A virtual power plant optimization method considering power-to-gas and tiered carbon trading

Abstract

The present invention relates to the technical field of virtual power plant optimization scheduling, and in particular to a virtual power plant optimization method taking into account power-to-gas and step-by-step carbon trading, including: pre-processing the collected wind power and photovoltaic output data, user-side load data, and meteorological historical data to form a complete resource load time series data set; constructing a two-stage power-to-gas model including electrolytic hydrogen production and hydrogen-to-methane; constructing a step-by-step carbon trading model for virtual power plants to participate in the electricity-carbon joint market; based on the deep coupling of the two-stage power-to-gas model and the carbon capture and storage system, constructing a multi-energy system for coordinated energy supply; based on the scheduling results of the two-stage power-to-gas model, the step-by-step carbon trading model, and the multi-energy system, constructing a virtual power plant optimization scheduling model; inputting the resource load time series data set into the virtual power plant optimization scheduling model, and outputting the virtual power plant optimization scheduling plan. Compared with the prior art, the present invention has the advantages of achieving optimized scheduling under low-carbon goals.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant optimization scheduling, and in particular to a virtual power plant optimization method taking into account power-to-gas and tiered carbon trading. Background Art

[0002] In the context of power system transformation and energy supply structure optimization, the promotion of multi-energy complementary systems and low-carbon energy technologies will help improve energy efficiency and reduce emissions. However, distributed power sources such as wind power and photovoltaics pose huge challenges to the stability and security of the power system due to their randomness and volatility.

[0003] As an advanced energy management method, virtual power plants provide technical support for the efficient access of distributed resources to the power grid through the aggregation and optimized scheduling of distributed power sources, while improving the economy and low-carbon nature of the system. Combined with low-carbon technologies such as power-to-gas (P2G) and carbon capture and storage (CCS), virtual power plants have shown great potential in achieving coordinated optimization of multiple energy forms and low-carbon operation.

[0004] Existing research on virtual power plant optimization scheduling mainly focuses on uncertainty modeling, low-carbon technology or electricity-carbon trading, and rarely systematically considers the uncertainty of wind and solar resources when virtual power plants participate in the electricity-carbon joint market. Therefore, how to further optimize the scheduling decision of virtual power plants under the premise of comprehensively considering uncertainty, low-carbon technology and carbon trading mechanism has become an important topic in current research and practice.

[0005] After searching, the Chinese invention application publication number CN117674073A discloses a coordinated optimization scheduling method for a wind power-carbon capture virtual power plant taking into account step-type carbon trading, including building a step-type carbon trading model, building a wind power-carbon capture virtual power plant model, introducing fuzzy opportunity constraints, building constraints, and solving the optimization scheduling model of the wind power-carbon capture virtual power plant taking into account step-type carbon trading under constraints to obtain the optimal output allocation plan for each unit. The optimization scheduling model includes step-type carbon trading costs, thermal power unit power generation costs, and flexible carbon capture power plants and wind power plant operation and maintenance costs. The output of each unit is optimized with the goal of minimizing the total system operating cost. This existing application does not consider the factor of power-to-gas, so there is a problem of poor virtual power plant scheduling optimization.

[0006] How to achieve virtual power plant optimization that comprehensively considers power-to-gas technology and tiered carbon trading has become a technical problem that needs to be solved. Summary of the invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a virtual power plant optimization method taking into account power-to-gas and tiered carbon trading.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] According to one aspect of the present invention, a virtual power plant optimization method taking into account power-to-gas and tiered carbon trading is provided, the method comprising: preprocessing the collected wind power and photovoltaic output data, user-side load data, and meteorological historical data to form a complete resource load time series data set;

[0010] Construct a two-stage power-to-gas model including hydrogen electrolysis and hydrogen-to-methane conversion;

[0011] Construct a tiered carbon trading model for virtual power plants to participate in the electricity-carbon joint market; build a multi-energy system for coordinated energy supply based on the deep coupling of the two-stage power-to-gas model and the carbon capture and storage system;

[0012] Based on the scheduling results of the two-stage power-to-gas model, the step-by-step carbon trading model and the multi-energy system, a virtual power plant optimization scheduling model is constructed; the resource load time series data set is input into the virtual power plant optimization scheduling model, and the virtual power plant optimization scheduling plan is output.

[0013] Preferably, the two-stage power-to-gas model includes a first-stage electrolysis hydrogen production sub-model and a second-stage hydrogen methanation sub-model;

[0014] The electrolysis hydrogen production sub-model is used to analyze the relationship between the power consumption and hydrogen production of the electrolyzer under different load conditions;

[0015] The hydrogen methanation sub-model is used to analyze the conversion efficiency of hydrogen and carbon dioxide in the methanation equipment and determine the mapping relationship between gas production and energy consumption.

[0016] More preferably, the hydrogen methanation sub-model is specifically:

[0017] ,

[0018] Where: and for The electrical power consumed by electrolysis and the hydrogen production power at each moment; and for The hydrogen consumption and gas production of the methanation equipment at all times; and are the conversion efficiencies of water electrolysis for hydrogen production and methanogenesis, respectively;

[0019] The hydrogen methanogen model is specifically:

[0020] ,

[0021] Where: and They are Carbon capture and storage technology is renewing The amount of methanogenization utilized and the amount stored; is the lower heating value of methane; for CO2 regenerated by carbon capture and storage technology 2 Remaining amount; For CO 2 density.

[0022] Preferably, the method further comprises constructing a comprehensive carbon emission model, the process of which comprises:

[0023] S3.1, based on historical operation data, conduct a detailed analysis of the carbon emission characteristics of various energy equipment such as wind and solar power generation, traditional power generation, user-side electricity and heat loads, and evaluate their carbon emission intensity per unit of power supply or heat supply as a benchmark for subsequent carbon emission optimization; the historical operation data includes the output and carbon emission data of each energy equipment over a period of time;

[0024] S3.2, allocate carbon emission quotas for virtual power plants based on carbon emission benchmarks, total system load requirements, and emission characteristics of different energy equipment, and set initial carbon emission limits within the total system load framework;

[0025] S3.3, dynamically adjust carbon emission limits based on system operation data and external environment, achieve a balance between carbon emissions and electricity demand and thermal energy supply, and provide feedback for the diversified energy system.

[0026] More preferably, the process of constructing the multi-energy system includes: based on the deep coupling of the two-stage power-to-gas model and the carbon capture and storage system, through the coordinated operation of the three energy forms of electricity, heat and gas, combining the output characteristics of wind and solar power and the user's electricity and heat load requirements, by introducing carbon emission limits and quota allocation results, optimizing the scheduling strategies of various energy forms of electricity, gas and heat, realizing the coordinated optimization of energy, and controlling the total carbon emissions during the scheduling process.

[0027] Preferably, the objective function of the virtual power plant optimization scheduling model is to minimize the total cost, specifically:

[0028] ,

[0029] In the formula, is the total cost, including carbon trading costs , Carbon storage costs , Start-up and shutdown costs of coal-fired power units , coal consumption cost , Gas purchase cost and curtailment costs .

[0030] More preferably, the carbon trading cost and carbon sequestration cost are calculated as follows:

[0031] ,

[0032] ,

[0033] Where: for Trading at all times Costs; For the mass of the storage unit Costs; is the scheduling period; for time The amount of storage;

[0034] The calculation of the start-up and shutdown cost and coal consumption cost of the coal-fired power unit is as follows:

[0035] ,

[0036] Where: is the start-up and shutdown cost coefficient of coal-fired power units; and are binary variables, representing Moment and t -1 The operating status of the unit at time , and All are coal consumption cost coefficients of coal-fired units;

[0037] The calculation of the wind abandonment cost and gas purchase cost is specifically as follows:

[0038] ,

[0039] ,

[0040] Where: is the penalty cost per unit of wind abandonment; for The amount of wind abandoned at the time; is the unit price of natural gas; for The power corresponding to the natural gas purchased by the system at that moment.

[0041] More preferably, the constraints of the virtual power plant optimization scheduling model include: power balance constraints, wind and solar constraints, gas turbine constraints, gas boiler climbing and output constraints, electric boiler climbing and output constraints, thermal power unit climbing and output constraints, carbon capture constraints, energy storage constraints, and upper and lower limits of power-to-hydrogen energy consumption and climbing constraints.

[0042] More preferably, the power balance constraint is specifically:

[0043] ,

[0044] Where: and for The heat and electricity demand on the load side at all times; and They are The electrical power consumed by electrolysis and the hydrogen production power at each moment; and They are Hydrogen consumption and gas production of methanation equipment at all times , , and They are t The electrical power of the hydrogen-doped gas turbine at any given moment, t The power generation of thermal power units at the moment, t The power generation of wind turbines at each moment and t The discharge amount of the energy storage device at any moment; , , , Respectively t The electric power of the electrolytic cell at the moment, t Total electrical power of the carbon capture system at the moment, t The power demand on the load side at all times and t The charge level of the energy storage device at any moment; , Respectively t The power of natural gas consumed by hydrogen-blended gas turbines and hydrogen-blended gas boilers at any given moment; for t The power corresponding to the natural gas purchased by the system at the moment; , , , Respectively t Thermal power of gas turbines and electrolyzers, and thermal power of charging and discharging of thermal energy storage equipment at all times; and They represent the hydrogen consumption power of the gas turbine and hydrogen-blended gas boiler at time t respectively;

[0045] The carbon capture constraints include: carbon capture operation energy consumption Do not exceed the maximum operating conditions :

[0046] ,

[0047] Liquid storage equipment capacity constraints:

[0048] ,

[0049] Where: and The upper and lower limits of the capacity of the rich liquid equipment; and The upper and lower limits of the capacity of the lean liquid equipment; and is the initial capacity of the liquid storage device; and are the final remaining capacity of the liquid storage device respectively; and They are t The capacity of the lean liquid equipment and the rich liquid equipment at all times;

[0050] Liquid storage equipment in one operating cycle T After the end, the capacity remains unchanged, and the constraints are as follows:

[0051] ,

[0052] Where: , They are t The volume of liquid flowing into the rich and poor liquid tanks at each moment; , are the outflow liquid volumes of the poor and rich liquid tanks at time t respectively.

[0053] More preferably, the power-to-hydrogen (P2H) energy consumption upper and lower limits and climbing constraints are:

[0054] ,

[0055] Where: , They are the upper and lower limits of the power consumed by electrolysis: and The upper and lower limits of the electrolytic cell power climbing; and They are t Moment and t -1 moment electrolysis consumes electrical power;

[0056] The energy storage constraints are as follows, where the constraints of heat, electricity, and hydrogen in energy storage are uniformly expressed by the following formula:

[0057] ,

[0058] Where: For the i Energy storage device Capacity at the moment; as well as are the lower and upper limits of the energy storage system’s capacity, respectively; as well as For the i Energy storage device Charging and discharging power at all times; Indicates i The upper limit of the charging power of each energy storage device; Indicates i The upper limit of energy storage device discharge power: as well as Respectively represent i A binary variable that indicates the charging and discharging state of an energy storage device at all times.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] (1) While establishing a virtual power plant to participate in the electricity-carbon joint market transaction, the present invention comprehensively considers the uncertainty of wind and solar power output, constructs a multi-energy system that is deeply coupled with a two-stage power-to-gas model and a carbon capture and storage system, and solves the contradiction between how to balance the volatility of renewable energy and the stability of system operation; by converting excess electricity into hydrogen and using it for methanation, the pressure on the power grid is further reduced; at the same time, carbon dioxide emissions are effectively reduced through carbon capture technology, promoting the realization of low-carbon goals; and introduces a stepped carbon trading mechanism. By dynamically adjusting the carbon emission cost and carbon penalty factor, and comprehensively considering uncertainty, low-carbon technology and carbon trading mechanism, the virtual power plant is optimized for scheduling under the low-carbon goal. This can be widely applied to different types of virtual power plants and multi-energy complementary systems, and improve the economy and low-carbon nature of virtual power plant scheduling.

[0061] (2) The present invention integrates the electricity-to-hydrogen and hydrogen methanation processes, fully utilizes the surplus electricity of wind and solar resources, converts it into storable fuel gas, and uses the captured carbon dioxide for methanation reaction, thereby realizing the recycling of carbon resources and the efficient consumption and conversion of renewable energy. By coordinating the operation of different energy types (electricity, heat, gas), a balance between low-carbon goals and cost optimization is achieved, thereby improving the overall energy utilization efficiency of the system and reducing the consumption of fossil energy.

[0062] (3) Based on historical operating data, the present invention evaluates the carbon emission intensity of unit power supply or heating power as a benchmark for subsequent carbon emission optimization; allocates carbon emission quotas for virtual power plants and sets initial carbon emission limits within the total load framework of the system to provide constraints for subsequent carbon emission optimization; dynamically adjusts carbon emission limits based on system operating data and external environment to achieve a balance between carbon emissions and power demand and thermal energy supply; dynamically monitors and optimizes total carbon emissions based on operating strategies to provide feedback for subsequent multi-energy system scheduling.

[0063] (4) The present invention introduces a tiered carbon trading mechanism and adjusts carbon quotas and carbon trading costs by setting different carbon trading price gradients according to different emissions. While optimizing power generation revenue, the project effectively reduces carbon emissions and reduces system operating costs, achieving a win-win situation in economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the flow of the virtual power plant optimization method in the present invention;

[0065] Figure 2 A schematic diagram showing the relationship between the hydrogen blending rate and the total cost of a gas turbine in the present invention;

[0066] Figure 3 A schematic diagram showing the relationship between the hydrogen blending rate and carbon emissions of a gas turbine in the present invention;

[0067] Figure 4 Schematic diagram of hydrogen power balance for scenario 1, which is the result of virtual power plant optimization scheduling of the method of the present invention;

[0068] Figure 5 The virtual power plant optimization scheduling result of the method of the present invention - scenario 1 CO 2 Power balance diagram;

[0069] Figure 6 It is a schematic diagram of the virtual power plant optimization scheduling result of the method of the present invention - scenario two hydrogen power balance;

[0070] Figure 7 The virtual power plant optimization scheduling result of the method of the present invention - scenario 2 CO 2 Power balance diagram;

[0071] Figure 8 It is the virtual power plant optimization scheduling result of the method of the present invention - schematic diagram of the three-hydrogen power balance of the scenario;

[0072] Fig. 9 The virtual power plant optimization scheduling result of the method of the present invention - scenario three CO 2 Power balance diagram;

[0073] Fig.10 It is a schematic diagram of system cost results under the step-by-step carbon trading mechanism of the present invention;

[0074] Fig.11 This is a schematic diagram of system cost results under a unified carbon trading mechanism;

[0075] Fig.12 It is a structural schematic diagram of the virtual power plant model of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0077] Example 1

[0078] This embodiment involves a virtual power plant optimization method taking into account power-to-gas and tiered carbon trading. The purpose is to solve the problems of low energy efficiency, insufficient resource utilization, and excessive system carbon emissions. It provides a multi-energy system based on two-stage power-to-gas and carbon capture, and achieves improved system energy efficiency, reduced carbon emissions, and lower operating costs by building an efficient energy conversion model, optimizing scheduling strategies, etc.

[0079] This embodiment solves the contradiction between the volatility of renewable energy and the stability of system operation by closely coupling the power-to-gas (P2G) technology with the carbon capture (CCS) system. By converting excess electricity into hydrogen and using it for methanation, the pressure on the power grid is further reduced. At the same time, carbon capture technology effectively reduces carbon dioxide emissions and promotes the realization of low-carbon goals. At the same time, a stepped carbon trading mechanism is introduced to optimize the allocation of carbon quotas by dynamically adjusting the carbon emission costs, so that virtual power plants can maximize economic benefits while meeting low-carbon goals. This method can fully consider the uncertainty of wind and solar resources, and further improve the energy absorption capacity of virtual power plants and reduce operating costs through optimized scheduling, thereby promoting the development of a low-carbon economy.

[0080] like Figure 1 , the method comprises the following steps:

[0081] Step S1: pre-process the user-side and meteorological historical data, obtain the complete time series data of wind and solar power output, user-side electrical load, and thermal load, clean and integrate them, and form a basic data set;

[0082] Step S2: innovatively construct a two-stage model of hydrogen production by electrolysis and hydrogen-to-methane conversion, combining energy storage with operation optimization strategies to achieve efficient consumption and conversion of renewable energy;

[0083] Step S3: According to the baseline method, combined with the carbon emission factors of different energy types (coal power, gas power, wind power, photovoltaic power, etc.), the carbon emission quota of the virtual power plant is scientifically allocated to build a comprehensive carbon emission model covering power generation consumption, P2G process and user load supply;

[0084] Step S4: Under the deep coupling of P2G-carbon capture system, by organically combining the hydrogen production and methanation process in P2G technology with carbon capture technology, further integrating multiple energy forms such as electricity, gas, and heat, the waste CO 2 Recycling, building a multi-energy system with coordinated energy supply, and optimizing energy structure and utilization efficiency;

[0085] Step S5: construct a carbon trading model for virtual power plants to participate in the electricity-carbon joint market, design a stepped carbon trading mechanism, divide multiple intervals according to carbon emissions, set different carbon price gradients, dynamically adjust transaction costs, and introduce carbon penalty costs in the scheduling model;

[0086] Step S6: Based on the scheduling objectives and constraints, develop a virtual power plant optimization scheduling model including power-to-gas technology and a tiered carbon trading mechanism;

[0087] Step S7: Input the actual wind and solar power output and the user-side electric load and thermal load data sets into the virtual power plant optimization scheduling model to obtain the actual optimization scheduling results of the virtual power plant participating in electricity-carbon trading.

[0088] The structure of a virtual power plant is as follows: Fig.12 , including power grid, heat grid, natural gas grid and hydrogen grid.

[0089] Step S1 specifically includes: preprocessing user-side and meteorological historical data to obtain complete time series data of electricity, heat, wind and solar resource loads, specifically: data collection and cleaning, collecting wind power and photovoltaic output, user power load, thermal load data and meteorological data (wind speed, temperature, humidity, irradiance, etc.), processing missing values, outliers and erroneous data to ensure data quality; time synchronization, synchronizing the timestamps of the collected resource load data with the meteorological data to ensure the time consistency of various types of data; feature analysis, extracting key features based on preprocessing, such as the output curve characteristics of wind power and photovoltaics, the daily change trends of user electricity and thermal loads, etc., to form a complete resource load time series data set.

[0090] Step S2 specifically includes:

[0091] S2.1, based on the power-to-hydrogen (P2H) technology, construct the first-stage electrolysis hydrogen production sub-model to analyze the relationship between the power consumption and hydrogen production of the electrolyzer under different load conditions;

[0092] S2.2, construct the second stage hydrogen methanation sub-model, analyze the conversion efficiency of hydrogen and carbon dioxide in the methanation equipment, and determine the mapping relationship between gas production and energy consumption;

[0093] S2.3, integrate the hydrogen production by electrolysis and hydrogen methanation processes, establish a two-stage power-to-gas (P2G) model, and provide basic model support for the multi-energy system. Fig.12 ,The two-stage P2G model includes the following steps:

[0094] (1) The first stage: hydrogen production by electrolysis:

[0095] ,

[0096] Where: and for The electrical power consumed by electrolysis and the hydrogen production power at each moment; and for The hydrogen consumption and gas production of the methanation equipment at all times; and are the conversion efficiencies of hydrogen production from water electrolysis and methanogenesis, respectively.

[0097] (2) Second stage: Hydrogen methanation:

[0098] ,

[0099] Where: and They are Carbon capture and storage (CCS) technology is renewing The amount of methanogenesis utilized and the amount stored.

[0100] The process of constructing a comprehensive carbon emission model in step S3 specifically includes:

[0101] S3.1 Based on historical operation data, conduct a detailed analysis of the carbon emission characteristics of various energy equipment such as wind and solar power generation, traditional power generation, and user-side electrical / thermal loads, and evaluate their carbon emission intensity per unit of power supply or heating power as a benchmark for subsequent carbon emission optimization;

[0102] S3.2 Allocate carbon emission quotas for virtual power plants based on carbon emission benchmarks, total system load requirements, and emission characteristics of different energy equipment, and set initial carbon emission limits within the total system load framework to provide constraints for subsequent carbon emission optimization;

[0103] S3.3, dynamically adjust the carbon emission limit based on system operation data and external environment to achieve a balance between carbon emissions and electricity demand and thermal energy supply, dynamically monitor and optimize the total carbon emissions based on operation strategies, and provide feedback for subsequent system scheduling.

[0104] In step S4, based on the deep coupling of the two-stage power-to-gas model and the carbon capture and storage system, combined with the wind and solar power output characteristics and the user's electricity and heat load requirements, a multi-energy system is constructed and optimized: through the coordinated operation of the three energy forms of electricity, heat and gas, it adapts to the fluctuations in wind and solar power output and user load requirements, and in the scheduling process of the multi-energy system, by introducing the carbon emission limits and quota allocation results in S3, the scheduling strategies of various energy forms such as electricity, gas, and heat are optimized to achieve coordinated optimization of energy, and the total amount of carbon emissions is controlled during the scheduling process to achieve coordinated optimization and low-carbon operation of electricity, gas and thermal energy.

[0105] Based on the two-stage power-to-gas (P2G) model, a refined CCS structure is constructed, including an absorption tower, a regeneration tower, and lean and rich liquid tanks, to better realize the conversion and utilization of multiple energy sources.

[0106] Balance of electricity and thermal energy: The combination of cogeneration and electric energy storage not only meets electricity demand, but also provides thermal energy support for the system, forming a good electric-thermal coupling.

[0107] Combination of wind power and P2G: The volatility of wind power can be absorbed through the flexible adjustment of the power-to-gas system, converting excess electricity into hydrogen, reducing wind curtailment, and converting hydrogen into methane for storage, enhancing the dispatchability of the system.

[0108] Synergy between renewable energy and energy storage equipment: By combining renewable energy with energy storage systems, the system's regulation capabilities are enhanced and seamless integration of different energy forms is achieved.

[0109] Combination of electric energy storage and thermal energy storage: Electric energy storage and thermal energy storage equipment not only serve as a means of regulating loads, but also provide the system with a variety of adjustment methods, further improving the adaptability of the system.

[0110] Conversion of hydrogen energy into methane: Through power-to-gas (P2G) technology, excess electricity is converted into hydrogen, and further stored as methane through methanation reaction, providing flexible scheduling space for subsequent energy utilization.

[0111] Synergy of wind energy and photovoltaics: By combining wind power and photovoltaic power generation, the reliability and flexibility of the energy system can be improved and more renewable energy can be absorbed.

[0112] Step S5 specifically includes:

[0113] Design a tiered carbon trading mechanism, divide carbon emissions into multiple intervals, set different carbon price gradients, and dynamically adjust transaction costs.

[0114] The calculation model of the tiered carbon trading cost is as follows:

[0115] ,

[0116] Where: It is the base price for carbon trading; is the length of the carbon emission interval; is the increase in carbon trading prices; is the compensation coefficient; For The carbon trading cost of the system at the moment, positive for purchase, negative for sale; is the carbon emissions in the carbon trading system.

[0117] The carbon trading cost is solved based on the different lengths of carbon emission intervals, and the step-by-step carbon trading mechanism is embedded in the scheduling optimization framework. The power generation priority and trading strategy under carbon emission constraints are clarified to achieve effective management and incentives for virtual power plant carbon trading.

[0118] The process of developing a virtual power plant optimization scheduling model including power-to-gas technology and a tiered carbon trading mechanism in step S6 specifically includes:

[0119] S6.1, construct the objective function of the virtual power plant optimization scheduling model with power-to-gas technology and tiered carbon trading, specifically:

[0120] ,

[0121] In the formula, The total cost includes six parts, namely: Carbon trading costs, Carbon storage costs, The start-up and shutdown costs of coal-fired power units, Coal consumption cost, Gas purchase cost, The cost of curtailing wind power.

[0122] (1) Calculation formula for carbon trading and storage costs:

[0123] ,

[0124] Where: for Trading at all times Costs; For the mass of the storage unit Costs; is the scheduling period; for time The amount of storage.

[0125] (2) and They are the start-up and shutdown costs and coal consumption costs of coal-fired units:

[0126] ,

[0127] Where: is the start-up and shutdown cost coefficient of coal-fired power units; and are binary variables, representing Moment and t -1 The operating status of the unit at time , and Both are coal consumption cost coefficients of coal-fired units.

[0128] (3) The cost of wind curtailment is:

[0129] ,

[0130] Where: is the penalty cost per unit of wind abandonment; for The amount of wind abandoned at the time.

[0131] (4) Gas purchase cost:

[0132] ,

[0133] Where: is the unit price of natural gas; for The power corresponding to the natural gas purchased by the system at that moment.

[0134] S6.2, set the constraints for the optimal dispatch model of the virtual power plant including power-to-gas technology and tiered carbon trading. The constraints include: power balance constraints, wind and solar constraints, gas turbine constraints, gas boiler climbing and output constraints, electric boiler climbing and output constraints, thermal power unit climbing and output constraints, carbon capture constraints, energy storage constraints, and power-to-hydrogen (P2H) energy consumption upper and lower limits and climbing constraints.

[0135] (1) Power balance constraints:

[0136] ,

[0137] Where: and for The heat and electricity demand on the load side at all times; and They are The electrical power consumed by electrolysis and the hydrogen production power at each moment; and They are Hydrogen consumption and gas production of methanation equipment at all times , , and They are t The electrical power of the hydrogen-doped gas turbine at any given moment, t The power generation of thermal power units at the moment, t The power generation of wind turbines at each moment and t The discharge amount of the energy storage device at any moment; , , , Respectively t The electric power of the electrolytic cell at the moment, t Total electrical power of the carbon capture system at the moment, t The power demand on the load side at all times and t The charge level of the energy storage device at any moment; , Respectively t The power of natural gas consumed by hydrogen-blended gas turbines and hydrogen-blended gas boilers at any given moment; for t The power corresponding to the natural gas purchased by the system at the moment; , , , Respectively t Thermal power of gas turbines and electrolyzers, and thermal power of charging and discharging of thermal energy storage equipment at all times; and They represent the hydrogen consumption power of the gas turbine and the hydrogen-blended gas boiler at time t respectively.

[0138] (2) Wind and solar constraints:

[0139] ,

[0140] Where: for t Predicted output of wind turbines at each moment; and They are t The amount of wind abandoned and the power generation of wind turbines at each moment.

[0141] (3) Gas turbine constraints:

[0142] ,

[0143] Where: and They are Moment and -Total output of gas generator set at time 1; and They are the gas turbine electrical output and the gas turbine thermal output respectively; , The upper and lower limits of the thermal output of the gas turbine; , The upper and lower limits of gas turbine electrical output; , The upper and lower limits of the gas turbine electrical output climb.

[0144] (4) Gas boiler climbing and output constraints:

[0145] ,

[0146] Where: , It is the upper and lower limits of the thermal output of the hydrogen-blended gas boiler (HB-GB); , The upper and lower limits of the climbing of hydrogen-blended gas boiler (HB-GB); and They are Moment and -1 hour thermal output of hydrogen-blended gas boiler (HB-GB).

[0147] (5) Electric boiler climbing and output constraints:

[0148] ,

[0149] Where: , The upper and lower limits of the output of the electric boiler; , The upper and lower limits of the electric boiler’s climbing; and They are Moment and -1 moment electric boiler output.

[0150] (6) Thermal power unit climbing and output constraints:

[0151] ,

[0152] Where: , They are the upper and lower limits of the power output of thermal power units: , They are the upper and lower climbing limits of thermal power units respectively; is a binary variable, indicating The operating status of the unit at all times; and They are the electrical output of thermal power units respectively.

[0153] (7) Carbon capture constraints:

[0154] The energy consumption of carbon capture operation shall not exceed the maximum operating condition :

[0155] ,

[0156] Capacity constraints of liquid storage equipment configured in CCS:

[0157] ,

[0158] Where: and The upper and lower limits of the capacity of the rich liquid equipment; and The upper and lower limits of the capacity of the lean liquid equipment; and is the initial capacity of the liquid storage device; and are the final remaining capacity of the liquid storage device respectively; and They are t The capacity of the lean liquid equipment and the rich liquid equipment at all times.

[0159] Liquid storage equipment in one operating cycle T After the end, the capacity remains unchanged, and the constraints are as follows:

[0160] ,

[0161] Where: , They are t The volume of liquid flowing into the rich and poor liquid tanks at each moment; , are the outflow liquid volumes of the poor and rich liquid tanks at time t respectively.

[0162] (8) Energy storage constraints:

[0163] Since the constraints of energy storage equipment are similar, a unified expression is used to describe the constraints of heat, electricity, and hydrogen, as follows:

[0164] ,

[0165] Where: For the i Energy storage device Capacity at the moment; as well as are the lower and upper limits of the energy storage system’s capacity, respectively; as well as For the i Energy storage device Charging and discharging power at all times; Indicates i The upper limit of the charging power of each energy storage device; Indicates i The upper limit of energy storage device discharge power: as well as Respectively represent i A binary variable that indicates the charging and discharging state of an energy storage device at all times.

[0166] (9) Upper and lower limits of energy consumption and climbing constraints for power-to-hydrogen (P2H):

[0167] ,

[0168] Where: , They are the upper and lower limits of the power consumed by electrolysis: and The upper and lower limits of the electrolytic cell power climbing; and They are t Moment and t -1 moment electrolysis consumes electrical power.

[0169] Example 2

[0170] This embodiment also involves a virtual power plant optimization method that takes into account power-to-gas and tiered carbon trading. By optimizing the energy dispatch of the virtual power plant, the synergistic efficiency of multiple energy forms such as electricity, gas, and heat is improved, thereby reducing operating costs and effectively controlling carbon emissions while meeting energy needs. Through the coordinated dispatch of multiple energy sources, not only the reliability and flexibility of the energy system are improved, but also carbon emissions can be effectively reduced, providing a low-carbon and economical operation plan for the virtual power plant.

[0171] First, we obtain complete time series data of the output electricity, heat, wind and solar resource loads and build a carbon trading model. Finally, we establish a virtual power plant optimization scheduling model with power-to-gas and step-by-step carbon trading. Based on actual virtual power plant data, we verify the economy and low carbon of the proposed scheduling decision model through multi-scenario comparison.

[0172] like Figure 2 As shown in the figure, the total cost changes with the hydrogen blending ratio. The total cost shows a gradual downward trend. The higher the hydrogen blending ratio, the lower the total cost. This is because a high hydrogen blending ratio reduces the system's dependence on natural gas and reduces the cost of purchasing gas. Figure 3It can be seen that carbon emissions gradually increase with the increase of hydrogen blending ratio, and show a step-by-step growth. Although the increase of hydrogen blending ratio reduces the consumption of some fossil fuels, the output of thermal power units also increases due to the increase in electricity demand for hydrogen electrolysis, resulting in an increase in carbon emissions.

[0173] Based on actual virtual power plant data, three scenarios are set, such as Figure 4~Figure 9 As shown in the figure, scenario one considers P2G-CCS coupling. By adopting a two-stage P2G model, when excess electricity is generated, it can be converted into hydrogen and stored through the methanation process, while carbon dioxide can be effectively captured and converted through the carbon capture system. This coupling technology not only improves the efficiency of energy conversion, but also reduces dependence on carbon emissions, which helps to achieve the goal of a low-carbon economy. Scenario two does not consider methanation, and only hydrogen is used for fuel gas hydrogen blending; scenario three does not consider CCS, and the emitted CO 2 Directly discharged into the atmosphere. After comparative verification, the results prove the economic and low-carbon nature of the scheduling decision model applied by the present invention.

[0174] like Figure 10~Figure 11 The following is a comparative analysis of the tiered carbon trading and unified carbon trading mechanisms. It can be seen that by setting different price tiers, the tiered carbon trading mechanism effectively encourages companies to reduce carbon emissions. As the basic price gradually increases, the total cost faced by companies increases, which prompts companies to take more active emission reduction measures to reduce costs. At the same time, carbon emissions decrease as prices rise, showing the effectiveness of the tiered carbon trading mechanism in promoting emission reduction.

[0175] This embodiment not only focuses on the optimization of multi-energy scheduling and carbon emission management, but also emphasizes how to achieve a balance between low-carbon goals and cost optimization by coordinating the operation of different energy types (electricity, heat, gas). In practical applications, it is particularly suitable for the consumption of large-scale renewable energy, the optimization of low-carbon power systems, and the energy scheduling needs of areas with strict carbon emission control. Through the implementation of this system, the virtual power plant can find the best balance between energy diversity, carbon emission control and economy, and promote green energy transformation and low-carbon economic development.

[0176] The invention application is particularly suitable for virtual power plants rich in renewable energy sources such as wind power and photovoltaics. Through the coordinated scheduling of multiple energy sources, it can not only effectively absorb renewable energy such as wind power, but also convert excess electricity into hydrogen or methane through power-to-gas technology, thereby increasing the utilization rate of renewable energy and optimizing the overall scheduling efficiency and energy absorption capacity of the energy system.

[0177] The present invention is applicable to areas with clear low-carbon emission targets, especially during peak hours of electricity and heat demand. Through the optimized scheduling of electricity, gas and heat resources and the precise control of carbon emissions, carbon emissions can be effectively reduced, and a sustainable solution can be provided during peak electricity demand with high carbon emissions. In addition, it is applicable to areas with carbon trading mechanism requirements, and can flexibly respond to market fluctuations in carbon emission trading and maintain low-carbon operation.

[0178] The present invention is applicable to integrated energy systems that need to comprehensively consider multiple factors such as electricity, heat, gas and carbon emissions, and is particularly widely used in smart cities, large industrial parks and areas with high-density electricity demand. Through the combination of multi-energy coordinated scheduling and carbon trading mechanisms, it is possible to reduce operating costs and carbon emissions while optimizing energy supply, providing efficient, economical and low-carbon solutions for complex energy systems.

[0179] Example 3

[0180] This embodiment also relates to an electronic device. If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0181] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading, characterized in that: The method comprises: preprocessing the collected wind power and photovoltaic output data, user-side load data and meteorological historical data to form a complete resource load time series data set; Construct a two-stage power-to-gas model including hydrogen electrolysis and hydrogen-to-methane conversion; Construct a tiered carbon trading model for virtual power plants to participate in the electricity-carbon joint market; build a multi-energy system for coordinated energy supply based on the deep coupling of the two-stage power-to-gas model and the carbon capture and storage system; Based on the scheduling results of the two-stage power-to-gas model, the step-by-step carbon trading model and the multi-energy system, a virtual power plant optimization scheduling model is constructed, wherein the virtual power plant optimization scheduling model introduces carbon penalty costs; the resource load time series data set is input into the virtual power plant optimization scheduling model, and the virtual power plant optimization scheduling plan is output; The method also includes constructing a comprehensive carbon emission model, the process of which includes: S3.1, based on historical operation data, conduct a detailed analysis of the carbon emission characteristics of various energy equipment such as wind and solar power generation, traditional power generation, user-side electricity and heat loads, and evaluate their carbon emission intensity per unit of power supply or heat supply as a benchmark for subsequent carbon emission optimization; the historical operation data includes the output and carbon emission data of each energy equipment over a period of time; S3.2, allocate carbon emission quotas for virtual power plants based on carbon emission benchmarks, total system load requirements, and emission characteristics of different energy equipment, and set initial carbon emission limits within the total system load framework; S3.3, dynamically adjust carbon emission limits based on system operation data and external environment, achieve a balance between carbon emissions and electricity demand and thermal energy supply, and provide feedback for the diversified energy system.

2. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 1, characterized in that: The two-stage power-to-gas model includes a first-stage electrolysis hydrogen production sub-model and a second-stage hydrogen methanation sub-model; The electrolysis hydrogen production sub-model is used to analyze the relationship between the power consumption and hydrogen production of the electrolyzer under different load conditions; The hydrogen methanation sub-model is used to analyze the conversion efficiency of hydrogen and carbon dioxide in the methanation equipment and determine the mapping relationship between gas production and energy consumption.

3. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 2, characterized in that: The hydrogen methanogen model is specifically: ， Where: and for The electrical power consumed by electrolysis and the hydrogen production power at each moment; and for The hydrogen consumption and gas production of the methanation equipment at all times; and are the conversion efficiencies of water electrolysis for hydrogen production and methanogenesis, respectively; The hydrogen methanogen model is specifically: ， Where: and They are Carbon capture and storage technology is renewing The amount of methanogenization utilized and the amount stored; is the lower heating value of methane; for The remaining amount of CO2 regenerated by carbon capture and storage technology at any time; is the density of CO2.

4. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 1, characterized in that: The process of constructing the multi-energy system includes: based on the deep coupling of the two-stage power-to-gas model and the carbon capture and storage system, through the coordinated operation of the three energy forms of electricity, heat and gas, combining the output characteristics of wind and solar power and the user's electricity and heat load requirements, by introducing carbon emission limits and quota allocation results, optimizing the scheduling strategies of multiple energy forms of electricity, gas and heat, realizing the coordinated optimization of energy, and controlling the total carbon emissions during the scheduling process.

5. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 1, characterized in that: The objective function of the virtual power plant optimization scheduling model is to minimize the total cost, specifically: ， In the formula, is the total cost, including carbon trading costs , Carbon storage costs , Start-up and shutdown costs of coal-fired power units , coal consumption cost , Gas purchase cost and curtailment costs .

6. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 5, characterized in that: The calculation of carbon trading costs and carbon sequestration costs is as follows: ， ， Where: for Trading at all times Costs; For the mass of the storage unit Costs; is the scheduling period; for time The amount of storage; The calculation of the start-up and shutdown cost and coal consumption cost of the coal-fired power unit is as follows: ， Where: is the start-up and shutdown cost coefficient of coal-fired power units; and are binary variables, representing Moment and t -1 The operating status of the unit at time , and All are coal consumption cost coefficients of coal-fired units; The calculation of the wind abandonment cost and gas purchase cost is specifically as follows: ， ， Where: is the penalty cost per unit of wind abandonment; for The amount of wind abandoned at the time; is the unit price of natural gas; for The power corresponding to the natural gas purchased by the system at that moment.

7. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 5, characterized in that: The constraints of the virtual power plant optimization scheduling model include: power balance constraints, wind and solar constraints, gas turbine constraints, gas boiler climbing and output constraints, electric boiler climbing and output constraints, thermal power unit climbing and output constraints, carbon capture constraints, energy storage constraints, and upper and lower limits of power-to-hydrogen energy consumption and climbing constraints.

8. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 7, characterized in that: The power balance constraint is specifically: ， Where: and for The heat and electricity demand on the load side at all times; and They are The electrical power consumed by electrolysis and the hydrogen production power at each moment; and They are Hydrogen consumption and gas production of methanation equipment at all times , , and They are t The electrical power of the hydrogen-blended gas turbine at all times, t The power generation of thermal power units at the moment, t The power generation of wind turbines at each moment and t The discharge amount of the energy storage device at any moment; , , , Respectively t The electric power of the electrolytic cell at the moment, t Total electrical power of the carbon capture system at the moment, t The power demand on the load side at all times and t The charge level of the energy storage device at any moment; , Respectively t The power of natural gas consumed by hydrogen-blended gas turbines and hydrogen-blended gas boilers at any given moment; for t The power corresponding to the natural gas purchased by the system at the moment; , , , Respectively t Thermal power of gas turbines and electrolyzers, and thermal power of charging and discharging of thermal energy storage equipment at all times; and They represent the hydrogen consumption power of the gas turbine and hydrogen-blended gas boiler at time t respectively; The carbon capture constraints include: carbon capture operation energy consumption Cannot exceed the maximum operating condition : ， Liquid storage equipment capacity constraints: ， Where: and The upper and lower limits of the capacity of the rich liquid equipment; and The upper and lower limits of the capacity of the lean liquid equipment; and is the initial capacity of the liquid storage device; and are the final remaining capacity of the liquid storage device respectively; and They are t The capacity of the lean liquid equipment and rich liquid equipment at all times; Liquid storage equipment in one operating cycle T After the end, the capacity remains unchanged, and the constraints are as follows: ， Where: , They are t The volume of liquid flowing into the rich and poor liquid tanks at each moment; , are the outflow liquid volumes of the poor and rich liquid tanks at time t respectively.

9. A virtual power plant optimization method taking into account power-to-gas and tiered carbon trading according to claim 7, characterized in that: The power-to-hydrogen (P2H) energy consumption upper and lower limits and climbing constraints are: ， Where: , They are the upper and lower limits of the power consumed by electrolysis: and The upper and lower limits of the electrolytic cell power climbing; and They are t Moment and t -1 moment electrolysis consumes electrical power; The energy storage constraints are as follows, where the constraints of heat, electricity, and hydrogen in energy storage are uniformly expressed by the following formula: ， Where: For the i Energy storage device Capacity at the moment; as well as are the lower and upper limits of the energy storage system’s capacity, respectively; as well as For the i Energy storage device Charging and discharging power at all times; Indicates i The upper limit of the charging power of each energy storage device; Indicates i The upper limit of energy storage device discharge power: as well as Respectively represent i A binary variable that indicates the charging and discharging state of an energy storage device at all times.

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