Scheduling optimization method, system and equipment of off-grid wind power AWE system and medium

Abstract

The invention belongs to the technical field of energy utilization optimization, and discloses an off-grid wind power AWE system scheduling optimization method, system, device and medium, and the method comprises the steps: constructing an off-grid wind power AWE system simulation model which comprises a wind power generation simulation model and a to-be-scheduled electrolytic cell simulation model; constructing an IES system scheduling framework, introducing a stepped carbon transaction mechanism into the IES system scheduling framework, and combining the off-grid wind power AWE system simulation model to construct an IES optimization operation model considering the stepped carbon transaction mechanism and electric hydrogen production; and solving the IES optimization operation model by taking the minimum total operation cost as a target to obtain an operation optimization strategy of the off-grid wind power AWE system. According to the technical scheme, the low carbon and economical efficiency of the IES are improved, and the operation optimization strategy of the off-grid wind power AWE system is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy utilization optimization, and in particular relates to a dispatch optimization method, system, equipment and medium for an off-grid wind power AWE system. Background Art

[0002] Energy is an important driving force for the development of human society and an important factor in driving and restricting the development of the national economy. Water electrolysis hydrogen production technology is a path with great development potential. This technology can use wind energy and light energy to produce hydrogen, reduce the neglect of wind energy and light energy, and improve the utilization rate and storage capacity of renewable energy. Alkaline water electrolysis (AWE) has its highlights in electrolysis hydrogen production technology. The single product specifications of the AWE hydrogen production system are much higher than the product specifications of proton exchange membrane water electrolysis hydrogen production. The power generation system has the advantages of low investment cost, long life, and scale, and is an important means to achieve the "carbon peak" goal at this stage. However, AWE has long had problems because the distance between the electrode and the diaphragm is long and the entire electrolytic cell is large, so its electrolysis performance is low (the current density is only about 300mA·cm-2 at 2.0V), which cannot meet industrial needs. In addition, the electrolysis reaction of alkaline aqueous solution is slow, and the electrolyte has strict requirements on equipment, making it difficult to install high-concentration intermittent renewable energy, which significantly increases the cost of hydrogen production equipment; in addition, the volatility of wind power has a great impact on the operation of the AWE hydrogen production system.

[0003] The scheduling optimization of off-grid wind power alkaline water electrolysis hydrogen production system involves many aspects, including energy management, equipment control, and improvement of the overall system operation efficiency. However, the scheduling optimization of existing off-grid wind power alkaline water electrolysis hydrogen production system has the disadvantages of high technical complexity, high requirements for wind power output prediction accuracy, high system investment cost, great influence by weather conditions, and difficulty in hydrogen storage and transportation. In practical applications, it is difficult to formulate a suitable scheduling optimization strategy based on the above shortcomings and limitations. Summary of the invention

[0004] The purpose of the present invention is to provide a scheduling optimization method, system, device and medium for an off-grid wind power AWE system to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above-mentioned objectives, the present invention provides a scheduling optimization method for an off-grid wind power AWE system, comprising: constructing an off-grid wind power AWE system simulation model, the off-grid wind power AWE system simulation model comprising a wind power generation simulation model and an electrolyzer simulation model to be scheduled; constructing an IES system scheduling framework, introducing a step-by-step carbon trading mechanism into the IES system scheduling framework, and combining the off-grid wind power AWE system simulation model to construct an IES optimization operation model considering the step-by-step carbon trading mechanism and electric hydrogen production; solving the IES optimization operation model with the goal of minimizing the total operating cost, and obtaining an operation optimization strategy for the off-grid wind power AWE system.

[0006] Optionally, the process of constructing the simulation model of the electrolytic cell to be scheduled includes: obtaining historical operating data of the electrolytic cell to be scheduled, obtaining data on the impact of temperature on the state switching process of electrolytic cells of different power levels based on the historical operating data, determining the relationship between the cold start duration and the idle speed of the electrolytic cell based on the impact data, and constructing a simulation model of the electrolytic cell to be scheduled that takes into account the variable load start-stop characteristics based on the relationship between the cold start duration and the idle speed of the electrolytic cell.

[0007] Optionally, the IES system scheduling framework includes a parent energy supply unit, a coupling device unit, an energy storage device unit, a final energy unit and a carbon trading market unit.

[0008] Optionally, the objective function of the IES optimization operation model considering the step-by-step carbon trading mechanism and electric hydrogen production is:

[0009]

[0010] In the formula, Energy purchase cost It is a tiered carbon trading cost. is the wind curtailment cost. F is the objective function.

[0011] Optionally, the constraints of the IES optimization operation model considering the tiered carbon trading mechanism and electric hydrogen production include wind power output constraints, electricity and gas purchase constraints, energy storage operation constraints, electric power balance constraints, natural gas balance constraints and hydrogen balance constraints.

[0012] Optionally, Yalmip is used to call the CPLEX solver to solve the IES optimization operation model to obtain the operation optimization strategy of the off-grid wind power AWE system.

[0013] A scheduling optimization system for an off-grid wind power AWE system comprises: a model building module for building an off-grid wind power AWE system simulation model, wherein the off-grid wind power AWE system simulation model comprises a wind power generation simulation model and an electrolyzer simulation model to be scheduled; an IES system scheduling framework is built, a step-by-step carbon trading mechanism is introduced into the IES system scheduling framework, and an IES optimization operation model considering the step-by-step carbon trading mechanism and electric hydrogen production is built in combination with the off-grid wind power AWE system simulation model; and a model solving module is used for solving the IES optimization operation model with the goal of minimizing the total operation cost, so as to obtain an operation optimization strategy for the off-grid wind power AWE system.

[0014] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program so that the electronic device executes the scheduling optimization method of an off-grid wind power AWE system.

[0015] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the scheduling optimization method of an off-grid wind power AWE system is implemented.

[0016] The technical effects of the present invention are:

[0017] The present invention converts complex system scheduling optimization problems into solvable mathematical models by constructing an off-grid wind power AWE system simulation model and an IES system scheduling framework, thereby reducing the difficulty of technical implementation; the detailed construction of the electrolyzer simulation model to be scheduled can more accurately reflect the actual operating status of the electrolyzer, thereby improving the accuracy of wind power output prediction and reducing the mismatch between supply and demand. The present invention can make full use of wind power resources, reduce unnecessary equipment investment and maintenance costs, and reduce the overall investment cost of the system by optimizing the scheduling strategy. By comprehensively considering multiple factors such as wind power output and energy storage operation, the optimized scheduling strategy can enhance the adaptability of the system to weather conditions and ensure the stable operation of the system under extreme weather conditions. By optimizing the scheduling strategy, the production and storage of hydrogen can be reasonably arranged, the ineffective storage and transportation of hydrogen can be reduced, and the related costs and risks can be reduced. The present invention proposes a stepped carbon trading mechanism for the joint operation of multiple electrolyzers and an IES optimized operation model for hydrogen production by electricity. In the model, the possibility of IES participating in the carbon trading market is taken into account, and a stepped carbon trading mechanism is introduced to guide IES to control carbon emissions. The energy demand of the electrolyzer will be coordinated with the demand of other energy facilities for overall scheduling, and optimized scheduling will be performed from the perspective of the electric-hydrogen-heat integrated energy system with multiple electrolyzers and multiple operating conditions, which can improve the low carbon and economy of IES. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a flow chart in an embodiment of the present invention;

[0020] Figure 2 is a flow chart in an embodiment of the present invention;

[0021] Figure 3 is a flow chart in an embodiment of the present invention;

[0022] Figure 4 is a wind energy utilization rate curve diagram of the pitch angle in the embodiment of the present invention;

[0023] Figure 5 Schematic diagram of an AWE electrolyzer in an embodiment of the present invention;

[0024] Figure 6 is a polarization curve diagram in an embodiment of the present invention;

[0025] Figure 7 Schematic diagram of a two-stage P2G process in an embodiment of the present invention;

[0026] Figure 8 is an operating condition diagram of an electrolytic cell array in an embodiment of the present invention;

[0027] Fig. 9 The thermoelectric ratio diagram of CHP in each period in the embodiment of the present invention;

[0028] Fig.10 is a power balance diagram in an embodiment of the present invention; wherein, Fig.10 (a) is an electric power balance diagram in an embodiment of the present invention, Fig.10 (b) is a thermal power balance diagram in an embodiment of the present invention, Fig.10 (c) is a hydrogen power balance diagram in an embodiment of the present invention, Fig.10 (d) is a gas load power balance diagram in an embodiment of the present invention.

[0029] Fig.11 It is an implementation flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0031] Embodiment 1

[0032] like Figure 1 - Fig.11 As shown, this embodiment provides a scheduling optimization method for an off-grid wind power AWE system, including: constructing an off-grid wind power AWE system simulation model, the off-grid wind power AWE system simulation model including a wind power generation simulation model and an electrolyzer simulation model to be scheduled; constructing an IES system scheduling framework, introducing a step-by-step carbon trading mechanism into the IES system scheduling framework, and combining the off-grid wind power AWE system simulation model to construct an IES optimization operation model considering the step-by-step carbon trading mechanism and electric hydrogen production; solving the IES optimization operation model with the goal of minimizing the total operating cost, and obtaining an operation optimization strategy for the off-grid wind power AWE system.

[0033] This embodiment studies the working characteristics of alkaline electrolytic cells of different power levels, considers the influence of temperature on the state switching process of the electrolytic cell, and establishes the relationship between the cold start duration and idle speed of the electrolytic cell through research and analysis of electrolytic cells of different power levels.

[0034] This embodiment proposes a tiered carbon trading mechanism for the joint operation of multiple electrolyzers and an IES optimized operation model for hydrogen production from electricity. At the same time, the operation of other energy facilities is combined with it to construct a comprehensive scheduling model. In the model, the energy demand of the electrolyzer is coordinated with the demand of other energy facilities for overall scheduling.

[0035] This embodiment takes actual wind power input as an example to optimize the electrolyzer array configuration scheme.

[0036] This example takes the efficiency of hydrogen production by electrolysis of water as the ultimate research goal, establishes a mathematical and physical model of the off-grid wind power AWE system configuration scheme and scheduling strategy, and uses simulation to carry out the performance study of the off-grid wind power AWE system. Analyze, discuss and study, learn and use MATLAB tools, and use solver programming to solve the model. Use examples to test the model and draw conclusions.

[0037] Principle and composition: Off-grid wind power generation system uses wind power to drive the wind turbine's rotor to rotate, which is then transmitted to the generator, causing the generator to generate electricity. At the same time, with the help of wind turbine direction adjustment, speed regulation mechanism, electrical control and converter, the rapidly changing wind energy is converted into stable electrical energy and can be directly used.

[0038] The components of the off-grid wind power system include wind rotor, generator, speed regulating mechanism, tower, electrical control system, energy storage device, inverter, etc. The wind rotor can convert wind energy into mechanical energy, and the rotating wind rotor shaft transmits the torque to the generator. Generators can be divided into synchronous generators and asynchronous generators, DC generators and AC generators, excitation generators and permanent magnet generators. Synchronous three-phase permanent magnet AC generators are the most commonly used generators. The function of the electrical control system is to ensure the safety and reliability of the wind turbine operation process, improve the operating efficiency of the unit and the quality of power generation. The brake is an indispensable and important safety protection component of the wind turbine. When encountering destructive strong winds or serious faults in the unit, effective braking must be implemented immediately to end the operation. The tower requires a certain degree of rigidity to ensure the safe and stable operation of the wind turbine. The main function of the inverter is to convert the output of DC or rectifier bridge into AC. The efficiency of the system is related to the power consumption of the off-grid inverter.

[0039] Operation mode: The operation modes of off-grid wind power generation systems include household single-machine, multi-household grid-connected power supply and community multi-energy auxiliary operation. The capacity of a single household power supply system is generally less than 1kW. It is suitable for single-family households with an average daily power consumption of about 1kW·h. The system mainly uses wind power generation, supplemented by a small number of solar cells to make up for the lack of wind power generation during weak winds. Electric energy is used for daily life and cultural and entertainment projects, such as lighting, electric stoves, refrigerators, televisions, etc. Multi-household grid-connected power supply is generated by connecting multiple small-capacity wind turbines to the grid. Small-capacity wind turbines are light and easy to install. When some units are shut down due to faults during use, other units still generate electricity to ensure normal power supply of the system. The community multi-energy complementary operation mode is that the average daily power consumption in the region is about 100kW·h, the power load is large, and multiple loads do not interfere with power supply. In terms of configuration, the main power supply is provided by wind turbines, equipped with solar cells, an appropriate number of batteries and diesel generators as emergency backup. This power supply method is suitable for remote mountainous areas, grasslands and islands without power grids, and can solve the problem of power consumption in production and life.

[0040] In order to effectively utilize the unstable wind energy, the battery needs to be equipped with energy storage devices. The current energy storage methods include: battery energy storage, compressed air energy storage, water electrolysis hydrogen energy storage, etc. However, due to the superiority of batteries, that is, they can adjust the distribution of electric energy according to the wind power and electrical load conditions to maintain the continuous and stable operation of the power supply of the wind power generation system, most of them use batteries as energy storage devices.

[0041] Alkaline water electrolysis produces hydrogen. The alternating current regulated by the inverter is passed through the electrolyzer to electrolyze water to produce the required hydrogen. At present, compared with high-temperature solid oxide electrolysis and solid polymer water electrolysis, alkaline water electrolysis is the most widely used and has great advantages. Wind turbines with large installed capacity need to adopt alkaline water electrolysis hydrogen production technology. This technology is simple and easy to use, but it also has problems of high energy consumption and high cost.

[0042] Working principle, hydrogen production by alkaline water electrolysis: Under the action of direct current, the water on the cathode side gains electrons, undergoes a hydrogen evolution reduction reaction, produces hydrogen and hydroxide ions, and passes through the physical separator under the action of the electric field and the concentration difference on the hydroxide side to lose electrons on the anode side. An oxygen evolution oxidation reaction occurs to produce oxygen and water. Water is decomposed into hydrogen and oxygen in the electrolytic cell. The alkaline hydrogen-liquid mixture is separated by a heat exchanger and a gas-liquid separator, and the electrolyte returns to the electrolytic cell. The liquid alkaline-oxygen mixture is exchanged. The heater and the gas-liquid separator separate the oxygen, and the electrolyte returns to the electrolytic cell. After washing and purification, the oxygen is collected or directly discarded.

[0043] Off-grid wind power AWE system modeling and simulation:

[0044] Implementation process of wind power generation modeling and simulation:

[0045] Mathematical model of wind power generation:

[0046] A wind turbine with a radius of R generates a mechanical power P when the wind speed is v. m

[0047] P m =K p C p (β,λ)V 3 (1-1)

[0048]

[0049] Where c1=0.5176, c2=116, c3=0.4, c4=5, c5=21, c6=0.0068

[0050]

[0051]

[0052] Where ρ is the air density, which is 1.205 kg / m at 20°C. 3 , ω is the blade rotation angular velocity, β is the blade pitch angle, λ is the tip speed ratio, T m is the mechanical torque.

[0053] Wind power generation simulation model: By learning the basic principles and mathematical models of wind power generation, use MATLAB / Simulink simulation software to build a wind power generation model. Figure 1-Figure 3 shown.

[0054] In the above model, the pitch angle β and the blade tip speed ratio λ are used as the input parameters of the model. By inputting different β values, the Cp curve corresponding to different pitch angles can be obtained. In addition, it can also be known that the change of λ is determined by the wind speed and the blade tip linear speed. By running the code, the following results can be obtained, as follows Figure 4 As shown:

[0055] From the curve of β = 0°, we can see the relationship between the wind energy utilization coefficient Cp and the blade tip speed ratio λ. As can be seen from the figure, the λ corresponding to the maximum Cp is called the optimal blade tip speed ratio λ. opt , the corresponding Cp is the maximum wind energy utilization coefficient. When λ is greater than or less than λ opt When the wind speed reaches 0°, Cp will be less than the maximum wind energy utilization coefficient, and the utilization efficiency will decrease. When the pitch angle of the blades gradually increases, the Cp curve will move downward. During the operation of the wind turbine, when the output power exceeds the rated power, the wind turbine will adjust the pitch angle to maintain the output power at the rated value; when the output power drops below the rated power, the wind turbine will control the pitch angle to remain at around 0°. In summary, the wind energy utilization rate in wind power generation is affected by the pitch angle of the wind turbine, so it needs to be reasonably controlled and adjusted.

[0056] Modeling and simulation analysis of electrolysis device: The process of producing hydrogen by electrolysis of water is actually a process from electrical energy to chemical energy and then to hydrogen energy, so the mathematical model of the electrolysis device mainly includes the electrical model and the electrochemical analytical model. From the perspective of the circuit, the electrolysis device is a nonlinear DC load, and factors such as input current, operating temperature and working pressure will affect the output voltage of the electrolyzer. The working characteristics of the electrolyzer can be expressed as:

[0057]

[0058] Where U el is the working voltage of the electrolytic cell, V; U r is the reversible voltage of the electrolytic cell that changes with temperature, V; I el is the working current of the electrolytic cell, A; r1, r2 are the ohmic resistance parameters of the electrolyte; T el is the working temperature of the electrolytic cell, °C; k el , k t1 , k t2 , k t3 is the overvoltage parameter on the electrolytic cell electrode; S is the surface area of ​​the electrolytic cell electrode, m 2 .

[0059] Reversible voltage U of the electrolytic cell r and the electrolytic cell temperature T el The relationship is shown as follows:

[0060] U r =U r 0 -k r (T el -298.15) (1-8)

[0061] Where U r 0 is the reversible voltage under standard conditions; k r is the empirical temperature coefficient of the reversible voltage.

[0062] In the large-scale hydrogen production process, it is often necessary to convert several (N c ) The single electrolyzers work in series, then the output voltage U ael :U ael =N el U el (1-9)

[0063] According to Faraday's law, the hydrogen production rate is directly proportional to the electrolysis current, so the hydrogen production rate by water electrolysis can be calculated by the following formula:

[0064]

[0065] In the formula, is the hydrogen production, in m 3 / h; η is the electrolysis efficiency of the electrolytic cell, %.

[0066] Simulation model of hydrogen production by electrolysis of water: Through the understanding of the working principle and mathematical model of the electrolyzer, the simulation model of the working characteristics of the electrolyzer and the hydrogen production rate model are reasonably established using MATLAB / Simulink simulation software, as shown in the figure below. Figure 5 shown.

[0067] In this model, the working current of the electrolyzer I elec and operating temperature T elec is the input parameter of the model, and voltage U is the output parameter. In the electrolyzer hydrogen production rate model, the input parameter is the operating current I, and the output parameter is the hydrogen production rate Temperature is an important factor affecting the operation of the electrolytic cell, so this embodiment will use the electrolytic cell characteristic simulation model to analyze the performance of the electrolytic cell. Table 1 shows the parameters used in the model.

[0068] Table 1 Parameters

[0069]

[0070]

[0071] Through modeling and operation, the polarization curves of the electrolytic cell at different operating temperatures can be obtained. Figure 6 As shown. Figure 6 It can be seen that when the working temperature increases, the working voltage and energy efficiency of the electrolyzer gradually decrease, which is mainly caused by the activation of the electrolytic catalyst in the electrolysis device. In addition, since the process of hydrogen production by electrolysis of water generates heat, the temperature of the electrolyzer increases. In order to ensure the working efficiency of the electrolyzer, the working temperature of the electrolysis of water hydrogen production equipment generally needs to be controlled at 40-80°C. This section introduces the types of hydrogen production by electrolysis of water and the working principle of the electrolyzer, and establishes a mathematical model and simulation model of the electrolyzer output characteristics and the electrolysis hydrogen production rate, and analyzes the influence of the electrolyzer working temperature on the hydrogen production efficiency, laying the foundation for the establishment of the simulation model and simulation analysis of the subsequent system.

[0072] Cold start characteristics of alkaline electrolyzers. There is a certain relationship between the cold start duration of the electrolyzer and the idle speed, which mainly depends on the design and operating conditions of the electrolyzer. Generally speaking, the cold start duration refers to the time required from the complete stop of the electrolyzer to the normal operating state. During the cold start process, the electrolyzer needs to gradually heat up and reach a stable operating temperature. At the same time, the concentration and current density of the electrolyte also need to be gradually adjusted to the normal operating level. Idle speed refers to the operating mode of the electrolyzer under low load or shutdown state. Under idle speed, the electrolyzer can be converted from shutdown state to normal operating state in a short time, because parameters such as electrolyte temperature and current density are close to normal operating levels. Therefore, under normal circumstances, the cold start duration of the electrolyzer will be longer than the time required from shutdown to normal operation under idle speed. In actual operation, in order to reduce the impact of cold start on the electrolyzer, some measures can be taken, such as maintaining the stable operation of the electrolyzer under idle speed and controlling the electrolyte temperature.

[0073] The cold start duration of the electrolyzer and the start time under idling state can be described by an empirical formula or mathematical model. In general, the cold start duration (T cold ) will be longer than the start time at idle speed (T idle )long.

[0074] Considering many dynamic factors and complexities, such as changes in the physical and chemical properties of the electrolyte, the preheating and insulation capabilities of the electrolyzer, changes in ambient temperature, and the response characteristics of the electrolyzer under different workloads, it is necessary to combine heat transfer and electrochemical kinetics for detailed modeling and simulation. The relationship between the cold start time and idle speed of the electrolyzer based on the heat transfer equation and the kinetic equation is as follows:

[0075] Tcold =α·T idle +β·(T env -T ele )

[0076] Among them: α and β are empirical coefficients that need to be fitted by experimental data, T env is the ambient temperature, T ele is the initial temperature of the electrolyte.

[0077] This relationship takes into account the influence of ambient temperature and initial electrolyte temperature on cold start time and can accurately describe the actual situation. By experimentally measuring the cold start time and idle start time under different conditions, the specific values ​​of α and β can be determined.

[0078] This embodiment constructs mathematical models of various parts based on the basic operating principles of wind power generation and alkaline electrolyzer, and establishes corresponding simulation models by using MATLAB / Simulink simulation software. Through the study of the model, the operating characteristics of the wind turbine are analyzed in detail, and it is found that the wind energy utilization coefficient of the wind turbine can be maintained at the optimal position by adjusting the pitch angle, thereby improving the wind energy utilization rate. The working characteristics of the electrolyzer are analyzed, and the results show that the working temperature is a key parameter that needs to be reasonably controlled. It is determined that the electrolyzer needs to work within a certain temperature range, and then the relationship between the cold start duration of the electrolyzer and the idle speed is also analyzed. The above analysis and research lays a solid foundation for the subsequent system simulation analysis.

[0079] A multi-level AWE system dispatch model with different power levels. The use of renewable energy to electrolyze water to produce hydrogen is a key technology for achieving energy transformation and decarbonization of electricity. However, power fluctuations and frequent switching on and off are the main factors that cause the service life of alkaline electrolyzers to decrease. To this end, a step-by-step carbon trading mechanism for the joint operation of multiple electrolyzers and an IES optimized operation model for hydrogen production are proposed. In addition, the variable load start-stop characteristics of the electrolyzer are also understood and considered, and it is planned and integrated with the operation of other energy facilities to establish a highly comprehensive energy dispatch model. In the model, the energy demand of the electrolyzer will be coordinated with the demand of other energy facilities for overall dispatch.

[0080] Operation framework, the integrated energy system IES includes a variety of energy sources for joint supply, which can meet the needs of terminal multi-energy loads. Its scheduling framework mainly includes five units: parent energy supply unit, coupling equipment unit, energy storage equipment unit, final energy unit and carbon trading market unit. Distributed power sources can provide IES with renewable clean energy; the role of the electrolyzer EL is to convert electrical energy into hydrogen energy, and then the hydrogen energy is further converted into natural gas through the methane reactor MR, which can actually be directly supplied to HFC for hydrogen production. Gas boilers burn natural gas to meet heat needs; gas load demand is determined by the overall natural gas network and MR supply; in addition, IES also includes electricity, gas, heat, hydrogen and other energy storage devices for energy storage, which can achieve CO2 absorption; emissions associated with the operation of each device are ultimately traded through the carbon dioxide trading market.

[0081] Adjustable heat-to-electricity ratio model, CHP unit burns natural gas for combined heat and power generation, by controlling the steam turbine extraction ratio and the inlet guide vane angle, the heat-to-electricity ratio is adjusted according to the real-time electric and thermal load conditions. The following is this model:

[0082]

[0083] Where: is the gas power consumption at time t; and are the power generation and heat generation at time t respectively; η CHP is the energy conversion efficiency of the combined heat and power unit; They are the upper and lower limits of gas power consumption; 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.

[0084] P2G (Power-to-Gas), the two-stage operation process of P2G is as follows Figure 7 shown.

[0085] 1) EL equipment.

[0086]

[0087] Where: P e,EL (t) is the electric energy input to EL during period t; is the hydrogen energy output by EL during period t; η EL is the energy conversion efficiency of EL; They are the upper and lower limits of the electric energy of the input EL respectively; They are the upper and lower limits of EL climbing respectively.

[0088] 2) MR equipment:

[0089]

[0090] Where: P is the hydrogen energy of MR input during period t; MR,g (t) is the natural gas power output by MR during period t; η MR is the energy conversion efficiency of MR; are the upper and lower limits of hydrogen energy input to MR, respectively; They are the upper and lower limits of MR climbing respectively.

[0091] 3) HFC equipment: Since the sum of the heat and electricity energy conversion efficiency of HFC is equivalent to a constant, and the electricity and heat conversion efficiency can be adjusted, the HFC model with adjustable thermoelectric ratio constructed in this embodiment is as follows.

[0092]

[0093] Where: P is the hydrogen energy input into HFC during period t; HFC,e , P HFC,h (t) are the electricity and heat energy output by HFC in period t, respectively; are the efficiency of HFC conversion into electricity and heat, respectively; HFC are the upper and lower limits of hydrogen energy for input HFC respectively; They are the upper and lower limits of HFC climbing respectively; They are the upper and lower limits of the heat-to-electricity ratio of HFC respectively.

[0094] 4) Methane reactor model MT: converts hydrogen energy into gas energy and supplies it to GB or CHP to generate electricity and heat energy. The model is as follows:

[0095]

[0096] Where: is the hydrogen power used at time t; is the hydrogen-to-gas conversion efficiency of MT; is the gas production power at time t; are the upper and lower limits of hydrogen power respectively; is the maximum climbing power.

[0097] Taking into account the variable load start-stop characteristics of the electrolyzer model, the use of electrolysis hydrogen production technology can strengthen the complementary coupling of multiple energy sources, improve wind power consumption, and enhance the flexible deployment capabilities of IES during periods of low electricity prices. The electrolyzer is a key equipment for electrolysis hydrogen production. This embodiment takes the AWE alkaline electrolyzer as an example to establish a mixed integer linear mathematical model of the electrolyzer considering the load variable start-stop characteristics. The operating state of AWE can be divided into shutdown state, cold standby state and working state. Taking into account the working characteristics of AWE, the working state can be divided into low load, variable load and overload state.

[0098] (1) Shutdown state (no hydrogen production): AWE can be shut down quickly under any conditions and is considered an interruptible load, excluding downtime. It usually takes 30 minutes to 1 hour to fully start in this state.

[0099] (2) Cold standby state (no hydrogen production): AWE is turned off but not stopped, keeping the control and antifreeze units running in low-power standby mode. In this case, AWE takes 5-10 minutes to perform a cold start.

[0100] (3) Working state (producing hydrogen): AWE operates under variable load conditions (30%-100%) within the power upper and lower limits of the electrolyzer. At the same time, AWE can operate for a short time under overload conditions (100%-150% of rated power) and under low load conditions (10%-30% of rated power), giving AWE excellent operating flexibility.

[0101] In order to illustrate the working characteristics of the alkaline electrolyzer, binary variables are used to represent the five working states of shutdown I, cold standby S, variable load L, overload R and low load V. At the same time, the binary variable Y is used to represent the complete startup interval. If it is 1, it means that the AWE is fully started from the shutdown state. The time until the AWE is fully started (set to 1 hour) is used as the optimized time scale. Since the cold start time (set to 10 minutes) is less than a time scale, the loss of hydrogen energy output during the cold start process must be considered. The relationship between the AWE power consumption and the hydrogen production power can be expressed as:

[0102] Where: is the hydrogen production power of the nth AWE at time t; is the power consumption of the nth AWE at time t; is the electricity-to-hydrogen conversion efficiency of AWE; is the standby power of AWE in cold standby state; AWE is the penalty coefficient of hydrogen production power during cold start, which is used to represent the hydrogen energy loss caused by inconsistent time scales; when the binary variable W is 1, it means that AWE switches from cold standby state to working state, satisfying the following formula.

[0103]

[0104] The power consumption of AWE satisfies the inequality relationship shown in the following formula. The coordinated control between different working states of AWE can be obtained through the value of binary variables.

[0105]

[0106] Where: is the rated operating power of AWE. When the binary variable is 1, it means it is in this state. At the same time, the transition of AWE working state needs to meet the following logical constraints.

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] Where: T Rmax and T Vmax They are the maximum time that AWE is allowed to run continuously in overload and underload states; from top to bottom, they are start-stop constraints, start interval constraints, operation state mutual exclusion constraints, and overload and underload maximum time limit constraints. In addition, in order to avoid frequent start and stop of the alkaline electrolyzer in a short period of time, it is necessary to limit the minimum shutdown and cold standby time.

[0113]

[0114] Where: T Imin , T Smin They are the shortest time that AWE can run continuously in shutdown and cold standby states respectively.

[0115] The ladder carbon trading mechanism model, the carbon trading mechanism achieves the goal of controlling carbon emissions by establishing legal carbon emission rights and allowing producers to trade carbon emission rights in the market. The ladder carbon trading mechanism model mainly includes the carbon emission quota model, the actual carbon emission model and the ladder carbon emission trading model.

[0116] Carbon emission quota model:

[0117] Where: E IES 、E e,buy 、E CHP 、E GB are the carbon emission quotas of IES, power purchase from higher authorities, CHP, and GB respectively; e , χ g are the carbon emission quotas per unit electricity consumption of coal-fired units and per unit natural gas consumption of natural gas-fired units; P e,buy (t) is the power purchased by the superior during period t; P CB，h (t) is the thermal energy output by GB during period t; T is the scheduling period.

[0118] The actual carbon emission model, because MR's hydrogen energy can absorb some CO2 during the conversion to natural gas, the actual carbon emission model needs to take it into account. The model is as follows:

[0119]

[0120] Where: E IES,a 、E e,buy,a are IES and the actual carbon emissions of electricity purchased by the superior; E total,a is the total actual carbon emissions of CHP, GB and MR; E MR,a is the amount of CO2 actually absorbed by MR; P total(t) is the equivalent output power of CHP, GB and MR in period t; a1, b1, c1 and a2, b2, c2 are the carbon emission calculation parameters of coal-fired units and natural gas-consuming energy supply equipment respectively; Parameters for absorbing CO2 in the MR equipment hydrogen-to-natural gas process.

[0121] In order to calculate the actual carbon emission rights trading amount in the carbon trading market in the tiered carbon emission trading model, it is necessary to calculate the IES carbon emission rights quota and actual carbon emissions.

[0122] E IES,t =E IES,a -E IES

[0123] Where: E IES,t is the carbon emission rights trading amount of IES.

[0124] This paper adopts a tiered pricing mechanism. That is, it is divided into multiple purchase intervals. The more carbon emission quotas need to be purchased, the higher the purchase price of the corresponding interval. The tiered carbon trading cost is:

[0125]

[0126] Where: is the step-by-step carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; α is the price growth rate.

[0127] IES optimization operation model considering the tiered carbon trading mechanism and electric hydrogen production:

[0128] 1. Objective function: This embodiment comprehensively considers the energy purchase cost of IES Tiered carbon trading costs and curtailment costs The low-carbon economic dispatching target with the minimum total operating cost F is as follows:

[0129]

[0130] Energy purchase cost

[0131] Where: P g,buy(t) is the gas purchase volume in period t; α t , β t are the electricity price and gas price in period t respectively.

[0132] Tiered carbon trading costs and the cost of wind curtailment

[0133] Where: DG P is the penalty cost for unit wind curtailment; DG,cut (t) is the wind power abandoned during period t.

[0134] 2. Constraints:

[0135] Wind power output constraints:

[0136] Where: P DG (t) is the wind power output power in period t; The upper limit of wind power output.

[0137] Constraints on purchasing electricity and gas:

[0138]

[0139] Where: is the amount of electricity and gas purchased by the system at time t; The maximum amount of electricity and gas purchased by the system.

[0140] GB operation constraints:

[0141]

[0142] Where: η GB is the energy conversion efficiency of GB; P g,GB (t) is the power input to GB during period t; They are the upper and lower limits of GB input power respectively; They are the upper and lower limits of GB’s climbing ability respectively.

[0143] Energy storage operation constraints: Electric, thermal, gas, and hydrogen energy storage devices all need to be modeled.

[0144]

[0145] Where: are the charging and discharging powers of the nth type of energy storage device during period t respectively; The maximum single charge and discharge power of the nth energy storage device; are binary variables, which are the charging and discharging state parameters of the nth energy storage device in period t. Indicates that it is in charging state. Indicates that it is in the state of releasing energy; P ES,n (t) is the final output power of the nth energy storage device during period t; are the charging and discharging efficiencies of the nth energy storage device; S n (t) is the capacity of the nth energy storage device during time period t; is the rated capacity of the nth type of energy storage device; They are respectively the upper and lower limits of the capacity of the nth type of energy storage device.

[0146] Electric power balance constraints.

[0147]

[0148] Where: P e_Load (t) is the electrical load during period t; is the power input to the storage during period t; It is the electricity purchase limit for each time period.

[0149] Thermal power balance constraints:

[0150] Where: P h_Load (t) is the heat load during period t; Enter the power of the thermal storage for period t.

[0151] Natural gas balance constraint: This embodiment does not consider the IES selling natural gas to the upper gas grid.

[0152]

[0153] Where: P g_Load (t) is the gas load during period t; The power of natural gas storage input for period t; It is the gas purchase limit for each time period.

[0154] Hydrogen balance constraints:

[0155] Where: Enter the power of the hydrogen storage for time period t.

[0156] The optimized scheduling is carried out according to a 24-hour cycle. The electrolyzer array consists of 6 alkaline electrolyzers connected in parallel with a rated power of Take 80kW, cold standby power Take 8kW. It also includes the unit cost of electrolyzer startup, energy coupling equipment, energy storage equipment parameters, time-of-use electricity price and natural gas price, all of which adopt actual parameters. This study is based on the combined operation of multiple electrolyzers and the working condition switching of electrolyzers and energy storage equipment in the real-time scheduling stage. The model is modeled on the Matlab2022a platform, and Yalmip is used to call the CPLEX solver for solution.

[0157] 1. Regarding alkaline electrolyzers, the duration of fluctuating power affects their operating life. The specific operating conditions of the following electrolyzer array are analyzed to demonstrate the protective effect of the flexible combination operation mode on the electrolyzer. It can be seen from the change of the electric power of each electrolyzer along time that between one o'clock and ten o'clock and from eight o'clock in the evening to midnight, more wind power is consumed, and AWE is operating at maximum or minimum power most of the time. The model proposed in this embodiment takes into account the operating characteristics and startup costs of AWE, effectively reduces the number of starts and stops of the electrolyzer, and the operating state is more flexible. In the period of 10:00-20:00, the demand for hydrogen is small, and most AWEs are in a shutdown state. At 20:00, due to the hydrogen storage capacity about to reach the lower limit, the hydrogen production power of AWE No. 4, 5, and 6 increases. Among them, electrolyzers 2, 4, and 5 are in a shutdown state for a long time, while electrolyzers 1, 3, and 6 are in working state for 16 hours and have more time in fluctuating working conditions, so they play a major role in the entire scheduling cycle. Long-term operation under fluctuating working conditions will damage the function of the alkaline electrolyzer and reduce its service life, so its fluctuating working condition is defined as an unhealthy operating state. Therefore, the electrolyzer array adopts a combined operation working mode to effectively alleviate the functional damage caused by fluctuating working conditions, significantly extend the service life of the electrolyzer, and indirectly reduce costs. Figure 8 shown.

[0158] from Figure 8 It can be seen from the line in the middle that the thermal load is at its peak during the night and early morning hours, and GB provides most of the thermal load, so GB is in full power, and the insufficient part is supplied by CHP; while the electric load is in a valley state during this period, and it is a period of low electricity prices. Since wind power can already meet most of the electricity demand, and CHP only supplies part of the thermal load, CHP is in the maximum heat-to-electricity ratio, which means more heat is generated and less electricity is generated. During the day, the thermal load is at a valley, so the power input to GB is reduced. However, at this time, the electric load has been at a high line. Due to the time-of-use electricity price, the electricity price is higher and the gas price is lower. Therefore, the system chooses to increase CHP to supply power to the electric load, and the CHP output increases, and it is also in the minimum heat-to-electricity ratio, which means more electricity is generated and less heat is generated. Therefore, the adjustable heat-to-electricity ratio of CHP and HFC is good, which can adjust the energy supply balance and reduce the system operation cost. Fig. 9 shown.

[0159] The combined operation mode of multiple electrolyzers and the real-time switching strategy of their working conditions are considered. The specific operating status of the equipment is as follows: Fig.10 shown.

[0160] In order to build a low-carbon economic operation model with the goal of cost saving and economical benefits, this embodiment introduces IES, CH, and P2G. Then a new electrolyzer model is proposed, the variable load start-stop characteristics of the electrolyzer are taken into consideration, and its idea is added to the constructed new model. Since the cogeneration strategy with adjustable heat-to-electricity ratio can improve the low carbon and economy of IES, the flexibility of the electrolysis hydrogen production process can be effectively improved by adjusting the working state of the equipment in real time according to the electric hydrogen load, which is also taken into consideration. Considering the possibility of IES participating in the carbon trading market, a step-by-step carbon trading mechanism is introduced to guide IES to control carbon emissions. In order to further study the multi-faceted benefits of hydrogen energy, the two-stage operation process of power-to-gas (P2G) is also refined, and electrolyzers, methane reactors and hydrogen fuel cells (HFC) are introduced to replace traditional P2G technology.

[0161] The off-grid wind power generation AWE system is mainly studied from two parts: off-grid wind power generation and AWE system.

[0162] The off-grid wind power generation system is an independent power generation and supply system. It is a new energy device that is very suitable for using wind energy resources to meet local electricity needs in areas where the power grid cannot reach. In remote areas far away from the power grid, using local wind energy resources to build an independently operated wind power generation system to meet local electricity needs has practical economic benefits and long-term social benefits. The wind power hydrogen production system uses wind power to drive the wind wheel of the wind turbine to rotate, and then transmits it to the generator, causing the generator to generate electricity. At the same time, with the help of wind turbine direction adjustment, speed regulation mechanism, electrical control and current conversion device, the rapidly changing wind energy is converted into stable electricity and can be directly used. The process goes through steps such as wind power generation, power storage, and electrolysis hydrogen production. The components include wind wheel, generator, speed regulation mechanism, tower, electrical control system, energy storage device, inverter, etc.

[0163] Compared with proton exchange membrane and solid oxide electrolysis cell hydrogen production technology, alkaline water electrolysis is the most widely used and has great advantages. Wind turbines with large installed capacity should adopt alkaline water electrolysis (AWE) hydrogen production technology. This technology is simple and easy to use, but it also has problems of high energy consumption and high cost.

[0164] This embodiment first analyzes the basic components of the off-grid wind power AWE system and its working principle and operation mode, then establishes a mathematical model, and uses MATLAB / Simulink simulation software to establish and analyze the simulation model of wind power generation and electrolysis equipment, and analyzes the impact of temperature on the operating state of the electrolyzer, and establishes the relationship between the cold start duration of the electrolyzer and the idle speed. Finally, a multi-level AWE system scheduling model with different power levels is established, and the electrolyzer array is optimized using the actual wind power input power as an example. The following conclusions are drawn:

[0165] (1) The results of the unit foundation analysis show that wind turbines are affected by wind speed. To improve the utilization rate of wind energy, it is necessary to adjust the pitch angle to make the wind turbine work in the best state. By analyzing the working characteristics of hydrogen production by electrolysis, the results show that temperature will affect the operation of the electrolyzer, and the electrolyzer needs to work within a certain temperature range. Then, the relationship between the cold start duration of the electrolyzer and the idle speed is also analyzed.

[0166] (2) System model simulation process: In order to improve energy utilization, a low-carbon economic operation model with the goal of cost saving and economical affordability was constructed. A new electrolyzer model was proposed, and the variable load start-stop characteristics of the electrolyzer were taken into consideration. A case analysis was conducted, and it was concluded through code running that the combined multi-electrolyzer operation is more economical. By adjusting the heat-to-electricity ratio to achieve energy supply balance, the heat and power cogeneration strategy with adjustable heat-to-electricity ratio can improve the low carbon and economy of IES. The possibility of IES participating in the carbon trading market was considered, and a step-by-step carbon trading mechanism was introduced to guide IES to control carbon emissions. In order to further study the multi-faceted benefits of hydrogen energy, the two-stage operation process of power-to-gas (P2G) was also studied.

[0167] This embodiment converts the complex system scheduling optimization problem into a solvable mathematical model by constructing an off-grid wind power AWE system simulation model and an IES system scheduling framework, thereby reducing the difficulty of technical implementation; through the detailed construction of the electrolyzer simulation model to be scheduled, the actual operating status of the electrolyzer can be more accurately reflected, thereby improving the accuracy of wind power output prediction and reducing the mismatch between supply and demand. This embodiment can make full use of wind power resources, reduce unnecessary equipment investment and maintenance costs, and reduce the overall investment cost of the system by optimizing the scheduling strategy. By comprehensively considering multiple factors such as wind power output and energy storage operation, the optimized scheduling strategy can enhance the system's adaptability to weather conditions and ensure the stable operation of the system under extreme weather conditions. By optimizing the scheduling strategy, the production and storage of hydrogen can be reasonably arranged, the ineffective storage and transportation of hydrogen can be reduced, and the related costs and risks can be reduced.

[0168] A scheduling optimization system for an off-grid wind power AWE system comprises: a model building module for building an off-grid wind power AWE system simulation model, wherein the off-grid wind power AWE system simulation model comprises a wind power generation simulation model and an electrolyzer simulation model to be scheduled; an IES system scheduling framework is built, a step-by-step carbon trading mechanism is introduced into the IES system scheduling framework, and an IES optimization operation model considering the step-by-step carbon trading mechanism and electric hydrogen production is built in combination with the off-grid wind power AWE system simulation model; and a model solving module is used for solving the IES optimization operation model with the goal of minimizing the total operation cost, so as to obtain an operation optimization strategy for the off-grid wind power AWE system.

[0169] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program so that the electronic device executes the scheduling optimization method of an off-grid wind power AWE system.

[0170] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the scheduling optimization method of an off-grid wind power AWE system is implemented.

[0171] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A dispatch optimization method for an off-grid wind power AWE system, characterized in that: include: Constructing an off-grid wind power AWE system simulation model, wherein the off-grid wind power AWE system simulation model includes a wind power generation simulation model and a to-be-dispatched electrolyzer simulation model; Construct an IES system dispatch framework, introduce a tiered carbon trading mechanism into the IES system dispatch framework, and construct an IES optimization operation model considering the tiered carbon trading mechanism and electric hydrogen production in combination with the off-grid wind power AWE system simulation model; The IES optimization operation model is solved with the goal of minimizing the total operation cost, and the operation optimization strategy of the off-grid wind power AWE system is obtained.

2. The scheduling optimization method of an off-grid wind power AWE system according to claim 1 is characterized in that: The construction process of the electrolyzer simulation model to be scheduled includes: Obtain historical operating data of the electrolyzer to be scheduled, obtain data on the impact of temperature on the state switching process of electrolyzers of different power levels based on the historical operating data, determine the relationship between the cold start duration and the idle speed of the electrolyzer based on the impact data, and construct a simulation model of the electrolyzer to be scheduled that takes into account the variable load start-stop characteristics based on the relationship between the cold start duration and the idle speed of the electrolyzer.

3. The scheduling optimization method of an off-grid wind power AWE system according to claim 1 is characterized in that: The IES system dispatch framework includes a parent energy supply unit, a coupling device unit, an energy storage device unit, a final energy unit and a carbon trading market unit.

4. The scheduling optimization method of an off-grid wind power AWE system according to claim 1, characterized in that: The objective function of the IES optimization operation model considering the step-by-step carbon trading mechanism and electric hydrogen production is: In the formula, Energy purchase cost It is a tiered carbon trading cost. is the wind curtailment cost. F is the objective function.

5. The scheduling optimization method of an off-grid wind power AWE system according to claim 1, characterized in that: The constraints of the IES optimization operation model considering the tiered carbon trading mechanism and electric hydrogen production include wind power output constraints, electricity and gas purchase constraints, energy storage operation constraints, electric power balance constraints, natural gas balance constraints and hydrogen balance constraints.

6. The scheduling optimization method of an off-grid wind power AWE system according to claim 1, characterized in that: Yalmip is used to call the CPLEX solver to solve the IES optimization operation model and obtain the operation optimization strategy of the off-grid wind power AWE system.

7. A dispatch optimization system for an off-grid wind power AWE system, characterized in that: include: A model building module, used to build an off-grid wind power AWE system simulation model, wherein the off-grid wind power AWE system simulation model includes a wind power generation simulation model and a to-be-dispatched electrolyzer simulation model; Construct an IES system dispatch framework, introduce a tiered carbon trading mechanism into the IES system dispatch framework, and construct an IES optimization operation model considering the tiered carbon trading mechanism and electric hydrogen production in combination with the off-grid wind power AWE system simulation model; The model solving module is used to solve the IES optimization operation model with the goal of minimizing the total operation cost, and obtain the operation optimization strategy of the off-grid wind power AWE system.

8. 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 a scheduling optimization method for an off-grid wind power AWE system according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: It stores a computer program, and when the computer program is executed by a processor, it implements a scheduling optimization method for an off-grid wind power AWE system as described in any one of claims 1-6.

Images