Power grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consumption park and related device

Abstract

The invention discloses a power grid collaborative optimization configuration method for extended hydrogen production load in a high-energy-consumption park, and belongs to the technical field of energy storage and power generation, and the method comprises the following steps: obtaining photovoltaic prediction output and load prediction data, and based on an extended hydrogen production load model in an industrial park, obtaining a power grid model; optimal scheduling is carried out by taking the minimum economic cost as a target to obtain the minimum economic cost, and then optimal scheduling is carried out by taking the optimal power grid frequency as a target to obtain the economic cost when the power grid frequency index is optimal; and setting a multi-objective optimization constraint condition, and according to the minimum economic cost and the economic cost when the power grid frequency index is optimal, performing optimization scheduling by taking the optimal power grid frequency as a target to obtain an optimization configuration result of the power grid frequency index and the economic cost. According to the invention, the problem that the current industrial park does not take the hydrogen production load as an independent adjustable load for modeling and scheduling and lacks deep coupling with the power grid frequency adjustment demand can be solved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage and power generation technology, specifically relating to a grid collaborative optimization configuration method and related devices for expanding hydrogen production load in high-energy-consuming industrial parks. Background Technology

[0002] The clean transformation of industry has become an important part of the current energy strategy. High-energy-consuming industrial parks often concentrate large amounts of electricity load and are highly dependent on the power grid under traditional production models. In recent years, the scale of renewable energy (such as photovoltaic and wind power) connected to the grid in these parks has been continuously expanding, but their intermittent and fluctuating characteristics have increased the difficulty of peak shaving and frequency regulation in the power system. To mitigate the fluctuation risks brought about by renewable energy grid connection, industrial parks typically deploy large-capacity energy storage systems and, through the concept of "park-grid coordinated dispatch," achieve the classification and orderly management of loads within the park.

[0003] On the other hand, with the maturity and widespread application of hydrogen energy technology, hydrogen production through water electrolysis has become an important pathway for "carbon emission reduction + energy storage" within industrial parks. Compared to traditional high-temperature cracking hydrogen production and power plant waste heat hydrogen production, water electrolysis hydrogen production has advantages such as less pollution and direct coupling with renewable energy. However, water electrolysis hydrogen production is a high-power, long-term operating load, and its start-up, shutdown, and operating power are highly dynamic and flexible. How to integrate "extended hydrogen production" as one of the dispatchable loads in the industrial park and combine it in an orderly manner with renewable energy power generation, energy storage systems, and grid frequency regulation needs has become an important research direction for the "clean and adaptive" operation of the next generation of high-energy-consuming industrial parks. Existing "industrial park-grid coordinated optimization" technologies mainly focus on the following two aspects: First, grid-connected dispatch of renewable energy and energy storage: Based on intraday or real-time market prices, load forecasts, and renewable power generation forecasts, mixed integer programming, two-stage stochastic or robust optimization methods are used to determine energy storage charging and discharging strategies to smooth out renewable output fluctuations and reduce operating costs. Secondly, tiered load management and orderly electricity use: Based on the interruptibility, time-shiftability and non-interruptionability of industrial loads, the loads within the park are divided into different levels, and the power grid frequency deviation or electricity price signal is responded to through load switching, peak shaving and valley filling, etc., to optimize the total energy consumption of the park under the premise of meeting production constraints.

[0004] While existing "industrial park-grid coordinated optimization" methods have been extensively studied for the coordinated scheduling of industrial loads, renewable energy, and energy storage systems, significant shortcomings remain in providing a systematic solution for "expanding hydrogen production load in high-energy-consuming industrial parks." These shortcomings are primarily manifested in the following ways: First, hydrogen production load is not modeled and scheduled as an independent, adjustable load: The impact of hydrogen production load on the overall load characteristics of the industrial park is ignored. This results in the inability to flexibly adjust hydrogen production power through intelligent scheduling when renewable power generation output fluctuates drastically or the grid frequency is abnormal, thus missing the potential to utilize hydrogen production load for "demand-side peak shaving and valley filling, and auxiliary frequency regulation." Second, there is a lack of deep coupling with grid frequency regulation requirements: Existing technologies mainly focus on the dual-objective trade-offs between the industrial park and the grid in terms of economic indicators (such as electricity costs and energy storage investment recovery) and capacity indicators (such as peak-valley difference reduction and optimal energy storage capacity), but fail to integrate "grid primary / secondary frequency regulation requirements" with "dynamic scheduling of hydrogen production load" in a unified design. Hydrogen production units are flexible enough to significantly increase or decrease load in a short period of time. However, if they are not directly linked to the grid frequency deviation signal, it is difficult to fully realize their auxiliary value for grid frequency regulation, resulting in high frequency regulation costs. At the same time, the park side cannot respond in time when the grid is under low frequency alarm, which affects the frequency stability of the overall power system. Summary of the Invention

[0005] The purpose of this invention is to provide a grid-coordinated optimization configuration method and related apparatus for expanding hydrogen production load in high-energy-consuming industrial parks, in order to solve the problem that current industrial parks do not model and schedule hydrogen production load as an independent adjustable load, and lack deep coupling with grid frequency regulation requirements.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Firstly, a method for grid-based coordinated optimization of hydrogen production load expansion in high-energy-consuming industrial parks includes the following steps:

[0008] We acquire photovoltaic power output and load forecast data, and based on the extended hydrogen production load model of the industrial park, we optimize the scheduling with the goal of minimizing economic cost to obtain the minimum economic cost. Then, we optimize the scheduling with the goal of the optimal grid frequency to obtain the economic cost when the grid frequency index is optimal.

[0009] Multi-objective optimization constraints are set, and based on the minimum economic cost and the economic cost when the power grid frequency index is optimal, the optimal power grid frequency is used as the objective for optimal scheduling, so as to obtain the optimal configuration result of the power grid frequency index and economic cost.

[0010] In some embodiments, the industrial park extended hydrogen production load model includes an electrolyzer model, a hydrogen storage tank model, and a battery model;

[0011] The electrolytic cell model is specifically as follows:

[0012] P elmin ≤P el (t)≤P elmax

[0013] |P el (t)-P el (t-1)|≤ΔP elmax

[0014]

[0015] Among them, P el (t) represents the operating power of the electrolytic cell in the mathematical model at time t, P elmin P represents the minimum operating power of the electrolyzer's mathematical model. elmax ΔP represents the maximum operating power of the electrolyzer's mathematical model. elmax s(t) represents the maximum allowable ramp power during normal operation of the electrolytic cell mathematical model, and t represents the 0-1 variable of the electrolytic cell's operating state. on This represents the shortest continuous operating time of the electrolytic cell, t. off Indicates the shortest continuous downtime of the electrolytic cell;

[0016] The hydrogen storage tank model is specifically as follows:

[0017]

[0018] Among them, W tank (t) represents the amount of hydrogen stored in the hydrogen storage tank at time t. Let be the filling rate of the hydrogen storage tank mathematical model at time t. Let s be the gas delivery rate of the hydrogen storage tank mathematical model at time t. in (t) represents a 0-1 variable representing the hydrogen storage tank's charging state, s out (t) represents a 0-1 variable representing the gas supply status of the hydrogen storage tank. This is the maximum rate at which gas enters the hydrogen storage tank. The maximum rate at which gas is supplied to the hydrogen storage tank;

[0019] The battery model is as follows:

[0020]

[0021]

[0022] δ SOCmin ≤δ SOC (t)≤δ SOCmax

[0023] in, Let be the charging power of the battery mathematical model at time t. Let s be the discharge power of the battery mathematical model at time t. ch (t) represents a 0-1 variable representing the battery's charging state, s dis (t) represents a 0-1 variable representing the battery's discharge state. This indicates the maximum charging power of the battery. This indicates the maximum discharge power of the battery. This indicates the charging efficiency of the battery. W represents the discharge efficiency of a storage battery. bat The value represents the battery capacity, Δt represents the sampling time interval, and δ represents the battery capacity. SOCmin δ represents the minimum state of charge during the operation of the battery mathematical model. SOCmax δ represents the maximum state of charge during the operation of the battery mathematical model. SOC (t) represents the state of charge of the battery mathematical model at time t.

[0024] In some implementations, the industrial park extended hydrogen production load model also includes an upstream power grid connection point model;

[0025] The model for the upstream power grid access point is as follows:

[0026] 0≤P grid (t)≤P gridmax

[0027] Among them, P grid (t) represents the power purchased by the industrial park from the grid at time t, P gridmax This refers to the maximum power that can be purchased from the power grid as stipulated by policy.

[0028] In some implementations, the step of optimizing scheduling with the goal of minimizing economic costs employs an economic cost objective function, specifically the following formula:

[0029] F1=min(f invest +f op +f sta +f grid -f sell )

[0030]

[0031]

[0032] Where F1 is the objective function value of economic cost, F invest f is the average daily amortization cost of the industrial park construction. op f represents the operating and maintenance costs of equipment during normal operation of the industrial park. sta f represents the start-up and shutdown costs of equipment during normal operation of the industrial park. gridf represents the cost of purchasing electricity from the grid for the industrial park. sell For the revenue generated from selling hydrogen in the industrial park, C pv C represents the operation and maintenance cost per unit power of a photovoltaic power generation system. bat For the maintenance cost per unit power of the battery, C el Let f be the maintenance cost per unit power of the electrolytic cell, el(t) be a 0-1 variable representing whether the electrolytic cell is started or stopped during the current time period, and f be the operating cost per unit power of the electrolytic cell. elsta For the cost of starting and stopping an electrolytic cell once, C price (t) represents the electricity price during this period. The price per unit volume of hydrogen. This represents the amount of hydrogen sold during the current time period.

[0033] In some implementations, the step of optimizing scheduling with the optimal grid frequency as the objective uses a grid frequency objective function, specifically the following formula:

[0034]

[0035] Where F2 is the target function value of the power grid frequency, P grid (t) represents the power purchased by the industrial park from the grid at time t, P gridplan (t) represents the expected power purchase capacity set by the power grid based on frequency regulation requirements.

[0036] In some implementations, the multi-objective optimization constraints are:

[0037] F1≤(1-λ)F 1min +λF 1max

[0038] Where F1 is the objective function value of economic cost, F 1min To minimize economic cost, F 1max λ represents the economic cost when the power grid frequency index is optimal, and λ is the weighting coefficient.

[0039] Secondly, a grid-coordinated optimization configuration system for expanding hydrogen production load in high-energy-consuming industrial parks includes:

[0040] The single-objective optimization module is used to acquire photovoltaic power output and load forecast data, and based on the extended hydrogen production load model of the industrial park, optimize scheduling with the goal of minimizing economic cost to obtain the minimum economic cost. Then, optimize scheduling with the goal of optimal grid frequency to obtain the economic cost when the grid frequency index is optimal.

[0041] The multi-objective optimization module is used to set multi-objective optimization constraints, and based on the minimum economic cost and the economic cost when the power grid frequency index is optimal, to perform optimized scheduling with the optimal power grid frequency as the objective, and obtain the optimized configuration result of the power grid frequency index and economic cost.

[0042] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of the grid-coordinated optimization configuration method for expanding hydrogen production load in a high-energy-consuming park.

[0043] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the grid-coordinated optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks.

[0044] Fifthly, a computer program product comprising a computer program, wherein when executed by a processor, the computer program implements the steps of the grid collaborative optimization configuration method for expanding hydrogen production load in a high-energy-consuming industrial park.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This invention is based on an extended hydrogen production load model for industrial parks. It optimizes scheduling to minimize economic costs, then optimizes scheduling again to achieve the optimal grid frequency, yielding the economic cost at which the grid frequency index is optimal. Multi-objective optimization constraints are set, and based on the minimum economic cost and the economic cost at which the grid frequency index is optimal, optimization scheduling is performed with the optimal grid frequency as the objective, resulting in an optimized configuration of grid frequency index and economic cost. Therefore, this invention addresses the current problem in industrial parks where hydrogen production load is not modeled and scheduled as an independent, adjustable load, lacking deep coupling with grid frequency regulation requirements.

[0047] Furthermore, the extended hydrogen production load model for the industrial park includes an electrolyzer model, a hydrogen storage tank model, a battery model, and an upstream grid connection point model. Therefore, this invention comprehensively configures an electrolytic hydrogen production device, a lithium battery energy storage unit, and renewable energy power generation equipment in the park's energy system. By establishing an integrated mathematical model covering new energy output, energy storage charging and discharging, and dynamic constraints on hydrogen production load, the joint scheduling and optimal configuration of the three are achieved: when renewable energy sources such as photovoltaic and wind power are abundant, the system can prioritize diverting excess power to the hydrogen production device to absorb it, and can also inject the remaining power into the battery energy storage; when renewable energy output is insufficient or the grid frequency deviates significantly, the lithium battery energy storage system can be quickly put into operation to compensate, while the hydrogen production load is gradually reduced or shut down according to preset rules, releasing power capacity to participate in grid frequency regulation.

[0048] Furthermore, this invention incorporates grid demand targets into the optimization objectives and, in conjunction with electricity price fluctuations, achieves deep synergy with the grid by linking hydrogen production, energy storage, and new energy power generation: during low-frequency alarm phases, priority is given to activating energy storage discharge and hydrogen production to reduce load; during high-frequency surplus phases, renewable power generation is used to increase hydrogen production or charge energy storage, thereby promoting the maximum absorption of renewable energy, reducing energy storage investment costs, and obtaining frequency regulation revenue, effectively improving the economic benefits of the industrial park and the safety of grid operation. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of an industrial park according to an embodiment of the present invention;

[0050] Figure 2 This is a flowchart of the adaptive method proposed in an embodiment of the present invention;

[0051] Figure 3 This is a graph showing the changing trends of economic cost and power grid frequency indicators for different λ values ​​in the adaptive method of this invention.

[0052] Figure 4 The following are the scheduling results of the adaptive method in the embodiments of the present invention under different values ​​of λ: (a) is the scheduling result when λ = 0, (b) is the scheduling result when λ = 0.00001, (c) is the scheduling result when λ = 0.03, (d) is the scheduling result when λ = 0.3, (e) is the scheduling result when λ = 0.7, and (f) is the scheduling result when λ = 1.

[0053] Figure 5 A flowchart illustrating a grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks, provided by an embodiment of the present invention;

[0054] Figure 6 This is a structural diagram of a grid-coordinated optimization configuration system for expanding hydrogen production load in a high-energy-consuming industrial park, provided in an embodiment of the present invention. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. The content described herein is for explanation rather than limitation of the present invention.

[0056] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this invention are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, systems, products, or devices.

[0057] like Figure 5As shown, this embodiment provides a grid-based collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks, including the following steps:

[0058] S1. Obtain photovoltaic power output and load forecast data, and based on the extended hydrogen production load model of the industrial park, optimize scheduling with the goal of minimizing economic cost to obtain the minimum economic cost. Then, optimize scheduling with the goal of optimal grid frequency to obtain the economic cost when the grid frequency index is optimal.

[0059] S2, set multi-objective optimization constraints, and based on the minimum economic cost and the economic cost when the power grid frequency index is optimal, perform optimized scheduling with the optimal power grid frequency as the objective, and obtain the optimized configuration result of the power grid frequency index and economic cost.

[0060] The details are as follows:

[0061] 1. Extended Hydrogen Production Load Model and Constraints in Industrial Parks

[0062] like Figure 1 As shown, the industrial park involved in this embodiment mainly includes a new energy power generation system, an electrolyzer, a hydrogen storage tank, a battery, an electrical load, and a hydrogen load. The extended hydrogen production load model and constraints for the industrial park are established as follows:

[0063] (1) Electrolyzer model: The electrolyzer model needs to satisfy the maximum and minimum power constraints, ramp-up power constraints, and start-up and shutdown time constraints, as follows:

[0064] P elmin ≤P el (t)≤P elmax

[0065] |P el (t)-P el (t-1)|≤ΔP elmax

[0066]

[0067] Among them, P el (t) represents the operating power of the electrolytic cell in the mathematical model at time t, P elmin P represents the minimum operating power of the electrolyzer's mathematical model. elmax ΔP represents the maximum operating power of the electrolyzer's mathematical model. elmax t represents the maximum allowable ramp power during normal operation of the electrolytic cell's mathematical model; s(t) represents a 0-1 variable indicating the electrolytic cell's operating state, where s(t) = 0 indicates the electrolytic cell is in a stopped state, and s(t) = 1 indicates the electrolytic cell is in a running state. on This represents the shortest continuous operating time of the electrolytic cell, t. off This indicates the shortest continuous downtime of the electrolytic cell.

[0068] (2) Mathematical model of hydrogen storage tank

[0069]

[0070] Among them, W tank (t) represents the amount of hydrogen stored in the hydrogen storage tank at time t. Let be the filling rate of the hydrogen storage tank mathematical model at time t. Let s be the gas delivery rate of the hydrogen storage tank mathematical model at time t. in (t) represents a 0-1 variable representing the hydrogen storage tank's charging state, s out (t) represents a 0-1 variable indicating the gas supply status of the hydrogen storage tank, s in (t) and s out (t) cannot both take the value 1 at the same time This is the maximum rate at which gas enters the hydrogen storage tank. The maximum rate at which gas is supplied to the hydrogen storage tank.

[0071] (3) The mathematical model of the battery mainly needs to meet the power constraint and the capacity constraint, as shown in the following formula:

[0072]

[0073] δ SOCmin ≤δ SOC (t)≤δ SOCmax

[0074] in, Let be the charging power of the battery mathematical model at time t. Let s be the discharge power of the battery mathematical model at time t. ch (t) represents a 0-1 variable representing the battery's charging state, s dis (t) represents a 0-1 variable representing the battery's discharge state, s ch (t) and s dis (t) is not simultaneously 1, This indicates the maximum charging power of the battery. This indicates the maximum discharge power of the battery. This indicates the charging efficiency of the battery. W represents the discharge efficiency of a storage battery. bat The value represents the battery capacity, Δt represents the sampling time interval, and δ represents the battery capacity. SOCmin δ represents the minimum state of charge during the operation of the battery mathematical model. SOCmax δ represents the maximum state of charge during the operation of the battery mathematical model. SOC (t) represents the state of charge of the battery mathematical model at time t.

[0075] (4) Upper-level power grid access point model

[0076] 0≤P grid (t)≤P gridmax

[0077] Among them, P grid (t) represents the power purchased by the industrial park from the grid at time t, P gridmax This refers to the maximum power that can be purchased from the power grid as stipulated by policy.

[0078] 2. Objective Function Design

[0079] (1) Economic cost objective function: The objective function can be established with the goal of minimizing the daily operating cost of the industrial park, as shown in the following formula:

[0080] F1=min(f invest +f op +f sta +f grid -f sell )

[0081]

[0082] Where F1 is the objective function value of economic cost, f invest f is the average daily amortization cost of the industrial park construction. op f represents the operating and maintenance costs of equipment during normal operation of the industrial park. sta f represents the start-up and shutdown costs of equipment during normal operation of the industrial park. grid f represents the cost of purchasing electricity from the grid for the industrial park. sell For the revenue generated from selling hydrogen in the industrial park, C pv C represents the operation and maintenance cost per unit power of a photovoltaic power generation system. bat The maintenance cost per unit power of the battery, c el Let f be the maintenance cost per unit power of the electrolytic cell, el(t) be a 0-1 variable representing whether the electrolytic cell is started or stopped during the current time period, and f be the operating cost per unit power of the electrolytic cell. elsta For the cost of starting and stopping an electrolytic cell once, C price (t) represents the electricity price during this period. The price per unit volume of hydrogen. This represents the amount of hydrogen sold during the current time period.

[0083] (2) Grid frequency objective function

[0084] When considering the frequency stability of the regional power grid, industrial parks need to adjust their electricity purchase behavior according to the frequency regulation instructions issued by the main grid during specific periods to assist the main grid in completing the frequency control task. Therefore, this embodiment introduces a power grid frequency objective function to measure the deviation between actual electricity purchase behavior and the planned frequency regulation electricity purchase curve, specifically expressed as follows:

[0085]

[0086] Where F2 is the target function value of the power grid frequency, P grid (t) represents the power purchased by the industrial park from the grid at time t, P gridplan (t) represents the expected power purchase capacity set by the power grid according to frequency regulation requirements, i.e., the power purchase value that the main grid hopes the industrial park will undertake in time period t.

[0087] 3. Multi-objective optimization method

[0088] like Figure 2 As shown, to seek a comprehensive optimization solution, this embodiment proposes an adaptive method that compromises between the first-stage objective and the second-stage objective of optimizing the power grid frequency under economic cost constraints. In practice, minimizing economic cost (set as the first-stage objective) is often the primary objective, while optimizing the power grid frequency is the second-stage objective.

[0089] The adaptive method seeks the optimal grid frequency solution while keeping economic costs within an acceptable range. The first stage involves single-objective optimization: minimizing economic costs and optimizing the grid frequency, respectively, to obtain the corresponding cost extrema F. 1min With F 1max The second stage introduces inequality constraints, i.e., multi-objective optimization constraints:

[0090] F1≤(1-λ)F 1min +λF 1max

[0091] Where F1 is the objective function value of economic cost, F 1min To minimize economic cost, F 1max λ represents the economic cost when the power grid frequency index is optimal, and λ is the weighting coefficient.

[0092] like Figure 3 and 4 As shown, based on the economic cost and power grid frequency index change trends corresponding to different λ values ​​in the adaptive method, the scheduling results of the adaptive method under different λ values ​​are obtained.

[0093] This multi-objective optimization constraint ensures that while pursuing optimal grid frequency, the increase in economic cost is limited to a reasonable range. Operators can adjust this range to seek the overall optimal solution. This stage focuses solely on minimizing grid frequency deviation, significantly simplifying the optimization problem. The adaptive method can flexibly balance objectives, adapt to dynamic changes, and obtain a solution with better overall performance.

[0094] This embodiment integrates an electrolytic hydrogen production unit, a lithium battery energy storage unit, and renewable energy power generation equipment into the park's energy system. By establishing an integrated mathematical model covering the dynamic constraints of renewable energy output, energy storage charging and discharging, and hydrogen production load, it achieves joint scheduling and optimal configuration of the three: When renewable energy sources such as photovoltaic and wind power are abundant, the system can prioritize diverting excess power to the hydrogen production unit to absorb it, and can also inject surplus power into the battery energy storage; when renewable energy output is insufficient or the grid frequency deviates significantly, the lithium battery energy storage system can quickly compensate, while the hydrogen production load is gradually reduced or shut down according to preset rules, releasing power capacity to participate in grid frequency regulation. In addition, grid demand targets are incorporated into the optimization objectives, and combined with electricity price fluctuations, deep synergy with the grid is achieved through the linkage of hydrogen production, energy storage, and renewable energy power generation: during low-frequency alarm phases, energy storage discharge and hydrogen production load reduction are prioritized; during high-frequency surplus phases, renewable power generation is used to increase hydrogen production or charge energy storage, thereby promoting maximum absorption of renewable energy, reducing energy storage investment costs, and obtaining frequency regulation benefits, effectively improving the park's economic benefits and grid operation safety.

[0095] like Figure 6 As shown, this embodiment also provides a grid-coordinated optimization configuration system for expanding hydrogen production load in high-energy-consuming industrial parks, including:

[0096] The single-objective optimization module is used to acquire photovoltaic power output and load forecast data, and based on the extended hydrogen production load model of the industrial park, optimize scheduling with the goal of minimizing economic cost to obtain the minimum economic cost. Then, optimize scheduling with the goal of optimal grid frequency to obtain the economic cost when the grid frequency index is optimal.

[0097] The multi-objective optimization module is used to set multi-objective optimization constraints, and based on the minimum economic cost and the economic cost when the power grid frequency index is optimal, to perform optimized scheduling with the optimal power grid frequency as the objective, and obtain the optimized configuration result of the power grid frequency index and economic cost.

[0098] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0099] This embodiment also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program (in this embodiment, the computer program includes computational components and iterative components, capable of model calculation and model updating). The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to realize the corresponding method flow or corresponding function. The processor described in this embodiment can be used in the operation of a grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming parks.

[0100] This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the grid-coordinated optimization configuration method for expanding hydrogen production load in a high-energy-consuming park as described in the above embodiment.

[0101] This embodiment also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the corresponding steps of the grid collaborative optimization configuration method for expanding hydrogen production load in a high-energy-consuming park as described in the above embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for grid-based coordinated optimization of hydrogen production load expansion in high-energy-consuming industrial parks, characterized in that, Includes the following steps: We acquire photovoltaic power output and load forecast data, and based on the extended hydrogen production load model of the industrial park, we optimize the scheduling with the goal of minimizing economic cost to obtain the minimum economic cost. Then, we optimize the scheduling with the goal of the optimal grid frequency to obtain the economic cost when the grid frequency index is optimal. Multi-objective optimization constraints are set, and based on the minimum economic cost and the economic cost when the power grid frequency index is optimal, the optimal power grid frequency is used as the objective for optimal scheduling, so as to obtain the optimal configuration result of the power grid frequency index and economic cost.

2. The grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks according to claim 1, characterized in that, The industrial park extended hydrogen production load model includes an electrolyzer model, a hydrogen storage tank model, and a battery model. The electrolytic cell model is specifically as follows: P elmin ≤P el (t)≤P elmax |P el (t)-P el (t-1)|≤ΔP elmax Among them, P el (t) represents the operating power of the electrolytic cell in the mathematical model at time t, P elmin P represents the minimum operating power of the electrolyzer's mathematical model. elmax ΔP represents the maximum operating power of the electrolyzer's mathematical model. elmax s(t) represents the maximum allowable ramp power during normal operation of the electrolytic cell mathematical model, and t represents the 0-1 variable of the electrolytic cell's operating state. on This represents the shortest continuous operating time of the electrolytic cell, t. off Indicates the shortest continuous downtime of the electrolytic cell; The hydrogen storage tank model is specifically as follows: Among them, W tank (t) represents the amount of hydrogen stored in the hydrogen storage tank at time t. Let be the filling rate of the hydrogen storage tank mathematical model at time t. Let s be the gas delivery rate of the hydrogen storage tank mathematical model at time t. in (t) represents a 0-1 variable representing the hydrogen storage tank's charging state, s out (t) represents a 0-1 variable representing the gas supply status of the hydrogen storage tank. This is the maximum rate at which gas enters the hydrogen storage tank. The maximum rate at which gas is supplied to the hydrogen storage tank; The battery model is as follows: d SOCmin ≤δ SOC (t)≤δ SOCmax in, Let be the charging power of the battery mathematical model at time t. Let s be the discharge power of the battery mathematical model at time t. ch (t) represents a 0-1 variable representing the battery's charging state, s dis (t) represents a 0-1 variable representing the battery's discharge state. This indicates the maximum charging power of the battery. This indicates the maximum discharge power of the battery. This indicates the charging efficiency of the battery. W represents the discharge efficiency of a storage battery. bat The value represents the battery capacity, Δt represents the sampling time interval, and δ represents the battery capacity. SOCmin δ represents the minimum state of charge during the operation of the battery mathematical model. SOCmax δ represents the maximum state of charge during the operation of the battery mathematical model. SOC (t) represents the state of charge of the battery mathematical model at time t.

3. The grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks according to claim 2, characterized in that, The industrial park extended hydrogen production load model also includes a model of the upper-level power grid access point. The model for the upstream power grid access point is as follows: 0≤P grid (t)≤P gridmax Among them, P grid (t) represents the power purchased by the industrial park from the grid at time t, P gridmax This refers to the maximum power that can be purchased from the power grid as stipulated by policy.

4. The grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks according to claim 1, characterized in that, The step of optimizing scheduling with the goal of minimizing economic costs adopts an economic cost objective function, specifically the following formula: F1=min(f invest +f op +f sta +f grid -f sell ) Where F1 is the objective function value of economic cost, f invest f is the average daily amortization cost of the industrial park construction. op f represents the operating and maintenance costs of equipment during normal operation of the industrial park. sta f represents the start-up and shutdown costs of equipment during normal operation of the industrial park. grid f represents the cost of purchasing electricity from the grid for the industrial park. sell For the revenue generated from selling hydrogen in the industrial park, C pv C represents the operation and maintenance cost per unit power of a photovoltaic power generation system. bat For the maintenance cost per unit power of the battery, C el Let f be the maintenance cost per unit power of the electrolytic cell, el(t) be a 0-1 variable representing whether the electrolytic cell is started or stopped during the current time period, and f be the operating cost per unit power of the electrolytic cell. elsta For the cost of starting and stopping an electrolytic cell once, C price (t) represents the electricity price during this period. The price per unit volume of hydrogen. This represents the amount of hydrogen sold during the current time period.

5. The grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks according to claim 1, characterized in that, The step of optimizing scheduling with the optimal grid frequency as the objective is to use the grid frequency objective function, which is as follows: Where F2 is the target function value of the power grid frequency, P grid (t) represents the power purchased by the industrial park from the grid at time t, P gridplan (t) represents the expected power purchase capacity set by the power grid based on frequency regulation requirements.

6. The grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming industrial parks according to claim 1, characterized in that, The multi-objective optimization constraints are as follows: F1≤(1-λ)F 1min +λF 1max Where F1 is the objective function value of economic cost, F 1min To minimize economic cost, F 1max λ represents the economic cost when the power grid frequency index is optimal, and λ is the weighting coefficient.

7. A grid-coordinated optimization configuration system for expanding hydrogen production load in high-energy-consuming industrial parks, characterized in that, include: The single-objective optimization module is used to acquire photovoltaic power output and load forecast data, and based on the extended hydrogen production load model of the industrial park, optimize scheduling with the goal of minimizing economic cost to obtain the minimum economic cost. Then, optimize scheduling with the goal of optimal grid frequency to obtain the economic cost when the grid frequency index is optimal. The multi-objective optimization module is used to set multi-objective optimization constraints, and based on the minimum economic cost and the economic cost when the power grid frequency index is optimal, to perform optimized scheduling with the optimal power grid frequency as the objective, and obtain the optimized configuration result of the power grid frequency index and economic cost.

8. An electronic device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, it implements the steps of the grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming parks as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming parks as described in any one of claims 1 to 6.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the grid collaborative optimization configuration method for expanding hydrogen production load in high-energy-consuming parks as described in any one of claims 1 to 6.

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