Renewable energy hydrogen production cooperative scheduling method based on biomass-geothermal energy hydrogen production

Abstract

The invention discloses a renewable energy hydrogen production cooperative scheduling method based on biomass-geothermal energy hydrogen production. The method comprises the following steps: establishing a biomass gasification power generation-hydrogen production model; establishing a geothermal energy-waste heat combined power generation model; establishing a biomass-assisted electrolytic hydrogen production model for waste heat utilization; constructing a renewable energy hydrogen production plant (REHPP) model; constructing an REHPP double-layer operation model in an electricity-hydrogen combined market environment; a KKT condition (Karush-Kuhn-Tucker condition) is used for converting the REHPP double-layer operation model in the established electricity-hydrogen combined market environment into a single-layer model; converting the single-layer model into a mixed integer second-order cone programming (MISOCP) model by using a large M method, a penalty function method, a segmented McCormick linearization method, a state space linearization method and a strong dual theorem; and according to the obtained mixed integer second-order cone programming (MISOCP) model, a solver Gurobi is used for solving in an MATLAB environment. By means of the method, the earnings of REHPP in the market can be effectively increased, and waste heat is effectively utilized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy market and integrated energy system scheduling, and in particular to a renewable energy hydrogen production coordinated scheduling method based on biomass-geothermal energy hydrogen production. Background Art

[0002] In the current energy system, fossil energy accounts for the majority, while renewable energy accounts for a small proportion. This model will bring about a large amount of carbon emissions. Hydrogen plays an important role in the decarbonization of the energy sector. Hydrogen has the advantages of high calorific value, clean and environmentally friendly, wide sources and renewability. It can significantly reduce greenhouse gas emissions and alleviate the adverse effects of climate change. Biomass and geothermal energy are renewable energy sources, and the thermal energy between the two is complementary. Combining biomass and geothermal power generation with biomass gasification and biomass-assisted electrolysis to produce hydrogen can help improve energy utilization, reduce energy consumption for hydrogen production, and reduce carbon emissions. Existing research still has the following problems: the direct use of hydrogen produced by biomass gasification to generate electricity and then produce hydrogen results in a large energy loss, and the waste heat of biomass power generation cannot be utilized. As recorded in the literature [1]: Chen S, Feng H, Zheng J, et al. Life Cycle Assessment and Economic Analysis of Biomass Energy Technology in China: A Brief Review [J]. Processes, 2020, 8(9): 1112.

[0003] The low efficiency of low-temperature geothermal resources in power generation has limited the development of geothermal energy, as recorded in the literature [2]: Wang D, Ali MA, Alizadeh A, et al. Thermoeconomic appraisal of a novel power and hydrogen cogeneration plant with integration of biomass and geothermal energies [J]. International Journal of Hydrogen Energy, 2024, 52: 385-400.

[0004] Electrolysis hydrogen production technology has high energy consumption, as recorded in the literature [3]: Li Jiayi, Zhou Wei, Chen Shuai, et al. Research progress on biomass-assisted water electrolysis for hydrogen production and synergistic preparation of high-value chemicals [J]. Journal of Engineering Thermophysics, 2022, 43(06): 1429-1443.

[0005] Therefore, integrating geothermal power generation systems, biomass gasification power generation, and biomass electrolysis hydrogen production into a renewable energy hydrogen production plant has broad prospects. Summary of the invention

[0006] The present invention provides a renewable energy hydrogen production coordinated scheduling method based on biomass-geothermal energy hydrogen production, which coordinates biomass power generation, geothermal energy power generation, biomass gasification, and biomass-assisted electrolysis hydrogen production, thereby improving energy utilization efficiency, promoting energy conservation and emission reduction, and reducing operating costs.

[0007] The technical solution adopted by the present invention is:

[0008] The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production includes the following steps:

[0009] Step 1: Based on the energy conversion and energy flow relationship of the gasifier, pressure swing adsorption device (PSA), gas turbine (GT), and hydrogen storage tank (HST), a biomass gasification power generation-hydrogen production model is established;

[0010] Step 2: Based on the complementary characteristics of thermal energy between geothermal power generation and biomass gasification power generation, a geothermal energy-waste heat combined power generation model is established;

[0011] Step 3: Based on the principle of biomass-assisted electrolysis hydrogen production and the relationship between the thermal energy flow of the electrolyzer, a biomass-assisted electrolysis hydrogen production model with waste heat utilization is established;

[0012] Step 4: Based on steps 1 to 3, establish energy balance constraints and build a renewable energy hydrogen production plant (REHPP) model; Step 5: Based on the renewable energy hydrogen production plant (REHPP) model established in step 4, build a REHPP two-layer operation model in the electricity-hydrogen joint market environment;

[0013] Step 6: Use the KKT condition (Karush-Kuhn-Tucker condition) to convert the REHPP two-layer operation model under the electricity-hydrogen joint market environment established in step 5 into a single-layer model;

[0014] Step 7: Use the big M method, penalty function method, piecewise McCormick linearization method, state space linearization method and strong duality theorem to transform the single-layer model in step 6 into a mixed integer second-order cone programming (MISOCP) model;

[0015] Step 8: Based on the mixed integer second-order cone programming (MISOCP) model obtained in step 7, use the solver Gurobi to solve it in the MATLAB environment.

[0016] In step 1, the biomass gasification power generation-hydrogen production model is established as follows:

[0017] ① Biomass gasifier: The gasifier gasifies biomass into synthesis gas. The mathematical model of the gasifier is as follows:

[0018] P Gf,t =η Gf,p M bio,t (1);

[0019]

[0020] Where P Gf,t is the electric power consumed by the gasifier; η Gf,p is the energy consumption rate of the gasifier; M bio,t is the mass of biomass consumed; is the maximum supply of biomass; is the volume of syngas produced by the gasifier; is the calorific value of the synthesis gas produced by the gasifier; η Gf,s is the energy conversion efficiency of the gasifier; is the calorific value of biomass.

[0021] ② Pressure swing adsorption device: Separate the hydrogen from the synthesis gas through the pressure swing adsorption device (PSA). The mathematical model of the pressure swing adsorption device (PSA) is as follows:

[0022]

[0023]

[0024] Where P PSA,t is the PSA energy consumption; η PSA is the PSA energy consumption rate; is the volume of hydrogen separated by PSA; Hydrogen calorific value; α PSA is the PSA hydrogen separation efficiency; is the proportion of hydrogen in the synthesis gas from the gasifier; It is the synthesis gas after passing through the pressure swing adsorption device; is the volume of syngas produced by the gasifier; The calorific value of the synthesis gas after hydrogen separation; is the energy loss rate of PSA.

[0025] ③ Gas turbine: The synthesis gas separated by the pressure swing adsorption device (PSA) enters the gas turbine (GT) to generate electricity, and the remaining heat is supplied to the geothermal power generation system. The mathematical model is as follows:

[0026]

[0027] Where P GT,t is the power generated by GT at time t; P GT,t-1is the power generated by GT at time t-1; is the maximum electrical power of the gas turbine; η GT is the energy conversion efficiency of GT, is the volume of syngas consumed by GT; are the downward and upward climbing rates respectively; The waste heat output by GT; is the energy loss rate of GT.

[0028] ④ Hydrogen storage tank: When the hydrogen load is at a low point or the energy is sufficient, the generated hydrogen can be stored in a hydrogen storage tank (HST), and when the hydrogen load peaks or the hydrogen price is high, the hydrogen can be released to obtain greater profits. The model of the hydrogen storage tank is established as follows.

[0029]

[0030] In the formula, Indicates the amount of hydrogen stored in the hydrogen storage tank at time t; represents the hydrogen storage capacity in the hydrogen storage tank at time t-1; η H The efficiency of storing and releasing hydrogen in the hydrogen storage tank; are the amounts stored and released by the hydrogen storage tank at time t respectively; They are the minimum and maximum storage capacity of the hydrogen storage tank respectively; Indicates the maximum amount of hydrogen that can be charged or discharged from the hydrogen storage tank; is a 0-1 variable, representing the charging and discharging status of the hydrogen storage tank; is the initial energy storage state; t represents the time index. In step 2, the geothermal energy-waste heat combined power generation model is established as follows:

[0031] The geothermal power generation system (GPGS) of the present invention adopts organic Rankine cycle (ORC) dry hot rock geothermal power generation. The dry hot rock geothermal power generation system injects low-temperature heat-conducting medium into the underground high-temperature rock mass through the injection well to heat it, and then the heat-conducting medium returns to the ground through the production well to provide power for the generator. The mathematical model of geothermal energy-waste heat combined power generation is established as follows:

[0032]

[0033] Where P EG,t is the power generation of GPGS; η exp is the average conversion efficiency of ORC; η EG is the generator efficiency; The waste heat generated by GT; Q geo,t is the energy transferred by convection heat transfer between the fluid and the hot dry rock at time t; is the mass flow rate of injected fluid into the production well at time t; h vis the heat transfer coefficient between rock and fluid; T t s is the temperature of the hot dry rock at time t; T t f is the temperature of the fluid at time t; d is the crack diameter; π is the circumference of the circle; k is a constant; ρ f is the fluid density; v is the fluid velocity; μ is the fluid viscosity; Re is the Reynolds number, which indicates the fluid flow state; Pr is the Prandtl number, which indicates the influence of fluid physical properties; c f is the specific heat capacity of the fluid.

[0034] In step 3, a biomass-assisted electrolysis hydrogen production model is established, comprising the following steps:

[0035] Step 3.1, the biomass-assisted hydrogen production electrolyzer can flexibly adjust the hydrogen production rate by adjusting the current and temperature. Since the hydrogen production of an electrolyzer is limited, multiple electrolyzers are connected in series to form an electrolyzer stack to increase the hydrogen production of the electrolyzer. The dynamic operation model of the electrolyzer is as follows:

[0036]

[0037] u ELZ,t =N c u c,t (19);

[0038] P ELZ,t =u ELZ,t I c,t (20);

[0039]

[0040] In the formula, u c,t is the voltage of a single electrolytic cell; ΔG bio is the free Gibbs energy change of biomass electrolysis reaction; F is the Faraday constant; z is the electrons transferred per mole of H2 e - Number; T c,t is the electrolytic cell temperature, unit: K; I c,t is the electrolysis current, unit: A; A c is the electrode area, unit: m 2 ; a1 and a2 are overvoltage parameters related to electrolysis temperature; u ELZ,t is the total voltage of the series connected electrolytic cells; N c is the number of electrolytic cells connected in series; P ELZ,t is the total power of the series electrolyzers; m ELZ,t is the hydrogen production rate of the electrolyzer stack; is the molar mass of hydrogen; η F is the Faraday efficiency of the electrolyzer.

[0041] Step 3.2: Biomass-assisted hydrogen production requires a large amount of heat energy to achieve the desired effect. Waste heat can be used to supplement the heat energy consumed by the electrolyzer, thereby reducing energy consumption. The thermodynamic model of the electrolyzer can be established using the concentrated parameter analysis method as follows:

[0042]

[0043] Q dis,t ≥0,Q in,t ≥0 (25);

[0044]

[0045] In the formula, the first term on the right side of formula (22) is the heat transfer from the electrolytic cell wall to the electrolyte, the second term is the injection of external heat energy, the third term is the internal Joule heat, and the fourth term is the heat loss of the endothermic hydrogen evolution reaction; Δt is the time interval; T w,t is the electrolytic cell wall temperature; T o,t is the outside temperature; C c is the heat capacity of the electrolyte; C w is the heat capacity of the electrolytic cell wall; Z c is the thermal resistance of the electrolyte; Z w is the thermal resistance of the electrolytic cell wall; Z o Represents the thermal resistance of the outside air; Q in,t The heat input is external heat energy; is the waste heat after passing through the evaporator; Q dis,t To discard heat.

[0046] In step 4, energy balance constraints are established and a renewable energy hydrogen production plant model is constructed, as follows:

[0047] The balance constraint for electricity and hydrogen in a renewable energy hydrogen production plant (REHPP) is:

[0048] P GT,t +P EG,t =P Gf,t +P PSA,t +P ELZ,t +P o,t (27);

[0049]

[0050] Where P o,t is the electrical energy exported from REHPP to the grid; h o,t The hydrogen exported by REHPP to the hydrogen grid; is the unit conversion constant.

[0051] The main equipment of the renewable energy hydrogen production plant (REHPP) includes a gasifier, a pressure swing adsorption device, a gas turbine, an electrolyzer, a geothermal power generation system and a hydrogen storage tank. The model of the renewable energy hydrogen production plant (REHPP) includes equations (1) to (28).

[0052] In step 5, constructing a REHPP two-tier operation model in the electricity-hydrogen joint market environment includes:

[0053] S5.1. Upper model:

[0054] In the upper model, the goal is to maximize the profit of the renewable energy hydrogen production plant (REHPP), and its objective function is shown below.

[0055]

[0056] In the formula, the objective function is REHPP's electricity sales revenue, hydrogen sales revenue, biomass acquisition cost and biomass power generation subsidy. h,t is the dual variable corresponding to the equilibrium constraint in the lower-level electricity market clearing model, representing the node marginal electricity price, P t H The electric energy provided by REHPP to the grid; α h,t is the dual variable corresponding to the equilibrium constraint in the lower hydrogen market clearing model, representing the node marginal hydrogen price, Hydrogen energy provided by REHPP to the hydrogen grid; C bio is the biomass acquisition cost of REHPP, C sub Subsidy for biomass power generation, c bio is the purchase price per unit of biomass, c sub Subsidize the price of biomass power generation.

[0057] The constraints of the upper model include the operation constraints of the gasifier, PSA, GT, geothermal energy-waste heat power generation, HST, biomass-assisted electrolysis hydrogen production constraints, and the balance constraints of electricity and hydrogen. The constraints of the upper model include equations (1) to (28).

[0058] The upper model is the operation model of the renewable energy hydrogen plant. The objective function is to maximize the profit of the hydrogen plant, and the constraints are equations (1) to (28).

[0059] S5.2, Lower model:

[0060] Renewable Energy Hydrogen Production Plant (REHPP) participates in both the electricity market and the hydrogen energy market. The lower model is divided into the electricity market clearing model and the hydrogen energy market clearing model, as shown below.

[0061] ① Electricity market clearing model: Assuming that REHPP is a price acceptor, its electricity market clearing model is as follows:

[0062]

[0063]

[0064] Where T is the scheduling time period, Ψ G is the set of generator sets, the variables in the brackets on the right represent the dual variables of the corresponding formula (the same applies to the following formula), θ slack,t represents the node voltage phase angle of the balanced node; represents the dual variable corresponding to the left-hand side formula; Ψ G,i represents the set of generators connected to node i; θ lf,t represents the node voltage phase angle of the starting node of line l; θ lt,t represents the node voltage phase angle of the end node of line l; represents the renewable energy hydrogen production plant located at node i; P o,t Represents REHPP power; is the electrical load; is the quotation of each generator set; P n,t is the power of generator set n; b l is the branch susceptance; P l,max is the maximum power of the line; Respectively represent the maximum and minimum output of the generator; Respectively represent the maximum downward and upward climbing power of the generator; ρ i,t , represents the dual variable of each corresponding formula; P n,t+1 represents the power of generator n at time t+1; T is the scheduling duration; n is the generator group index; o is the REHPP index; l is the line index.

[0065] ② Hydrogen energy market clearing model: The size of the hydrogen flow in the hydrogen pipeline is determined by the node pressure on both sides of the pipeline. The present invention adopts the Weymouth equation to describe the relationship between the hydrogen flow and the node pressure. The hydrogen energy market clearing model is shown below.

[0066]

[0067] In the formula, the variables in the brackets on the right side represent dual variables, Provide quotation for hydrogen suppliers, h s,t Help hydrogen suppliers, represents the hydrogen supplier at node i, represents the passive pipeline set with node i as the end node, represents the passive pipeline set with node i as the starting node, represents the active pipeline set with node i as the starting node, represents the active pipeline set with node i as the terminal node, h o,t Indicates the REHPP hydrogen output, represents the renewable energy hydrogen production plant located at node i; h pl,t represents the hydrogen flow rate in the passive pipeline, h c,t represents the hydrogen flow rate in the active pipeline, represents the node hydrogen load; and They represent the upper and lower limits of the hydrogen supplier's output respectively; Π represents the square of the node pressure; R c,max Indicates the maximum compression ratio of the compressor; π i,max and π i,min Respectively represent the maximum and minimum values ​​of the node pressure; M pl represents the Weymouth coefficient; sgn represents the sign function.

[0068] s represents the hydrogen supplier index; pl represents the ordinary hydrogen pipeline index; c represents the compressor pipeline index; o represents the REHPP index; Π plf,t Represents the square of the node pressure of the starting node of a common pipeline; Π plt,t It represents the square of the node pressure at the end node of a common pipeline; represents the minimum output of hydrogen supplier s; represents the maximum output of hydrogen supplier s; Π ct,t represents the square of the node pressure of the end node of the compressor pipeline c; i,t represents the square of the air pressure at node i; α i,t , ω c,t , They represent the dual variables of the corresponding formulas on their left sides respectively.

[0069] In actual operation, the flow direction of hydrogen in the hydrogen pipeline will basically not change in the short term and can be predicted. That is, in the model, assuming that the flow direction of hydrogen is fixed, the Weymouth equation (44) can be simplified to the following form.

[0070]

[0071] Since equation (48) is a quadratic non-convex constraint and it is very difficult to solve a model containing non-convex constraints, the present invention relaxes equation (48) into the following convex constraint:

[0072]

[0073] The feasible domain of the model obtained by relaxing equation (48) will be enlarged, resulting in inaccurate results. It is necessary to introduce a penalty term in the objective function to tighten equation (49). Therefore, the objective function becomes the following form.

[0074]

[0075] Where: χ is the penalty factor, which is a very small positive constant.

[0076] Convert equation (49) into the standard form of second-order cone constraint.

[0077]

[0078] In the above formula, represents the dual variable of the second-order cone constraint in the above equation.

[0079] S5.3. The established two-layer operation model can be expressed as follows:

[0080]

[0081] In step 6, the two-layer operation model is converted into a single-layer model, including the following steps:

[0082] S6.1. To facilitate the solution, the lower model is converted into KKT conditions and added to the upper model, so that the double-layer model is converted into a single-layer model. The KKT conditions include the KKT conditions of the electricity market clearing model and the KKT conditions of the hydrogen energy market clearing model.

[0083] ①. KKT conditions of the electricity market clearing model:

[0084]

[0085] Where l∈Ψ i represents the set of lines connected to node i; represents the set of another node j of the line l connected to the node i; represents the set of lines with i as the starting node; represents the set of lines with i as the end node; represents the value of the dual variable of equation (37) at time t+1; represents the value of the dual variable of equation (38) at time t+1.

[0086] ②. KKT conditions of hydrogen energy market clearing model:

[0087]

[0088] In the above formula, ⊥ represents the complementary slack constraint, and ||·||2 represents the 2-norm.

[0089] S6.2. After KKT conditional transformation, the single-layer model is:

[0090]

[0091] In step 7, the single-layer model is converted into a mixed integer second-order cone programming (MISOCP) model, including the following steps:

[0092] S7.1. KKT conditional complementary relaxation constraints of the electricity market and hydrogen energy market: Equations (55) and (61) are linearized using the following large M method. That is, μ i ≥0⊥f i (x)≤0 is transformed into:

[0093]

[0094] In the formula, f i (x) represents the complementary relaxation constraint: The right-hand side of equation (55) and equation (61), μ i f i (x) the corresponding dual variable; ν i is a 0-1 auxiliary variable; M is a sufficiently large constant.

[0095] S7.2. For the complementary relaxation constraints of the second-order cone constraints contained in the hydrogen energy market: Formula (62) is solved by the penalty function method in Formula (50), and then Formula (62) becomes:

[0096]

[0097] S7.3, Upper Constraint Linearization:

[0098] The bilinear term T in equations (18) and (20) is transformed into c,t I c,t 、u ELZ,t I c,t Linearize. Assume that there is a bilinear term w = xy, x and y are both continuous variables, if the maximum and minimum values ​​of x are x max and x min , the maximum and minimum values ​​of y are max and min , then w=xy can be approximately expressed by the following inequality.

[0099]

[0100] y s,min =y min +(y max -y min )(s-1) / |S| (68);

[0101] y s,max =y min +(y max -y min )s / |S| (69);

[0102] y s,min z s ≤y s ≤y s,max z s (70);

[0103] x min z s ≤x s ≤x max z s (71);

[0104] In the above formula, z s is a 0-1 auxiliary variable; S represents the number of segments; y s,min ,y s,max Respectively represent the minimum and maximum values ​​of the sth segment of y; s ,y s is a continuous auxiliary variable.

[0105] S7.4. For differential equations (22) and (23), the state space linearization method is used to process them and the following equations are obtained.

[0106]

[0107]

[0108] In the formula, A1, B1, A w , B w 、E w 、a w , b w represents the intermediate auxiliary parameter; T c,t+1 represents the temperature of the electrolytic cell at time t+1; △t represents the time interval; Q in,t Represents the input heat energy of the electrolytic cell; P ELZ,t represents the power of the electrolytic cell at time t; m ELZ,t represents the hydrogen production of the electrolyzer at time t; Indicates the calorific value of hydrogen; T o,t Indicates the external environment temperature; Z c is the thermal resistance of the electrolytic cell; Z w is the thermal resistance of the electrolytic cell wall; Z o Represents the thermal resistance of the external environment; C w Represents the heat capacity of the electrolytic cell wall.

[0109] S7.5, Linearization of upper objective function:

[0110] In the upper model, the objective function contains bilinear terms, and contains the dual variables of the lower variables. The strong duality theorem can be used for linearization, and the upper objective function is transformed into:

[0111]

[0112] S7.6. After the transformation in steps S7.1 to S7.5, the single-layer model formula (63) is transformed into a mixed integer second-order cone programming (MISOCP) model.

[0113] In step 8, the solution process is as follows:

[0114] The mixed integer second-order cone programming (MISOCP) model obtained according to the above steps is solved by calling the Gurobi solver in the MATLAB environment.

[0115] The invention provides a renewable energy hydrogen production coordinated scheduling method based on biomass-geothermal energy hydrogen production, which has the following beneficial effects:

[0116] 1) The biomass gasification power generation-hydrogen production model established by the present invention can separate the hydrogen in the synthesis gas produced by biomass gasification to participate in the hydrogen market and reduce energy loss.

[0117] 2) The geothermal energy-waste heat combined power generation model established by the present invention can fully utilize the waste heat generated by biomass gasification power generation, increase the power generation of the geothermal power generation system, and thus reduce the power generation cost.

[0118] 3) The biomass-assisted electrolysis hydrogen production model with waste heat utilization established in the present invention will reduce the energy consumption of the electrolyzer and the waste of waste heat, and can realize flexible hydrogen production when combined with biomass gasification hydrogen production.

[0119] 4) The renewable energy hydrogen production plant model established by the present invention uses biomass and geothermal energy as raw materials, is sustainable, and can solve the problems of high energy consumption of electrolytic hydrogen production, low efficiency of low-temperature geothermal resource power generation, and high pollution caused by biomass incineration treatment, which is of great significance to promoting energy conservation and emission reduction.

[0120] 5) The double-layer operation model of the renewable energy hydrogen production plant in the electricity-hydrogen joint market environment established by the present invention can maximize the revenue of the renewable energy hydrogen production plant and improve the competitiveness of the renewable energy hydrogen production plant in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0122] Figure 1 It is a system framework structure diagram of the present invention.

[0123] Figure 2 It is a 7-node hydrogen network-5-node power grid network topology.

[0124] Figure 3 This is a data chart of electric energy load and hydrogen energy load.

[0125] Figure 4 It is the node electricity price and node hydrogen price curve of the node where REHPP is located.

[0126] Figure 5 Provides support for generators and hydrogen suppliers.

[0127] Figure 6 This is the REHPP power dispatch result.

[0128] Figure 7 Results for REHPP hydrogen dispatch.

[0129] Figure 8 This is the REHPP market revenue chart. DETAILED DESCRIPTION

[0130] A market operation method for a renewable energy hydrogen production plant based on biomass-geothermal energy hydrogen production includes establishing a biomass gasification power generation-hydrogen production model based on energy conversion and energy flow relationships of a gasifier, a pressure swing adsorption device, a gas turbine, and a hydrogen storage tank; establishing a geothermal energy-waste heat combined power generation model based on the thermal energy complementary characteristics of geothermal power generation and biomass gasification power generation; establishing a biomass-assisted electrolysis hydrogen production model using waste heat based on the biomass-assisted electrolysis hydrogen production principle and the thermal energy flow relationship of an electrolyzer; establishing an energy balance constraint and constructing a renewable energy hydrogen production plant (REHPP) model; establishing a REHPP double-layer operation model under an electricity-hydrogen joint market environment; using KKT conditions to convert the established double-layer operation model into a single-layer model, and then using the large M method, penalty function method, piecewise McCormick linearization method, state space linearization method, and strong duality theorem to convert the single-layer model into a MISOCP model; finally, according to the obtained MISOCP model, using the solver Gurobi to solve it in a MATLAB environment. This method can effectively increase the revenue of REHPP in the market and make effective use of waste heat.

[0131] The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production includes the following steps:

[0132] Step 1: Based on the energy conversion and energy flow relationship of the gasifier, pressure swing adsorption device (PSA), gas turbine (GT), and hydrogen storage tank (HST), a biomass gasification power generation-hydrogen production model is established;

[0133] Step 2: Based on the complementary characteristics of thermal energy between geothermal power generation and biomass gasification power generation, a geothermal energy-waste heat combined power generation model is established;

[0134] Step 3: Based on the principle of biomass-assisted electrolysis hydrogen production and the relationship between the thermal energy flow of the electrolyzer, a biomass-assisted electrolysis hydrogen production model with waste heat utilization is established;

[0135] Step 4: Based on steps 1 to 3, establish energy balance constraints and build a renewable energy hydrogen production plant (REHPP) model; Step 5: Based on the renewable energy hydrogen production plant (REHPP) model established in step 4, build a REHPP two-layer operation model in the electricity-hydrogen joint market environment;

[0136] Step 6: Use the KKT condition (Karush-Kuhn-Tucker condition) to convert the REHPP two-layer operation model under the electricity-hydrogen joint market environment established in step 5 into a single-layer model;

[0137] Step 7: Use the big M method, penalty function method, piecewise McCormick linearization method, state space linearization method and strong duality theorem to transform the single-layer model in step 6 into a mixed integer second-order cone programming (MISOCP) model;

[0138] Step 8: Based on the mixed integer second-order cone programming (MISOCP) model obtained in step 7, use the solver Gurobi to solve it in the MATLAB environment.

[0139] The solution process is as follows:

[0140] According to the MISOCP model obtained in the above steps, program in the MATLAB environment and input parameters to call the solver Gurobi for solution.

[0141] The following steps are involved:

[0142] Step 1: Write a deterministic program in MATLAB based on the obtained MISOCP model;

[0143] Step 2: Input the parameters of REHPP, load, transmission line, hydrogen pipeline, load, hydrogen suppliers and generators;

[0144] Step 3: Call the solver Gurobi in the written program to solve and obtain the scheduling plan.

[0145] System block diagram Figure 1As shown. The system is mainly composed of a gasifier, a pressure swing adsorption device, a hydrogen storage tank, a gas generator, an evaporator, a biomass-assisted hydrogen production electrolyzer, and a geothermal power generation system. First, the biomass is gasified in a gasifier to produce synthesis gas. Since the synthesis gas contains hydrogen, the hydrogen is separated by a pressure swing adsorption device. The separated hydrogen can be stored in a hydrogen storage tank, and the remaining synthesis gas is supplied to the gas turbine for power generation. The waste heat generated by the gas turbine can be passed through the evaporator to increase the power generation of the geothermal power generation system, and the waste heat passing through the evaporator can be supplied to the heat energy consumption of the electrolyzer. Since the hydrogen production of a single electrolyzer is small, 14,000 electrolyzers are connected in series for large-scale production, and the maximum current of the electrolyzer is 600A. Table 1 shows the relevant parameters of the geothermal power generation system, and Table 2 shows the relevant parameters of REHPP. Figure 2 The network topology diagram of the simulation test case used is shown in Table 3. Table 3 shows the parameters of the power supplier, and Table 4 shows the parameters of the hydrogen supplier. Figure 3 It is the electricity load and hydrogen load data.

[0146] Table 1 Geothermal power generation system parameters

[0147]

[0148] Table 2 REHPP related parameters

[0149]

[0150] Table 3 Electricity supplier parameters

[0151]

[0152] Table 4 Hydrogen supplier parameters

[0153]

[0154] Based on the quotations from power generators and hydrogen suppliers, the settlement prices for REHPP electricity and hydrogen can be derived, such as Figure 4 As shown. During 5:00-23:00, due to the large hydrogen load, the node hydrogen price during this period is high, and the HP1 output reaches the maximum at this time. When the hydrogen demand is low, that is, during the 1:00-4:00 and 24:00 time periods, the HP1 output can meet the demand, and the node hydrogen price during this period is low. When the hydrogen load increases further, the HP1 output with a lower bid reaches the upper limit, which will force the hydrogen energy supplier with a high bid to increase its output, and the corresponding cost will also increase, resulting in an increase in the node hydrogen price.

[0155] Similarly, according to Figure 5From the output of each generator, we can see that during the period of 6:00-11:00, the output of generator G1 reaches the upper limit, which makes the node electricity price higher. This is because G1 has a low bid and G1 has priority output among generators G1-G3. When G1 reaches the upper limit of power generation, generator G2 increases its output, which leads to an increase in node electricity price. During the period of 12:00-15:00, the load increases further and the output of generator G2 reaches the upper limit, which further increases the electricity price.

[0156] Figure 6 This is the result of REHPP power dispatch. Since the REHPP quotation is 0, the gas turbine and geothermal power generation system have stable output and provide most of the energy. At 6:00, the gas turbine output in REHPP is the lowest, which reduces the power sales. This is because in order to make more profit from power sales, REHPP reduces the power output and forces G1 to reach the upper limit, thereby increasing the node electricity price. In the day-ahead energy market, the clearing price trend of electricity and hydrogen is close to the load change trend, which is manifested as high clearing prices when the load is high.

[0157] Figure 7 The dispatch result of hydrogen energy within REHPP is Figure 5 The output of each hydrogen supplier in China and Figure 7 The hydrogen energy dispatch results show that Figure 7 From 5:00 in the figure, the hydrogen output of REHPP has dropped significantly, while the hydrogen price has risen at 5:00, and the hydrogen load is gradually rising. In the period of 5:00-8:00, the hydrogen storage tank is storing hydrogen, but REHPP does not store hydrogen when the hydrogen price is low. According to the above scheduling results, it is not difficult to find the reason why hydrogen is stored when the hydrogen price is high but not when the hydrogen price is low. This is because in the period of 1:00-4:00, the hydrogen load is small and there is hydrogen produced by PSA in REHPP, which cannot reach the full output of HP1. REHPP cannot raise the hydrogen price of the node by changing the bid amount. As the hydrogen load gradually increases, from 5:00 to 8:00, REHPP reduces the hydrogen bid amount, so that the HP1 output reaches the limit, thereby increasing the hydrogen price. The excess hydrogen produced by REHPP is stored in the hydrogen storage tank and released when the hydrogen load peaks and the hydrogen price is high, so as to obtain more profits. If hydrogen is stored between 1:00 and 4:00, it may not be possible to raise the hydrogen price to obtain more profits between 5:00 and 8:00. The same is true between 21:00 and 23:00 to raise the hydrogen price to obtain more profits. When the electricity price is high, that is, between 12:00 and 15:00 and 19:00 and 20:00, the output of the electrolyzer is reduced, and the hydrogen storage tank begins to release hydrogen to obtain more profits in both markets at the same time.

[0158] Figure 8The revenue of REHPP in the electricity market and hydrogen market is shown. It can be clearly seen that the revenue of REHPP in the electricity market is higher than that in the hydrogen market. This is due to the limitation of electrolyzer power size and the small hydrogen load. The total revenue of REHPP in the electricity market for one day is $40,529.41, and the total revenue in the hydrogen market for one day is $9,824.98. Figure 5 It can be seen that the output power of REHPP has been relatively flat with little change, while the revenue in the electricity market has increased significantly during the period from 9:00 to 21:00. The reason for this difference is that the power load is large during this period, which makes the electricity price higher. In the hydrogen energy market, the revenue is small during the period around 6:00 and 22:00, and the revenue is large in other time periods. The reason for this trend is that in order to maximize the overall profit, REHPP reduces the amount of hydrogen market bidding during the period around 6:00 and 21:00 to increase the hydrogen price, and transfers the excess hydrogen to the time when the hydrogen price is high. However, during the period from 1:00 to 4:00 and 24:00, REHPP cannot increase the hydrogen price by changing the bidding strategy, and can only rely on increasing the amount of hydrogen sold to obtain greater profits.

Claims

1. A coordinated dispatching method for renewable energy hydrogen production based on biomass-geothermal energy hydrogen production, characterized in that The following steps are involved: Step 1: Based on the energy conversion and energy flow relationship of the gasifier, pressure swing adsorption device, gas turbine, and hydrogen storage tank, a biomass gasification power generation-hydrogen production model is established; Step 2: Based on the complementary characteristics of thermal energy between geothermal power generation and biomass gasification power generation, a geothermal energy-waste heat combined power generation model is established; Step 3: Based on the principle of biomass-assisted electrolysis hydrogen production and the relationship between the thermal energy flow of the electrolyzer, a biomass-assisted electrolysis hydrogen production model with waste heat utilization is established; Step 4: Based on steps 1 to 3, establish energy balance constraints and build a renewable energy hydrogen production plant model; Step 5: Based on the renewable energy hydrogen production plant model established in step 4, a REHPP two-tier operation model is constructed in the electricity-hydrogen joint market environment; Step 6: Use the KKT condition to convert the REHPP two-layer operation model under the electricity-hydrogen joint market environment established in step 5 into a single-layer model; Step 7: Use the big M method, penalty function method, piecewise McCormick linearization method, state space linearization method and strong duality theorem to transform the single-layer model in step 6 into a mixed integer second-order cone programming model; Step 8: Use the solver to solve the mixed integer second-order cone programming model obtained in step 7.

2. The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production according to claim 1 is characterized in that: In step 1, the biomass gasification power generation-hydrogen production model is established as follows: ① Biomass gasifier: The gasifier gasifies biomass into synthesis gas. The mathematical model of the gasifier is as follows: P Gf,t =the Gf,p M bio,t (1); Where P Gf,t is the electric power consumed by the gasifier; η Gf,p is the energy consumption rate of the gasifier; M bio,t is the mass of biomass consumed; is the maximum supply of biomass; is the volume of syngas produced by the gasifier; is the calorific value of the synthesis gas produced by the gasifier; η Gf,s is the energy conversion efficiency of the gasifier; is the calorific value of biomass; ② Pressure swing adsorption device: The hydrogen in the synthesis gas is separated by the pressure swing adsorption device; the mathematical model of the pressure swing adsorption device (PSA) is as follows: Where P PSA,t is the PSA energy consumption; η PSA is the PSA energy consumption rate; is the volume of hydrogen separated by PSA; Hydrogen calorific value; α PSA is the PSA hydrogen separation efficiency; γ H2 is the proportion of hydrogen in the synthesis gas from the gasifier; It is the synthesis gas after passing through the pressure swing adsorption device; is the volume of syngas produced by the gasifier; The calorific value of the synthesis gas after hydrogen separation; is the energy loss rate of PSA; ③ Gas turbine: The synthesis gas separated by the pressure swing adsorption device enters the gas turbine (GT) to generate electricity, and the remaining heat is supplied to the geothermal power generation system. The mathematical model is as follows: Where P GT,t is the power generated by GT at time t; P GT,t-1 is the power generated by GT at time t-1; is the maximum electrical power of the gas turbine; η GT is the energy conversion efficiency of GT, is the volume of syngas consumed by GT; are the downward and upward climbing rates respectively; The waste heat output by GT; is the energy loss rate of GT; ④ Hydrogen storage tank: When the hydrogen load is at a low point or the energy is sufficient, the generated hydrogen is stored in the hydrogen storage tank, and when the hydrogen load is at a peak or the hydrogen price is high, the hydrogen is released to obtain greater profits; the model of the hydrogen storage tank is established as follows; In the formula, Indicates the amount of hydrogen stored in the hydrogen storage tank at time t; represents the hydrogen storage capacity in the hydrogen storage tank at time t-1; η H The efficiency of storing and releasing hydrogen in the hydrogen storage tank; are the amounts stored and released by the hydrogen storage tank at time t respectively; They are the minimum and maximum storage capacity of the hydrogen storage tank respectively; Indicates the maximum amount of hydrogen that can be charged or discharged from the hydrogen storage tank; is a 0-1 variable, representing the charging and discharging status of the hydrogen storage tank; is the initial energy storage state; t represents the time index.

3. The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production according to claim 2 is characterized in that: In step 2, the geothermal energy-waste heat combined power generation model is established as follows: The mathematical model of geothermal energy-waste heat combined power generation is established as follows: Where P EG,t is the power generation of GPGS; η exp is the average conversion efficiency of ORC; η EG is the generator efficiency; The waste heat generated by GT; Q geo,t is the energy transferred by convection heat transfer between the fluid and the hot dry rock at time t; is the mass flow rate of injected fluid into the production well at time t; h v is the heat transfer coefficient between rock and fluid; T t s is the temperature of the hot dry rock at time t; T t f is the temperature of the fluid at time t; d is the crack diameter; π is the circumference of the circle; k is a constant; ρ f is the fluid density; v is the fluid velocity; μ is the fluid viscosity; Re is the Reynolds number, which indicates the fluid flow state; Pr is the Prandtl number, which indicates the influence of fluid physical properties; c f is the specific heat capacity of the fluid.

4. The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production according to claim 3 is characterized by: In step 3, a biomass-assisted electrolysis hydrogen production model is established, comprising the following steps: Step 3.1, multiple electrolyzers are connected in series to form an electrolyzer stack to increase the hydrogen production of the electrolyzer; the dynamic operation model of the electrolyzer is as follows: u ELZ,t =N c u c,t (19); P ELZ,t =in ELZ,t AND c,t (20); In the formula, u c,t is the voltage of a single electrolytic cell; ΔG bio is the free Gibbs energy change of biomass electrolysis reaction; F is the Faraday constant; z is the electrons transferred per mole of H2 e - Number; T c,t is the electrolytic cell temperature; I c,t is the electrolysis current; A c is the electrode area; a1 and a2 are overvoltage parameters related to the electrolysis temperature; u ELZ,t is the total voltage of the series connected electrolytic cells; N c is the number of electrolytic cells connected in series; P ELZ,t is the total power of the series electrolyzers; m ELZ,t is the hydrogen production rate of the electrolyzer stack; is the molar mass of hydrogen; η F is the Faraday efficiency of the electrolyzer; Step 3.2: The thermodynamic model of the electrolyzer is established using the lumped parameter analysis method as follows: Q dis,t ≥0,Q in,t ≥0 (25); In the formula, the first term on the right side of formula (22) is the heat transfer from the electrolytic cell wall to the electrolyte, the second term is the injection of external heat energy, the third term is the internal Joule heat, and the fourth term is the heat loss of the endothermic hydrogen evolution reaction; Δt is the time interval; T w,t is the electrolytic cell wall temperature; T o,t is the outside temperature; C c is the heat capacity of the electrolyte; C w is the heat capacity of the electrolytic cell wall; Z c is the thermal resistance of the electrolyte; Z w is the thermal resistance of the electrolytic cell wall; Z o Represents the thermal resistance of the outside air; Q in,t The heat input is external heat energy; is the waste heat after passing through the evaporator; Q dis,t To discard heat.

5. The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production according to claim 4 is characterized in that: In step 4, energy balance constraints are established and a renewable energy hydrogen production plant model is constructed, as follows: The balance constraint of electricity and hydrogen in a renewable energy hydrogen production plant is: P GT,t +P EG,t =P Gf,t +P PSA,t +P ELZ,t +P o,t (27); Where P o,t is the electrical energy exported from REHPP to the grid; h o,t The hydrogen exported by REHPP to the hydrogen grid; is the unit conversion constant; The equipment of the renewable energy hydrogen production plant includes a gasifier, a pressure swing adsorption device, a gas turbine, an electrolyzer, a geothermal power generation system and a hydrogen storage tank. The model of the renewable energy hydrogen production plant includes equations (1) to (28).

6. The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production according to claim 1, characterized in that: In step 5, constructing a REHPP two-tier operation model in the electricity-hydrogen joint market environment includes: S5.

1. Upper model: In the upper model, the goal is to maximize the profit of the renewable energy hydrogen plant, and its objective function is as follows; In the formula, the objective function is REHPP's electricity sales revenue, hydrogen sales revenue, biomass acquisition cost and biomass power generation subsidy; ρ h,t is the dual variable corresponding to the equilibrium constraint in the lower-level electricity market clearing model, representing the node marginal electricity price, P t H The electric energy provided by REHPP to the grid; α h,t is the dual variable corresponding to the equilibrium constraint in the lower hydrogen market clearing model, representing the node marginal hydrogen price, Hydrogen energy provided by REHPP to the hydrogen grid; C bio is the biomass acquisition cost of REHPP, C sub Subsidy for biomass power generation, c bio is the purchase price per unit of biomass, c sub Subsidize prices for biomass power generation; S5.2, Lower model: Renewable energy hydrogen production plants participate in both the electricity market and the hydrogen energy market. The lower model is divided into the electricity market clearing model and the hydrogen energy market clearing model, as shown below; ① Electricity market clearing model: Assuming that REHPP is a price acceptor, its electricity market clearing model is as follows: Where T is the scheduling time period, Ψ G is the set of generator sets, the variables in the brackets on the right represent the dual variables of the corresponding equation, θ slack,t represents the node voltage phase angle of the balanced node; represents the dual variable corresponding to the left-hand side formula; Ψ G,i represents the set of generators connected to node i; θ lf,t represents the node voltage phase angle of the starting node of line l; θ lt,t represents the node voltage phase angle of the end node of line l; represents the renewable energy hydrogen production plant located at node i; P o,t Represents REHPP power; is the electrical load; is the quotation of each generator set; P n,t is the power of generator set n; b l is the branch susceptance; P l,max is the maximum power of the line; Respectively represent the maximum and minimum output of the generator; Respectively represent the maximum downward and upward climbing power of the generator; ρ i,t , represents the dual variable of each corresponding formula; P n,t+1 represents the power of generator n at time t+1; T is the scheduling duration; n represents the generator group index; o represents the REHPP index; l represents the line index; ② Hydrogen energy market clearing model: Weymouth equation is used to describe the relationship between hydrogen flow and node gas pressure. The hydrogen energy market clearing model is shown below; In the formula, the variables in the brackets on the right side represent dual variables, Provide quotation for hydrogen suppliers, h s,t Help hydrogen suppliers, represents the hydrogen supplier at node i, represents the passive pipeline set with node i as the end node, represents the passive pipeline set with node i as the starting node, represents the active pipeline set with node i as the starting node, represents the active pipeline set with node i as the terminal node, h o,t Indicates the REHPP hydrogen output, represents the renewable energy hydrogen production plant located at node i; h pl,t represents the hydrogen flow rate in the passive pipeline, h c,t represents the hydrogen flow rate in the active pipeline, represents the node hydrogen load; and They represent the upper and lower limits of the hydrogen supplier's output respectively; Π represents the square of the node pressure; R c,max Indicates the maximum compression ratio of the compressor; π i,max and π i,min Respectively represent the maximum and minimum values ​​of the node pressure; M pl represents the Weymouth coefficient; sgn represents the sign function; s represents the hydrogen supplier index; pl represents the ordinary hydrogen pipeline index; c represents the compressor pipeline index; o represents the REHPP index; Π plf,t Represents the square of the node pressure of the starting node of a common pipeline; Π plt,t It represents the square of the node pressure at the end node of a common pipeline; represents the minimum output of hydrogen supplier s; represents the maximum output of hydrogen supplier s; Π ct,t represents the square of the node pressure of the end node of the compressor pipeline c; i,t represents the square of the air pressure at node i; α i,t , ω c,t , They represent the dual variables of the corresponding formulas on their left sides respectively; In actual operation, the flow direction of hydrogen in the hydrogen pipeline will basically not change in the short term and can be predicted. That is, in the model, assuming that the flow direction of hydrogen is fixed, the Weymouth equation (44) can be simplified to the following form; Since equation (48) is a quadratic non-convex constraint, relax equation (48) into the following convex constraint: The feasible domain of the model obtained by relaxing equation (48) will be expanded, resulting in inaccurate results. It is necessary to introduce a penalty term in the objective function to tighten equation (49); therefore, the objective function becomes as follows: Where: χ is the penalty factor; Transform equation (49) into the second-order cone constraint standard form; In the above formula, represents the dual variable of the second-order cone constraint in the above equation; S5.

3. The established two-layer operation model is expressed as follows:

7. The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production according to claim 6 is characterized by: In step 6, the two-layer operation model is converted into a single-layer model, including the following steps: S6.

1. To facilitate the solution, the lower model is converted into KKT conditions and added to the upper model, so that the double-layer model is converted into a single-layer model. The KKT conditions include the KKT conditions of the electricity market clearing model and the KKT conditions of the hydrogen energy market clearing model. ①. KKT conditions of the electricity market clearing model: Where l∈Ψ i represents the set of lines connected to node i; represents the set of another node j of the line l connected to the node i; represents the set of lines with i as the starting node; represents the set of lines with i as the end node; represents the value of the dual variable of equation (37) at time t+1; represents the value of the dual variable of equation (38) at time t+1; ②. KKT conditions of hydrogen energy market clearing model: In the above formula, ⊥ represents the complementary relaxation constraint, and ||·||2 represents the 2-norm; S6.

2. After KKT conditional transformation, the single-layer model is:

8. The method for coordinated dispatching of renewable energy hydrogen production based on biomass-geothermal energy hydrogen production according to claim 4 is characterized by: In step 7, the single-layer model is converted into a mixed integer second-order cone programming (MISOCP) model, including the following steps: S7.

1. KKT conditional complementary relaxation constraints for the electricity market and hydrogen energy market: Equations (55) and (61) are linearized using the following large M method; that is, μ i ≥0⊥f i (x)≤0 is transformed into: In the formula, f i (x) represents the complementary relaxation constraint: The right-hand side of equation (55) and equation (61), μ i f i (x) the corresponding dual variable; ν i is a 0-1 auxiliary variable; M is a sufficiently large constant; S7.

2. For the complementary relaxation constraints of the second-order cone constraints contained in the hydrogen energy market: Formula (62) is solved by the penalty function method in Formula (50), and then Formula (62) becomes: S7.3, Upper Constraint Linearization: The bilinear term T in equations (18) and (20) is transformed into c,t I c,t 、u ELZ,t I c,t Linearize; Assume there is a bilinear term w = xy, x and y are both continuous variables, if the maximum and minimum values ​​of x are x max and x min , the maximum and minimum values ​​of y are max and min , then w=xy can be approximately expressed by the following inequality; and s,min =and min +(and max -and min )(s-1) / |S| (68); and s,max =and min +(and max -and min )s / |S|(69); y s,min z s ≤y s ≤y s,max z s (70); x min z s ≤x s ≤x max z s (71); In the above formula, z s is a 0-1 auxiliary variable; S represents the number of segments; y s,min ,y s,max Respectively represent the minimum and maximum values ​​of the sth segment of y; s ,y s is a continuous auxiliary variable; S7.4, and for differential equations (22) and (23), the state space linearization method is used to process them and the following equation is obtained; In the formula, A1, B1, A w , B w 、E w 、a w 、b w represents the intermediate auxiliary parameter; T c,t+1 represents the temperature of the electrolytic cell at time t+1; △t represents the time interval; Q in,t Represents the input heat energy of the electrolytic cell; P ELZ,t represents the power of the electrolytic cell at time t; m ELZ,t represents the hydrogen production of the electrolyzer at time t; Indicates the calorific value of hydrogen; T o,t Indicates the external environment temperature; Z c is the thermal resistance of the electrolytic cell; Z w is the thermal resistance of the electrolytic cell wall; Z o Represents the thermal resistance of the external environment; C w represents the heat capacity of the electrolytic cell wall; S7.5, Linearization of upper objective function: In the upper model, the objective function contains bilinear terms, and contains the dual variables of the lower variables. The strong duality theorem can be used for linearization, and the upper objective function is transformed into: S7.

6. After the transformation in steps S7.1 to S7.5, the single-layer model formula (63) is transformed into a mixed integer second-order cone programming model.

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