Low-carbon scheduling method, system and equipment for multi-energy complementary virtual power plant

Abstract

The invention discloses a low-carbon scheduling method, system and equipment for a multi-energy complementary virtual power plant. The method comprises the following steps: constructing a virtual power plant operation framework comprising a hydrogen circulation system and an EMC (Electro Magnetic Compatibility) carbon capture power plant; establishing a mathematical model of the hydrogen circulation system and the EMC carbon capture power plant; and constructing a scheduling model of the multi-energy complementary virtual power plant, and determining an optimal operation scheme of the virtual power plant by optimizing and solving the scheduling model. According to the low-carbon scheduling method for the virtual power plant, coupling cooperation of multiple energy sources of electricity, heat, gas and hydrogen is promoted, the carbon emission and the operation cost of the virtual power plant are effectively reduced, and low-carbon development of the virtual power plant is achieved.

Description

Technical Field

[0001] The present invention relates to the field of day-ahead optimization scheduling of virtual power plants, and in particular to a low-carbon scheduling method, system and equipment for a multi-energy complementary virtual power plant. Background Art

[0002] Virtual power plants are an important development direction for new power systems. They can gather a considerable amount of distributed energy, which plays a vital role in promoting the transformation of power systems to more environmentally friendly systems. However, in existing research, the virtual power plant system is relatively simple, and the coordinated scheduling of various resources is not studied in depth. The hydrogen cycle system and electrolytic molten carbonate (EMC) carbon capture technology power plants are not included in the virtual power plant optimization scheduling research, ignoring the huge decarbonization potential of virtual power plants. Summary of the invention

[0003] Purpose of the invention: In view of the above problems, the purpose of the present invention is to provide a low-carbon scheduling method, system and equipment for a multi-energy complementary virtual power plant. From the perspective of low carbonization, it introduces EMC carbon capture technology suitable for gas turbines, and constructs a hydrogen circulation system with zero-carbon energy hydrogen as the link, expanding the interconnection and coordination of hydrogen energy with electricity, heat energy and natural gas, and proposing a low-carbon operation strategy for a multi-energy complementary virtual power plant, thereby reducing the operating cost and carbon emission level of the virtual power plant.

[0004] Technical solution: In the first aspect, the present invention provides a low-carbon dispatching method for a multi-energy complementary virtual power plant, comprising the following steps:

[0005] Step 1: Build a virtual power plant operation framework including a hydrogen cycle system and an EMC carbon capture power plant;

[0006] Step 2, establish a mathematical model of the hydrogen cycle system and the EMC carbon capture power plant;

[0007] Step 3: construct a dispatching model for a multi-energy complementary virtual power plant, and determine the optimal operation plan of the virtual power plant by optimizing and solving the dispatching model.

[0008] Further, the hydrogen circulation system includes an electrolyzer, a methane reactor, a hydrogen blending unit, a hydrogen fuel cell and a hydrogen storage tank;

[0009] Electric energy is passed into an electrolyzer to generate hydrogen, and the hydrogen is passed into a methane reactor, a hydrogen blending unit, a hydrogen fuel cell or a hydrogen storage tank.

[0010] Further, the EMC carbon capture power plant includes a gas turbine, an organic Rankine cycle low temperature waste heat power generation device, an EMC device and a waste heat boiler;

[0011] The high-temperature hot gas discharged from the gas turbine is passed into the EMC device to convert CO2 The waste heat from the gas turbine and EMC device is absorbed and converted into solid carbon, and the waste heat resources from the gas turbine and EMC device are introduced into the organic Rankine cycle low-temperature waste heat power generation device or waste heat boiler.

[0012] Furthermore, the mathematical model of the electrolyzer is:

[0013]

[0014] In the formula, are the electric power consumed by the electrolyzer and the hydrogen power output at time t; η EL is the conversion efficiency of the electrolyzer; They are the upper and lower limits of the electric power input to the electrolyzer and the upper and lower limits of the ramp rate;

[0015] The mathematical model of the methane reactor is:

[0016]

[0017] In the formula, are the hydrogen power consumed by the methane reactor and the gas power output at time t; η MR is the conversion efficiency of the methane reactor; They are the upper and lower limits of hydrogen power and the upper and lower limits of ramping input into the methane reactor respectively; is the CO consumed by the methane reactor at time t 2 Quantity; q gas is the calorific value of natural gas; For CO 2 density; is the thermal power generated by the methane reactor at time t; η MR,h is the heat generation coefficient per unit power consumption of the methane reactor;

[0018] The mathematical model of a hydrogen fuel cell is:

[0019]

[0020] In the formula, is the hydrogen power consumed by the hydrogen fuel cell at time t; are the electrical power and thermal power output of the hydrogen fuel cell at time t; η HFC is the conversion efficiency of hydrogen fuel cells; They are the upper and lower limits of the thermal-to-electricity ratio of hydrogen fuel cells; They are the upper and lower limits of hydrogen power and the upper and lower limits of ramp speed input into the hydrogen fuel cell respectively;

[0021] The hydrogen-doped unit includes a hydrogen-doped gas turbine and a hydrogen-doped gas boiler, wherein the mathematical model of the hydrogen-doped gas turbine is:

[0022]

[0023] In the formula, are the gas power and hydrogen power consumed by the gas turbine at time t respectively; are the electrical power and thermal power output of the gas turbine at time t respectively; η GT,e , η GT,h are the electricity and heat conversion efficiency of gas turbine respectively; Y GT,t is the hydrogen blending ratio of the gas turbine at time t; They are the upper and lower limits of hydrogen blending ratio of gas turbines respectively; is the calorific value of hydrogen; They are the upper and lower limits of gas turbine output electric power and the upper and lower limits of ramp power respectively; are the upper and lower limits of the gas turbine output thermal power respectively; is the CO generated by the gas turbine at time t 2 quantity; is the carbon emission per unit volume of natural gas combustion, L gas The energy obtained by burning a unit volume of natural gas;

[0024] The mathematical model of hydrogen-blended gas boiler is:

[0025]

[0026] In the formula, are the gas power and hydrogen power consumed by the gas boiler at time t respectively; is the thermal power output of the gas boiler at time t; η GB is the heat conversion efficiency of the gas boiler; Y GB,t is the hydrogen blending ratio of the gas boiler at time t; are the upper and lower limits of hydrogen blending ratio of gas boilers; ρ gas , H2 is the density of natural gas and hydrogen; m gas 、m H2 is the relative molecular mass of natural gas and hydrogen; They are the upper and lower limits of the thermal power output and the upper and lower limits of the ramp power of the gas boiler respectively; is the CO generated by the gas boiler at time t 2 quantity;

[0027] The mathematical model of the hydrogen storage tank is:

[0028]

[0029] In the formula, They are the charging and discharging power of hydrogen energy storage at time t respectively; They are the charging and discharging state variables of hydrogen energy storage at time t respectively; They are the maximum charging and discharging power of hydrogen energy storage respectively; They are the charging and discharging efficiency of hydrogen energy storage respectively; is the power loss coefficient of hydrogen energy storage; S H,t is the hydrogen storage capacity at time t; They are the upper and lower limits of the hydrogen storage capacity of hydrogen energy storage.

[0030] Furthermore, the mathematical model of the EMC carbon capture power plant is:

[0031]

[0032] In the formula, is the CO produced by EMC electrolysis at time t 2 quantity; is the EMC electrolysis efficiency; Γ is the flue gas split ratio; is the electrical power consumed by the EMC and the waste heat power generated at time t; η EMC,e , η EMC,h EMC electrolysis CO 2 Power consumption coefficient and heat generation efficiency; The upper limit of the input EMC power; is the amount of solid carbon generated by EMC at time t; η EMC,C To produce solid carbon efficiency; are the thermal powers input to the organic Rankine cycle low-temperature waste heat power generation device and the waste heat boiler at time t respectively; are the electric power output of the organic Rankine cycle low-temperature waste heat power generation device and the thermal power output of the waste heat boiler at time t respectively; η ORC , η WHB They are the power generation efficiency of the organic Rankine cycle low-temperature waste heat power generation device and the heating efficiency of the waste heat boiler.

[0033] Furthermore, the objective function is constructed with the minimum total operating cost of the virtual power plant, which includes the gas purchase cost F gas,t , electricity sales revenue Environmental cost pol,t , Electric vehicle compensation cost F EV,t , alternative response load adjustment cost F alt,t , Carbon trading costs and solid carbon yield F C,t , the objective function is:

[0034]

[0035] F gas,t =c gas P gas,t

[0036]

[0037] Where P gas,t is the gas purchase volume at time t; c gas is the unit price of gas purchase; are the power purchased and sold at time t; c pur,t 、c sale,t are the electricity purchase and sales prices at time t; Q i is the emission coefficient of the i-th pollutant gas; S i 、N i is the penalty and environmental loss cost coefficient of the i-th pollutant gas; c EV The cost coefficient for discharging compensation for electric vehicles; is the discharge power of the electric vehicle at time t; ΔP load,e,t , ΔP load,h,t , ΔP load,g,t They are the load changes of electricity, heat and gas substitution response at time t respectively; are the cost coefficients of alternative response loads for electricity, heat, and gas, respectively; is the carbon trading price; is the carbon quota trading volume at time t, positive means buying, negative means selling; c C is the solid carbon benefit coefficient;

[0038] The power balance constraint conditions of the virtual power plant are established, where the power supply and demand balance constraint is:

[0039]

[0040] In the formula, The day-ahead forecast values ​​of wind and solar output at time t respectively; is the energy storage charging and discharging power at time t; P is the charging power of the electric vehicle at time t; el,t is the electrical load at time t;

[0041] The thermal power supply and demand balance constraint is:

[0042]

[0043] In the formula, are the thermal energy storage charging and discharging power at time t; P hl,t is the heat load at time t;

[0044] The gas power supply and demand balance constraint is:

[0045]

[0046] The hydrogen power supply and demand balance constraint is:

[0047]

[0048] The carbon supply and demand balance constraint is:

[0049]

[0050] In the formula, is the carbon quota obtained by the virtual power plant at time t; are the carbon quota coefficients of gas turbines and gas boilers respectively; ξ is the electric-heat conversion coefficient of the carbon quota of gas turbines;

[0051] The uncertainty of wind power and photovoltaic output in the virtual power plant is described by an uncertainty set U, which is expressed as:

[0052]

[0053] Where P win,t P pv,t are the actual wind and solar outputs at time t respectively; is a 0-1 variable representing the uncertainty of wind and solar power; are wind and solar output forecast deviations respectively; Γ win , Γ pv are the robustness coefficients of wind power and photovoltaic power respectively;

[0054] The compact form of the dispatch model of the multi-energy complementary virtual power plant is expressed as:

[0055]

[0056] In the formula, y is the first-stage decision variable, including the state variables of each device; x is the second-stage decision variable, including the power of each device in the virtual power plant, as well as the gas purchase volume and electricity purchase and sales power; They are the overestimation and underestimation of wind and solar power output respectively; are the penalty cost coefficients for overestimation and underestimation respectively; a, b, C, d, E, G, h, J are constant matrices.

[0057] In a second aspect, the present invention provides a low-carbon dispatching system for a multi-energy complementary virtual power plant, and a low-carbon dispatching method for running the multi-energy complementary virtual power plant, the system comprising:

[0058] Framework building modules for building a virtual power plant operation framework that considers hydrogen cycle systems and EMC carbon capture technology;

[0059] Low-carbon technology module, used to build mathematical models of hydrogen cycle systems and EMC carbon capture power plants;

[0060] The optimization scheduling module is used to comprehensively consider the uncertainty of wind and solar power output, build a scheduling model for a multi-energy complementary virtual power plant, and determine the optimal operation plan of the virtual power plant through optimization solution.

[0061] In a third aspect, the present invention provides a computer device comprising a processor and a storage medium, the storage medium being used to store instructions; the processor being used to operate according to the instructions to execute the low-carbon scheduling method of the multi-energy complementary virtual power plant.

[0062] In a fourth aspect, the present invention provides a readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the low-carbon scheduling method of the multi-energy complementary virtual power plant.

[0063] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0064] 1. The present invention takes into account the hydrogen energy circulation system and EMC carbon capture technology, reduces the carbon emission level and operating cost of the virtual power plant, and promotes the improvement of the comprehensive utilization efficiency of energy;

[0065] 2. The present invention proposes a carbon capture technology suitable for gas turbines, and based on this, constructs an EMC carbon capture power plant containing a gas turbine-ORC low-temperature waste heat power generation device-waste heat boiler, which realizes the effective fixation of carbon dioxide and the flexible adjustment and mutual assistance of power supply and heat supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flowchart of a low-carbon dispatch method for a multi-energy complementary virtual power plant;

[0067] Figure 2 Provides a framework diagram for virtual power plant operation;

[0068] Figure 3 Optimize the dispatch results for virtual power plants;

[0069] Figure 4 This is a framework diagram of a low-carbon dispatching system for a multi-energy complementary virtual power plant. DETAILED DESCRIPTION

[0070] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0071] The low-carbon scheduling method of a multi-energy complementary virtual power plant described in this embodiment is as follows: Figure 1 As shown, the method at least includes the following steps 1 to 3.

[0072] Step 1: Build a virtual power plant operation framework including a hydrogen cycle system and an EMC carbon capture power plant;

[0073] Step 2, establish a mathematical model of the hydrogen cycle system and the EMC carbon capture power plant;

[0074] Step 3: construct a dispatching model for a multi-energy complementary virtual power plant, and determine the optimal operation plan of the virtual power plant by optimizing and solving the dispatching model.

[0075] Further, the hydrogen circulation system includes an electrolyzer, a methane reactor, a hydrogen blending unit, a hydrogen fuel cell and a hydrogen storage tank;

[0076] Electric energy is passed into an electrolyzer to generate hydrogen, and the hydrogen is passed into a methane reactor, a hydrogen blending unit, a hydrogen fuel cell or a hydrogen storage tank.

[0077] Further, the EMC carbon capture power plant includes a gas turbine, an organic Rankine cycle low temperature waste heat power generation device, an EMC device and a waste heat boiler;

[0078] The high-temperature hot gas discharged from the gas turbine is passed into the EMC device to convert CO 2 The waste heat from the gas turbine and EMC device is absorbed and converted into solid carbon, and the waste heat resources from the gas turbine and EMC device are introduced into the organic Rankine cycle low-temperature waste heat power generation device or waste heat boiler.

[0079] The virtual power plant in this example is based on the hydrogen cycle system and EMC carbon capture technology to build a low-carbon operation framework that coordinates multiple energy sources such as electricity, heat, gas and hydrogen. The hydrogen cycle system includes an electrolyzer, a methane reactor, a hydrogen blending unit, a hydrogen fuel cell (HFC) and a hydrogen storage tank. Electricity is passed into the electrolyzer to produce hydrogen, and hydrogen is passed into the methane reactor, the hydrogen blending unit, and the HFC to supply electricity, gas, and heat, or stored in a hydrogen storage tank for subsequent conversion and utilization at any time, thereby realizing the interconnection of hydrogen energy with electricity, heat and natural gas. The EMC carbon capture power plant includes a gas turbine, an organic Rankine cycle (ORC) low-temperature waste heat power generation device, an EMC device and a waste heat boiler. The high-temperature hot gas emitted by the gas turbine can be directly passed into the EMC device to convert CO 2 The waste heat resources of the gas turbine and EMC device can be introduced into the ORC or waste heat boiler, thereby achieving flexible and efficient electricity and heat supply of the EMC carbon capture power plant and effective treatment of carbon dioxide.

[0080] Furthermore, the mathematical model of the electrolyzer is:

[0081]

[0082] In the formula, are the electric power consumed by the electrolyzer and the hydrogen power output at time t; η EL is the conversion efficiency of the electrolyzer; They are the upper and lower limits of the electric power input to the electrolyzer and the upper and lower limits of the ramp rate;

[0083] The mathematical model of the methane reactor is:

[0084]

[0085] In the formula, are the hydrogen power consumed by the methane reactor and the gas power output at time t; η MR is the conversion efficiency of the methane reactor; They are the upper and lower limits of hydrogen power and the upper and lower limits of ramping input into the methane reactor respectively; is the CO consumed by the methane reactor at time t 2 Quantity; q gas is the calorific value of natural gas; For CO 2 density; is the thermal power generated by the methane reactor at time t; η MR,h is the heat generation coefficient per unit power consumption of the methane reactor;

[0086] The mathematical model of a hydrogen fuel cell is:

[0087]

[0088] In the formula, is the hydrogen power consumed by the hydrogen fuel cell at time t; are the electrical power and thermal power output of the hydrogen fuel cell at time t; η HFC is the conversion efficiency of hydrogen fuel cells; They are the upper and lower limits of the thermal-to-electricity ratio of hydrogen fuel cells; They are the upper and lower limits of hydrogen power and the upper and lower limits of ramp speed input into the hydrogen fuel cell respectively;

[0089] The hydrogen-doped unit includes a hydrogen-doped gas turbine and a hydrogen-doped gas boiler, wherein the mathematical model of the hydrogen-doped gas turbine is:

[0090]

[0091] In the formula, are the gas power and hydrogen power consumed by the gas turbine at time t respectively; are the electrical power and thermal power output of the gas turbine at time t respectively; η GT,e , η GT,h are the electricity and heat conversion efficiency of gas turbine respectively; Y GT,t is the hydrogen blending ratio of the gas turbine at time t; They are the upper and lower limits of hydrogen blending ratio of gas turbines respectively; is the calorific value of hydrogen; They are the upper and lower limits of gas turbine output electric power and the upper and lower limits of ramp power respectively; are the upper and lower limits of the thermal power output of the gas turbine respectively; is the CO generated by the gas turbine at time t2 quantity; is the carbon emission per unit volume of natural gas combustion, L gas The energy obtained by burning a unit volume of natural gas;

[0092] The mathematical model of hydrogen-blended gas boiler is:

[0093]

[0094] In the formula, are the gas power and hydrogen power consumed by the gas boiler at time t respectively; is the thermal power output of the gas boiler at time t; η GB is the heat conversion efficiency of the gas boiler; Y GB,t is the hydrogen blending ratio of the gas boiler at time t; are the upper and lower limits of hydrogen blending ratio of gas boilers; ρ gas , is the density of natural gas and hydrogen; m gas , is the relative molecular mass of natural gas and hydrogen; They are the upper and lower limits of the thermal power output and the upper and lower limits of the ramp power of the gas boiler respectively; is the CO generated by the gas boiler at time t 2 quantity;

[0095] The mathematical model of the hydrogen storage tank is:

[0096]

[0097] In the formula, They are the charging and discharging power of hydrogen energy storage at time t respectively; They are the charging and discharging state variables of hydrogen energy storage at time t respectively; They are the maximum charging and discharging power of hydrogen energy storage respectively; They are the charging and discharging efficiency of hydrogen energy storage respectively; is the power loss coefficient of hydrogen energy storage; S H,t is the hydrogen storage capacity at time t; They are the upper and lower limits of the hydrogen storage capacity of hydrogen energy storage.

[0098] Furthermore, the mathematical model of the EMC carbon capture power plant is:

[0099]

[0100] In the formula, is the CO produced by EMC electrolysis at time t 2 quantity; is the EMC electrolysis efficiency; Γ is the flue gas split ratio; is the electrical power consumed by the EMC and the waste heat power generated at time t; η EMC,e , η EMC,h EMC electrolysis CO 2 Power consumption coefficient and heat generation efficiency; The upper limit of the input EMC power; is the amount of solid carbon generated by EMC at time t; η EMC,C To produce solid carbon efficiency; are the thermal powers input to the organic Rankine cycle low-temperature waste heat power generation device and the waste heat boiler at time t respectively; are the electric power output of the organic Rankine cycle low-temperature waste heat power generation device and the thermal power output of the waste heat boiler at time t respectively; η ORC , η WHB They are the power generation efficiency of the organic Rankine cycle low-temperature waste heat power generation device and the heating efficiency of the waste heat boiler.

[0101] Furthermore, the objective function is constructed with the minimum total operating cost of the virtual power plant, which includes the gas purchase cost F gas,t , electricity sales revenue Environmental cost pol,t , Electric vehicle compensation cost F EV,t , alternative response load adjustment cost F alt,t , Carbon trading costs and solid carbon yield F C,t , the objective function is:

[0102]

[0103] F gas,t =c gas P gas,t

[0104]

[0105]

[0106] Where P gas,t is the gas purchase volume at time t; c gas is the unit price of gas purchase; are the power purchased and sold at time t; c pur,t 、c sale,t are the electricity purchase and sales prices at time t; Q i is the emission coefficient of the i-th pollutant gas; S i 、N i is the penalty and environmental loss cost coefficient of the i-th pollutant gas; c EV The cost coefficient for discharging compensation for electric vehicles; is the discharge power of the electric vehicle at time t; ΔP load,e,t , ΔPload,h,t , ΔP load,g,t They are the load changes of electricity, heat and gas substitution response at time t respectively; are the cost coefficients of alternative response loads for electricity, heat, and gas, respectively; is the carbon trading price; is the carbon quota trading volume at time t, positive means buying, negative means selling; c C is the solid carbon benefit coefficient;

[0107] The power balance constraint conditions of the virtual power plant are established, where the power supply and demand balance constraint is:

[0108]

[0109] In the formula, The day-ahead forecast values ​​of wind and solar output at time t respectively; is the charging and discharging power of the energy storage at time t; P is the charging power of the electric vehicle at time t; el,t is the electrical load at time t;

[0110] The thermal power supply and demand balance constraint is:

[0111]

[0112] In the formula, are the thermal energy storage charging and discharging power at time t; P hl,t is the heat load at time t;

[0113] The gas power supply and demand balance constraint is:

[0114]

[0115] The hydrogen power supply and demand balance constraint is:

[0116]

[0117] The carbon supply and demand balance constraint is:

[0118]

[0119] In the formula, is the carbon quota obtained by the virtual power plant at time t; are the carbon quota coefficients of gas turbines and gas boilers respectively; ξ is the electric-heat conversion coefficient of the carbon quota of gas turbines;

[0120] The uncertainty of wind power and photovoltaic output in the virtual power plant is described by an uncertainty set U, which is expressed as:

[0121]

[0122] Where P win,t P pv,t are the actual wind and solar outputs at time t respectively; is a 0-1 variable representing the uncertainty of wind and solar power; are wind and solar output forecast deviations respectively; Γ win , Γ pv are the robustness coefficients of wind power and photovoltaic power respectively;

[0123] The compact form of the dispatch model of the multi-energy complementary virtual power plant is expressed as:

[0124]

[0125] In the formula, y is the first-stage decision variable, including the state variables of each device, that is, the charging and discharging state of energy storage and electric vehicles; x is the second-stage decision variable, including the power of each device in the virtual power plant, as well as the gas purchase volume, electricity purchase and sales power, etc. They are the overestimation and underestimation of wind and solar power output respectively; are the penalty cost coefficients for overestimation and underestimation respectively; a, b, C, d, E, G, h, J are constant matrices.

[0126] By solving the scheduling model through optimization algorithms such as the Column-and-Constraint Generation (C&CG) algorithm and the Benders decomposition algorithm, the output and energy purchase situation of each equipment in the virtual power plant can be determined, and a low-carbon optimized operation plan for the multi-energy complementary virtual power plant can be obtained.

[0127] In one example, the virtual power plant includes wind and solar power generators, gas turbines, gas boilers, HFC, EMC devices, ORC low-temperature waste heat power generation, waste heat boilers, electrolyzers, methane reactors, electric thermal hydrogen energy storage, electric vehicles, and alternative response loads. The specific framework diagram is as follows: Figure 2 The relevant parameters are shown in Table 1.

[0128] Table 1 Virtual power plant operating parameters

[0129]

[0130] In order to verify the superiority of the low-carbon dispatch method of the multi-energy complementary virtual power plant described in this example, the following four different scenarios are set for comparison:

[0131] Scenario 1: The virtual power plant does not consider the EMC carbon capture power plant and hydrogen cycle system.

[0132] Scenario 2: The virtual power plant only considers the hydrogen cycle system and does not consider the EMC carbon capture power plant.

[0133] Scenario 3: The virtual power plant only considers the EMC carbon capture power plant, does not consider the hydrogen cycle system but considers the traditional power-to-gas technology.

[0134] Scenario 4: Virtual power plant considers EMC carbon capture power plant and hydrogen circulation system, that is, the method proposed in the present invention.

[0135] The optimization scheduling results of the virtual power plant based on the method proposed in this invention (scenario 4) are as follows: Figure 3 As shown, the supply and demand balance of various energy sources of the virtual power plant is displayed, where Figure (a) is the supply and demand balance diagram of electric power, Figure (b) is the supply and demand balance diagram of thermal power, Figure (c) is the supply and demand balance diagram of gas power, Figure (d) is the supply and demand balance diagram of hydrogen power, and Figure (e) is the supply and demand balance diagram of carbon power. The cost comparison of virtual power plants under the four scenarios is shown in Table 2.

[0136] Table 2 Comparison of virtual power plant costs under different scenarios

[0137]

[0138]

[0139] It can be seen from Table 2 that based on the low-carbon scheduling method of the multi-energy complementary virtual electric field described in the present invention, the total operating cost in scenario 4 is reduced by at least 86.5 yuan (0.43%) compared with other scenarios, and is reduced by 5100.6 yuan (20.43%) compared with scenario 1 without considering the hydrogen circulation system and EMC carbon capture technology. In addition, the carbon trading cost in scenario 4 is reduced by at least 110.73 yuan (46.45%) compared with other scenarios, which further illustrates that the method proposed in the present invention has better low-carbon economy.

[0140] In one embodiment, a low-carbon dispatching system for a multi-energy complementary virtual power plant is provided, such as Figure 4 As shown, the low-carbon dispatching method for operating the multi-energy complementary virtual power plant includes:

[0141] Framework building modules for building a virtual power plant operation framework that considers hydrogen cycle systems and EMC carbon capture technology;

[0142] Low-carbon technology module, used to build mathematical models of hydrogen cycle systems and EMC carbon capture power plants;

[0143] The optimization scheduling module is used to comprehensively consider the uncertainty of wind and solar power output, build a scheduling model for a multi-energy complementary virtual power plant, and determine the optimal operation plan of the virtual power plant through optimization solution.

[0144] In one embodiment, a computer device is provided, including a processor and a storage medium, the storage medium being used to store instructions; the processor being used to operate according to the instructions to execute the low-carbon scheduling method of the multi-energy complementary virtual power plant.

[0145] In one embodiment, a readable storage medium is provided, wherein the readable storage medium stores a computer program, and when the computer program is executed by a processor, the low-carbon scheduling method of the multi-energy complementary virtual power plant is implemented.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A low-carbon dispatching method for a multi-energy complementary virtual power plant, characterized in that: The steps include: Step 1: Build a virtual power plant operation framework including a hydrogen cycle system and an EMC carbon capture power plant; Step 2, establish a mathematical model of the hydrogen cycle system and the EMC carbon capture power plant; Step 3: construct a dispatching model for a multi-energy complementary virtual power plant, and determine the optimal operation plan of the virtual power plant by optimizing and solving the dispatching model.

2. A low-carbon dispatching method for a multi-energy complementary virtual power plant according to claim 1, characterized in that: The hydrogen circulation system includes an electrolyzer, a methane reactor, a hydrogen blending unit, a hydrogen fuel cell and a hydrogen storage tank; Electric energy is passed into an electrolyzer to generate hydrogen, and the hydrogen is passed into a methane reactor, a hydrogen blending unit, a hydrogen fuel cell or a hydrogen storage tank.

3. A low-carbon dispatching method for a multi-energy complementary virtual power plant according to claim 2, characterized in that: The EMC carbon capture power plant includes a gas turbine, an organic Rankine cycle low-temperature waste heat power generation unit, an EMC unit and a waste heat boiler; The high-temperature hot gas emitted by the gas turbine is passed into the EMC device to absorb CO2 and convert it into solid carbon. The waste heat resources of the gas turbine and EMC device are passed into the organic Rankine cycle low-temperature waste heat power generation device or waste heat boiler.

4. A low-carbon dispatching method for a multi-energy complementary virtual power plant according to claim 3, characterized in that: The mathematical model of the electrolyzer is: In the formula, are the electric power consumed by the electrolyzer and the hydrogen power output at time t; η EL is the conversion efficiency of the electrolyzer; They are the upper and lower limits of the electric power input to the electrolyzer and the upper and lower limits of the ramp rate; The mathematical model of the methane reactor is: In the formula, are the hydrogen power consumed and gas power output by the methane reactor at time t respectively; η MR is the conversion efficiency of the methane reactor; They are the upper and lower limits of hydrogen power and the upper and lower limits of ramping input into the methane reactor respectively; is the amount of CO2 consumed by the methane reactor at time t; q gas is the calorific value of natural gas; is the density of CO2; is the thermal power generated by the methane reactor at time t; η MR,h is the heat generation coefficient per unit power consumption of the methane reactor; The mathematical model of a hydrogen fuel cell is: In the formula, is the hydrogen power consumed by the hydrogen fuel cell at time t; are the electrical power and thermal power output of the hydrogen fuel cell at time t respectively; η HFC is the conversion efficiency of hydrogen fuel cells; They are the upper and lower limits of the thermal-to-electricity ratio of hydrogen fuel cells; They are the upper and lower limits of hydrogen power and the upper and lower limits of ramp speed input into the hydrogen fuel cell respectively; The hydrogen-doped unit includes a hydrogen-doped gas turbine and a hydrogen-doped gas boiler, wherein the mathematical model of the hydrogen-doped gas turbine is: In the formula, are the gas power and hydrogen power consumed by the gas turbine at time t respectively; are the electrical power and thermal power output of the gas turbine at time t respectively; η GT,e , η GT,h are the electricity and heat conversion efficiency of gas turbine respectively; Y GT,t is the hydrogen blending ratio of the gas turbine at time t; They are the upper and lower limits of hydrogen blending ratio of gas turbines respectively; is the calorific value of hydrogen; They are the upper and lower limits of gas turbine output electric power and the upper and lower limits of ramp power respectively; are the upper and lower limits of the gas turbine output thermal power respectively; is the amount of CO2 produced by the gas turbine at time t; is the carbon emission per unit volume of natural gas combustion, L gas The energy obtained by burning a unit volume of natural gas; The mathematical model of hydrogen-blended gas boiler is: In the formula, are the gas power and hydrogen power consumed by the gas boiler at time t respectively; is the thermal power output of the gas boiler at time t; η GB is the heat conversion efficiency of the gas boiler; Y GB,t is the hydrogen blending ratio of the gas boiler at time t; are the upper and lower limits of hydrogen blending ratio of gas boilers; ρ gas , is the density of natural gas and hydrogen; m gas , is the relative molecular mass of natural gas and hydrogen; They are the upper and lower limits of the thermal power output and the upper and lower limits of the ramp power of the gas boiler respectively; is the amount of CO2 produced by the gas boiler at time t; The mathematical model of the hydrogen storage tank is: In the formula, They are the charging and discharging power of hydrogen energy storage at time t respectively; They are the charging and discharging state variables of hydrogen energy storage at time t respectively; They are the maximum charging and discharging power of hydrogen energy storage respectively; They are the charging and discharging efficiency of hydrogen energy storage respectively; is the power loss coefficient of hydrogen energy storage; S H,t is the hydrogen storage capacity at time t; They are the upper and lower limits of the hydrogen storage capacity of hydrogen energy storage.

5. A low-carbon dispatching method for a multi-energy complementary virtual power plant according to claim 4, characterized in that: The mathematical model of the EMC carbon capture power plant is: In the formula, is the amount of CO2 electrolyzed by the EMC at time t; is the EMC electrolysis efficiency; Γ is the flue gas split ratio; is the electrical power consumed by the EMC and the waste heat power generated at time t; η EMC,e , η EMC,h They are the power consumption coefficient and heat generation efficiency of EMC CO2 electrolysis; The upper limit of the input EMC power; is the amount of solid carbon generated by EMC at time t; η EMC,C To produce solid carbon efficiency; are the thermal powers input to the organic Rankine cycle low-temperature waste heat power generation device and the waste heat boiler at time t respectively; are the electric power output of the organic Rankine cycle low-temperature waste heat power generation device and the thermal power output of the waste heat boiler at time t respectively; η ORC , η WHB They are the power generation efficiency of the organic Rankine cycle low-temperature waste heat power generation device and the heating efficiency of the waste heat boiler.

6. A low-carbon dispatching method for a multi-energy complementary virtual power plant according to claim 5, characterized in that: The objective function is constructed by minimizing the total operating cost of the virtual power plant. The total operating cost includes the gas purchase cost F gas,t , electricity sales revenue Environmental cost pol,t , Electric vehicle compensation cost F EV,t , alternative response load adjustment cost F alt,t , Carbon trading costs and solid carbon yield F C,t , the objective function is: F gas,t =c gas P gas,t Where P gas,t is the gas purchase volume at time t; c gas is the unit price of gas purchase; are the power purchased and sold at time t; c pur,t 、c sale,t are the electricity purchase and sales prices at time t; Q i is the emission coefficient of the i-th pollutant gas; S i 、N i is the penalty and environmental loss cost coefficient of the i-th pollutant gas; c EV The cost coefficient for discharging compensation for electric vehicles; is the discharge power of the electric vehicle at time t; ΔP load,e,t , ΔP load,h,t , ΔP load,g,t They are the load changes of electricity, heat and gas substitution response at time t respectively; are the cost coefficients of alternative response loads for electricity, heat, and gas, respectively; is the carbon trading price; is the carbon quota trading volume at time t, positive means buying, negative means selling; c C is the solid carbon benefit coefficient; The power balance constraint conditions of the virtual power plant are established, where the power supply and demand balance constraint is: In the formula, The day-ahead forecast values ​​of wind and solar output at time t respectively; is the charging and discharging power of the energy storage at time t; P is the charging power of the electric vehicle at time t; el,t is the electrical load at time t; The thermal power supply and demand balance constraint is: In the formula, are the thermal energy storage charging and discharging power at time t; P hl,t is the heat load at time t; The gas power supply and demand balance constraint is: The hydrogen power supply and demand balance constraint is: The carbon supply and demand balance constraint is: In the formula, is the carbon quota obtained by the virtual power plant at time t; are the carbon quota coefficients of gas turbines and gas boilers respectively; ξ is the electric-heat conversion coefficient of the carbon quota of gas turbines; The uncertainty of wind power and photovoltaic output in the virtual power plant is described by an uncertainty set U, which is expressed as: Where P win,t P pv,t are the actual wind and solar outputs at time t respectively; is a 0-1 variable representing the uncertainty of wind and solar power; are wind and solar output forecast deviations respectively; Γ win , Γ pv are the robustness coefficients of wind power and photovoltaic power respectively; The compact form of the dispatch model of the multi-energy complementary virtual power plant is expressed as: In the formula, y is the first-stage decision variable, including the state variables of each device; x is the second-stage decision variable, including the power of each device in the virtual power plant, as well as the gas purchase volume and electricity purchase and sales power; They are the overestimation and underestimation of wind and solar power output respectively; are the penalty cost coefficients for overestimation and underestimation respectively; a, b, C, d, E, G, h, J are constant matrices.

7. A low-carbon dispatching system for a multi-energy complementary virtual power plant, characterized in that: The low-carbon dispatching method for a multi-energy complementary virtual power plant according to any one of claims 1 to 6 is operated, and the system comprises: Framework building modules for building a virtual power plant operation framework that considers hydrogen cycle systems and EMC carbon capture technology; Low-carbon technology module, used to build mathematical models of hydrogen cycle systems and EMC carbon capture power plants; The optimization scheduling module is used to comprehensively consider the uncertainty of wind and solar power output, build a scheduling model for a multi-energy complementary virtual power plant, and determine the optimal operation plan of the virtual power plant through optimization solution.

8. A computer device comprising a processor and a storage medium, characterized in that: The storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the low-carbon scheduling method of the multi-energy complementary virtual power plant according to any one of claims 1 to 6.

9. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the low-carbon scheduling method for a multi-energy complementary virtual power plant as described in any one of claims 1 to 6.

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