Comprehensive energy system low-carbon optimization scheduling method of green certificate-carbon transaction mechanism

Abstract

The invention provides a low-carbon optimal scheduling method for an integrated energy system based on a green certificate-carbon transaction mechanism, so as to reduce the carbon emission of the integrated energy system and improve the renewable energy consumption capability. In order to improve the running flexibility of the IES containing the electro-hydrogen coupling unit, a proton exchange membrane electrolytic cell is introduced, and refined modeling is carried out on the hydrogen energy using process and equipment. A transaction model of a common stepped carbon transaction is improved and combined with a green certificate transaction mechanism, and a combined heat and power energy system model with an adjustable heat and power ratio is constructed; secondly, taking the minimum total cost of the system as an objective function, coupling a carbon transaction mechanism and a green certificate strategy, and performing optimization solution by using a Cplex solver; analyzing the influence of different carbon transaction mechanism parameters and green evidence parameters on the system; finally, four typical scenes are established, the proposed strategy is obtained through comparative analysis, the carbon emission can be remarkably reduced, the consumption amount of renewable energy sources is increased, meanwhile, good economic benefits are achieved, and reference can be provided for low-carbon economic dispatching of the comprehensive energy system.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon scheduling technology, specifically relating to a low-carbon optimization scheduling method for integrated energy systems. Background Technology

[0002] Under the new circumstances of grid parity for renewable energy, China has established a green certificate trading mechanism (tradable green certificates, TGC) and a national carbon emissions trading scheme (emissions trading scheme, ETS). Meanwhile, promoting research on electrolyzer exchange membrane technology and power-to-gas (P2G) hydrogen production technology is an effective way to boost renewable energy consumption.

[0003] Currently, the power industry accounts for over 40% of carbon emissions, necessitating the construction of a new power system primarily based on renewable energy. However, the large-scale grid connection of fluctuating power sources such as wind and solar power in existing technologies has led to significant issues of wind and solar curtailment, and traditional thermal power units lack sufficient regulation capacity, making it difficult to achieve a high proportion of renewable energy consumption. Meanwhile, electro-hydrogen technology can convert surplus renewable energy into hydrogen energy storage through water electrolysis, but existing systems generally suffer from low efficiency in multi-energy coupling of electricity, hydrogen, and heat, and poor operational economics.

[0004] Combining carbon trading with green certificate trading mechanisms can better reflect the development of a low-carbon economy within an integrated energy system. Summary of the Invention

[0005] This invention aims to provide a low-carbon optimized scheduling method for integrated energy systems based on a green certificate-carbon trading mechanism. By integrating an improved tiered carbon trading mechanism based on green certificates with hydrogen production from electricity, the method enhances the operational efficiency of the integrated energy system. This scheme utilizes the green certificate trading incentive mechanism to encourage energy producers to reduce carbon emissions, while simultaneously using hydrogen production from electricity to convert excess renewable energy into hydrogen for storage, thereby improving energy utilization efficiency and reducing dependence on fossil fuels. Furthermore, by optimizing system operation strategies, it achieves efficient allocation of multiple energy forms such as electricity, heat, and gas, reducing system operating costs and enhancing the stability and flexibility of energy supply. Thus, while achieving low-carbon goals, it also improves the economy and reliability of the energy system, strongly supporting the realization of the national "dual carbon" goals.

[0006] Technical Solution: To address the aforementioned technical problems, this invention provides a low-carbon optimized scheduling method for an integrated energy system based on a green certificate-carbon trading mechanism, thereby resolving the problems existing in the prior art. The method includes the following steps:

[0007] S1: Modeling a novel cogeneration system containing a proton exchange membrane electrolyzer;

[0008] S2: Construct an improved tiered carbon emission trading model and a green certificate trading model;

[0009] S3: Under the joint interaction mechanism of green certificate-carbon trading, construct a comprehensive energy system low-carbon optimization scheduling model that considers green certificate-carbon trading and hydrogen energy demand;

[0010] S4: Optimize the equipment operation model of the integrated energy system, set optimization objectives and constraints, solve the optimized equipment operation model to obtain the optimal scheduling strategy, use the YALMIP toolbox to call the CPLEX commercial solver to solve the problem, and verify the carbon reduction effect.

[0011] Furthermore, S1 establishes a novel cogeneration system model containing a proton exchange membrane electrolyzer, with the following process:

[0012] A proton exchange membrane (PEM) electrolyzer model considering variable load start-up and shutdown characteristics is used. To illustrate the operating characteristics of the PEM, five operating states are represented by binary variables: shutdown (I), cold standby (S), variable load (L), overload (R), and low load (V). A binary variable Y represents the full start-up interval, with a value of 1 indicating that the PEM has fully started from the shutdown state.

[0013] The time for PEM to fully start up (set as 1 hour in this paper) is used as the optimization time scale. Since the cold start time (set as 10 minutes in this paper) is smaller than a single time scale, the loss of hydrogen energy output during the cold start process needs to be considered. The relationship between PEM power consumption and hydrogen production power can be expressed as:

[0014]

[0015] In the formula, Let be the hydrogen production power of the nth PEM at time t; Let be the power consumption of the nth PEM at time t; The electro-hydrogen conversion efficiency of PEM; This refers to the standby power of the PEM in cold standby mode; ζ PEM The penalty coefficient for hydrogen production power during cold start is used to represent the hydrogen energy loss caused by the inconsistency of time scale; when the binary variable W is 1, it means that the PEM has switched from the cold standby state to the working state, satisfying equation (2).

[0016]

[0017] The power consumption of PEM satisfies the inequality relationship shown in equation (3). The coordinated control between different working states of the electrolytic cell can be achieved by taking the values ​​of binary variables.

[0018]

[0019] In the formula, This represents the rated operating power of the PEM; a binary variable of 1 indicates that it is in this state.

[0020] The adjustable combined heat and power (CHP) unit consists of a hydrogen fuel cell (HFC) and a CHP unit. The HFC burns hydrogen to provide both electricity and heat. In traditional models, the heat-to-power ratio is a constant, typically operating in a "heat-driven power generation" or "power-driven heat generation" mode, resulting in poor operational flexibility. By changing the flow rate of the cooling circulating water and the hydrogen input rate, the fuel cell can adjust the heat-to-power ratio according to the real-time electrothermal load, as shown in the model below:

[0021]

[0022] In the formula, Let be the hydrogen power at time t; and These represent the power generated by electricity and the power generated by heat at time t, respectively; η HFC The energy conversion efficiency of fuel cells; and These are the upper and lower limits of hydrogen power utilization, respectively; Maximum climbing power; and These represent the upper and lower limits of the adjustable range for the electrothermal ratio of a fuel cell.

[0023] CHP units burn natural gas for combined heat and power (CHP). By controlling the turbine extraction ratio and the inlet guide vane angle, the heat-to-power ratio can be adjusted according to the real-time electro-thermal load. The model is as follows:

[0024]

[0025]

[0026] In the formula, Let be the gas consumption power at time t; and These represent the power generated by electricity and the power generated by heat at time t, respectively; η CHP Energy conversion efficiency of combined heat and power units; and These are the upper and lower limits of gas consumption power, respectively; Maximum climbing power; and This refers to the upper and lower limits of the adjustable range of the electrothermal ratio for combined heat and power (CHP) units.

[0027] A methane reactor (MR) converts hydrogen energy into gaseous energy and supplies it to GB or CHP to generate electricity and heat. The model is as follows:

[0028]

[0029] In the formula, The hydrogen energy input to MR at time t; η represents the natural gas power output of MR at time t; MR The energy conversion efficiency of MR; and These are the upper and lower limits of hydrogen energy input to MR, respectively; and These represent the upper and lower limits of MR's climbing ability.

[0030] Gas-fired boilers (GB) can be integrated with hydrogen-driven cogeneration systems to achieve peak-valley complementarity of electrical and thermal loads, driven by electricity and gas prices. The model is as follows:

[0031]

[0032] In the formula: Let be the gas consumption power at time t; The gas-heat conversion efficiency is defined in GB standards. Let t be the heat generation power at time t.

[0033] In step S2, a green certificate trading model is constructed by incorporating the green certificate trading mechanism:

[0034] The Green Certificate Trading (GCT) mechanism requires energy companies to fulfill their obligations to absorb renewable energy. If an energy company fails to meet its renewable energy absorption quota, it can offset heavy penalties by purchasing green certificates at a low price. Conversely, if an energy company exceeds its renewable energy absorption quota, the excess can be sold as green certificates for profit. Increased renewable energy absorption reduces traditional power generation to some extent, indirectly reducing carbon emissions. The GCT cost is calculated as follows:

[0035] The new energy quota model is established as follows:

[0036]

[0037] Among them, P q P is the electricity required for the renewable energy quota, λ is the renewable energy quota coefficient, and P is the renewable energy quota. s ζ is the total power consumption of the system, ε is the influence coefficient of historical renewable energy quota completion, δ is the influence weight of historical renewable energy quota completion, and δ is the completion coefficient of the system's historical renewable energy quota. m It is the average of the historical new energy quota completion volume.

[0038] Based on the requirements of the renewable energy quota system, the number of green certificates required by this system is:

[0039] N IES,ne =P q Δt / 1000 (69)

[0040] Where, N IES,neIt is the number of green certificates required for the system to meet the renewable energy consumption responsibility weight, and Δt is the unit planning period.

[0041] The expression for the number of green certificates obtained is as follows:

[0042]

[0043] Where, N IES,ob It refers to the number of green certificates obtained by new energy power generation companies, P t N ψ is the renewable energy power generation capacity in period T, where T is the planned period, ψ is the coefficient affecting renewable energy output forecast, θ is the weight affecting renewable energy output forecast, and τ is the forecast accuracy of renewable energy output in the previous cycle. m It is the standard value for the accuracy of new energy output prediction.

[0044] The costs of participating in the GCT are as follows:

[0045] f GCT =μ GCT (N IES,ne -N IES,ob (71)

[0046] Among them, f GCT It is the transaction cost of the system participating in green certificate transactions, μ GCT That's the price of the green certificate.

[0047] In step S2, based on carbon trading market theory, an improved tiered carbon trading mechanism model is constructed:

[0048] The IES (Environmental Engineering Society) identifies three main sources of carbon emissions: purchased electricity, GB (Government Standards in China), CHP (Consumer Power Utilization) units, and gas load. Currently, China primarily uses the unpaid carbon allowance allocation method, assuming purchased electricity originates from coal-fired power plants. The carbon emission allowance allocation model is established as follows:

[0049]

[0050] In the formula, The total carbon emission allowance for an Integrated Energy System (IES); These are carbon emission allowances for CHP units, GB, externally purchased electricity, and gas load, respectively. These are the carbon emission allowances obtained from the unit thermal power produced by gas-fired units, the unit electrical power produced by coal-fired units, and the unit gas load consumed, respectively. The externally purchased electricity amount at time t; Let t be the gas load during the time period.

[0051] The actual carbon emission model is established as follows:

[0052]

[0053] In the formula, E IES E represents the actual carbon emissions of the IES; CHP E GB , E L,G These represent the actual carbon emissions from the CHP unit, CB, externally purchased electricity, and gas load, respectively; λ h ,λ e ,λ g These represent the actual carbon emissions obtained from the production unit thermal power of gas-fired units, the production unit electrical power of coal-fired units, and the consumption unit gas load, respectively.

[0054] Within a carbon trading settlement cycle, the trading volume of IES participating in the carbon trading market is determined by the difference between the IES's actual carbon emissions and its carbon emission allowances, i.e.

[0055]

[0056] In the formula, This refers to carbon trading quotas. Traditional tiered carbon trading mechanisms limit carbon emissions by setting tiered carbon prices across different carbon trading quota ranges. Each time the carbon trading quota increases by one level, the base price for that range increases, thus limiting emissions. Building upon the traditional tiered carbon pricing system, an improved carbon trading mechanism is introduced by further optimizing the process within each carbon trading quota range. The formula is as follows:

[0057]

[0058] In the formula, denoted as the system carbon trading cost; d represents the carbon trading volume range; α represents the carbon price growth rate; and x represents the carbon trading base price.

[0059] Step S3 constructs the objective function of the IES low-carbon economic dispatch model for green certificate-carbon trading and hydrogen demand. Taking into account the operating costs of the IES low-carbon system, the transaction costs of the improved tiered carbon trading (CET), and GCT costs, where operating costs include energy purchase costs, wind curtailment costs, energy storage charging and discharging costs, and equipment operation and maintenance costs, the following low-carbon economic dispatch objective (F) with the lowest total operating cost is constructed:

[0060]

[0061] Energy purchase cost:

[0062]

[0063] in, It represents the amount of natural gas purchased during time period t; and These are the electricity price and gas price for time period t, respectively.

[0064] Costs of wind curtailment:

[0065]

[0066] Where, δ w It is the cost of the unit's wind curtailment penalty. To provide power for wind power, The actual wind power output at time t. It is the wind curtailment power during period t.

[0067] The cost of energy storage charging and discharging is:

[0068]

[0069] In the formula, and Let γ be the charging and discharging power of the nth energy storage unit at time t. n is the charging and discharging cost coefficient for the nth type of energy storage.

[0070] Operating costs:

[0071]

[0072] In the formula, These are the overall operation and maintenance unit price for the CHP device and the operation and maintenance unit price for the GB device, respectively.

[0073] Furthermore, step S3 constructs the constraint function for the IES low-carbon economic dispatch model that considers green certificate-carbon trading and hydrogen demand:

[0074] The transition of PEM working state must meet the following logical constraints.

[0075]

[0076] In the formula, T Rmax and These represent the maximum time that the PEM can continuously operate in overload and low-load states, respectively; Y is a binary variable, where 1 represents the PEM transitioning from a shutdown state to a working state, and 0 represents other states.

[0077] In addition, to avoid frequent start-ups and shutdowns of the PEM in a short period of time, it is necessary to limit the minimum downtime and cold standby time.

[0078]

[0079]

[0080] In the formula, T Imin TSmin These represent the shortest time that the PEM can continuously operate in a stopped or cold standby state, respectively.

[0081] GB's constraint functions:

[0082]

[0083] In the formula, and These are the upper and lower limits of gas consumption power, respectively; This represents the maximum climbing power.

[0084] Considering the similarities between the models for electric energy storage, hydrogen energy storage, and thermal energy storage, a general modeling approach is adopted for energy storage:

[0085]

[0086] In the formula, and Let be the charging and discharging power of the nth type of energy storage unit at time t; The maximum charge / discharge power of the nth type of energy storage unit; This represents the charge / discharge status identifier of the nth type of energy storage unit at time t. Indicates charging. Indicates energy release; η ES,n and χ ES,n Let be the charge / discharge efficiency and self-loss rate of the nth type of energy storage unit; Let be the charging and discharging power of the nth type of energy storage unit at time t; Let be the energy of the nth type of energy storage unit at time t; and These represent the upper and lower limits of the capacity of the nth type of energy storage unit.

[0087] Constraints on electricity and gas purchases:

[0088]

[0089] In the formula, and The system's electricity and gas purchases at time t; and This represents the maximum amount of electricity and gas that the system can purchase.

[0090] Wind power output and curtailment constraints:

[0091]

[0092] In the formula, ω w The maximum wind curtailment rate at time t; The actual wind power output at time t; The maximum wind power output at time t is given.

[0093] Power balance constraints:

[0094]

[0095] In the formula, Let t represent the electrical load, thermal load, and gas load at time t; N represents the number of PEMs connected in parallel in the electrolytic cell array.

[0096] This invention offers the following advantages over existing technologies: First, to improve the operational flexibility of IES (Environmentally Integrated Systems) containing electro-hydrogen coupling units, a proton exchange membrane electrolyzer is introduced, enabling refined modeling of the hydrogen energy consumption process and equipment. Second, the trading model of ordinary tiered carbon trading is improved and combined with a green certificate trading mechanism, and a combined heat and power (CHP) energy system model with an adjustable heat-to-power ratio is constructed. Then, with the minimum total system cost as the objective function, the carbon trading mechanism and green certificate strategy are coupled, and the Cplex solver is used for optimization. Finally, the impact of different carbon trading mechanism parameters and green certificate parameters on the system is analyzed. Finally, four typical scenarios are established, and comparative analysis shows that the proposed strategy can significantly reduce carbon emissions, increase the absorption of renewable energy, and has good economic benefits, providing a reference for low-carbon economic dispatch of integrated energy systems. Attached Figure Description

[0097] Figure 1 This is a diagram illustrating the coordination mechanism between green certificate trading and carbon trading. Detailed Implementation

[0098] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0099] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0100] Example 1:

[0101] This embodiment provides a low-carbon optimization scheduling method for an integrated energy system based on a green certificate-carbon trading mechanism, including:

[0102] Modeling a novel cogeneration system containing a proton exchange membrane electrolyzer;

[0103] Develop and improve tiered carbon emission trading models and green certificate trading models;

[0104] Under the joint interaction mechanism of green certificate-carbon trading, a comprehensive low-carbon optimization scheduling model for the energy system that considers green certificate-carbon trading and hydrogen energy demand is constructed.

[0105] The operation model of the integrated energy system equipment was optimized, optimization objectives and constraints were set, and the optimal scheduling strategy was obtained by solving the optimized equipment operation model. The CPLEX commercial solver was called using the YALMIP toolbox to solve the problem and verify the carbon reduction effect.

[0106] S1. Establish a model of a novel cogeneration system containing a proton exchange membrane electrolyzer.

[0107] A proton exchange membrane (PEM) electrolyzer model considering variable load start-up and shutdown characteristics is used. To illustrate the operating characteristics of the PEM, five operating states are represented by binary variables: shutdown (I), cold standby (S), variable load (L), overload (R), and low load (V). A binary variable Y represents the full start-up interval, with a value of 1 indicating that the PEM has fully started from the shutdown state.

[0108] The time for PEM to fully start up (set as 1 hour in this paper) is used as the optimization time scale. Since the cold start time (set as 10 minutes in this paper) is smaller than a single time scale, the loss of hydrogen energy output during the cold start process needs to be considered. The relationship between PEM power consumption and hydrogen production power can be expressed as:

[0109]

[0110] In the formula, Let be the hydrogen production power of the nth PEM at time t; Let be the power consumption of the nth PEM at time t; The electro-hydrogen conversion efficiency of PEM; This refers to the standby power of the PEM in cold standby mode; ζ PEM The penalty coefficient for hydrogen production power during cold start is used to represent the hydrogen energy loss caused by the inconsistency of time scale; when the binary variable W is 1, it means that the PEM has switched from the cold standby state to the working state, satisfying equation (2).

[0111]

[0112] The power consumption of PEM satisfies the inequality relationship shown in equation (3). The coordinated control between different working states of the electrolytic cell can be achieved by taking the values ​​of binary variables.

[0113]

[0114] In the formula, This represents the rated operating power of the PEM; a binary variable of 1 indicates that it is in this state.

[0115] The adjustable combined heat and power (CHP) unit consists of a hydrogen fuel cell (HFC) and a CHP unit. The HFC burns hydrogen to provide both electricity and heat. In traditional models, the heat-to-power ratio is a constant, typically operating in a "heat-driven power generation" or "power-driven heat generation" mode, resulting in poor operational flexibility. By changing the flow rate of the cooling circulating water and the hydrogen input rate, the fuel cell can adjust the heat-to-power ratio according to the real-time electrothermal load, as shown in the model below:

[0116]

[0117] In the formula, Let be the hydrogen power at time t; and These represent the power generated by electricity and the power generated by heat at time t, respectively; η HFC The energy conversion efficiency of fuel cells; and These are the upper and lower limits of hydrogen power utilization, respectively; Maximum climbing power; and These represent the upper and lower limits of the adjustable range for the electrothermal ratio of a fuel cell.

[0118] CHP units burn natural gas for combined heat and power (CHP). By controlling the turbine extraction ratio and the inlet guide vane angle, the heat-to-power ratio can be adjusted according to the real-time electro-thermal load. The model is as follows:

[0119]

[0120] In the formula, Let be the gas consumption power at time t; and These represent the power generated by electricity and the power generated by heat at time t, respectively; η CHP Energy conversion efficiency of combined heat and power units; and These are the upper and lower limits of gas consumption power, respectively; Maximum climbing power; and This refers to the upper and lower limits of the adjustable range of the electrothermal ratio for combined heat and power (CHP) units.

[0121] A methane reactor (MR) converts hydrogen energy into gaseous energy and supplies it to GB or CHP to generate electricity and heat. The model is as follows:

[0122]

[0123] In the formula, The hydrogen energy input to MR at time t; η represents the natural gas power output of MR at time t; MR The energy conversion efficiency of MR; and These are the upper and lower limits of hydrogen energy input to MR, respectively; and These represent the upper and lower limits of MR's climbing ability.

[0124] Gas-fired boilers (GB) can be integrated with hydrogen-driven cogeneration systems to achieve peak-valley complementarity of electrical and thermal loads, driven by electricity and gas prices. The model is as follows:

[0125]

[0126] In the formula: Let be the gas consumption power at time t; The gas-heat conversion efficiency is defined in GB standards. Let t be the heat generation power at time t.

[0127] S2. Establish a green certificate trading mechanism and improve the tiered carbon trading mechanism.

[0128] The Green Certificate Trading (GCT) mechanism requires energy companies to fulfill their obligations to absorb renewable energy. If an energy company fails to meet its renewable energy absorption quota, it can offset heavy penalties by purchasing green certificates at a low price. Conversely, if an energy company exceeds its renewable energy absorption quota, the excess can be sold as green certificates for profit. Increased renewable energy absorption reduces traditional power generation to some extent, indirectly reducing carbon emissions. The GCT cost is calculated as follows:

[0129] The new energy quota model is established as follows:

[0130]

[0131] Among them, P q P is the electricity required for the renewable energy quota, λ is the renewable energy quota coefficient, and P is the renewable energy quota. s ζ is the total power consumption of the system, ε is the influence coefficient of historical renewable energy quota completion, δ is the influence weight of historical renewable energy quota completion, and δ is the completion coefficient of the system's historical renewable energy quota. m It is the average of the historical new energy quota completion volume.

[0132] Based on the requirements of the renewable energy quota system, the number of green certificates required by this system is:

[0133] N IES,ne =P q Δt / 1000 (121)

[0134] Where, N IES,ne It is the number of green certificates required for the system to meet the renewable energy consumption responsibility weight, and Δt is the unit planning period.

[0135] The expression for the number of green certificates obtained is as follows:

[0136]

[0137] Where, N IES,ob It refers to the number of green certificates obtained by new energy power generation companies, P t N ψ is the renewable energy power generation capacity in period T, where T is the planned period, ψ is the coefficient affecting renewable energy output forecast, θ is the weight affecting renewable energy output forecast, and τ is the forecast accuracy of renewable energy output in the previous cycle. m It is the standard value for the accuracy of new energy output prediction.

[0138] The costs of participating in the GCT are as follows:

[0139] f GCT =μ GCT (N IES,ne -N IES,ob (123)

[0140] Among them, f GCT It is the transaction cost of the system participating in green certificate transactions, μ GCT That's the price of the green certificate.

[0141] The IES (Environmental Engineering Society) identifies three main sources of carbon emissions: purchased electricity, GB (Government Standards in China), CHP (Consumer Power Utilization) units, and gas load. Currently, China primarily uses the unpaid carbon allowance allocation method, assuming purchased electricity originates from coal-fired power plants. The carbon emission allowance allocation model is established as follows:

[0142]

[0143] In the formula, This represents the total carbon emission allowance for the IES; These are carbon emission allowances for CHP units, GB, externally purchased electricity, and gas load, respectively. These are the carbon emission allowances obtained from the unit thermal power produced by gas-fired units, the unit electrical power produced by coal-fired units, and the unit gas load consumed, respectively. The externally purchased electricity amount at time t; Let t be the gas load during the time period.

[0144] The actual carbon emission model is established as follows:

[0145]

[0146] In the formula, E IES E represents the actual carbon emissions of the IES; CHP E GB , E L,G These represent the actual carbon emissions from the CHP unit, CB, externally purchased electricity, and gas load, respectively; λ h ,λ e ,λ gThese represent the actual carbon emissions obtained from the production unit thermal power of gas-fired units, the production unit electrical power of coal-fired units, and the consumption unit gas load, respectively.

[0147] Within a carbon trading settlement cycle, the trading volume of IES participating in the carbon trading market is determined by the difference between the IES's actual carbon emissions and its carbon emission allowances, i.e.

[0148]

[0149] In the formula, This refers to carbon trading quotas. Traditional tiered carbon trading mechanisms limit carbon emissions by setting tiered carbon prices across different carbon trading quota ranges. Each time the carbon trading quota increases by one level, the base price for that range increases, thus limiting emissions. Building upon the traditional tiered carbon pricing system, an improved carbon trading mechanism is introduced by further optimizing the process within each carbon trading quota range. The formula is as follows:

[0150]

[0151] In the formula, denoted as the system carbon trading cost; d represents the carbon trading volume range; α represents the carbon price growth rate; and x represents the carbon trading base price.

[0152] S3. Construct the objective function and constraint functions of the IES low-carbon economic dispatch model for green certificate-carbon trading and hydrogen demand. Taking into account the operating costs of the IES low-carbon system, the improved tiered CET trading costs, and GCT costs, where operating costs include energy purchase costs, wind curtailment costs, energy storage charging and discharging costs, and equipment operation and maintenance costs, the following low-carbon economic dispatch objective with the lowest total operating cost (F) is constructed:

[0153]

[0154] Energy purchase cost:

[0155]

[0156] in, It represents the amount of natural gas purchased during time period t; and These are the electricity price and gas price for time period t, respectively.

[0157] Costs of wind curtailment:

[0158]

[0159] Where, δ w It is the cost of the unit's wind curtailment penalty. To provide power for wind power, The actual wind power output at time t. It is the wind curtailment power during period t.

[0160] The cost of energy storage charging and discharging is:

[0161]

[0162] In the formula, and Let γ be the charging and discharging power of the nth energy storage unit at time t. n is the charging and discharging cost coefficient for the nth type of energy storage.

[0163] Operating costs:

[0164]

[0165] In the formula, These are the overall operation and maintenance unit price for the CHP device and the operation and maintenance unit price for the GB device, respectively.

[0166] The transition of PEM working state must meet the following logical constraints:

[0167]

[0168] In the formula, T Rmax and These represent the maximum time that the PEM can continuously operate in overload and low-load states, respectively; Y is a binary variable, where 1 represents the PEM transitioning from a shutdown state to a working state, and 0 represents other states.

[0169] In addition, to avoid frequent start-ups and shutdowns of the PEM in a short period of time, it is necessary to limit the minimum downtime and cold standby time.

[0170]

[0171] In the formula, T Imin T Smin These represent the shortest time that the PEM can continuously operate in a stopped or cold standby state, respectively.

[0172] GB's constraint functions:

[0173]

[0174]

[0175] In the formula, and These are the upper and lower limits of gas consumption power, respectively; This represents the maximum climbing power.

[0176] Considering the similarities between the models for electric energy storage, hydrogen energy storage, and thermal energy storage, a general modeling approach is adopted for energy storage:

[0177]

[0178] In the formula, and Let be the charging and discharging power of the nth type of energy storage unit at time t; The maximum charge / discharge power of the nth type of energy storage unit; This represents the charge / discharge status identifier of the nth type of energy storage unit at time t. Indicates charging. Indicates energy release; η ES,n and χ ES,n Let be the charge / discharge efficiency and self-loss rate of the nth type of energy storage unit; Let be the charging and discharging power of the nth type of energy storage unit at time t; Let be the energy of the nth type of energy storage unit at time t; and These represent the upper and lower limits of the capacity of the nth type of energy storage unit.

[0179] Constraints on electricity and gas purchases:

[0180]

[0181] In the formula, and The system's electricity and gas purchases at time t; and This represents the maximum amount of electricity and gas that the system can purchase.

[0182] Wind power output and curtailment constraints:

[0183]

[0184] In the formula, ω w The maximum wind curtailment rate at time t; The actual wind power output at time t; The maximum wind power output at time t is given.

[0185] Power balance constraints:

[0186]

[0187] In the formula, Let t represent the electrical load, thermal load, and gas load at time t; N represents the number of PEMs connected in parallel in the electrolytic cell array.

[0188] S4. Optimize the equipment operation model of the integrated energy system, set optimization objectives and constraints, solve the optimized equipment operation model to obtain the optimal scheduling strategy, use the YALMIP toolbox to call the CPLEX commercial solver to solve the problem, and verify the carbon reduction effect.

[0189] S5. Taking a small CHP-type source-load system in a certain region of southern China as an example, with a scheduling cycle of 24 hours and a step size of 1 hour, we study the low-carbon economic optimization scheduling strategy for the CHP-type source-load system.

[0190] The carbon emission allowance coefficient for coal-fired power units is set at 0.728 kg / (kW·h), the carbon emission allowance coefficient for natural gas power units is 0.367 kg / (kW·h), and the carbon emission allowance coefficient per unit of gas load consumed is 0.180 kg / (kW·h). The carbon trading benchmark price is 50 yuan / t, the carbon trading price growth rate is 0.25%, the interval length is 2t, the green certificate price is 200 yuan, the natural gas price is 0.35 yuan / kW, and the unit wind curtailment penalty cost is 0.35 yuan / (kW·h).

[0191] This embodiment verifies the effectiveness of the model and scheduling strategy introduced in the paper through four scenarios. Scenario 1 contains only the ordinary tiered CET mechanism; Scenario 2 contains only the improved tiered CET mechanism; Scenario 3 includes both the ordinary tiered CET mechanism and the GCT mechanism; Scenario 4 involves the combined action of the improved tiered CET mechanism and the GCT mechanism. Table 1 compares the cost results for the four scenarios.

[0192] Table 1: Scheduling Results for Four Scenarios

[0193]

[0194]

[0195] According to Table 1, carbon emissions in Scenario 3 decreased by 0.42% compared to Scenario 1, indicating that the GCT mechanism played a certain indirect role in reducing carbon emissions, but the effect was not significant. Carbon emissions in Scenario 2 decreased by 14.58% compared to Scenario 1 and by 14.21% compared to Scenario 3. It can be seen that the carbon reduction effect of the improved step-type CET mechanism is significantly better than that of the ordinary step-type CET mechanism, and it is also significantly better than the GCT-ordinary step-type CET mechanism. Scenario 4 showed a further reduction of 12.10% compared to Scenario 2, demonstrating that the GCT mechanism can indirectly help the system further reduce carbon emissions by absorbing new energy sources. After combining the GCT mechanism and the improved stepped CET mechanism, the total carbon reduction was 2189.22 kg, a decrease of 24.92%, compared to the ordinary stepped CET mechanism, and a decrease of 24.60% compared to the GCT-ordinary stepped CET mechanism. This shows that the proposed GCT-improved stepped CET mechanism utilizes the green certificate mechanism to indirectly reduce carbon emissions in each generation calculation process, and the combination with the improved CET mechanism more efficiently leverages the carbon reduction capability of the green certificate mechanism itself.

[0196] It is evident that although the improved step-by-step CET mechanism has a slight decrease in economic efficiency, when combined with the GCT mechanism, it can continuously optimize economic efficiency in each optimization process, fully making up for the economic shortcomings of the improved GCT mechanism, and at the same time more fully reflecting the economic efficiency of the GCT mechanism itself.

[0197] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A low-carbon optimization scheduling method for an integrated energy system based on a green certificate-carbon trading mechanism, characterized in that, Includes the following steps: S1: Modeling a novel cogeneration system containing a proton exchange membrane electrolyzer; S2: Construct an improved tiered carbon emission trading model and a green certificate trading model; S3: Under the joint interaction mechanism of green certificate-carbon trading, construct a low-carbon optimization scheduling model for the integrated energy system of green certificate-carbon trading and hydrogen energy demand; S4: Optimize the operation model of the integrated energy system equipment, set optimization objectives and constraints, solve the optimized equipment operation model to obtain the optimal scheduling strategy, and verify the carbon reduction effect.

2. The integrated energy system low-carbon optimization scheduling method based on the green certificate-carbon trading mechanism according to claim 1, characterized in that, In step S1, a novel cogeneration system model containing a proton exchange membrane electrolyzer is established. Five operating states are represented by binary variables: shutdown I, cold standby S, variable load L, overload R, and low load V. At the same time, the binary variable Y represents the full start interval, where 1 indicates that the proton exchange membrane electrolyzer has been fully started from the shutdown state. The time for the proton exchange membrane electrolyzer to fully start up is used as the optimization time scale. Since the cold start time is less than one time scale, the loss of hydrogen energy output during the cold start process needs to be considered. The relationship between the power consumption and hydrogen production power of the proton exchange membrane electrolyzer can be expressed as: In the formula, Let be the hydrogen production power of the nth proton exchange membrane electrolyzer at time t; Let be the power consumption of the nth proton exchange membrane electrolyzer at time t; The electro-hydrogen conversion efficiency of a proton exchange membrane electrolyzer; This represents the standby power of the proton exchange membrane electrolyzer in cold standby mode; ζ PEM This is the penalty coefficient for hydrogen production power during cold start, used to represent the loss of hydrogen energy due to inconsistencies in time scales; When the binary variable W is 1, it indicates that the proton exchange membrane electrolyzer has switched from a cold standby state to a working state, satisfying equation (2): The power consumption of the proton exchange membrane electrolyzer satisfies the inequality relationship shown in equation (3). The coordinated control between different working states of the electrolyzer can be achieved by taking the values ​​of binary variables. In the formula, This represents the rated operating power of the proton exchange membrane electrolyzer; a binary variable of 1 indicates that it is in this state. The adjustable cogeneration unit consists of a hydrogen fuel cell and a cogeneration unit. The hydrogen fuel cell burns hydrogen to generate electricity and heat. In traditional models, the heat-to-power ratio is a constant, operating in a "heat-driven power generation" or "power-driven heat generation" mode, resulting in poor operational flexibility. By changing the flow rate of the cooling circulating water and the hydrogen input rate, the fuel cell can adjust the heat-to-power ratio according to the real-time electrothermal load, as shown in the model below: In the formula, Let be the hydrogen power at time t; and These represent the power generated by electricity and the power generated by heat at time t, respectively; η HFC The energy conversion efficiency of fuel cells; and These are the upper and lower limits of hydrogen power utilization, respectively; Maximum climbing power; and The upper and lower limits of the adjustable range of the electrothermal ratio of a fuel cell; A combined heat and power (CHP) unit burns natural gas for combined heat and power generation. By controlling the turbine extraction ratio and the inlet guide vane angle, the heat-to-power ratio can be adjusted according to the real-time electro-thermal load. The model is as follows: In the formula, Let be the gas consumption power at time t; and These represent the power generated by electricity and the power generated by heat at time t, respectively; η CHP Energy conversion efficiency of combined heat and power units; and These are the upper and lower limits of gas consumption power, respectively; Maximum climbing power; and The upper and lower limits of the adjustable range of the electro-thermal ratio for combined heat and power units; A methane reactor converts hydrogen energy into gas energy, which is then supplied to a gas boiler or combined heat and power plant to generate electricity and heat. The model is as follows: In the formula, The hydrogen energy input into the methane reactor at time t; η represents the natural gas power output of the methane reactor at time t; MR The energy conversion efficiency of the methane reactor; and These are the upper and lower limits of hydrogen energy input to the methane reactor, respectively; and The upper and lower limits of MR's ramp rate; Gas-fired boilers can be combined with hydrogen-driven cogeneration systems, driven by electricity and gas prices, to achieve peak-valley complementarity of electrical and thermal loads, as shown in the following model: In the formula: Let be the gas consumption power at time t; The gas-to-heat conversion efficiency of a gas-fired boiler; Let t be the heat generation power at time t.

3. The integrated energy system low-carbon optimization scheduling method based on the green certificate-carbon trading mechanism according to claim 1, characterized in that, In step S2, the green certificate trading mechanism is constructed. The green certificate trading mechanism requires energy providers to fulfill their obligations to absorb renewable energy. If an energy provider fails to meet its renewable energy absorption quota, it can offset the hefty penalties by purchasing green certificates at a lower price. Conversely, if an energy provider exceeds its renewable energy absorption quota, the excess can be sold as green certificates for profit. Increased renewable energy absorption reduces traditional power generation to some extent, indirectly reducing carbon emissions. The calculation method for green certificate trading costs is as follows: The new energy quota model is established as follows: Among them, P q P is the electricity required for the renewable energy quota, λ is the renewable energy quota coefficient, and P is the renewable energy quota. s ζ is the total power consumption of the system, ε is the influence coefficient of historical renewable energy quota completion, δ is the influence weight of historical renewable energy quota completion, and δ is the completion coefficient of the system's historical renewable energy quota. m It is the average of the historical new energy quota completion volume; Based on the requirements of the renewable energy quota system, the number of green certificates required by this system is: N IES,ne =P q Δt / 1000 (17) Where, N IES,ne It is the number of green certificates required for the system to meet the renewable energy consumption responsibility weight, and Δt is the unit planning period; The expression for the number of green certificates obtained is as follows: Where, N IES,ob It refers to the number of green certificates obtained by new energy power generation companies, P t N ψ is the renewable energy power generation capacity in period T, where T is the planned period, ψ is the coefficient affecting renewable energy output forecast, θ is the weight affecting renewable energy output forecast, and τ is the forecast accuracy of renewable energy output in the previous cycle. m This is the standard value for the accuracy of new energy output prediction; The fees for participating in green certificate trading are as follows: f GCT =μ GCT (N IES,ne -N IES,ob ) (19) Among them, f GCT It is the transaction cost of the system participating in green certificates, μ GCT That's the price of the green certificate.

4. The integrated energy system low-carbon optimization scheduling method based on the green certificate-carbon trading mechanism according to claim 1, characterized in that, In step S2, which involves constructing an improved tiered carbon trading mechanism... The main carbon emission sources of integrated energy systems are: purchased electricity, gas-fired boilers, combined heat and power (CHP) units, and gas load; the main approach is to allocate carbon allowances without payment, assuming that purchased electricity comes from coal-fired power generation units; the carbon emission allowance allocation model is established as follows: In the formula, The total carbon emission allowance for the integrated energy system; These are carbon emission allowances for combined heat and power units, gas-fired boilers, externally purchased electricity, and gas load, respectively. These are the carbon emission allowances obtained from the unit thermal power produced by gas-fired units, the unit electrical power produced by coal-fired units, and the unit gas load consumed, respectively. The externally purchased electricity amount at time t; The gas load during time period t; The actual carbon emission model is established as follows: In the formula, E IES E represents the actual carbon emissions of the integrated energy system. CHP E GB , E L,G These represent the actual carbon emissions from combined heat and power units, gas-fired boilers, externally purchased electricity, and gas load, respectively; λ h ,λ e ,λ g These are the actual carbon emissions obtained from the production unit thermal power of gas-fired units, the production unit electrical power of coal-fired units, and the unit gas load consumed, respectively. Within a carbon trading settlement cycle, the trading volume of an integrated energy system participating in the carbon trading market is determined by the difference between the integrated energy system's actual carbon emissions and its carbon emission allowances. In the formula, This refers to carbon trading quotas. Traditional tiered carbon trading mechanisms limit carbon emissions by setting tiered carbon prices across different carbon trading quota ranges. Each time the carbon trading quota increases by one level, the base price for that range increases, thus limiting emissions. Building upon the traditional tiered carbon pricing system, an improved carbon trading mechanism is introduced, further optimized within each carbon trading quota range. The formula is as follows: In the formula, denoted as the system carbon trading cost; d represents the carbon trading volume range; α represents the carbon price growth rate; and x represents the carbon trading base price.

5. The integrated energy system low-carbon optimization scheduling method based on the green certificate-carbon trading mechanism according to claim 1, characterized in that, Step S3 constructs the objective function of the integrated energy system low-carbon economic dispatch model for green certificate-carbon trading and hydrogen energy demand. This function comprehensively considers the operating costs of the integrated energy system's low-carbon system, the trading costs of the improved tiered carbon trading system, and the trading costs of green certificate transactions. The operating costs include energy purchase costs, wind curtailment costs, energy storage charging and discharging costs, and equipment operation and maintenance costs. The resulting low-carbon economic dispatch objective with the lowest total operating cost (F) is as follows: Energy purchase cost: in, It represents the amount of natural gas purchased during time period t; and These are the electricity price and gas price for time period t, respectively. Costs of wind curtailment: Where, δ w It is the cost of the unit's wind curtailment penalty. To provide power for wind power, The actual wind power output at time t. It is the wind curtailment power during period t; The cost of energy storage charging and discharging is: In the formula, and Let γ be the charging and discharging power of the nth energy storage unit at time t. n The charging and discharging cost coefficient for the nth type of energy storage; Operating costs: In the formula, These are the overall operation and maintenance unit price for combined heat and power (CHP) units and the operation and maintenance unit price for gas-fired boilers, respectively.

6. The integrated energy system low-carbon optimization scheduling method based on the green certificate-carbon trading mechanism according to claim 1, characterized in that, Step S3 involves constructing a constraint function for a comprehensive energy system low-carbon economic dispatch model that considers green certificate-carbon trading and hydrogen energy demand. The transition of the working state of a proton exchange membrane electrolyzer must meet the following logical constraints: In the formula, T Rmax and These refer to the maximum allowable continuous operation time of a proton exchange membrane electrolyzer under overload and underload conditions, respectively. Y is a binary variable, where 1 represents the proton exchange membrane electrolyzer changing from a shutdown state to a working state, and 0 represents other states. To avoid frequent start-ups and shutdowns of the proton exchange membrane electrolyzer in a short period of time, it is necessary to limit the minimum downtime and cold standby time. In the formula, T Imin T Smin These are the shortest times that the PEM can continuously operate in a stopped or cold standby state, respectively. Constraint functions for gas-fired boilers: In the formula, and These are the upper and lower limits of gas consumption power, respectively; This represents the maximum climbing power. Considering the similarities between the models for electric energy storage, hydrogen energy storage, and thermal energy storage, a general modeling approach is adopted for energy storage: In the formula, and Let be the charging and discharging power of the nth type of energy storage unit at time t; The maximum charge / discharge power of the nth type of energy storage unit; This represents the charge / discharge status identifier of the nth type of energy storage unit at time t. Indicates charging. Indicates the release of energy; η ES,n and χ ES,n Let be the charge / discharge efficiency and self-loss rate of the nth type of energy storage unit; Let be the charging and discharging power of the nth type of energy storage unit at time t; Let be the energy of the nth type of energy storage unit at time t; and These are the upper and lower limits of the capacity of the nth type of energy storage unit; Constraints on electricity and gas purchases: In the formula, and The system's electricity and gas purchases at time t; and This represents the maximum amount of electricity and gas that the system can purchase. Wind power output and curtailment constraints: In the formula, ω w The maximum wind curtailment rate at time t; The actual wind power output at time t; The maximum wind power output at time t; Power balance constraints: In the formula, Let t represent the electrical load, thermal load, and gas load at time t; N represents the number of PEMs connected in parallel in the electrolytic cell array.

7. The integrated energy system low-carbon optimization scheduling method based on the green certificate-carbon trading mechanism according to claim 1, characterized in that, In step S4, the YALMIP toolbox is used to call the CPLEX commercial solver to solve the problem and verify the carbon reduction effect.

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