Comprehensive energy system scheduling optimization method and device considering source-charge-hydrogen coupling

Abstract

The invention discloses an integrated energy system scheduling optimization method and device considering source-charge-hydrogen coupling, and the method comprises the steps: constructing an integrated energy system operation model which comprises wind power generation, photovoltaic power generation, a hydropower station, an electrolytic cell, a fuel cell, a methane reactor, a carbon capture and storage system, electric energy storage and hydrogen energy storage; electric power, thermal power, hydrogen energy balance and equipment operation constraints are set; according to the method, a multi-objective optimization function including cost minimization, consumption rate maximization and operation stability is constructed, a rolling time sequence optimization strategy is adopted to solve the multi-objective optimization function, an optimal operation scheme of each device in a scheduling period is obtained, and multi-energy collaborative and low-carbon efficient operation is realized. According to the invention, through multi-source-charge-hydrogen equipment coupling modeling and multi-target rolling optimization, the operation economy, cleanliness and stability of the comprehensive energy system are cooperatively improved.

Description

Technical Field

[0001] This application belongs to the field of energy system dispatching technology, specifically relating to a comprehensive energy system dispatching optimization method and apparatus that considers source-load-hydrogen coupling. Background Technology

[0002] With the advancement of carbon peaking and carbon neutrality strategies, multi-energy synergistic systems centered on renewable energy sources such as wind and solar power are gradually replacing traditional fossil fuels and becoming an important component of new power systems. At the same time, hydrogen energy, as a key carrier for achieving deep decarbonization of energy, is playing an increasingly prominent role in integrated energy systems. Constructing a source-load-hydrogen coupling system that integrates multiple functions such as wind power, solar power, hydropower, electrolyzers, fuel cells, energy storage, and carbon capture and storage has become an important path to improve the utilization rate of new energy sources, achieve multi-energy complementarity, and promote the clean and low-carbon operation of the system.

[0003] Existing integrated energy dispatch methods primarily focus on single energy types or multi-energy coupling of electricity, heat, and gas, making it difficult to fully characterize the interactive characteristics of multiple energy forms such as wind, solar, hydro, and hydrogen. In particular, they lack system modeling and operational strategies for the coupled control of hydrogen production, storage, and utilization, carbon capture reaction processes, and the power system. Furthermore, most methods only optimize economic indicators, neglecting the synergistic constraints of renewable energy absorption capacity and operational stability. This easily leads to problems such as wind and solar curtailment and equipment instability, failing to meet the dual requirements of flexibility and low carbon emissions for current high-proportion renewable energy systems. Summary of the Invention

[0004] To address the problems of existing technologies, this application proposes a method and apparatus for scheduling optimization of integrated energy systems that considers source-load-hydrogen coupling. This application improves the operational economy, cleanliness, and stability of integrated energy systems through multi-source-load-hydrogen device coupling modeling and multi-objective rolling optimization.

[0005] This application is achieved through the following technical solution:

[0006] A comprehensive energy system scheduling optimization method considering source-load-hydrogen coupling includes:

[0007] Construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electric energy storage and hydrogen energy storage, and set the balance of electricity, heat and hydrogen energy and equipment operation constraints;

[0008] A multi-objective optimization function is constructed, which includes cost minimization, absorption rate maximization, and operational stability. The rolling time-series optimization strategy is used to solve the multi-objective optimization function to obtain the optimal operation scheme of each device within the scheduling cycle, thereby achieving multi-energy synergy and low-carbon and efficient operation.

[0009] On the other hand, this application also proposes a comprehensive energy system scheduling optimization device considering source-load-hydrogen coupling, comprising:

[0010] The first modeling unit is used to construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electrical energy storage and hydrogen energy storage, and to set hydraulic, thermal and hydrogen energy balance and equipment operation constraints.

[0011] The second modeling unit is used to construct a multi-objective optimization function that includes cost minimization, absorption rate maximization, and operational stability.

[0012] In addition, a solution unit is used to solve the multi-objective optimization function using a rolling time-series optimization strategy to obtain the optimal operating scheme of each device within the scheduling cycle, thereby achieving multi-energy collaboration and low-carbon, high-efficiency operation.

[0013] This application proposes a comprehensive energy system scheduling optimization method and device that considers source-load-hydrogen coupling. By constructing a multi-source collaborative system operation model, it finely models the operating characteristics and energy conversion mechanisms of various devices, and sets a multi-objective optimization function with the objectives of minimum operating cost, maximum renewable energy absorption rate, and minimum power deviation. By combining weighted combination and rolling time-series optimization strategies, the optimal scheduling scheme under the whole cycle is obtained, thereby improving the overall economy, stability and low-carbon operation capability of the system. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and form part of this application, do not constitute a limitation on the embodiments of this application. In the drawings:

[0015] Figure 1 This is a flowchart of the integrated energy system scheduling optimization method proposed in the embodiments of this application;

[0016] Figure 2 This is a schematic diagram of the integrated energy system scheduling optimization device proposed in the embodiments of this application;

[0017] Figure 3 This is a schematic diagram of the integrated energy system scheduling optimization system architecture proposed in the embodiments of this application;

[0018] Figure 4 This is a schematic diagram of the electronic device proposed in the embodiments of this application;

[0019] Figure 5 This is a schematic diagram of a computer-readable storage medium proposed in an embodiment of this application;

[0020] Figure reference numerals and corresponding component names:

[0021] 200-Integrated Energy System Scheduling and Optimization Device, 201-First Modeling Unit, 202-Second Modeling Unit, 203-Solving Unit, 300-Integrated Energy System Scheduling and Optimization System, 301-Input Device, 302-Output Device, 303-Processor A, 304-Memory A, 400-Electronic Device, 410-Memory B, 420-Processor B, 411-Computer Program A, 500-Computer-readable Storage Medium, 511-Computer Program B. Detailed Implementation

[0022] In the following, the terms “comprising” or “may include” as used in the various embodiments of this application indicate the presence of a function, operation, or element of the invention and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in the various embodiments of this application, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or adding one or more combinations of the foregoing.

[0023] In various embodiments of this application, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0024] The terms used in the various embodiments of this application (such as "first," "second," etc.) may modify various constituent elements in the various embodiments, but do not limit the corresponding constituent elements. For example, the above terms do not limit the order and / or importance of the elements. The above terms are only used for the purpose of distinguishing one element from other elements. For example, a first user device and a second user device refer to different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of this application, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element.

[0025] It should be noted that if a description is made of "connecting" one component to another, then the first component can be directly connected to the second component, and a third component can be "connected" between the first and second components. Conversely, when a component is "directly connected" to another component, it can be understood that there is no third component between the first and second components.

[0026] The terminology used in the various embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. The terms (such as those defined in a generally used dictionary) are to be interpreted as having the same meaning as in the context of the relevant technical field and are not to be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0028] like Figure 1 As shown in the embodiments of this application, a comprehensive energy system scheduling optimization method considering source-load-hydrogen coupling is proposed, including the following steps:

[0029] Step 1: Construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electrical energy storage and hydrogen energy storage, and set the balance of electricity, heat and hydrogen energy and equipment operation constraints;

[0030] Step 2: Construct a multi-objective optimization function that includes cost minimization, absorption rate maximization, and operational stability, and use a rolling time-series optimization strategy to solve the multi-objective optimization function to obtain the optimal operating scheme of each device within the scheduling cycle, thereby achieving multi-energy collaboration and low-carbon, high-efficiency operation.

[0031] Furthermore, in step 1 of this application embodiment, the process of constructing the integrated energy system operation model includes:

[0032] Wind power generation output model: Wind turbines (or simply wind turbines) utilize the kinetic energy of the wind to drive the turbine blades to rotate, further converting kinetic energy into mechanical energy, and then using the principle of electromagnetic induction to convert it into electrical energy. The output power of a wind turbine is closely related to wind speed; changes in wind speed directly determine the operating state of the turbine and the magnitude of its output power. Based on different wind speeds, the operating state of a wind turbine can be divided into four stages: **Shutdown and Waiting for Wind Stage:** When the wind speed is lower than the cut-in wind speed, the wind cannot drive the blades to rotate, the wind turbine cannot start, and the unit's output power is 0. **Climbing Operation Stage:** When the wind speed is between the cut-in wind speed and the rated wind speed, the blades begin to rotate, the turbine is in operation, and the unit's power increases with the wind speed. **Rated Power Stage:** When the wind speed reaches or exceeds the rated wind speed but does not exceed the cut-out wind speed, the turbine reaches its rated operating state, and the output power remains at the rated value. **High Wind Speed ​​Protection Stage:** When the wind speed exceeds the cut-out wind speed, to protect equipment safety, the turbine will stop operating, and the output power will drop to 0. Based on this, the embodiments of this application divide the output of wind power generation into four operating states according to wind speed: standby, ramping operation, rated power, and high wind speed protection. The wind power generation is expressed as:

[0033] ;

[0034] in, Representing wind turbine units Power generation at any given moment; This represents the rated power of the wind turbine. Representative at Wind speed at any given moment; Represents the cut-in wind speed of the wind turbine; This represents the cut-out wind speed of the wind turbine. This represents the rated wind speed of the wind turbine generator set.

[0035] Photovoltaic power generation output model: Photovoltaic power generation is a technology that uses the photovoltaic effect of semiconductors to convert solar energy into electrical energy. Photovoltaic power generation relies on solar panels to absorb solar radiation, thereby exciting free electron currents and realizing the conversion of solar energy into electrical energy. Its power generation is usually directly related to light intensity and ambient temperature. Photovoltaic power generation output is expressed as:

[0036] ;

[0037] ;

[0038] in, Indicates photovoltaics in Power generation at any given moment; The energy conversion efficiency of photovoltaic power generation is typically taken as 0.9; This indicates the maximum output power of the photovoltaic system under standard test conditions; represent The actual radiation intensity received by the photovoltaic system at any given moment; The rated light intensity of photovoltaics is generally ; Temperature power coefficient; For photovoltaic panels in The actual temperature at that moment; The rated temperature of the photovoltaic panel is generally... ; Representative at The air temperature in the environment where the photovoltaic system is located at any given time.

[0039] Hydropower Station Output Model: The factors influencing the output power of a hydropower station include not only the total water demand at various times and changes in reservoir water level, but also the turbine head and generator efficiency. Therefore, the output power model of a hydropower station can be represented by the law of energy conservation, whereby the potential energy difference between the upstream and downstream water bodies corresponds to the output electrical energy.

[0040] ;

[0041] in, For the first A reservoir-capacity hydropower station Efforts made at all times; The density of water; It is the acceleration due to gravity; For power generation efficiency; For power generation flow; For generating water head.

[0042] Electrolyzer Model: P2H technology, where the electrolyzer produces hydrogen and oxygen after being energized. Depending on the electrolyte, electrolyzers can be classified as alkaline electrolyzers (AE), proton exchange membrane electrolyzers, and solid oxide electrolyzers. Alkaline electrolyzers have the highest technological maturity, with an energy conversion rate of up to 60%, and a unit investment cost of approximately 2000 to 10000 yuan / kW. Therefore, this application uses an alkaline electrolyzer, including both hydrogen production power and heat production power, and the energy conversion efficiency considers both hydrogen production and heat production separately. Its mathematical model can be expressed as:

[0043] ;

[0044] ;

[0045] in, for The hydrogen production power of AE at time t; It's AE in Power consumption at any given moment; It's AE in Heat production capacity at any given time; and These represent the hydrogen production efficiency and heat production efficiency of AE, respectively.

[0046] Fuel Cell Model: Because fuel cells are not limited by the Carnot cycle, their energy utilization rate is very high. Hydrogen fuel cells are a new type of high-efficiency device for generating electricity using hydrogen. Under current technological conditions, the theoretical maximum power generation efficiency from hydrogen production through water electrolysis to hydrogen fuel cell power generation can reach 80%. In addition, hydrogen fuel cells also generate a large amount of heat during operation, which can be used as a heat source in a combined cycle with a gas turbine to achieve hydrogen-electricity-heat conversion. Since proton exchange membrane hydrogen fuel cells are highly efficient and relatively mature, this application uses a proton exchange membrane hydrogen fuel cell. Its power generation is related to the hydrogen consumption rate and operating efficiency. The power generation process is accompanied by heat output for heat load supply, as shown in the model below:

[0047] ;

[0048] in, This refers to the output power of the fuel cell; The rate at which hydrogen is consumed by the fuel cell; For optimal fuel cell operating efficiency, 45% is preferred. Hydrogen consumed to produce one unit of electricity for fuel cells.

[0049] Methane reactor model: The methane reactor reacts hydrogen produced in the P2H process with carbon dioxide to produce methane. Its gas production power is proportional to the hydrogen consumption power, and the required carbon dioxide consumption mass is related to the calorific value of the natural gas. The model is expressed as follows:

[0050] ;

[0051] in, For IES in The gas production capacity of the methane reactor at any given time. Is the system in The hydrogen consumption power of the methane reactor at any given time. This refers to the conversion efficiency of MP.

[0052] Since the volume of carbon dioxide consumed in the MP stage is the same as the volume of methane produced, the mass of carbon dioxide required for the MP process is calculated as follows:

[0053] ;

[0054] in, for Carbon dioxide consumption during the MP process at time point; The density of carbon dioxide; This refers to the calorific value of natural gas.

[0055] Carbon capture and storage system model: The carbon capture and storage system includes flue gas carbon capture, regeneration tower, and carbon storage equipment. A dynamic relationship model between carbon energy consumption and carbon dioxide flow rate is constructed, specifically including:

[0056] The total energy consumption model for carbon capture and storage (CCUS) is expressed as follows:

[0057] ;

[0058] in, In order to be in Total power consumption of CCUS at any given time; The basic energy consumption of CCUS can be considered a constant. In order to be in The power consumption of CCUS at any given time is related to the amount of carbon dioxide captured.

[0059] The calculation model for the power consumption of carbon capture is as follows:

[0060] ;

[0061] in, In order to be in The mass of carbon dioxide processed by the regeneration tower of CCUS at any given time is determined by the actual operating conditions and optimized scheduling of the system itself. The energy consumption coefficient for CCUS to capture a unit mass of carbon dioxide is generally taken as 0.269 MW·h / .

[0062] To rationally control the energy consumption of carbon capture, a carbon capture system with flue gas diversion is adopted. The diversion model for carbon capture is expressed as follows:

[0063] ;

[0064] in, In order to be in The mass of carbon dioxide absorbed by CCUS at any given time; In order to be in The total carbon dioxide emissions of the carbon emission unit at all times; In order to be in Carbon dioxide is constantly being indirectly emitted into the air through flue gas diversion.

[0065] The carbon capture regeneration tower plays a crucial role in the above process, and the mass model of the carbon dioxide treated by the regeneration tower is expressed as follows:

[0066] ;

[0067] in, For the system in The quality of carbon dioxide captured by a carbon capture and storage (CCS) device.

[0068] Carbon sequestration equipment models: Carbon sequestration equipment includes lean solution storage devices (LSSD) and rich solution storage devices (RSSD). LSSD and RSSD are... The reserve model at time t is:

[0069] ;

[0070] ;

[0071] in, and They are respectively in The liquid level of the LSSD at any given time and in The liquid level of the LSSD at any given time; and They are respectively in The volumetric flow rate of the liquid entering and leaving the LSSD at all times; and They are respectively in The reservoir volume of RSSD at any given time and in The liquid level of the RSSD at any given time; and They are respectively in The volumetric flow rate of the liquid entering and leaving the RSSD at all times.

[0072] Among them, The inflow and outflow of lean and rich liquids at any given time satisfy the following relationship:

[0073] ;

[0074] ;

[0075] To avoid nonlinear issues arising from modeling, we assume that a unit volume of enriched liquid can absorb 25 times its own volume of carbon dioxide. Therefore, the carbon absorption and release model for enriched liquid is expressed as follows:

[0076] ;

[0077] in, The carbon capture efficiency coefficient is typically between 80% and 95%, with a preferred value of 90% in the embodiments of this application. .

[0078] Electric energy storage model: An electric energy storage system typically consists of energy storage batteries, controllers, inverters, etc. During operation, the system includes charging and releasing processes. The charging process involves storing electrical energy in the battery, at which point the battery can be considered a load. The releasing process involves releasing the stored energy to supply the load, at which point the battery can be considered a power supply device. During operation, a battery can be divided into three states: charging, discharging, and standby. Its energy storage capacity can be represented as follows:

[0079] ;

[0080] When the energy storage system is in the charging state, the charging process of the device can be represented as:

[0081] ;

[0082] When the energy storage system is in a discharging state, the energy release process of the device can be represented as:

[0083] ;

[0084] in, Indicates that the energy storage system is in The capacity of a given moment; The self-consumption rate of the energy storage system; , They represent in time, The state of charge of the battery at all times; , They represent in The charging and discharging power of the battery at all times; , These represent the charging efficiency and discharging efficiency of the battery, respectively. Indicates the rated capacity of the battery; Indicates time difference.

[0085] Hydrogen energy storage model: Energy storage technology plays a buffering role in addressing the imbalance between energy supply and user-side load demand. The introduction of P2G enables electrical coupling, allowing excess electricity generated from renewable energy sources to be converted to gas during off-peak hours at night. Additional gas storage tanks are needed to store the produced natural gas for supplying gas to users during peak demand periods or to fuel gas-fired equipment within the system. Furthermore, this application's embodiments consider the two-stage process of P2G technology; to ensure stable operation of the hydrogen fuel cell, hydrogen storage tanks are required to store hydrogen energy. The reservoir capacity of the hydrogen energy storage device is expressed as:

[0086] ;

[0087] in, , They are respectively The storage capacity of the hydrogen storage tank at any time and The storage capacity of the hydrogen storage tank at any given time; , They are respectively Hydrogen storage and release power at any given time; , These represent hydrogen storage efficiency and hydrogen release efficiency, respectively.

[0088] Furthermore, the core requirements of the source-load-hydrogen coupling system in this application embodiment are: high proportion of new energy consumption, low carbon emission reduction, and economical and efficient operation. Currently, the function is constructed as a multi-objective optimization function, which integrates the sub-objectives through weighted coefficients, specifically expressed as follows:

[0089] ;

[0090] in, , , These are the normalized weighting coefficients; The goal is to minimize the overall operating cost of the system; To maximize the utilization of new energy sources; The goal is to ensure the stability of system operation.

[0091] The goal of minimizing the overall system operating cost encompasses the power generation cost, the operation and maintenance cost of hydrogen production and storage equipment, the loss cost of energy storage equipment, and the cost of purchasing and selling electricity, and is expressed as:

[0092] ;

[0093] In the formula, A collection of power-side devices. For the first Power supply The cost of operation at all times includes the cost of hydropower, which includes fixed maintenance fees and water wastage losses, while the cost of wind and solar power is zero marginal cost. A collection of hydrogen energy-side equipment. For the first Hydrogen-like energy equipment Real-time operation and maintenance costs, including electricity costs for the electrolyzer, hydrogen storage losses, and fuel cell catalyst replacement costs; A collection of energy storage devices, For the first Energy storage devices The cost of constant wear and tear includes charging and discharging losses and equipment depreciation. for The electricity purchase and sale cost is calculated based on the real-time electricity price, while the electricity sale revenue is calculated based on the on-grid electricity price.

[0094] The goal of maximizing the absorption of new energy sources, with the minimization of wind and solar curtailment rates as its core, is expressed as:

[0095] ;

[0096] in, , They are respectively Wind power and photovoltaic power are theoretically contributing at all times; for The power curtailed from wind and solar power is absorbed through hydrogen production loads, energy storage charging, and grid interaction.

[0097] The system operational stability objective, characterized by minimizing the source-load power deviation, is expressed as:

[0098] ;

[0099] in, for Total system load at any time; for Total power output on the power supply side at all times; For fuel cells Always put in the effort; Electrolytic cell Power consumption at all times; , Energy storage devices Constant charging and discharging power.

[0100] The constraints of the aforementioned multi-objective optimization function include the balance constraints of electricity, heat and hydrogen energy, as well as the power boundary and conversion characteristic constraints of P2G, fuel cells, carbon capture and storage systems, which are used to ensure the safe and stable operation of the multi-energy collaborative system and energy conservation.

[0101] Among these constraints, energy balance is a key requirement: the system must maintain real-time balance between electrical and thermal energy supply and demand, encompassing the coordinated balance between the power system and the hydrogen-thermal coupling system. The power balance constraint is as follows: The total power supply of the time system equals the total power consumption, expressed as:

[0102] ;

[0103] in, For various power supplies Always putting in actual effort; For hydrogen fuel cells Power generation at all times Electrolytic cell Power consumption at all times; , They are respectively Real-time grid power purchase and sale, non-negative constraints; for The system's electrical load at all times; for The power grid transmission loss and equipment self-consumption at all times are estimated at 2%-5% of the total power.

[0104] Thermal balance constraints, addressing the waste heat utilization of hydrogen fuel cells and industrial heat load requirements. The constant heat supply and demand balance is expressed as:

[0105] ;

[0106] in, For fuel cell power generation efficiency; Other heat sources (such as industrial waste heat, gas-fired boilers) Provides heat at all times; for System heat load at all times; for Constant heat transfer loss.

[0107] Hydrogen energy balance constraints: The hydrogen energy side must meet the dynamic balance between hydrogen production, storage, and utilization. The hydrogen energy balance at time t is expressed as:

[0108] ;

[0109] in, Hydrogen storage tank Hydrogen storage capacity at all times, to meet ; Electrolytic cell Hydrogen production capacity at all times For hydrogen storage efficiency; For fuel cells Hydrogen power consumption at all times For hydrogen release efficiency; Hydrogen storage tank Hydrogen loss occurs constantly.

[0110] Two-stage P2G constraints:

[0111] ;

[0112] in, For the system in The power consumption of the electrolytic cell at all times. This indicates the lower limit of the power consumption of the electrolytic cell; This indicates the upper limit of the power consumption of the electrolytic cell; This indicates the lower limit of the power consumption ramp-up of the electrolytic cell; This indicates the upper limit of the power consumption ramp-up of the electrolytic cell.

[0113] Fuel cell constraints: The input power of hydrogen fuel cells must meet the equipment capacity constraints. Furthermore, considering the response speed during energy conversion and the potential for safety issues due to rapid power changes, the input power must also meet ramping constraints.

[0114] ;

[0115] in, for Output power of hydrogen fuel cells during specific time periods; The equipment capacity; , These are the upper and lower limits of the equipment's ramp power, respectively.

[0116] Carbon capture constraints:

[0117] ;

[0118] in, This indicates the maximum power consumption of CCUS.

[0119] Carbon sequestration constraints:

[0120] ;

[0121] in, Indicates the lower limit of the liquid storage capacity of the LSSD device; Indicates the upper limit of the liquid storage capacity of the LSSD device; Indicates the lower limit of the liquid storage capacity of the RSSD device; This indicates the upper limit of the liquid storage capacity of the RSSD device.

[0122] ;

[0123] ;

[0124] in, This indicates the maximum carbon dioxide storage capacity of the CSE equipment. Represents the time slice number within the scheduling period. This represents the total number of time slices in the scheduling cycle.

[0125] Energy storage constraints include electrical energy storage constraints and hydrogen energy storage constraints. Electrical energy storage constraints are expressed as:

[0126] ;

[0127] ;

[0128] in, This indicates the maximum state of charge of the energy storage battery; This indicates the minimum state of charge of the energy storage battery; This indicates the charging efficiency of the energy storage battery; This indicates the discharge efficiency of the energy storage battery; This indicates the maximum charging power of the energy storage battery; This indicates the maximum discharge power of the energy storage battery.

[0129] The hydrogen energy storage constraint is expressed as:

[0130] ;

[0131] in, , These are the upper and lower limits of hydrogen storage capacity, respectively. , These are the maximum hydrogen storage and hydrogen release power per cycle, respectively. , These are variables ranging from 0 to 1, representing the charging and discharging states, respectively. A value of 1 for both states indicates that the hydrogen storage tank cannot simultaneously perform hydrogen storage and hydrogen release. The scheduling period is [number].

[0132] Furthermore, the rolling time-series optimization strategy used in step 2 of this application embodiment is implemented on the Matlab platform for solving the multi-objective optimization function. Through data preprocessing, the output prediction data (wind, solar, hydro, etc.), load demand data, equipment parameters, etc. within the scheduling cycle are imported into the Matlab workspace and the time-series data is divided into time slices. Through normalization processing, the three objectives of cost, absorption rate, and operational stability are converted into dimensionless indicators and integrated into a single-objective optimization function by combining weight coefficients. Constraints such as energy balance constraints, upper and lower limits of equipment output, and hydrogen energy storage capacity limits are converted into constraint expressions that can be recognized by the Matlab optimization toolbox. The Matlab optimization function is called to iteratively calculate in a rolling time-series manner to obtain the optimal operating scheme of each device within the scheduling cycle. The optimal operating scheme obtained by the solution (equipment output curve, cost data, etc.) is exported to complete the generation of the scheduling scheme for multi-energy synergy and low-carbon high-efficiency operation.

[0133] The integrated energy system scheduling optimization method proposed in this application constructs an integrated energy system operation model that integrates wind power, photovoltaic power, hydropower, electrolyzers, fuel cells, methane reactors, carbon capture and storage systems, electrical energy storage, and hydrogen energy storage devices. It introduces multi-energy balance constraints and equipment operation boundary constraints for electricity, heat, and hydrogen energy to achieve deep coupling between energy flows of source, load, and hydrogen, providing a physical basis and control boundary for the safe and stable operation of multi-energy systems. At the same time, by setting multi-objective optimization functions including minimizing operating costs, maximizing the absorption rate of new energy sources, and minimizing source-load deviation, and combining the joint optimization of key processes such as wind and solar power output, hydropower regulation, energy storage charging and discharging, and hydrogen energy conversion, it improves the system's economic efficiency, cleanliness, and flexible control capabilities at multiple time scales, and promotes the development of integrated energy systems towards low-carbon, high-efficiency, and coordinated operation.

[0134] Based on the same technical concept described above, this application also proposes a comprehensive energy system scheduling optimization device that considers source-load-hydrogen coupling, such as... Figure 2 As shown, the integrated energy system dispatch optimization device 200 includes:

[0135] The first modeling unit 201 is used to construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electrical energy storage, and hydrogen energy storage, and to set the balance of electricity, heat, and hydrogen energy, as well as equipment operation constraints. The specific methods are as described in the above methods and will not be repeated here.

[0136] The second modeling unit 202 is used to construct a multi-objective optimization function that includes cost minimization, absorption rate maximization, and operational stability. The specific method is as described above and will not be repeated here.

[0137] Furthermore, the solution unit 203 is used to solve the multi-objective optimization function using a rolling time-series optimization strategy to obtain the optimal operating scheme for each device within the scheduling cycle, thereby achieving multi-energy collaboration and low-carbon, high-efficiency operation. The specific process is as described in the above method and will not be repeated here.

[0138] Based on the same technical concept described above, this application also proposes a comprehensive energy system scheduling optimization system considering source-load-hydrogen coupling, such as... Figure 3 As shown, the integrated energy system scheduling optimization system 300 proposed in this application includes:

[0139] The system comprises an input device 301, an output device 302, a processor A303, and a memory A304; wherein the number of processors A303 and memory A304 can be one or more. Figure 3The following description uses a processor A303 and a memory A304 as an example. The input device 301, output device 302, processor A303, and memory A304 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0140] Specifically, by calling the operation instructions stored in memory A304, processor A303 executes the following steps:

[0141] Construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electric energy storage and hydrogen energy storage, and set the balance of electricity, heat and hydrogen energy and equipment operation constraints;

[0142] A multi-objective optimization function is constructed, which includes cost minimization, absorption rate maximization, and operational stability. A rolling time-series optimization strategy is used to solve the multi-objective optimization function to obtain the optimal operating scheme of each device within the scheduling cycle, thereby achieving multi-energy synergy and low-carbon, high-efficiency operation.

[0143] Optionally, by calling the operation instructions stored in memory A304, processor A303 is also used to execute any of the corresponding embodiments in the above-described integrated energy system scheduling optimization method.

[0144] Based on the same technical concept described above, this application also proposes an electronic device, such as... Figure 4 As shown, the electronic device 400 includes: a memory B410, a processor B420, and a computer program A411 stored in the memory B410 and executable on the processor B420. When the processor B420 executes the computer program A411, it performs the following steps:

[0145] Construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electric energy storage and hydrogen energy storage, and set the balance of electricity, heat and hydrogen energy and equipment operation constraints;

[0146] A multi-objective optimization function is constructed, which includes cost minimization, absorption rate maximization, and operational stability. A rolling time-series optimization strategy is used to solve the multi-objective optimization function to obtain the optimal operating scheme of each device within the scheduling cycle, thereby achieving multi-energy synergy and low-carbon, high-efficiency operation.

[0147] Optionally, when processor B420 executes computer program A411, it can implement any of the corresponding embodiments in the above-described integrated energy system scheduling optimization method.

[0148] It should be noted that the electronic device proposed in this application embodiment is a device used to implement the above-mentioned integrated energy system scheduling optimization method. Therefore, based on the above-mentioned integrated energy system scheduling optimization method proposed in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this application embodiment. Therefore, how the electronic device specifically implements the above-mentioned integrated energy system scheduling optimization method will not be described in detail here. Any electronic device used by those skilled in the art to implement the above-mentioned integrated energy system scheduling optimization method falls within the scope of protection of this application.

[0149] Based on the same technical concept described above, embodiments of this application also propose a computer-readable storage medium, such as... Figure 5 As shown, the computer-readable storage medium 500 stores a computer program B511, which, when executed by a processor, performs the following steps:

[0150] Construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electric energy storage and hydrogen energy storage, and set the balance of electricity, heat and hydrogen energy and equipment operation constraints;

[0151] A multi-objective optimization function is constructed, which includes cost minimization, absorption rate maximization, and operational stability. A rolling time-series optimization strategy is used to solve the multi-objective optimization function to obtain the optimal operating scheme of each device within the scheduling cycle, thereby achieving multi-energy synergy and low-carbon, high-efficiency operation.

[0152] Optionally, when the computer program B511 is executed by the processor, it can implement any of the embodiments corresponding to the above-described integrated energy system scheduling optimization method.

[0153] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0154] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0155] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] 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.

[0157] 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.

[0158] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for integrated energy system scheduling optimization considering source-load-hydrogen coupling, characterized in that, include: Construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electric energy storage and hydrogen energy storage, and set the balance of electricity, heat and hydrogen energy and equipment operation constraints; A multi-objective optimization function is constructed, which includes cost minimization, absorption rate maximization, and operational stability. The rolling time-series optimization strategy is used to solve the multi-objective optimization function to obtain the optimal operation scheme of each device within the scheduling cycle, thereby achieving multi-energy synergy and low-carbon and efficient operation. 2.The method of claim 1, wherein, The integrated energy system operation model includes: The wind power output model divides the wind power output into four operating states according to wind speed: shutdown and waiting for wind, uphill operation, rated power, and high wind speed protection. The photovoltaic power generation output model is established based on light intensity and ambient temperature. The power output model of the hydropower station is established based on the water head, water flow and turbine power generation efficiency, and using the principle of energy conservation. The electrolyzer model uses an alkaline electrolyzer, including hydrogen production power and heat production power, and the energy conversion efficiency considers both hydrogen production and heat production separately. The fuel cell model uses a proton exchange membrane hydrogen fuel cell. Its power generation is related to the hydrogen consumption rate and operating efficiency. The power generation process is accompanied by heat output for heat load supply. The methane reactor model shows that its gas production power is proportional to its hydrogen consumption power, and the required carbon dioxide consumption mass is related to the calorific value of the natural gas. A carbon capture and storage system model, including carbon capture, regeneration tower and carbon storage equipment, is used to construct a dynamic relationship model between carbon energy consumption and carbon dioxide flow rate; The electric energy storage model and the hydrogen energy storage model respectively characterize their charging and discharging processes through charge state and hydrogen capacity change models. 3.The method of claim 2, wherein, The wind power output model is expressed as follows: ； in, Representing wind turbine units Power generation at any given moment; This represents the rated power of the wind turbine. Representative at Wind speed at any given moment; Represents the cut-in wind speed of the wind turbine; This represents the cut-out wind speed of the wind turbine. This represents the rated wind speed of the wind turbine generator set. The photovoltaic power generation output model is expressed as follows: ； ； in, Indicates photovoltaics in Power generation at any given moment; The energy conversion efficiency of photovoltaic power generation; This indicates the maximum output power of the photovoltaic system under standard test conditions; represent The actual radiation intensity received by the photovoltaic system at any given moment; This refers to the rated light intensity of photovoltaics. Temperature power coefficient; For photovoltaic panels in The actual temperature at that moment; This refers to the rated temperature of the photovoltaic panel. Representative at The air temperature in the environment where the photovoltaic system is located at all times; The power output model of the hydropower station is represented as follows: ； in, For the first A reservoir-capacity hydropower station Efforts made at all times; The density of water; It is the acceleration due to gravity; For power generation efficiency; For power generation flow; For generating water head.

4. The integrated energy system scheduling optimization method considering source-load-hydrogen coupling according to claim 3, characterized in that, The electrolytic cell model is represented as follows: ； ； in, for Hydrogen production power of the electrolyzer at any given time; It is the electrolytic cell in Power consumption at any given moment; It is the electrolytic cell in Heat production capacity at any given time; and These are the hydrogen production efficiency and heat production efficiency of the electrolyzer, respectively. The fuel cell model is represented as follows: ； in, This refers to the output power of the fuel cell; The rate at which hydrogen is consumed by the fuel cell; For fuel cell operating efficiency; Hydrogen consumed per unit of electricity produced by fuel cells; The methane reactor model is represented as follows: ； in, For IES in The gas production power of MP at any given time; For MP conversion efficiency; For IES in Hydrogen consumption power at time MP; The mass of carbon dioxide required for the MP process is expressed as follows: ； in, for Carbon dioxide consumption during the MP process at time point; The density of carbon dioxide; This refers to the calorific value of natural gas.

5. The integrated energy system scheduling optimization method considering source-load-hydrogen coupling according to claim 4, characterized in that, The carbon capture and storage system model includes: The total energy consumption model for carbon capture is expressed as: ； in, In order to be in Total energy consumption of carbon capture at any given time; This represents the basic energy consumption for carbon capture; In order to be in Power consumption of carbon capture at any given time; The power consumption for carbon capture is expressed as: ； in, In order to be in The quality of carbon dioxide processed by the regeneration tower of the carbon capture system at any given time; The energy consumption coefficient for capturing a unit mass of carbon dioxide by carbon capture; The splitting model for carbon capture is expressed as: ； in, In order to be in The mass of carbon dioxide absorbed by CCUS at any given time; In order to be in The total carbon dioxide emissions of the carbon emission unit at all times; In order to be in Carbon dioxide is constantly being indirectly emitted into the air through flue gas diversion; The mass model for carbon dioxide treated by the regeneration tower is expressed as follows: ； in, For the system in The mass of carbon dioxide captured by a carbon capture and storage (CCS) device; The carbon sequestration equipment model is represented as follows: ； ； in, and They are respectively in The storage capacity of the lean liquid storage device at any time and in The liquid storage capacity of the lean liquid storage device at any given time; and They are respectively in The volumetric flow rate of the liquid entering and leaving the lean liquid storage device at all times; and They are respectively in The liquid storage capacity of the instantaneous rich liquid storage device and in The liquid storage capacity of the instantaneous rich liquid storage device; and They are respectively in The volumetric flow rate of the liquid entering and leaving the rich liquid storage device at all times; Among them The inflow and outflow of lean and rich liquids at any given time satisfy the following relationship: ； ； The carbon uptake and release model in rich liquid is expressed as follows: ； in, This represents the carbon capture efficiency coefficient.

6. The integrated energy system scheduling optimization method considering source-load-hydrogen coupling according to claim 5, characterized in that, The energy storage model is expressed as follows: ； When the energy storage system is in the charging state, the charging process of the device can be represented as follows: ； When the energy storage system is in a discharging state, the energy release process of the device can be represented as follows: ； in, Indicates that the energy storage system is in The capacity of a given moment; The self-consumption rate of the energy storage system; , They represent in time, The state of charge of the battery at all times; , They represent in The charging and discharging power of the battery at all times; , These represent the charging efficiency and discharging efficiency of the battery, respectively. Indicates the rated capacity of the battery; Indicates time difference; The hydrogen energy storage model is expressed as follows: ； in, , They are respectively The storage capacity of the hydrogen storage tank at any time and The storage capacity of the hydrogen storage tank at any given time; , They are respectively Hydrogen storage and release power at any given time; , These represent hydrogen storage efficiency and hydrogen release efficiency, respectively.

7. A comprehensive energy system scheduling optimization method considering source-load-hydrogen coupling according to any one of claims 1-6, characterized in that, The multi-objective optimization function is expressed as: ； in, , , These are the normalized weighting coefficients; The goal is to minimize the overall operating cost of the system; To maximize the utilization of new energy sources; The goal is to ensure the stability of system operation; The goal of minimizing the overall operating cost of the system includes the power generation cost on the power supply side, the operation and maintenance cost of hydrogen production and storage equipment, the loss cost of energy storage equipment, and the cost of purchasing and selling electricity. The goal of maximizing the consumption of new energy sources is characterized by minimizing the wind and solar curtailment rate. The system's operational stability objective is characterized by minimizing the source-load power deviation.

8. The integrated energy system scheduling optimization method considering source-load-hydrogen coupling according to claim 7, characterized in that, The constraints of the multi-objective optimization function include the balance constraints of electricity, heat and hydrogen energy, as well as the power boundary and conversion characteristic constraints of the electrolyzer, fuel cell, carbon capture and storage system, in order to ensure the safe and stable operation and energy conservation of the multi-energy collaborative system.

9. The integrated energy system scheduling optimization method considering source-load-hydrogen coupling according to claim 7, characterized in that, The method of solving the multi-objective optimization function using a rolling time-series optimization strategy includes: The power output forecast data, load demand data, and equipment parameters within the scheduling cycle are divided into time-series data according to time slices; By normalizing the three objectives of cost, absorption rate and operational stability, they are converted into dimensionless indicators and then integrated into a single-objective optimization function by combining weighting coefficients. Transform constraints into recognizable constraint expressions; The optimization function is invoked to iteratively calculate in a rolling time sequence manner to obtain the optimal operating plan for each device within the scheduling cycle. The optimal operating scheme obtained from the solution is output, thus completing the generation of a scheduling scheme for multi-energy collaboration and low-carbon, high-efficiency operation.

10. A comprehensive energy system scheduling optimization device considering source-load-hydrogen coupling, characterized in that, include: The first modeling unit is used to construct an integrated energy system operation model that includes wind power generation, photovoltaic power generation, hydropower station, electrolyzer, fuel cell, methane reactor, carbon capture and storage system, electrical energy storage and hydrogen energy storage, and to set hydraulic, thermal and hydrogen energy balance and equipment operation constraints. The second modeling unit is used to construct a multi-objective optimization function that includes cost minimization, absorption rate maximization, and operational stability. In addition, a solution unit is used to solve the multi-objective optimization function using a rolling time-series optimization strategy to obtain the optimal operating scheme of each device within the scheduling cycle, thereby achieving multi-energy collaboration and low-carbon, high-efficiency operation.

Images