Hydrogen-driven comprehensive energy refined modeling methods, systems, equipment and media

Abstract

The present invention discloses a hydrogen-driven comprehensive energy refined modeling method, system, equipment and medium, which relates to the field of energy utilization. The method includes: obtaining the operation data and equipment parameters of the comprehensive energy system; constructing the regulation characteristic model of energy coupling and energy storage unit in the comprehensive energy system according to the equipment parameters of the comprehensive energy system; the electrolyzer model in the regulation characteristic model is a mixed integer linear mathematical model of the electrolyzer considering the variable load start-stop characteristics, and the fuel cell model is a mathematical model considering the thermal variable thermoelectric characteristics; according to the operation data and regulation characteristic model of the comprehensive energy system, a multi-energy flow operation optimization model of the comprehensive energy system is established; the operation strategy of the comprehensive energy system is solved with the goal of minimizing the sum of the operation and maintenance power, power purchase, gas purchase and wind power consumption of the comprehensive energy system. The present invention can improve the operation flexibility of the comprehensive energy system.

Description

Technical Field

[0001] The present invention relates to the field of energy utilization, and in particular to a method, system, equipment and medium for comprehensive energy refinement modeling driven by hydrogen energy. Background Art

[0002] At present, the scale of energy consumption continues to expand, and the problems of low energy utilization and environmental pollution are becoming more and more serious. The Integrated Energy System (IES) contains a variety of energy coupling units, which can achieve the complementary advantages of energy supply and provide a solution to the above problems. Wind power absorption capacity, economic operation capacity and energy efficient utilization capacity are important bases for measuring the operational flexibility of IES. Hydrogen energy, as a secondary energy with diverse and efficient conversion forms, can complement other energy sources to form a comprehensive energy utilization architecture driven by hydrogen energy. The electric-hydrogen interaction process consisting of electrolysis hydrogen production, methanation, and hydrogen power generation is a bridge for the two-stage operation of P2G (Power-to-Gas). The energy conversion efficiency of electrolysis hydrogen production exceeds 80%, while the efficiency of electrolysis natural gas production is less than 60%. And because hydrogen has a higher combustion efficiency, high-grade use of hydrogen in the electric-hydrogen coupling link is preferred, which can improve the economic efficiency of IES. Therefore, the refined modeling of hydrogen energy utilization has a positive effect on improving the operational flexibility of IES.

[0003] In the field of refined modeling of hydrogen energy utilization, existing studies have refined the hydrogen energy consumption process and modeled the energy coupling unit with a simple linear conversion relationship, ignoring the energy efficiency characteristics of the electric-hydrogen coupling equipment. The operating state transformation of equipment such as electrolyzers is diverse, which will affect the operational flexibility of IES. At present, the refined modeling technology for electric-hydrogen coupling equipment mostly considers the nonlinear relationship between the operating efficiency and the input electric power, and establishes a non-fixed efficiency energy efficiency model. However, such models are non-convex, which is not conducive to large-scale calculation and solution, and the operating characteristics of the electrolyzer are not considered. In fact, the electrolyzer has a variable load characteristic and can be flexibly switched between overload, variable load, and low load states. Fuel cells are important cogeneration units in hydrogen-driven IES. The heat-to-electricity ratio is usually regarded as a constant, which cannot accurately match the energy demand, resulting in low energy utilization and is not conducive to wind power consumption. Therefore, how to improve the operational flexibility of the integrated energy system is still an urgent problem to be solved. Summary of the invention

[0004] Based on this, an embodiment of the present invention provides a hydrogen-driven integrated energy refined modeling method, system, equipment and medium, taking into account the variable load start-stop characteristics of the electrolyzer and the variable thermal-to-electric ratio characteristics of the fuel cell to enhance the operational flexibility of the integrated energy system.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A comprehensive energy refinement modeling method driven by hydrogen energy, including:

[0007] Obtaining operation data and equipment parameters of the integrated energy system; the integrated energy system is a hydrogen-driven and electric-heat integrated energy system; the integrated energy system comprises: an energy supply unit, an energy coupling and energy storage unit, and an energy consumption unit connected in sequence; the energy coupling and energy storage unit comprises: an electrolyzer, a fuel cell, a cogeneration unit, a gas boiler, a methane reactor, and an energy storage unit;

[0008] According to the equipment parameters of the integrated energy system, a regulation characteristic model of the energy coupling and energy storage unit is constructed; the regulation characteristic model includes: an electrolyzer model, a fuel cell model, a cogeneration unit model, a gas boiler model, a methane reactor model and an energy storage unit model; the electrolyzer model is a mixed integer linear mathematical model of the electrolyzer that takes into account the variable load start-stop characteristics; the fuel cell model is a mathematical model that takes into account the thermally variable thermoelectric characteristics;

[0009] According to the operation data of the integrated energy system and the regulation characteristic model, a multi-energy flow operation optimization model of the integrated energy system is established; the multi-energy flow operation optimization model includes an operation and maintenance optimization unit, an energy self-sufficiency optimization unit and a wind power consumption optimization unit;

[0010] The multi-energy flow operation optimization model is solved with the goal of minimizing the sum of the operation and maintenance electricity, electricity purchase volume, gas purchase volume and wind power consumption of the integrated energy system to obtain the operation strategy of the integrated energy system.

[0011] Optionally, according to the operation data of the integrated energy system and the regulation characteristic model, a multi-energy flow operation optimization model of the integrated energy system is established, which specifically includes:

[0012] The operating data of the integrated energy system is reduced by a scenario reduction method based on probability distance to obtain operating data of a set number of typical scenarios;

[0013] A multi-energy flow operation optimization model of the integrated energy system is established based on the operating data of a set number of typical scenarios and the regulation characteristic model.

[0014] Optionally, the electrolytic cell model includes: an electrolytic cell power relationship expression and electrolytic cell constraints;

[0015] The electrolyzer power relationship expression represents the relationship between the electric power consumption and hydrogen production power of the electrolyzer; the electrolyzer constraint conditions include: electric-hydrogen conversion constraints, electric power consumption constraints, logical constraints for conversion of operating states, minimum shutdown time constraints, and minimum cold standby time constraints.

[0016] Optionally, the electrolytic cell power relationship expression is:

[0017]

[0018] in, is the hydrogen production power of the nth proton exchange membrane electrolyzer at time t; is the power consumption of the nth proton exchange membrane electrolyzer at time t; is the electricity-to-hydrogen conversion efficiency of the proton exchange membrane electrolyzer; is the standby power of the proton exchange membrane electrolyzer in cold standby state; PEM is the penalty coefficient of hydrogen production power during cold start of proton exchange membrane electrolyzer; is the conversion binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, When indicates that the nth proton exchange membrane electrolyzer switches from the cold standby state to the working state; is the binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, : It indicates that the nth proton exchange membrane electrolyzer is in cold standby state.

[0019] Optionally, the fuel cell model is:

[0020]

[0021] in, is the hydrogen power used by the fuel cell at time t; is the power generated by the fuel cell at time t; is the heat generation power of the fuel cell at time t; η HFC The energy conversion efficiency of the fuel cell; The upper limit of hydrogen power consumption; is the lower limit of hydrogen power; is the maximum climbing power; is the hydrogen power used by the fuel cell at time t+1; The upper limit of the adjustable range of the fuel cell's thermal-to-electric ratio; It is the lower limit of the adjustable range of the fuel cell's thermal-to-electric ratio.

[0022] Optionally, the operating data includes: wind power output.

[0023] Optionally, the equipment parameters include: technical output limit parameters, climbing rate limit parameters and energy limit parameters.

[0024] The present invention also provides a hydrogen-driven comprehensive energy refined modeling system, comprising:

[0025] A data acquisition module is used to acquire the operation data and equipment parameters of the integrated energy system; the integrated energy system is a hydrogen-driven and electric-heat integrated energy system; the integrated energy system comprises: an energy supply unit, an energy coupling and energy storage unit, and an energy consumption unit connected in sequence; the energy coupling and energy storage unit comprises: an electrolyzer, a fuel cell, a cogeneration unit, a gas boiler, a methane reactor, and an energy storage unit;

[0026] A regulating characteristic model building module is used to build a regulating characteristic model of the energy coupling and energy storage unit according to the equipment parameters of the integrated energy system; the regulating characteristic model includes: an electrolyzer model, a fuel cell model, a cogeneration unit model, a gas boiler model, a methane reactor model and an energy storage unit model; the electrolyzer model is a mixed integer linear mathematical model of the electrolyzer that takes into account the variable load start-stop characteristics; the fuel cell model is a mathematical model that takes into account the thermally variable thermoelectric characteristics;

[0027] An optimization model building module, used to establish a multi-energy flow operation optimization model of the integrated energy system according to the operation data of the integrated energy system and the regulation characteristic model; the multi-energy flow operation optimization model includes an operation and maintenance optimization unit, an energy self-sufficiency optimization unit and a wind power consumption optimization unit;

[0028] The operation strategy solving module is used to solve the multi-energy flow operation optimization model with the goal of minimizing the sum of the operation and maintenance electricity, electricity purchase volume, gas purchase volume and wind power consumption of the integrated energy system, so as to obtain the operation strategy of the integrated energy system.

[0029] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned hydrogen-driven comprehensive energy refined modeling method.

[0030] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned hydrogen-driven comprehensive energy refined modeling method.

[0031] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0032] The embodiment of the present invention proposes a hydrogen-driven integrated energy refined modeling method, system, equipment and medium, and constructs a regulation characteristic model of energy coupling and energy storage units in the integrated energy system; the electrolyzer model in the regulation characteristic model is a mixed integer linear mathematical model of the electrolyzer that considers the variable load start-stop characteristics, and the fuel cell model is a mathematical model that considers the thermally variable thermoelectric ratio characteristics; according to the operation data and the regulation characteristic model of the integrated energy system, a multi-energy flow operation optimization model of the integrated energy system is established; based on the regulation characteristic model, with the goal of minimizing the sum of the operation and maintenance power, purchased power, purchased gas and wind power consumption of the integrated energy system, the multi-energy flow operation optimization model is solved to obtain the operation strategy of the integrated energy system. The present invention considers the variable load start-stop characteristics of the electrolyzer and the variable thermoelectric ratio characteristics of the fuel cell, and can improve the operation flexibility of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 A flow chart of a method for refined modeling of comprehensive energy driven by hydrogen energy provided in an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of the operation architecture of a hydrogen-driven integrated energy system provided in an embodiment of the present invention;

[0036] Figure 3 A schematic diagram of the electrolytic cell operation state conversion provided by an embodiment of the present invention;

[0037] Figure 4 A structural diagram of the hydrogen-driven comprehensive energy refined modeling system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Embodiment 1

[0041] In view of the deficiencies existing in the refined modeling process of existing hydrogen energy-consuming equipment, this embodiment considers the variable load start-stop characteristics of the electrolyzer and the variable thermal-to-electric ratio characteristics of the fuel cell, and proposes a technology for improving the flexibility of the comprehensive energy system by considering the refined modeling of hydrogen energy utilization.

[0042] See also Figure 1 The hydrogen-driven comprehensive energy refined modeling method of this embodiment includes:

[0043] Step 101: Acquire operation data and equipment parameters of an integrated energy system; the integrated energy system is a hydrogen-driven and electric-heat integrated energy system.

[0044] The operation architecture of the hydrogen-driven integrated energy system of this embodiment is as follows: Figure 2 As shown, see Figure 2 The integrated energy system includes: an energy supply unit, an energy coupling and energy storage unit, and an energy consumption unit connected in sequence. The energy coupling and energy storage unit includes: an energy coupling unit and an energy storage unit. The energy coupling unit includes: an electrolyzer, a fuel cell, a cogeneration unit, a gas boiler, and a methane reactor.

[0045] The operation data includes: wind power output. The equipment parameters include: technical output limit parameters, ramp rate limit parameters and energy limit parameters.

[0046] Step 102: Constructing a regulation characteristic model of the energy coupling and energy storage unit according to the equipment parameters of the integrated energy system.

[0047] The regulation characteristic model includes: an electrolyzer model, a fuel cell model, a cogeneration unit model, a gas boiler model, a methane reactor model and an energy storage unit model. The electrolyzer model is a mixed integer linear mathematical model of the electrolyzer that takes into account the variable load start-stop characteristics; the fuel cell model is a mathematical model that takes into account the thermally variable thermoelectric characteristics.

[0048] The electrolytic cell model includes: an electrolytic cell power relationship expression and electrolytic cell constraint conditions.

[0049] The electrolyzer power relationship expression represents the relationship between the electric power consumption and hydrogen production power of the electrolyzer; the electrolyzer constraint conditions include: electric-hydrogen conversion constraints, electric power consumption constraints, logical constraints for conversion of operating states, minimum shutdown time constraints, and minimum cold standby time constraints.

[0050] The electrolytic cell power relationship expression is:

[0051]

[0052] in, is the hydrogen production power of the nth proton exchange membrane electrolyzer (PEM) at time t; is the power consumption of the nth proton exchange membrane electrolyzer at time t; is the electricity-to-hydrogen conversion efficiency of the proton exchange membrane electrolyzer; is the standby power of the proton exchange membrane electrolyzer in cold standby state; PEM is the penalty coefficient of hydrogen production power during cold start of proton exchange membrane electrolyzer; is the conversion binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, When indicates that the nth proton exchange membrane electrolyzer switches from the cold standby state to the working state; is the binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, : It indicates that the nth proton exchange membrane electrolyzer is in cold standby state.

[0053] The fuel cell model is:

[0054]

[0055] in, is the hydrogen power used by the fuel cell at time t; is the power generated by the fuel cell at time t; is the heat generation power of the fuel cell at time t; η HFC The energy conversion efficiency of the fuel cell; The upper limit of hydrogen power consumption; is the lower limit of hydrogen power; is the maximum climbing power; is the hydrogen power used by the fuel cell at time t+1; The upper limit of the adjustable range of the fuel cell's thermal-to-electric ratio; It is the lower limit of the adjustable range of the fuel cell's thermal-to-electric ratio.

[0056] Step 103: Establishing a multi-energy flow operation optimization model of the integrated energy system according to the operation data of the integrated energy system and the regulation characteristic model.

[0057] The multi-energy flow operation optimization model includes an operation and maintenance optimization unit, an energy self-sufficiency optimization unit and a wind power consumption optimization unit.

[0058] Step 103 specifically includes:

[0059] A scenario reduction method based on probability distance is used to reduce the operating data of the integrated energy system to obtain operating data of a set number of typical scenarios; based on the operating data of the set number of typical scenarios and the regulation characteristic model, a multi-energy flow operation optimization model of the integrated energy system is established.

[0060] Step 104: Solve the multi-energy flow operation optimization model with the goal of minimizing the sum of the operation and maintenance electricity, electricity purchase volume, gas purchase volume and wind power consumption of the integrated energy system to obtain the operation strategy of the integrated energy system.

[0061] The following is a detailed description of a specific process in the practical application of the above-mentioned hydrogen-driven comprehensive energy refined modeling method, and verifies the effectiveness of this method.

[0062] This specific example aims at the problem of operational flexibility of integrated energy systems, and proposes a method for refined modeling of integrated energy driven by hydrogen energy, including: establishing an operational architecture of an integrated energy system driven by hydrogen energy, collecting system operational data (wind power output, electricity prices, gas prices, etc.) and equipment parameters of each energy coupling unit; establishing a mixed integer model of an electrolyzer and a fuel cell with variable thermoelectric ratio characteristics that takes into account variable load start-stop characteristics; considering the uncertainty of wind power and multi-energy loads, establishing an operational cost optimization model for the integrated energy system; establishing evaluation indicators for the operational flexibility of the integrated energy system, and evaluating the role of the proposed technology in improving flexibility based on the optimization results. The method of the embodiment of the present invention has a certain universality for integrated energy systems including electrolysis for hydrogen production and hydrogen fuel cells, and has practical significance for improving the operational flexibility of integrated energy systems in the context of the continuous expansion of energy consumption scale and electrification. Specifically as follows:

[0063] Step S1: Establish a hydrogen-driven integrated energy system operation framework, collect system operation data (wind power output, electricity price, gas price, etc.) and equipment parameters of each energy coupling unit.

[0064] Specifically, a hydrogen-driven integrated energy system operation architecture is established. The hydrogen driven electricity heat integrated energy system (HEH-IES) driven by hydrogen and mainly composed of electricity and heat can realize the flexible operation of multi-energy supply and load demand within the system. The present invention introduces a fuel cell with an adjustable heat-to-electricity ratio on the basis of the traditional system containing a combined heat and power unit (CHP) and a gas boiler (GB), while taking into account the operating characteristics of the electrolyzer, and refining the hydrogen energy consumption process and equipment energy consumption characteristics of the electric-hydrogen coupling unit.

[0065] See also Figure 2HEH-IES consists of four parts: energy supply unit, energy coupling unit, energy storage unit and energy consumption unit. The energy supply unit mainly includes wind power and upper power grid and gas grid. The energy coupling unit includes electrolyzer, fuel cell, cogeneration unit, gas boiler, methane reactor, etc. The energy storage unit includes heat storage, gas storage and electricity storage. The energy consumption unit includes electricity load, heat load and gas load.

[0066] Wind turbines provide clean electricity, and IES can make up for the energy shortage in the system by purchasing energy from the upper power grid and gas grid. The electrolyzer array consists of multiple PEM electrolyzers in parallel, and the electric-hydrogen coupling unit composed of hydrogen fuel cells (HFC) and methane reactors (MR) can effectively reduce the cascade loss of energy and improve the comprehensive utilization rate. In addition, the electric thermal hydrogen multi-energy storage equipment and the comprehensive demand response of the load will provide certain guarantees for the flexible operation of HEH-IES.

[0067] On this basis, the equipment parameters of the energy supply unit, energy coupling unit, and energy storage unit in the integrated energy system operation architecture (including technical output limit, ramp rate limit, energy limit, etc.) and the historical operation data of the integrated energy system (including wind power output, electricity price, gas price, etc.) are collected. The equipment parameters are input into step S2 to finely model the hydrogen energy equipment.

[0068] Secondly, obtain typical scenarios of the operation of the integrated energy system. The typical scenarios are obtained by reducing the historical operation data in S1. This embodiment uses a scenario reduction method based on probability distance to reduce the wind power and multi-energy load sample sets to obtain a certain number of typical scenarios. The reduction steps are as follows:

[0069] Step 1: Determine the cut scenes (k*) ,k * ∈(1,…,K). Calculate the geometric distance d(s) between any two scenes in the historical sample S. (n) ,s (m) ), considering the probability of scene occurrence p (n) Probability distance D from Euclidean distance d (n) , filter out and cut scenes (k*) The scene with the smallest sum of probability distances is the eliminated scene s( k′) , the calculation formula is:

[0070]

[0071] Among them, s (n) and (m) are scenes m and n in the historical sample S; d(s (n) ,s (m) ) is the Euclidean distance between scene m and scene n.

[0072] Step 2: Change and remove scenes (k′) The probability of the scene with the closest probability distance p (k′) , the calculation formula is:

[0073] p (k′) =p (k′) +p (k*) .

[0074] Among them, p (k*) To reduce the probability of the scene.

[0075] Step 3: Determine whether the number of remaining scenes meets the requirement, otherwise return to step 1.

[0076] Thus, a typical scenario of the operation of the integrated energy system can be obtained, and the typical scenario operation data is input into step S3 for the optimized operation of the integrated energy system.

[0077] Step S2: Considering the variable load start-stop characteristics of the electrolyzer and the variable heat-to-electricity ratio characteristics of the fuel cell, a regulation characteristic model of the energy coupling unit and the energy storage unit in the integrated energy system is established.

[0078] Specifically, step S2 includes: establishing a regulation characteristic model of the electrolyzer, fuel cell, cogeneration unit, gas boiler, methane reactor and general energy storage model based on the integrated energy system operation architecture and energy coupling unit and energy storage unit equipment parameters constructed by S1.

[0079] (1) Electrolyzer model: The use of electrolytic hydrogen production technology can strengthen the complementary coupling of multiple energy sources and improve the flexible scheduling capabilities of wind power consumption and IES during off-peak electricity price periods. The electrolyzer is a key equipment for electrolytic hydrogen production. The proton exchange membrane electrolyzer (PEM) has higher conversion efficiency and adjustment flexibility than the alkaline electrolyzer. Therefore, the present invention takes PEM as an example to establish a mixed integer linear mathematical model of the electrolyzer that takes into account the operating characteristics. The electrolyzer model includes: an electrolyzer power relationship expression and electrolyzer constraints. The electrolyzer constraints include: electric-hydrogen conversion constraints, power consumption constraints, logical constraints for operating state conversion, minimum shutdown time constraints, and minimum cold standby time constraints.

[0080] The operating state of PEM can be divided into shutdown state, cold standby state and working state. Considering the working characteristics of PEM, the working state can be further divided into low load, variable load and overload state. Figure 3 shown.

[0081] (1) Shutdown state (no hydrogen production): PEM can be shut down quickly in any state and is considered as an interruptible load, regardless of downtime. In this state, it usually takes 30 minutes to 1 hour to fully start.

[0082] (2) Cold standby state (no hydrogen production): The PEM is turned off but does not stop, and maintains the operation of the control and antifreeze units at low power standby

[10] . In this state, the PEM takes 5-10 minutes to complete a cold start.

[0083] (3) Working state (hydrogen production): To ensure the safety of hydrogen production in the electrolyzer, that is, hydrogen has an upper and lower explosion volume limit, PEM operates in a variable load state (30%-100% of rated power) most of the time. At the same time, PEM can operate in an overload state (100%-150% of rated power) and a low load state (10%-30% of rated power) for a short period of time, which makes PEM have excellent operating flexibility.

[0084] To illustrate the operating characteristics of PEM, five working states are represented by binary variables: shutdown I, cold standby S, variable load L, overload R, and low load V. When it is 1, it means it is in this state. At the same time, the binary variable Y is used to represent the full startup interval. When it is 1, it means that PEM is fully started from the shutdown state.

[0085] The time for PEM to fully start is used as the optimized time scale. Since the cold start time is less than one time scale, the loss of output hydrogen energy caused by the cold start process needs to be taken into account. The power relationship expression of the electrolyzer, that is, the relationship between the PEM power consumption and the hydrogen production power can be expressed as:

[0086]

[0087] in, is the hydrogen production power of the nth proton exchange membrane electrolyzer (PEM) at time t; is the power consumption of the nth proton exchange membrane electrolyzer at time t; is the electricity-to-hydrogen conversion efficiency of the proton exchange membrane electrolyzer; is the standby power of the proton exchange membrane electrolyzer in cold standby state; PEM is the penalty coefficient of hydrogen production power during cold start of proton exchange membrane electrolyzer; is the conversion binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, When indicates that the nth proton exchange membrane electrolyzer switches from the cold standby state to the working state; is the binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, : It indicates that the nth proton exchange membrane electrolyzer is in cold standby state.

[0088] The above expression satisfies the electricity-hydrogen conversion constraint, namely:

[0089]

[0090] in, and is the binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, When the electrolytic cell is in cold standby state, When , it indicates that the electrolytic cell is in a variable load state. When the electrolytic cell is in overload state, It indicates that the electrolytic cell is in low load state.

[0091] The power consumption constraint of the PEM electrolyzer can be expressed as:

[0092]

[0093] Where: is the rated operating power of the proton exchange membrane electrolyzer (PEM). The coordinated control between different working states of the electrolyzer can be achieved through the value of the binary variable. When the binary variable is 1, it represents the state. This formula represents the electric power operation constraint of the electrolyzer equipment, which is used to limit the operating range of the electric power.

[0094] In addition to satisfying the above-mentioned electricity-hydrogen conversion constraints and power consumption constraints, the electrolyzer must also satisfy the logical constraints of the operation state conversion. The logical constraints of the operation state conversion include start-stop constraints, start interval constraints, operation state mutual exclusion constraints, and overload and underload maximum time limit constraints, which are expressed as follows:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] Where: is the binary variable of the working state of the proton exchange membrane electrolyzer at time t, When T Rmax and T Vmax They are the maximum time that the proton exchange membrane electrolyzer (PEM) is allowed to operate continuously in overload and underload states respectively; is the full startup interval of the proton exchange membrane electrolyzer at time t, t represents the complete startup from the shutdown state; τ represents the time variable.

[0101] In addition, in order to avoid frequent start and stop of PEM in a short period of time, it is necessary to limit the minimum shutdown and cold standby time, that is, the electrolyzer must also meet the minimum shutdown time constraint and the minimum cold standby time constraint:

[0102]

[0103]

[0104] Where: T Imin 、T Smin They are respectively the shortest time that the proton exchange membrane electrolyzer (PEM) operates continuously in shutdown and cold standby states.

[0105] (2) Fuel cell model: Fuel cells burn hydrogen to generate electricity and heat. Traditional fuel cells have a fixed heat-to-electricity ratio and generally operate in a "heat-to-electricity" or "electricity-to-heat" mode, with poor operating flexibility. By changing the cooling circulation water flow rate and hydrogen input rate, the fuel cell can adjust the heat-to-electricity ratio according to the real-time electric and thermal load conditions. The model is shown below.

[0106]

[0107] in, is the hydrogen power used by the fuel cell at time t; is the power generated by the fuel cell at time t; is the heat generation power of the fuel cell at time t; η HFC The energy conversion efficiency of the fuel cell; The upper limit of hydrogen power consumption; is the lower limit of hydrogen power; is the maximum climbing power; is the hydrogen power used by the fuel cell at time t+1; The upper limit of the adjustable range of the fuel cell's thermal-to-electric ratio; It is the lower limit of the adjustable range of the fuel cell's thermal-to-electric ratio.

[0108] (3) Combined heat and power unit model: The combined heat and power unit burns methane to provide combined heat and power. By controlling the steam turbine extraction ratio and the inlet guide vane angle, the combined heat and power unit can adjust the heat-to-electricity ratio according to the real-time electric and thermal load conditions. The combined heat and power unit model is:

[0109]

[0110]

[0111]

[0112]

[0113] Where: is the gas power consumption of the cogeneration unit at time t; and are the electricity generation power and heat generation power of the cogeneration unit at time t respectively; η CHP is the energy conversion efficiency of the combined heat and power unit; and are the upper and lower limits of gas power respectively; is the maximum climbing power; and The upper and lower limits of the adjustable range of the power-to-heat ratio of the cogeneration unit.

[0114] (4) Gas boiler model: Gas boilers can be used in conjunction with hydrogen-driven cogeneration systems under the incentives of electricity and gas prices to complement the peaks and valleys of electric and thermal loads and improve the operational flexibility of the system.

[0115]

[0116]

[0117]

[0118] Where: is the gas power of the gas boiler at time t; is the gas-heat conversion efficiency; is the heating power of the gas boiler at time t; and are the upper and lower limits of gas power respectively; is the maximum climbing power.

[0119] (5) Methane reactor model: The methane reactor converts hydrogen energy into gas energy and supplies it to a gas boiler or a cogeneration unit to generate electricity and heat energy. The model is as follows:

[0120]

[0121]

[0122]

[0123] Where: is the hydrogen power used by the methane reactor at time t; is the hydrogen-gas conversion efficiency; is the gas production power of the methane reactor at time t; and are the upper and lower limits of hydrogen power respectively; is the maximum climbing power.

[0124] (6) Universal energy storage unit model: Considering the similarities between electricity storage, hydrogen storage, and heat storage models, a universal modeling of energy storage units is performed.

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] Where: and is the charging and discharging power of the nth energy storage unit at time t; is the maximum charging and discharging power of the nth type of energy storage unit; is the charge and discharge status identifier of the nth energy storage unit at time t, Indicates charging, Indicates energy release; η ES,n and χ ES,n is the charging and discharging efficiency and self-consumption rate of the nth energy storage unit; is the charging and discharging power of the nth energy storage unit at time t; is the energy of the nth energy storage unit at time t; is the energy of the nth energy storage unit at time t+1; is the energy of the nth energy storage unit at the initial moment; is the energy of the nth energy storage unit at time T (end time); and It is the upper and lower limits of the capacity of the nth energy storage unit.

[0132] Step S2 models the key energy coupling units and energy storage unit equipment in the integrated energy system, and the model is input as a whole into step S3 to participate in the operation optimization of the integrated energy system.

[0133] Step S3: Considering the uncertainty of wind power and multi-energy loads, a multi-energy flow operation optimization model of the integrated energy system is established.

[0134] The step S3 includes: considering the uncertainty of wind power and multi-energy loads, establishing a multi-energy flow operation optimization model of the integrated energy system, and the multi-energy flow operation optimization model includes three optimization objectives: an operation and maintenance optimization unit of an energy coupling unit and an energy storage unit, an energy self-sufficiency optimization unit, and a wind power consumption optimization unit.

[0135] The operation and maintenance optimization unit can be expressed as:

[0136]

[0137] Where: C PEM,s , C HFC,s , C CHP, , C GB,s , C MT,s and C ESS,s are the operation and maintenance power of the electrolyzer, fuel cell, cogeneration unit, gas boiler, methane reactor and general energy storage equipment in step S2 respectively; δ is the conversion coefficient between different energies, where δ H2 is the conversion coefficient between hydrogen energy and electrical energy, δ G is the conversion coefficient between gas energy and electrical energy, δ n is the conversion coefficient between the nth type of energy storage and electric energy; C OP,s is the total operation and maintenance electricity of each unit under scenario s (representing the total operation and maintenance cost); Δt is the time scale.

[0138] The energy self-sufficiency optimization unit can be expressed as:

[0139] C BUY,s =C BUY,E,s +C BUY,G,s

[0140]

[0141] Where: and is the power of electricity and gas purchased by the system at time t; C BUY,E,s and C BUY,G,s The amount of electricity and gas purchased by the comprehensive energy system; C BUY,s It is the sum of the electricity purchase amount and gas purchase amount under scenario s (i.e., the constitutive energy, representing the system energy purchase cost).

[0142] The wind power consumption optimization unit can be expressed as:

[0143]

[0144] Where: is the actual wind power consumption at time t; is the maximum wind power output at time t; C W,s is the wind power consumption under scenario s (representing the cost of wind power abandonment).

[0145] Therefore, the overall optimization model, that is, the multi-energy flow operation optimization model can be expressed as:

[0146]

[0147] Where: s is the typical scenario number containing uncertainty information of wind power and multi-energy loads, S is the number of scenarios, π s is the probability of scene s occurring, obtained by step S1.

[0148] As an optional implementation, the multi-energy flow operation optimization model can also be expressed as:

[0149]

[0150] C DR,s is the comprehensive demand response compensation cost under scenario s.

[0151] In step S3, the operation strategy of each unit in the system is obtained by solving the multi-energy flow operation optimization model of the integrated energy system considering the uncertainty of wind power and multi-energy loads, and this is used as a benchmark to input into the evaluation system of each indicator in step S4 to evaluate the flexibility of the integrated energy system.

[0152] Step S4: Establish an evaluation system for the operational flexibility of the integrated energy system, and evaluate the effect of the proposed technology on improving flexibility based on the optimization results of step S3.

[0153] The step S4 includes: constructing an evaluation index for the operational flexibility of the integrated energy system, the operational flexibility of the integrated energy system can be quantified by wind power absorption capacity, energy self-sufficiency capacity and energy efficient utilization capacity. The effect of the proposed technology on improving flexibility is evaluated based on the optimization results.

[0154] The wind power absorption capacity can be quantified by the actual amount of wind power absorbed, expressed as:

[0155]

[0156] Where: is the actual wind power consumption at time t; is the maximum wind power output at time t; ω W is the maximum wind abandonment rate at time t.

[0157] Energy self-sufficiency can be quantified by the amount of purchased energy, expressed as:

[0158] E BUY =E BUY,E +E BUY,G

[0159]

[0160] Where: and E is the power of electricity and gas purchased by the system at time t; Δt is the time scale. BUY,E and E BUY,G The amount of electricity and gas purchased for the integrated energy system.

[0161] The ability to efficiently utilize energy can be quantified by the comprehensive energy utilization rate. IES includes the conversion process of multiple energy flows, and different forms of energy have both quantitative connections and qualitative differences. Due to the cascade loss of energy, the energy quality continues to decrease. In order to illustrate the role of this technology in improving energy utilization, the energy quality coefficient is used to quantify the energy quality, which is expressed as:

[0162]

[0163] Where: η IEU is the comprehensive energy utilization rate; E , G , W , H are the energy quality coefficients of electric energy, gas energy, wind energy, and thermal energy respectively; T is the scheduling period. The operation strategies of each energy coupling device obtained by solving the multi-energy flow operation optimization model in step S3 are used as the benchmark to input the above indicators to evaluate the flexibility of the integrated energy system. The greater the system's wind power absorption capacity, energy self-sufficiency capacity, and energy efficient utilization capacity, the better the system's flexibility.

[0164] In order to comprehensively score the flexibility of the system, each flexibility indicator must be weighted. The entropy weight double base point method is used, and the weights are corrected by considering the subjective will of the integrated energy system dispatcher, and the solution with the highest relative closeness among the dispatching schemes is obtained as the comprehensive score of system flexibility. The specific steps are as follows:

[0165] (1) Establish three flexibility indicators and an evaluation matrix R of M scheduling schemes.

[0166]

[0167] Where: r ij Represents the i-th flexibility index value corresponding to the j-th scheduling scheme.

[0168] (2) Calculate the entropy weight vector α of the three flexibility indicators i The size of the entropy weight reflects the amount of information that the flexibility indicator can provide, which is determined by the degree of difference between different scheduling schemes of the flexibility indicator.

[0169]

[0170]

[0171] Where: e i represents the entropy value of the i-th flexibility index; e j represents the entropy value of the jth flexibility index; 1-e i represents the coefficient of variation of the i-th flexibility index.

[0172] (3) Modify the weights according to the subjective wishes of the integrated energy system dispatchers.

[0173]

[0174] Where: λ is the weight set by the dispatcher’s preference; ω i is the modified weight coefficient.

[0175] (4) Constructing a weighted evaluation matrix Weighted evaluation matrix It not only reflects the preferences of dispatchers subjectively, but also objectively reflects the importance of each flexibility index, making the measurement of comprehensive flexibility evaluation more convincing in theory.

[0176]

[0177] (5) Determine the double base point F + / - , and calculate the relative closeness TJ of each scheduling scheme j .

[0178]

[0179]

[0180]

[0181]

[0182]

[0183] Where: F + is a positive ideal point; F - is a negative ideal point; are the coordinates of the positive ideal point, indicating the most ideal situation; is the coordinate of the negative ideal point, indicating the most undesirable situation; is the weighted evaluation matrix Items in is the Euclidean distance from the jth scheduling solution to the positive and negative ideal points. Relative closeness TJ j The larger the value of , the greater the distance between the solution and the positive ideal point.

[0184] Therefore, the above-mentioned entropy weight double base point method can be used to comprehensively evaluate the hydrogen energy refined modeling technology for improving the flexibility of the integrated energy system in the present invention.

[0185] The hydrogen-driven comprehensive energy refined modeling method of this specific example refines the two-stage operation process of electricity-to-gas, and the hydrogen produced by the electrolyzer is preferentially supplied to the fuel cell for cogeneration, and the remaining part is then supplied to the cogeneration unit and the gas boiler for energy supply, avoiding the intermediate energy ladder loss. The refinement of the hydrogen energy use process gives full play to the high efficiency and flexibility of hydrogen energy utilization, and improves the energy utilization rate of the comprehensive energy system. The present invention considers the variable load start-stop characteristics of the electrolyzer and the variable thermal-electric ratio characteristics of the fuel cell to establish a mixed integer model. Compared with the existing hydrogen energy utilization refined modeling technology, it avoids the disadvantage that the nonlinear model is difficult to solve on a large scale. The output of the equipment can be adjusted in real time according to the energy demand, further improving the wind power consumption capacity. Based on the present invention, the dispatcher of the comprehensive energy system can flexibly adjust the operating status of the electrolyzer and the fuel cell according to the energy balance of the system's multi-energy flow, which has important application and reference value for promoting wind power consumption, economic operation and efficient utilization of energy, and improving the flexibility of the comprehensive energy system.

[0186] Embodiment 2

[0187] In order to execute the method corresponding to the above-mentioned embodiment 1 to achieve the corresponding functions and technical effects, a hydrogen-driven comprehensive energy refined modeling system is provided below.

[0188] See also Figure 4 , the system comprising:

[0189] The data acquisition module 401 is used to obtain the operating data and equipment parameters of the integrated energy system; the integrated energy system is a hydrogen-driven and electric-thermal integrated energy system; the integrated energy system includes: an energy supply unit, an energy coupling and energy storage unit, and an energy consumption unit connected in sequence; the energy coupling and energy storage unit includes: an electrolyzer, a fuel cell, a cogeneration unit, a gas boiler, a methane reactor, and an energy storage unit.

[0190] The regulation characteristic model construction module 402 is used to construct the regulation characteristic model of the energy coupling and energy storage unit according to the equipment parameters of the integrated energy system; the regulation characteristic model includes: an electrolyzer model, a fuel cell model, a cogeneration unit model, a gas boiler model, a methane reactor model and an energy storage unit model; the electrolyzer model is a mixed integer linear mathematical model of the electrolyzer that takes into account the variable load start-stop characteristics; the fuel cell model is a mathematical model that takes into account the thermally variable thermoelectric characteristics.

[0191] The optimization model building module 403 is used to establish a multi-energy flow operation optimization model of the integrated energy system according to the operation data of the integrated energy system and the regulation characteristic model; the multi-energy flow operation optimization model includes an operation and maintenance optimization unit, an energy self-sufficiency optimization unit and a wind power consumption optimization unit.

[0192] The operation strategy solving module 404 is used to solve the multi-energy flow operation optimization model with the goal of minimizing the sum of the operation and maintenance electricity, purchased electricity, purchased gas and wind power consumption of the integrated energy system to obtain the operation strategy of the integrated energy system.

[0193] Embodiment 3

[0194] This embodiment provides an electronic device, including a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the hydrogen-driven comprehensive energy refined modeling method of embodiment 1.

[0195] Optionally, the above-mentioned electronic device may be a server.

[0196] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the hydrogen-driven comprehensive energy refined modeling method of embodiment 1.

[0197] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0198] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A hydrogen-driven comprehensive energy refined modeling method, characterized in that: include: Obtain operating data and equipment parameters of the integrated energy system; The comprehensive energy system is a hydrogen-driven and electric-heat integrated energy system; The comprehensive energy system comprises: an energy supply unit, an energy coupling and energy storage unit and an energy consumption unit connected in sequence; the energy coupling and energy storage unit comprises: an electrolyzer, a fuel cell, a cogeneration unit, a gas boiler, a methane reactor and an energy storage unit; According to the equipment parameters of the integrated energy system, a regulation characteristic model of the energy coupling and energy storage unit is constructed; the regulation characteristic model includes: an electrolyzer model, a fuel cell model, a cogeneration unit model, a gas boiler model, a methane reactor model and an energy storage unit model; the electrolyzer model is a mixed integer linear mathematical model of the electrolyzer that takes into account the variable load start-stop characteristics; the fuel cell model is a mathematical model that takes into account the thermally variable thermoelectric characteristics; The electrolytic cell model includes: an electrolytic cell power relationship expression and electrolytic cell constraint conditions; The electrolyzer power relationship expression represents the relationship between the electric power consumption and hydrogen production power of the electrolyzer; the electrolyzer constraint conditions include: electric-hydrogen conversion constraint, electric power consumption constraint, logical constraint of operation state conversion, shortest shutdown time constraint and shortest cold standby time constraint; The electrolytic cell power relationship expression is: in, is the hydrogen production power of the nth proton exchange membrane electrolyzer at time t; is the power consumption of the nth proton exchange membrane electrolyzer at time t; is the electricity-to-hydrogen conversion efficiency of the proton exchange membrane electrolyzer; is the standby power of the proton exchange membrane electrolyzer in cold standby state; PEM is the penalty coefficient of hydrogen production power during cold start of proton exchange membrane electrolyzer; is the conversion binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, When indicates that the nth proton exchange membrane electrolyzer switches from the cold standby state to the working state; is the binary variable of the working state of the nth proton exchange membrane electrolyzer at time t, When , it indicates that the nth proton exchange membrane electrolyzer is in cold standby state; According to the operation data of the integrated energy system and the regulation characteristic model, a multi-energy flow operation optimization model of the integrated energy system is established; the multi-energy flow operation optimization model includes an operation and maintenance optimization unit, an energy self-sufficiency optimization unit and a wind power consumption optimization unit; the operation and maintenance optimization unit is the total operation and maintenance electricity of the energy coupling and energy storage unit; the energy self-sufficiency optimization unit is the sum of the purchased electricity and gas volume of the integrated energy system; the wind power consumption optimization unit is the wind power consumption; The multi-energy flow operation optimization model is solved with the goal of minimizing the sum of the operation and maintenance electricity, electricity purchase volume, gas purchase volume and wind power consumption of the integrated energy system to obtain the operation strategy of the integrated energy system.

2. The method for refined modeling of comprehensive energy driven by hydrogen energy according to claim 1 is characterized in that: According to the operation data of the integrated energy system and the regulation characteristic model, a multi-energy flow operation optimization model of the integrated energy system is established, which specifically includes: The operating data of the integrated energy system is reduced by a scenario reduction method based on probability distance to obtain operating data of a set number of typical scenarios; A multi-energy flow operation optimization model of the integrated energy system is established based on the operating data of a set number of typical scenarios and the regulation characteristic model.

3. The method for refined modeling of comprehensive energy driven by hydrogen energy according to claim 1 is characterized in that: The fuel cell model is: in, is the hydrogen power used by the fuel cell at time t; For fuel cells t The power generated at the time; is the heat generation power of the fuel cell at time t; η HFC The energy conversion efficiency of the fuel cell; The upper limit of hydrogen power consumption; is the lower limit of hydrogen power; is the maximum climbing power; is the hydrogen power used by the fuel cell at time t+1; The upper limit of the adjustable range of the fuel cell's thermal-to-electric ratio; It is the lower limit of the adjustable range of the fuel cell's thermal-to-electric ratio.

4. The method for refined modeling of comprehensive energy driven by hydrogen energy according to claim 1 is characterized in that: The operating data include: wind power output.

5. The method for refined modeling of comprehensive energy driven by hydrogen energy according to claim 1 is characterized in that: The equipment parameters include: technical output limit parameters, ramp rate limit parameters and energy limit parameters.

6. A hydrogen-driven comprehensive energy refined modeling system, applied to the hydrogen-driven comprehensive energy refined modeling method according to any one of claims 1 to 5, characterized in that: include: Data acquisition module, used to obtain the operation data and equipment parameters of the integrated energy system; The comprehensive energy system is a hydrogen-driven and electric-heat integrated energy system; The comprehensive energy system comprises: an energy supply unit, an energy coupling and energy storage unit and an energy consumption unit connected in sequence; the energy coupling and energy storage unit comprises: an electrolyzer, a fuel cell, a cogeneration unit, a gas boiler, a methane reactor and an energy storage unit; A regulating characteristic model building module is used to build a regulating characteristic model of the energy coupling and energy storage unit according to the equipment parameters of the integrated energy system; the regulating characteristic model includes: an electrolyzer model, a fuel cell model, a cogeneration unit model, a gas boiler model, a methane reactor model and an energy storage unit model; the electrolyzer model is a mixed integer linear mathematical model of the electrolyzer that takes into account the variable load start-stop characteristics; the fuel cell model is a mathematical model that takes into account the thermally variable thermoelectric characteristics; An optimization model building module, used to establish a multi-energy flow operation optimization model of the integrated energy system according to the operation data of the integrated energy system and the regulation characteristic model; the multi-energy flow operation optimization model includes an operation and maintenance optimization unit, an energy self-sufficiency optimization unit and a wind power consumption optimization unit; The operation strategy solving module is used to solve the multi-energy flow operation optimization model with the goal of minimizing the sum of the operation and maintenance electricity, electricity purchase volume, gas purchase volume and wind power consumption of the integrated energy system, so as to obtain the operation strategy of the integrated energy system.

7. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the hydrogen-driven comprehensive energy refined modeling method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the hydrogen-driven comprehensive energy refined modeling method as described in any one of claims 1 to 5.

Images