Electro-hydrogen system optimization regulation and control method and system based on system energy efficiency characteristic modeling, medium, equipment and product

Abstract

The invention discloses an electro-hydrogen system optimization regulation and control method and system based on system energy efficiency characteristic modeling, a medium, equipment and a product in the technical field of power system operation and intelligent control. The method comprises the following steps: according to energy efficiency characteristics of a target electricity hydrogen system, constructing an electricity hydrogen system nonlinear physical energy efficiency model, and according to the electricity hydrogen system nonlinear physical energy efficiency model, constructing an electricity hydrogen system optimization scheduling model taking the minimum daily operation total net cost of the electricity hydrogen system as a target function; and according to the real-time operation data of the electro-hydrogen system, solving the electro-hydrogen system optimization scheduling model by adopting a nonlinear programming algorithm to obtain an optimal power distribution instruction. According to the method, the energy efficiency characteristics of the electrolytic cell in the medium-low load interval can be accurately identified, the energy estimation deviation caused by a traditional fixed efficiency model is effectively avoided, and the economic benefit of a wind-hydrogen system is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system operation and intelligent control, in particular to an electrolytic hydrogen system optimization control method, system, medium, equipment and product based on system energy efficiency characteristic modeling. BACKGROUND

[0002] With the increase of renewable energy penetration, using electrolytic water to produce hydrogen to accommodate fluctuating wind power has become an important trend. In the optimization scheduling of the electrolytic hydrogen coupling system, the electrolytic cell is the core equipment for electric-hydrogen energy conversion, and the accuracy of its energy efficiency model directly determines the economy and rationality of the scheduling strategy.

[0003] The existing electrolytic cell modeling and scheduling strategy control usually assumes that the hydrogen production efficiency of the electrolytic cell is constant, however, the actual physical characteristics show that the operating efficiency of the electrolytic cell presents significant nonlinear characteristics with the change of the load rate. In the traditional efficiency model modeling process, only the polarization curve of the stack itself is considered, and the fixed basic operating power consumption of auxiliary systems including water pumps, thermal management systems, power electronic converters and the like is ignored. At low load rates, due to the sharp rise of the fixed power consumption ratio, the system-level efficiency will be seriously attenuated.

[0004] Due to the lack of a detailed energy efficiency model, the existing scheduling strategy tends to run the electrolytic cell at full power to accommodate wind power, while ignoring the efficiency decline caused by the large ohmic heat loss of the stack at full load. Since the fixed efficiency model cannot reflect the real energy efficiency change of the PEMEL system at different load rates, the scheduling strategy cannot locate the optimal energy efficiency working point, resulting in high system operation cost. SUMMARY

[0005] The present application aims to overcome the problem that the existing fixed efficiency model cannot reflect the real energy efficiency change of the PEMEL system at different load rates, resulting in the scheduling strategy being unable to locate the optimal energy efficiency working point, and causing high system operation cost, and provides an electrolytic hydrogen system optimization control method, system, medium, equipment and product based on system energy efficiency characteristic modeling, which designs a system-level electrolytic hydrogen system nonlinear physical energy efficiency model that can accurately describe the coupling of stack characteristics and auxiliary characteristics, and applies it to optimization scheduling to determine the power distribution point with the highest marginal revenue, output the coal-fired unit output and electrolytic cell input power instruction in each period, realize energy saving and efficiency improvement of the electrolytic hydrogen system, and reduce the operation cost.

[0006] To solve the above technical problems, the present application is implemented by using the following technical solutions:

[0007] In a first aspect, the present application provides an electrolytic hydrogen system optimization control method based on system energy efficiency characteristic modeling, comprising:

[0008] According to the energy efficiency characteristics of the target electric hydrogen system, a nonlinear physical energy efficiency model of the electric hydrogen system is constructed;

[0009] According to the nonlinear physical energy efficiency model of the electric hydrogen system, an electric hydrogen system optimization scheduling model with the minimum total net cost of daily operation of the electric hydrogen system as an objective function is constructed;

[0010] According to real-time operation data of the electric hydrogen system, a nonlinear programming algorithm is used to solve the electric hydrogen system optimization scheduling model to obtain optimal power distribution instructions.

[0011] Optionally, the nonlinear physical energy efficiency model of the electric hydrogen system includes a stack efficiency model, an auxiliary machine efficiency model, and a system energy efficiency characteristic curve.

[0012] The nonlinear physical energy efficiency model of the electric hydrogen system is constructed according to the energy efficiency characteristics of the target electric hydrogen system, including:

[0013] The stack efficiency in the target electric hydrogen system is modeled as a linear decay function of the load rate to obtain a stack efficiency model.

[0014] The effective utilization rate of the auxiliary machine in the target electric hydrogen system is set, and the load-independent auxiliary machine basic power consumption is quantified to obtain an auxiliary machine efficiency model.

[0015] According to the coupling relationship between the stack efficiency and the auxiliary machine efficiency, the stack efficiency model and the auxiliary machine efficiency model are solved to obtain a system energy efficiency characteristic curve.

[0016] Optionally, the expression of the stack efficiency model is as follows:

[0017] ,

[0018] wherein, η (r) represents the stack efficiency when the electrolyzer load rate is r, η0 represents the theoretical efficiency of the stack, k represents the load loss coefficient, and r represents the electrolyzer load rate.

[0019] The expression of the auxiliary machine efficiency model is as follows:

[0020] ,

[0021] wherein, η (r) represents the auxiliary machine efficiency when the electrolyzer load rate is r, P (r) represents the real-time input total power of the electrolyzer system, P0 represents the fixed operation power consumption of the auxiliary machine system, and γ represents the auxiliary machine fixed power consumption coefficient.

[0022] Optionally, the expression of the system energy efficiency characteristic curve is as follows:

[0023] ,

[0024] wherein, represents the system energy efficiency when the electrolyzer load rate is r, represents the theoretical efficiency of the stack, k represents the load loss coefficient, r represents the electrolyzer load rate, and γ represents the auxiliary fixed power consumption coefficient, represents the stack efficiency when the electrolyzer load rate is r, represents the auxiliary efficiency when the electrolyzer load rate is r.

[0025] Optionally, the objective function of the optimized scheduling model of the electro-hydrogen system is as follows:

[0026] ,

[0027] wherein, min represents a minimization function, J represents the total net cost of daily operation of the electro-hydrogen system, and T represents the time period of the optimized scheduling, represents the coal consumption cost at t, represents the investment cost, represents the hydrogen sales revenue;

[0028] The hydrogen sales revenue is obtained by the following formula:

[0029] ,

[0030] wherein, represents the total real-time input power of the electrolyzer system at t, represents the theoretical efficiency of the stack, and k represents the load loss coefficient, is the rated power of the electrolyzer system, and γ represents the auxiliary fixed power consumption coefficient, represents the hydrogen sales price.

[0031] Optionally, the optimized scheduling model of the electro-hydrogen system follows the power balance constraint, the upper and lower limits of the output of the coal-fired unit and the ramp rate constraint, the electrolyzer operation constraint, and the abandoned wind power constraint.

[0032] The expression of the power balance constraint is:

[0033] ,

[0034] wherein, represents the output of the gth coal-fired unit at t, represents the predicted available power of the wind farm at t, represents the abandoned wind power at t, represents the conventional electrical load demand at t, represents the actual power consumption of the electrolyzer system at t;

[0035] The expression of the upper and lower limits and the ramp rate constraint of the coal-fired unit is:

[0036] ,

[0037] wherein, represents the lower limit of the output of the gth coal-fired unit, represents the upper limit of the output of the gth coal-fired unit, represents the output of the gth coal-fired unit at t-1, is the upward ramp rate limit of the coal-fired unit, represents the maximum downward ramp power of the coal-fired unit, represents the maximum upward ramp power of the coal-fired unit;

[0038] The expression of the electrolytic tank operation constraint is:

[0039] ,

[0040] wherein, represents the maximum power that can be output by the electrolytic tank, represents the minimum power that can be output by the electrolytic tank;

[0041] The expression of the abandoned wind amount constraint is:

[0042] .

[0043] In a second aspect, the present application provides an electric-hydrogen system optimization and regulation system based on system energy efficiency characteristic modeling, which is used to realize the electric-hydrogen system optimization and regulation method based on system energy efficiency characteristic modeling in any one of the first aspect, and comprises:

[0044] An electric-hydrogen system nonlinear physical energy efficiency model construction module is configured to: construct an electric-hydrogen system nonlinear physical energy efficiency model according to the energy efficiency characteristics of a target electric-hydrogen system;

[0045] An electric-hydrogen system optimization scheduling model construction module is configured to: construct an electric-hydrogen system optimization scheduling model with the minimum total net cost of daily operation of the electric-hydrogen system as an objective function according to the electric-hydrogen system nonlinear physical energy efficiency model;

[0046] An electric-hydrogen system optimization scheduling module is configured to: according to real-time operation data of the electric-hydrogen system, solve the electric-hydrogen system optimization scheduling model by using a nonlinear programming algorithm to obtain optimal power distribution instructions.

[0047] In a third aspect, the present application provides a computer readable storage medium having computer instructions stored thereon, and the computer instructions are executed by a processor to realize the steps of the electric-hydrogen system optimization and regulation method based on system energy efficiency characteristic modeling in the first aspect.

[0048] Fourthly, the present invention provides a computer device, comprising: a memory for storing computer instructions; and a processor for executing the computer instructions to implement the steps of the method for optimizing and controlling an electric hydrogen system based on system energy efficiency characteristics modeling as described in the first aspect.

[0049] Fifthly, the present invention provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method for optimizing and controlling an electric hydrogen system based on system energy efficiency characteristic modeling as described in the first aspect.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0051] 1. By constructing a nonlinear physical energy efficiency model for the electric hydrogen system that can accurately describe the coupling between the characteristics of the fuel cell stack and the auxiliary equipment, not only is the heat loss of the fuel cell stack under high load considered, but also the efficiency drop caused by the fixed energy consumption of the auxiliary equipment under low load is accurately characterized, thus correcting the deviation of the traditional constant efficiency model.

[0052] 2. By introducing nonlinear efficiency characteristics into the objective function of the optimization scheduling model of the electro-hydrogen system, the optimization algorithm can automatically identify and utilize the high-efficiency zone of the electrolyzer, avoiding energy waste caused by blindly operating at full load, thereby significantly reducing system operating costs in multi-condition scenarios with fluctuating wind power.

[0053] 3. An overall technical solution for optimizing and controlling the electro-hydrogen system based on system energy efficiency characteristic modeling was designed. It can accurately identify the energy efficiency characteristics of the electrolyzer in the medium and low load range, effectively avoid the energy estimation deviation caused by the traditional fixed efficiency model, and significantly improve the economic benefits of the wind-hydrogen system. Attached Figure Description

[0054] Figure 1 The flowchart is provided below for an optimized control method of an electric hydrogen system based on system energy efficiency characteristic modeling according to an embodiment of the present invention.

[0055] Figure 2 This is a schematic diagram of a simulation model of an electro-hydrogen system provided according to an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of a typical daily simulation result without an electrolytic hydrogen production unit, provided according to an embodiment of the present invention.

[0057] Figure 4 A schematic diagram of the simulation results of the optimized control method of the hydrogen-electric system that uses only surplus wind energy to produce hydrogen under a typical day, according to an embodiment of the present invention.

[0058] Figure 5 This is a schematic diagram of the simulation results of the optimized control method for an electric hydrogen system based on system energy efficiency characteristic modeling provided in an embodiment of the present invention under a typical day. Detailed Implementation

[0059] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0060] It should be noted that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0061] Example 1:

[0062] This invention discloses an optimization and control method for an electro-hydrogen system based on system energy efficiency characteristic modeling, with reference to... Figure 1 As shown, the specific steps include the following:

[0063] Based on the energy efficiency characteristics of the target hydrogen-electric system, a nonlinear physical energy efficiency model of the hydrogen-electric system is constructed.

[0064] Based on the nonlinear physical energy efficiency model of the electric hydrogen system, an optimal scheduling model for the electric hydrogen system is constructed with the objective function of minimizing the total net daily operating cost of the electric hydrogen system.

[0065] Based on the real-time operating data of the electric hydrogen system, a nonlinear programming algorithm is used to solve the optimal scheduling model of the electric hydrogen system to obtain the optimal power allocation command.

[0066] In renewable energy-driven hydrogen production scenarios, electrolyzers often operate under variable loads for extended periods. This is because the polarization loss mechanisms of the stack differ under high and low current densities, and auxiliary equipment consumes power regardless of the load. Therefore, in this embodiment, taking a proton exchange membrane electrolyzer (PEMEL) as an example, a nonlinear physical energy efficiency model of the electrohydrogen system, including the stack-auxiliary equipment-system coupling, is constructed to obtain system energy efficiency characteristic curves that can be used for scheduling optimization, energy management, and economic evaluation.

[0067] In step S1, the nonlinear physical energy efficiency model of the electro-hydrogen system includes the stack efficiency model, the auxiliary equipment efficiency model, and the system energy efficiency characteristic curve. According to this embodiment, the stack terminal voltage increases with increasing current density, which originates from the superposition of reversible voltage and various polarizations. Specifically, within the typical operating range of the PEMEL electrolyzer, ohmic polarization loss typically increases approximately monotonically with current density. Modeling the stack efficiency as a linear decay function of the load rate yields the following stack efficiency model:

[0068] ,

[0069] in, This represents the stack efficiency when the electrolytic cell load is r. denoted by , where k represents the theoretical efficiency of the fuel cell stack, r represents the load loss coefficient, and r represents the electrolytic cell load rate.

[0070] The electrolytic cell system includes auxiliary equipment such as circulating pumps, cooling units, gas handling, sensing, and control systems. A significant portion of the power consumption in this type of equipment exhibits weak load dependence or near-constant characteristics during operation. Therefore, this embodiment defines the effective utilization rate of auxiliary equipment and quantifies the fixed base power consumption of the auxiliary equipment system, independent of the load, to obtain an auxiliary equipment efficiency model:

[0071] ,

[0072] in, This represents the auxiliary machine efficiency when the electrolytic cell load rate is r. This indicates the real-time total input power of the electrolytic cell system. γ represents the fixed operating power consumption of the auxiliary system, and γ represents the fixed power consumption coefficient of the auxiliary system.

[0073] The energy efficiency of an electric hydrogen system is jointly determined by the efficiency decay caused by stack polarization losses and the decrease in effective utilization rate due to the fixed power consumption of auxiliary equipment. The coupling relationship between stack efficiency and auxiliary equipment efficiency is defined as follows:

[0074] ,

[0075] in, This indicates the system energy efficiency when the electrolytic cell load is r.

[0076] Based on the coupling relationship between the fuel cell stack efficiency and the auxiliary equipment efficiency, the system energy efficiency characteristic curve is obtained by combining the fuel cell stack efficiency model and the auxiliary equipment efficiency model:

[0077] ,

[0078] in, γ represents the theoretical efficiency of the fuel cell stack, k represents the load loss coefficient, r represents the electrolytic cell load rate, and γ represents the auxiliary equipment fixed power consumption coefficient. This represents the stack efficiency when the electrolytic cell load is r. This represents the auxiliary machine efficiency when the electrolytic cell load rate is r.

[0079] In step S2, the objective function J in this embodiment aims to minimize the total net daily operating cost of the electric hydrogen system within the scheduling period T; in the electric hydrogen system optimization scheduling model, the expression of the objective function is:

[0080] ,

[0081] Where min represents the minimization function, J represents the total daily net operating cost of the hydrogen-electric system, and T represents the time period for optimal scheduling. This represents the coal consumption cost at time t. Indicates investment cost, The revenue from hydrogen sales is represented by the following formula:

[0082] ,

[0083] in, This represents the real-time total input power of the electrolytic cell system at time t. This represents the theoretical efficiency of the fuel cell stack, and k represents the load loss coefficient. The rated power of the electrolytic cell system is given by γ, which represents the fixed power consumption coefficient of the auxiliary equipment. This indicates the price of hydrogen.

[0084] This embodiment also sets operational constraints for the optimized scheduling model of the hydrogen-electric system, including power balance constraints, upper and lower limits of coal-fired unit output and ramp rate constraints, electrolyzer operation constraints, and curtailment constraints.

[0085] The expression for the power balance constraint is:

[0086] ,

[0087] in, This represents the output of the g-th coal-fired unit at time t. This represents the predicted available power of the wind farm at time t. This represents the wind power curtailment at time t, i.e., the wind power forgone to balance the hydrogen-electricity system. This represents the normal electrical load demand at time t. This represents the actual power consumption of the electrolytic cell system at time t;

[0088] The expressions for the upper and lower limits of the output and the gradient rate constraints of the coal-fired unit are as follows:

[0089] ,

[0090] in, This represents the lower limit of the output of the g-th coal-fired unit. This represents the upper limit of the output of the g-th coal-fired unit. This represents the output of the g-th coal-fired unit at time t-1. To limit the upward ramp rate of coal-fired power units. This indicates the maximum downward ramp power of the coal-fired power unit. This indicates the maximum upward climbing power of the coal-fired power unit;

[0091] The expression for the electrolytic cell operating constraints is:

[0092] ,

[0093] in, This indicates the maximum power that the electrolytic cell can output. This indicates the minimum power that the electrolytic cell can output;

[0094] The expression for the air curtailment constraint is:

[0095] .

[0096] In step S3, due to the introduction of the variable efficiency function, the objective function of the electric hydrogen system optimization scheduling model is non-linear. Therefore, this embodiment uses the non-linear programming algorithm SQP (Sequential Quadratic Programming) as the core solver to solve the constructed electric hydrogen system optimization scheduling model, find the power allocation point with the highest marginal benefit, and output the output power of the coal-fired unit and the input power of the electrolyzer for each time period. This algorithm has good convergence in handling small-to-medium-scale optimization problems with non-linear constraints.

[0097] This embodiment further verifies the effectiveness of the proposed method for optimizing the scheduling of an electric hydrogen system through simulation examples; the electric hydrogen system simulation model used in MATLAB is as follows: Figure 2 As shown in the figure, G1, G2, and G3 represent three units of different capacity levels; the simulation scheduling cycle is set to 24 hours to simulate the complete operating conditions of a typical day. The rated power of the PEMEL electrolyzer system is set to 60 MW as the benchmark for normalized load rate calculation. Meanwhile, the lower calorific value of hydrogen is set to 33.3 kWh / kg and the price of hydrogen is set to 30 yuan / kg. These two parameters together constitute the core calculation basis for hydrogen production revenue, directly determining when the electrolyzer should be started and at what power level it is economically viable.

[0098] To simulate the real power supply structure, constraint sets were defined for three units of different capacity levels (G1, G2, and G3) based on the operating characteristics of coal-fired power units. These constraints include upper and lower output limits defined as 5 / 40 MW, 20 / 100 MW, and 50 / 250 MW; ramp rates set as 10 MW / h, 25 MW / h, and 60 MW / h; and coal consumption cost coefficients (…). ), where the coefficients of the quadratic term a, b, and c are set to 0.005, 0.003, and 0.001, respectively.

[0099] PEMEL's nonlinear physical energy efficiency parameters are precisely defined to drive variable efficiency optimization. The program characterizes the conversion capability under ideal low current using a stack baseline efficiency of 0.7, and introduces an ohmic loss factor of 0.15 to describe the linear efficiency decay caused by internal resistance heating as the load increases. Simultaneously, a fixed auxiliary power consumption percentage of 0.035 is set to characterize the basic energy consumption of the water pump and thermal management system. Regarding scenario inputs and decision variables, the simulation uses a typical bi-peak electrical load curve as a rigid requirement and configures a 220 MW wind power installed capacity. Based on this, the optimization solver automatically seeks the scheduling strategy with the lowest system operating cost while satisfying power balance constraints, using coal-fired power output.

[0100] Simulation results are as follows Figure 3 , Figure 4 and Figure 5 As shown in the figure, the operational characteristics of this solution in active optimization mode are intuitively demonstrated.

[0101] During the early morning (01:00-07:00) and nighttime (21:00-24:00) periods, wind power resources are extremely abundant, and the system exhibits a "full load at night" characteristic. At this time, the electrolyzers operate at full power to maximize the absorption of curtailed wind power. Although the hydrogen production efficiency decreases slightly due to the high load rate, the total hydrogen production is still considerable due to the abundant electricity supply.

[0102] In contrast, during the daytime period from 08:00 to 18:00, this scheme demonstrated a unique proactive optimization capability. Although wind power capacity is less abundant during this time than at night, the optimization algorithm accurately identified the high-efficiency range under partial load. Instead of shutting down the electrolyzer directly as in traditional schemes, it was controlled to operate at half load (approximately 30-40 MW). At this time, the system avoided the high ohmic losses at full load and the high auxiliary power consumption ratio at low load, operating in the range with the highest overall energy efficiency, maximizing hydrogen production per unit of electricity consumption.

[0103] In summary, the electric hydrogen system optimization and control method based on system energy efficiency characteristic modeling proposed in this embodiment not only effectively utilizes inexpensive renewable energy at night, but also accurately locates the optimal operating point during the day through variable efficiency active scheduling, significantly improving the system's economy and energy utilization rate throughout the entire cycle.

[0104] Example 2:

[0105] Based on the same inventive concept as Embodiment 1, this embodiment of the invention discloses an optimization and control system for an electro-hydrogen system based on system energy efficiency characteristic modeling, comprising:

[0106] The module for constructing a nonlinear physical energy efficiency model of an electric hydrogen system is used to: construct a nonlinear physical energy efficiency model of the electric hydrogen system based on the energy efficiency characteristics of the target electric hydrogen system;

[0107] The module for constructing an optimal scheduling model for an electric hydrogen system is used to: construct an optimal scheduling model for an electric hydrogen system with the objective function of minimizing the total net daily operating cost of the electric hydrogen system, based on the nonlinear physical energy efficiency model of the electric hydrogen system.

[0108] The electric hydrogen system optimization scheduling module is used to: solve the electric hydrogen system optimization scheduling model using a nonlinear programming algorithm based on the real-time operating data of the electric hydrogen system, and obtain the optimal power allocation command.

[0109] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0110] Example 3:

[0111] This embodiment provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the steps of the method for optimizing and controlling an electric hydrogen system based on system energy efficiency characteristic modeling as described in Embodiment 1.

[0112] Example 4:

[0113] This embodiment provides a computer device, including:

[0114] Memory, used to store computer instructions;

[0115] A processor is used to execute the computer instructions to implement the steps of the method for optimizing and controlling an electric hydrogen system based on system energy efficiency characteristics modeling as described in Embodiment 1.

[0116] Example 5:

[0117] This embodiment provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the optimized control method for an electric hydrogen system based on system energy efficiency characteristic modeling as described in any one of Embodiments 1 and 2.

[0118] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for optimizing and controlling an electrohydrogen system based on system energy efficiency characteristic modeling, characterized in that, include: Based on the energy efficiency characteristics of the target hydrogen-electric system, a nonlinear physical energy efficiency model of the hydrogen-electric system is constructed. Based on the nonlinear physical energy efficiency model of the electric hydrogen system, an optimal scheduling model for the electric hydrogen system is constructed with the objective function of minimizing the total net daily operating cost of the electric hydrogen system. Based on the real-time operating data of the electric hydrogen system, a nonlinear programming algorithm is used to solve the optimal scheduling model of the electric hydrogen system to obtain the optimal power allocation command.

2. The method for optimizing and controlling an electro-hydrogen system based on system energy efficiency characteristic modeling according to claim 1, characterized in that, The nonlinear physical energy efficiency model of the electric hydrogen system includes a stack efficiency model, an auxiliary equipment efficiency model, and system energy efficiency characteristic curves. The construction of a nonlinear physical energy efficiency model for the target hydrogen-electric system, based on its energy efficiency characteristics, includes: The stack efficiency in the target hydrogen-electric system is modeled as a linear decay function of the load rate, resulting in the stack efficiency model. The effective utilization rate of auxiliary equipment in the target hydrogen-electric system is set, and the basic power consumption of auxiliary equipment independent of load is quantified to obtain the auxiliary equipment efficiency model. Based on the coupling relationship between fuel cell stack efficiency and auxiliary machine efficiency, the fuel cell stack efficiency model and the auxiliary machine efficiency model are combined to obtain the system energy efficiency characteristic curve.

3. The method for optimizing and controlling an electro-hydrogen system based on system energy efficiency characteristic modeling according to claim 2, characterized in that, The expression for the stack efficiency model is as follows: ， in, This represents the stack efficiency when the electrolytic cell load is r. denoted by , where k represents the load loss coefficient and r represents the electrolytic cell load rate; The expression for the auxiliary machine efficiency model is as follows: ， in, P represents the auxiliary machine efficiency when the electrolytic cell load is r. in P represents the real-time total input power of the electrolytic cell system. fix γ represents the fixed operating power consumption of the auxiliary system, and γ represents the fixed power consumption coefficient of the auxiliary system.

4. The method for optimizing and controlling an electro-hydrogen system based on system energy efficiency characteristic modeling according to claim 2, characterized in that, The expression for the system's energy efficiency characteristic curve is as follows: ， in, This indicates the system energy efficiency when the electrolytic cell load is r. γ represents the theoretical efficiency of the fuel cell stack, k represents the load loss coefficient, r represents the electrolytic cell load rate, and γ represents the auxiliary equipment fixed power consumption coefficient. This represents the stack efficiency when the electrolytic cell load is r. This represents the auxiliary machine efficiency when the electrolytic cell load rate is r.

5. The method for optimizing and controlling an electro-hydrogen system based on system energy efficiency characteristic modeling according to claim 1, characterized in that, The objective function of the optimized scheduling model for the hydrogen-electric system is as follows: ， Where min represents the minimization function, J represents the total daily net operating cost of the hydrogen-electric system, and T represents the time period for optimal scheduling. This represents the coal consumption cost at time t. Indicates investment cost, This indicates revenue from hydrogen sales; The revenue from hydrogen sales is obtained using the following formula: ， in, This represents the real-time total input power of the electrolytic cell system at time t. This represents the theoretical efficiency of the fuel cell stack, and k represents the load loss coefficient. The rated power of the electrolytic cell system is given by γ, which represents the fixed power consumption coefficient of the auxiliary equipment. This indicates the price of hydrogen.

6. The method for optimizing and controlling an electro-hydrogen system based on system energy efficiency characteristic modeling according to claim 5, characterized in that, The optimized scheduling model of the electric hydrogen system follows the constraints of power balance, the upper and lower limits of output of coal-fired units and the ramp rate, the operation constraints of electrolyzers and the curtailment of air volume. The expression for the power balance constraint is: ， in, This represents the output of the g-th coal-fired unit at time t. This represents the predicted available power of the wind farm at time t. This represents the wind curtailment power at time t. This represents the normal electrical load demand at time t. This represents the actual power consumption of the electrolytic cell system at time t; The expressions for the upper and lower limits of the output and the gradient rate constraints of the coal-fired unit are as follows: ， in, This represents the lower limit of the output of the g-th coal-fired unit. This represents the upper limit of the output of the g-th coal-fired unit. This represents the output of the g-th coal-fired unit at time t-1. To limit the upward ramp rate of coal-fired power units. This indicates the maximum downward ramp power of the coal-fired power unit. This indicates the maximum upward climbing power of the coal-fired power unit; The expression for the electrolytic cell operating constraints is: ， in, This indicates the maximum power that the electrolytic cell can output. This indicates the minimum power that the electrolytic cell can output; The expression for the air curtailment constraint is: 。 7. An optimized control system for an electro-hydrogen system based on system energy efficiency characteristic modeling, characterized in that, include: The module for constructing a nonlinear physical energy efficiency model of an electric hydrogen system is used to: construct a nonlinear physical energy efficiency model of the electric hydrogen system based on the energy efficiency characteristics of the target electric hydrogen system; The module for constructing an optimal scheduling model for an electric hydrogen system is used to: construct an optimal scheduling model for an electric hydrogen system with the objective function of minimizing the total net daily operating cost of the electric hydrogen system, based on the nonlinear physical energy efficiency model of the electric hydrogen system. The electric hydrogen system optimization scheduling module is used to: solve the electric hydrogen system optimization scheduling model using a nonlinear programming algorithm based on the real-time operating data of the electric hydrogen system, and obtain the optimal power allocation command.

8. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instruction is executed by the processor, it implements the steps of the method for optimizing and controlling an electric hydrogen system based on system energy efficiency characteristics modeling as described in any one of claims 1-6.

9. A computer device, characterized in that, include: Memory, used to store computer instructions; A processor for executing the computer instructions to implement the steps of the method for optimizing and controlling an electric hydrogen system based on system energy efficiency characteristics modeling as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that, When the computer instruction is executed by the processor, it implements the steps of the method for optimizing and controlling an electric hydrogen system based on system energy efficiency characteristic modeling as described in any one of claims 1-6.

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