regulation, has become an urgent problem to be solved. Solving this problem will help improve the operating efficiency and economic benefits of virtual power plants and promote the healthy development of the power market. Summary of the invention

[0007] In view of the shortcomings of the above-mentioned prior art, the present invention provides a virtual power plant carbon-green trading optimization scheduling method that takes into account the coupling relationship between green electricity and carbon emissions. It can ensure that the operation and scheduling of the virtual power plant fully conforms to and optimizes the complex operating mechanism of the electricity market while taking into account the carbon trading and green certificate trading costs, thereby achieving precise regulation.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] A virtual power plant carbon green trading optimization scheduling method considering the green electricity and carbon emission coupling relationship includes the following steps:

[0010] S1. Taking the lowest total cost as the objective function, an optimal scheduling model for virtual power plants to participate in carbon trading and green certificate trading is constructed; the optimal scheduling model includes a calculation model or calculation formula for carbon trading cost, green certificate trading cost and the operating cost of each equipment;

[0011] Among them, the objective function of the optimization scheduling model is:

[0012] F=min(C CET +C GCT +C CHP +C MT +C P2G +C CCS +C wind +C pv +C ES );

[0013] In the formula, C CET represents the carbon trading cost of the virtual power plant; C GCT represents the green certificate transaction cost of the virtual power plant; C CHP represents the operating cost of the combined heat and power unit; C MT represents the gas purchase and operation cost of the gas turbine; C P2G represents the operating cost of the P2G device; C CCS represents the operating cost of the CCS device; C wind represents the operating cost of wind and solar power generation; C pv C represents the penalty cost for abandoning wind and solar power; ES represents the operating cost of the electric energy storage system;

[0014] S2. Solve the optimization scheduling model according to the actual parameters of the virtual power plant to be optimized, and obtain the optimal scheduling plan for the virtual power plant to participate in carbon trading and green certificate trading;

[0015] S3. Use the optimized dispatching plan obtained in S2 to optimize the operation dispatching of the virtual power plant.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. This method can achieve comprehensive cost optimization. This method first takes the lowest total cost as the objective function and constructs an optimization scheduling model that includes carbon trading costs, green certificate trading costs, and the operating costs of each equipment. Compared with the existing technology, this model not only takes into account the traditional equipment operating costs, but also takes into account the costs of carbon trading and green certificate trading for the first time, achieving comprehensive optimization of the operating costs of virtual power plants. By accurately calculating these costs, this method can ensure that virtual power plants comply with environmental protection policy requirements and have economic competitiveness when participating in market transactions.

[0018] 2. This method can accurately regulate and respond to the electricity market mechanism. While taking into account the transaction costs of carbon trading and green certificates, this method ensures that the operation and dispatch of virtual power plants can fully adapt to and optimize the complex operation mechanism of the electricity market by optimizing the dispatch model. The various cost calculation formulas in the model, such as carbon trading costs and green certificate trading costs, are closely linked to market dynamics, allowing virtual power plants to flexibly adjust their operating strategies according to market changes. This ability to accurately regulate enables virtual power plants to better seize opportunities in the electricity market, reduce operating costs, and improve profitability.

[0019] 3. This method achieves efficient scheduling under complex resource composition. Considering that virtual power plants usually contain multiple types of distributed power sources and flexible resources, such as cogeneration units, gas turbines, P2G devices, CCS devices, etc., this method achieves efficient scheduling of these complex resources by optimizing the scheduling model. The model can comprehensively consider the operating costs and characteristics of various resources, as well as the impact of carbon trading and green certificate trading, so as to formulate the optimal operating strategy. This efficient scheduling capability not only improves the operating efficiency of virtual power plants, but also reduces operating costs and enhances their competitiveness in the electricity market.

[0020] 4. This method can achieve green and sustainable development. By participating in carbon trading and green certificate trading, this method also promotes the green and sustainable development of virtual power plants. Carbon trading and green certificate trading, as important means to promote the consumption of renewable energy and reduce carbon emissions, can encourage virtual power plants to actively adopt clean energy and energy-saving and emission reduction technologies. By incorporating these factors into the optimization scheduling model, this method enables virtual power plants to pay more attention to environmental protection and sustainability during operation, contributing to the realization of the "dual carbon" goal.

[0021] In summary, this method can ensure that the operation and dispatch of virtual power plants fully conform to and optimize the complex operation mechanism of the power market while taking into account the transaction costs of carbon trading and green certificates, and achieve precise regulation. This capability not only improves the operational efficiency and economic benefits of virtual power plants, but also promotes the healthy development of the power market and promotes green and sustainable development.

[0022] Preferably, in the optimization scheduling model, the carbon transaction cost C is calculated by the carbon transaction cost model. CET ; Among them, the carbon trading cost model is:

[0023]

[0024] In the formula, C CET ,t represents the carbon trading cost of the virtual power plant in period t; represents the actual carbon emissions of the virtual power plant in period t; represents the total carbon emission quota of the virtual power plant in period t; α is the carbon trading cost coefficient.

[0025] Such a setting can accurately quantify the carbon trading costs. The carbon trading cost model enables the virtual power plant to accurately quantify its carbon trading costs at different times. By comparing the actual carbon emissions with the total carbon emission quota, the model can calculate the cost changes caused by over-emissions or savings in carbon emissions, thereby guiding the virtual power plant to pay more attention to carbon emission management during operation. Based on the calculation results of the carbon trading cost model, the optimization scheduling model can formulate a more reasonable operation strategy.

[0026] Preferably, according to the carbon emission quota model, the total carbon emission quota of the virtual power plant in period t is calculated;

[0027] The carbon emission quota model is:

[0028]

[0029] In the formula, represents the total carbon emission quota of the virtual power plant in period t; ε MT , ε CHP , ε wind , ε pv , ε ES Respectively represent the unit power carbon emission quota of gas turbine, cogeneration unit, wind power, photovoltaic, and electric energy storage, t / MW; P MT,t , P CHP,t , P wind,t , P pv,t , P ES-ch,t , P ES-dch,tThey represent the output power of gas turbine, cogeneration unit, wind power and photovoltaic at time t and the charging power and discharging power of electric energy storage respectively.

[0030] Such a setting, 3. According to the carbon emission quota model, this method can accurately calculate the total carbon emission quota of the virtual power plant in a specific period (period t). This model comprehensively considers the unit power carbon emission quota of various distributed power sources and flexible resources such as gas turbines, cogeneration units, wind power, photovoltaics, and electric energy storage, as well as their actual output power or charging and discharging power, thereby achieving accurate prediction and control of the total carbon emissions of virtual power plants.

[0031] 2. It can accurately predict the total carbon emissions. The carbon emission quota model can accurately predict the total carbon emissions of the virtual power plant in period t by integrating the carbon emission data of various distributed power sources and flexible resources. This prediction result is of great significance for the virtual power plant to formulate energy-saving and emission reduction strategies, participate in carbon trading, and optimize operation and scheduling.

[0032] 3. It can optimize resource allocation. Based on the calculation results of the carbon emission quota model, virtual power plants can more reasonably configure various distributed power sources and flexible resources to reduce overall carbon emissions. For example, when the unit power carbon emission quota of a certain resource is high, the virtual power plant may reduce its usage ratio and use resources with lower carbon emissions instead.

[0033] Preferably, according to the actual carbon emission model, the actual carbon emission of the virtual power plant in period t is calculated;

[0034] The actual carbon emissions model is:

[0035]

[0036] In the formula, represents the actual carbon emissions of the virtual power plant in period t; They represent the carbon emissions of the gas turbine and the cogeneration unit in period t respectively; E CCS,t represents the amount of carbon dioxide captured by CCS during period t; a MT , b MT 、c MT is the carbon emission coefficient of the gas turbine; a CHP , b CHP 、c CHP is the carbon emission coefficient of the cogeneration unit.

[0037] With this setup, 1. Based on the actual carbon emission model, this method can accurately calculate the actual carbon emissions of the virtual power plant in a specific period (period t). This model comprehensively considers the carbon dioxide capture of major carbon emission sources such as gas turbines and cogeneration units, as well as CCS (carbon capture and storage) technology, thereby achieving real-time monitoring and accurate evaluation of the carbon emission status of the virtual power plant.

[0038] 2. Real-time monitoring of carbon emissions. The actual carbon emissions model can monitor the carbon emissions of the virtual power plant in time period t in real time, and provide timely and accurate carbon emissions data for the virtual power plant. This is of great significance for the virtual power plant to timely discover problems, adjust operation strategies, and reduce carbon emissions.

[0039] 3. It can accurately assess carbon emissions. By integrating the emission data of various carbon emission sources and the amount captured by CCS technology, the model can accurately assess the actual carbon emissions of the virtual power plant in period t. This assessment result not only helps the virtual power plant understand its own carbon emissions, but also provides strong support for it to formulate energy-saving and emission reduction strategies and participate in carbon trading.

[0040] Preferably, in the optimization scheduling model, the green certificate transaction cost is calculated through the green certificate transaction model;

[0041] The green certificate trading model is:

[0042]

[0043] In the formula, C GCT,t represents the green certificate transaction cost of the virtual power plant in period t; β represents the unit price of green certificate transaction; They represent the number of green certificates for system quota and the number of green certificates obtained by the system for generating green electricity; ε0 and ε1 represent the system green certificate quota coefficient and the conversion coefficient between renewable energy and the number of green certificates respectively; P load,t , P wind,t , P pv,t They respectively represent the actual output of the system's electric load, wind power, and photovoltaic power during period t.

[0044] Such a setting, 1. By introducing the green certificate trading model, this method can accurately calculate the green certificate trading cost of the virtual power plant in period t. This model comprehensively considers multiple factors such as the unit price of green certificate trading, the number of system quota green certificates, and the number of green certificates obtained by the system issuing green electricity, thereby achieving a comprehensive evaluation of the green certificate trading cost of the virtual power plant.

[0045] 2. The green certificate transaction cost can be accurately quantified. The green certificate transaction model enables the virtual power plant to accurately quantify its green certificate transaction costs in different periods of time. By comparing the number of green certificates in the system quota with the number of green certificates obtained by the system's green electricity, the model can calculate the cost of green certificate transactions, providing an important reference for the virtual power plant to formulate operating strategies.

[0046] 3. It can optimize the use of renewable energy. The system green certificate quota coefficient and the conversion coefficient between renewable energy and the number of green certificates in the green certificate trading model reflect the value of renewable energy generation in green certificate trading. By optimizing the scheduling model, the virtual power plant can more reasonably allocate renewable energy resources and improve the utilization rate of renewable energy, thereby increasing the number of green certificates and reducing the cost of green certificate trading.

[0047] Through green certificate trading, virtual power plants can transform the environmental attributes of renewable energy generation into economic value, thereby encouraging more consumers to choose green electricity and promoting the green development of the electricity market.

[0048] Preferably, in the optimal scheduling model, the operating cost of the cogeneration unit C CHP The calculation formula is:

[0049]

[0050] Where a1, b1, c1 are the CHP unit operation cost calculation coefficients; P CHP,t represents the total electric power generated by CHP at time t; T is the total dispatching time;

[0051]

[0052] In the formula, Indicates the electrical power output from CHP to CCS; Indicates the electrical power output from CHP to P2G; Indicates the CHP grid-connected power;

[0053] Gas turbine gas purchase and operating costs C MT The calculation formula is:

[0054]

[0055] In the formula, ρ gas is the price of natural gas; represents the natural gas power consumed by the gas turbine at time t; represents the gas production power of the P2G device at time t; P MT,t represents the power generation of the gas turbine at time t; a2, b2, c2 are the coefficients for calculating the operating cost of the gas turbine;

[0056]

[0057] Where η MT Indicates the gas-to-electricity conversion efficiency of the gas turbine; L gas Indicates the lower calorific value of natural gas.

[0058] Such a setting, 1. By introducing the CHP unit operating cost calculation coefficient, the model can accurately estimate the operating cost of CHP under different operating conditions, including fuel consumption, equipment maintenance, depreciation and other expenses. The model takes into account the output power of CHP to CCS, P2G devices and the power grid, which helps to flexibly allocate the output of CHP in the optimal scheduling to maximize the overall benefit of the system. By accurately calculating the operating cost of CHP, the operation between CHP and other energy conversion devices (such as P2G, CCS) can be better coordinated to achieve multi-energy complementarity and improve energy utilization efficiency.

[0059] 2. The model takes into account the impact of gas turbine natural gas consumption and P2G device gas production on operating costs, which helps optimize natural gas consumption and reduce gas purchase costs. By introducing parameters such as gas turbine gas-to-electricity conversion efficiency and natural gas lower calorific value, the model can reflect the power generation efficiency of the gas turbine, thereby guiding the scheduling strategy and improving the overall power generation efficiency. The introduction of the gas turbine operating cost calculation coefficient enables the model to flexibly adjust the cost estimate according to actual conditions, improving the applicability and accuracy of the model.

[0060] Preferably, in the optimization scheduling model, the P2G device operation cost C P2G The calculation formula is:

[0061]

[0062] In the formula, λ P2G is the P2G operation cost coefficient; represents the power consumption of the P2G device at time t; κ P2G E is the cost coefficient of carbon dioxide consumed by P2G; CCS ,t represents the amount of carbon dioxide captured by CCS at time t;

[0063]

[0064] Where ξ is the conversion coefficient between P2G power consumption and carbon dioxide consumption; η P2G It represents the electricity-to-gas conversion efficiency of P2G; It represents the gas production power of the P2G device at time t; The maximum power consumption per unit time of the P2G device;

[0065] CCS device operating cost CCS The calculation formula is:

[0066]

[0067] In the formula, λ CCS represents the CCS operation cost coefficient; κ CCS represents the CO2 storage cost coefficient of CCS; It represents the electric power consumed by CCS at time t;

[0068]

[0069] In the formula, are the fixed energy consumption and operating energy consumption of the CSS device respectively; η CCS Efficiency factor for capturing CO2 for CCS; It is the maximum power consumption of the CCS device per unit time.

[0070] With this setting, 1. The model takes into account the power consumption and carbon dioxide cost of the P2G device, which helps to evaluate the energy consumption and environmental protection cost of the P2G device during operation. By introducing the electricity-to-gas conversion efficiency of P2G, the model can reflect the conversion efficiency of the P2G device, thereby guiding the scheduling strategy and improving the overall energy conversion efficiency. The model takes into account the interaction between the P2G device and energy systems such as CCS and the power grid, which helps to coordinate multiple energy flows in the optimal scheduling and achieve overall optimization of the system.

[0071] 2. By introducing the CCS operating cost coefficient and the carbon dioxide storage cost coefficient, the model can accurately estimate the operating cost of the CCS device under different operating conditions, including energy consumption, equipment maintenance, depreciation, and carbon dioxide storage costs. The model takes into account the fixed energy consumption and operating energy consumption of the CCS device, as well as the carbon dioxide capture efficiency coefficient, which helps to optimize the energy consumption of the CCS device and improve energy efficiency. By setting the CCS carbon capture amount not to exceed the carbon emission limit of the CHP unit, the model can ensure that the CCS device operates within a reasonable range and avoid cost waste caused by excessive capture.

[0072] Preferably, in the optimization scheduling model, the wind and solar power generation operating cost C wind The penalty cost of wind and solar curtailment C pv The calculation formulas are:

[0073]

[0074] In the formula, λ w , p are the operation and maintenance cost coefficients of wind power and photovoltaic power respectively; μ w , μ p are the penalty coefficients for wind and solar abandonment, respectively; are the predicted values ​​of wind power and photovoltaic output power respectively; P wind,t , P pv,tare the actual values ​​of wind power and photovoltaic output power respectively;

[0075] Electric energy storage system operating cost C ES The calculation formula is:

[0076]

[0077] In the formula, λ ES is the energy storage operation cost coefficient; P ES-ch,t The charging power for the electric energy storage; P ES-dch,t is the discharge power of the electric energy storage;

[0078]

[0079] Where Y t is 01 variable, Y t =1 means the energy storage system is in charging state, Y t =0 means the energy storage system is in a discharging state; SoC t is the state of charge of the energy storage system at time t; η ch is the charging efficiency of the energy storage system; η dch SoC is the discharge efficiency of the energy storage system; min The minimum state of charge required for safe operation of the energy storage system; SoC max is the highest state of charge of the energy storage system; P ES-ch,max P is the maximum power of a single charge of the energy storage system; ES-dch,max It is the maximum single discharge power of the energy storage system.

[0080] Such a setting, 1. The model takes into account the wind power and photovoltaic operation and maintenance cost coefficients and the wind and solar power abandonment penalty coefficients, which helps to improve the reliability of wind and solar power generation while ensuring the economy of the energy system. By comparing the predicted values ​​of wind power and photovoltaic output power with the actual values, the model can evaluate the prediction accuracy of wind and solar power generation and provide data support for optimized scheduling. The introduction of wind and solar power abandonment penalty costs can help reduce the abandonment of wind and solar power due to inaccurate predictions or improper scheduling, and reduce energy waste and cost losses.

[0081] 2. The model takes into account the energy storage operation cost coefficient and the charging power and discharging power of the electric energy storage, which helps to optimize the charging and discharging strategy of the energy storage system and reduce the operating cost. t To represent the charging state of the energy storage system, the model can monitor the operating status of the energy storage system in real time and adjust the charging and discharging strategy as needed to improve the flexibility and reliability of the system. The model also considers other cost factors related to the operation of the energy storage system (although the specific parameters are not listed in detail), such as equipment depreciation and maintenance costs, which helps to more comprehensively evaluate the economic feasibility of the energy storage system.

[0082] In summary, through the above technologies, this method can accurately estimate the operating cost of the energy system, optimize the energy conversion and utilization efficiency, improve the reliability and flexibility of the energy system, and reduce energy waste and cost losses.

[0083] Preferably, the constraints of the optimization scheduling model include power balance constraints, interconnection line power constraints, unit climbing constraints, unit climbing constraints and output limit constraints, and wind and solar output constraints.

[0084] Such settings,optimizing the constraints of the dispatch model are of great significance to ensure the safe,stable and efficient operation of the energy system.,By satisfying these constraints, we can achieve optimal allocation of resources,improve energy efficiency, ensure grid security and promote the effective use of renewable energy.

[0085] Preferably, the power balance constraint is:

[0086]

[0087] Where P VPP,t is the total aggregated power of the virtual power plant system at time t;

[0088] The tie line power constraint is:

[0089]

[0090]

[0091] In the formula, are the lower and upper limits of VPP aggregation capacity, respectively;

[0092] The unit climbing constraint is:

[0093] P CHP,dn ≤P CHP,t -P CHP,t-1 ≤P CHP,up ;

[0094] Where P CHP,dn , P CHP,up They are the lower and upper limits of the ramp rate of the CHP unit respectively;

[0095] The unit climbing constraint and output limit constraint are:

[0096]

[0097] Where P MT,dn , P MT,up are the lower and upper limits of the gas turbine ramp rate, respectively;

[0098] P MT,min , P MT,maxare the lower and upper limits of gas turbine power generation respectively;

[0099] The wind and solar power output constraints are:

[0100]

[0101] In the formula, are the predicted values ​​of wind power and photovoltaic output power respectively.

[0102] Such a setting, 1. Through power balance constraints, can ensure the balance between total power generation and total load power in the power system, which is the basic requirement for stable operation of the power system. Prevent system frequency deviation and voltage fluctuation caused by power imbalance, thereby maintaining the stability and reliability of the power system.

[0103] 2. Through tie-line power constraints, the power range transmitted through tie-lines between different regions or different power grids in the power system is limited to ensure the safe operation of tie-lines. This prevents grid failures or power outages caused by tie-line overloads and improves the reliability and stability of the grid. It promotes coordinated operation and energy complementarity between regional power grids and optimizes resource allocation.

[0104] 3. Through the unit ramp constraint, the rate of change of the unit output in adjacent time periods, i.e. the ramp rate, is limited to prevent the unit from being damaged due to too fast output changes. Ensure that the unit can smoothly adjust the output when the load changes to maintain the stability of the power system. Improve the operation flexibility and reliability of the unit to adapt to load fluctuations and changes.

[0105] 4. Combine the unit climbing constraint and output limit constraint to ensure that the unit will not be damaged due to rapid changes during the output adjustment process, nor will it exceed its maximum or minimum output range. Improve the operating efficiency and safety of the unit and optimize the dispatching strategy of the power system. Adapt to different load demands and energy supply conditions to achieve economic dispatch and optimized operation of the power system.

[0106] 5. Consider the uncertainty of renewable energy generation such as wind power and photovoltaic power, and limit the range of their output power by setting wind and solar power output constraints. Ensure the stable access and effective use of renewable energy generation in the power system to avoid impact on the power system. Improve the utilization rate of renewable energy and the flexibility of the power system, and promote the sustainable development of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0108] Figure 1 A flowchart of the method is shown in FIG.

[0109] Figure 2 It is a schematic diagram of the output power of each unit of the virtual power plant under four scenarios in the example of the second embodiment;

[0110] Figure 3 A schematic diagram of carbon emissions of each unit of a virtual power plant and carbon captured by a P2G-CCS system in different scenarios in an example of Embodiment 2;

[0111] Figure 4 This is a schematic diagram comparing the operating costs of a virtual power plant in different scenarios in the example of Embodiment 2. DETAILED DESCRIPTION

[0112] The following is a further detailed description through specific implementation methods:

[0113] Embodiment 1

[0114] like Figure 1 As shown, this embodiment discloses a virtual power plant carbon green trading optimization scheduling method considering the green electricity carbon emission coupling relationship, including the following steps:

[0115] S1. Taking the lowest total cost as the objective function, an optimal scheduling model for virtual power plants to participate in carbon trading and green certificate trading is constructed; the optimal scheduling model includes a calculation model or formula for carbon trading costs, green certificate trading costs, and the operating costs of each equipment.

[0116] Among them, the equipment operating cost includes the operating cost of the cogeneration unit C CHP , Gas turbine gas purchase and operation costs C MT 、P2G device operating cost C P2G 、CCS device operating cost C CCS , Wind and solar power generation operating costs C wind , Penalty cost for wind and solar power abandonment C pv 、Operation cost of electric energy storage system C ES In this way, the consideration of equipment operating costs in the optimization scheduling model is of great significance for improving the overall economic benefits of the system, reducing energy consumption and emissions, and enhancing the flexibility and reliability of the system. By comprehensively considering the operating costs of various equipment and taking corresponding optimization measures, the optimal operation strategy of the system can be achieved, and energy utilization efficiency and economy can be improved.

[0117] Operating costs of combined heat and power units:

[0118] It reflects the energy consumption, maintenance, personnel operation and equipment depreciation costs generated during the operation of the cogeneration unit. Optimizing this cost can help cogeneration companies improve energy efficiency, reduce energy consumption and maintenance costs, and thus improve economic benefits.

[0119] Gas turbine gas purchase and operating costs:

[0120] It covers the fuel cost (such as natural gas, liquefied petroleum gas, etc.) and operation and maintenance costs of gas turbines. By optimizing the load of gas turbines and improving fuel utilization efficiency, the gas purchase and operation costs can be reduced and the economy of gas turbines can be improved.

[0121] P2G device operating costs:

[0122] P2G (Power to Gas) devices can convert electrical energy into gas energy. Its operating costs include electricity consumption, equipment maintenance, catalyst replacement, etc. Optimizing the operating costs of P2G devices can improve their energy conversion efficiency, reduce operating costs, and thus enhance the flexibility and reliability of the system.

[0123] CCS device operating costs:

[0124] CCS (Carbon Capture and Storage) devices are used to capture and store carbon dioxide, and their operating costs include the costs of capture, transportation, storage, etc. By improving CCS technology, increasing capture efficiency, and reducing energy consumption, the operating costs of CCS devices can be reduced, which helps achieve carbon emission reduction goals.

[0125] Wind and solar power generation operating costs:

[0126] The operating costs of wind and solar power generation mainly include equipment maintenance, personnel operation, land rent, and possible penalty costs for wind and solar power abandonment. Optimizing the operating costs of wind and solar power generation can be achieved by improving power generation efficiency, reducing equipment failure rates, and lowering maintenance costs.

[0127] Penalty costs for curtailing wind and solar power:

[0128] When the actual output of wind power and photovoltaic power generation is lower than its potential output, wind and solar power may be abandoned and penalized. By optimizing the dispatch strategy and reducing wind and solar power abandonment, the penalty cost can be reduced and the overall economic benefits of the system can be improved.

[0129] Electric energy storage system operating costs:

[0130] The electric energy storage system is used to balance the supply and demand of electricity. Its operating costs include energy consumption during charging and discharging, equipment maintenance, depreciation, etc. Optimizing the operation strategy of the electric energy storage system, such as reasonably arranging the charging and discharging time and improving the charging and discharging efficiency, can reduce its operating costs and improve the energy storage efficiency of the system.

[0131] The objective function of the optimization scheduling model is:

[0132] F=min(C CET +C GCT +C CHP +CMT +C P2G +C CCS +C wind +C pv +C ES );

[0133] In the formula, C CET represents the carbon trading cost of the virtual power plant; C GCT represents the green certificate transaction cost of the virtual power plant; C CHP represents the operating cost of the combined heat and power unit; C MT represents the gas purchase and operation cost of the gas turbine; C P2G represents the operating cost of the P2G device; C CCS represents the operating cost of the CCS device; C wind represents the operating cost of wind and solar power generation; C pv C represents the penalty cost for abandoning wind and solar power; ES Represents the operating cost of the electric energy storage system.

[0134] In specific implementation, the carbon trading cost C is calculated through the carbon trading cost model. CET ; Among them, the carbon trading cost model is:

[0135]

[0136] In the formula, C CET ,t represents the carbon trading cost of the virtual power plant in period t; represents the actual carbon emissions of the virtual power plant in period t; represents the total carbon emission quota of the virtual power plant in period t; α is the carbon trading cost coefficient.

[0137] In this way, the carbon trading costs can be accurately quantified. The carbon trading cost model enables virtual power plants to accurately quantify their carbon trading costs at different times. By comparing actual carbon emissions with total carbon emission quotas, the model can calculate the cost changes caused by over-emissions or savings in carbon emissions, thereby guiding virtual power plants to pay more attention to carbon emission management during operation. Based on the calculation results of the carbon trading cost model, the optimization scheduling model can formulate a more reasonable operation strategy.

[0138] According to the carbon emission quota model, calculate the total carbon emission quota of the virtual power plant in period t;

[0139] The carbon emission quota model is:

[0140]

[0141] In the formula, represents the total carbon emission quota of the virtual power plant in period t; ε MT , ε CHP , εwind , ε pv , ε ES Respectively represent the unit power carbon emission quota of gas turbine, cogeneration unit, wind power, photovoltaic, and electric energy storage, t / MW; P MT,t , P CHP,t , P wind,t , P pv,t , P ES-ch,t , P ES-dch,t They represent the output power of gas turbine, cogeneration unit, wind power and photovoltaic at time t and the charging power and discharging power of electric energy storage respectively.

[0142] In this way, according to the carbon emission quota model, this method can accurately calculate the total carbon emission quota of the virtual power plant in a specific period (period t). This model comprehensively considers the unit power carbon emission quota of various distributed power sources and flexible resources such as gas turbines, cogeneration units, wind power, photovoltaics, and electric energy storage, as well as their actual output power or charging and discharging power, thereby realizing the accurate prediction and control of the total carbon emissions of the virtual power plant. The total carbon emissions can also be accurately predicted. The carbon emission quota model can accurately predict the total carbon emissions of the virtual power plant in period t by integrating the carbon emission data of various distributed power sources and flexible resources. This prediction result is of great significance for virtual power plants to formulate energy-saving and emission reduction strategies, participate in carbon trading, and optimize operation and scheduling. In addition, resource allocation can be optimized. Based on the calculation results of the carbon emission quota model, virtual power plants can more reasonably configure various distributed power sources and flexible resources to reduce overall carbon emissions. For example, when the unit power carbon emission quota of a certain resource is high, the virtual power plant may reduce its usage ratio and use resources with lower carbon emissions instead.

[0143] According to the actual carbon emission model, calculate the actual carbon emission of the virtual power plant in period t;

[0144] The actual carbon emissions model is:

[0145]

[0146] In the formula, represents the actual carbon emissions of the virtual power plant in period t; They represent the carbon emissions of the gas turbine and the cogeneration unit in period t respectively; E CCS,t represents the amount of carbon dioxide captured by CCS during period t; a MT , b MT 、c MT is the carbon emission coefficient of the gas turbine; a CHP , b CHP 、c CHP is the carbon emission coefficient of the cogeneration unit.

[0147] In this way, according to the actual carbon emission model, this method can accurately calculate the actual carbon emissions of the virtual power plant in a specific period (period t). This model comprehensively considers the carbon dioxide capture of major carbon emission sources such as gas turbines and cogeneration units and CCS (carbon capture and storage) technology, thereby realizing real-time monitoring and accurate evaluation of the carbon emission status of the virtual power plant. Carbon emissions can also be monitored in real time. The actual carbon emission model can monitor the carbon emissions of the virtual power plant in period t in real time and provide timely and accurate carbon emission data for the virtual power plant. This is of great significance for the virtual power plant to find problems in time, adjust operation strategies, and reduce carbon emissions. In addition, carbon emissions can be accurately evaluated. By integrating the emission data of various carbon emission sources and the capture of CCS technology, the model can accurately evaluate the actual carbon emissions of the virtual power plant in period t. This evaluation result not only helps the virtual power plant understand its own carbon emission status, but also provides strong support for it to formulate energy-saving and emission reduction strategies and participate in carbon trading.

[0148] In specific implementation, in the optimization scheduling model, the green certificate transaction cost is calculated through the green certificate transaction model;

[0149] The green certificate trading model is:

[0150]

[0151] In the formula, C GCT,t represents the green certificate transaction cost of the virtual power plant in period t; β represents the unit price of green certificate transaction; They represent the number of green certificates for system quota and the number of green certificates obtained by the system for generating green electricity; ε0 and ε1 represent the system green certificate quota coefficient and the conversion coefficient between renewable energy and the number of green certificates respectively; P load,t , P wind,t , P pv,t They respectively represent the actual output of the system's electric load, wind power, and photovoltaic power during period t.

[0152] In this way, by introducing the green certificate trading model, this method can accurately calculate the green certificate trading cost of the virtual power plant in period t. This model comprehensively considers multiple factors such as the unit price of green certificate trading, the number of system quota green certificates, and the number of green certificates obtained by the system issuing green electricity, thereby realizing a comprehensive evaluation of the green certificate trading cost of the virtual power plant. The green certificate trading cost can also be accurately quantified. The green certificate trading model enables the virtual power plant to accurately quantify its green certificate trading cost in different periods. By comparing the number of system quota green certificates with the number of green certificates obtained by the system issuing green electricity, the model can calculate the cost of green certificate trading, providing an important reference for the virtual power plant to formulate operating strategies. In addition, the use of renewable energy can be optimized. The system green certificate quota coefficient and the conversion coefficient between renewable energy and the number of green certificates in the green certificate trading model reflect the value of renewable energy generation in green certificate trading. By optimizing the scheduling model, the virtual power plant can more reasonably allocate renewable energy resources and improve the utilization rate of renewable energy, thereby increasing the number of green certificates and reducing the cost of green certificate trading.

[0153] Through green certificate trading, virtual power plants can transform the environmental attributes of renewable energy generation into economic value, thereby encouraging more consumers to choose green electricity and promoting the green development of the electricity market.

[0154] In specific implementation, the operating cost of the cogeneration unit is C CHP The calculation formula is:

[0155]

[0156] Where a1, b1, c1 are the CHP unit operation cost calculation coefficients; P CHP,t represents the total electric power generated by CHP at time t; T is the total scheduling time, h;

[0157]

[0158] In the formula, Indicates the electrical power output from CHP to CCS; Indicates the electrical power output from CHP to P2G; Indicates the CHP grid-connected power;

[0159] Gas turbine gas purchase and operating costs C MT The calculation formula is:

[0160]

[0161] In the formula, ρ gas is the price of natural gas; represents the natural gas power consumed by the gas turbine at time t; represents the gas production power of the P2G device at time t; P MT,trepresents the power generation of the gas turbine at time t; a2, b2, c2 are the coefficients for calculating the operating cost of the gas turbine;

[0162]

[0163] Where η MT Indicates the gas-to-electricity conversion efficiency of the gas turbine; L gas Indicates the lower calorific value of natural gas.

[0164] P2G device operating cost C P2G The calculation formula is:

[0165]

[0166] In the formula, λ P2G is the P2G operation cost coefficient; represents the power consumption of the P2G device at time t; κ P2G E is the cost coefficient of carbon dioxide consumed by P2G; CCS ,t represents the amount of carbon dioxide captured by CCS at time t;

[0167]

[0168] Where ξ is the conversion coefficient between P2G power consumption and carbon dioxide consumption; η P2G It represents the electricity-to-gas conversion efficiency of P2G; It represents the gas production power of the P2G device at time t; It is the maximum power consumption of the P2G device per unit time.

[0169] In this way, by introducing the CHP unit operation cost calculation coefficient, the model can accurately estimate the operation cost of CHP under different operating conditions, including fuel consumption, equipment maintenance, depreciation and other expenses. The model takes into account the output power of CHP to CCS, P2G devices and power grid, which helps to flexibly allocate the output of CHP in the optimization scheduling to maximize the overall benefit of the system. By accurately calculating the operation cost of CHP, the operation between CHP and other energy conversion devices (such as P2G and CCS) can be better coordinated to achieve multi-energy complementarity and improve energy utilization efficiency. In addition, the model takes into account the impact of gas turbine natural gas power consumption and P2G device gas production power on the operation cost, which helps to optimize natural gas consumption and reduce gas purchase costs. By introducing parameters such as gas turbine gas-to-electricity conversion efficiency and natural gas low calorific value, the model can reflect the power generation efficiency of the gas turbine, thereby guiding the scheduling strategy and improving the overall power generation efficiency. The introduction of the gas turbine operation cost calculation coefficient enables the model to flexibly adjust the cost estimate according to the actual situation, improving the applicability and accuracy of the model. In addition, the model takes into account the power consumption and carbon dioxide costs of the P2G device, which helps to evaluate the energy consumption and environmental protection costs of the P2G device during operation. By introducing the P2G power-to-gas conversion efficiency, the model can reflect the conversion efficiency of the P2G device, thereby guiding the scheduling strategy and improving the overall energy conversion efficiency. The model takes into account the interaction between the P2G device and energy systems such as CCS and the power grid, which helps to coordinate multiple energy flows in the optimal scheduling and achieve overall optimization of the system.

[0170] CCS device operating cost CCS The calculation formula is:

[0171]

[0172] In the formula, λ CCS represents the CCS operation cost coefficient; κ CCS represents the CO2 storage cost coefficient of CCS; It represents the electric power consumed by CCS at time t;

[0173]

[0174] In the formula, are the fixed energy consumption and operating energy consumption of the CSS device respectively; η CCS Efficiency factor for capturing CO2 for CCS; It is the maximum power consumption of the CCS device per unit time.

[0175] CCS captures carbon dioxide from the CHP unit, so the amount of carbon captured by CCS cannot exceed the carbon emissions of the CHP unit:

[0176]

[0177] Wind and solar power generation operating cost C wind The penalty cost of wind and solar curtailment C pv The calculation formulas are:

[0178]

[0179] In the formula, λ w , p are the operation and maintenance cost coefficients of wind power and photovoltaic power respectively; μ w , μ p are the penalty coefficients for wind and solar abandonment, respectively; are the predicted values ​​of wind power and photovoltaic output power respectively; P wind,t , P pv,t are the actual values ​​of wind power and photovoltaic output power respectively;

[0180] Electric energy storage system operating cost C ES The calculation formula is:

[0181]

[0182] In the formula, λ ES is the energy storage operation cost coefficient; P ES-ch,t The charging power for the electric energy storage; P ES-dch,t is the discharge power of the electric energy storage;

[0183]

[0184] Where Y t is 01 variable, Y t =1 means the energy storage system is in charging state, Y t =0 means the energy storage system is in a discharging state; SoC t is the state of charge of the energy storage system at time t; η ch is the charging efficiency of the energy storage system; η dch SoC is the discharge efficiency of the energy storage system; min The minimum state of charge required for safe operation of the energy storage system; SoC max is the highest state of charge of the energy storage system; P ES-ch,max P is the maximum power of a single charge of the energy storage system; ES-dch,max It is the maximum single discharge power of the energy storage system.

[0185] In this way, by introducing the CCS operating cost coefficient and the carbon dioxide storage cost coefficient, the model can accurately estimate the operating cost of the CCS device under different operating conditions, including energy consumption, equipment maintenance, depreciation, and carbon dioxide storage costs. The model takes into account the fixed energy consumption and operating energy consumption of the CCS device, as well as the carbon dioxide capture efficiency coefficient, which helps to optimize the energy consumption of the CCS device and improve energy utilization efficiency. By setting the limit that the CCS carbon capture amount cannot exceed the carbon emission limit of the CHP unit, the model can ensure that the CCS device operates within a reasonable range and avoid cost waste caused by excessive capture. In addition, the model takes into account the wind power and photovoltaic operation and maintenance cost coefficients and the wind and solar abandonment penalty coefficients, which helps to improve the reliability of wind and solar power generation while ensuring the economy of the energy system. By comparing the predicted values ​​and actual values ​​of wind power and photovoltaic output power, the model can evaluate the prediction accuracy of wind and solar power generation and provide data support for optimized scheduling. The introduction of penalty costs for wind and solar abandonment helps to reduce the wind and solar abandonment caused by inaccurate predictions or improper scheduling, and reduce energy waste and cost losses. In addition, the model takes into account the energy storage operation cost coefficient and the charging power and discharging power of the electric energy storage, which helps to optimize the charging and discharging strategy of the energy storage system and reduce the operating cost. t To represent the charging state of the energy storage system, the model can monitor the operating status of the energy storage system in real time and adjust the charging and discharging strategy as needed to improve the flexibility and reliability of the system. The model also considers other cost factors related to the operation of the energy storage system (although the specific parameters are not listed in detail), such as equipment depreciation and maintenance costs, which helps to more comprehensively evaluate the economic feasibility of the energy storage system.

[0186] During specific implementation, the constraints of the optimized scheduling model include power balance constraints, interconnection line power constraints, unit climbing constraints, unit climbing constraints and output limit constraints, and wind and solar output constraints.

[0187] The power balance constraint is:

[0188]

[0189] Where P VPP,t is the total aggregated power of the virtual power plant system at time t;

[0190] The tie line power constraint is:

[0191]

[0192]

[0193] In the formula, are the lower and upper limits of VPP aggregation capacity, respectively;

[0194] The unit climbing constraint is:

[0195] P CHP,dn ≤P CHP,t -P CHP,t-1 ≤P CHP,up ;

[0196] Where P CHP,dn , P CHP,up They are the lower and upper limits of the ramp rate of the CHP unit respectively;

[0197] The unit climbing constraint and output limit constraint are:

[0198]

[0199] Where P MT,dn , P MT,up are the lower and upper limits of the gas turbine ramp rate, respectively;

[0200] P MT,min , P MT,max are the lower and upper limits of gas turbine power generation respectively;

[0201] The wind and solar power output constraints are:

[0202]

[0203] In the formula, are the predicted values ​​of wind power and photovoltaic output power respectively.

[0204] In this way, the constraints of the optimal dispatch model are of great significance to ensure the safe, stable and efficient operation of the energy system. By meeting these constraints, we can achieve optimal allocation of resources, improve energy utilization efficiency, ensure grid security and promote the effective use of renewable energy.

[0205] Through power balance constraints, the balance between total power generation and total load power in the power system can be ensured, which is the basic requirement for the stable operation of the power system. Prevent system frequency deviation and voltage fluctuation caused by power imbalance, thereby maintaining the stability and reliability of the power system. Through tie line power constraints, limit the power range transmitted through tie lines between different regions or different power grids in the power system to ensure the safe operation of tie lines. Prevent power grid failures or power outages caused by tie line overloads, and improve the reliability and stability of the power grid. Promote coordinated operation and energy complementarity between regional power grids and optimize resource allocation. Through unit ramp constraints, limit the rate of change of unit output in adjacent time periods, that is, the ramp rate, to prevent the unit from being damaged due to excessively fast output changes. Ensure that the unit can smoothly adjust its output when the load changes to maintain the stability of the power system. Improve the operating flexibility and reliability of the unit to adapt to load fluctuations and changes.

[0206] Combined with the unit climbing constraint and output limit constraint, it is ensured that the unit will not be damaged due to rapid changes during the output adjustment process, nor will it exceed its maximum or minimum output range. Improve the operating efficiency and safety of the unit and optimize the dispatching strategy of the power system. Adapt to different load demands and energy supply conditions to achieve economic dispatch and optimized operation of the power system. Consider the uncertainty of renewable energy generation such as wind power and photovoltaic power generation, and limit the range of its output power by setting wind and solar output constraints. Ensure the stable access and effective use of renewable energy generation in the power system to avoid impact on the power system. Improve the utilization rate of renewable energy and the flexibility of the power system, and promote the sustainable development of renewable energy.

[0207] S2. Solve the optimization scheduling model according to the actual parameters of the virtual power plant to be optimized, and obtain the optimal scheduling plan for the virtual power plant to participate in carbon trading and green certificate trading;

[0208] S3. Use the optimized dispatching plan obtained in S2 to optimize the operation dispatching of the virtual power plant.

[0209] This method can achieve comprehensive cost optimization. First, this method takes the lowest total cost as the objective function and constructs an optimization scheduling model that includes carbon trading costs, green certificate trading costs, and the operating costs of each device. Compared with the existing technology, this model not only takes into account the traditional equipment operating costs, but also takes into account the costs of carbon trading and green certificate trading for the first time, thus achieving comprehensive optimization of the operating costs of virtual power plants. By accurately calculating these costs, this method can ensure that virtual power plants comply with environmental protection policy requirements and have economic competitiveness when participating in market transactions. In addition, this method can accurately regulate and respond to the power market mechanism. While taking into account the costs of carbon trading and green certificate trading, this method ensures that the operation and scheduling of virtual power plants can fully adapt to and optimize the complex operating mechanism of the power market by optimizing the scheduling model. The various cost calculation formulas in the model, such as carbon trading costs and green certificate trading costs, are closely linked to market dynamics, enabling virtual power plants to flexibly adjust their operating strategies according to market changes. This ability to accurately regulate enables virtual power plants to better seize opportunities in the power market, reduce operating costs, and improve profitability.

[0210] In addition, this method achieves efficient scheduling under complex resource composition. Considering that virtual power plants usually contain multiple types of distributed power sources and flexible resources, such as cogeneration units, gas turbines, P2G devices, CCS devices, etc., this method achieves efficient scheduling of these complex resources by optimizing the scheduling model. The model can comprehensively consider the operating costs and characteristics of various resources, as well as the impact of carbon trading and green certificate trading, so as to formulate the optimal operation strategy. This efficient scheduling capability not only improves the operating efficiency of virtual power plants, but also reduces operating costs and enhances their competitiveness in the power market. In addition, this method can achieve green and sustainable development. By participating in carbon trading and green certificate trading, this method also promotes the green and sustainable development of virtual power plants. Carbon trading and green certificate trading, as important means to promote the consumption of renewable energy and reduce carbon emissions, can encourage virtual power plants to actively adopt clean energy and energy-saving and emission reduction technologies. By incorporating these factors into the optimization scheduling model, this method enables virtual power plants to pay more attention to environmental protection and sustainability during operation, and contribute to the realization of the "dual carbon" goals.

[0211] In summary, this method can ensure that the operation and dispatch of virtual power plants fully conform to and optimize the complex operation mechanism of the power market while taking into account the transaction costs of carbon trading and green certificates, and achieve precise regulation. This capability not only improves the operational efficiency and economic benefits of virtual power plants, but also promotes the healthy development of the power market and promotes green and sustainable development.

[0212] Embodiment 2

[0213] In order to better illustrate the effect of this method, a specific example is used for illustration. In the example, the virtual power plant includes a gas unit, a wind turbine, a photovoltaic unit and a battery energy storage system. The installed capacity of the energy storage system is 100MW·h, the initial capacity is 50%, the maximum output power is 20MW, and the charging and discharging efficiency is 0.9. The carbon trading price is set at 200 yuan / t. Based on the historical green certificate trading platform information, the green certificate price is 180 yuan / (MW·h). In order to analyze the benefits of virtual power plants participating in the green certificate and carbon trading markets, the present invention considers 4 scenarios: ① The virtual power plant participates in both carbon trading and green certificate trading, that is, the model proposed by the present invention; ② The virtual power plant only participates in carbon trading and does not participate in green certificate trading; ③ The virtual power plant only participates in green certificate trading and does not participate in carbon trading; ④ The virtual power plant operates alone and does not participate in carbon trading or green certificate trading.

[0214] In one embodiment of the present invention, the output dispatching scheme of VPP units in each scenario is analyzed. The output power of each unit in the virtual power plant under the above four scenarios is as follows: Figure 2 As shown in (a), (b), (c) and (d).

[0215] Carbon trading and green certificate trading will promote the consumption of renewable energy and reduce the output of CHP and gas units. In the four scenarios, the grid-connected output of CHP units was 530.97, 731.70, 701.80, and 944.12 MWh, respectively. It can be seen that as the degree of virtual power plant participation in green-carbon trading deepens, the grid-connected power of CHP is gradually reduced. In scenario 4, which does not participate in carbon trading and green certificate trading, the grid-connected power of CHP units maintains the maximum output limit for a long time, while in the remaining scenarios participating in green-carbon trading, the grid-connected power of CHP units fluctuates with the fluctuations of the predicted power of renewable energy and the actual load.

[0216] In the four scenarios, the grid-connected renewable energy output is 552.48, 551.84, 556.88, and 299.52 MWh respectively; compared with scenario 4, the renewable energy utilization rate in scenarios 1-3 is higher. This shows that when virtual power plants are allowed to participate in carbon trading and green certificate trading, although there are certain operating and maintenance costs for renewable energy units, compared with the available carbon-green trading income and the operating and environmental costs of CHP / MT unit power generation, virtual power plants are more inclined to use renewable energy and reduce the power output of CHP / MT units.

[0217] In terms of the output of gas-fired units, the power generation in the four scenarios is 60.60, 67.70, 70.16, and 70.16 MWh respectively. On the one hand, as can be seen from the figure, the gas-fired units are mostly in the minimum power output state in the four scenarios, so the total power generation of the gas-fired units in the four scenarios is relatively close; this shows that compared with CHP unit power generation and renewable energy, gas-fired units have a higher unit cost of consuming natural gas for power generation. On the other hand, since the power generation of CHP units will not only participate in the supply of actual loads, but also supply a part of the P2G-CCS system to reduce carbon emissions, the unit power generation cost of CHP units is indirectly reduced in scenarios 1-2 that can participate in the carbon trading market, so the output of gas-fired units in scenarios 1-2 is relatively lower.

[0218] In addition, the energy storage system participates more in scenarios 1 and 2. In scenarios 3 and 4, it only discharges at 1:00 to supply the load due to the ramp rate limit of the CHP unit. In scenarios 1 and 2, it can be observed that the energy storage system is usually charged at 2:00-4:00, 9:00-16:00, and 22:00-23:00, and discharged at other times. This is because the CHP unit has a high grid-connected power and a large amount of wind and solar power output. The virtual power plant transfers wind and solar power to the CHP unit when the grid-connected power is low through the energy storage system to supply the load and suppress the peak and fill the valley.

[0219] The carbon emissions of VPP units in each scenario are analyzed. The carbon emissions of each unit in the virtual power plant under different scenarios and the carbon captured by the P2G-CCS system are shown in Figure 2. Figure 3 shown.

[0220] Corresponding to the power generation of the units, the main carbon source in the four scenarios is the CHP unit. The carbon emissions of the gas units increased only during the peak load period (13:00-15:00, 17:00-22:00), and the minimum emissions were maintained during the rest of the time. This is because during the peak load period, although the wind and solar output increased, it still could not meet the load demand, and the virtual power plant was forced to call on the gas units for power generation. In scenario 1, which participates in both the carbon trading market and the green certificate trading market, the total carbon emissions are low most of the time, and only suddenly increase to 67t at 17:00, which is also due to the sudden increase in the power generation of the gas units at this time.

[0221] The carbon trading mechanism will promote the P2G-CCS system to increase its carbon capture and reduce the total carbon emissions of virtual power plants. By comparing the carbon emission curves of each scenario, it can be concluded that in scenarios 1-2 participating in carbon trading, the virtual power plant will allocate part of the power generated by the CHP unit to the P2G-CCS system when the load is low. This is because when allowed to participate in carbon trading, the sale of carbon emission rights indirectly obtained through the P2G-CCS system can generate revenue, which is greater than the operating cost of the P2G-CCS system and the increased power generation cost to meet the load. Similarly, since the P2G-CCS system requires a certain cost to capture carbon, in scenarios 3 and 4 that do not participate in the carbon trading market, the virtual power plant does not allow the P2G-CCS system to operate in order to reduce the total operating cost.

[0222] Green certificate trading can also reduce the total carbon emissions of virtual power plants. By comparing Scenario 3 with Scenario 4, it can be concluded that when green certificate trading is allowed, virtual power plants will make more use of renewable energy, supplying loads while generating green certificate trading revenue. However, since the operation and maintenance of renewable energy units require certain costs, in Scenario 4, which does not participate in carbon trading or green certificate trading, virtual power plants tend to use CHP units or gas units for power generation, resulting in the highest carbon emissions.

[0223] The comparison of carbon capture, power consumption, P2G methane production, and gas consumption of the gas unit in each scenario of the P2G-CCS system is shown in Table 1.

[0224] Table 1

[0225]

[0226] As can be seen from Table 1, scenario 1, which participates in both green certificate trading and carbon trading markets, has the highest P2G-CCS carbon capture and the lowest gas consumption of gas units, proving that the virtual power plant has the highest environmental benefits at this time. At the same time, since the methane generated by P2G can be supplied to the gas units for power generation, the gas purchase cost of the gas units is further reduced.

[0227] In one embodiment of the present invention, the benefits of VPP units in various scenarios are analyzed. Figure 4 The operating costs and benefits of virtual power plants under different scenarios are shown in Table 2.

[0228] Table 2

[0229]

[0230] The comparison of various costs under different scenarios is shown in the table above. It can be concluded from the table that through the carbon trading and green certificate trading mechanism, scenarios 1-3 obtain the income from the sale of carbon emission rights / green certificates, reduce the system operation costs, fully absorb renewable energy, and reduce the total carbon emissions of the system. In general, scenario 1, which participates in both carbon trading and green certificate trading, is most suitable for system operation. Optimizing the scheduling of energy supply in this scenario will achieve optimal operation of the system. Compared with scenario 1, the total operating costs of the other three scenarios will increase by 65.59%, 49.23%, and 86.40%, respectively, and carbon emissions will increase by 16.30%, 13.77%, and 33.77%, respectively, thereby verifying the effectiveness of the green-carbon joint trading mechanism model of the virtual power plant proposed by this method.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A virtual power plant carbon green trading optimization scheduling method considering the coupling relationship between green electricity and carbon emissions, characterized in that: The following steps are involved: S1. Taking the lowest total cost as the objective function, an optimal scheduling model for virtual power plants to participate in carbon trading and green certificate trading is constructed; the optimal scheduling model includes a calculation model or formula for carbon trading costs, green certificate trading costs, and the operating costs of each device; Among them, the objective function of the optimization scheduling model is: F=min(C CET +C GCT +C CHP +C MT +C P2G +C CCS +C wind +C pv +C ES ); In the formula, C CET represents the carbon trading cost of the virtual power plant; C GCT represents the green certificate transaction cost of the virtual power plant; C CHP represents the operating cost of the combined heat and power unit; C MT represents the gas purchase and operation cost of the gas turbine; C P2G represents the operating cost of the P2G device; C CCS represents the operating cost of the CCS device; C wind represents the operating cost of wind and solar power generation; C pv C represents the penalty cost for abandoning wind and solar power; ES represents the operating cost of the electric energy storage system; S2. Solve the optimization scheduling model according to the actual parameters of the virtual power plant to be optimized, and obtain the optimal scheduling plan for the virtual power plant to participate in carbon trading and green certificate trading; S3. Use the optimized dispatching plan obtained in S2 to optimize the operation dispatching of the virtual power plant.

2. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 1 is characterized by: In the optimization scheduling model, the carbon trading cost C is calculated by the carbon trading cost model. CET ; Among them, the carbon trading cost model is: In the formula, C CET ,t represents the carbon trading cost of the virtual power plant in period t; represents the actual carbon emissions of the virtual power plant in period t; represents the total carbon emission quota of the virtual power plant in period t; α is the carbon trading cost coefficient.

3. The virtual power plant carbon green trading optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 2 is characterized by: According to the carbon emission quota model, calculate the total carbon emission quota of the virtual power plant in period t; The carbon emission quota model is: In the formula, represents the total carbon emission quota of the virtual power plant in period t; ε MT , ε CHP , ε wind , ε pv , ε ES Respectively represent the unit power carbon emission quota of gas turbine, cogeneration unit, wind power, photovoltaic, and electric energy storage, t / MW; P MT,t , P CHP,t , P wind,t , P pv,t , P ES-ch,t , P ES-dch,t They represent the output power of gas turbine, cogeneration unit, wind power and photovoltaic at time t and the charging power and discharging power of electric energy storage respectively.

4. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 3 is characterized by: According to the actual carbon emission model, calculate the actual carbon emission of the virtual power plant in period t; The actual carbon emissions model is: In the formula, represents the actual carbon emissions of the virtual power plant in period t; They represent the carbon emissions of the gas turbine and the cogeneration unit in period t respectively; E CCS,t represents the amount of carbon dioxide captured by CCS during period t; a MT 、b MT 、c MT is the carbon emission coefficient of the gas turbine; a CHP 、b CHP 、c CHP is the carbon emission coefficient of the cogeneration unit.

5. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 4 is characterized by: In the optimization scheduling model, the green certificate transaction cost is calculated through the green certificate transaction model; The green certificate trading model is: In the formula, C GCT,t represents the green certificate transaction cost of the virtual power plant in period t; β represents the unit price of green certificate transaction; They represent the number of green certificates for system quota and the number of green certificates obtained by the system for generating green electricity; ε0 and ε1 represent the system green certificate quota coefficient and the conversion coefficient between renewable energy and the number of green certificates respectively; P load,t , P wind, t、P pv,t They respectively represent the actual output of the system's electric load, wind power, and photovoltaic power during period t.

6. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 5 is characterized by: In the optimal dispatch model, the operating cost of the cogeneration unit C CHP The calculation formula is: Where a1, b1, c1 are the CHP unit operation cost calculation coefficients; P CHP,t represents the total electric power generated by CHP at time t; T is the total dispatching duration; In the formula, Indicates the electrical power output from CHP to CCS; Indicates the electrical power output from CHP to P2G; Indicates the CHP grid-connected power; Gas turbine gas purchase and operating costs C MT The calculation formula is: In the formula, ρ gas is the price of natural gas; represents the natural gas power consumed by the gas turbine at time t; represents the gas production power of the P2G device at time t; P MT,t represents the power generation of the gas turbine at time t; a2, b2, c2 are the coefficients for calculating the operating cost of the gas turbine; Where η MT Indicates the gas-to-electricity conversion efficiency of the gas turbine; L gas Indicates the lower calorific value of natural gas.

7. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 6 is characterized by: In the optimal scheduling model, the operating cost of the P2G device C P2G The calculation formula is: In the formula, λ P2G is the P2G operation cost coefficient; represents the power consumption of the P2G device at time t; κ P2G The cost factor for CO2 consumption by P2G; E CCS ,t represents the amount of carbon dioxide captured by CCS at time t; Where ξ is the conversion coefficient between P2G power consumption and carbon dioxide consumption; η P2G It represents the electricity-to-gas conversion efficiency of P2G; It represents the gas production power of the P2G device at time t; The maximum power consumption per unit time of the P2G device; CCS device operating cost CCS The calculation formula is: In the formula, λ CCS represents the CCS operation cost coefficient; κ CCS represents the CO2 storage cost coefficient of CCS; It represents the electric power consumed by CCS at time t; In the formula, are the fixed energy consumption and operating energy consumption of the CSS device respectively; η CCS Efficiency factor for capturing CO2 for CCS; It is the maximum power consumption of the CCS device per unit time.

8. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 7 is characterized by: In the optimal dispatch model, the wind and solar power generation operating cost C wind The penalty cost of wind and solar curtailment C pv The calculation formulas are: In the formula, λ w , p are the operation and maintenance cost coefficients of wind power and photovoltaic power respectively; μ w , μ p are the penalty coefficients for wind and solar abandonment, respectively; are the predicted values ​​of wind power and photovoltaic output power respectively; P wind,t , P pv,t are the actual values ​​of wind power and photovoltaic output power respectively; Electric energy storage system operating cost C ES The calculation formula is: In the formula, λ ES is the energy storage operation cost coefficient; P ES-ch,t The charging power for the electric energy storage; P ES-dch,t is the discharge power of the electric energy storage; Where Y t is 01 variable, Y t =1 means the energy storage system is in charging state, Y t =0 means the energy storage system is in a discharging state; SoC t is the state of charge of the energy storage system at time t; η ch is the charging efficiency of the energy storage system; η dch SoC is the discharge efficiency of the energy storage system; min The minimum state of charge required for safe operation of the energy storage system; SoC max is the highest state of charge of the energy storage system; P ES-ch,max P is the maximum power of a single charge of the energy storage system; ES-dch,max It is the maximum single discharge power of the energy storage system.

9. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 8 is characterized by: The constraints of the optimization scheduling model include power balance constraints, interconnection line power constraints, unit climbing constraints, unit climbing constraints and output limit constraints, and wind and solar output constraints.

10. The virtual power plant carbon green transaction optimization scheduling method considering the green electricity and carbon emission coupling relationship as claimed in claim 9 is characterized by: The power balance constraint is: Where P VPP,t is the total aggregated power of the virtual power plant system at time t; The tie line power constraint is: In the formula, are the lower and upper limits of VPP aggregation capacity, respectively; The unit climbing constraint is: P CHP,dn ≤P CHP,t -P CHP,t-1 ≤P CHP,up ; Where P CHP,dn , P CHP,up They are the lower and upper limits of the ramp rate of the CHP unit respectively; The unit climbing constraint and output limit constraint are: Where P MT,dn , P MT,up are the lower and upper limits of the gas turbine ramp rate, respectively; P MT,min , P MT,max are the lower and upper limits of gas turbine power generation respectively; The wind and solar power output constraints are: In the formula, are the predicted values ​​of wind power and photovoltaic output power respectively.

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