IES low-carbon scheduling method and equipment considering refined utilization of hydrogen and ammonia, and medium

Abstract

The invention relates to an IES low-carbon scheduling method and device considering refined utilization of hydrogen and ammonia and a medium, and the method comprises the following steps: constructing a comprehensive energy system model considering refined utilization of hydrogen and ammonia, which comprises a hydrogen energy refined utilization sub-model and an ammonia energy refined utilization sub-model; introducing a green hydrogen energy certificate based on a renewable energy source forestation system, and establishing a green hydrogen certificate transaction model; based on the integrated energy system model and the green hydrogen certificate transaction model, considering the uncertainty of renewable energy sources, and constructing a two-stage robust low-carbon optimization scheduling model of the integrated energy system by taking the minimum total operation cost as a target; and solving the two-stage robust low-carbon optimal scheduling model of the integrated energy system, outputting a low-carbon scheduling scheme, and completing a low-carbon scheduling process. Compared with the prior art, the system has the advantages that the renewable energy consumption level is improved, and low-carbon, economical and flexible operation of the system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimal scheduling, and in particular to a low-carbon scheduling method, device and medium for IES considering refined utilization of hydrogen and ammonia. Background Technique

[0002] IES (Integrated Energy System) is mainly characterized by the coupling and complementarity of multiple energy subsystems such as electricity, gas, cooling and heating. It integrates various energy resources on the source side to achieve unified planning, unified scheduling, optimal operation and complementary mutual assistance among different energy systems. While effectively improving energy utilization efficiency, it promotes the consumption of renewable energy, and thus realizes energy conservation and emission reduction.

[0003] As a low-carbon and clean secondary green energy, hydrogen energy has broad application prospects in the optimal operation of integrated energy systems. At present, the research on the application of hydrogen energy in integrated energy systems mainly focuses on the optimal operation of hydrogen energy storage, electrolysis hydrogen technology, hydrogen fuel cells and related equipment involved in hydrogen production in integrated energy systems. The wind power electrolysis hydrogen technology can effectively solve the current large-scale wind abandonment problem and is one of the important measures to improve the wind power consumption capacity in integrated energy systems. Ammonia has a calorific value equivalent to that of ordinary coal, and compared with hydrogen, ammonia has lower liquefaction conditions, better economy, safety in transportation and storage, and is considered a zero-carbon energy source. The power-to-ammonia technology realizes the conversion of electrical energy to ammonia through the ammonia synthesis reaction after electrolysis hydrogen, and is an effective way to solve the problem of hydrogen storage and transportation and realize large-scale storage of renewable energy. In terms of ammonia utilization, in recent years, researchers have proposed the idea of co-firing ammonia in coal-fired boilers to reduce carbon emissions, which has great potential for promoting carbon reduction in coal-based energy systems.

[0004] With the rapid increase in the installed capacity of renewable energy, the problem of its accommodation has gradually become prominent. To effectively improve the accommodation level of renewable energy, scholars at home and abroad have carried out a large number of studies on the application of hydrogen energy in IES. Reference [Deng Jie, Jiang Fei, Wang Wenye, et al. Low-carbon operation of integrated energy system considering electro-thermal flexible load and refined hydrogen energy modeling [J]. Power System Technology, 2022, 46(05): 1692-1704.] introduced a two-stage hydrogen energy utilization model of electrolytic cell hydrogen production, hydrogen-to-methane, and hydrogen fuel cell power generation into the IES structure, realizing the recycling of electricity-hydrogen-electricity. Reference [Xiong Yufeng, Chen Laijun, Zheng Tianwen, et al. Optimal configuration of hydrogen energy storage in a low-carbon park integrated energy system considering electro-thermal-gas coupling characteristics [J]. Electric Power Automation Equipment, 2021, 41(9): 31-38.] conducted electrochemical and thermodynamic analyses on hydrogen energy storage, proposed an optimization model of IES with hydrogen energy storage in the park, and gave the optimal configuration method of hydrogen energy storage. Reference [Yuansheng. Dynamic planning and energy management strategy of integrated charging and hydrogen refueling at highway energy supply stations considering on-site green hydrogen production [J]. International Journal of Hydrogen Energy, 2023, 48(77): 29835-29851.] proposed an IES scheduling model considering hydrogen energy utilization and hydrogen blending with natural gas with hydrogen energy as the medium, expanding the utilization scenarios of hydrogen energy. Reference [Hu Junjie, Tong Yuxuan, Liu Xuetao, et al. Multi-time scale robust optimization strategy of integrated energy system considering refined hydrogen energy utilization [J]. Transactions of China Electrotechnical Society, 2024, 39(05): 1419-1435.] proposed an operation method of IES with electric-hydrogen coupling and wind power hydrogen production, and carried out refined modeling for the hydrogen utilization stage. Similar to hydrogen energy, ammonia energy is also a zero-carbon energy source. It has a calorific value equivalent to that of ordinary coal, and compared with hydrogen, ammonia has a safer transportation and storage environment and higher economy. Regarding the application in ammonia production, Reference [Zhou Buxiang, Zhu Wencong, Zhu Jie, et al. Analysis of multi-period dispatchable domain of wind-solar hydrogen production ammonia synthesis system [J]. Proceedings of the CSEE, 2024, 44(1): 160-173.] studied the technical principle of power-to-ammonia, analyzed the internal reaction principle of power-to-ammonia, and proposed a multi-period dispatch analysis model of wind-solar hydrogen production ammonia synthesis system.The literature [Yuan Wenteng, Chen Liang, Wang Chunbo, et al. Two - layer optimal scheduling of power - to - ammonia coupled wind - solar - thermal integrated energy system based on ammonia energy storage technology [J]. Proceedings of the CSEE, 2023, 43(18): 6992 - 7003.] proposed power - to - ammonia technology and ammonia - blended combustion technology for thermal power units, constructed an IES architecture with power - to - ammonia coupling, and effectively improved the utilization rate of wind and light. The literature [Cui Yang, Sun Xibin, Fu Xiaobiao, et al. Low - carbon scheduling method for rural chemical integrated energy system considering power - to - ammonia and biomass waste energy conversion [J]. Power System Technology, 2024, 48(08): 3350 - 3360.] considered the carbon - ammonia coupling process, combined green ammonia with carbon capture equipment, and constructed an IES model for chemical industrial parks with two - stage optimization of power - to - ammonia.

[0005] With the high - proportion access of renewable energy to the power grid, the problem of renewable energy consumption has gradually become prominent. To promote the large - scale development of renewable - energy - based hydrogen production, it is of great significance to deeply explore the environmental value of hydrogen energy utilization for improving the consumption of renewable energy. The literature [Liu Shijian. Research on key issues in the market promotion of new - energy - based hydrogen production under the background of energy transformation [D]. Beijing: North China Electric Power University, 2021.] defined green hydrogen energy and studied the green hydrogen certificate trading mechanism between hydrogen - production companies and hydrogen - selling companies. The literature [Luo Zhao, Liu Dewen, Jia Yunrui, et al. Optimal operation of integrated energy system considering green hydrogen certificate and hydrogen production from hydropower [J]. Power System Technology, 1 - 14.] proposed a two - way auction model for green hydrogen certificate trading (GHCT) based on the auction trading principle, laying a foundation for the market - oriented development of green hydrogen. The literature [Yang Dongfeng, Zuo Shengyu, Yang Jingying, et al. Low - carbon economic scheduling of integrated energy system considering green hydrogen certificate trading mechanism and carbon quota of new - energy vehicles [J]. Power System Technology, 2025, 49(02): 562 - 571.] considered both green hydrogen certificate trading and new - energy vehicle quota system, and proposed a low - carbon optimization model for IES with green hydrogen certificates, promoting the mutual conversion of green electricity and green hydrogen. Most of the above - mentioned literature focuses on the market - oriented application of the GHCT mechanism and does not deeply analyze the impact of the GHCT mechanism and its green hydrogen trading price on the operation optimization of hydrogen - containing IES.

[0006] Renewable energy sources such as wind power and photovoltaic power have extremely high uncertainties. Currently, the main methods for dealing with the uncertainties of renewable energy are stochastic optimization and robust optimization. The reliability of stochastic optimization is related to the complexity of the probability distribution model. The more precise the probability distribution model, the more difficult it is to obtain accurate results. In contrast, robust optimization has high solution efficiency and strong reliability. It does not require accurate modeling of the distribution function of uncertain variables, which can reduce the difficulty of model solution. The literature [Qiu Bin, Yang Ruixue, Wang Kai, et al. Two-stage robust optimization of integrated energy systems considering flexible coordinated response of source-load [J / OL]. Proceedings of the CSU-EPSA, 1-12.] proposed a two-stage robust optimization method for IES considering flexible coordination of source-load, taking into account the fluctuation characteristics of wind power, charge, and biogas digesters. The literature [Li Xin, Chen Yingzhang, Li Hanwen, et al. Two-stage robust optimization of low-carbon economic dispatch for integrated electricity-heat energy systems considering carbon trading [J]. Electric Power Construction, 2024, 45(06): 58-69.] used a box-type uncertainty set to characterize the uncertainties of wind power and photovoltaic power, and constructed a two-stage robust optimization model for IES with a min-max-min structure.

[0007] Existing studies rarely deeply analyze the coupling and complementarity between green hydrogen and green ammonia and their comprehensive utilization in multiple links, do not further explore the application of refined utilization of hydrogen and ammonia in IES, and fail to achieve deep coupling of multiple energies such as electricity-heat-hydrogen-ammonia. Summary of the Invention

[0008] The purpose of the present invention is to provide a low-carbon scheduling method, device, and medium for IES considering refined utilization of hydrogen and ammonia to improve the accommodation level.

[0009] The purpose of the present invention can be achieved through the following technical solutions:

[0010] A low-carbon scheduling method for IES considering refined utilization of hydrogen and ammonia, comprising the following steps:

[0011] Construct an integrated energy system model considering refined utilization of hydrogen and ammonia, including a refined utilization sub-model of hydrogen energy and a refined utilization sub-model of ammonia energy;

[0012] Based on the renewable energy quota system, introduce green hydrogen certificates and establish a green hydrogen certificate trading model;

[0013] Based on the integrated energy system model and the green hydrogen certificate trading model, considering the uncertainties of renewable energy, construct a two-stage robust low-carbon optimization scheduling model for the integrated energy system with the goal of minimizing the total operating cost;

[0014] Solve the two-stage robust low-carbon optimization scheduling model of the integrated energy system, output a low-carbon scheduling plan, and complete the low-carbon scheduling process.

[0015] Furthermore, the refined hydrogen energy utilization sub-model includes the following:

[0016] Hydrogen production by electrolysis:

[0017]

[0018] P EL,h (t) = η EL,h P EL,e (t)

[0019] In the formula, f[P EL,e (t)] is the efficiency function of EL at time t, a EL , b EL and c EL are the efficiency function coefficients of EL respectively, P EL,e (t) is the electric power input into EL at time t, is the amount of hydrogen-producing substance of EL at time t, P EL,eN is the rated value of the electric power input into EL; Q EL,N is the rated capacity of EL, is the hydrogen production amount of EL at time t; P EL,h (t) is the recovered residual heat amount of EL at time t; η EL,h is the heat recovery efficiency coefficient of EL; is the molar mass of hydrogen; is the density of hydrogen; is the lower calorific value of hydrogen;

[0020] Hydrogen fuel cell:

[0021]

[0022] In the formula, a1 / b1 and a2 / b2 / c2 are the adjustment coefficients in the electric and heat conversion efficiencies of HFC respectively; is the rated electric output power of HFC; and are the rated electric and heat conversion efficiencies of HFC respectively;

[0023] Hydrogen-blended CHP unit:

[0024]

[0025] In the formula, is the lower calorific value of the natural gas-hydrogen mixture; P CHP (t) is the total amount of natural gas-hydrogen mixture input into CHP at time t; is the amount of hydrogen input into CHP at time t; is the amount of natural gas input into CHP; is the lower calorific value of natural gas; λ(t) is the natural gas-hydrogen blending ratio;

[0026] Hydrogen storage:

[0027]

[0028] where S HES (t) is the energy storage capacity of HES; and are the upper and lower limits of S HES (t); η HES,c and η HES,d are the hydrogen charging and discharging efficiency coefficients of HES respectively; and are the hydrogen charging and discharging powers of HES respectively.

[0029] Furthermore, the refined ammonia energy utilization sub-model includes:

[0030] Methane reactor:

[0031]

[0032] where and are the hydrogen consumption and gas production of MR respectively; η MR is the conversion efficiency of MR;

[0033] Ammonia production section:

[0034]

[0035] where is the energy consumption of APE at time t; is the unit energy consumption of APE for ammonia production; is the heat released per unit ammonia produced by APE; is the heat release ratio of APE for heat supply; is the ammonia produced by APE at time t; and are the ammonia charging and discharging amounts of the ammonia storage tank respectively; is the ammonia storage amount of the ammonia storage tank in APE at time t; is the heat power provided by APE at time t;

[0036] Ammonia-doped combustion in a carbon capture power plant:

[0037]

[0038] where P coal (t) is the total power generation of CCPP; P CCPP,e (t) is the net output power of CCPP; P base is the basic energy consumption of CCPP; P work(t) is the operating energy consumption of the CCPP; ε ope is the operating energy consumption coefficient of the CCPP; E rele (t) is the amount of CO2 processed by the regeneration tower; λ yq (t) is the flue gas split ratio; E coal (t) and δ coal are respectively the total amount of CO2 generated by the CCPP and its carbon emission intensity; μ1 and μ2 are respectively the absorption and regeneration efficiencies in the CCPP; E rich (t) is the CO2 outflow from the rich liquid tank; E absorb (t) is the amount of CO2 absorbed by the absorption tower; and M coal (t) are respectively the coal consumption during ammonia - doped and non - ammonia - doped combustion of the CCPP; a1, b1, c1 are respectively the coal consumption coefficients of the CCPP; is the ammonia doping amount of the CCPP; P coal (t) is the output power of the CCPP; and are respectively the low calorific values of ammonia and coal; is the ammonia doping ratio of the CCPP; is the maximum ammonia doping ratio; is the maximum capacity of the ammonia storage tank; Q rich (t) and Q poor (t) are respectively the solution outflow / inflow of the lean and rich liquid storage tanks; is the CO2 solution density; W rich (t) and W poor (t) are respectively the storage capacities of the lean and rich liquid storage tanks.

[0039] Furthermore, the expression of the green hydrogen certificate trading model is:

[0040]

[0041] In the formula, F GHCT is the green hydrogen trading cost, λ GHCT is the trading unit price of the green hydrogen certificate, ω and θ are respectively the reward coefficient and the penalty coefficient, d is the length of the green hydrogen quota interval, E GHCT is the number of green hydrogen certificates traded by the system.

[0042] Furthermore, the expression of the number of green hydrogen certificates E GHCT is:

[0043] E GHCT = E d - E s

[0044]

[0045] where, E d and E s are respectively the quota index of green hydrogen certificates that the integrated energy system needs to hold and the number of green hydrogen certificates obtained through wind power, is the green hydrogen certificate quota coefficient; is the total hydrogen energy demand at time t; is the number of green hydrogen certificates obtained from wind power electrolysis; is the quantization coefficient for converting hydrogen production into green hydrogen certificates.

[0046] Furthermore, the two-stage robust low-carbon optimal scheduling model of the integrated energy system includes an objective function and corresponding constraint conditions. The expression of the objective function is:

[0047] min F IES = F CCPP + F Buy + F CET + F GHCT + F M + F Aban

[0048] where, F CET is the carbon trading cost, F Buy is the outsourcing cost, F GHCT is the green hydrogen certificate trading cost, F M is the unit operation and maintenance cost of the unit, F Aban is the curtailment cost of wind power, F CCPP is the carbon capture operation cost;

[0049] Among them:

[0050] (1) The carbon capture operation cost F CCPP is expressed as:

[0051] F CCPP = f cce + f store + f RY

[0052]

[0053] where, f cce is the operation and maintenance cost of CCPP, f store is the carbon dioxide sequestration cost, f RY is the solution loss cost, λ cce is the operation and maintenance coefficient of CCPP; δ store is the sequestration cost coefficient per unit of CO2; is the CO2 capture rate; E MEA (t) is the amount of CO2 to be captured supplied by the liquid storage tank to CCPP at time t; α L is the loss coefficient of the liquid storage tank; k Lis the operation coefficient of the liquid storage tank; is the total amount of CO2 captured by CCPP at time t;

[0054] (2) The coal consumption cost F of the thermal power unit coal is expressed as:

[0055]

[0056] In the formula, c coal is the unit coal price;

[0057] (3) The carbon trading cost F CET is expressed as:

[0058]

[0059] In the formula, is the total amount of carbon quotas for the integrated energy system; is the externally purchased electric power of the integrated energy system at time t; is the output power of GB; δ e and δ h are the carbon quota coefficients per unit of electricity and per unit of heat respectively; is the actual total carbon emissions of the integrated energy system; λ e-h is the electricity - heat conversion coefficient; δ gas is the carbon emission intensity of the gas - fired unit; δ MR is the carbon emission absorption coefficient per unit of MR; c CET is the unit carbon trading cost;

[0060] (4) The energy purchase cost F Buy is expressed as:

[0061]

[0062] In the formula, π e,b (t) is the time - of - use electricity price; π g,b (t) is the gas purchase price; is the natural gas consumption of GB;

[0063] (5) The equipment operation and maintenance cost F main is expressed as:

[0064]

[0065] In the formula, n is the type of energy supply equipment; P n (t), ε n are the output power and operation and maintenance coefficient of the energy supply equipment n respectively; j is the type of energy storage equipment; β j is the operation and maintenance coefficient of the energy storage equipment j; and The charging and discharging powers of the energy storage device j, respectively;

[0066] (6) Curtailment cost F Aban It is expressed as:

[0067]

[0068] In the formula, is the unit curtailment penalty coefficient; P WT (t) and P WT,p (t) are the actual wind power consumption power and the predicted value, respectively.

[0069] Furthermore, the constraint conditions include:

[0070] (1) CCPP operation constraint:

[0071]

[0072] In the formula, and are the upper and lower limits of P coal (t); θ max is the maximum operating condition coefficient of the compressor and the regeneration tower; and are the upper and lower limits of the ramp rate of P coal (t); and are the upper and lower limits of the flue gas split ratio;

[0073] (2) Upstream power and gas purchase constraints:

[0074]

[0075] In the formula, P Gas (t) is the upstream gas purchase power of the integrated energy system; and are the upper limit of the power purchase and the upper limit of the gas purchase power, respectively;

[0076] (3) Power balance constraint:

[0077]

[0078] In the formula, L e (t) and L h (t) are the actual electricity and heat loads, respectively; and are the charging and discharging powers of BT, respectively; and are the charging and heat release powers of TST, respectively.

[0079] Furthermore, the C&CG algorithm and CPLEX solver are used to solve the two-stage robust low-carbon optimal scheduling model of the integrated energy system. The specific solution process is as follows:

[0080] The objective function and constraint conditions of the two-stage robust low-carbon optimal scheduling model of the integrated energy system are matrixized to construct a two-stage robust optimization model with a min-max-min structure. The expression is:

[0081]

[0082] In the formula, f is the coefficient matrix of the objective function; x is the output variable of the integrated energy system; u is the state variable of the integrated energy system; A, B, C, D, and E y are the coefficient matrices corresponding to the constraint conditions respectively; α d and α h are the operating parameters of the integrated energy system respectively; y is the uncertainty vector in the worst-case scenario;

[0083] Equation (1) is decomposed into a master problem and a sub-problem, that is, an outer min problem and an inner max-min problem. The expression is:

[0084]

[0085] In the formula, θ represents the inner sub-problem; k is the current iteration number; is the value of renewable energy in the worst-case scenario after the kth iteration; u k is the solution of the sub-problem after the kth iteration; γ, λ, φ, are the dual variables respectively;

[0086] For the inner max-min problem, the big M method is used for linearization, that is:

[0087]

[0088] In the formula: Δy = [y WT (t), y PV (t)] and H' = [H' WT (t), H' PV (t)] are the added auxiliary variables respectively, H is the inverse matrix of H'; and M is taken as the upper bound of the dual variable where M is a sufficiently large positive real number;

[0089] After the above C&CG algorithm processing, CPLEX is used for iterative solution to complete the solution process.

[0090] The present invention also provides an electronic device, comprising: one or more processors; a memory; and one or more programs stored in the memory, the one or more programs including instructions for executing the IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia as described above.

[0091] The present invention also provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for executing the IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia as described above.

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] (1) To give play to the clean characteristics of hydrogen and ammonia energy, the present invention constructs a comprehensive energy system model including refined utilization of hydrogen energy and refined utilization of ammonia energy; in order to deeply analyze the impact of the green hydrogen certificate trading mechanism and its green hydrogen trading price on the operation optimization of the hydrogen-containing IES, a green hydrogen certificate trading model is established, which can increase the amount of green hydrogen used by the system and improve the proportion of renewable energy consumption; considering the uncertainty of renewable energy and taking into account the low-carbon and economic characteristics of the system, a two-stage robust low-carbon optimization scheduling model is constructed, which can effectively improve the level of renewable energy consumption and realize the low-carbon, economic and flexible operation of the IES.

[0094] (2) The present invention constructs a refined utilization sub-model of hydrogen energy including hydrogen production, hydrogen use, and hydrogen blending in gas. The refined utilization of hydrogen energy can convert the surplus wind power resources at night into hydrogen energy, and realize the maximum consumption of hydrogen through hydrogen use, hydrogen storage, and hydrogen blending, which can effectively improve the economic and low-carbon characteristics of the system operation and the level of renewable energy consumption. It also constructs a refined utilization sub-model of ammonia energy including nitrogen production, ammonia production, and ammonia combustion in thermal power. The refined utilization of ammonia energy can replace part of ordinary coal, not only reducing the carbon emissions of thermal power units, but also improving the level of wind power consumption.

[0095] (3) Existing research has rarely delved deeply into the coupling and complementarity between green hydrogen and green ammonia, as well as their comprehensive utilization across multiple links. It has not further explored the application of refined hydrogen and ammonia utilization in the IES, failed to achieve deep coupling of electricity, heat, hydrogen, and ammonia, and has not analyzed in depth the GHCT mechanism and the impact of the green hydrogen trading price on the operation optimization of hydrogen-containing IES. The present invention constructs a refined hydrogen utilization sub-model including hydrogen production, hydrogen use, and gas blending with hydrogen, and a refined ammonia utilization sub-model including nitrogen production, ammonia production, and ammonia combustion in thermal power generation. Based on the renewable energy quota system, a green hydrogen certificate trading model is established to promote the mutual conversion of green electricity and green hydrogen. Considering the uncertainty of renewable energy, with the goal of minimizing carbon trading costs, external purchase costs, green hydrogen certificate trading costs, unit operation and maintenance costs, curtailment costs, and carbon capture operation costs, a two-stage robust low-carbon optimal scheduling model for the integrated energy system is constructed. The refined hydrogen utilization can convert the surplus wind power resources at night into hydrogen energy, and achieve the maximum consumption of hydrogen through links such as hydrogen use, hydrogen storage, and hydrogen blending, effectively improving the system operation economy and low-carbon performance, and enhancing the level of renewable energy consumption. The refined ammonia utilization link composed of nitrogen production, ammonia production, and ammonia blending in thermal power generation can utilize the surplus wind power resources to generate ammonia and blend it into the thermal power unit for combustion, thus replacing part of the ordinary coal, not only reducing the carbon emissions of the thermal power unit, but also improving the level of wind power consumption. Description of the Drawings

[0096] Figure 1 It is a schematic flowchart of the method of the present invention;

[0097] Figure 2 It is a structural diagram of the integrated energy system considering refined hydrogen and ammonia utilization of the present invention;

[0098] Figure 3 It is a graph of the comparison results of electric energy in Scenario 1 and Scenario 2 of the present invention;

[0099] Figure 4 It is a graph of the relationship between the green hydrogen certificate trading price and the system wind power consumption and green hydrogen certificate revenue of the present invention. Detailed Embodiment

[0100] The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives detailed implementation manners and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0101] Embodiment 1

[0102] This embodiment provides an IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia. First, to give full play to the clean characteristics of hydrogen and ammonia energy, a refined hydrogen energy utilization model including hydrogen production, hydrogen use, and gas hydrogen blending links and a refined ammonia energy utilization model including nitrogen production, ammonia production, and thermal power ammonia blending combustion are respectively constructed. Second, based on the renewable energy quota system, green hydrogen certificates are introduced, and a green hydrogen certificate trading mechanism is proposed to promote the mutual conversion of green electricity and green hydrogen. Finally, considering the uncertainty of renewable energy, an IES two-stage robust low-carbon optimal scheduling model is constructed, and the C&CG algorithm and CPLEX solver are used for solution. The example results show that the proposed method can effectively improve the renewable energy consumption level and achieve the low-carbon, economic, and flexible operation of IES. Specifically, as Figure 1 shown, the method includes the following steps:

[0103] Step 1: Construct a comprehensive energy system model considering the refined utilization of hydrogen and ammonia:

[0104] (1) Establishment of the electrolytic hydrogen model:

[0105]

[0106] In the formula, f[P EL,e (t)] is the efficiency function of EL at time t; P EL,e (t) is the electric power input into EL at time t; a EL , b EL and c EL are the efficiency function coefficients of EL respectively; is the amount of hydrogen substance produced by EL at time t; P EL,eN is the rated value of the electric power input into EL; Q EL,N is the rated capacity of EL.

[0107] Considering that there is a large amount of heat loss during the electrolysis process, the lost heat can be recovered to fully improve the energy utilization efficiency of the system.

[0108]

[0109] P EL,h (t) = η EL,h P EL,e (t);

[0110] In the formula, is the hydrogen production amount of EL at time t; P EL,h (t) is the recovered waste heat amount of EL at time t; η EL,h is the heat recovery efficiency coefficient of EL; is the molar mass of hydrogen; is the density of hydrogen; is the low calorific value of hydrogen.

[0111] (2) Hydrogen fuel cell model establishment:

[0112]

[0113] Wherein, a1 / b1 and a2 / b2 / c2 are the adjustment coefficients in the electrical and thermal conversion efficiencies of HFC respectively; is the rated electrical output power of HFC; and are the rated electrical and thermal conversion efficiencies of HFC respectively.

[0114] (3) Methane reactor model establishment:

[0115]

[0116] Wherein, and are the hydrogen consumption and gas production of MR respectively; η MR is the conversion efficiency of MR.

[0117] (4) Hydrogen-doped CHP unit model establishment:

[0118]

[0119] Wherein, is the lower calorific value of the natural gas hydrogen mixture; P CHP (t) is the total amount of natural gas hydrogen mixture input to CHP at time t; is the amount of hydrogen input to CHP at time t; is the amount of natural gas input to CHP; is the lower calorific value of natural gas; λ(t) is the natural gas hydrogen mixture ratio.

[0120] (5) Ammonia production link:

[0121]

[0122] Wherein, is the energy consumption of APE at time t; is the unit energy consumption of APE for ammonia production; is the heat released per unit ammonia produced by APE; is the heat release ratio of APE for heat supply; is the ammonia produced by APE at time t; and are the ammonia charging and discharging amounts of the ammonia storage tank respectively; is the ammonia storage amount of the ammonia storage tank in APE at time t; is the heat power provided by APE at time t.

[0123] (6) Establishment of the ammonia - blended combustion model for a carbon capture power plant (CCPP):

[0124]

[0125] In the formula, P coal (t) is the total power generation of the CCPP; P CCPP,e (t) is the net output power of the CCPP; P base is the basic energy consumption of the CCPP; P work (t) is the operating energy consumption of the CCPP; ε ope is the operating energy consumption coefficient of the CCPP; E rele (t) is the amount of CO2 processed by the regeneration tower; λ yq (t) is the flue gas split ratio; E coal (t) and δ coal are the total amount of CO2 generated by the CCPP and its carbon emission intensity respectively; μ1 and μ2 are the absorption and regeneration efficiencies in the CCPP respectively; E rich (t) is the CO2 outflow from the rich liquid tank; E absorb (t) is the amount of CO2 absorbed by the absorption tower.

[0126]

[0127] In the formula, and M coal (t) are the coal consumption of the CCPP during ammonia - blended and non - ammonia - blended combustion respectively; a1, b1, c1 are the coal consumption coefficients of the CCPP; is the ammonia injection amount of the CCPP; P coal (t) is the output power of the CCPP; and are the lower calorific values of ammonia and coal respectively; is the ammonia blending ratio of the CCPP.

[0128]

[0129] In the formula, is the maximum ammonia blending ratio, which is taken as 20% in this paper; is the maximum capacity of the ammonia storage tank.

[0130]

[0131] In the formula, Q rich (t) and Q poor (t) are the solution outflow / inflow of the lean and rich liquid storage tanks respectively; is the density of the CO2 solution; W rich (t) and W poor (t) are the storage capacities of the lean and rich liquid storage tanks respectively.

[0132] (7) Hydrogen storage model establishment:

[0133]

[0134] Wherein, S HES (t) is the energy storage capacity of HES; and are respectively the upper and lower limits of S HES (t); η HES,c and η HES,d are respectively the hydrogen charging and discharging efficiency coefficients of HES; and are respectively the hydrogen charging and discharging powers of HES.

[0135] Step 2: Establish a green hydrogen certificate trading model:

[0136] E GHCT = E d - E s ;

[0137]

[0138] Wherein, E GHCT is the number of green hydrogen certificates for the system to conduct transactions; E d and E s are respectively the green hydrogen certificate quota index that the integrated energy system needs to hold and the number of green hydrogen certificates obtained through wind power; is the green hydrogen certificate quota coefficient; is the total hydrogen energy demand at time t; is the number of green hydrogen certificates obtained from wind power electrolysis of hydrogen; is the quantization coefficient for converting the hydrogen production amount into green hydrogen certificates. In this paper, it is quantified as 1 green hydrogen certificate corresponding to 1 MWh of green hydrogen energy.

[0139] If E GHCT > 0, it means that the IES needs to purchase green hydrogen quotas from the green hydrogen trading market. On the contrary, it means that the IES can sell the excess green hydrogen quantity to obtain benefits. Then the green hydrogen trading cost F GHCT can be expressed as:

[0140]

[0141] Wherein, λ GHCT is the trading unit price of green hydrogen certificates; ω and θ are respectively the reward coefficient and the penalty coefficient; d is the length of the green hydrogen quota interval.

[0142] Step 3: Establish a two-stage robust low-carbon optimal scheduling model for the integrated energy system:

[0143] Objective function:

[0144] min F IES = F CCPP + F Buy + F CET + F GHCT + F M + F Aban ;

[0145] In the formula, the carbon trading cost F CET , the outsourcing cost F Buy , the green hydrogen certificate trading cost F GHCT , the unit operation and maintenance cost F M , the curtailment cost F Aban , and the carbon capture operation cost F CCPP , the expressions of each other part are as follows:

[0146] (1) CCPP operation cost

[0147] The CCPP operation cost F CCPP includes the CCPP operation and maintenance cost f cce , the carbon dioxide sequestration cost f store , and the solution loss cost f RY , which can be specifically expressed as follows:

[0148] F CCPP = f cce + f store + f RY ;

[0149]

[0150] In the formula, λ cce is the operation and maintenance coefficient of CCPP; δ store is the sequestration cost coefficient per unit of CO2; is the CO2 capture rate; E MEA (t) is the amount of CO2 to be captured supplied by the liquid storage tank to the CCPP at time t; α L is the loss coefficient of the liquid storage tank; k L is the operation coefficient of the liquid storage tank; is the total amount of CO2 captured by the CCPP at time t.

[0151] (2) Coal consumption cost of thermal power unit

[0152]

[0153] In the formula, c coal is the unit coal price.

[0154] (3) Carbon trading cost

[0155] The carbon emission sources of the present invention mainly come from four parts: CHP, GB, purchased electricity from the superior (considered as coal-fired power generation), and CCPP. The benchmark line method is used to allocate the carbon emission sources. Therefore, the carbon emission quota and the actual carbon emissions of the integrated energy system can be expressed as:

[0156]

[0157]

[0158] In the formula, is the total carbon quota of the integrated energy system; is the purchased electricity power of the integrated energy system at time t; is the output power of GB; δ e and δ h are the carbon quota coefficients per unit of electricity and per unit of heat respectively; is the actual total carbon emissions of the integrated energy system; λ e-h is the electricity-heat conversion coefficient; δ gas is the carbon emission intensity of the gas turbine unit; δ MR is the carbon emission coefficient absorbed by the MR unit.

[0159] Therefore, the carbon trading cost can be expressed as:

[0160]

[0161] In the formula, c CET is the unit carbon trading cost.

[0162] (4) Energy purchase cost

[0163] The energy purchase cost of the integrated energy system includes the electricity purchase cost and the gas purchase cost, which can be specifically expressed as follows:

[0164]

[0165] In the formula: π e,b (t) is the time-of-use electricity price; π g,b (t) is the gas purchase price; is the natural gas consumption of GB.

[0166] (5) Equipment operation and maintenance cost

[0167]

[0168] In the formula, n is the type of energy supply equipment; P n (t), ε n are the output power and operation and maintenance coefficient of the energy supply equipment n respectively; j is the type of energy storage equipment; β j is the operation and maintenance coefficient of the energy storage equipment j; and They are the charging and discharging powers of the energy storage device j respectively.

[0169] (6) Curtailment cost

[0170]

[0171] In the formula, is the unit curtailment penalty coefficient; P WT (t) and P WT,p (t) are the actual absorbed power and the predicted value of wind power respectively.

[0172] The constraint conditions are as follows:

[0173] (1) CCPP operation constraints

[0174]

[0175] In the formula, and are the upper and lower limits of P coal (t) respectively; θ max is the maximum operating condition coefficient of the compressor and the regeneration tower; and are the upper and lower limits of the ramp rate of P coal (t) respectively; and are the upper and lower limits of the flue gas split ratio respectively.

[0176] (2) Upstream power and gas purchase

[0177]

[0178] In the formula, P Gas (t) is the upstream gas purchase power of the integrated energy system; and are the upper limit of the power purchase and the upper limit of the gas purchase power respectively.

[0179] (3) Power balance constraints

[0180]

[0181] In the formula, L e (t) and L h (t) are the actual electricity and heat loads respectively; and are the charging and discharging powers of BT respectively; and are the charging and heat release powers of TST respectively.

[0182] Step 4: Use the C&CG algorithm and the CPLEX solver to solve:

[0183] For the two-stage robust low-carbon optimal scheduling model of the integrated energy system established above, the C&CG algorithm and the CPLEX solver are used to solve the low-carbon scheduling plan to achieve low-carbon scheduling. The model solving steps are as follows:

[0184] In order to reduce the impact of the volatility and intermittency of renewable energy on system operation, two-stage robust optimization is used to describe the uncertainty of wind power in the system, and a two-stage robust optimization model with a min-max-min structure is constructed. For the convenience of solution, the above objective function and constraint conditions are matrixized to obtain:

[0185]

[0186] Where: f is the coefficient matrix of the objective function; x is the output variable of the IES; u is the state variable of the IES; A, B, C, D, and E y are the coefficient matrices of the corresponding constraint conditions; α d and α h are the operating parameters of the IES respectively; y is the uncertainty vector in the worst-case scenario.

[0187] The original problem equation is decomposed into a master problem and a sub-problem, that is, an outer-layer min problem and an inner-layer max-min problem, namely:

[0188]

[0189] Where: θ represents the inner-layer sub-problem; k is the current iteration number; is the value of renewable energy in the worst-case scenario after the kth iteration; u k is the solution of the sub-problem after the kth iteration.

[0190]

[0191] Where: γ, λ, φ, are the dual variables respectively.

[0192] Since there are bilinear terms in the maximization problem generated after the dual transformation of the inner-layer max-min problem, the big M method can be used for linearization, that is:

[0193]

[0194] Where: Δy = [y WT (t), y PV (t)] and H' = [H' WT (t), H' PV (t)] are the added auxiliary variables respectively, H is the inverse matrix of H'; and M is taken as the dual variable The upper bound, where M is a sufficiently large positive real number. After the above processing, CPLEX can be used for iterative solution.

[0195] In this embodiment, through case simulations and comparisons of different scenarios, the effectiveness of the proposed method is verified. Taking the industrial park system with hydrogen production and ammonia production as an example, the effectiveness of the IES low-carbon scheduling model proposed in the present invention is verified. Among them, the structure diagram of IES is shown in Figure 2 as follows; the equipment parameters of IES are shown in Table 1; the unit carbon trading price cCET is 0.268 yuan / kg [3]; the penalty cost coefficient for curtailed wind is 0.3; the unit price of green hydrogen trading is 150 yuan / book. Based on the MATLAB R2019b platform, the present invention uses the Yalmip modeling method and calls the commercial solver Yalmip to solve the IES low-carbon scheduling model proposed in the present invention.

[0196] Table 1 Operating parameters of IES equipment

[0197]

[0198]

[0199] To verify the effectiveness analysis of the method proposed in this embodiment, the following 5 scenarios are set for comparative analysis: 1) Scenario 1: Traditional IES scheduling scheme; 2) Scenario 2: On the basis of Scenario 1, a hydrogen energy utilization model including EL, HFC, MR, and hydrogen blending in gas is introduced, but EL and HFC participate in operation at a constant efficiency; 3) Scenario 3: On the basis of Scenario 2, EL and HFC participate in operation at a variable efficiency; 4) Scenario 4: On the basis of Scenario 3, a liquid storage tank is introduced to form a CCPP comprehensive flexible operation mode; 5) Scenario 5: On the basis of Scenario 4, links including nitrogen production, ammonia production, and ammonia blending in thermal power are introduced; 6) Scenario 6: On the basis of Scenario 5, a green hydrogen certificate trading mechanism is introduced. The optimization results of each scenario are shown in Table 2:

[0200] Table 2 Optimization results of Scenario 1 - Scenario 6

[0201]

[0202] 1) Effectiveness analysis of the refined utilization link of hydrogen energy

[0203] Comparing Scenario 1 and Scenario 2, in Scenario 2, on the basis of Scenario 1, a hydrogen energy utilization model for hydrogen production, hydrogen use, hydrogen storage, and hydrogen blending in gas is introduced. It can be calculated from Table 2 that when a diversified hydrogen energy utilization model including EL, HFC, MR, and hydrogen blending in gas is introduced, the curtailed wind cost of Scenario 2 decreases by 43.02%, and the total cost and carbon emissions of IES decrease by 5.70% and 8.50% respectively. It not only improves the consumption level of night-time wind power in IES, but also reduces the system operation cost and carbon emissions.

[0204] The present invention analyzes the electric energy dispatching results. Figure 3 The electric energy dispatching results for Scenario 1 and Scenario 2 are as follows. As can be seen from the bar chart corresponding to BT in Figure 3 , due to the low electric load and high heat load at night, without the introduction of the hydrogen energy utilization link in Scenario 1, the wind power consumption level during 01:00 - 07:00 and 22:00 - 24:00 at night is limited, and there is a relatively large cost of wind abandonment. After the introduction of the hydrogen energy utilization link in Scenario 2, the excess wind power at night can be converted into hydrogen through electrolysis of water by using EL, greatly improving the utilization rate of night-time wind power. And through hydrogen energy utilization links such as HFC, MR, and hydrogen blending in gas, it is respectively converted into electric, heat, and gas energy. The electric and heat energy generated by HFC can be directly used for power supply and heating, and the remaining hydrogen can be transported to MR and CHP to reduce the gas purchase cost of the system. Although the operation and maintenance cost of Scenario 2 increases due to the introduction of hydrogen energy-related equipment, due to the effective reduction of the wind abandonment cost and carbon trading cost, the total cost of IES is also effectively reduced.

[0205] 2) Analysis of the impact of the flexible operation of CCPP on the system

[0206] Compared with Scenario 3, Scenario 4 introduces a liquid storage tank, forming a flexible operation mode of CCPP. Since the carbon dioxide absorption process and the capture process are coupled in Scenario 3, when the electric load is high, due to the high output power of CCPP, the carbon capture energy consumption during this period increases, and at the same time, the system carbon emissions increase. After the introduction of the liquid storage tank in Scenario 4, through the decoupling effect of the liquid storage tank, the〖CO〗_2 generated by CCPP can be stored in the rich liquid tank during the peak electricity consumption period, reducing the carbon capture energy consumption during this period. And during the low electricity consumption period at night, the〖CO〗_2 stored in the rich liquid tank and the〖CO〗_2 from the absorption tower are input into the regeneration tower for treatment together, increasing the carbon capture energy consumption at night, realizing the time shift of the carbon capture energy consumption of CCPP.

[0207] As can be seen from Table 2, in Scenario 4, part of the operation, loss and other costs of the liquid storage tank are borne, and the operation cost of CCPP increases, but the system wind abandonment cost, electricity purchase cost, and carbon trading cost are reduced. Therefore, the total cost of IES and carbon emissions decrease by 3.64% and 9.98% respectively.

[0208] 3) Analysis of the effectiveness of the efficient utilization of ammonia energy

[0209] For Scenario 5, on the basis of Scenario 4, an ammonia energy utilization model of nitrogen production, ammonia production, and ammonia blending in thermal power is introduced. After the introduction of the ammonia production utilization link, since ammonia has a calorific value equivalent to that of ordinary coal and ammonia belongs to zero-carbon clean energy, the coal consumption of the thermal power unit is effectively reduced under the condition of ammonia blending.

[0210] It is calculated from Table 2 that, compared with Scenario 4, the carbon trading cost in Scenario 5 decreased by 40.37%, and the total IES cost and total carbon emissions decreased by 3.87% and 9.44% respectively.

[0211] 4) Verification of the effectiveness of the green hydrogen certificate trading mechanism

[0212] For Scenario 6, the green hydrogen certificate trading mechanism was further introduced on the basis of Scenario 5. For Scenario 5, when the green hydrogen certificate trading mechanism was not considered, its highest green hydrogen ratio was about 0.9. After the introduction of the green hydrogen certificate trading mechanism in Scenario 6, the system could convert the hydrogen energy generated by renewable energy into green hydrogen certificates and trade them in the green hydrogen market, enabling Scenario 6 to obtain a green hydrogen certificate income of 103,500 yuan. Therefore, the hydrogen production input into the electrolyzer was all provided by renewable energy, with a green hydrogen ratio of 1, effectively improving the renewable energy consumption level and reducing the curtailment cost of wind power to 0. As can be seen from Table 2, compared with Scenario 5, the total IES cost in Scenario 6 decreased by 5.02%, and the total carbon emissions decreased by 8.37%.

[0213] Next, the impact of the green hydrogen certificate trading price on the system's wind power consumption is discussed. Figure 4 shows the relationship between the green hydrogen certificate trading price and the system's wind power consumption and green hydrogen certificate income. It can be seen that as the green hydrogen trading price increases, the system's wind power consumption level continuously rises, and the rising amplitude shows a trend of being fast first and then slow and gradually stabilizing, and the full consumption of wind power is achieved when the green hydrogen trading price is 150 yuan / certificate. The reason is that the green hydrogen trading price can enable the IES to obtain part of the green hydrogen trading income and reduce the total system cost. Similarly, as the green hydrogen trading price increases, the green hydrogen trading income obtained by the IES also continuously increases, effectively motivating the system to adopt renewable energy for hydrogen production and increasing the green hydrogen ratio.

[0214] 5) Uncertainty analysis

[0215] The two-stage robust optimization is used to handle the uncertainty of wind power. Based on the existing prediction accuracy, the prediction deviation rates of wind power are set to 15%, 20% and 25% respectively, and the uncertainty coefficients are 6, 12 and 24 respectively. Taking the final Scenario 6 in this paper as the scheduling model, the robust characteristics of the IES are analyzed, and the optimization results are shown in Table 3.

[0216] Table 3 Analysis of the robust characteristics of the IES

[0217]

[0218] As can be seen from Table 3, the total cost and carbon emissions of the IES are the lowest under the determined optimization method. The reason is that this method does not consider the uncertainty of renewable energy, and its optimization result is too conservative and ideal. Although it is economically optimal, its robustness is the worst and does not meet the actual production requirements. When the wind power deviation coefficient is determined, after the wind power uncertainty increases from 6 to 12, the total system cost and carbon emissions increase by 49,300 yuan and 420.5 tons of carbon dioxide respectively. After the uncertainty increases from 12 to 24, the total system cost and carbon emissions increase by 74,000 yuan and 560.5 tons of carbon dioxide respectively. And due to the increase in wind power uncertainty, the curtailment cost also shows an upward trend. The results show that the wind power uncertainty is positively correlated with the system robustness and negatively correlated with the economy and low-carbon characteristics of the system.

[0219] In addition, when the wind power uncertainty is determined, when the wind power deviation coefficient increases from 15% to 20%, the total system cost and carbon emissions increase by 33,100 yuan and 302.4 tons respectively. When it increases from 20% to 25%, the total system cost and carbon emissions increase by 34,300 yuan and 258.1 tons respectively, and the curtailment cost also increases by 19.85%. In summary, the greater the uncertainty or deviation coefficient, the better the robustness of the system, but the worse the economy and low-carbon performance of the system. On the contrary, the worse the robustness of the system, the better the economy and low-carbon performance.

[0220] In summary, the present invention provides a low-carbon economic dispatch method for an integrated energy system considering the refined utilization of hydrogen and ammonia. A hydrogen energy utilization model integrating hydrogen production, hydrogen use, and hydrogen blending from renewable energy is constructed. The refined utilization of hydrogen energy can convert the surplus wind power resources at night into hydrogen energy, and achieve the maximum consumption of hydrogen through links such as hydrogen use, hydrogen storage, and hydrogen blending, which can effectively improve the operation economy and low-carbon performance of the system and improve the consumption level of renewable energy. At the same time, an ammonia energy utilization model including nitrogen production, ammonia production, and ammonia blending combustion in CCPP is constructed, which can replace part of ordinary coal, not only reducing the carbon emissions of thermal power units but also improving the consumption level of wind power. In order to deeply analyze the impact of the green hydrogen certificate trading (GHCT) mechanism and its green hydrogen trading price on the operation optimization of the hydrogen-containing IES, a green hydrogen certificate trading model is established, which can increase the amount of green hydrogen used in the system and improve the consumption ratio of renewable energy. Considering the uncertainty of renewable energy and taking into account the low-carbon and economic characteristics of the system, a two-stage robust low-carbon optimization dispatch model of the IES is constructed. The example results show that the proposed model can effectively improve the consumption level of renewable energy and achieve the low-carbon, economic, and flexible operation of the IES.

[0221] Example 2

[0222] This embodiment provides an electronic device, including: one or more processors; a memory; and one or more programs stored in the memory, where the one or more programs include instructions for executing the IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia as described in Embodiment 1 above.

[0223] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0224] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0225] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0226] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means embodying the functionality specified in the flowchart(s) Figure 1 or flowcharts and / or block(s) Figure 1 or blocks.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the functionality specified in the flowchart(s) Figure 1 or flowcharts and / or block(s) Figure 1 or blocks.

[0228] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0229] It is obvious that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A low-carbon scheduling method for IES considering the refined utilization of hydrogen and ammonia, characterized in that It includes the following steps: Construct a comprehensive energy system model considering the refined utilization of hydrogen and ammonia, including a refined hydrogen utilization sub-model and a refined ammonia utilization sub-model; Based on the renewable energy quota system, introduce green hydrogen certificates and establish a green hydrogen certificate trading model; Based on the comprehensive energy system model and the green hydrogen certificate trading model, considering the uncertainty of renewable energy, construct a two-stage robust low-carbon optimal scheduling model for the comprehensive energy system with the goal of minimizing the overall operating cost; Solve the two-stage robust low-carbon optimal scheduling model of the comprehensive energy system, output a low-carbon scheduling plan, and complete the low-carbon scheduling process.

2. The IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia according to claim 1, characterized in that The refined hydrogen utilization sub-model includes the following: Hydrogen production by electrolysis: P EL,h P(t) = η EL,h P EL,e P(t) where f[P EL,e (t)] is the efficiency function of EL at time t, a EL , b EL and c EL are the coefficient of the efficiency function of EL respectively, P EL,e (t) is the electric power input into EL at time t, is the amount of hydrogen produced by EL at time t, P EL,eN is the rated value of the electric power input into EL; Q EL,N is the rated capacity of EL, is the hydrogen production of EL at time t; P EL,h (t) is the recovered residual heat of EL at time t; η EL,h is the heat recovery efficiency coefficient of EL; is the molar mass of hydrogen; is the density of hydrogen; is the lower calorific value of hydrogen; Hydrogen fuel cell: Wherein, a1 / b1 and a2 / b2 / c2 are the adjustment coefficients in the electrical and thermal conversion efficiencies of the HFC, respectively; is the rated electrical output power of the HFC; and are the rated electrical and thermal conversion efficiencies of the HFC, respectively; Hydrogen-blended CHP unit: In the formula, is the lower calorific value of the natural gas and hydrogen mixture gas; P CHP (t) is the total amount of natural gas and hydrogen mixture input into the CHP at time t; is the amount of hydrogen input into the CHP at time t; is the amount of natural gas input into the CHP; is the lower calorific value of natural gas; λ(t) is the natural gas and hydrogen mixture ratio; Hydrogen storage: Where S HES (t) is the energy storage capacity of HES; and are the upper and lower limits of S HES (t) respectively; η HES,c and η HES,d are the hydrogen charging and discharging efficiency coefficients of HES respectively; and are the hydrogen charging and discharging powers of HES respectively.

3. The IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia according to claim 1, wherein, The refined ammonia utilization sub-model includes: Methane reactor: In the formula, and are the hydrogen consumption and gas production of MR respectively; η MR is the conversion efficiency of MR. Ammonia production link: Wherein, is the energy consumption of APE at time t; is the unit energy consumption of ammonia production by APE; is the heat released per unit ammonia produced by APE; is the heat release ratio of APE for heat supply; is the ammonia produced by APE at time t; and are the ammonia charging and discharging amounts of the ammonia storage tank respectively; is the ammonia storage amount of the ammonia storage tank in APE at time t; is the heat power provided by APE at time t; Ammonia combustion in a carbon capture power plant with ammonia blending: Where, P coal (t) is the total power generation of the CCPP; P CCPP,e (t) is the net output power of the CCPP; P base is the basic energy consumption of the CCPP; P work (t) is the operating energy consumption of the CCPP; ε ope is the operating energy consumption coefficient of the CCPP; E rele (t) is the amount of CO2 treated by the regeneration tower; λ yq (t) is the flue gas split ratio; E coal (t) and δ coal are respectively the total amount of CO2 generated by the CCPP and its carbon emission intensity; μ1 and μ2 are respectively the absorption and regeneration efficiencies in the CCPP; E rich (t) is the CO2 outflow from the rich liquid tank; E absorb (t) is the amount of CO2 absorbed by the absorption tower; and M coal (t) are respectively the coal consumption during ammonia - blended and non - ammonia - blended combustion of the CCPP; a1, b1, c1 are respectively the coal consumption coefficients of the CCPP; is the ammonia injection amount of the CCPP; P coal (t) is the output power of the CCPP; and are respectively the low calorific values of ammonia and coal; is the ammonia injection ratio of the CCPP; is the maximum ammonia injection ratio; is the maximum capacity of the ammonia storage tank; Q rich (t) and Q poor (t) are respectively the solution outflow / inflow of the lean and rich liquid storage tanks; is the density of the CO2 solution; W rich (t) and W poor (t) are respectively the storage capacities of the lean and rich liquid storage tanks.

4. The IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia according to claim 1, wherein The expression of the green hydrogen certificate trading model is: In the formula, F GHCT is the green hydrogen trading cost, λ GHCT is the unit price of green hydrogen certificate trading, ω and θ are the reward coefficient and penalty coefficient respectively, d is the length of the green hydrogen quota interval, and E GHCT is the number of green hydrogen certificates traded by the system.

5. The IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia according to claim 4, characterized in that, The quantity E of the green hydrogen certificates GHCT has the following expression: Where E d and E s are respectively the green hydrogen certificate quota index that the integrated energy system needs to hold and the number of green hydrogen certificates obtained through wind power, is the green hydrogen certificate quota coefficient; is the total hydrogen energy demand at time t; is the number of green hydrogen certificates obtained from wind power electrolysis; is the quantization coefficient for converting hydrogen production into green hydrogen certificates.

6. The IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia according to claim 1, characterized in that The two-stage robust low-carbon optimal scheduling model of the comprehensive energy system includes an objective function and corresponding constraint conditions. The expression of the objective function is: min F IES = F CCPP + F Buy + F CET + F GHCT + F M + F Aban where F CET is the carbon trading cost, F Buy is the outsourcing cost, F GHCT is the green hydrogen certificate trading cost, F M is the unit operation and maintenance cost, F Aban is the curtailment cost, F CCPP is the carbon capture operation cost; Where: (1) Carbon capture operating cost F CCPP Expressed as: F CCPP = f cce + f store + f RY where, f cce is the CCPP operation and maintenance cost, f store is the carbon dioxide sequestration cost, f RY is the solution loss cost, λ cce is the operation and maintenance coefficient of CCPP; δ store is the sequestration cost coefficient per unit of CO2; is the CO2 capture rate; E MEA (t) is the amount of CO2 to be captured supplied by the liquid storage tank to the CCPP at time t; α L is the loss coefficient of the liquid storage tank; k L is the operation coefficient of the liquid storage tank; is the total amount of CO2 captured by the CCPP at time t; (2) Coal consumption cost F of thermal power unit coal It is expressed as: where c coal is the unit coal price; (3) Carbon trading cost F CET It is expressed as: In the formula, is the total carbon quota of the integrated energy system; is the purchased electricity power of the integrated energy system at time t; is the output power of GB; δ e and δ h are the carbon quota coefficients per unit of electricity and per unit of heat respectively; is the actual total carbon emissions of the integrated energy system; λ e-h is the electricity-heat conversion coefficient; δ gas is the carbon emission intensity of the gas turbine unit; δ MR is the carbon emission coefficient absorbed by the MR unit; c CET is the unit carbon trading cost; (4) Energy purchase cost F Buy Expressed as: where, π e,b (t) is the time-of-use electricity price; π g,b (t) is the gas purchase price; is the natural gas consumption of GB; (5) Equipment operation and maintenance cost F main Expressed as: where n is the type of energy supply equipment; P n (t), ε n are respectively the output power and operation and maintenance coefficient of the energy supply equipment n; j is the type of energy storage equipment; β j is the operation and maintenance coefficient of the energy storage equipment j; and are respectively the charging and discharging powers of the energy storage equipment j; (6) Curtailment cost F Aban It is expressed as: Wherein, is the unit penalty coefficient for abandoned wind; P WT (t) and P WT,p (t) are the actual absorbed power and predicted value of wind power respectively.

7. A low-carbon scheduling method for IES considering the refined utilization of hydrogen and ammonia according to claim 6, characterized in that The constraint conditions include: (1) CCPP operation constraints: In the formula, and are the upper and lower limits of P coal (t) respectively; θ max is the maximum operating condition coefficient of the compressor and the regeneration tower; and are the upper and lower limits of the ramp rate of P coal (t) respectively; and are the upper and lower limits of the flue gas diversion ratio respectively; (2) Constraints on purchasing electricity and gas from higher levels: where P Gas (t) is the upper-level gas purchase power of the integrated energy system; and are the upper limits of the power purchase and the gas purchase power respectively; (3) Power balance constraints: Where, L e (t) and L h (t) are the actual electrical and thermal loads respectively; and are the charging and discharging powers of BT respectively; and are the charging and heat release powers of TST respectively.

8. The IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia according to claim 1, wherein, Use the C&CG algorithm and CPLEX solver to solve the two-stage robust low-carbon optimal scheduling model of the comprehensive energy system. The specific solution process includes: Matrixize the objective function and constraint conditions of the two-stage robust low-carbon optimal scheduling model of the comprehensive energy system, and construct a two-stage robust optimization model with a min-max-min structure. The expression is: where f is the coefficient matrix of the objective function; x is the output variable of the integrated energy system; u is the state variable of the integrated energy system; A, B, C, D, and E y are the coefficient matrices of the corresponding constraint conditions; α d and α h are the operating parameters of the integrated energy system; y is the uncertainty vector under the worst-case scenario; Decompose Equation (1) into a master problem and a sub-problem, that is, an outer-layer min problem and an inner-layer max-min problem. The expression is: where, θ represents the inner sub - problem; k is the current iteration number; is the value of renewable energy under the most severe scenario after the k - th iteration; u k is the solution of the sub - problem after the k - th iteration; γ, λ, φ, are dual variables respectively; For the inner-layer max-min problem, use the big M method for linearization, that is: where: Δy = [y WT (t), y PV (t)] and H' = [H' WT (t), H' PV (t)] are respectively the added auxiliary variables, H is the inverse matrix of H'; and M is taken as the upper bound of the dual variable where M is a sufficiently large positive real number; After the above C&CG algorithm processing, then use CPLEX for iterative solution to complete the solution process.

9. An electronic device, characterized in that, It includes: One or more processors; A memory; and One or more programs stored in the memory. The one or more programs include instructions for executing the IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, One or more programs for execution by one or more processors of a power supply device. The one or more programs include instructions for executing the IES low-carbon scheduling method considering the refined utilization of hydrogen and ammonia as described in any one of claims 1-8.

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