A virtual power plant low-carbon optimization scheduling method containing carbon-green certificate interaction and demand response

Abstract

The application discloses a kind of virtual power plant low carbon optimization scheduling methods containing carbon-green certificate interaction and demand response, belong to electric power dispatching technical field.The application promotes the consumption of renewable energy while reducing carbon emissions by introducing a combined operation model of cogeneration units coupled with carbon capture and power-to-gas in virtual power plants;By introducing a comprehensive demand response model of price type and substitution type on the load side, the low-carbon and flexible operation potential of the demand side of virtual power plants can be fully tapped;By introducing a carbon-green certificate joint trading mechanism, the virtual power plant electricity, carbon and green certificate markets are combined, taking into account both economic and environmental considerations.The method of the present application not only avoids the storage cost of CO2 to the greatest extent, but also reduces the carbon emissions of virtual power plants;It fully taps the low-carbon and flexible operation potential of the demand side of virtual power plants, reducing the operation cost, energy purchase cost and carbon emissions of virtual power plants.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a low-carbon optimized dispatching method and system for virtual power plants that incorporates carbon-green certificate interaction and demand response. Background Technology

[0002] The increasing consumption of traditional energy sources has led to increasingly serious environmental pollution problems. Therefore, vigorously developing low-carbon and clean energy power generation has become an important measure to maintain a sustainable development path. Against this backdrop, Virtual Power Plants (VPPs) couple multiple forms of energy and aggregate source and load connections to the power system, fully mobilizing supply and demand flexibility resources and improving energy utilization efficiency.

[0003] Carbon Capture Systems (CCS) and Power-to-Gas (P2G) technologies are effective ways to reduce CO2 emissions from VPPs (Vehicle Power Plants). CCS can capture a portion of the CO2 produced by coal-fired and gas-fired power plants, while P2G utilizes this CO2 to react with hydrogen to produce methane. Current research largely neglects the combined operation mode of CHP units in VPPs, which couples CCS and P2G.

[0004] Most current studies lack sophisticated demand response models for multiple energy sources on the load side, and the potential of demand-side flexibility resources to regulate the low-carbon economy of VPPs needs further exploration. With the diversification of energy demand, how to achieve multi-energy complementarity within VPPs and mobilize load-side flexibility resources has become a problem that needs to be explored.

[0005] Carbon emissions trading (CET) and green certificate trading (GCT) are important means of guiding the low-carbon economic operation of VPPs. However, most current research focuses on CET or GCT mechanisms alone, neglecting the further impact of carbon-green certificate joint trading on the optimal scheduling of VPP low-carbon economy.

[0006] Therefore, low-carbon optimization scheduling strategies for virtual power plants that consider carbon-green certificate interaction and demand response are urgent problems to be solved in this field. Summary of the Invention

[0007] This invention provides a low-carbon optimized scheduling method for virtual power plants that incorporates carbon-green certificate interaction and demand response. By introducing a joint operation model of cogeneration units coupled with carbon capture and electricity-to-gas conversion into the virtual power plant, it promotes the consumption of renewable energy while reducing carbon emissions. On the load side, it introduces a comprehensive demand response model based on price and substitution to fully tap the low-carbon and flexible operation potential of the virtual power plant on the demand side. Furthermore, it introduces a carbon-green certificate joint trading mechanism to promote the integration of the electricity, carbon, and green certificate markets of the virtual power plant, while taking into account both economic efficiency and environmental protection.

[0008] The technical solution of this invention is:

[0009] According to a first aspect of the present invention, a low-carbon optimization scheduling method for a virtual power plant with carbon-green certificate interaction and demand response is provided, comprising: Step 1, constructing a virtual power plant architecture with external power grid and gas grid for energy supply, mainly consisting of source-side components including wind power, photovoltaic, power-to-gas, carbon capture, cogeneration units, micro gas turbines, and electric chillers, and load-side components including electrical loads, electric vehicles, cooling loads, and heating loads; Step 2, based on the virtual power plant architecture, establishing a virtual power plant source-side flexible response model and a virtual power plant load-side comprehensive flexible demand response model; wherein, the virtual power plant source-side flexible response model includes a cogeneration unit model coupled with carbon capture and power-to-gas, a micro gas turbine model, and an electric chiller model, and the virtual power plant... The load-side integrated flexible demand response model includes a price-based demand response model and a substitution-based demand response model; Step 3: Establish a virtual power plant carbon-green certificate joint trading model; Step 4: Based on the virtual power plant source-side flexible response model, the virtual power plant load-side integrated flexible demand response model, and the virtual power plant carbon-green certificate joint trading model, construct an objective function that minimizes the comprehensive operating cost of the virtual power plant, including carbon-green certificate interaction and demand response, and construct constraints; Under the constraints, solve the problem with minimizing the comprehensive operating cost of the virtual power plant, including carbon-green certificate interaction and demand response, as the low-carbon economic optimization scheduling objective of the virtual power plant, to obtain the low-carbon optimization scheduling strategy of the virtual power plant, including carbon-green certificate interaction and demand response.

[0010] Furthermore, the model expression for the combined heat and power unit that couples carbon capture and electricity-to-gas conversion is as follows:

[0011]

[0012] In the formula: Net output power of the combined heat and power unit at time t; The power supplied to the combined heat and power unit at time t; The electrical power supplied to the combined heat and power unit for power-to-gas conversion at time t; The electrical power supplied for carbon capture to the cogeneration unit at time t; These are the maximum and minimum power supplies for the combined heat and power (CHP) unit, respectively. Let t be the output thermal power of the cogeneration unit; k1 and k2 are the electro-to-heat coefficients corresponding to the minimum and maximum power supplied by the cogeneration unit, respectively; k is the power supply heat conversion coefficient of the cogeneration unit. To output the minimum thermal power of the combined heat and power unit; The maximum and minimum electrical power supplied to the combined heat and power unit for power-to-gas conversion are respectively determined. The maximum and minimum electrical power supplied to the cogeneration unit for carbon capture are respectively; α and β are the coefficients of the power-to-gas conversion efficiency and the amount of carbon dioxide captured by carbon capture, respectively; τ is the conversion coefficient of carbon capture power consumption to carbon dioxide capture. These are the maximum and minimum net output power of the combined heat and power unit, respectively. Let t be the output gas power of the electro-pneumatic converter; Let t be the amount of carbon dioxide absorbed by the electro-gas conversion process.

[0013] Furthermore, the price-based demand response model is expressed as follows:

[0014]

[0015] In the formula: e t,j ΔE is the value in the t-th row and j-th column of the elasticity matrix. c,t This represents the change in load type at time t after the demand response; Let Δe be the initial value of each type of load at time t; j Let J represent the change in the price of each energy source at time j after the demand response. Let ΔE be the initial price of each energy source at time j; cut,t ΔE tran,t These represent the changes in load reduction and transfer for each type of load at time t after the demand response; These represent the initial load that can be reduced and transferred at time t; E cut (t,j), E tran (t,j) represent the price elasticity matrices for various types of load reduction and transfer, respectively; e j Let be the energy price at time j; where each type of load includes electricity, heat, and cooling loads; and T be the dispatch period.

[0016] Furthermore, the alternative demand response model is expressed as follows:

[0017]

[0018] In the formula: These represent the electrical load replaced by heat load and the electrical load replaced by cooling load, respectively; ε e,h ε e,c These are the substitution coefficients for electric heating and electric cooling, respectively. These represent the heat and cooling loads replaced by electrical loads, respectively; υ e υ h υ c These are the unit calorific value of electricity, heat, and cold energy, respectively. These are the utilization rates of electrical, thermal, and cold energy, respectively. These are the minimum replacement amounts for the electrical, heating, and cooling loads being replaced, respectively. These represent the maximum replacement amounts for the electrical, heating, and cooling loads, respectively.

[0019] Furthermore, the green certificate trading expression for the virtual power plant carbon-green certificate joint trading model is as follows:

[0020]

[0021]

[0022] In the formula: For the number of green certificate quotas for virtual power plants; k green Green certificate quota coefficients allocated to virtual power plants; Let t be the initial electrical load. The number of green certificates obtained by the virtual power plant for renewable energy generation; σ green The conversion factor for converting new energy power generation into green certificates; These represent the wind power output and solar power output at time t, respectively; C GCT For green certificate transaction costs; p GCT T represents the green certificate trading price; T represents the scheduling period.

[0023] Furthermore, the carbon trading expression for the virtual power plant carbon-green certificate joint trading model is as follows:

[0024]

[0025] In the formula: D VPP , E VPP These are, respectively, the free carbon allowances of the virtual power plant, the actual carbon emissions, and the actual carbon emission rights trading amount participated in carbon trading; δ e v h These are carbon quotas per unit of electricity and heat, respectively; a co2 b co2 c co2 These are the actual carbon emission coefficients of combined heat and power units; d co2 e co2 are the actual carbon emission coefficients for micro gas turbines and purchased electricity, respectively; k1 is the electro-to-heat coefficient corresponding to the minimum power output of the combined heat and power unit; D GREEN To offset carbon emissions by connecting new energy sources to the grid; gλ is the conversion factor; c is the carbon trading base price; λ is the price growth rate; h is the length of the carbon emission range. These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. C represents the power purchased from the grid at time t; CET For carbon trading costs; The net output power of the combined heat and power unit at time t; These represent the wind power output and photovoltaic power output at time t, respectively. Let t be the output thermal power of the cogeneration unit; Let t be the amount of carbon dioxide absorbed by the electro-gas converter at time t; T is the scheduling period.

[0026] Furthermore, the objective function is expressed as:

[0027] min G VPP =C op +C ab +C buy +C CET +C GCT

[0028] In the formula: C VPP C represents the total operating cost. op For unit operation and maintenance costs; C buy Energy purchase cost; C ab To incur penalties for abandoning scenic views; C CET For carbon trading costs; C GCT Costs associated with green certificate transactions.

[0029] Furthermore, the unit operation and maintenance cost is expressed as:

[0030]

[0031] In the formula: c1, c2, and c3 are the operation and maintenance cost coefficients of the combined heat and power unit; c P2G c CCS c MT c AC These represent the operation and maintenance cost coefficients for power-to-gas conversion, carbon capture, micro gas turbines, and electric chillers, respectively; M represents the number of electric vehicles. Net output power of the combined heat and power unit at time t; Let t be the output thermal power of the cogeneration unit; The electrical power supplied to the combined heat and power unit for power-to-gas conversion at time t; The electrical power supplied for carbon capture to the cogeneration unit at time t; Let t be the output electrical power of the micro gas turbine; Let t be the electrical power consumed by the electric chiller; η is the discharge power of the electric vehicle at time t.EV,dis μ EV These represent the electric vehicle's discharge efficiency and self-loss coefficient, respectively; x EV This refers to the driving distance of electric vehicles.

[0032] Furthermore, the constraints include:

[0033] The power balance constraint is expressed as follows:

[0034]

[0035] Unit operating constraints, expressed as:

[0036]

[0037] The charging and discharging constraints for electric vehicles are expressed as follows:

[0038]

[0039]

[0040] In the formula: These represent the wind power output and photovoltaic power output at time t, respectively. Let t be the power purchased from the grid at time t; The power supplied to the combined heat and power unit at time t; These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. These represent the initial electrical, heating, and cooling loads at time t; ΔP cut,t ΔH cut,t ΔC cut,t These represent the reductions in electrical, heating, and cooling loads at time t, respectively; ΔP tran,t ΔH tran,t ΔC tran,t These represent the electrical, thermal, and cooling load transfer amounts at time t, respectively. These represent the electrical load replaced by heat load and the electrical load replaced by cooling load, respectively. These are the amounts of heat and cooling loads replaced by electrical loads, respectively. These represent the charging and discharging power of the electric vehicle at time t, respectively. Let t be the electrical power consumed by the electric chiller; Let t be the output thermal power of the cogeneration unit; Let t be the output cooling power of the electric chiller; Let t be the output cooling power of the micro gas turbine; These are the maximum and minimum power supplies for the combined heat and power (CHP) unit, respectively. The maximum and minimum net output power of the combined heat and power unit; The maximum and minimum electrical power supplied to the combined heat and power unit for power-to-gas conversion are respectively determined. The maximum and minimum electrical power for carbon capture are supplied to the cogeneration unit, respectively. To output the minimum thermal power of the combined heat and power unit; To output the maximum thermal power of the combined heat and power unit; These are the upper and lower limits of the output electrical power of the micro gas turbine; These represent the upper and lower limits of the ramp speed for micro gas turbines; These are the upper and lower limits of the output cooling power of the electric chiller; These represent the predicted output of wind power and solar power at time t, respectively. These represent the wind power and photovoltaic power curtailment at time t, respectively. These represent the charging and discharging states of the electric vehicle at time t; These are the upper limits for charging and discharging electric vehicles, respectively; η EV,ch η EV,dis To improve the charging and discharging efficiency of electric vehicles; S represents the state of charge of the electric vehicle at time t; ev The rated capacity of the electric vehicle battery is taken as 0.05; These represent the upper and lower limits of the electric vehicle's state of charge, respectively; Δt represents each moment.

[0041] According to a second aspect of the present invention, a low-carbon optimized scheduling system for virtual power plants with carbon-green certificate interaction and demand response is provided, comprising: a module including any one of the methods described above.

[0042] The beneficial effects of this invention are:

[0043] (1) A joint operation mode of CHP unit with CCS and P2G was introduced at the VPP source, in which CCS captures CO2 generated by CHP and supplies it to P2G, which not only avoids the storage cost of CO2; simulation also demonstrates that the carbon emissions of VPP were reduced by 190.47t.

[0044] (2) Introducing a comprehensive demand response strategy that combines price-based and substitution-based approaches on the load side of the VPP can fully tap the potential for low-carbon and flexible operation on the demand side of the VPP. Simulations also demonstrate that the method of this invention reduces energy purchase costs and carbon emissions by 33.16% and 57.69t, respectively.

[0045] (3) The introduction of a carbon-green certificate joint trading mechanism in VPP promotes the integration of electricity, carbon and green certificate markets; simulation also demonstrates that the system operating cost was reduced by RMB 7,100 and carbon emissions by 9.04t, taking into account both the economic and environmental benefits of VPP.

[0046] In summary, the method of this invention not only minimizes CO2 storage costs but also reduces carbon emissions from VPPs; it fully taps the low-carbon and flexible operation potential of VPPs on the demand side, thereby reducing VPP operating costs, energy purchase costs, and carbon emissions. Attached Figure Description

[0047] Figure 1 This is a flowchart of the present invention;

[0048] Figure 2 This is a diagram of a virtual power plant structure.

[0049] Figure 3 Produce power, electricity, heat, and cooling load diagrams for wind and solar power forecasts;

[0050] Figure 4 This is the power balance diagram for scenario 5.

[0051] Figure 5 This is the power balance diagram for scenario 6.

[0052] Figure 6 This is a graph showing the relationship between electricity price and electricity load changes in scenario 1. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0054] Example 1: As Figure 1-6As shown, a low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response includes: Step 1, constructing a virtual power plant architecture with external power grid and gas grid for energy supply, mainly composed of source-side components including wind power, photovoltaic, power-to-gas, carbon capture, cogeneration units, micro gas turbines, and electric chillers, and load-side components including electric loads, electric vehicles, cooling loads, and heating loads; Step 2, based on the virtual power plant architecture, establishing a virtual power plant source-side flexible response model and a virtual power plant load-side comprehensive flexible demand response model; wherein, the virtual power plant source-side flexible response model includes a coupled carbon capture and power-to-gas cogeneration unit model, a micro gas turbine model, and an electric chiller model, and the virtual power plant load-side comprehensive flexible demand response model... The flexible demand response model includes a price-based demand response model and a substitution-based demand response model; Step 3: Establish a virtual power plant carbon-green certificate joint trading model; Step 4: Based on the virtual power plant source-side flexible response model, the virtual power plant load-side integrated flexible demand response model, and the virtual power plant carbon-green certificate joint trading model, construct an objective function that minimizes the comprehensive operating cost of the virtual power plant, including carbon-green certificate interaction and demand response, and construct constraints; Under the constraints, solve the problem with minimizing the comprehensive operating cost of the virtual power plant, including carbon-green certificate interaction and demand response, as the low-carbon economic optimization scheduling objective of the virtual power plant, to obtain the low-carbon optimization scheduling strategy of the virtual power plant, including carbon-green certificate interaction and demand response.

[0055] Furthermore, the following describes optional specific embodiments of the present invention in conjunction with experimental data:

[0056] The model expression for the combined heat and power unit that couples carbon capture and power-to-gas conversion is:

[0057]

[0058] The expression for the micro gas turbine model is:

[0059]

[0060] The model expression for the electric chiller is:

[0061]

[0062] In the formula: Net output power of the combined heat and power unit at time t; The power supplied to the combined heat and power unit at time t; The electrical power supplied to the combined heat and power unit for power-to-gas conversion at time t; The electrical power supplied for carbon capture to the cogeneration unit at time t. The maximum and minimum power supplies to the cogeneration unit are respectively taken as 35 and -15; , k1 and k2 are the maximum and minimum net output power of the cogeneration unit, respectively, taken as 35 and 10; k1 and k2 are the electricity-to-heat conversion coefficients corresponding to the minimum and maximum power supplied by the cogeneration unit, respectively, taken as 0.15 and 0.20; k is the power supply-to-heat conversion coefficient of the cogeneration unit, taken as 0.85; Let t be the output thermal power of the cogeneration unit; To ensure the minimum thermal power output of the cogeneration unit, we set it to 0; The maximum and minimum electrical power supplied to the combined heat and power unit for power-to-gas conversion are set to 15 and 0, respectively. The maximum and minimum electrical power supplied to the cogeneration unit for carbon capture are 10 and 0, respectively; α and β are the coefficients of the power-to-gas conversion efficiency and the amount of carbon dioxide captured by carbon capture, respectively, and are 0.55 and 1.02, respectively; τ is the conversion coefficient of carbon capture power consumption to carbon dioxide capture, and is 0.5. Let t be the output gas power of the electro-pneumatic converter; Let t be the amount of carbon dioxide absorbed by the electro-gas conversion process. These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. Let t be the output cooling power of the micro gas turbine; Let η be the gas power consumed by the micro gas turbine at time t; e The gas-to-electricity conversion coefficient of the micro gas turbine is taken as 0.6; η h The heating coefficient is taken as 0.95; η y The waste heat recovery coefficient is taken as 0.05; η c The cooling coefficient is set to 0.95. The electric chiller outputs cooling power; σ AC The conversion efficiency of the electric chiller is taken as 3; Let t be the electrical power consumed by the electric chiller.

[0063] It should be noted that carbon capture can capture a portion of the CO2 produced by the cogeneration unit and supply it to the power-to-gas (EPG) system, thereby reducing CO2 emissions from the CHP unit. The EPG operation consists of two parts: first, the EPG uses electricity from the cogeneration unit to electrolyze water to produce hydrogen and oxygen; second, the EPG uses the CO2 captured from carbon capture to react with hydrogen to produce natural gas, thus reducing both the system's carbon dioxide emissions and the operating costs of purchasing natural gas.

[0064] The price-based demand response model is expressed as follows:

[0065]

[0066] In the formula: e t,j Let E(t,j) be the elasticity matrix at row t and column j; ΔEc,t This represents the change in load type at time t after the demand response; Let Δe be the initial value of each type of load at time t; j Let J represent the change in the price of each energy source at time j after the demand response. Let j be the initial prices of each energy source (including...). These represent the changes in load reduction and transfer at time t after the demand response (load types include electricity, heat, and cooling loads, ΔE). cut,t Including ΔP cut,t ΔH cut,t ΔC cut,t ΔE represents the reduction in electricity, heat, and cooling loads at time t, respectively. tran,t Including ΔP tran,t ΔH tran,t ΔC tran,t , which represent the transfer amounts of electricity, heat, and cooling loads at time t; each energy source refers to electricity, heat, and cooling energy, which corresponds to the load. E represents the initial load that can be reduced and transferred at time t, respectively, and is 0.15 and 0.2 times the initial value of each type of load; cut (t,j), E tran (t,j) represent the price elasticity matrices for various types of load reduction and transfer, respectively; e j Let P be the price of each energy source at time j. j H j C j ).

[0067] The alternative demand response model is expressed as follows:

[0068]

[0069]

[0070] In the formula: These are the amounts of heat and cooling loads replaced by electrical loads, respectively. These represent the electrical load replaced by heat load and the electrical load replaced by cooling load, respectively; ε e,h ε e,c The substitution coefficients for electric heating and electric cooling are 1.83 and 1.78, respectively; υ e υ h υ c The unit calorific value of electricity, heat, and cold energy are respectively taken as 3.6MJ, 37MJ, and 10.8MJ; The utilization rates of electricity, heat, and cold energy are respectively taken as 0.85, 0.8, and 0.75; These represent the minimum replacement amounts for the electrical, heating, and cooling loads being replaced, respectively, and are all set to 0. These represent the maximum substitution amounts for the electricity, heat, and cooling loads being replaced, respectively. These represent the initial electrical, heating, and cooling loads at time t, respectively.

[0071] It should be noted that price-based demand response refers to users adjusting their energy consumption habits based on differences in electricity prices at different times to achieve peak shaving and valley filling. Substitution-based demand response is an important way to improve users' energy flexibility. Users can choose the optimal energy source to meet their energy needs based on the prices of different energy sources at the same time, thereby achieving mutual substitution between different energy sources.

[0072] The proposed virtual power plant carbon-green certificate joint trading model has the following green certificate trading expression:

[0073]

[0074] In the formula: For the number of green certificate quotas for virtual power plants; k green The green certificate quota coefficient allocated to the virtual power plant is set to 0.2; Let t be the initial electrical load. The number of green certificates obtained by the virtual power plant for renewable energy generation; σ green The conversion factor for converting renewable energy power generation into green certificates is set to 1. These represent the wind power output and solar power output at time t, respectively; C GCT For green certificate transaction costs; p GCT The transaction price for the green certificate is set at 50 yuan.

[0075] The carbon trading expression for the virtual power plant carbon-green certificate joint trading model is as follows:

[0076]

[0077]

[0078] In the formula: D VPP , E VPP These are, respectively, the free carbon allowances of the virtual power plant, the actual carbon emissions, and the actual carbon emission rights trading amount participated in carbon trading; δ e δ h The carbon allowances per unit of electricity and heat are 0.798 and 0.385, respectively; a co2 b co2 c co2 These are the actual carbon emission coefficients for combined heat and power (CHP) units, taken as 0.89, 0.0017, and 26.15 respectively; d co2 e co2The actual carbon emission coefficients for micro gas turbines and purchased electricity are taken as 1.03 and 1.08, respectively; D GREEN To offset carbon emissions by connecting new energy sources to the grid; g λ is the conversion coefficient, taken as 0.08; c is the carbon trading base price, taken as 100 yuan / t; λ is the price growth rate, taken as 0.1; h is the length of the carbon emission range, taken as 10t; These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. C represents the power purchased from the grid at time t; CET For carbon trading costs; The net output power of the combined heat and power unit at time t; These represent the wind power output and photovoltaic power output at time t, respectively. Let t be the output thermal power of the cogeneration unit; Let t be the amount of carbon dioxide absorbed by the electro-gas conversion at time t; T is 24.

[0079] It should be noted that the green certificate trading mechanism refers to a certificate issued by regulatory authorities for the electricity generated from renewable energy sources and for users consuming green electricity. The carbon trading mechanism refers to a system where companies, based on carbon emission allowances allocated by regulatory authorities, conduct reasonable production to reduce carbon emissions. If a VPP's actual carbon emissions are lower than its allocated carbon allowances, it can sell the remaining allowances through the carbon trading market to profit; conversely, it needs to purchase the corresponding carbon allowances.

[0080] The objective function is expressed as follows:

[0081] min C VPP =C op +C ab +C buy +C CET +C GCT

[0082] In the formula: C VPP C represents the total operating cost. op For unit operation and maintenance costs; C buy Energy purchase cost; C ab To incur penalties for abandoning scenic views; C CET For carbon trading costs; C GCT Costs associated with green certificate transactions.

[0083] The specific calculation model is as follows:

[0084]

[0085] In the formula: c1, c2, and c3 are the operation and maintenance cost coefficients of the combined heat and power unit, respectively, and are taken as 13.29, 0.004, and 39; c P2G cCCS c MT c AC The operation and maintenance cost coefficients for power-to-gas conversion, carbon capture, micro gas turbine, and electric chiller are 22, 22, 60, and 90, respectively; M is the number of electric vehicles, taken as 1000. Net output power of the combined heat and power unit at time t; Let t be the output thermal power of the cogeneration unit; The electrical power supplied to the combined heat and power unit for power-to-gas conversion at time t; The electrical power supplied for carbon capture to the cogeneration unit at time t; Let t be the output electrical power of the micro gas turbine; Let t be the electrical power consumed by the electric chiller; η is the discharge power of the electric vehicle at time t. EV,dis μ EV x represents the electric vehicle's discharge efficiency and self-loss coefficient, respectively, taken as 0.9 and 0.1; EV For electric vehicle driving distance;

[0086]

[0087] In the formula: c WT c PV The penalty cost coefficients for wind curtailment and solar curtailment are set to 0.25 and 0.2, respectively. These represent the wind power and photovoltaic power curtailment at time t, respectively.

[0088]

[0089] In the formula: Let t be the power purchased from the grid at time t; Let t be the power of purchasing gas from the gas network; These represent the electricity and gas prices at time t, respectively.

[0090] The constraints include:

[0091] The power balance constraint is expressed as follows:

[0092]

[0093] Unit operating constraints, expressed as:

[0094]

[0095] The charging and discharging constraints for electric vehicles are expressed as follows:

[0096]

[0097]

[0098] In the formula: These represent the wind power output and photovoltaic power output at time t, respectively. Let t be the power purchased from the grid at time t; The power supplied to the combined heat and power unit at time t; These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. These represent the initial electrical, heating, and cooling loads at time t; ΔP cut,t ΔH cut,t ΔC cut,t These represent the reductions in electrical, heating, and cooling loads at time t, respectively; ΔP tran,t ΔH tran,t ΔC tran,t These represent the electrical, thermal, and cooling load transfer amounts at time t, respectively. These represent the electrical load replaced by heat load and the electrical load replaced by cooling load, respectively. These are the amounts of heat and cooling loads replaced by electrical loads, respectively. These represent the charging and discharging power of the electric vehicle at time t, respectively. Let t be the electrical power consumed by the electric chiller; Let t be the output thermal power of the cogeneration unit; Let t be the output cooling power of the electric chiller; Let t be the output cooling power of the micro gas turbine; To ensure the minimum thermal power output of the cogeneration unit, we set it to 0; To determine the maximum thermal power output of the cogeneration unit, we take 40. These are the upper and lower limits of the output electric power of the micro gas turbine, respectively, and are set to 30 and 5. These represent the upper and lower limits of the ramp rate for the micro gas turbine, respectively set to 20 and -20. These are the upper and lower limits of the output cooling power of the electric chiller, respectively set to 4 and 0; These represent the predicted output of wind power and solar power at time t, respectively. These represent the wind power and photovoltaic power curtailment at time t, respectively; η EV,ch η EV,dis For the charging and discharging efficiency of electric vehicles, we take 0.9 and 0.9, respectively. These represent the charging and discharging states of the electric vehicle at time t; These are the upper limits for charging and discharging electric vehicles, respectively, and are set to 0.057 and 0.057. S represents the state of charge of the electric vehicle at time t; ev The rated capacity of the electric vehicle battery is taken as 0.05; Δt represents the upper and lower limits of the electric vehicle's state of charge, respectively, and is set to 0.9 and 0.2; Δt represents each moment, and is set to 1 hour.

[0099] Furthermore, to analyze the impact of the proposed model on the carbon emissions and economic benefits of low-carbon optimized scheduling of virtual power plants with carbon-green certificates and demand response, this invention sets up six scenarios for comparative analysis, namely:

[0100] Scenario 1: Low-carbon optimization scheduling strategy for virtual power plants with carbon-green certificates and demand response, i.e., the method proposed in this invention;

[0101] Scenario 2: Based on Scenario 1, without considering the carbon-green certificate joint trading model;

[0102] Scenario 3: Based on Scenario 1, without considering carbon capture and carbon trading;

[0103] Scenario 4: Based on Scenario 1, without considering carbon trading;

[0104] Scenario 5: Based on Scenario 1, without considering price-based and substitution-based demand response models;

[0105] Scenario 6: Based on Scenario 1, without considering price-based and substitution-based demand response models and electric vehicle charging and discharging constraints;

[0106] The scheduling results of the virtual power plant are analyzed for the above six scenarios.

[0107] To verify the effectiveness and applicability of the proposed model, this invention uses a 24-hour period and a 1-hour time step for solving the problem. Solution process:

[0108] 1) Input raw data and parameters, including predicted wind power output. Photovoltaic power forecast Initial electrical load Initial heat load Initial cooling load With time-of-use electricity pricing Gas purchase price Data. For example... Figure 3 As shown in Table 1.

[0109] Table 1 Energy Prices

[0110]

[0111] 2) Input variables And satisfy the model of a combined heat and power unit that couples carbon capture and power to gas conversion, with input variables And satisfy the micro gas turbine model, input variables And it satisfies the electric chiller model.

[0112] 3) Input variable ΔE c,t ΔEcut,t ΔE tran,t And satisfy the price-based demand response model, input variables And it meets the alternative demand response model.

[0113] 4) Input variables And satisfy the green certificate trading model, input variables And it meets the requirements of the carbon trading model.

[0114] 5) Input variables And build a system based on operation and maintenance costs C op The cost of abandoning scenic views (C) ab Energy purchase cost C buy Carbon trading costs C CET Green certificate transaction costs C GCT Total operating cost C VPP The minimum objective function must simultaneously satisfy constraints including power balance, unit operation, and electric vehicle charging and discharging.

[0115] 6) Using a 24-hour period and a 1-hour step, solve the above variables using the CPLEX solver, and then calculate Δe based on the output. j , C GCT D VPP , e VPP C CET C ab C buy C op Thus, the total operating cost C is obtained. VPP Ultimately, a low-carbon optimization scheduling strategy for virtual power plants was obtained, which includes carbon-green certificate interaction and demand response.

[0116] Table 2 shows a comparison of virtual power plant scheduling results under different scenarios.

[0117] Table 2 Comparison of Virtual Power Plant Scheduling Results in Different Scenarios

[0118]

[0119] As shown in Table 2, compared with Scenario 1, Scenario 2 saw an increase of 9.04 tons in carbon emissions and 6560.06 yuan in carbon trading costs, with a total operating cost increase of 7,100 yuan. This is because in Scenario 1, when the carbon-green certificate joint trading system is in operation, the VPP participates in the carbon trading market by obtaining green certificates to offset part of its carbon emission reductions, resulting in a significant decrease in carbon trading costs and demonstrating the low-carbon economic benefits of the carbon-green certificate joint trading mechanism. Compared with Scenario 4, Scenario 3 saw an increase of 190.47 tons in carbon emissions and 2621.95 yuan in green certificate costs. This is because in Scenario 4, when the carbon capture and power-to-gas cogeneration units are coupled, carbon capture can capture some of the CO2 generated by the cogeneration units and supply it to the power-to-gas system to produce natural gas. This not only reduces the VPP's external energy purchase costs by 15,200 yuan but also lowers carbon emissions. Compared to Scenario 1, Scenario 5 increases carbon emissions by 57.69 tons and operating costs by 2,100 yuan. This is because considering price-based and substitution-based demand responses can better guide users to change their energy consumption strategies, balancing the economics and low-carbon nature of VPPs. Compared to Scenario 6, Scenario 5 reduces carbon trading costs and green certificate trading costs by 891.18 yuan and 618.36 yuan, respectively. This is because introducing electric vehicles into VPPs allows unused energy to be stored during periods of high wind and solar activity and released during periods of low wind and solar activity to meet load demand. This not only reduces CO2 emissions from cogeneration units and micro gas turbine units but also increases wind and solar utilization rates.

[0120] In summary, introducing a carbon-green certificate joint trading mechanism into a Virtual Power Plant (VPP) can promote the coupling of the electricity, carbon, and green certificate markets, balancing system economics and low-carbon characteristics. Secondly, coupling carbon capture and cogeneration (CHP) unit operation modes with electricity-to-gas conversion can effectively improve the wind and solar utilization rate of the VPP and reduce carbon emissions. Furthermore, considering electric vehicles and demand response by utilizing load-side flexibility resources further reduces VPP carbon emissions and operating costs, validating the effectiveness of the proposed low-carbon optimized scheduling strategy for virtual power plants incorporating carbon-green certificates and demand response.

[0121] The power balance diagrams for scenarios 1 and 6 are as follows: Figure 4 , 5 As shown in the diagram. Compared to Scenario 6, Scenario 1, with the introduction of electric vehicles and integrated demand response (EDR) for electricity, heat, and cooling, enhances the source-load coordination of the VPP and makes the system's energy supply more flexible. During periods of abundant wind and solar power, electric vehicles can store unused curtailed wind and solar energy and release it during periods of low wind and solar power to meet the energy demand on the load side, reducing the VPP's energy purchase costs. Simultaneously, the load curve shows a more pronounced change with wind and solar power output, further reducing the VPP's carbon emissions.

[0122] Scenario 1: Relationship between electricity price and electricity load Figure 6As shown, during peak electricity price periods (11:00-15:00, 18:00-21:00), price-based load response and demand response strategies reduce some of the electricity load and shift a large portion of peak load to off-peak periods (01:00-07:00) to achieve the goal of "peak shaving and valley filling." During periods when electricity prices are significantly higher than heating and cooling prices (08:00-24:00), some electricity load is replaced by heating and cooling loads to reduce system operating costs. Figure 5 It can be seen that when price-based and substitution-based demand responses work together, the load curve after the response shows a more pronounced correlation with wind and solar power output, and the load curve is also smoother, which can better promote the consumption of renewable energy and achieve peak shaving and valley filling.

[0123] Example 2: A low-carbon optimized scheduling system for a virtual power plant with carbon-green certificate interaction and demand response, comprising: a construction module for constructing a virtual power plant architecture with external power grid and gas grid for energy supply, and internally mainly composed of source-side components including wind power, photovoltaic, power-to-gas, carbon capture, cogeneration units, micro gas turbines, and electric chillers, and load-side components including electric loads, electric vehicles, cooling loads, and heating loads; a first establishment module for establishing a virtual power plant source-side flexible response model and a virtual power plant load-side integrated flexible demand response model based on the virtual power plant architecture; wherein, the virtual power plant source-side flexible response model includes a cogeneration unit model coupled with carbon capture and power-to-gas, a micro gas turbine model, and an electric chiller model, and a virtual power plant load-side integrated flexible demand response model; The flexible demand response model includes a price-based demand response model and a substitution demand response model; the second module is used to establish a virtual power plant carbon-green certificate joint trading model; the scheduling module is used to construct an objective function that minimizes the comprehensive operating cost of virtual power plants containing carbon-green certificate interaction and demand response based on the virtual power plant source-side flexible response model, the virtual power plant load-side integrated flexible demand response model, and the virtual power plant carbon-green certificate joint trading model, and to construct constraints; under the constraints, the objective function of optimizing the low-carbon economy scheduling of virtual power plants with carbon-green certificate interaction and demand response is solved to obtain the virtual power plant low-carbon optimization scheduling strategy with carbon-green certificate interaction and demand response.

[0124] Example 3: A virtual power plant system includes an electronic device, which includes a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the low-carbon optimization scheduling method for virtual power plants with carbon-green certificate interaction and demand response as described in Example 1.

[0125] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0126] Although specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A low-carbon optimization scheduling method for virtual power plants incorporating carbon-green certificate interaction and demand response, characterized in that, include: Step 1: Construct a virtual power plant architecture with external power grid and gas grid for energy supply, and internal power sources including wind power, photovoltaic, power-to-gas, carbon capture, cogeneration units, micro gas turbines, and electric chillers, and loads including electric loads, electric vehicles, cooling loads, and heating loads. Step 2: Based on the virtual power plant architecture, establish a virtual power plant source-side flexible response model and a virtual power plant load-side integrated flexible demand response model. The virtual power plant source-side flexible response model includes a combined heat and power unit model coupled with carbon capture and power-to-gas conversion, a micro gas turbine model, and an electric chiller model. The virtual power plant load-side integrated flexible demand response model includes a price-based demand response model and a substitution demand response model. Step 3: Establish a virtual power plant carbon-green certificate joint trading model; Step 4: Based on the virtual power plant source-side flexible response model, the virtual power plant load-side integrated flexible demand response model, and the virtual power plant carbon-green certificate joint trading model, construct an objective function that minimizes the comprehensive operating cost of the virtual power plant, including carbon-green certificate interaction and demand response, and construct constraints. Under the constraints, solve the problem with the goal of minimizing the comprehensive operating cost of the virtual power plant, including carbon-green certificate interaction and demand response, to obtain the virtual power plant low-carbon economic optimization scheduling strategy. The carbon trading expression for the virtual power plant carbon-green certificate joint trading model is as follows: In the formula: D VPP , E VPP These are, respectively, the free carbon allowances of the virtual power plant, the actual carbon emissions, and the actual carbon emission rights trading amount participated in carbon trading; δ e δ h These are carbon quotas per unit of electricity and heat, respectively; a co2 b co2 c co2 These are the actual carbon emission coefficients of combined heat and power units; d co2 e co2 are the actual carbon emission coefficients for micro gas turbines and purchased electricity, respectively; k1 is the electro-to-heat coefficient corresponding to the minimum power output of the combined heat and power unit; D GREEN To offset carbon emissions by connecting new energy sources to the grid; k g λ is the conversion factor; c is the carbon trading base price; λ is the price growth rate; h is the length of the carbon emission range. These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. C represents the power purchased from the grid at time t; CET For carbon trading costs; The net output power of the combined heat and power unit at time t; These represent the wind power output and photovoltaic power output at time t, respectively. Let t be the output thermal power of the cogeneration unit; The amount of carbon dioxide absorbed by the electro-gas converter at time t; T is the scheduling period. The unit operation and maintenance cost is expressed as: In the formula: c1, c2, and c3 are the operation and maintenance cost coefficients of the combined heat and power unit; c P2G c CCS c MT c AC These represent the operation and maintenance cost coefficients for power-to-gas conversion, carbon capture, micro gas turbines, and electric chillers, respectively; M represents the number of electric vehicles. Net output power of the combined heat and power unit at time t; Let t be the output thermal power of the cogeneration unit; The electrical power supplied to the combined heat and power unit for power-to-gas conversion at time t; The electrical power supplied for carbon capture to the cogeneration unit at time t; Let t be the output electrical power of the micro gas turbine; Let t be the electrical power consumed by the electric chiller; η is the discharge power of the electric vehicle at time t. EV,dis μ EV These represent the electric vehicle's discharge efficiency and self-loss coefficient, respectively; x EV For electric vehicle driving distance; Solution process: 1) Input raw data and parameters, including predicted wind power output. Photovoltaic power forecast Initial electrical load Initial heat load Initial cooling load With time-of-use electricity pricing Gas purchase price data; 2) Input variables And satisfy the model of a combined heat and power unit that couples carbon capture and power to gas conversion, with input variables And satisfy the micro gas turbine model, input variables And satisfy the electric chiller model; among which, These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. Let t be the output cooling power of the micro gas turbine; Let t be the gas power consumed by the micro gas turbine; To output cooling power to the electric chiller; Let t be the electrical power consumed by the electric chiller; 3) Input variable ΔE c,t ΔE cut,t ΔE tran,t And satisfy the price-based demand response model, input variables And it satisfies the alternative demand response model; where ΔE c,t ΔE represents the change in load type at time t after demand response; cut,t ΔE tran,t These represent the changes in load reduction and transfer for each type of load at time t after the demand response; These are the amounts of heat and cooling loads replaced by electrical loads, respectively. These represent the electrical load replaced by heat load and the electrical load replaced by cooling load, respectively. 4) Input variables And satisfy the green certificate trading model, input variables And satisfy the carbon trading model; among which, These represent the wind power output and photovoltaic power output at time t, respectively. Let t be the power purchased from the grid at time t; 5) Input variables And build a system based on operation and maintenance costs C op The cost of abandoning scenic views (C) ab Energy purchase cost C buy Carbon trading costs C CET Green certificate transaction costs C GCT Total operating cost C VPP The minimum objective function simultaneously satisfies constraints including power balance, unit operation, and electric vehicle charging and discharging; among which, These represent the charging and discharging power of the electric vehicle at time t, respectively. These represent the wind power and photovoltaic power curtailment at time t, respectively. These represent the charging and discharging states of the electric vehicle at time t; The electric vehicle's state of charge at time t; 6) Solve using the CPLEX solver to obtain the total operating cost c. VPP .

2. The virtual power plant low-carbon optimization scheduling method with carbon-green certificate interaction and demand response as described in claim 1, characterized in that, The model expression for the combined heat and power unit that couples carbon capture and power-to-gas conversion is: In the formula: Net output power of the combined heat and power unit at time t; The power supplied to the combined heat and power unit at time t; The electrical power supplied to the combined heat and power unit for power-to-gas conversion at time t; The electrical power supplied for carbon capture to the cogeneration unit at time t; These are the maximum and minimum power supplies for the combined heat and power (CHP) unit, respectively. Let t be the output thermal power of the cogeneration unit; k1 and k2 are the electro-to-heat coefficients corresponding to the minimum and maximum power supplied by the cogeneration unit, respectively; k is the power supply heat conversion coefficient of the cogeneration unit. To output the minimum thermal power of the combined heat and power unit; The maximum and minimum electrical power supplied to the combined heat and power unit for power-to-gas conversion are respectively determined. The maximum and minimum electrical power supplied to the cogeneration unit for carbon capture are respectively; α and β are the coefficients of the power-to-gas conversion efficiency and the amount of carbon dioxide captured by carbon capture, respectively; τ is the conversion coefficient of carbon capture power consumption to carbon dioxide capture. These are the maximum and minimum net output power of the combined heat and power unit, respectively. Let t be the output gas power of the electro-pneumatic converter; Let t be the amount of carbon dioxide absorbed by the electro-gas conversion process.

3. The virtual power plant low-carbon optimization scheduling method with carbon-green certificate interaction and demand response as described in claim 1, characterized in that, The price-based demand response model is expressed as follows: In the formula: e t,j ΔE is the value in the t-th row and j-th column of the elasticity matrix. c,t This represents the change in load type at time t after the demand response; Let Δe be the initial value of each type of load at time t; j Let J represent the change in the price of each energy source at time j after the demand response. Let ΔE be the initial price of each energy source at time j; cut,t ΔE tran,t These represent the changes in load reduction and transfer for each type of load at time t after the demand response; These represent the initial load that can be reduced and transferred at time t; E cut (t,j), E tran (t,j) represent the price elasticity matrices for various types of load reduction and transfer, respectively; e j Let be the energy price at time j; where each type of load includes electricity, heat, and cooling loads; and T be the dispatch period.

4. The virtual power plant low-carbon optimization scheduling method with carbon-green certificate interaction and demand response as described in claim 1, characterized in that, The alternative demand response model is expressed as follows: In the formula: These represent the electrical load replaced by heat load and the electrical load replaced by cooling load, respectively. ε e,h ε e,c These are the substitution coefficients for electric heating and electric cooling, respectively. These represent the heat and cooling loads replaced by electrical loads, respectively; v e v h v c These are the unit calorific value of electricity, heat, and cold energy, respectively. These are the utilization rates of electrical, thermal, and cold energy, respectively. These are the minimum replacement amounts for the electrical, heating, and cooling loads being replaced, respectively. These represent the maximum replacement amounts for the electrical, heating, and cooling loads, respectively.

5. The virtual power plant low-carbon optimization scheduling method with carbon-green certificate interaction and demand response as described in claim 1, characterized in that, The proposed virtual power plant carbon-green certificate joint trading model has the following green certificate trading expression: In the formula: For the number of green certificate quotas for virtual power plants; k green Green certificate quota coefficients allocated to virtual power plants; Let t be the initial electrical load. The number of green certificates obtained by the virtual power plant for renewable energy generation; σ green The conversion factor for converting new energy power generation into green certificates; These represent the wind power output and solar power output at time t, respectively; C GCT For green certificate transaction costs; p GCT T represents the green certificate trading price; T represents the scheduling period.

6. The virtual power plant low-carbon optimization scheduling method with carbon-green certificate interaction and demand response as described in claim 1, characterized in that, The objective function is expressed as follows: min C VPP =C op +C ab +C buy +C CET +C GCT In the formula: C VPP C represents the total operating cost. op For unit operation and maintenance costs; C buy Energy purchase cost; C ab To incur penalties for abandoning scenic views; C CET For carbon trading costs; C GCT Costs associated with green certificate transactions.

7. The virtual power plant low-carbon optimization scheduling method with carbon-green certificate interaction and demand response as described in claim 1, characterized in that, The constraints include: The power balance constraint is expressed as follows: Unit operating constraints, expressed as: The charging and discharging constraints for electric vehicles are expressed as follows: In the formula: These represent the wind power output and photovoltaic power output at time t, respectively. Let t be the power purchased from the grid at time t; The power supplied to the combined heat and power unit at time t; These represent the electrical and thermal power output of the micro gas turbine at time t, respectively. These represent the initial electrical, heating, and cooling loads at time t; ΔP cut,t ΔH cut,t ΔC cut,t These represent the reductions in electrical, heating, and cooling loads at time t, respectively; ΔP tran,t ΔH tran,t ΔC tran,t These represent the electrical, thermal, and cooling load transfer amounts at time t, respectively. These represent the electrical load replaced by heat load and the electrical load replaced by cooling load, respectively. These are the amounts of heat and cooling loads replaced by electrical loads, respectively. These represent the charging and discharging power of the electric vehicle at time t, respectively. Let t be the electrical power consumed by the electric chiller; Let t be the output thermal power of the cogeneration unit; Let t be the output cooling power of the electric chiller; Let t be the output cooling power of the micro gas turbine; These are the maximum and minimum power supplies for the combined heat and power (CHP) unit, respectively. The maximum and minimum net output power of the combined heat and power unit; The maximum and minimum electrical power supplied to the combined heat and power unit for power-to-gas conversion are respectively determined. The maximum and minimum electrical power for carbon capture are supplied to the cogeneration unit, respectively. To output the minimum thermal power of the combined heat and power unit; To output the maximum thermal power of the combined heat and power unit; These are the upper and lower limits of the output electrical power of the micro gas turbine; These represent the upper and lower limits of the ramp speed for micro gas turbines; These are the upper and lower limits of the output cooling power of the electric chiller; These represent the predicted output of wind power and solar power at time t, respectively. These represent the wind power and photovoltaic power curtailment at time t, respectively. These represent the charging and discharging states of the electric vehicle at time t; These are the upper limits for charging and discharging electric vehicles, respectively; η EV,ch η EV,dis To improve the charging and discharging efficiency of electric vehicles; S represents the state of charge of the electric vehicle at time t; ev The rated capacity of the electric vehicle battery is taken as 0.05; These represent the upper and lower limits of the electric vehicle's state of charge, respectively; Δt represents each moment.

8. A virtual power plant low-carbon optimization scheduling system incorporating carbon-green certificate interaction and demand response, characterized in that, include: The module includes the method described in any one of claims 1-7.

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