RIES source load storage optimization scheduling method considering green power-green hydrogen certificate joint transaction mechanism

Abstract

The invention provides an RIES source load storage optimization scheduling method considering a green power-green hydrogen certificate joint transaction mechanism. In order to solve the problems of insufficient renewable energy consumption, high green hydrogen production cost and limited market acceptance, the method comprises the following steps: firstly, constructing an electricity-heat-hydrogen-gas IRES operation framework, taking a carbon transaction mechanism and a green certificate transaction mechanism as references, further providing a green hydrogen energy certificate transaction mechanism and model, and forming a green electricity-green hydrogen certificate joint transaction mechanism; the advantages of green electricity and green hydrogen are exerted to realize green energy utilization and synergistic carbon reduction. And secondly, considering the adjustment characteristics of power, heat and gas loads, establishing a comprehensive energy load demand response model, and further constructing an RIES source load storage low-carbon economic dispatching model considering a green power-green hydrogen certificate joint transaction mechanism.

Description

Technical Field

[0001] This invention relates to the field of regional integrated energy system dispatching technology. Specifically, it is a source-load-storage optimization dispatching method for regional integrated energy systems (RIES) that considers a green electricity-green hydrogen certificate joint trading mechanism. It is applicable to solving problems such as insufficient renewable energy consumption, high green hydrogen production costs and limited market acceptance, and realizes low-carbon economic operation and multi-energy flow synergistic optimization of regional integrated energy systems. Background Technology

[0002] At this critical juncture of energy structure transformation, insufficient renewable energy absorption capacity, high operating costs of green hydrogen production, and limited market acceptance significantly constrain the efficient utilization and deep synergy of clean energy. Meanwhile, although existing market trading mechanisms have flourished under a sound policy framework, strengthening environmental value transformation and economic incentives, and providing multi-layered institutional guarantees and market drivers for clean energy systems, they still fall short in effectively connecting the green electricity and green hydrogen markets and incentivizing their deep synergy in carbon reduction.

[0003] Existing technologies have two significant limitations: The market-based trading mechanisms for green electricity and green hydrogen are disconnected: The green certificate trading (GCT) system has been applied to the electricity market to promote the consumption of green electricity. Some technical solutions propose the green hydrogen certificate trading (GHCT) mechanism to quantify the green value of green hydrogen. However, GCT and GHCT are mostly disconnected and have not formed a joint trading mechanism model that can fully stimulate the synergistic benefits of green electricity to hydrogen production and carbon reduction potential.

[0004] The RIES optimization scheduling method has shortcomings: While existing research focuses on the potential of Demand Response (DR) technology to adjust user electricity / heat / gas load curves and attempts to introduce single GCT or GHCT mechanisms into scheduling models, two key issues remain. First, the trading mechanism design emphasizes a single certificate type, failing to integrate the green value correlation and synergistic carbon reduction advantages of green electricity and green hydrogen, thus limiting the market's guiding role in the synergistic coupling of electricity and hydrogen. Second, the utilization of load-side regulation resources is insufficient; the load types and regulation characteristics involved in DR are not adapted to the actual complexity of multi-energy flow coupling within a comprehensive energy system, and the diverse flexible load resources are not deeply integrated with the source-side green electricity-green hydrogen collaborative production and certificate joint trading mechanism for optimization.

[0005] 1) Existing technologies mainly focus on traditional hydrogen energy utilization pathways, and have not fully explored the potential of electricity-hydrogen synergy in dynamic synergistic regulation and multi-energy flow coupling, especially in the deep synergy between green electricity consumption and green hydrogen value, which still needs breakthroughs. This invention innovatively utilizes market trading mechanisms to achieve quantitative certification and market incentives for the carbon reduction value of green hydrogen by dynamically coupling green electricity consumption and green hydrogen production processes. At the same time, it establishes a multi-energy load synergistic response model of electricity-heat-gas, driving the source-load-storage resources and certificate trading mechanism to form a closed-loop synergistic optimization, ultimately achieving a triple breakthrough in RIES: increased renewable energy consumption rate, increased green hydrogen production, and reduced carbon emission intensity.

[0006] 2) Existing technologies mostly explore and study the effects of single mechanisms such as GCT and GHCT, without fully considering the synergistic effects of multiple mechanisms and their comprehensive impact. This invention, however, is the first to propose a GHCT mechanism model and introduce the GCT and GHCT mechanisms into RIES for optimized operation. At the same time, it uniformly converts green electricity certificates and green hydrogen certificates into deductible carbon quotas and embeds them into the carbon emission quota model to promote the coordinated trading and operation of green electricity and green hydrogen, and give full play to their synergistic role in absorbing new energy and reducing carbon emissions.

[0007] 3) While existing technologies consider the scheduling potential of demand-side load (DR), the types of loads participating in DR are relatively limited, and they are not optimized in conjunction with source-side equipment and the green electricity-green hydrogen certificate joint trading mechanism, resulting in limited demand-side resource regulation capabilities. In contrast, this invention considers the regulation characteristics of electricity, heat, and gas loads and optimizes the energy supply and demand relationship of the system. It can form a "source-load complementarity" with the joint trading mechanism, jointly promoting system stability, flexibility, and economy.

[0008] In summary, existing RIES optimization scheduling methods are insufficient to simultaneously achieve global optimization at the system level, including efficient green electricity consumption, large-scale economic utilization of green hydrogen, low-carbon cost optimization, and flexible response of multi-energy loads. There is an urgent need to explore new optimization scheduling technologies that leverage the synergistic advantages of green electricity and green hydrogen in the energy market and achieve coordinated response of multi-energy loads. Summary of the Invention

[0009] Purpose of the invention: To address the problems of insufficient renewable energy consumption, high green hydrogen production costs and limited market acceptance in existing technologies, the disconnect between green electricity and green hydrogen trading mechanisms, and insufficient RIES optimization scheduling capabilities, this invention provides a RIES source-load-storage optimization scheduling method that considers a green electricity-green hydrogen certificate joint trading mechanism. This method achieves dynamic synergy between the multi-energy flow optimization scheduling of source, load, and storage within the RIES and the joint trading of green electricity and green hydrogen certificates, thereby improving the low-carbon economy and renewable energy consumption rate of the system operation.

[0010] Technical Solution: A RIES source-load-storage optimization scheduling method considering a green electricity-green hydrogen certificate joint trading mechanism, the method comprising: (1) Establish and construct an electricity-heat-hydrogen-gas IRES operating system, taking carbon trading mechanism and green certificate trading mechanism as reference. The system includes energy forms including electricity, gas, heat and hydrogen. Energy is obtained through wind power, photovoltaic, upper-level power grid and external gas network to meet the supply and demand balance. Its electrolyzer electrolyzes water and supplies the heat generated in the process to the heat load to form electricity-hydrogen energy conversion. The methanation reactor uses the hydrogen generated by the electrolyzer and absorbs CO2 to produce natural gas to form hydrogen-gas energy conversion, converting hydrogen energy into electricity and heat to form hydrogen-electricity-heat energy coupling. (2) Construct a green hydrogen energy certificate trading mechanism and model to form a green electricity-green hydrogen certificate joint trading mechanism. The trading mechanism includes issuing green electricity certificates representing the power generation of renewable energy power generation systems, allowing the certificates to be traded in the market, and obtaining the corresponding green electricity certificate quota after the renewable energy power generation system submits an application to the renewable energy information management center to participate in the green electricity certificate trading. If the quota is not met, the system needs to purchase the certificate. The market trading promotes the consumption of renewable energy and the optimization of the energy structure. (3) Considering the regulation characteristics of electricity, heat and gas loads, a comprehensive energy load demand response model is established. This model considers the regulation characteristics of electricity, heat and gas loads, classifies load types and formulates corresponding regulation rules to achieve peak shaving and valley filling of the load curve. (4) Construct a low-carbon economic dispatch model for RIES source-load-storage that considers the joint trading mechanism of green electricity and green hydrogen certificates. The low-carbon economic dispatch model of RIES source-load-storage integrates the joint trading mechanism of green electricity and green hydrogen certificates in step (2) and the comprehensive energy load demand response model in step (3) into the dispatch model to achieve low-carbon economic optimization dispatch of RIES source-load-storage.

[0011] Furthermore, in step (1), the system also includes a combined heat and power unit, a gas boiler, a hydrogen fuel cell, and energy storage equipment; The combined heat and power unit generates electricity by driving a generator through fuel combustion, while recovering waste heat from flue gas to produce steam or hot water for supply to the heating network. The gas-fired boiler supplies energy by heating circulating water through the combustion of natural gas. The hydrogen fuel cell converts hydrogen energy into electricity and heat; The energy storage devices include electrical energy storage, thermal energy storage, hydrogen energy storage, and gas energy storage, used to realize the storage and on-demand release of energy.

[0012] The aforementioned green hydrogen certificate trading mechanism involves converting the amount of green hydrogen certificates generated by the system through electrolysis of water using renewable energy. If the amount of green hydrogen certificates is lower than the daily quota, the system needs to purchase more green hydrogen certificates or pay a penalty. If the amount of green hydrogen certificates is higher than the daily quota, the system can profit by selling the excess green hydrogen certificates.

[0013] Furthermore, in step (2), when constructing the green hydrogen energy certificate trading model, it is necessary to correct the problem of duplicate billing. The duplicate billing is the cost of green electricity certificates corresponding to wind power consumed by wind power hydrogen production. The corrected green hydrogen energy certificate trading cost needs to deduct the duplicate billing cost.

[0014] Furthermore, in step (3), the load types include fixed load, transferable load and alternative load; for transferable load, the total load is constrained to remain unchanged within the scheduling cycle, thereby achieving horizontal shift on the time scale; for alternative load, the total energy demand of users is constrained to remain unchanged within the same time period, thereby achieving vertical conversion of energy form.

[0015] Furthermore, in step (4), the objective function of the RIES source-load-storage low-carbon economic dispatch model is to minimize the overall system cost. The overall cost includes operating cost, transaction cost, wind curtailment cost, and demand response compensation cost. The operating cost covers energy purchase cost and equipment maintenance cost. The transaction cost covers green electricity certificate transaction cost, green hydrogen certificate transaction cost, and carbon transaction cost.

[0016] The carbon trading cost is calculated based on the carbon emission quota model, setting a carbon emission ceiling and allocating carbon quotas. If the actual emissions of the system are lower than the carbon quota, the remaining carbon quota can be sold; if the actual emissions are higher than the carbon quota, the difference in carbon quota must be purchased. The carbon emission sources of the system include upstream electricity purchases, CHP, GB, and direct consumption of natural gas at the terminal, while also taking into account the carbon capture effect of the power-to-gas conversion device, and converting green electricity certificates and green hydrogen certificates into deductible emission reductions.

[0017] Furthermore, in step (4), the RIES source-load-storage low-carbon economic dispatch model must meet the constraints of electric power balance, thermal power balance, natural gas balance, hydrogen energy balance, and energy storage equipment output. The electric power balance constraint ensures that the power generation of the cogeneration unit, the power generation of the hydrogen fuel cell, the power purchased from the upstream, and the charging and discharging of the electric energy storage are matched with the electric load demand during the time period. The thermal power balance constraint ensures that the heat generated by the hydrogen fuel cell, the heat generated by the cogeneration unit, the heat generated by the gas boiler, the heat generated by the electrolyzer, and the charging and discharging of the thermal energy storage are matched with the heat load demand during the time period. The natural gas balance constraint ensures that the gas purchased from the upstream, the amount of natural gas synthesized by the methanation reactor, and the charging and discharging of the gas storage are matched with the gas load demand during the time period. The hydrogen energy balance constraint ensures that the hydrogen produced by the electrolyzer, the charging and discharging of the hydrogen storage are matched with the hydrogen load demand during the time period. The energy storage equipment output constraint limits the charging and discharging power and capacity of the energy storage equipment to within the range required by the system operation.

[0018] Furthermore, the electrolyzer is a proton exchange membrane electrolyzer, and its operation constraints include: limiting the upper and lower limits of the input power and ramping limits of the electrolyzer, while considering the heat transfer during the electrolysis process, constraining the balance of the electrolyzer's heat generation power, heat loss power, output heat power and input heat network heat power, and the relationship between the electrolyzer temperature and the ambient temperature.

[0019] In step (4), a backup capacity mechanism is adopted to ensure power supply reliability in response to the uncertainty of wind power output and system electrical load; The reserve capacity includes upward reserve capacity and downward reserve capacity, which is jointly provided by electric energy storage, electrolyzers, cogeneration units, and hydrogen fuel cells. The reserve capacity coefficient is set based on the probability distribution characteristics of wind power prediction errors and the fluctuation characteristics of load curves.

[0020] Beneficial Effects: The technical solution provided by this invention, on the one hand, draws on carbon trading and green certificate trading mechanisms to propose a green hydrogen energy certificate trading mechanism and model, and constructs a green electricity-green hydrogen certificate joint trading mechanism. On the other hand, it comprehensively considers the regulation characteristics of multiple loads such as electricity, heat, and gas, and establishes a demand response model. Through the coordinated transfer and reduction of electricity, heat, and gas loads, it forms a dynamic source-load interaction capability, deeply embedding demand-side resources into the green electricity-green hydrogen certificate joint trading framework. Ultimately, it achieves dynamic coordination between the optimized scheduling of multiple energy flows (source, load, and storage) within the RIES and the joint trading of green electricity-green hydrogen certificates. This invention can provide a reference for promoting the synergistic advantages of green electricity and green hydrogen in the energy market, improving the low-carbon economy of system operation, and increasing the renewable energy absorption rate. Compared with existing technologies, its effects include the following three aspects: Achieving a triple breakthrough: Compared to existing technologies that have not fully explored the potential of electricity-hydrogen synergy, this invention utilizes a market trading mechanism to dynamically couple the green electricity consumption and green hydrogen production processes, enabling quantitative certification and market-based incentives for the carbon reduction value of green hydrogen. Simultaneously, it establishes a multi-energy load synergistic response model of electricity, heat, and gas, driving the source-load-storage resources and certificate trading mechanism to form a closed-loop synergistic optimization, ultimately achieving a triple breakthrough in RIES: increased renewable energy consumption rate, increased green hydrogen production, and reduced carbon emission intensity.

[0021] Leveraging the advantages of collaborative trading: Existing technologies mostly focus on single mechanisms such as GCT and GHCT. This invention is the first to propose a GHCT mechanism model and integrate the two into RIES for optimized operation. At the same time, green electricity certificates and green hydrogen certificates are uniformly converted into deductible carbon quotas and embedded into the carbon emission quota model, promoting the collaborative trading and operation of green electricity and green hydrogen. Its collaborative absorption of new energy and carbon reduction effect is better than that of a single mechanism, enabling RIES to achieve dual benefits in the two certificate trading markets.

[0022] Enhancing load-side regulation capabilities: Addressing the issues of existing DR technologies having a single load type and lacking coordinated optimization with source-side equipment and joint trading mechanisms, this invention considers the regulation characteristics of electricity, heat, and gas loads to optimize the energy supply and demand relationship, forming a "source-load mutual complementarity" with the joint trading mechanism. This not only guides users to proactively adjust their energy consumption behavior and reduce the overall system cost, but also fully taps into the flexibility resources on the load side, significantly improving system stability, flexibility, and economy. Attached Figure Description

[0023] Figure 1 This is the RIES framework diagram of the electro-thermal-hydrogen-gas system; Figure 2 This is a schematic diagram illustrating the principle of green electricity certificate trading; Figure 3 This is a schematic diagram illustrating the principle of green hydrogen energy certificate trading; Figure 4 This is a schematic diagram illustrating the principle of the green electricity-green hydrogen certificate joint trading mechanism; Figure 5 This is a graph showing the total predicted wind power output and the predicted load. Figure 6 This is a framework diagram for the coordinated emission reduction of carbon quotas and green electricity and green hydrogen certificates; Figure 7 This is a diagram showing the power supply and demand balance in scenario 1; Figure 8 This is a diagram illustrating the heat supply and demand balance in scenario 1. Figure 9 This is a comparison chart of the green electricity ratio and renewable energy consumption rate for scenarios 1-5; Figure 10 This is a diagram showing the EL output in scenarios 1-5; Figure 11 This is a diagram showing the power supply and demand balance in scenario 4; Figure 12 This is a diagram showing the heat supply and demand balance in scenario 4. Figure 13 This is a diagram of wind power output under different quota coefficients in the green electricity trading model; Figure 14 This is a diagram showing the system operation results under different quota coefficients in the green electricity trading model; Figure 15 This is a graph showing the wind power output under the price gradient of the green hydrogen trading model; Figure 16 This is a graph showing the system operation results of the green hydrogen trading model under different trading prices; Figure 17 This is a diagram showing the demand response situation in scenario 5. Detailed Implementation

[0024] Based on the above technical solutions, the implementation process of the present invention will be further described below with reference to the accompanying drawings.

[0025] Overall, this invention provides a RIES source-load-storage optimization scheduling method considering a green electricity-green hydrogen certificate joint trading mechanism. Addressing the issues of insufficient renewable energy consumption and the high cost and limited market acceptance of green hydrogen production, this invention first constructs an electricity-heat-hydrogen-gas IRES operating framework. Referring to carbon trading and green certificate trading mechanisms, it then proposes a green hydrogen energy certificate trading mechanism and model, forming a green electricity-green hydrogen certificate joint trading mechanism to leverage the advantages of green electricity and green hydrogen to achieve green energy utilization and synergistic carbon reduction. Secondly, considering the regulation characteristics of electricity, heat, and gas loads, a comprehensive energy load demand response model is established, and then a low-carbon economic scheduling model for RIES source-load-storage considering the green electricity-green hydrogen certificate joint trading mechanism is constructed.

[0026] Specifically, the implementation steps of the above plan are as follows: (1) Established Electric-Heat-Hydrogen-Gas RIES Operating System Framework. This system includes various equipment for energy conversion, transmission, and storage, involving energy forms such as electricity, gas, heat, and hydrogen, and encompassing various load types such as electricity, gas, and heat. The energy supply equipment of this system includes combined heat and power (CHP) units, gas boilers (GB), electrolyzers (EL), methanation reactors (MR), hydrogen fuel cells (HFC), electrical energy storage, thermal energy storage, hydrogen energy storage, and gas energy storage. In addition, the system can also obtain energy from wind power, photovoltaics, the upstream power grid, and external gas networks to meet the supply and demand balance. The main equipment in the multi-stage utilization of hydrogen energy is EL, MR, HFC, and heat exchangers. EL produces hydrogen through the electrolysis of water, realizing the conversion of electricity to hydrogen energy, and is the main source of hydrogen energy supply in the system. In addition, the heat exchanger can recover the waste heat generated by the EL and supply it to the heat load, realizing the efficient use of energy; the MR uses the hydrogen generated by the EL and absorbs CO2 to produce natural gas, realizing hydrogen-gas energy conversion and is the main source of natural gas supply in the system; the HFC converts hydrogen energy into electricity and heat, realizing hydrogen-electricity-thermal energy coupling.

[0027] CHP (Continuous Power Generation) generates electricity by burning fuel in a gas turbine to drive a generator, while simultaneously introducing high-temperature flue gas into a waste heat boiler to produce steam or hot water for supplying the heating network. Its core lies in the cascade utilization of energy: the chemical energy of the fuel is first converted into high-grade electrical energy, and then medium- and low-grade waste heat is recovered to meet the heating or industrial steam demand, realizing the simultaneous production of both electricity and heat from fuel input.

[0028] GB mixes natural gas and air and ignites it through a burner to generate a high-temperature flame that heats the radiant heating surface; the high-temperature flue gas then flows through the convection tube bundle, transferring heat energy to the circulating water medium inside the tube.

[0029] When direct current is applied to the electrolyzer (EL), an oxygen evolution reaction occurs at the anode and a hydrogen evolution reaction occurs at the cathode, resulting in the dissociation of water molecules to generate high-purity hydrogen and oxygen. The MR process combines the hydrogen produced in the electrolyzer with the captured carbon. Methane is produced through a Sabatier reaction under high temperature and pressure conditions. The two devices are coupled to a waste heat circulation pipeline via a hydrogen buffer tank, forming a power-to-gas (P2G) technology that converts surplus electrical energy into storable and transportable chemical energy.

[0030] HFC is a device that directly converts the chemical energy of hydrogen and oxygen into electrical energy. During operation, hydrogen is catalytically decomposed into protons and electrons at the anode. The protons move to the cathode through the proton exchange membrane, while the electrons form an electric current through an external circuit. Finally, at the cathode, they combine with oxygen to form water, achieving highly efficient energy conversion.

[0031] Energy storage devices are devices that can convert energy into other forms of energy or compress and convert it into different states for storage, and then convert the stored energy back into conventional energy forms for output when needed. Their working principle mainly involves two processes: energy charging and energy release.

[0032] Based on the above technical solutions, the present invention can be implemented through the following steps: S1. Constructing the Electric-Thermal-Hydrogen-Gas RIES Framework As a multi-energy complementary system, RIES can dynamically coordinate the conversion ratios of various energy sources such as electricity, heat, gas, and hydrogen through coupling devices and energy storage devices, achieving dynamic matching of multi-energy demands and minimizing energy waste. This invention constructs an IRES containing electricity, heat, gas, and hydrogen loads, achieving mutual complementarity between different energy sources through various energy conversion devices and energy storage devices. The electricity-heat-hydrogen-gas RIES framework is as follows: Figure 1 As shown, wind turbines and the power grid meet the main electrical load demand, while the remaining electrical load is provided by HFC, CHP, and energy storage. Excess wind power generation is supplied to P2G, where it is converted into other energy forms. GB, HFC, CHP, and thermal energy storage jointly meet the thermal load demand. In addition, heat from the electrolytic cell (EL) process is recovered and utilized through heat exchangers to supply energy for the thermal load and alleviate the energy supply pressure on heating equipment. Hydrogen-to-methane (MR) is used to capture CO2 and synthesize natural gas with hydrogen, and the gas grid and gas storage jointly meet the gas load demand. P2G converts electrical energy into hydrogen energy, which, together with hydrogen storage, meets the hydrogen load demand.

[0033] S2. Optimization of source-load-storage scheduling for regional integrated energy systems, considering a joint trading mechanism for green electricity and green hydrogen certificates. This specifically includes the following processes: S21, Principles and Analysis of the Joint Trading Mechanism for Green Electricity and Green Hydrogen Certificates and Demand Response The GCT mechanism incentivizes renewable energy generation systems to increase green electricity production and consumption by issuing green electricity certificates representing their generated electricity and allowing these certificates to be traded in the market. Renewable energy generation systems apply to the Renewable Energy Information Management Center to participate in green electricity certificate trading and obtain corresponding green electricity certificate quotas. If a renewable energy system exceeds its quota, it can sell the excess certificates on the green electricity certificate trading platform. If it fails to meet its quota, it must purchase certificates to ensure compliance with the prescribed assessment standards. This green electricity certificate market trading promotes renewable energy consumption and optimizes the energy structure. The trading principle of GCT is as follows: Figure 2 As shown.

[0034] A Green Hydrogen Certificate (GHCT) is a green attribute certificate used to prove that the produced hydrogen is obtained through the electrolysis of water using renewable energy sources (such as solar and wind power). When a system produces hydrogen by electrolyzing water using renewable energy, it is converted into a corresponding Green Hydrogen Certificate, proving that the hydrogen source is green hydrogen and will not cause environmental pollution. Similar to the GCT mechanism, if the number of Green Hydrogen Certificates obtained by the system is lower than the quota, the corresponding difference must be purchased; otherwise, a penalty will be paid. Conversely, excess certificates can be sold on the trading market to generate revenue. The GHCT mechanism provides additional economic incentives for renewable energy power generation, encouraging power generation companies to increase renewable energy generation and improve energy efficiency. It also supports the development of green hydrogen, enhancing market trust and acceptance of green hydrogen by certifying its quality, thus creating a more favorable market environment for green hydrogen. A schematic diagram of the GHCT is shown below. Figure 3 As shown.

[0035] The Green Electricity and Green Hydrogen Certificate (GCT) mechanism aims to reflect the environmental value of renewable energy to promote its consumption. However, the uncertainty of wind power generation can easily lead to wind curtailment. The Green Energy and Green Hydrogen Certificate (GHCT) mechanism, by promoting green hydrogen production and utilizing surplus wind power for hydrogen production, can effectively solve the wind curtailment problem and promote green electricity consumption. The joint trading of these two mechanisms can improve the overall utilization level of renewable energy. Specifically, the GCT mechanism incentivizes companies to purchase green electricity, promoting the direct consumption of new energy. When wind power generation is surplus, it converts surplus into green hydrogen assets with clear environmental value and generates GHCT revenue. The GHCT mechanism, by promoting green hydrogen production, further stimulates the consumption of new energy. During peak load periods or periods of high electricity prices, green hydrogen produced and stored at a lower cost during periods of wind power surplus can be efficiently generated through HFCs (Hydrogen Fuel Cells), replacing the electricity that would otherwise be purchased at high prices, directly reducing electricity purchase costs and generating GCT revenue. Therefore, the green electricity and green hydrogen certificate joint trading mechanism can form a closed loop, improving the renewable energy consumption capacity and economic efficiency of the energy system. The linkage principle of green electricity and green hydrogen certificate trading is as follows: Figure 4 As shown.

[0036] As a core adjustment tool for load-side flexibility resources, DR (Distributed Load Management) becomes a key path to improve the economy and flexibility of RIES (Restoration Energy Systems) through "source-load interaction" with the trading mechanism. Essentially, it relies on economic incentives to adjust user load curves, optimize supply-demand matching by activating adjustable load resources, and schedule flexible load resources of various energy sources within RIES to achieve supply-demand coordination, complementarity of multiple energy sources, and the "peak shaving and valley filling" function of adjusting load curves. Considering the individual scheduling response potential of electricity, heat, and gas loads and their mutual substitutability within the same time period, different types of loads are classified into fixed loads, transferable loads, and substitutable loads.

[0037] S22. Construct a joint trading mechanism and demand response model for green electricity and green hydrogen certificates.

[0038] 1) GCT mechanism model

[0039] Within the pre-set quota standard framework, the economic cost of GCT is calculated by evaluating the grid connection and consumption of wind power, and a cost model for RIES participation in GCT is established.

[0040] (1)

[0041] In the formula, For GCT cost, , , These are the unit price for green electricity certificate trading, the system's green electricity certificate quota coefficient, and the quantitative coefficient for converting wind power generation into green electricity certificates. , These are the quota indicators for the number of green electricity certificates the system needs to hold and the number of green electricity certificates obtained by wind power generation, respectively. , The system is respectively in Electricity demand and wind power output during different time periods.

[0042] 2) Constructing the GHCT mechanism model

[0043] GHCT operates in two scenarios: First, if the amount of green hydrogen produced by the system and the resulting certificates are lower than the daily quota, green hydrogen certificates must be purchased to meet the quota; otherwise, penalties will be incurred. Second, if the amount of green hydrogen produced and the resulting certificates are higher than the daily quota, green hydrogen certificates can be sold to generate profit. This invention combines the carbon trading mechanism and the GCT mechanism in market trading mechanisms to construct the GHCT model, with the following mathematical expression: (2); (3); In the formula, For GHCT cost, , These are the unit transaction prices for purchasing and selling green hydrogen certificates, respectively. It is a 0-1 variable, set to 1 when a certificate is purchased, and 0 otherwise. This is the transaction penalty coefficient; , , , These are the green hydrogen energy certificate quota indicators, the number of green hydrogen energy certificates obtained from wind power hydrogen production, and the number of green hydrogen energy certificates required for power generation companies and users under the new energy quota. The green hydrogen energy certificate quota ratio; The conversion factor for converting wind power hydrogen production into the number of green hydrogen energy certificates; , , , , These represent the wind power hydrogen production, hydrogen energy demand, and hydrogen input power for CHP, HFC, GB, and MR during time period t.

[0044] Considering that hydrogen production through water electrolysis will consume green electricity, the GHCT cost calculated by equations (2) and (3) includes the duplicate billing of green electricity certificates. To avoid duplicate billing, the cost of the corresponding green electricity certificate is deducted when the green hydrogen energy certificate is generated, so the GHCT model is modified.

[0045] (4) (5) In the formula: The cost of repeated billing is the cost of green electricity certificates corresponding to the wind power absorbed by wind power hydrogen production. This is the revised cost of GHCT.

[0046] 3) Demand Response Model

[0047] Considering the individual dispatch response potential of electricity, heat, and gas loads and their mutual substitutability within the same time period, different types of loads are divided into fixed loads, transferable loads, and substitutable loads, namely: (6); In the formula: For load type, ; , , They represent the first Type of load Power of time-limited fixed loads, transferable loads, and alternative loads.

[0048] It should be noted that stationary loads do not participate in DR. Stationary charge includes electrical, thermal, and gaseous loads.

[0049] The transferable load can be adjusted within a specific time period, and the total amount of load transferred during the adjustment process is kept to zero, that is, the total load size remains unchanged within the scheduling cycle, thereby realizing the horizontal shift of the load on the time scale, as shown in equation (7): (7) In the formula for Time of the first Power after DR of transferable load, For transferable load power, , They are respectively Time period The state variables for the transfer of transferable loads, including the state variables for the transfer of loads into and out of the system. , They are respectively Time period The transfer-in and transfer-out power of transferable loads, , These represent the lower and upper limits of the transferable power, respectively.

[0050] To meet users' energy demands within the same time period, one form of energy can be replaced with another, achieving a vertical load shift, i.e., load substitution. When the substitute load participates in DR, the user's total energy demand remains unchanged, as shown in equation (8): (8); In the formula: for Time period Power after a load DR can be replaced; The power involved in DR; , They are respectively Time period A state variable for the transfer of alternative loads; , Tables Time period Power input and output that can be used as a substitute load; , These represent the upper and lower limits of the substitutable power, respectively.

[0051] S3. Construct a regional integrated energy system source-load-storage optimization scheduling model that considers the joint trading mechanism of green electricity and green hydrogen certificates.

[0052] 1) Objective function

[0053] Taking into account the costs of system participation in GCT and GHCT, carbon emission costs, wind curtailment penalty costs, DR compensation costs, as well as the operation and maintenance costs of wind power, P2G and other equipment and system energy purchase costs, the objective function is to minimize the overall cost, i.e.: (9); In the formula: , , , , These are the overall system cost, operating cost, transaction cost, wind curtailment cost, and DR compensation cost.

[0054] The operating cost model is as follows:

[0055] In the formula, , , They are respectively Real-time electricity and gas purchase volume; , They are respectively Electricity and gas prices at any given time; , , The first Type of wind turbine, the first Type of controllable unit and the first Maintenance cost coefficient of various energy storage devices; , These are the number of types of controllable generating units and energy storage devices, respectively. For the first Wind turbine Efforts during a specific time period; For controllable unit output, It provides power for energy storage equipment.

[0056] To deeply integrate green electricity, expand the application of green hydrogen, and simultaneously reduce carbon emissions, this invention introduces a carbon quota mechanism, converting green electricity and green hydrogen certificates into deductible emission reductions. Under quota constraints, the system must prioritize the allocation of green electricity and the use of green hydrogen, thereby reducing carbon trading costs through market mechanisms and achieving a synergistic optimization of environmental benefits and economic incentives. Its carbon quota and green electricity / green hydrogen certificate emission reduction framework is as follows: Figure 6 As shown.

[0057] The expression for transaction cost modeling is: (11); In the formula, , The transaction costs for green electricity certificates and green hydrogen certificates are calculated using equations (1) to (5). This refers to the cost of carbon trading.

[0058] Carbon trading costs are calculated based on a carbon emission quota model. The principle of this carbon trading mechanism is as follows: by setting a legal carbon emission cap, assigning corresponding carbon quotas to each emission source, and promoting the circulation and trading of these carbon quotas within the market system, the goal of effectively controlling carbon emissions is achieved. In the initial stage of this mechanism, regulatory agencies will first allocate carbon quotas reasonably to each emission source, and RIES will produce and emit according to their own carbon quotas. When the actual carbon emissions of the system are lower than the carbon quotas, the remaining carbon quotas can enter the market for trading; if the emissions exceed the carbon quotas, the difference must be purchased through the market. The carbon emission quota model mainly includes a carbon quota allocation mechanism, emission accounting methods, and a market-based trading system.

[0059] Currently, initial carbon emission allowances are typically allocated free of charge using a baseline method. This invention uniformly converts purchased electricity into coal-fired power generation; therefore, the main carbon emission sources in RIES are: upstream power purchases, CHP, GB, and direct consumption of end-user natural gas, while also considering the carbon capture effect of P2G devices. Therefore, the carbon emission allowance allocation, accounting method, and certificate-discounted carbon emission reduction are shown in equation (12): (12) The cost model for wind curtailment penalty is as follows: (13); In the formula: This is the wind curtailment penalty coefficient; for Wind curtailment power during a given time period.

[0060] Demand response cost modeling is as follows: (14) In the formula: , These are the subsidy coefficients for movable loads and replaceable loads, respectively.

[0061] 2) Constraints include electrical power balance, thermal power balance, natural gas balance, hydrogen energy balance, energy storage equipment output constraints, and electrolyzer hydrogen production unit operation constraints, as detailed below: The power balance modeling expression is: (15) In the formula, , They are respectively Power generation of CHP and HFC during the same period; for The power of the input energy storage and EL during the time period; The upper limit for electricity purchases in each time period.

[0062] The thermal power balance model is as follows: (16); In the formula: , , , They are respectively Heat production capacity of HFC, CHP, GB, and EL during the specified time period; for Heat load during a given period; for The power input to the thermal storage is constantly being adjusted.

[0063] The natural gas balance model is as follows: (17); In the formula, , , They are respectively Input CHP, GB, and gas storage power during the time period; , They are respectively Real-time gas load, MR synthetic natural gas power; This refers to the maximum gas purchase limit for each time period.

[0064] The hydrogen energy balance constraint is modeled as follows: (18) In the formula, for EL hydrogen production power during the time period for The power input to hydrogen storage during specific time periods; The amount of hydrogen doping in CHP and GB; The output constraint of energy storage equipment is: (19); In the formula, , They are respectively Time of the first The charging and discharging power of this type of energy storage device; , The variables 0 and 1 represent the charging and discharging states, respectively. No. The maximum charge / discharge power of this type of energy storage device; for Time of the first The output power of this type of energy storage device; , For the charging and discharging efficiency of energy storage devices; , , They are respectively Time of the first The capacity and upper and lower limits of various energy storage devices.

[0065] The operational constraints of the electrolyzer hydrogen production unit are modeled as follows: Proton exchange membrane electrolyzers (PEMES) possess advantages such as high hydrogen purity, excellent hydrogen production efficiency, high integration, and good coupling with fluctuating power sources. Therefore, this invention selects a PEMES and considers its electrolysis and heat transfer processes. The electro-hydrogen energy coupling can be achieved through the EL, making it a key device for "green electricity-green hydrogen" conversion. Its operating constraints are shown in equation (20). (20); In the formula: for EL hydrogen production efficiency at any given time; , , These are the parameters of the efficiency function; Rated power; , These are the upper and lower limits of the input power, respectively. , These represent the upper and lower limits of the uphill climb, respectively.

[0066] The electrolysis process of the proton exchange membrane electrolyzer is an exothermic reaction, releasing a large amount of heat energy, which is recovered through a heat exchanger and transported to the heating network to meet the heat load demand, thereby reducing the pressure of the heating equipment. The heat transfer process is constrained as shown in equations (21) and (22): (twenty one); (twenty two); In the formula: , , , They are respectively Real-time EL heat generation power, heat loss power, output heat power, and input heat power to the heating network; For EL heat generation efficiency; The equivalent thermal resistance of EL; The convective heat transfer coefficient; , They are respectively EL temperature and ambient temperature at any given time.

[0067] 3) Uncertainty handling

[0068] To address the uncertainty in wind power output and system load, this invention employs a reserve capacity mechanism to ensure power supply reliability. First, based on the probability distribution characteristics of wind power forecasting errors, prediction deviations are quantified through confidence analysis, thereby determining the reserve capacity requirement to cope with wind power fluctuations. Simultaneously, a fixed load error threshold is set based on the fluctuation characteristics of the load curve, thus generating the reserve capacity corresponding to load uncertainty. Reserve capacity constraints include upward reserve capacity and downward reserve capacity.

[0069] As an energy storage resource, electrical energy storage can provide uplink and downlink backup for the system through charging and discharging, and it satisfies the following constraints: (twenty three) In the formula: , They are respectively The upside and downside reserve capacity provided by the energy storage system at all times; , These are the charging and discharging power, respectively. , These are the upper limits for charging and discharging power, respectively. for The capacity of electrical energy storage at any given time; , These are the upper and lower limits of the energy storage capacity, respectively. , These refer to the charging and discharging efficiencies of the energy storage system.

[0070] EL converts electrical energy into hydrogen energy through water electrolysis to provide both uplink and downlink backup for the system, satisfying the following constraints: (twenty four); In the formula, , They are respectively The EL device provides the system with both upward and downward backup capacity.

[0071] CHP can convert input gas power into output electrical and thermal power, and provide up and down backup power to the system by adjusting the electrical power output, satisfying the following constraints: (25) In the formula, , They are respectively At any given moment, CHP represents the upward and downward reserve capacity provided by the system. This represents the maximum gradeability of CHP. , These are the upper and lower limits of the CHP output power, respectively.

[0072] HFCs utilize stored or produced hydrogen to convert hydrogen power into electrical power, thereby providing uplink and downlink backup for the system, satisfying the following constraints: (26); In the formula: , They are respectively The upside and downside reserve capacity provided by HFC at any given moment; , These are the upper and lower limits of the HFC output power, respectively. for The capacity of hydrogen storage at any given time; , These represent the upper and lower limits of hydrogen energy storage capacity, respectively. Energy conversion efficiency.

[0073] The total upward and downward spare constraints within the system are as follows:

[0074] In the formula: , These are the reserve capacity coefficients set to account for wind power and load uncertainties, respectively. Based on the probability distribution characteristics of wind power forecasting errors, confidence analysis determines the reserve capacity factor to be 15% of the forecasted wind power. 2% of the electrical load power.

[0075] S4. Solution and Analysis of the Regional Integrated Energy System Source-Load-Storage Optimization Scheduling Model Considering the Green Electricity-Green Hydrogen Certificate Joint Trading Mechanism

[0076] Based on the constructed optimization model and objective function, the Yalmip toolbox is used in conjunction with the commercial optimization software GUROBI to solve the problem. This yields the economically optimal output of typical Sunrise wind turbines, the operating status and output of other energy supply equipment units and energy storage equipment, and the actual situation of their participation in peak shaving and valley filling, renewable energy consumption, and green hydrogen production and use, all of which meet the constraints.

[0077] To verify the effectiveness of the proposed optimized scheduling strategy, a specific RIES scenario was selected for validation. The predicted results of various loads and turbine output within the RIES are shown below. Figure 5 The natural gas price is set at 0.35 yuan / kWh, and external electricity purchases are subject to time-of-use pricing (see Table 2). The purchase and sale price of green electricity certificates is 150 yuan / certificate, and the trading price of green hydrogen certificates is set at 200 yuan / certificate, with a penalty price of 300 yuan / certificate. The capacity and operating parameters of each device are shown in Table 3, and the installed capacity and parameters of energy storage are shown in Table 4. Assuming the green electricity-green hydrogen certificate joint trading mechanism is successfully implemented, five scenarios are set up for comparative analysis, and the operating results of each scenario are shown in Table 1.

[0078] Scenario 1: In the traditional RIES optimization scheduling model, consider the recovery of heat from electric hydrogen production.

[0079] Scenario 2: Based on Scenario 1, introduce the GCT mechanism.

[0080] Scenario 3: Based on Scenario 1, introduce GHCT.

[0081] Scenario 4: Based on Scenario 1, both GCT and GHCT mechanisms are introduced.

[0082] Scene 5: Building on Scene 4, introduce a demand response model.

[0083] Table 1. Optimization results for scenarios 1-5 with different market trading mechanisms or DR participation.

[0084] Table 2. Prices of externally purchased electricity during different time periods

[0085] Table 3. Capacity and Operating Parameters of Each Equipment

[0086] Table 4. Installed capacity and parameters of electric, gas, thermal, and hydrogen energy storage devices

[0087] 1) Analysis of the impact of heat recovery from electro-hydrogen production on RIES

[0088] The electricity and heat supply and demand balance in scenario 1 is as follows: Figure 7 , 8 As shown. From Figure 7 , 8 It can be seen that during the peak electricity consumption period from 8:00 to 22:00 during the daytime, the system achieves a balance between electricity supply and demand by prioritizing wind power consumption. During the peak periods from 01:00 to 06:00 and from 19:00 to 23:00, the heat load is at its peak, and GB, CHP, and HFC work together to meet the heat load demand. At this time, the electricity load is at its low point, and there is a surplus of wind power. To avoid wind curtailment, EL is used to convert the redundant wind power into green hydrogen. The electrolysis process is an exothermic reaction, which releases a large amount of heat. The heat is recovered by a heat exchanger and input into the heating network to supply energy to the heat load. This can maintain the stable operating temperature of EL, avoid efficiency decline due to insufficient heat dissipation, and can also use the recovered heat energy to replace part of the conventional heating equipment, thereby improving the efficiency of renewable energy utilization.

[0089] 2) Benefit Analysis of the Green Electricity-Green Hydrogen Certificate Joint Trading Mechanism

[0090] The proportion of green electricity, renewable energy consumption rate, and EL output in various scenarios are as follows: Figure 9 and Figure 10 As shown in Table 1 and Figure 9 As shown, compared to Scenario 1, Scenario 2 saw a 32.86% increase in the proportion of green electricity, a 51.50% increase in renewable energy integration rate, a 22.50% reduction in system carbon emissions, and a 14.37% decrease in overall costs. This indicates that the GCT mechanism can promote renewable energy integration, reduce carbon emissions, and lower overall costs. This is because the introduction of the GCT mechanism effectively promoted the grid connection and integration of wind power, and also enabled the system to sell surplus green electricity certificates, bringing additional economic benefits to the system. At the same time, the grid connection and integration of wind power reduced the use of traditional energy sources, lowering overall costs and carbon emissions.

[0091] According to Table 1 and Figure 9 , 10 As shown, compared to Scenario 1, Scenario 3 reduces system carbon emissions by 16.46 tons, lowers overall costs by 8,900 yuan, and increases the proportion of green electricity and renewable energy consumption rate by 15.22% and 42.57%, respectively. The EL hydrogen production capacity also significantly increases compared to Scenario 1 and Scenario 2. This indicates that the GHCT mechanism can effectively reduce system carbon emissions, improve economic efficiency, and promote wind power consumption and stimulate green hydrogen production. This is because the introduction of the GHCT mechanism allows surplus wind power to be converted into green hydrogen for storage and utilization. On the one hand, it not only reduces energy waste caused by wind curtailment but also significantly reduces system carbon emissions by replacing traditional energy sources with green hydrogen. On the other hand, the increased green hydrogen production raises the proportion of green hydrogen in the system, providing strong support for the large-scale promotion and application of green hydrogen in RIES.

[0092] According to Table 1 and Figure 9 , 10 It can be seen that, compared to scenarios 1-3, scenario 4 further reduces the total system cost and carbon emissions, and significantly improves the renewable energy integration rate, the proportion of green electricity, and the production of green hydrogen. Combined with... Figure 11 , 12 Analysis of the supply and demand balance of electricity and hydrogen reveals that during nighttime, when electricity load is low, wind power cannot be absorbed in large quantities. The EL (Electrolysis) device converts wind power into green hydrogen to promote wind power absorption and obtain corresponding green hydrogen energy certificate revenue. Furthermore, the green hydrogen obtained from electrolysis can be converted into its energy form to alleviate supply and storage pressures. During the daytime, when electricity load is high, priority is given to absorbing as much wind power as possible to obtain corresponding green electricity certificate revenue. Green hydrogen from hydrogen storage tanks is also utilized to generate electricity through HFC (Hydrogen Fuel Cell) power, feeding back into the grid to replace expensive externally purchased electricity and obtaining a certain amount of green electricity certificate revenue. The results show that the green electricity-green hydrogen certificate joint trading mechanism can further effectively optimize the production, use, and mutual promotion of green hydrogen and green electricity, better matching their renewable energy generation capacity. By introducing the green electricity-green hydrogen certificate joint trading mechanism, RIES can achieve dual benefits in both the green hydrogen energy certificate trading market and the green electricity certificate trading market.

[0093] 3) Analysis of the impact of trading parameters of the green certificate-green hydrogen certificate joint trading mechanism on the operation of RIES

[0094] Impact Analysis of Green Electricity Certificate Quota Factors on System Operation and GCT Mechanism

[0095] Since the cost of GCT is influenced by the price of green electricity certificates, quota volume, and quota factor, with the quota factor being a relatively constant parameter and adjusted infrequently, it is necessary to conduct an in-depth analysis of the impact of the quota factor, especially its effect on system operation under the GCT mechanism. This analysis aims to find a reasonable quota factor that ensures effective system operation while fully adapting to the GCT mechanism. A relationship diagram related to the quota factor is therefore drawn, as shown below. Figure 13 , 14 As shown.

[0096] Depend on Figure 13 It can be seen that when the quota coefficient is 0.1 and 0.15, although wind power output increases with the increase of the base transaction price of green electricity certificates, the improvement in wind power output is not significant, revealing that the quota coefficient is low at this stage, and the incentive effect of price changes on wind power output is limited. However, as the quota coefficient continues to increase, the sensitivity of the system's wind power output to the increase of the base transaction price of green electricity certificates becomes stronger, and the trend of power increase becomes particularly prominent. This indicates that the setting of the green electricity certificate quota coefficient has a significant impact on the economics of system operation and the promotion of wind power consumption.

[0097] To quantify the impact of green electricity certificate quota coefficients on system economics and renewable energy consumption, a set of... Sensitivity analysis was performed with a gradient of 0.1-0.3. For example... Figure 14 As shown, when As the cost increases from 0.1 to 0.3, the total system cost first decreases and then increases, while the wind power absorption rate continues to improve. At 5 o'clock, the total system cost is compared to the baseline scenario ( The wind power consumption rate increased by 15.1%, while the quota coefficient decreased by 7.4%. This result demonstrates that appropriately increasing the quota coefficient can significantly improve wind power consumption efficiency within a controllable cost range by enhancing the market incentive effect of green electricity certificate trading, thus validating the... The rationality of the parameters.

[0098] The impact of GHCT prices on RIES operation

[0099] To study the impact of GHCT prices on system operation, the price range of GHCT was set to 50-250 yuan / unit. The revenue from GHCT and the output of EL units and wind power were compared under different price conditions. Figure 15 , 16 As shown.

[0100] Depend on Figure 15 , 16 It is evident that when the GHCT price is below 100 yuan / certificate, GHCT revenue is low, and the EL unit operating load is also in a low operating range, accompanied by significant wind curtailment. This reflects the dual limitation of green hydrogen production economics and renewable energy consumption efficiency caused by insufficient market incentives. As the GHCT price rises to the critical value of over 150 yuan / certificate, GHCT revenue increases significantly, and the overall output of EL units and wind power shows a significant upward trend. This reflects the mechanism by which the green hydrogen certificate premium significantly improves the system's operating economics through marginal incentive effects, enabling EL units to have dynamic adjustment capabilities to absorb wind power output fluctuations, thereby achieving synergistic optimization of green hydrogen production and renewable energy consumption.

[0101] 4) The impact of synergistic optimization of DR and green electricity-green hydrogen certificate joint trading on RIES

[0102] Figure 17This is the DR (Demand Reduction) scenario for electricity, heat, and gas loads in Scenario 5. Considering DR, during peak electricity demand from 8:00 AM to 10:00 PM, due to higher electricity prices than gas prices and energy losses from converting natural gas into heat and electricity, the system substitutes a portion of the electricity load with gas and heat loads during this period. This "peak shaving" is achieved through electricity load transfer and gas / heat load substitution, freeing up grid space for the injected wind power to produce hydrogen via EL (Electric Hydrogen Production Line), thus generating GHCT (High-Gross Transmission and Control) revenue to cover DR costs. During off-peak hours from 1:00 AM to 6:00 AM and from 10:00 PM to 12:00 AM, to further absorb wind power, a portion of the gas and heat loads is replaced by electricity. This "valley filling" is achieved through electricity load transfer and substitution of gas and heat loads, reducing the maximum peak-valley difference from 2075 kW to 1627 kW, a reduction of 21.59%. Simultaneously, EL utilizes low-cost wind power to accelerate hydrogen production, which not only increases green hydrogen production and allows GHCT revenue to cover part of the DR costs but also lowers the unit hydrogen production cost. Similarly, during peak periods of heat load (01:00-06:00 and 19:00-23:00), "peak shaving" is achieved through heat load transfer and electricity / gas load substitution. The released HFC capacity can be converted into electricity to feed back into the grid, reducing the amount of electricity purchased from upstream and simultaneously generating GCT revenue. During off-peak periods (14:00-17:00), "valley filling" is achieved through heat load transfer and electricity / gas load substitution, reducing the maximum peak-valley difference from 4351kW to 3746kW, a reduction of 13.90%. Through dynamic coordinated regulation and multi-energy flow coupling among various energy sources, not only is "peak shaving and valley filling" achieved on the load side, but it also interacts with source-side equipment, promoting deep synergy between green electricity consumption and the value of green hydrogen.

[0103] according to Figure 10 , 11 As shown in Tables 17 and 1, through the synergistic optimization of the DR and green electricity-green hydrogen certificate joint trading mechanism, Scenario 5, compared with Scenario 4, not only reduced the upstream power purchase during peak electricity load periods and increased the wind power absorption rate and wind power hydrogen production during off-peak electricity load periods, but also reduced the overall cost of RIES by 12.81% and carbon emissions by 5.74%. Although the amount of DR subsidies increased, operating costs, wind curtailment penalty costs, and transaction costs all decreased, indicating that synergistic optimization has significant advantages in terms of economy, flexibility, and environmental benefits.

[0104] In summary, introducing DR into the green electricity-green hydrogen certificate joint trading mechanism leverages the complementary nature of multiple energy sources. Without affecting user experience, it can not only reduce the overall cost of RIES by adjusting energy consumption behavior, but also increase wind power absorption rate and green hydrogen production, and significantly reduce carbon emissions.

[0105] To promote the synergistic utilization of green electricity and green hydrogen in the energy market and improve the renewable energy absorption rate, this paper proposes a RIES source-load-storage optimization scheduling strategy considering a joint trading mechanism for green electricity and green hydrogen certificates. The following conclusions are drawn through analysis and simulation examples: 1) This paper proposes a joint trading mechanism for green electricity and green hydrogen certificates, and incorporates it into the RIES source-load-storage optimization scheduling, enabling RIES to achieve dual benefits in both the green hydrogen certificate trading market and the green electricity certificate trading market. This effectively optimizes the production and use of green hydrogen and green electricity, and their mutual promotion, better matching them with renewable energy generation capacity and further reducing carbon emissions. Compared to the GET and GHCT mechanisms considered separately, this joint mechanism performs better in improving the grid connection rate of green electricity and green hydrogen and the renewable energy consumption rate.

[0106] 2) Based on the joint trading of green electricity and green hydrogen certificates, multiple types of DR (Renewable Energy) such as electricity, gas and heat are introduced to achieve source-load synergistic optimization. This can guide users to actively adjust their energy consumption behavior, reduce the overall system cost, fully explore the flexibility resources on the load side, and improve the system's renewable energy absorption capacity.

Claims

1. A method for optimal scheduling of RIES source-load-storage considering a joint trading mechanism of green electricity and green hydrogen certificates, characterized in that, The method includes: (1) Establish and construct an electricity-heat-hydrogen-gas IRES operating system, taking carbon trading mechanism and green certificate trading mechanism as reference. The system includes energy forms including electricity, gas, heat and hydrogen. Energy is obtained through wind power, photovoltaic, upper-level power grid and external gas network to meet the supply and demand balance. Its electrolyzer electrolyzes water and supplies the heat generated in the process to the heat load to form electricity-hydrogen energy conversion. The methanation reactor uses the hydrogen generated by the electrolyzer and absorbs CO2 to produce natural gas to form hydrogen-gas energy conversion, converting hydrogen energy into electricity and heat to form hydrogen-electricity-heat energy coupling. (2) Construct a green hydrogen energy certificate trading mechanism and model to form a green electricity-green hydrogen certificate joint trading mechanism. The trading mechanism includes issuing green electricity certificates representing the power generation of renewable energy power generation systems, allowing the certificates to be traded in the market, and obtaining the corresponding green electricity certificate quota after the renewable energy power generation system submits an application to the renewable energy information management center to participate in the green electricity certificate trading. If the quota is not met, the system needs to purchase the certificate. The market trading promotes the consumption of renewable energy and the optimization of the energy structure. (3) Considering the regulation characteristics of electricity, heat and gas loads, a comprehensive energy load demand response model is established. This model considers the regulation characteristics of electricity, heat and gas loads, classifies load types and formulates corresponding regulation rules to achieve peak shaving and valley filling of the load curve. (4) Construct a low-carbon economic dispatch model for RIES source-load-storage that considers the joint trading mechanism of green electricity and green hydrogen certificates. The low-carbon economic dispatch model of RIES source-load-storage integrates the joint trading mechanism of green electricity and green hydrogen certificates in step (2) and the comprehensive energy load demand response model in step (3) into the dispatch model to achieve low-carbon economic optimization dispatch of RIES source-load-storage.

2. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 1, characterized in that, In step (1), the system also includes a combined heat and power unit, a gas boiler, a hydrogen fuel cell, and energy storage equipment; The combined heat and power unit generates electricity by driving a generator through fuel combustion, while recovering waste heat from flue gas to produce steam or hot water for supply to the heating network. The gas-fired boiler supplies energy by heating circulating water through the combustion of natural gas. The hydrogen fuel cell converts hydrogen energy into electricity and heat; The energy storage devices include electrical energy storage, thermal energy storage, hydrogen energy storage, and gas energy storage, used to realize the storage and on-demand release of energy.

3. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 1, characterized in that, The aforementioned green hydrogen certificate trading mechanism involves converting the amount of green hydrogen certificates generated by the system through electrolysis of water using renewable energy. If the amount of green hydrogen certificates is lower than the daily quota, the system needs to purchase more green hydrogen certificates or pay a penalty. If the amount of green hydrogen certificates is higher than the daily quota, the system can profit by selling the excess green hydrogen certificates.

4. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 3, characterized in that, In step (2), when constructing the green hydrogen energy certificate trading model, it is necessary to correct the problem of duplicate billing. The duplicate billing is the cost of green electricity certificates corresponding to wind power consumed by wind power hydrogen production. The corrected green hydrogen energy certificate trading cost needs to deduct this duplicate billing cost.

5. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 1, characterized in that, In step (3), the load types include fixed load, transferable load and alternative load; for transferable load, the total load is constrained to remain unchanged within the scheduling cycle, thus achieving horizontal shift on the time scale; for alternative load, the total energy demand of users is constrained to remain unchanged within the same time period, thus achieving vertical conversion of energy form.

6. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 1, characterized in that, In step (4), the objective function of the RIES source-load-storage low-carbon economic dispatch model is to minimize the overall system cost. The overall cost includes operating cost, transaction cost, wind curtailment cost, and demand response compensation cost. The operating cost covers energy purchase cost and equipment maintenance cost. The transaction cost covers green electricity certificate transaction cost, green hydrogen certificate transaction cost, and carbon trading cost.

7. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 6, characterized in that, The carbon trading cost is calculated based on the carbon emission quota model, setting a carbon emission ceiling and allocating carbon quotas. If the actual emissions of the system are lower than the carbon quota, the remaining carbon quota can be sold; if the actual emissions are higher than the carbon quota, the difference in carbon quota must be purchased. The carbon emission sources of the system include upstream electricity purchases, CHP, GB, and direct consumption of natural gas at the terminal, while also taking into account the carbon capture effect of the power-to-gas conversion device, and converting green electricity certificates and green hydrogen certificates into deductible emission reductions.

8. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 1, characterized in that, In step (4), the RIES source-load-storage low-carbon economic dispatch model must meet the constraints of electric power balance, thermal power balance, natural gas balance, hydrogen energy balance, and energy storage equipment output. The electric power balance constraint ensures that the power generation of the cogeneration unit, the power generation of the hydrogen fuel cell, the power purchased from the upstream, and the charging and discharging of the energy storage are matched with the electric load demand during the time period. The thermal power balance constraint ensures that the heat generation of the hydrogen fuel cell, the heat generation of the cogeneration unit, the heat generation of the gas boiler, the heat generation of the electrolyzer, and the charging and discharging of the thermal energy storage are matched with the heat load demand during the time period. The natural gas balance constraint ensures that the gas purchased from the upstream, the amount of natural gas synthesized by the methanation reactor, and the charging and discharging of the gas storage are matched with the gas load demand during the time period. The hydrogen energy balance constraint ensures that the hydrogen production of the electrolyzer and the charging and discharging of the hydrogen storage are matched with the hydrogen load demand during the time period. The energy storage equipment output constraint limits the charging and discharging power and capacity of the energy storage equipment to within the range of system operation requirements.

9. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 8, characterized in that, The electrolyzer is a proton exchange membrane electrolyzer, and its operation constraints include: limiting the upper and lower limits of the input power and ramping limits of the electrolyzer, while considering the heat transfer during the electrolysis process, constraining the balance of the electrolyzer's heat generation power, heat loss power, output heat power and input heat power of the heating network, and the relationship between the electrolyzer temperature and the ambient temperature.

10. The RIES source-load-storage optimization scheduling method considering the green electricity-green hydrogen certificate joint trading mechanism according to claim 1, characterized in that, In step (4), a backup capacity mechanism is adopted to ensure power supply reliability in response to the uncertainty of wind power output and system electrical load; The reserve capacity includes upward reserve capacity and downward reserve capacity, which is jointly provided by electric energy storage, electrolyzers, cogeneration units, and hydrogen fuel cells. The reserve capacity coefficient is set based on the probability distribution characteristics of wind power prediction errors and the fluctuation characteristics of load curves.

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