Robust optimization scheduling method of hydrogen-containing integrated energy system considering dynamic green certificate-carbon emission collaborative transaction mechanism

Abstract

The invention discloses a hydrogen-containing integrated energy system robust optimization scheduling method considering a dynamic green certificate-carbon emission collaborative transaction mechanism. The method comprises the following steps: establishing the dynamic green certificate-carbon emission collaborative transaction mechanism considering reward and punishment characteristics; establishing a hydrogen-containing comprehensive energy system equipment model according to a hydrogen-containing comprehensive energy system architecture; determining a target function and constraint conditions according to the dynamic green certificate-carbon emission collaborative transaction mechanism considering the reward and punishment characteristics and the hydrogen-containing integrated energy system equipment model so as to establish a hydrogen-containing integrated energy system robust optimization scheduling model considering the dynamic green certificate-carbon emission collaborative transaction mechanism; and solving the hydrogen-containing integrated energy system robust optimization scheduling model considering the dynamic green certificate-carbon emission collaborative transaction mechanism based on a robust linear optimization theory. According to the method provided by the invention, the running flexibility of the hydrogen-containing comprehensive energy system is improved, and meanwhile, the running economy and the carbon emission level of the system are improved.

Description

Technical Field

[0001] This invention relates to a robust optimization scheduling method for hydrogen-containing integrated energy systems that takes into account a dynamic green certificate-carbon emission collaborative trading mechanism, and belongs to the field of hydrogen-containing integrated energy system scheduling. Background Technology

[0002] Carbon trading mechanisms (CTM) and green certificate trading (GCT) are considered effective means to balance electricity economics and low-carbon environmental protection, effectively incentivizing market participants to control total carbon emissions through appropriate carbon reduction actions. Integrated energy systems (IES), as key participants in both carbon and green certificate markets, can promote the clean transformation of the power system and energy conservation and emission reduction through multi-energy complementarity and electricity-to-hydrogen conversion. Therefore, how to coordinate the optimal integrated dispatch efficiency of hydrogen-containing IES in the coupled carbon-green certificate market is a pressing issue that needs to be addressed to further advance emission reduction efforts. Summary of the Invention

[0003] This invention provides a robust optimization scheduling method for hydrogen-containing integrated energy systems that considers a dynamic green certificate-carbon emission collaborative trading mechanism. First, a dynamic green certificate-carbon emission collaborative trading mechanism is established to reflect the real-time supply and demand relationship between the carbon market and the green certificate market, promoting effective coordination between the two markets and exploring the low-carbon benefits of coupling the green certificate with hydrogen energy for the integrated energy system (IES). Specifically, a nonlinear price mapping function is used to quantify the relationship between green certificate prices and carbon prices in each time period, determining the conversion coefficient and realizing dynamic integrated trading of green certificates and carbon emissions, providing a new approach to carbon-green certificate market coupling. Second, a hydrogen-containing IES is constructed using a reversible solid oxide cell (RSOC) as the electricity-heat-gas-hydrogen conversion hub to locally absorb surplus renewable energy output. Finally, an improved robust linear optimization theory is used to address the system operation risks caused by source-load uncertainties. A robust optimization scheduling model for hydrogen-containing IES considering the dynamic green certificate-carbon emission collaborative trading mechanism and ROSC is established. Case simulations verify the effectiveness of the proposed model in exploring the low-carbon economic potential of the system and reducing system operation risks.

[0004] The technical solution of this invention is:

[0005] A robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism includes the following steps: establishing a dynamic green certificate trading mechanism considering reward and penalty characteristics; establishing a dynamic carbon trading mechanism considering reward and penalty characteristics; establishing a dynamic green certificate-carbon emission collaborative trading mechanism considering reward and penalty characteristics based on the dynamic green certificate trading mechanism and the dynamic carbon trading mechanism considering reward and penalty characteristics; establishing an equipment model of the hydrogen-containing integrated energy system based on the hydrogen-containing integrated energy system architecture; determining the objective function and constraints based on the dynamic green certificate-carbon emission collaborative trading mechanism considering reward and penalty characteristics and the equipment model of the hydrogen-containing integrated energy system to establish a robust optimization scheduling model of the hydrogen-containing integrated energy system considering the dynamic green certificate-carbon emission collaborative trading mechanism; and solving the robust optimization scheduling model of the hydrogen-containing integrated energy system considering the dynamic green certificate-carbon emission collaborative trading mechanism based on robust linear optimization theory.

[0006] Furthermore, the establishment of the dynamic green certificate trading mechanism that takes into account reward and punishment characteristics is specifically as follows:

[0007] Based on the number of green certificates required by the system at time t and the number of green certificates obtained by the system at time t, determine the number of green certificates to participate in the green certificate trading market at time t without considering the conversion of green certificates;

[0008] By introducing a two-stage linearization function, a dynamic green certificate trading price model that takes into account reward and punishment characteristics is constructed, as shown in the following expression:

[0009]

[0010] In the formula: c GCT,t The dynamic green certificate trading price at time t; This is the floor price for green certificate trading; The penalty price for green certificate trading; The threshold for green certificate trading; k1 and k2 are the linear price slopes of the first and second segments of the green certificate trading linearization function, respectively; N GCT,t This takes into account the number of green certificates participating in the green certificate trading market at time t under the conversion of green certificates.

[0011] The green certificate trading threshold is calculated as follows:

[0012]

[0013] In the formula: The threshold for green certificate transactions; ρ com The proportion of paid carbon allowances; E IES,t Let θ be the carbon quota of the system at time t; θ1 is the conversion coefficient between carbon quota and green certificate.

[0014] Furthermore, the establishment of the dynamic carbon trading mechanism that takes into account reward and punishment characteristics specifically includes:

[0015] A carbon emission and carbon quota model is established, expressed as follows:

[0016]

[0017] In the formula: E C,t Let t be the actual carbon emissions of the system at time t; The system's power purchase at time t; These represent the electrical and thermal power output by CHP at time t, respectively. Let be the heat production power of GB at time t; a1 be the carbon emission parameters of the upstream power purchase; a2 be the carbon emission parameters of the natural gas-consuming equipment; a3 be the carbon emission parameters of wind power considering the life cycle assessment method; χ1 be the carbon emission quota parameters of the upstream power purchase; χ2 be the carbon emission quota parameters of the natural gas-consuming equipment; χ3 be the carbon emission quota parameters of wind power considering the life cycle assessment method. This does not take into account the carbon emissions generated at time t when participating in the carbon market under the real-time green certificate carbon credit. The maximum value of purchasable paid carbon allowances; ρ com The proportion of paid carbon allowances; E IES,t P represents the system's carbon quota at time t; WT,t Let t be the system's wind power output.

[0018] By introducing a two-stage linearization function, a dynamic carbon trading price model that takes into account reward and punishment characteristics is constructed, as shown in the following expression:

[0019]

[0020] In the formula: c CTM,t The dynamic carbon trading price at time t; The floor price for carbon trading; The penalty price for carbon trading; k3 and k4 are the linear price slopes of the first and second segments of the carbon trading linearization function, respectively; E CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deductions for participation in the carbon trading market.

[0021] Furthermore, the establishment of a dynamic green certificate-carbon emission collaborative trading mechanism that takes into account reward and punishment characteristics, based on the dynamic green certificate trading mechanism and the dynamic carbon trading mechanism that take into account reward and punishment characteristics, specifically includes:

[0022] Discretize the time scale of the green certificate trading mechanism and the carbon trading mechanism to be consistent with the time scale of the hydrogen-containing integrated energy system;

[0023] The carbon emission reductions associated with obtaining green certificates

[0024] The carbon quota and green certificate conversion coefficient θ1 is determined based on the nonlinear price mapping function of the natural logarithm.

[0025] Based on the carbon emission reductions, carbon quotas, and green certificate conversion coefficients associated with green certificates, the trading volume of green certificates and carbon quotas for a hydrogen-containing integrated energy system after considering dynamic green certificate-carbon emission coordinated trading is calculated. The expression is as follows:

[0026]

[0027] In the formula: N represents the number of green certificates participating in the green certificate trading market at time t, without considering the conversion of green certificates; trans,t N represents the number of green certificates participating in the conversion. ne,t Let be the number of green certificates the system needs at time t; This excludes carbon emissions from participating in the carbon trading market at time t under the real-time green certificate carbon credit; N GCT,t E represents the amount of green certificates traded in the green certificate trading market at time t, considering the conversion of green certificates; CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deduction and participation in the carbon trading market;

[0028] Based on the trading volume of green certificates and carbon quotas after considering the coordinated trading of dynamic green certificates and carbon emissions in the hydrogen-containing integrated energy system, the dynamic green certificate trading price model and the dynamic carbon trading price model in the dynamic carbon trading mechanism that take into account the reward and punishment characteristics are called to determine the dynamic green certificate trading price and the dynamic carbon trading price.

[0029] Furthermore, the hydrogen-containing integrated energy system equipment model includes the RSOC model, the source-load uncertainty model, the cogeneration unit model, the integrated demand response model, the energy storage model, the gas boiler model, and the hydrogen fuel cell vehicle model.

[0030] Furthermore, the RSOC model is expressed as follows:

[0031]

[0032] In the formula: P SOEC,t These represent the hydrogen production power and power consumption in SOEC mode at time t, respectively. These represent the power generation, heat generation, and hydrogen consumption in SOFC mode at time t, respectively; η SOEC Hydrogen production efficiency in SOEC mode; These are the power generation and heat generation efficiencies of SOFC mode, respectively. These are the maximum power consumption and hydrogen consumption power of the RSOC, respectively. and These are the upper and lower limits of ramp power in SOFC mode, respectively. and These represent the upper and lower limits of ramp power in SOEC mode, respectively.

[0033] Furthermore, the source load uncertainty model is expressed as follows:

[0034]

[0035] In the formula: P WT,t , These represent the power output, electrical load, thermal load, and gas load of the system's new energy units at time t. These represent the predicted values ​​of the system's new energy unit output, electrical load, thermal load, and gas load at time t. These represent the deviations of the system's new energy output, electrical load, thermal load, and gas load at time t; Γ WT , To robustly adjust parameters.

[0036] Furthermore, the objective function aims to minimize the sum of carbon trading costs, green certificate trading costs, energy purchase costs, equipment maintenance costs, wind curtailment costs, and demand response costs; the constraints include electrical power balance constraints, thermal power balance constraints, gas power balance constraints, and hydrogen power balance constraints.

[0037] Furthermore, the robust optimization scheduling model for the hydrogen-containing integrated energy system based on robust linear optimization theory and the dynamic green certificate-carbon emission collaborative trading mechanism is specifically as follows:

[0038] Based on robust linear optimization theory, and introducing y WT,t , The source-load uncertainty model in the hydrogen-containing integrated energy system equipment model is transformed into a robust equivalent model, as shown in the following expression:

[0039]

[0040] In the formula: P WT,t , These represent the power output, electrical load, thermal load, and gas load of the system's new energy units at time t. These represent the predicted values ​​of the system's new energy unit output, electrical load, thermal load, and gas load at time t. These represent the deviations of the system's new energy output, electrical load, thermal load, and gas load at time t; v WT,t q WT,t , The auxiliary variable introduced; y WT,t , For binary variables; τ WT , The uncertainty of the system's new energy output, electrical load, heat load and gas load;

[0041] For the dynamic green certificate trading price model in the dynamic green certificate trading mechanism that takes into account reward and punishment characteristics, and the dynamic carbon trading price model in the dynamic carbon trading mechanism that takes into account reward and punishment characteristics, Q+1 continuous auxiliary variables and Q binary auxiliary variables are introduced to convert the corresponding models into linear forms.

[0042] For the robust optimization scheduling model of a hydrogen-containing integrated energy system that considers the dynamic green certificate-carbon emission coordinated trading mechanism, the range of variable values ​​for the carbon trading cost expression in the objective function is set, and then discrete units are used to analyze E in the carbon trading cost expression. CEA,t Discretization is performed to obtain an approximately linearized expression for the carbon trading cost expression; where E CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deduction and participation in the carbon trading market;

[0043] For the robust optimization scheduling model of a hydrogen-containing integrated energy system that considers the dynamic green certificate-carbon emission collaborative trading mechanism, the range of variable values ​​for the variables involved in the green certificate trading cost expression of the objective function is set. Then, discrete units are used to analyze N in the green certificate trading cost expression. GCT,t Discretization is performed to obtain an approximate linearized expression for the green certificate transaction cost; N GCT,t This represents the volume of green certificates traded in the green certificate trading market at time t, considering the conversion of green certificates.

[0044] Solve using a solver.

[0045] The beneficial effects of this invention are:

[0046] 1) When considering the green certificate-carbon emission coordinated trading mechanism that takes into account the dynamic trading supply and demand curve, the carbon emission liability offsetting capacity of green electricity and green certificates in different time periods is clarified by the coordinated discretization of the time scale of the green certificate market and the carbon market. This reflects the real-time supply and demand relationship between the carbon market and the green certificate market, and provides dynamic market supply and demand price signals to guide enterprises or users to adjust their electricity consumption behavior. This achieves a certain balance between the total system cost and carbon emissions, promotes coordinated trading in the carbon-green certificate market, and has higher market efficiency.

[0047] 2) The proposed joint operation strategy of RSOC and carbon-green certificate trading helps to improve the flexibility and economy of hydrogen-containing IES dispatch. During off-peak hours, RSOC converts surplus renewable energy and low-yield electricity into hydrogen, improving renewable energy utilization and thus increasing green certificate revenue. During peak hours, RSOC uses hydrogen-to-electricity conversion to reduce load peaks, alleviating high carbon emissions from natural gas-consuming equipment during peak electricity demand and increasing carbon trading revenue.

[0048] 3) To address the source-load uncertainty problem in the optimization scheduling process of hydrogen-containing IES, this paper uses an improved robust linear optimization theory to effectively improve the uncertainty risk resistance of hydrogen-containing IES. At the same time, hydrogen-containing IES can flexibly adjust between economy and robustness according to the actual risk preference and make the optimal choice. Attached Figure Description

[0049] Figure 1 This is a flowchart of the present invention;

[0050] Figure 2 A dynamic supply and demand curve for green certificate trading that takes into account reward and punishment characteristics;

[0051] Figure 3 For green certificates and carbon emission timescale coordinated trading;

[0052] Figure 4 It is a hydrogen-containing IES architecture;

[0053] Figure 5 Forecasting power output and load for new energy sources;

[0054] Figure 6 To account for uncertainties in the electrical load and wind power forecast output curves before and after;

[0055] Figure 7 To account for uncertainties, the heat load and gas load curves before and after adjustment;

[0056] Figure 8 It is the relationship between the trading of green certificates and changes in green certificate prices;

[0057] Figure 9 The relationship between carbon allowance trading and changes in carbon allowance prices;

[0058] Figure 10 The impact curves of different green certificate quota coefficients on various system costs;

[0059] Figure 11 To assess the impact of robust uncertainty parameters on dynamic green certificate-carbon emission collaborative trading;

[0060] Figure 12 To account for the uncertainties in the source load of the RSOC running results;

[0061] Figure 13 To contribute to the equipment related to the SOEC model. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0063] Example 1: As Figures 1-13 As shown, a robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism includes the following steps:

[0064] Step 1: Establish a dynamic green certificate trading mechanism that takes into account reward and penalty characteristics; establish a dynamic carbon trading mechanism that takes into account reward and penalty characteristics; based on the dynamic green certificate trading mechanism and the dynamic carbon trading mechanism that take into account reward and penalty characteristics, establish a dynamic green certificate-carbon emission collaborative trading mechanism that takes into account reward and penalty characteristics.

[0065] Step 2: Based on the architecture of the hydrogen-containing integrated energy system, establish a model of the hydrogen-containing integrated energy system equipment.

[0066] Step 3: Based on the dynamic green certificate-carbon emission collaborative trading mechanism that takes into account the reward and punishment characteristics and the equipment model of the hydrogen-containing integrated energy system, determine the objective function and constraints in order to establish a robust optimization scheduling model of the hydrogen-containing integrated energy system that takes into account the dynamic green certificate-carbon emission collaborative trading mechanism.

[0067] Step 4: Solve the robust optimization scheduling model of the hydrogen-containing integrated energy system based on robust linear optimization theory and dynamic green certificate-carbon emission collaborative trading mechanism.

[0068] The architecture of the hydrogen-containing integrated energy system (IES) model is shown below. Figure 4 The system is powered by an external power grid and gas grid. This architecture includes an energy supply side, a load side, and an energy storage side, achieving complementarity among four energy sources: electricity, heat, gas, and hydrogen. The energy supply side includes wind power (WT), gas-fired boilers (GB), combined heat and power units (CHP), and reversible solid oxide batteries (RSOC). The gas-fired boilers (GB) couple heat and gas energy; the CHP couples couple electricity, heat, and gas energy; and the RSOC couples electricity, heat, and hydrogen energy within the hydrogen-containing fuel cell ecosystem. The load side includes electrical load, heat load, and gas load. The energy storage side includes hydrogen fuel cell vehicles, energy storage devices, thermal storage devices, and gas storage devices.

[0069] Furthermore, Step 1 includes:

[0070] S1.1: Establish a dynamic green certificate trading mechanism that takes into account reward and penalty characteristics. Most existing green certificate trading operates on an annual timescale, covering the allocation and fulfillment of renewable energy quotas. Therefore, green certificate pricing is generally achieved through centralized auctions or bilateral negotiations. This leads to a mismatch between the annual renewable energy quota requirements and the daily, hourly, or other time-scale operational needs of the power system, potentially resulting in hefty penalties at the end of the assessment period for failing to meet quota targets. To guide future new power systems to better integrate green certificate trading, this paper assumes that the system pre-allocates the required renewable energy quotas (the number of green certificates required by the system) based on the seasonality and daily variation characteristics of its production, and that this allocation varies by time period, thus discretizing green certificate trading into the required sub-periods.

[0071] The conversion relationship between green certificates and green electricity is that one green certificate is equivalent to 1MW of green electricity. Therefore, the calculation method for the number of green certificates obtained by the system is as follows:

[0072]

[0073] Where: N ob,t P represents the number of green certificates obtained by the system at time t. WT,t Δt represents the system's wind power output at time t; Δt represents the unit dispatch period.

[0074] According to the renewable energy quota mechanism, a portion of the electricity generated and consumed by enterprises or users must come from renewable energy sources. The calculation formula for the number of green certificates required by the system is as follows:

[0075]

[0076] Where: N ne,t χ represents the number of green certificates the system needs at time t; GCT P represents the green certificate quota coefficient. load,t Let t be the load demand within the system at time t; Δt is the unit scheduling period.

[0077] Based on the above analysis, the formula for calculating the number of green certificates traded by the system in the green certificate trading market is as follows:

[0078]

[0079] In the formula: N represents the number of green certificates participating in the green certificate trading market at time t, without considering the conversion of green certificates; ne,t N represents the number of green certificates the system needs at time t. ob,t Let t be the number of green certificates obtained by the system at time t.

[0080] According to economic principles, there is a specific relationship between the demand for green certificates and their price, generally described as a linear function. Considering the current low level of activity in green certificate trading in my country, this paper introduces a linear compensation coefficient to transform the monotonically penalized linear function into a two-stage linear function that takes into account reward and penalty characteristics, such as... Figure 2 As shown, when the demand for green certificates gradually increases (vertical axis to the right), the price of green certificates increases linearly until it reaches a fixed, punitive high price at a threshold. Conversely, as the sales volume of green certificates increases (vertical axis to the left), the selling price of green certificates increases accordingly, encouraging more participants to join the green certificate trading market and thus enhancing its liquidity and vitality. The dynamic green certificate trading price model, taking into account the reward and punishment characteristics, is as follows:

[0081]

[0082] In the formula: c GCT,t The dynamic green certificate trading price at time t; This is the floor price for green certificate trading; The penalty price for green certificate trading; The threshold for green certificate trading; k1 and k2 are the linear price slopes of the first and second segments of the green certificate trading linearization function, respectively (the first segment is... Figure 2 (The middle segment is BC, the second segment is CD); N GCT,t This takes into account the number of green certificates participating in the green certificate trading market at time t under the conversion of green certificates.

[0083] The green certificate trading threshold is calculated as follows:

[0084]

[0085] In the formula: The threshold for green certificate transactions; ρ com The proportion of paid carbon allowances; E IES,t Let θ be the carbon quota of the system at time t; θ1 is the conversion coefficient between carbon quota and green certificate.

[0086] S1.2: Establish a dynamic carbon trading mechanism that takes into account reward and penalty characteristics. CTM refers to a market mechanism based on economic incentives to promote carbon emission reduction. To achieve the integration of CTM with the future scheduling of hydrogen-containing IES, this section assumes that the total allowable carbon emission allowances for hydrogen-containing IES are allocated in advance according to the seasonal and daily cycles of production, and that the carbon emission allowance E varies across different time periods. IES,t Unlike traditional CTM, which only considers carbon emissions from the power generation stage, this paper employs a life cycle assessment (CTM) approach to quantitatively analyze carbon emissions from different energy conversion chains within hydrogen-containing IESs, including wind turbines (WT), carbon monoxide (CHP), and bulk carbon (GB), and calculates standardized carbon emission factors. The carbon emission and carbon quota models are as follows:

[0087]

[0088] In the formula: E C,t Let t be the actual carbon emissions of the system at time t; The system's power purchase at time t; These represent the electrical and thermal power output by CHP at time t, respectively. Let be the heat production power of GB at time t; a1 be the carbon emission parameters of the upstream power purchase; a2 be the carbon emission parameters of the natural gas-consuming equipment; a3 be the carbon emission parameters of wind power considering the life cycle assessment method; χ1 be the carbon emission quota parameters of the upstream power purchase; χ2 be the carbon emission quota parameters of the natural gas-consuming equipment; χ3 be the carbon emission quota parameters of wind power considering the life cycle assessment method. This does not take into account the carbon emissions generated at time t when participating in the carbon market under the real-time green certificate carbon credit. The maximum value of purchasable paid carbon allowances; ρ com The proportion of paid carbon allowances; E IES,t P represents the system's carbon quota at time t; WT,t Let t be the system's wind power output at time t.

[0089] Similar to the supply and demand curve for dynamic green certificate trading, the dynamic carbon trading supply and demand curve reflects the carbon price formation mechanism of the carbon market in real time. Therefore, the dynamic carbon trading price model that takes into account the reward and punishment characteristics can be expressed as follows:

[0090]

[0091] In the formula: c CTM,t The dynamic carbon trading price at time t; The floor price for carbon trading; The penalty price for carbon trading; k3 and k4 are the linear price slopes of the first and second segments of the linearization function for carbon trading, respectively (similar to...). Figure 2 (The principle); E CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deductions for participation in the carbon trading market.

[0092] S1.3: Establish a dynamic green certificate-carbon emission coordinated trading mechanism that takes into account reward and penalty characteristics. After clarifying the environmental rights of green certificates, the carbon market and green certificate market are discretized into the required subdivided time periods. The trading time scales of the two markets are coordinated. Then, based on the carbon emission offset corresponding to the green certificate, a portion of the carbon emission responsibility is offset during the carbon trading assessment period, indirectly affecting the carbon market. At the same time, based on the nonlinear price mapping function of the natural logarithm, the green certificate price and carbon price for each time period are quantified to determine the green certificate-carbon quota conversion relationship, and the green certificate re-participates in the dynamic trading of the two markets. Thus, a linkage mechanism between GCT and CTM is established based on factors such as the supply and demand relationship of green certificates and trading prices, realizing dynamic interaction and reducing the trading costs of both. The detailed steps of dynamic green certificate-carbon emission coordinated trading are as follows:

[0093] 1) Coordinated trading across multiple time scales in the carbon-green certificate market

[0094] The timescales of the green certificate and carbon trading mechanisms are discretized to align with the timescale of the hydrogen-containing integrated energy system (IES). Since the hydrogen-containing IES optimizes scheduling on a 1-hour timescale, this study also discretizes the 24-hour trading cycle for green certificate and carbon trading to a 1-hour trading timescale. The hydrogen-containing IES simultaneously executes trading plans in both the green certificate and carbon markets within a single time period, achieving carbon-green certificate trading on the same timescale, and finally performs unified settlement. A schematic diagram is attached. Figure 3 .

[0095] 2) Calculate the number of green certificates and the carbon emission reductions behind them.

[0096] The number of green certificates is determined by the green electricity produced or consumed by hydrogen-containing IES, and the carbon emission reduction represented by each green certificate is obtained by comparing the carbon emissions generated by traditional energy power generation and new energy power generation, thus assigning a clear environmental value to each green certificate.

[0097] 3) Determine the ownership of green rights

[0098] Based on the principle of "uniqueness of environmental rights certification" for green certificates, the carbon emission reduction attributes of green certificates already sold in the green certificate trading market have been transferred from the seller to the buyer. To avoid double counting of environmental rights for green certificates, this information should be deducted from the seller's record. Similarly, this information should be added to the buyer's record accordingly.

[0099] 4) Determining the conversion coefficients based on a nonlinear price mapping function using the natural logarithm.

[0100] Based on the relationship between real-time green certificate prices and carbon prices, a conversion coefficient is determined to obtain the converted green certificate and carbon quota quantities, which are then re-entered into the green certificate and carbon markets for trading, thus realizing dynamic green certificate-carbon emission coordinated trading.

[0101]

[0102] In the formula: θ1 is the carbon quota to green certificate conversion coefficient; c GCT,t The dynamic green certificate trading price at time t; The penalty price for green certificate trading; c CTM,t The dynamic carbon trading price at time t; ω represents the penalty price for carbon trading; ω is the quantification coefficient.

[0103] 5) Calculate the trading volume of hydrogen-containing IES, taking into account dynamic green certificates and carbon emission coordinated trading.

[0104]

[0105] In the formula: N represents the number of green certificates participating in the green certificate trading market at time t, without considering the conversion of green certificates; trans,t The number of green certificates participating in the conversion; The carbon emission reductions corresponding to the green certificates; N ne,t Let be the number of green certificates the system needs at time t; This represents the carbon emissions emitted at time t during carbon trading, excluding real-time green certificate carbon credits. The first line of the above formula contains N. GCT,t This represents the amount of green certificates traded in the green certificate trading market at time t, considering the conversion of green certificates. The second line of the above formula, E, represents this. CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deductions for participation in the carbon trading market.

[0106] Furthermore, Step 2 is... Figure 4 The modeling involves RSOC, source-load uncertainty (wind power and electricity, heat, gas, and load), combined heat and power (CHP) unit model, integrated demand response (DR) model, energy storage model, gas boiler (GB) model, and hydrogen fuel cell vehicle model. Step 2 includes:

[0107] S2.1: Establish a reversible solid oxide battery (RSOC) model. The energy conversion process of RSOC can be divided into solid oxide fuel cell (SOFC) mode and solid oxide electrolytic cell (SOEC) mode. During peak load periods, RSOC operates in SOFC mode, utilizing hydrogen stored in hydrogen storage devices to perform load shaving through hydrogen-to-electricity conversion, mitigating the high carbon emissions of natural gas-consuming equipment during peak electricity demand and thus increasing carbon trading revenue. During off-peak periods, RSOC operates in SOEC mode, converting surplus renewable energy output and low-yield electricity into hydrogen, improving renewable energy utilization and thereby increasing the amount of green certificates issued by the National Renewable Energy Information Management Center, thus increasing green certificate revenue. The RSOC model is as follows:

[0108]

[0109] In the formula: P SOEC,t These represent the hydrogen production power and power consumption in SOEC mode at time t, respectively. These represent the power generation, heat generation, and hydrogen consumption in SOFC mode at time t, respectively; η SOEC Hydrogen production efficiency in SOEC mode; These are the power generation and heat generation efficiencies of SOFC mode, respectively. These are the maximum power consumption and hydrogen consumption power of the RSOC, respectively. and These are the upper and lower limits of ramp power in SOFC mode, respectively. and These represent the upper and lower limits of ramp power in SOEC mode, respectively.

[0110] S2.2: Uncertainty model of source load.

[0111] The output and load power of new energy sources consist of two parts: predicted values ​​and deviation values. The allowable deviation value in China is within 20%, and this paper uses 15%. The source-load model considering uncertainties is as follows:

[0112]

[0113] In the formula: P WT,t , These represent the power output, electrical load, thermal load, and gas load of the system's new energy units at time t. These represent the predicted values ​​of the system's new energy unit output, electrical load, thermal load, and gas load at time t. These represent the deviations of new energy output, electrical load, thermal load, and gas load at time t, respectively; Γ WT , To robustly adjust parameters.

[0114] In addition, the system also includes the CHP model, the integrated DR model, the energy storage model, the GB model, and the hydrogen fuel cell vehicle model. These models have been extensively studied and will not be elaborated upon here.

[0115] Furthermore, Step 3 includes:

[0116] S3.1: Establish the objective function. This paper uses the carbon trading cost F of hydrogen-containing IES as an example. CTM Green certificate transaction cost F GCT Energy purchase cost F buy Equipment maintenance cost F M Cost of wind curtailment F CW and demand response cost F DR The robust optimization scheduling is performed with the goal of minimizing the sum F. The model is as follows:

[0117] F = min(F) CTM +F GCT +F buy +F M +F CW +F DR )

[0118] The costs of carbon trading, green certificate trading, energy purchase, equipment maintenance, wind curtailment, and demand response are detailed below:

[0119]

[0120] In the formula: T is the scheduling period; N GCT,t E represents the number of green certificates traded at time t. CEA,t Let c be the amount of carbon allowances traded in the carbon market at time t; GCT,t c represents the dynamic green certificate trading price at time t; CTM,t The dynamic carbon trading price at time t; The electricity purchase price; For the gas purchase price; The system's power purchase at time t; The system purchases natural gas power at time t; Let be the electrical power output by CHP at time t; Let GB be the heat generation power at time t; These are CHP, RSOC, GB, and unit maintenance cost of wind turbine units, respectively; c CW The cost of wind curtailment penalty per unit; P SOEC,t These are the power consumption in SOEC mode; Power generated in SOFC mode; P WT,t P represents the system's wind power output at time t. CW,t Let c be the wind curtailment power at time t; cut To participate in the vertical DR cost coefficient; c tra To participate in the horizontal DR cost coefficient; These represent the load amounts (i.e., electrical, heat, and gas loads) of the i-th type of load participating in the longitudinal DR and transverse DR, respectively.

[0121] S3.2: Constraints

[0122] 1) Power balance constraint

[0123]

[0124] In the formula: The system's power purchase at time t; Let t be the electrical load after DR in the system; Let t be the electrical power stored in the energy storage device; Let t be the electrical power released by the energy storage. The system's power purchase limit; P represents the electrical power output of CHP at time t; WT,t Let t be the system's wind power output. P represents the power generated in SOFC mode at time t. SOEC,t Let t be the power consumption in SOEC mode.

[0125] 2) Thermal power balance constraint

[0126]

[0127] In the formula: Let t be the system heat load after DR; Let t be the thermal power stored in the thermal storage device at time t; Let t be the thermal power released by the thermal storage device at time t; Let t be the thermal power output of CHP; Let GB be the heat generation power at time t; Let t be the heat output power generated in SOFC mode.

[0128] 3) Gas power balance constraint

[0129]

[0130] In the formula: Let t be the system's gas purchase power. Let t be the hydrogen doping power of the system at time t; Let t be the system's post-DR gas load; The gas consumption power of CHP at time t; Let GB be the gas consumption power at time t; Let t be the gas storage power of the gas storage device; Let t be the gas discharge power of the gas storage device; This is the limit for the gas purchase capacity of the system.

[0131] 4) Hydrogen power balance constraint

[0132]

[0133] In the formula: The hydrogen production power in SOEC mode at time t; Let t be the hydrogen doping power of the system at time t; The hydrogen consumption power in SOFC mode at time t; Let t be the hydrogen storage power at time t; Let t be the hydrogen power released by the hydrogen storage at time t. The hydrogen storage refers to hydrogen refueling stations and hydrogen fuel cell vehicles.

[0134] Furthermore, Step 4 includes:

[0135] A robust optimization scheduling model for a hydrogen-containing integrated energy system, incorporating a dynamic green certificate-carbon emission collaborative trading mechanism, is solved based on robust linear optimization theory and the Gurobi solver. This paper employs robust linear optimization theory to handle source-load uncertainty. Considering the relationship between economics and robustness, and to increase the sensitivity of the robust adjustment coefficient, constraints on source-load uncertainty are introduced during actual scheduling to adjust and limit the number of time periods during which uncertainty factors may reach extreme values ​​within a preset interval. The robust equivalence model obtained based on the improved robust linear optimization theory is as follows:

[0136]

[0137] In the formula: P WT,t , These represent the power output, electrical load, thermal load, and gas load of the system's new energy units at time t. These represent the predicted values ​​of the system's new energy unit output, electrical load, thermal load, and gas load at time t. These represent the deviations of new energy output, electrical load, heat load, and gas load at time t; v WT,t q WT,t , The auxiliary variable introduced; y WT,t , This is a binary variable; a value of 0 indicates that the uncertain source load model has transformed into a deterministic model, while a value of 1 indicates that the uncertain source load model has reached the extreme value of the uncertain set; τ WT , The value represents the uncertainty.

[0138] about All are continuous nonlinear expressions. To enable them to be solved directly in a linear solver, Q+1 continuous auxiliary variables [w] are introduced. 1,t ,w 2,t ,…,w Q+1,t ] and Q binary auxiliary variables [z 1,t ,z 2,t ,…,z Q,t ], as follows, c GCT,t For example, let's transform it into the linear form shown below:

[0139] c GCT,t =w 1,t c GCT,t (b1)+w 2,t c GCT,t (b2)+…+w Q+1,t c GCT,t (b Q+1 )

[0140]

[0141] Among them, [b1,b2,…,b Q+1 ] represents Q+1 segmentation points.

[0142] for by To explain, first let variable c be defined. CTM,t and E CEA,t The range of values ​​is and Use 2 K Discrete unit E CEA,t For discrete processing, a larger K value yields better results but is less efficient, as shown below:

[0143]

[0144] Among them, u k For binary variables; ΔE CEA,t For discrete elements, the expression is as follows:

[0145]

[0146] From the above two equations, we can obtain F. CTM The approximate expression is as follows:

[0147]

[0148] Let γ k =c CTM,t u k Then F CTM The approximate linearized expression can be represented as:

[0149]

[0150] for The linearization process is similar and will not be elaborated here.

[0151] According to one aspect of the present invention, a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism is provided, comprising: a first establishment module for establishing a dynamic green certificate-carbon emission collaborative trading mechanism; a second establishment module for establishing a model of relevant equipment of the hydrogen-containing integrated energy system; a third establishment module for establishing a robust optimization scheduling model of the hydrogen-containing integrated energy system considering the dynamic green certificate-carbon emission collaborative trading mechanism; and a solution module for solving the robust optimization scheduling model of the hydrogen-containing integrated energy system considering the dynamic green certificate-carbon emission collaborative trading mechanism based on robust linear optimization theory.

[0152] Furthermore, to verify the effectiveness of the proposed model, this paper sets up four scenarios for comparative analysis, namely:

[0153] Scenario 1 considers robust optimization scheduling of hydrogen-containing integrated energy systems (IES) under the parallel operation of traditional carbon trading and green certificate trading mechanisms; Scenario 2 considers robust optimization scheduling of hydrogen-containing IES under the interaction of traditional carbon-green certificate trading mechanisms; Scenario 3 considers robust optimization scheduling of hydrogen-containing IES under the parallel operation of dynamic carbon trading and dynamic green certificate trading mechanisms; Scenario 4 considers robust optimization scheduling of hydrogen-containing IES under the dynamic green certificate-carbon emission coordinated trading mechanism. The scheduling results of the hydrogen-containing integrated energy system are analyzed for these four scenarios.

[0154] To verify the effectiveness and applicability of the proposed model, the present invention provides examples in the appendix. Figure 4 Based on the hydrogen-containing IES architecture, the system was validated with a scheduling cycle of 24 hours. The original load and wind power forecast curves are attached. Figure 5 The relevant parameters are shown in Table 1, and the time-of-use electricity price information is shown in Table 2.

[0155] Table 1 Relevant Parameters

[0156]

[0157] Table 2 Time-of-use Electricity Prices

[0158] name Time Time-of-use electricity price (RMB / kW·h) Peak hours 12:00—14:00、19:00—22:00 1.20 Normal period 08:00—11:00、15:00—18:00 0.75 Valley period 01:00—07:00、23:00—24:00 0.40

[0159] 1) Analysis of scheduling results for each scenario

[0160] Taking the scheduling result with a robust adjustment parameter of 0.5 and an uncertainty of 18 as an example, the severe source-load scenario considering the uncertainty is shown in the attached figure. Figure 6 -Appendix Figure 7 As shown in Table 3, the scheduling results for different scenarios are as follows. Table 3 shows that Scenario 2 reduces total cost and carbon emissions by 5.27% and 49.22% respectively compared to Scenario 1, indicating that the interactive operation of the carbon market and the green certificate market can effectively reduce system operating costs while reducing carbon emissions. Scenario 3 reduces total cost and carbon trading costs by 1.39% and 13.14% respectively compared to Scenario 1, while increasing green certificate revenue by 43.05%, indicating that the dynamic trading supply and demand curve proposed in this paper can reduce system operating costs while increasing green certificate trading revenue. The carbon emissions in Scenario 3 are slightly higher than in Scenario 1 because the dynamic trading supply and demand curve strategy proposed in this paper provides time-varying market supply and demand price signals, enabling hydrogen-containing IES to control the risk of their carbon-green certificate costs, thereby appropriately increasing some power generation supply load demand and reducing DR costs. Scenario 4, due to the consideration of dynamic green certificate-carbon emission coordinated trading, has lower total cost and carbon emissions than the other three scenarios, indicating that the model proposed in this paper can increase green certificate revenue while reducing system operating costs and carbon emissions.

[0161] Table 3 Scheduling results for different scenarios

[0162] type Scene 1 Scene 2 Scene 3 Scene 4 Total cost F / yuan 31714.2 30043.8 31273.3 29673.3 <![CDATA[Carbon trading cost F CTM / yuan]]> 2403.3 732.2 2087.6 854.2 <![CDATA[Green certificate trading revenue F GCT / yuan]]> 828.6 828.6 1455.1 1439.4 <![CDATA[DR cost F DR / yuan]]> 1040.9 1042.1 1034.4 1022.3 <![CDATA[Carbon emission E CEA,t / kg]]> 12016.7 6102.1 12246.1 5976.6

[0163] 2) Research on the impact of dynamic green certificate-carbon emission collaborative trading mechanism on the operating characteristics of hydrogen-containing IES

[0164] To verify the effectiveness of the proposed dynamic green certificate-carbon emission coordinated trading mechanism, four scenarios were set up for comparative analysis with Scenario 4. Scenario 5 considers the traditional carbon trading model with the lowest carbon price for robust optimization scheduling; Scenario 6 considers the traditional carbon trading model with the highest carbon price for robust optimization scheduling; Scenario 7 considers the traditional green certificate trading model with the lowest green certificate price for robust optimization scheduling; Scenario 8 considers the traditional green certificate trading model with the highest green certificate price for robust optimization scheduling.

[0165] Table 4 shows a comparative analysis of different scenarios. As can be seen from Table 4, Scenario 5, by setting the carbon price to a fixed minimum value, has a lower total cost than Scenario 4. However, due to the relaxed carbon emission restrictions caused by the excessively low carbon price, carbon emissions are higher than in Scenario 4. Scenario 6, by setting the carbon price to a fixed maximum value, reduces carbon emissions by 1061.6 kg compared to Scenario 4, but the corresponding cost increases by 2237.5 yuan. This indicates that setting the carbon price to the maximum value increases carbon emission control, but leads to a significant increase in cost. Furthermore, Scenario 6 reduces carbon emissions by 17.76% compared to Scenario 4, while increasing costs by 7.54%. This is because the carbon price is higher than the green certificate price, resulting in a larger conversion coefficient. Therefore, the system purchases additional green certificates to convert into carbon allowances to offset carbon obligations, reducing actual carbon emissions. This means sacrificing some benefits to increase carbon emission reduction efforts, but overall, the reduction in carbon emissions is more significant. This also indirectly confirms the effectiveness of the dynamic green certificate trading and the green certificate-carbon emission conversion interaction based on real-time prices proposed in this paper. Scenario 7 sets the green certificate price to a fixed minimum value, increasing the total system cost by 2.04% compared to Scenario 4 and reducing green certificate revenue by 46.75%. Scenario 8 sets the green certificate price to a fixed maximum value, reducing the total system cost by 5.18% compared to Scenario 4, but the excessively high green certificate price hinders green certificate trading activity, increasing carbon emissions by 2.45% compared to Scenario 4. Scenario 4, by considering dynamic trading supply and demand curves, flexibly reflects the real-time supply and demand relationship between the carbon market and the green certificate market. Therefore, its economic efficiency and low-carbon characteristics achieve a balance between Scenario 5 and Scenario 8, further promoting the effective synergy of the carbon-green certificate coupled market.

[0166] Table 4 Comparison Analysis of Different Scenarios

[0167] type Scene 5 Scene 6 Scene 7 Scene 8 Total cost / yuan 29001.0 31910.8 30278.7 28135.4 Carbon trading cost / yuan 300.9 2703.3 870.7 884.5 Green certificate trading profit / yuan 1439.4 957.2 766.5 2985.1 Carbon emissions / kg 6018.7 4915.0 5859.4 6123.0

[0168] Scenario 4: The relationship between the trading of green certificates and changes in green certificate prices is as follows: Figure 8 As shown. Combined with Figure 8 and Figure 5It is observed that during the periods of 01:00–07:00 and 22:00–24:00, wind power output is sufficient, resulting in a large number of green certificates issued. During these periods, a significant number of green certificates are sold after meeting quota targets, leading to a higher green certificate price, closer to the incentive zone. From 13:00 to 18:00, wind power output is lower, and the issued green certificates are insufficient to meet renewable energy quota targets. Therefore, it is necessary to purchase green certificates from the green certificate market to offset these quotas. The demand for green certificates is highest at 15:00, resulting in a price closer to the penalty zone, with the highest purchase price. Furthermore, from 13:00 to 18:00, both the purchase and sale prices of green certificates are lower, while the demand for carbon allowances is higher. Therefore, increasing the conversion of green certificates into carbon allowances to offset carbon emissions optimizes system operation.

[0169] Scenario 4: The relationship between carbon allowance trading and carbon price changes, as follows Figure 9 As shown. By Figure 9 It can be seen that during most periods, hydrogen-containing IES as a whole needs to purchase carbon allowances. During 01:00, hydrogen-containing IES as a whole requires fewer carbon allowances, thus allowing them to sell excess allowances for profit. During 16:00, due to the highest demand for carbon allowances, the carbon price for purchasing carbon allowances is the highest, at 167.92 yuan / t. This reflects a close dynamic supply and demand relationship between carbon allowance trading and carbon prices. Furthermore, considering dynamic green certificate-carbon emission coordinated trading, during 15:00-17:00, carbon emissions are high, and carbon prices tend towards the penalty zone. Therefore, during these periods, increasing the number of green certificates converted into carbon allowances to offset carbon emissions optimizes the supply and demand balance of green certificates and carbon allowances. The above analysis shows that the prices of green certificates and carbon prices exhibit a dynamic supply and demand relationship with the trading volume of green certificates and carbon quotas at different times. This reflects the dynamic changes of the dynamic collaborative trading mechanism and more flexibly reflects the supply and demand relationship in the green certificate market and the carbon market. Furthermore, the supply and demand relationship of green certificates and carbon quotas is similar to the changing trends of the corresponding green certificate prices and carbon prices.

[0170] The impact of different green certificate quota coefficients on system operation is as follows: Figure 10 As shown. By Figure 10 It is evident that as the green certificate quota coefficient increases, system carbon emissions decrease, but the economic efficiency of system operation is sacrificed. When the green certificate quota coefficient is between 0.15 and 0.20, both the economic efficiency and low-carbon performance of the system are good. Analysis indicates that when generating scheduling schemes, the green certificate quota coefficient should be set reasonably to ensure both the economic efficiency and low-carbon benefits of system operation.

[0171] 3) The impact of robust uncertainty parameters on dynamic green certificate-carbon emission coordinated trading

[0172] The impact of robust uncertainty parameters on dynamic green certificate-carbon emission coordinated trading, such as Figure 11As shown, the collaborative benefits gradually decrease as the robust uncertainty parameter increases. With a fixed robust adjustment parameter Γ of 0.5, the collaborative benefits are negatively correlated with the uncertainty value; the collaborative benefits decrease as the uncertainty increases. This is because with increasing uncertainty, the system purchases more energy within the allowable operating range of the equipment, resulting in higher carbon emissions. With a fixed uncertainty τ of 24, the collaborative benefits of hydrogen-containing IES dynamic green certificate-carbon emission trading are highest when Γ∈[0,0.25], but the flexibility is insufficient, and the ability to cope with uncertainty is weak. When Γ∈[0.75,1], the hydrogen-containing IES scheduling scheme has a stronger ability to cope with uncertainty factors, but the collaborative benefits of dynamic green certificate-carbon emission trading are poor. When Γ∈[0.25,0.75], the collaborative benefits of hydrogen-containing IES dynamic green certificate-carbon emission trading reach equilibrium. The analysis shows that the reasonable setting of uncertainty and robust adjustment parameters has a significant positive effect on the benefits of green certificate-carbon emission trading.

[0173] 4) RSOC Operation Analysis Considering Source Load Uncertainty

[0174] RSOC running results taking into account source load uncertainties are as follows Figure 12 As shown. By Figure 12 It is evident that the RSOC fully leverages the flexibility of bidirectional hydrogen-electricity conversion. During periods of abundant renewable energy and low electricity load, when wind power output is high and electricity prices are low, the RSOC operates in SOEC mode, converting low-priced and surplus electricity in the system locally into hydrogen for storage, thereby increasing wind power absorption and thus enhancing green certificate trading revenue. During peak load periods, the RSOC operates in SOFC mode, consuming hydrogen to produce thermal power, which to some extent alleviates the power supply burden on CHP units. Simultaneously, hydrogen power generation is more environmentally friendly, effectively reducing carbon emissions and thus increasing carbon trading revenue. Analysis shows that the introduction of the RSOC improves the spatiotemporal complementarity of hydrogen-containing IES power supply and demand, effectively enhancing the flexibility of hydrogen-containing IES dispatch.

[0175] SOEC mode output such as Figure 13 As shown. By Figure 13 It is understood that RSOC primarily produces hydrogen through SOEC mode during periods of high wind power generation and off-peak electricity prices, and blends a portion of the produced hydrogen into the natural gas system via natural gas pipelines. Hydrogen fuel cell vehicles consume energy during periods such as 10:00-12:00, 14:00-17:00, and 01:00, and recharge during periods of 02:00-07:00 and 23:00-24:00, consuming a portion of the hydrogen converted from wind power. By rationally scheduling the SOEC mode operation time and the recharging plan for hydrogen fuel cell vehicles, not only can wind power resources be fully utilized and wind curtailment reduced, but load demand can also be effectively balanced.

[0176] As can be seen from the above technical solution, the method proposed in this invention improves the operational flexibility of hydrogen-containing integrated energy systems while also improving the economic efficiency and carbon emission levels of the system operation.

[0177] Example 2: A robust optimization scheduling system for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism, comprising: a first module for establishing a dynamic green certificate trading mechanism considering reward and penalty characteristics; establishing a dynamic carbon trading mechanism considering reward and penalty characteristics; and establishing a dynamic green certificate-carbon emission collaborative trading mechanism considering reward and penalty characteristics based on the dynamic green certificate trading mechanism and the dynamic carbon trading mechanism considering reward and penalty characteristics; a second module for establishing an equipment model of the hydrogen-containing integrated energy system based on the architecture of the hydrogen-containing integrated energy system; a third module for determining the objective function and constraints based on the dynamic green certificate-carbon emission collaborative trading mechanism considering reward and penalty characteristics and the equipment model of the hydrogen-containing integrated energy system, in order to establish a robust optimization scheduling model of the hydrogen-containing integrated energy system considering the dynamic green certificate-carbon emission collaborative trading mechanism; and a fourth module for solving the robust optimization scheduling model of the hydrogen-containing integrated energy system considering the dynamic green certificate-carbon emission collaborative trading mechanism based on robust linear optimization theory.

[0178] The technical features and effects of the robust optimization scheduling system for hydrogen-containing integrated energy systems, which incorporates a dynamic green certificate-carbon emission collaborative trading mechanism, proposed in this embodiment of the invention are the same as those of the method proposed in Embodiment 1 of the invention, and will not be repeated here. Each module in the aforementioned robust optimization scheduling system for hydrogen-containing integrated energy systems, which incorporates a dynamic green certificate-carbon emission collaborative trading mechanism, can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0179] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism, characterized in that, Includes the following steps: Establish a dynamic green certificate trading mechanism that takes into account reward and punishment characteristics; establish a dynamic carbon trading mechanism that takes into account reward and punishment characteristics; based on the dynamic green certificate trading mechanism and the dynamic carbon trading mechanism that take into account reward and punishment characteristics, establish a dynamic green certificate-carbon emission coordinated trading mechanism that takes into account reward and punishment characteristics. Based on the architecture of a hydrogen-containing integrated energy system, establish an equipment model for the hydrogen-containing integrated energy system; Based on the dynamic green certificate-carbon emission collaborative trading mechanism that takes into account the reward and punishment characteristics and the equipment model of the hydrogen-containing integrated energy system, the objective function and constraints are determined in order to establish a robust optimization scheduling model of the hydrogen-containing integrated energy system that takes into account the dynamic green certificate-carbon emission collaborative trading mechanism. A robust optimization scheduling model for a hydrogen-containing integrated energy system is developed based on robust linear optimization theory and a dynamic green certificate-carbon emission collaborative trading mechanism.

2. The robust optimization scheduling method for hydrogen-containing integrated energy systems considering dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 1, characterized in that, The establishment of the dynamic green certificate trading mechanism that takes into account reward and punishment characteristics is specifically as follows: Based on the number of green certificates required by the system at time t and the number of green certificates obtained by the system at time t, determine the number of green certificates to participate in the green certificate trading market at time t without considering the conversion of green certificates; By introducing a two-stage linearization function, a dynamic green certificate trading price model that takes into account reward and punishment characteristics is constructed, as shown in the following expression: In the formula: c GCT,t The dynamic green certificate trading price at time t; This is the floor price for green certificate trading; The penalty price for green certificate trading; The threshold for green certificate trading; k1 and k2 are the linear price slopes of the first and second segments of the green certificate trading linearization function, respectively; N GCT,t This takes into account the number of green certificates participating in the green certificate trading market at time t under the conversion of green certificates.

3. The robust optimization scheduling method for hydrogen-containing integrated energy systems considering dynamic green certificate-carbon emission collaborative trading mechanism according to claim 2, characterized in that, The green certificate trading threshold is calculated as follows: In the formula: The threshold for green certificate transactions; ρ com The proportion of paid carbon allowances; E IES,t Let θ be the carbon quota of the system at time t; θ1 is the conversion coefficient between carbon quota and green certificate.

4. The robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 1, characterized in that, The establishment of the dynamic carbon trading mechanism that takes into account reward and punishment characteristics is specifically as follows: A carbon emission and carbon quota model is established, expressed as follows: Where: E C,t Let t be the actual carbon emissions of the system at time t; The system's power purchase at time t; These represent the electrical and thermal power output by CHP at time t, respectively. Let be the heat production power of GB at time t; a1 be the carbon emission parameters of the upstream power purchase; a2 be the carbon emission parameters of the natural gas-consuming equipment; a3 be the carbon emission parameters of wind power considering the life cycle assessment method; χ1 be the carbon emission quota parameters of the upstream power purchase; χ2 be the carbon emission quota parameters of the natural gas-consuming equipment; χ3 be the carbon emission quota parameters of wind power considering the life cycle assessment method. This does not take into account the carbon emissions generated at time t when participating in the carbon market under the real-time green certificate carbon credit. The maximum value of purchasable paid carbon allowances; ρ com The proportion of paid carbon allowances; E IES,t P represents the system's carbon quota at time t; WT,t Let t be the system's wind power output. A two-stage linearization function is introduced to construct a dynamic carbon trading price model that takes into account reward and punishment characteristics, as shown in the following expression: In the formula: c CTM,t The dynamic carbon trading price at time t; The floor price for carbon trading; The penalty price for carbon trading; k3 and k4 are the linear price slopes of the first and second segments of the carbon trading linearization function, respectively; E CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deductions for participation in the carbon trading market.

5. The robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 1, characterized in that, Based on the dynamic green certificate trading mechanism and the dynamic carbon trading mechanism that take into account reward and penalty characteristics, a dynamic green certificate-carbon emission coordinated trading mechanism that takes into account reward and penalty characteristics is established, specifically as follows: Discretize the time scale of the green certificate trading mechanism and the carbon trading mechanism to be consistent with the time scale of the hydrogen-containing integrated energy system; The carbon emission reductions associated with obtaining green certificates The carbon quota and green certificate conversion coefficient θ1 is determined based on the nonlinear price mapping function of the natural logarithm. Based on the carbon emission reductions, carbon quotas, and green certificate conversion coefficients associated with green certificates, the trading volume of green certificates and carbon quotas for a hydrogen-containing integrated energy system after considering dynamic green certificate-carbon emission coordinated trading is calculated. The expression is as follows: In the formula: N represents the number of green certificates participating in the green certificate trading market at time t, without considering the conversion of green certificates; trans,t N represents the number of green certificates participating in the conversion. ne,t Let be the number of green certificates the system needs at time t; This excludes carbon emissions from participating in the carbon trading market at time t under the real-time green certificate carbon credit; N GCT,t E represents the amount of green certificates traded in the green certificate trading market at time t, considering the conversion of green certificates; CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deductions for participation in the carbon trading market.

6. The robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 1, characterized in that, The hydrogen-containing integrated energy system equipment model includes the RSOC model, the source-load uncertainty model, the cogeneration unit model, the integrated demand response model, the energy storage model, the gas boiler model, and the hydrogen fuel cell vehicle model.

7. The robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 6, characterized in that, The RSOC model is expressed as follows: In the formula: P SOEC,t These represent the hydrogen production power and power consumption in SOEC mode at time t, respectively. These represent the power generation, heat generation, and hydrogen consumption in SOFC mode at time t, respectively; η SOEC Hydrogen production efficiency in SOEC mode; These are the power generation and heat generation efficiencies of SOFC mode, respectively. These are the maximum power consumption and hydrogen consumption power of the RSOC, respectively. and These are the upper and lower limits of ramp power in SOFC mode, respectively. and These represent the upper and lower limits of ramp power in SOEC mode, respectively.

8. The robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 6, characterized in that, The source load uncertainty model is expressed as follows: In the formula: P WT,t , These represent the power output, electrical load, thermal load, and gas load of the system's new energy units at time t. These represent the predicted values ​​of the system's new energy unit output, electrical load, thermal load, and gas load at time t. These represent the deviations of the system's new energy output, electrical load, thermal load, and gas load at time t; Γ WT , To robustly adjust parameters.

9. The robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 1, characterized in that, The objective function aims to minimize the sum of carbon trading costs, green certificate trading costs, energy purchase costs, equipment maintenance costs, wind curtailment costs, and demand response costs. The constraints include electrical power balance constraints, thermal power balance constraints, gas power balance constraints, and hydrogen power balance constraints.

10. The robust optimization scheduling method for a hydrogen-containing integrated energy system considering a dynamic green certificate-carbon emission collaborative trading mechanism as described in claim 1, characterized in that, The robust optimization scheduling model for a hydrogen-containing integrated energy system, based on robust linear optimization theory and a dynamic green certificate-carbon emission collaborative trading mechanism, is as follows: Based on robust linear optimization theory, and introducing y WT,t , The source-load uncertainty model in the hydrogen-containing integrated energy system equipment model is transformed into a robust equivalent model, as shown in the following expression: In the formula: P WT,t , These represent the power output, electrical load, thermal load, and gas load of the system's new energy units at time t. These represent the predicted values ​​of the system's new energy unit output, electrical load, thermal load, and gas load at time t. These represent the deviations of the system's new energy output, electrical load, thermal load, and gas load at time t; v WT,t q WT,t , The auxiliary variable introduced; y WT,t , For binary variables; τ WT , The uncertainty of the system's new energy output, electrical load, heat load and gas load; For the dynamic green certificate trading price model in the dynamic green certificate trading mechanism that takes into account reward and punishment characteristics, and the dynamic carbon trading price model in the dynamic carbon trading mechanism that takes into account reward and punishment characteristics, Q+1 continuous auxiliary variables and Q binary auxiliary variables are introduced to convert the corresponding models into linear forms. For the robust optimization scheduling model of a hydrogen-containing integrated energy system that considers the dynamic green certificate-carbon emission collaborative trading mechanism, the range of variable values ​​for the carbon trading cost expression in the objective function is set, and then discrete units are used to analyze E in the carbon trading cost expression. CEA,t Discretization is performed to obtain an approximately linearized expression for the carbon trading cost expression; where E CEA,t This represents the carbon emissions at time t after real-time green certificate carbon deduction and participation in the carbon trading market; For the robust optimization scheduling model of a hydrogen-containing integrated energy system that considers the dynamic green certificate-carbon emission collaborative trading mechanism, the range of variable values ​​for the variables involved in the green certificate trading cost expression of the objective function is set. Then, discrete units are used to analyze N in the green certificate trading cost expression. GCT,t Discretization is performed to obtain an approximate linearized expression for the green certificate transaction cost; N GCT,t This represents the volume of green certificates traded in the green certificate trading market at time t, considering the conversion of green certificates. Solve using a solver.

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