A virtual power plant low-carbon optimization scheduling method, system, device, medium and product

Abstract

The application discloses a kind of virtual power plant low carbon optimization scheduling method, system, equipment, medium and product, relating to power distribution network optimization scheduling technical field, to minimum operating cost as optimization goal, set constraint condition, with power plant scheduling data as decision variable, construct virtual power plant optimization scheduling model. Obtain the equipment data of power distribution network, based on the oxygen-enriched combustion carbon capture unit energy flow characteristics of power distribution network, corresponding oxygen-enriched combustion carbon capture unit model is constructed according to equipment data. Mechanism modeling is carried out according to oxygen-enriched combustion carbon capture unit model and equipment data, and corresponding virtual power plant model is obtained. The virtual power plant optimization scheduling model is optimized and solved using the virtual power plant model, and the corresponding low carbon optimization scheduling scheme is obtained. The existing virtual power plant low carbon scheduling multi-focus single carbon capture technology or single-side demand response mechanism is solved, but it is difficult to realize carbon cycle, oxygen cycle closed loop utilization, which reduces the reliability of virtual power plant low carbon optimization scheduling.

Description

Technical Field

[0001] This invention relates to the field of power distribution network optimization scheduling technology, and in particular to a virtual power plant low-carbon optimization scheduling method, system, equipment, medium and product. Background Technology

[0002] Driven by the dual-carbon development strategy, the energy industry is accelerating its transformation towards cleaner, lower-carbon, and smarter energy sources. Traditional high-carbon power sources are gradually being phased out, while renewable energy sources such as photovoltaics and wind power are being connected to the grid on a large scale. However, their output is highly volatile and intermittent, posing challenges to the safe and stable operation of the power system. Simultaneously, energy demand from industry, construction, and transportation continues to grow, and the energy consumption structure exhibits multi-energy coupling characteristics, making it difficult for a single energy system to simultaneously achieve efficient utilization and low-carbon emission reduction. Virtual power plants, as aggregation and control platforms integrating distributed power sources, energy storage, flexible loads, and electric vehicles, can achieve synergistic interaction among multiple types of distributed energy resources, improve renewable energy absorption capacity, enhance system flexibility, and reduce carbon emissions, thus becoming an important support for the construction of new power systems.

[0003] Currently, existing virtual power plant low-carbon dispatching methods mostly focus on single carbon capture technologies or one-sided demand response mechanisms, but they are difficult to achieve closed-loop utilization of carbon and oxygen cycles, which reduces the reliability of low-carbon optimized dispatching of virtual power plants. Summary of the Invention

[0004] This invention provides a method, system, equipment, medium, and product for low-carbon optimized scheduling of virtual power plants. It solves the technical problem that existing low-carbon scheduling technologies for virtual power plants often focus on single carbon capture or single-sided demand response mechanisms, but these technologies are difficult to implement in a closed-loop utilization of carbon and oxygen cycles, thus reducing the reliability of low-carbon optimized scheduling of virtual power plants.

[0005] The first aspect of this invention provides a low-carbon optimized scheduling method for virtual power plants, comprising:

[0006] With the goal of minimizing operating costs, constraints are set, and power plant scheduling data is used as decision variables to construct a virtual power plant optimal scheduling model;

[0007] Obtain equipment data from the power distribution network, and based on the energy flow characteristics of the oxygen-enriched combustion carbon capture unit in the power distribution network, construct a corresponding oxygen-enriched combustion carbon capture unit model according to the equipment data.

[0008] Based on the oxygen-enriched combustion carbon capture unit model and the equipment data, a mechanistic model is performed to obtain the corresponding virtual power plant model.

[0009] The virtual power plant model is used to optimize and solve the virtual power plant optimal scheduling model to obtain the corresponding low-carbon optimal scheduling scheme.

[0010] Optionally, the constraints include electrical power balance constraints, thermal power balance constraints, natural gas balance constraints, and hydrogen power balance constraints.

[0011] Optionally, the step of performing mechanistic modeling based on the oxygen-enriched combustion carbon capture unit model and the equipment data to obtain the corresponding virtual power plant model includes:

[0012] The equipment data was used to model the two-stage electro-gas conversion mechanism, resulting in the corresponding two-stage electro-gas conversion model.

[0013] Based on the law of conservation of energy and the law of heat conversion in combustion, a corresponding gas-fired boiler model is constructed according to the equipment data.

[0014] The device data is input into a preset carbon trading model to obtain the corresponding target carbon trading model;

[0015] A corresponding electric vehicle charging and discharging model is constructed based on a preset electric vehicle orderly charging and discharging model and the device data.

[0016] The oxygen-enriched combustion carbon capture unit model, the two-stage power-to-gas conversion model, the gas boiler model, the target carbon trading model, the electric vehicle charging and discharging model, and the preset demand response model are coupled to obtain the corresponding virtual power plant model.

[0017] Optionally, the step of using the equipment data to perform two-stage electro-gas conversion mechanism modeling to obtain the corresponding two-stage electro-gas conversion model includes:

[0018] Based on the principle of electrochemical energy conversion, a corresponding electrolyzer hydrogen production model is constructed according to the equipment data.

[0019] Based on the law of conservation of energy, a corresponding hydrogen fuel cell cogeneration model and hydrogen methanation model are constructed according to the equipment data.

[0020] By combining the electrolyzer hydrogen production model, the hydrogen fuel cell cogeneration model, and the hydrogen methanation model, a corresponding two-stage electro-gas conversion model is obtained.

[0021] Optionally, the step of constructing a corresponding electric vehicle charging and discharging model based on a preset electric vehicle orderly charging and discharging model and the device data includes:

[0022] A corresponding disordered charging model for electric vehicles is constructed based on the pre-acquired probability distribution data and the device data;

[0023] The electric vehicle disordered charging model and the preset electric vehicle ordered charging and discharging model are coupled to obtain the corresponding electric vehicle charging and discharging model.

[0024] Optionally, the step of using the virtual power plant model to optimize and solve the virtual power plant optimal scheduling model to obtain the corresponding low-carbon optimal scheduling scheme includes:

[0025] The virtual power plant model and the virtual power plant optimization scheduling model are coupled to obtain the corresponding target virtual power plant optimization scheduling model.

[0026] The preset solver is invoked to optimize and solve the target virtual power plant scheduling model, thereby obtaining the corresponding low-carbon optimized scheduling scheme.

[0027] A second aspect of the present invention provides a virtual power plant low-carbon optimization scheduling system, comprising:

[0028] The first construction module is used to construct a virtual power plant optimization scheduling model with the goal of minimizing operating costs, setting constraints, and using power plant scheduling data as decision variables.

[0029] The data acquisition module is used to acquire equipment data of the power distribution network and, based on the energy flow characteristics of the oxygen-enriched combustion carbon capture unit of the power distribution network, construct a corresponding oxygen-enriched combustion carbon capture unit model according to the equipment data.

[0030] The second construction module is used to perform mechanistic modeling based on the oxygen-enriched combustion carbon capture unit model and the equipment data to obtain the corresponding virtual power plant model.

[0031] The optimization module is used to optimize and solve the virtual power plant optimization scheduling model using the virtual power plant model to obtain the corresponding low-carbon optimization scheduling scheme.

[0032] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the virtual power plant low-carbon optimization scheduling method as described above.

[0033] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the virtual power plant low-carbon optimization scheduling method as described above.

[0034] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the virtual power plant low-carbon optimization scheduling method as described above.

[0035] As can be seen from the above technical solutions, the present invention has the following advantages:

[0036] This invention constructs a virtual power plant optimal scheduling model by setting constraints and using power plant dispatch data as decision variables, with the goal of minimizing operating costs. Equipment data from the distribution network is acquired, and based on the energy flow characteristics of the oxy-fuel carbon capture (OFC) units in the distribution network, a corresponding OFC unit model is constructed. Mechanistic modeling is then performed based on the OFC unit model and equipment data to obtain the corresponding virtual power plant model. The virtual power plant model is then used to optimize and solve the virtual power plant optimal scheduling model, yielding the corresponding low-carbon optimal scheduling scheme. This overcomes the technical problem that existing virtual power plant low-carbon scheduling often focuses on a single carbon capture technology or a one-sided demand response mechanism, making it difficult to achieve closed-loop utilization of carbon and oxygen cycles, thus reducing the reliability of virtual power plant low-carbon optimal scheduling. Compared with traditional virtual power plant low-carbon dispatching methods, this approach aims to minimize operating costs. It constructs an optimized dispatching model based on power plant dispatching data to precisely quantify dispatching decisions. By combining distribution network equipment data with the energy flow characteristics of oxygen-enriched combustion carbon capture units, a refined unit model is established to accurately characterize the coupling laws of unit energy consumption, output, and carbon capture. Through mechanistic modeling, the interaction relationships of multiple devices and energy flows are integrated to form an integrated virtual power plant model, achieving closed-loop utilization of carbon and oxygen cycles. Driven by the virtual power plant model, the optimization solution balances cost optimization and low-carbon emission reduction, significantly improving renewable energy absorption capacity, reducing energy purchase and carbon trading costs, and decreasing carbon emissions. Simultaneously, it enhances the flexibility and operational reliability of low-carbon optimized dispatching of virtual power plants. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the steps of a low-carbon optimization scheduling method for a virtual power plant according to Embodiment 1 of the present invention.

[0039] Figure 2 This is a flowchart illustrating the steps of a low-carbon optimization scheduling method for a virtual power plant provided in Embodiment 2 of the present invention.

[0040] Figure 3 This is a schematic diagram of the virtual power plant structure provided in Embodiment 2 of the present invention;

[0041] Figure 4 This is a schematic diagram of the structure of the oxygen-enriched combustion carbon capture unit provided in Embodiment 2 of the present invention;

[0042] Figure 5 This is a schematic diagram of the energy flow characteristics of an oxygen-enriched combustion carbon capture unit provided in Embodiment 2 of the present invention;

[0043] Figure 6 This is a schematic diagram of a two-stage electro-gas conversion structure provided in Embodiment 2 of the present invention;

[0044] Figure 7 This is a schematic diagram of the power balance curve of Scheme 3 provided in Embodiment 2 of the present invention;

[0045] Figure 8 This is a schematic diagram of the heat power balance curve of Scheme 3 provided in Embodiment 2 of the present invention;

[0046] Figure 9 This is a schematic diagram of the hydrogen power balance curve of Scheme 3 provided in Embodiment 2 of the present invention;

[0047] Figure 10 This is a schematic diagram of the power balance curve under strategy 4 provided in Embodiment 2 of the present invention;

[0048] Figure 11 This is a schematic diagram of the thermal power balance curve under strategy 4 provided in Embodiment 2 of the present invention;

[0049] Figure 12 This is a schematic diagram of EV charging power under different charging methods provided in Embodiment 2 of the present invention;

[0050] Figure 13 This is a schematic diagram of the demand response scheduling results under strategy 4 provided in Embodiment 2 of the present invention;

[0051] Figure 14 This invention provides a probability density distribution map of electric vehicles for Embodiment 2 of the present invention;

[0052] Figure 15 This is a distribution map of electric vehicle parking times provided in Embodiment 2 of the present invention;

[0053] Figure 16 This is a schematic diagram of the response characteristics of an electric vehicle when connected to the grid, provided in Embodiment 2 of the present invention;

[0054] Figure 17 This is a structural block diagram of a virtual power plant low-carbon optimization scheduling system provided in Embodiment 3 of the present invention;

[0055] Figure 18 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0056] This invention provides a method, system, equipment, medium, and product for low-carbon optimized scheduling of virtual power plants. It addresses the technical problem that existing low-carbon scheduling technologies for virtual power plants often focus on single carbon capture or single-sided demand response mechanisms, but these technologies struggle to achieve closed-loop utilization of carbon and oxygen cycles, thus reducing the reliability of low-carbon optimized scheduling of virtual power plants.

[0057] 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. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

[0058] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a low-carbon optimization scheduling method for a virtual power plant provided in Embodiment 1 of the present invention.

[0059] This invention provides a low-carbon optimized scheduling method for virtual power plants, comprising:

[0060] Step 101: With the goal of minimizing operating costs, set constraints and use power plant scheduling data as decision variables to construct a virtual power plant optimization scheduling model.

[0061] Operating costs refer to the total expenses incurred by a virtual power plant (VPP) during a set dispatch period for activities such as energy procurement, equipment operation and maintenance, carbon trading, wind and solar power consumption compensation, carbon sequestration, and load and electric vehicle response incentives.

[0062] Power plant dispatch data refers to the set of real-time and predictive operating parameters that can be dynamically adjusted in virtual power plant optimization dispatching to determine the optimal operating scheme. This includes unit output data for each time period, energy storage charging and discharging power data, power-to-gas conversion equipment operating power data, load response data, electric vehicle charging and discharging power data, carbon capture and storage (CCS) scale data, energy purchase and sale data, equipment start-up and shutdown status data, ramp-up rate data, carbon trading quota data, wind and solar power output forecast data, load forecast data, hydrogen / oxygen storage tank charging and discharging data, gas boiler output data, hydrogen fuel cell output data, methanation reactor operating data, air separation oxygen production unit operating data, CCS self-consumption data, and operating status parameter data for each piece of equipment. Specifically, unit output data includes the actual output power values ​​of power generation / heating equipment such as oxygen-enriched combustion CCS units, hydrogen fuel cells, and gas turbines for each time period. Energy storage charging and discharging power data includes the charging / charging and discharging / releasing power values ​​of electric energy storage, hydrogen storage tanks, and oxygen storage tanks for each time period. The power output data for the power-to-gas (EPG) equipment includes the input / output power values ​​for the electrolyzer and methanation reactor at each time period. Load response data includes the adjustable power values ​​for flexible loads that can be shifted, reduced, or substituted at each time period. Electric vehicle charging / discharging power data includes the charging power of a single electric vehicle and the V2G discharging power of hybrid electric vehicles at each time period. Carbon capture and storage (CCS) scale data includes the amount of carbon dioxide captured, stored, and supplied to the methanation reactor by the CCS unit at each time period. Energy purchase and sale data includes the quantity of electricity purchased from the main grid, natural gas purchased from external sources, and electricity and gas sold externally at each time period. Equipment start-up / shutdown status data includes the 0-1 status flags for the operation / shutdown of various types of equipment at each time period.

[0063] In this embodiment of the invention, with the goal of minimizing operating costs, constraints are set, and power plant scheduling data is used as decision variables to construct a virtual power plant optimal scheduling model. These constraints include electrical power balance constraints, thermal power balance constraints, natural gas balance constraints, and hydrogen power balance constraints.

[0064] It should be noted that the virtual power plant optimization scheduling model is as follows:

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] in, For the operating costs of virtual power plants, The energy purchase cost for time period t. The maintenance cost for time period t. The carbon trading cost for period t. The cost of scenic penalty during time period t. The cost of carbon sequestration during time period t. Let be the compensation cost for time period t, where t is the time period index and T is the scheduling period. This refers to the compensation cost when flexible loads on the load side participate in incentive-based demand response during time period t. The discharge compensation cost of an electric vehicle participating in V2G orderly charging and discharging during time period t. As a replacement load compensation factor, This represents the upper limit of the alternative load demand response for the i-th type. To reduce the load compensation factor, This represents the upper limit of the demand response that can reduce the load for the i-th type. This is the unit capacity compensation coefficient for the shifted load. Let be the demand response power of the i-th type of load that can be shifted during time period t. This is the compensation coefficient for the unit discharge power of electric vehicles. Let represent the discharge power of the nth electric vehicle during time period t, where i is the load type index and n is the electric vehicle index. The time-of-use electricity price for the VPP system to purchase electricity from the main grid during time period t. for, The price of natural gas purchased by the VPP system from the main grid during time period t. The gas purchase volume of the system during time period t. Let be the unit power operating cost coefficient of the j-th generating unit. The actual output of the j-th generating unit during time period t. The penalty unit price for curtailment of wind / solar power. The wind curtailment power of the wind turbine generators (WT) in the virtual power plant during time period t. Let j be the curtailed power of photovoltaic (PV) generators in the virtual power plant during time period t, and j be the generator index. This is the unit fuel cost coefficient. This represents the amount of fuel consumed by the virtual power plant during time period t. For the wind turbine output during time period t, For the photovoltaic unit to output power during period t, For the time period t, the output of conventional power sources (such as oxygen-enriched combustion units) For the time period t, the power output of the hydrogen fuel cell is... This represents the purchased power exchanged with the main grid during time period t. The energy storage discharge power during time period t is... The V2G discharge power of the electric vehicle during time period t. The power consumption of the electrolytic cell during time period t. The charging power of the energy storage during time period t. For time period t, the foundation is a rigid electrical load. For time period t, the electrical load can be shifted / reduced. The amount of electrical load reduction that can be substituted for time period t. The time-sharing response of electrical load during period t. The charging power of electric vehicles during time period t. The heating power of the gas turbine during time period t. The heating power of the gas-fired boiler during the time period t. The heating power of the hydrogen fuel cell during time period t. The waste heat power supplied by the methanation reactor during the specified time period (t) The rigid heat load of the foundation during time period t. The heat load can be shifted / reduced for time period t. The amount of heat load reduction that can be substituted for time period t. The time-sharing response of heat load during period t. Let t be the volume of natural gas consumed by the gas-fired boiler during the time period. Let t be the volume of natural gas consumed by the gas turbine during time period t. Let t be the volume of purchased natural gas. Let t be the volume of methane generated during the period t of the electro-gas (P2G) conversion. The hydrogen production power of the electrolyzer is calculated for time period t. The hydrogen release power of the hydrogen storage tank is calculated for time period t. The hydrogen charging power of the hydrogen storage tank is calculated for time period t. The hydrogen consumption power of the hydrogen fuel cell is calculated for time period t. The hydrogen consumption power of the methanation reactor is calculated for time period t.

[0076] Step 102: Obtain equipment data from the distribution network. Based on the energy flow characteristics of the oxygen-enriched combustion carbon capture unit in the distribution network, construct the corresponding oxygen-enriched combustion carbon capture unit model according to the equipment data.

[0077] It should be noted that the equipment data includes, but is not limited to, parameters of the oxygen-enriched combustion carbon capture unit (including overall performance data such as rated output, carbon emission intensity, heat-to-power ratio range, start-up and shutdown energy consumption, and minimum stable output), rated power and heat-to-power ratio of the gas turbine, energy consumption and oxygen production efficiency of the air separation oxygen production unit, capture efficiency and self-consumption of the carbon capture unit, oxygen storage tank capacity and oxygen charging and discharging rate, rated capacity and charge-discharge efficiency of the electric energy storage, rated power and hydrogen production efficiency of the electrolyzer, output range and cogeneration efficiency of the hydrogen fuel cell, hydrogen consumption coefficient and waste heat recovery efficiency of the methanation reactor, rated thermal power and thermal efficiency of the gas boiler, rated capacity and output curves of photovoltaic and wind turbines, number of electric vehicles, battery capacity and charge-discharge parameters, line impedance and node voltage limits, start-up and shutdown status of each device, ramp rate, loss coefficient, rated voltage, rated current, operating temperature, pressure, and real-time operation monitoring data.

[0078] In this embodiment of the invention, equipment data of the power distribution network is obtained, and the coupling law of power consumption, oxygen generation, flue gas circulation, carbon dioxide capture and emission during the energy flow process of the oxygen-enriched combustion carbon capture unit is combined with the equipment data to construct a corresponding oxygen-enriched combustion carbon capture unit model.

[0079] It should be noted that the specific model of the oxygen-enriched combustion carbon capture unit is as follows:

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] in, The power generation capacity of the gas turbine during time period t. The heating power of the gas turbine during time period t. The total energy utilization efficiency of a gas turbine. The total chemical energy of natural gas consumed by the gas turbine during time period t. Let t be the volume of natural gas consumed by the gas turbine during time period t. The lower heating value of natural gas. This is the energy conversion factor. This is the minimum permissible input energy for a gas turbine. This represents the maximum permissible input energy of the gas turbine. This represents the maximum permissible downhill ramp rate of the gas turbine. This represents the total chemical energy of natural gas consumed by the gas turbine in time period t+1. This refers to the maximum permissible uphill climb rate of the gas turbine. This is the minimum permissible thermoelectric ratio for a gas turbine. This represents the maximum permissible thermoelectric ratio of the gas turbine. The net power output of the unit during time period t is the power output of the unit to the outside world. The self-consumption power of the carbon capture device during time period t. The self-consumption power of the air separation oxygen generator during time period t. This represents the unit carbon dioxide capture power consumption coefficient of the carbon capture device. This represents the actual CO2 mass flow rate captured by the carbon capture device during time period t. The carbon capture efficiency of the carbon capture device during time period t is given. The carbon emission intensity coefficient of the gas turbine / unit. This represents the maximum permissible downhill ramp rate for the carbon capture device. The self-consumption power of the carbon capture device in time period t+1. This represents the maximum permissible uphill climbing rate for the carbon capture device. The unit oxygen production power consumption coefficient of the air separation unit. The oxygen production of the air separation unit during time period t. This represents the maximum permissible carbon capture efficiency of the carbon capture device. This represents the minimum load factor for the air separation unit. This represents the maximum power consumption / rated output of the air separation unit. This represents the amount of oxygen directly purchased from external sources during time period t. Let t be the amount of oxygen released from the oxygen storage tank (OST). The oxygen supply to the oxygen storage tank (OST) during time period t. This refers to the oxygen consumption coefficient per unit of power generation in an oxygen-enriched combustion chamber. This represents the actual oxygen consumption in the oxygen-enriched combustion chamber (OCC) during time period t. This represents the actual CO2 mass flow rate captured by the carbon capture device during time period t. Let t be the mass flow rate of CO2 that is not captured and is directly emitted into the atmosphere. Let t be the mass flow rate of CO2 that is sealed or disposed of during time period t. The CO2 mass flow rate (kg / h) supplied to the methanation reactor (MR) for time period t. Let y be the current energy storage / state of charge of energy storage device y during time period t. Let y be the self-discharge / self-loss coefficient of the energy storage device. For the previous period ( The energy stored in energy storage device y. Let y be the charging / energy-charging power of the energy storage device during time period t. For charging / energy efficiency, For discharge / energy release efficiency, The maximum rated capacity of energy storage device y is given. Let y be the initial stored energy of energy storage device y at the start of the scheduling cycle. This refers to the energy storage capacity of energy storage device y at the end of the scheduling cycle (e.g., the 24th hour). This is a binary variable (0 / 1) representing the charging / discharging state during time period t, where 1 indicates charging and 0 indicates discharging. The maximum charging / energy-charging power of energy storage device y. Let y be the maximum discharge / release power of the energy storage device, where y is the energy storage type index.

[0086] Step 103: Perform mechanistic modeling based on the oxygen-enriched combustion carbon capture unit model and equipment data to obtain the corresponding virtual power plant model.

[0087] Mechanistic modeling refers to a modeling method that uses mathematical equations to accurately depict the energy conversion, mass transfer, and dynamic response laws within a device or system, based on the laws of conservation of energy and mass and the physical operating mechanism of the device.

[0088] In this embodiment of the invention, a two-stage electricity-to-gas mechanism model is constructed using equipment data to obtain a corresponding two-stage electricity-to-gas model. Based on the law of conservation of energy and the law of combustion heat conversion, a corresponding gas-fired boiler model is constructed based on the equipment data. The equipment data is input into a preset carbon trading model to obtain a corresponding target carbon trading model. A corresponding electric vehicle charging and discharging model is constructed based on a preset electric vehicle orderly charging and discharging model and the equipment data. The oxygen-enriched combustion carbon capture unit model, the two-stage electricity-to-gas model, the gas-fired boiler model, the target carbon trading model, the electric vehicle charging and discharging model, and the preset demand response model are coupled to obtain a virtual power plant model that can fully characterize the energy interaction, resource coordination, and low-carbon operation patterns within the virtual power plant.

[0089] Step 104: Use the virtual power plant model to optimize and solve the virtual power plant optimization scheduling model to obtain the corresponding low-carbon optimization scheduling scheme.

[0090] Low-carbon optimization scheduling scheme refers to a scheduling execution document that determines the optimal operating strategy and parameter configuration of each device in a virtual power plant within a scheduling cycle, with the goal of minimizing operating costs and optimizing carbon emissions.

[0091] In this embodiment of the invention, the virtual power plant model and the virtual power plant optimal scheduling model are coupled and correlated, unifying the variable dimensions, time scale, and constraint boundaries to obtain the corresponding target virtual power plant optimal scheduling model. An adapted optimization algorithm is then used to optimize and solve the target virtual power plant optimal scheduling model, yielding the corresponding low-carbon optimal scheduling scheme.

[0092] It should be noted that optimization algorithms refer to numerical computation methods used to solve complex nonlinear and multi-constraint optimization problems, such as linear programming, quadratic programming, and heuristic algorithms.

[0093] In this embodiment of the invention, a virtual power plant optimal scheduling model is constructed by setting constraints with the goal of minimizing operating costs and using power plant scheduling data as the decision variable. Equipment data from the distribution network is acquired, and based on the energy flow characteristics of the oxy-fuel carbon capture (OFC) units in the distribution network, a corresponding OFC unit model is constructed. Mechanistic modeling is performed based on the OFC unit model and the equipment data to obtain the corresponding virtual power plant model. The virtual power plant model is then used to optimize and solve the virtual power plant optimal scheduling model, yielding the corresponding low-carbon optimal scheduling scheme. This overcomes the technical problem that existing virtual power plant low-carbon scheduling often focuses on a single carbon capture technology or a one-sided demand response mechanism, but struggles to achieve closed-loop utilization of carbon and oxygen cycles, thus reducing the reliability of virtual power plant low-carbon optimal scheduling. Compared with traditional virtual power plant low-carbon dispatching methods, this approach aims to minimize operating costs. It constructs an optimized dispatching model based on power plant dispatching data to precisely quantify dispatching decisions. By combining distribution network equipment data with the energy flow characteristics of oxygen-enriched combustion carbon capture units, a refined unit model is established to accurately characterize the coupling laws of unit energy consumption, output, and carbon capture. Through mechanistic modeling, the interaction relationships of multiple devices and energy flows are integrated to form an integrated virtual power plant model, achieving closed-loop utilization of carbon and oxygen cycles. Driven by the virtual power plant model, the optimization solution balances cost optimization and low-carbon emission reduction, significantly improving renewable energy absorption capacity, reducing energy purchase and carbon trading costs, and decreasing carbon emissions. Simultaneously, it enhances the flexibility and operational reliability of low-carbon optimized dispatching of virtual power plants.

[0094] Please see Figure 2 , Figure 2 The flowchart illustrates the steps of a low-carbon optimization scheduling method for a virtual power plant provided in Embodiment 2 of the present invention.

[0095] This invention provides a low-carbon optimized scheduling method for virtual power plants, comprising:

[0096] Step 201: With the goal of minimizing operating costs, set constraints and use power plant scheduling data as decision variables to construct a virtual power plant optimization scheduling model.

[0097] In this embodiment of the invention, with the goal of minimizing operating costs, constraints are set based on the multi-energy coupling operation characteristics of the virtual power plant and the requirements for safe operation of equipment. Power plant scheduling data is used as the decision variable to construct an optimized scheduling model for the virtual power plant.

[0098] It should be noted that the constraints include electrical power balance constraints, thermal power balance constraints, natural gas balance constraints, and hydrogen power balance constraints.

[0099] The specific power balance constraints are as follows:

[0100]

[0101] The specific thermal power balance constraint is as follows:

[0102]

[0103] The specific natural gas balance constraints are:

[0104]

[0105] The hydrogen power balance constraint is specifically as follows:

[0106]

[0107] Step 202: Obtain equipment data from the distribution network. Based on the energy flow characteristics of the oxygen-enriched combustion carbon capture unit in the distribution network, construct the corresponding oxygen-enriched combustion carbon capture unit model according to the equipment data.

[0108] In this embodiment of the invention, equipment data of the power distribution network is obtained, and based on the energy flow patterns of internal power consumption, oxygen preparation, oxygen-enriched combustion, flue gas circulation, carbon dioxide capture and medium transportation of the oxygen-enriched combustion carbon capture unit, an oxygen-enriched combustion carbon capture unit model is constructed based on the equipment data, which can accurately reflect the energy flow direction, output characteristics and low-carbon operation patterns of the unit under different operating conditions.

[0109] It is worth mentioning that, see Figure 3As shown, the virtual power plant (VPP system) of the distribution network consists of a photovoltaic array, wind turbine, oxy-fuel combustion carbon capture unit (OCC), gas boiler, energy storage equipment, electrolyzer (EL), hydrogen fuel cell (HFC), methane reactor (MR), and electric vehicle (EV). The oxy-fuel combustion carbon capture unit mainly includes a gas turbine, an air separation unit (ASU), and a carbon capture and compression unit (CPU). The electrolyzer equipment utilizes renewable energy, supplying the byproduct oxygen to the OCC unit. This, combined with an oxygen storage tank (OST), reduces the oxygen supply to the ASU (Automatic Substation) and enhances the peak-shaving capacity of the OCC unit. The produced hydrogen is preferentially supplied to the HFC (Hydrogen Fuel Cell), utilizing its electrothermal properties for combined heat and power (CHP) to alleviate electrothermal coupling within the VPP (Vehicle Power Plant). The remaining hydrogen is used for methanation and storage in a hydrogen storage tank (HST). The natural gas produced from hydrogen methanation can be supplied to gas-fired equipment, reducing the system's gas purchase costs and lowering carbon emissions from the VPP system, thus achieving low-carbon, economical, and optimized scheduling of the VPP system.

[0110] It is worth mentioning that, see Figure 4 As shown, the oxy-fuel combustion carbon capture unit is a combustion device with an oxygen concentration higher than that of air. By utilizing high-concentration O2 and CO2 recovered from flue gas recirculation to replace air for combustion, the resulting high-concentration CO2 is easily captured and stored by the CPU equipment. Simultaneously, the oxy-fuel combustion carbon capture unit recovers and reuses oxygen, a byproduct of the P2G process, thus expanding the unit's oxygen supply sources and reducing energy waste.

[0111] It should be noted that, for reference Figure 5As shown, the energy flow characteristics of an oxy-fuel combustion carbon capture unit (OCC) are mainly characterized by the fact that the power loss of the OCC is concentrated in the ASU (Air Supply Unit) and CPU (Power Processing Unit). To cope with load changes, the ASU is typically equipped with an OST (Oxygen Transfer Unit) to achieve load transfer. At night, when wind power is abundant and electrical load demand is low, the output of the ASU is increased in conjunction with the EL (Electric Power Supply) unit, and part of the generated O2 is used for oxy-fuel combustion, while the other part is stored through the OST. When the electrical load is at its peak, the OCC unit prioritizes meeting the electrical load demand, reducing the operating output of the CPU and ASU. At this time, the OST supplies the O2 stored during off-peak hours to the OCC unit, alleviating the output pressure on the OCC unit, ensuring the CPU is in normal operating condition, and reducing the unit's carbon emissions.

[0112] Step 203: Use equipment data to model the two-stage electro-gas conversion mechanism and obtain the corresponding two-stage electro-gas conversion model.

[0113] It should be noted that, for reference Figure 6 As shown, the two-stage electro-gas conversion refers to dividing the electro-gas conversion into two stages: water electrolysis for hydrogen production and hydrogen methanation. In the first stage, the hydrogen produced by water electrolysis is primarily used for hydrogen fuel cell cogeneration. In the second stage, natural gas is produced through the reaction of methane, and any excess hydrogen is stored in a hydrogen storage tank. Furthermore, the oxygen produced as a byproduct of the electrolyzer in the first stage is recovered and reused, and can be supplied to the OCC unit for oxygen-enriched combustion, reducing the output of the ASU equipment and improving the net output and energy utilization rate of the OCC unit.

[0114] Furthermore, step 203 includes the following sub-steps:

[0115] S11. Based on the principle of electrochemical energy conversion, construct the corresponding electrolyzer hydrogen production model according to the equipment data.

[0116] The principle of electrochemical energy conversion refers to the energy conversion mechanism that uses electrical energy to drive an electrolyte solution to undergo an oxidation-reduction reaction, directly converting electrical energy into chemical energy. It is the core theoretical basis for hydrogen production by water electrolysis.

[0117] In this embodiment of the invention, an electrolyzer hydrogen production model is constructed based on the principle of electrochemical energy conversion and equipment data, which includes energy balance equations, hydrogen production characteristic equations, and operational constraint equations.

[0118] It should be noted that the specific model for hydrogen production using an electrolyzer is as follows:

[0119] ;

[0120] in, Let t be the equivalent power of hydrogen produced by the electrolyzer during time period t. The energy conversion efficiency of the electrolyzer (0~1). The input electrical power of the electrolyzer during time period t. This is the minimum allowable input electrical power for the electrolytic cell. This is the maximum permissible input electrical power of the electrolytic cell. This represents the maximum permissible downward ramp rate of the electrolytic cell. The input electrical power of the electrolytic cell is given by time period t+1. This represents the maximum permissible upward ramp rate of the electrolytic cell. Let t be the volumetric flow rate of hydrogen produced by the electrolyzer during time period t. The volumetric flow rate of oxygen produced by the electrolyzer during time period t is given. For unit conversion factors, This is the lower heating value of hydrogen.

[0121] S12. Based on the law of conservation of energy, construct corresponding hydrogen fuel cell cogeneration models and hydrogen methanation models according to equipment data.

[0122] In this embodiment of the invention, based on equipment data, the law of conservation of energy is used to establish an energy balance relationship between hydrogen energy input and electrical and thermal energy output of the hydrogen fuel cell. Simultaneously, output constraints, ramp-up constraints, and thermoelectric ratio adjustment range are introduced to construct a hydrogen fuel cell cogeneration model that characterizes the efficient cascade utilization of hydrogen energy and the synergistic thermoelectric output characteristics. Furthermore, based on the law of conservation of energy and combined with equipment data, energy and mass balance equations are established between hydrogen consumption, carbon dioxide participation in the reaction, methane production, and waste heat recovery. Finally, equipment operating limits and dynamic adjustment constraints are superimposed to construct a corresponding hydrogen methanation model.

[0123] It should be noted that the specific model for hydrogen fuel cell cogeneration is as follows:

[0124] ;

[0125] in, The power generation of the hydrogen fuel cell during time period t. The heating power of the hydrogen fuel cell during time period t. The total efficiency of combined heat and power generation in hydrogen fuel cells. Let t be the equivalent power of hydrogen energy consumed by the hydrogen fuel cell during time period t. The maximum permissible hydrogen power consumption of a hydrogen fuel cell. This represents the minimum permissible hydrogen power consumption of a hydrogen fuel cell. This represents the maximum permissible downhill ramp rate for hydrogen fuel cells. This represents the equivalent power of hydrogen energy consumed by the hydrogen fuel cell in time period t+1. This represents the maximum permissible uphill ramp rate for a hydrogen fuel cell. The minimum permissible thermoelectric ratio for hydrogen fuel cells, This represents the maximum permissible thermoelectric ratio of a hydrogen fuel cell.

[0126] The hydrogen methanation model is as follows:

[0127] ;

[0128] in, The volumetric flow rate of methane (natural gas) produced by the methanation reactor during time period t. For the efficiency of the methanation reaction, This represents the equivalent power of hydrogen energy consumed by the methanation reactor during time period t. The lower heating value of methane. This represents the minimum permissible hydrogen consumption power of the methanation reactor. This represents the maximum permissible hydrogen consumption power of the methanation reactor. This represents the maximum permissible downhill ramp rate for the methanation reactor. This represents the equivalent power of hydrogen energy consumed by the methanation reactor at time t+1. This represents the maximum permissible uphill ramp rate for the methanation reactor. The recoverable waste heat power of the methanation reactor during the time period t is the heating power. The waste heat recovery coefficient per unit of methane production. The volumetric flow rate of CO2 consumed by the methanation reactor during time period t. The CO2 mass flow rate consumed by the methanation reactor during time period t. This is the standard density of CO2.

[0129] S13. Combine the electrolyzer hydrogen production model, the hydrogen fuel cell cogeneration model, and the hydrogen methanation model to obtain the corresponding two-stage electro-gas conversion model.

[0130] In this embodiment of the invention, the electrolyzer hydrogen production model, the hydrogen fuel cell cogeneration model, and the hydrogen methanation model are combined and integrated to obtain the corresponding two-stage electro-gas conversion model.

[0131] Step 204: Based on the law of conservation of energy and the law of heat conversion of combustion, construct the corresponding gas boiler model according to the equipment data.

[0132] In this embodiment of the invention, based on equipment data, the total balance relationship between the chemical energy of natural gas and the output thermal energy is determined according to the law of conservation of energy. The conversion process of heat release, heat transfer and heat dissipation loss of natural gas combustion is characterized by combining the law of heat conversion of combustion. A quantitative equation is established between the input natural gas flow rate, combustion calorific value, thermal efficiency and output thermal power. At the same time, operating constraints such as boiler output adjustment range, ramp rate limit, start-up and shutdown loss are superimposed to form a gas boiler model that includes energy balance, heat conversion characteristics and operating boundaries.

[0133] It should be noted that the gas boiler model is as follows:

[0134] ;

[0135] in, The actual heating power of the gas-fired boiler during time period t. For the thermal efficiency of gas-fired boilers The volumetric flow rate of natural gas consumed by the gas-fired boiler during time period t. This refers to the minimum allowable heating power of a gas-fired boiler. This refers to the maximum allowable heating capacity of the gas-fired boiler. This refers to the maximum permissible downward ramp rate of the gas-fired boiler. This represents the actual heating power of the gas-fired boiler during time period t+1. This represents the maximum permissible upward ramp rate for a gas-fired boiler.

[0136] Step 205: Input the equipment data into the preset carbon trading model to obtain the corresponding target carbon trading model.

[0137] In this embodiment of the invention, parameters such as unit carbon emission intensity, output level, carbon capture efficiency, wind and solar power consumption, electricity purchase and carbon sequestration scale from the equipment data are input into a preset carbon trading model to obtain the corresponding target carbon trading model.

[0138] It should be noted that carbon trading models include carbon emission quota models, actual carbon emission models, and reward / penalty tiered carbon trading models.

[0139] The carbon emission quota model is as follows:

[0140] ;

[0141] in, The total primary energy consumption of the virtual power plant in time period t is divided by its carbon emission intensity. The primary energy / carbon emission contribution of a gas turbine (GT) cogeneration unit for time period t. The primary energy / carbon emission contribution corresponding to heating provided by a gas-fired boiler (GB) for a time period t. The contribution of primary energy consumption / carbon emissions to electricity purchased from the main grid during time period t. The conversion factor for heat / electricity to primary energy / carbon emissions. This is the equivalent conversion factor for electro-thermal conversion. The power generation capacity of the gas turbine during time period t. The heating power of the gas turbine during time period t. The heating power of the gas-fired boiler during time period t. For time period t, the virtual power plant purchases electricity from the main grid. The average carbon emissions of the power grid from purchased electricity / primary energy conversion factor.

[0142] The actual carbon emission model is as follows:

[0143] ;

[0144] in, The total carbon emissions / pollutant emissions of the virtual power plant during time period t. Emissions generated by heating from a gas-fired boiler (GB) for a given period of time (t). For time period t, the emissions corresponding to purchasing electricity from the main grid. The mass flow rate of CO2 that is not captured and directly emitted by the oxygen-enriched combustion unit during time period t. The coefficient of the quadratic term of the emission characteristics of the gas-fired boiler. The heating power of the gas-fired boiler during time period t. The coefficient for the first-order term of the emission characteristics of a gas-fired boiler. This is a constant term representing the emission characteristics of gas-fired boilers. The coefficient of the quadratic term of the emission characteristics of purchased electricity. The coefficient for the first-order term of the emission characteristics of purchased electricity. For time period t, the virtual power plant purchases electricity from the main grid. This is a constant term for the emission characteristics of purchased electricity.

[0145] The reward and punishment tiered carbon trading model is as follows:

[0146] ;

[0147] ;

[0148] in, The carbon emission trading volume of the VPP system during time period t. The carbon trading cost of the VPP system during time period t. The base price for carbon trading. For price growth rate, This represents the length of the carbon emission range.

[0149] Step 206: Construct the corresponding electric vehicle charging and discharging model based on the preset electric vehicle orderly charging and discharging model and equipment data.

[0150] Furthermore, step 206 includes the following sub-steps:

[0151] S21. Construct a corresponding disordered charging model for electric vehicles based on pre-acquired probability distribution data and device data.

[0152] Probability distribution data refers to data that describes the statistical patterns of random behaviors such as the arrival and departure times of electric vehicles.

[0153] In this embodiment of the invention, based on the pre-acquired probability distribution data and device data, and according to the behavioral characteristics of users randomly accessing and leaving the network under disordered charging, a probability density function for access and departure times is established. Combining battery capacity, charging efficiency, and state of charge constraints, the calculation relationship between charging time and charging power of a single electric vehicle is derived. Then, by aggregating the charging behavior of all vehicles and superimposing time period continuity constraints, a disordered charging model for electric vehicles that can describe users' random charging behavior and load random fluctuation characteristics is formed.

[0154] It should be noted that the disordered charging model for electric vehicles is as follows:

[0155] ;

[0156] ;

[0157] in, The time t for electric vehicle to be connected (start charging) s The probability density function, The standard deviation of the access time reflects the dispersion of user access time. For the moment when an electric vehicle is connected / starts charging, This represents the average time it takes for a user to typically return home / connect to the power grid. The time t when the electric vehicle leaves (ends charging) e The probability density function, For the time when the electric vehicle leaves / ends charging, The mean at the time of departure. The standard deviation of the departure time. The theoretical charging time (in hours) for the nth electric vehicle, i.e., the time required to complete charging. The initial state of charge (SOC, 0~1) of the battery when the nth electric vehicle is connected to the grid. The target battery state of charge (SOC, 0~1) when the nth electric vehicle leaves the grid represents the minimum charge required for the user's trip. The rated battery capacity for a single electric vehicle. Let be the charging power of the nth electric vehicle during time period t. For the charging efficiency of electric vehicles. Let t be the total charging power of all electric vehicles in the virtual power plant during time period t. Let be the charging power of the nth electric vehicle during time period t. Let be the charging power of the nth electric vehicle in time period t+24 (i.e., the same time period on the next day), used to consider the continuity of charging across days.

[0158] It is worth mentioning that, see Figures 14-15 As shown, the main parking time for EVs is concentrated between 17:00 and 8:00, while the departure times during this period span the day-ahead scheduling interval and are distributed into the next day. To address parking situations where this occurs, an auxiliary time period t is introduced. e +24, at this time the EV's stopping time is [t s ,t e +24].

[0159] See Figure 16 As shown in the figure, the shaded area represents the feasible region for charging and discharging electric vehicles. These represent the State of Charge (SOC) of an electric vehicle when it is connected to and disconnected from the power grid, respectively. These represent the upper and lower limits of the State of Charge (SOC) of an electric vehicle, respectively, and in this invention, they are taken as 0.9 and 0.2, respectively.

[0160] S22. Couple the disordered charging model of electric vehicles with the preset ordered charging and discharging model of electric vehicles to obtain the corresponding charging and discharging model of electric vehicles.

[0161] In this embodiment of the invention, the disordered charging model of electric vehicles and the preset ordered charging and discharging model of electric vehicles are combined to obtain the corresponding charging and discharging model of electric vehicles.

[0162] It should be noted that the specific orderly charging and discharging model for electric vehicles is as follows:

[0163] ;

[0164] in, Let n be the state of charge of the battery of the nth electric vehicle during time period t. Let n be the state of charge of the battery of the nth electric vehicle in time period t-1. Let n be the net charging and discharging power of the nth electric vehicle during time period t. The length of the scheduling period. Let n be the rated battery capacity of the nth electric vehicle. Let be the charging power of the nth electric vehicle during time period t. For the charging efficiency of electric vehicles. Let be the discharge power of the nth electric vehicle in time period t. For the discharge efficiency of electric vehicles. Let n be the minimum permissible SOC for the nth electric vehicle. For the maximum permissible SOC of the nth electric vehicle, Let SOC be the state of charge (SOC) of the nth electric vehicle at the start of the scheduling cycle. Let be a binary variable (0 / 1) representing the charging status of the nth electric vehicle during time period t, where 1 indicates charging is allowed and 0 indicates charging is not allowed. Let n be the maximum charging power of the nth electric vehicle. Let n be the maximum discharge power of the nth electric vehicle. Let t be the total charging power of all electric vehicles in the virtual power plant during time period t. Let the charging power of the nth electric vehicle be the charging power at the same time t+24 on the next day. Let t be the total discharge power of all electric vehicles in the virtual power plant during time period t. Let be the discharge power of the nth electric vehicle at the same time t+24 on the next day.

[0165] Step 207: Couple the oxygen-enriched combustion carbon capture unit model, the two-stage power-to-gas conversion model, the gas boiler model, the target carbon trading model, the electric vehicle charging and discharging model, and the preset demand response model to obtain the corresponding virtual power plant model.

[0166] In this embodiment of the invention, the oxygen-enriched combustion carbon capture unit model, the two-stage power-to-gas conversion model, the gas boiler model, the target carbon trading model, the electric vehicle charging and discharging model, and the preset demand response model are combined to obtain the corresponding virtual power plant model.

[0167] It should be noted that demand response models include shiftable load models, reduceable load models, and alternative load models.

[0168] The specific model of the transferable load is as follows:

[0169] ;

[0170] in, Let be the net power response of the i-th type of movable load during time period t. The load transfer state variable for time period t is (0 / 1), where 1 indicates that load was transferred in from other time periods during this period. Let be the transferred power (kW) of the i-th type of load in time period t, that is, the amount of load shifted from other time periods to this time period. The load transfer status variable (0 / 1) represents the load transfer from time period t, where 1 indicates that load is transferred to other time periods during that period. Let be the power transferred out of the i-th type of load in time period t (kW), that is, the amount of load that is shifted from this time period to other time periods. Let i be the maximum allowable transfer power of the i-th type of transferable load in a single time period. Let be the maximum allowable transfer power of the i-th type of transferable load in a single time period.

[0171] The load reduction model is as follows:

[0172] ;

[0173] in, Let t be the actual power reduction of the i-th type of load that can be reduced. This represents the maximum allowable power reduction for the i-th type of load that can be reduced.

[0174] The alternative load model is as follows:

[0175] ;

[0176] in, The net substitution / transfer power of the electrical load during time period t. The conversion efficiency coefficient for electricity-heat load substitution. The net replacement / transfer power of heat load in time period t. Let be the absolute value of the minimum permissible transfer / replacement power for the i-th type of load. The absolute value of the maximum permissible transfer / replacement power for the i-th type of load. The net replacement / transfer power of the i-th type (electric or thermal) load during time period t.

[0177] Step 208: Use the virtual power plant model to optimize and solve the virtual power plant optimization scheduling model to obtain the corresponding low-carbon optimization scheduling scheme.

[0178] Furthermore, step 208 includes the following sub-steps:

[0179] S31. Couple the virtual power plant model and the virtual power plant optimal scheduling model to obtain the corresponding target virtual power plant optimal scheduling model.

[0180] In this embodiment of the invention, the virtual power plant model and the virtual power plant optimal scheduling model are combined to obtain the corresponding target virtual power plant optimal scheduling model.

[0181] S32. Call the preset solver to optimize and solve the target virtual power plant optimization scheduling model to obtain the corresponding low-carbon optimization scheduling scheme.

[0182] In this embodiment of the invention, the constructed target virtual power plant optimization scheduling model is input into a preset solver. Calculation parameters such as convergence accuracy, number of iterations, and solution time limits are set. The solver reads the model's objective function, constraints, and parameter data, and iteratively searches for the optimal solution within the feasible region using a built-in optimization algorithm. It calculates the unit output, energy storage charging and discharging power, electricity-to-gas operation power, load response, electric vehicle charging and discharging power, and carbon capture and allocation strategies for each time period. The solution results are then constrained and their feasibility is assessed. Infeasible solutions are eliminated, and iterative optimization continues until the convergence conditions are met, resulting in a low-carbon optimization scheduling scheme that balances the lowest operating cost and optimal carbon emissions.

[0183] It is worth mentioning that, in order to verify the effectiveness of the virtual power plant low-carbon optimization scheduling method of this application, five schemes were set up for comparison.

[0184] Option 1: The oxygen-enriched combustion carbon capture unit is put into operation, without considering the optimized scheduling of VPP under P2G equipment;

[0185] Option 2: Optimized VPP scheduling under coordinated operation of oxygen-enriched combustion carbon capture unit and P2G equipment;

[0186] Option 3: Coordinate the operation of the oxygen-enriched combustion carbon capture unit with the refined two-stage P2G and introduce VPP optimization scheduling under HFC equipment.

[0187] Option 4: Based on Option 3, the VPP system does not consider low-carbon optimized scheduling under the trading mechanism.

[0188] Option 5: Based on Option 3, the VPP system considers low-carbon optimized scheduling under the conventional carbon trading mechanism.

[0189] The total cost and individual costs corresponding to the above scheme are shown in Table 1 and Table 2.

[0190] Table 1

[0191]

[0192] Table 2

[0193]

[0194] The power balance curve of Scheme 3 is as follows: Figure 7 As shown. The heat power balance curve of Scheme 3 is as follows. Figure 8 As shown. The hydrogen power balance curve for Scheme 3 is shown in the figure. Figure 9 As shown. From Figure 7 The power balance curve of Scheme 3 clearly shows that the oxygen-enriched combustion carbon capture unit significantly increases its output during the midday and evening peak load periods, taking on the supporting role of the basic electrical load. Meanwhile, the hydrogen fuel cell can respond quickly to fluctuations in wind or solar power output, filling the power gap. At the same time, during the night when there is a surplus of wind power, the electrolyzer absorbs a large amount of wind power that might otherwise be abandoned, converting the excess electricity into hydrogen energy storage. This reduces the penalty cost for wind and solar curtailment to zero throughout the day, achieving a highly friendly absorption of renewable energy. Figure 8 The thermal power balance curve further shows that in Scheme 3, the hydrogen fuel cell can stably output thermal power while supplying electricity, effectively alleviating the rigid coupling of traditional cogeneration units that "determines power based on heat". The gas boiler is only used as a supplement under extreme heat load demand, and its operating time is significantly reduced. The waste heat recovered by the methanation reactor also participates in the heating. The overall thermal power scheduling is more flexible and efficient, and fuel consumption and carbon emissions decrease accordingly. Figure 9 The hydrogen power balance curve fully demonstrates the closed-loop path of hydrogen energy "production-storage-use-conversion" in Scheme 3: the electrolyzer produces a large amount of hydrogen during periods of surplus wind / solar power, which is preferentially supplied to hydrogen fuel cells for combined heat and power (CHP). Excess hydrogen is stored in hydrogen storage tanks and released when needed. Simultaneously, some hydrogen reacts with carbon dioxide captured by the oxygen-enriched combustion carbon capture unit in a methanation reactor to produce natural gas, reducing the cost of purchasing natural gas and achieving closed-loop utilization of carbon and oxygen cycles. Figure 7 , Figure 8 , Figure 9 Based on the cost data in Tables 1 and 2, Scheme 3 not only has the lowest total cost (42,978.5 yuan) compared to Schemes 1 and 2, but also significantly reduces energy purchase costs, carbon trading costs, and wind and solar penalty costs. Furthermore, it achieves a high-precision balance and synergy among electricity, heat, and hydrogen power, fully demonstrating the comprehensive advantages of the virtual power plant in terms of low carbon emissions, economy, and operational flexibility after refining the two-stage electricity-to-gas conversion and introducing hydrogen fuel cells. Table 3 shows the different charging strategies for EVs under Scheme 5. Table 4 shows the total cost and individual costs corresponding to strategies 1-4 in Scheme 5.

[0195] Table 3

[0196]

[0197] Table 4

[0198]

[0199] The power balance curve under strategy 4 is as follows: Figure 10 As shown. The heat power balance curve under strategy 4 is as follows. Figure 11 As shown. EV charging power under different charging methods is as follows: Figure 12 As shown. The demand response scheduling results under Strategy 4 are as follows. Figure 13 As shown. From Figure 10 The power balance curve of Strategy 4 shows that the oxygen-enriched combustion carbon capture unit outputs power stably during peak load periods, the hydrogen fuel cell and energy storage system respond quickly when wind and solar power output fluctuates, and the electrolyzer absorbs a large amount of wind curtailment during the night when wind power is surplus, resulting in almost no wind or solar curtailment throughout the day. The power load tracking accuracy is high, and the amount of electricity purchased is significantly reduced. Figure 11 The thermal power balance curve shows that in Strategy 4, the hydrogen fuel cell continuously provides thermal output while supplying electricity, the gas boiler only supplements briefly during extremely cold periods, and the waste heat recovered by the methanation reactor also participates in heating. The overall thermal power scheduling is smooth and efficient, and fuel consumption is further reduced. Figure 12The comparison chart of EV charging power under different charging methods shows that the orderly charging and discharging strategy (V2G) adopted by Strategy 4 can shift the electric vehicle charging load from the evening peak period to the early morning low load period, and support the grid through V2G discharge during the peak period. This not only alleviates the grid pressure, but also reduces the charging cost for users, while increasing the space for renewable energy consumption. Figure 13 The demand response scheduling results further illustrate that in Strategy 4, the shiftable, reduceable, and substitute loads all respond rationally according to the excitation signals. Shiftable loads transfer some daytime electricity consumption to nighttime, reduceable loads proactively decrease electricity consumption during peak hours, and substitute loads achieve flexible conversion between electricity and heat, thereby significantly reducing the system's peak-to-valley difference and total operating costs. Figures 10 to 13 Based on the cost data in Table 4, Strategy 4 demonstrates the best performance in terms of electric and thermal balance capability, electric vehicle-grid interaction efficiency, and demand response flexibility compared to Strategies 1 to 3. It also has the lowest total cost, and significantly reduces energy purchase costs and carbon trading costs, fully demonstrating the technical advantages of orderly charging and discharging and multi-type demand response synergistic optimization.

[0200] In this embodiment of the invention, a virtual power plant optimal scheduling model is constructed by setting constraints with the goal of minimizing operating costs and using power plant scheduling data as the decision variable. Equipment data from the distribution network is acquired, and based on the energy flow characteristics of the oxy-fuel carbon capture (OFC) units in the distribution network, a corresponding OFC unit model is constructed. Mechanistic modeling is performed based on the OFC unit model and the equipment data to obtain the corresponding virtual power plant model. The virtual power plant model is then used to optimize and solve the virtual power plant optimal scheduling model, yielding the corresponding low-carbon optimal scheduling scheme. This overcomes the technical problem that existing virtual power plant low-carbon scheduling often focuses on a single carbon capture technology or a one-sided demand response mechanism, but struggles to achieve closed-loop utilization of carbon and oxygen cycles, thus reducing the reliability of virtual power plant low-carbon optimal scheduling. Compared with traditional virtual power plant low-carbon dispatching methods, this approach aims to minimize operating costs. It constructs an optimized dispatching model based on power plant dispatching data to precisely quantify dispatching decisions. By combining distribution network equipment data with the energy flow characteristics of oxygen-enriched combustion carbon capture units, a refined unit model is established to accurately characterize the coupling laws of unit energy consumption, output, and carbon capture. Through mechanistic modeling, the interaction relationships of multiple devices and energy flows are integrated to form an integrated virtual power plant model, achieving closed-loop utilization of carbon and oxygen cycles. Driven by the virtual power plant model, the optimization solution balances cost optimization and low-carbon emission reduction, significantly improving renewable energy absorption capacity, reducing energy purchase and carbon trading costs, and decreasing carbon emissions. Simultaneously, it enhances the flexibility and operational reliability of low-carbon optimized dispatching of virtual power plants.

[0201] Please see Figure 17 , Figure 17 This is a structural block diagram of a virtual power plant low-carbon optimization scheduling system provided in Embodiment 3 of the present invention.

[0202] This invention provides a virtual power plant low-carbon optimized scheduling system, comprising:

[0203] The first construction module 301 is used to construct a virtual power plant optimization scheduling model with the goal of minimizing operating costs, setting constraints, and using power plant scheduling data as decision variables.

[0204] The acquisition module 302 is used to acquire equipment data of the distribution network and construct a corresponding oxygen-enriched combustion carbon capture unit model based on the energy flow characteristics of the oxygen-enriched combustion carbon capture unit of the distribution network and the equipment data.

[0205] The second construction module 303 is used to perform mechanistic modeling based on the oxygen-enriched combustion carbon capture unit model and equipment data to obtain the corresponding virtual power plant model.

[0206] The optimization module 304 is used to optimize and solve the virtual power plant optimization scheduling model using the virtual power plant model, and obtain the corresponding low-carbon optimization scheduling scheme.

[0207] Furthermore, the constraints include electrical power balance constraints, thermal power balance constraints, natural gas balance constraints, and hydrogen power balance constraints.

[0208] Furthermore, the second building module 303 includes:

[0209] The first construction submodule is used to model the two-stage electro-gas conversion mechanism using equipment data to obtain the corresponding two-stage electro-gas conversion model.

[0210] The second construction submodule is used to build the corresponding gas boiler model based on the law of conservation of energy and the law of heat conversion of combustion, according to the equipment data.

[0211] The third construction submodule is used to input equipment data into a preset carbon trading model to obtain the corresponding target carbon trading model;

[0212] The fourth sub-module is used to construct the corresponding electric vehicle charging and discharging model based on the preset electric vehicle orderly charging and discharging model and equipment data;

[0213] The first coupling submodule is used to couple the oxygen-enriched combustion carbon capture unit model, the two-stage power-to-gas conversion model, the gas boiler model, the target carbon trading model, the electric vehicle charging and discharging model, and the preset demand response model to obtain the corresponding virtual power plant model.

[0214] Furthermore, the first construction submodule includes:

[0215] The first building unit is used to construct a corresponding electrolyzer hydrogen production model based on the principle of electrochemical energy conversion and equipment data.

[0216] The second building unit is used to construct the corresponding hydrogen fuel cell cogeneration model and hydrogen methanation model based on the law of conservation of energy and equipment data.

[0217] The first coupling unit is used to combine the electrolyzer hydrogen production model, the hydrogen fuel cell cogeneration model, and the hydrogen methanation model to obtain the corresponding two-stage electro-gas conversion model.

[0218] Furthermore, the fourth construction submodule includes:

[0219] The third building unit is used to build a corresponding disordered charging model for electric vehicles based on pre-acquired probability distribution data and device data;

[0220] The second coupling unit is used to couple the disordered charging model of electric vehicles with the preset ordered charging and discharging model of electric vehicles to obtain the corresponding charging and discharging model of electric vehicles.

[0221] Furthermore, module 304 is optimized, including:

[0222] The second coupling submodule is used to couple the virtual power plant model and the virtual power plant optimization scheduling model to obtain the corresponding target virtual power plant optimization scheduling model.

[0223] The optimization submodule is used to call a preset solver to optimize and solve the target virtual power plant optimization scheduling model, and obtain the corresponding low-carbon optimization scheduling scheme.

[0224] Please see Figure 18 , Figure 18 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0225] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes the virtual power plant low-carbon optimization scheduling method as described in any of the above embodiments.

[0226] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to execute the various steps in the virtual power plant low-carbon optimization scheduling method described above.

[0227] Embodiment 5 of the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the virtual power plant low-carbon optimization scheduling method as described in any of the above embodiments.

[0228] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the virtual power plant low-carbon optimization scheduling method as described in any of the above embodiments.

[0229] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0230] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0231] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0232] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0233] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0234] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-carbon optimized scheduling method for virtual power plants, characterized in that, include: With the goal of minimizing operating costs, constraints are set, and power plant scheduling data is used as decision variables to construct a virtual power plant optimal scheduling model; Obtain equipment data from the power distribution network, and based on the energy flow characteristics of the oxygen-enriched combustion carbon capture unit in the power distribution network, construct a corresponding oxygen-enriched combustion carbon capture unit model according to the equipment data. Based on the oxygen-enriched combustion carbon capture unit model and the equipment data, a mechanistic model is performed to obtain the corresponding virtual power plant model. The virtual power plant model is used to optimize and solve the virtual power plant optimal scheduling model to obtain the corresponding low-carbon optimal scheduling scheme.

2. The virtual power plant low-carbon optimization scheduling method according to claim 1, characterized in that, The constraints include electrical power balance constraints, thermal power balance constraints, natural gas balance constraints, and hydrogen power balance constraints.

3. The virtual power plant low-carbon optimization scheduling method according to claim 1, characterized in that, The step of performing mechanistic modeling based on the oxygen-enriched combustion carbon capture unit model and the equipment data to obtain the corresponding virtual power plant model includes: The equipment data was used to model the two-stage electro-gas conversion mechanism, resulting in the corresponding two-stage electro-gas conversion model. Based on the law of conservation of energy and the law of heat conversion in combustion, a corresponding gas-fired boiler model is constructed according to the equipment data. The device data is input into a preset carbon trading model to obtain the corresponding target carbon trading model; A corresponding electric vehicle charging and discharging model is constructed based on a preset electric vehicle orderly charging and discharging model and the device data. The oxygen-enriched combustion carbon capture unit model, the two-stage power-to-gas conversion model, the gas boiler model, the target carbon trading model, the electric vehicle charging and discharging model, and the preset demand response model are coupled to obtain the corresponding virtual power plant model.

4. The virtual power plant low-carbon optimization scheduling method according to claim 3, characterized in that, The step of using the equipment data to perform two-stage electro-gas conversion mechanism modeling to obtain the corresponding two-stage electro-gas conversion model includes: Based on the principle of electrochemical energy conversion, a corresponding electrolyzer hydrogen production model is constructed according to the equipment data. Based on the law of conservation of energy, a corresponding hydrogen fuel cell cogeneration model and hydrogen methanation model are constructed according to the equipment data. By combining the electrolyzer hydrogen production model, the hydrogen fuel cell cogeneration model, and the hydrogen methanation model, a corresponding two-stage electro-gas conversion model is obtained.

5. The virtual power plant low-carbon optimization scheduling method according to claim 3, characterized in that, The step of constructing a corresponding electric vehicle charging and discharging model based on a preset electric vehicle orderly charging and discharging model and the device data includes: A corresponding disordered charging model for electric vehicles is constructed based on the pre-acquired probability distribution data and the device data; The electric vehicle disordered charging model and the preset electric vehicle ordered charging and discharging model are coupled to obtain the corresponding electric vehicle charging and discharging model.

6. The virtual power plant low-carbon optimization scheduling method according to claim 1, characterized in that, The step of using the virtual power plant model to optimize and solve the virtual power plant optimal scheduling model to obtain the corresponding low-carbon optimal scheduling scheme includes: The virtual power plant model and the virtual power plant optimization scheduling model are coupled to obtain the corresponding target virtual power plant optimization scheduling model. The preset solver is invoked to optimize and solve the target virtual power plant scheduling model, thereby obtaining the corresponding low-carbon optimized scheduling scheme.

7. A virtual power plant low-carbon optimized dispatching system, characterized in that, include: The first construction module is used to construct a virtual power plant optimization scheduling model with the goal of minimizing operating costs, setting constraints, and using power plant scheduling data as decision variables. The data acquisition module is used to acquire equipment data of the power distribution network and, based on the energy flow characteristics of the oxygen-enriched combustion carbon capture unit of the power distribution network, construct a corresponding oxygen-enriched combustion carbon capture unit model according to the equipment data. The second construction module is used to perform mechanistic modeling based on the oxygen-enriched combustion carbon capture unit model and the equipment data to obtain the corresponding virtual power plant model. The optimization module is used to optimize and solve the virtual power plant optimization scheduling model using the virtual power plant model to obtain the corresponding low-carbon optimization scheduling scheme.

8. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the virtual power plant low-carbon optimization scheduling method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the virtual power plant low-carbon optimization scheduling method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the virtual power plant low-carbon optimization scheduling method as described in any one of claims 1-6.

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