An energy flow-substance flow coordination-based electric-hydrogen-carbon coupling multi-energy system optimal operation method

Abstract

The application discloses an energy flow-substance flow based electric-hydrogen-carbon coupled multi-energy system optimal operation method, first, a collaborative operation framework of the electric-hydrogen-carbon coupled multi-energy system is constructed, and various power generation units, electrolytic hydrogen production devices, hydrogen energy storage devices, carbon capture devices and chemical cogeneration devices are uniformly embedded in a multi-node network structure; secondly, the establishment of the power subsystem, the hydrogen subsystem and the carbon capture and carbon-based chemical utilization subsystem establishes the energy-substance flow balance constraint, the network power flow and capacity constraint and the equipment operation logic constraint; finally, the maximum annual economic benefit is taken as the target to construct the electric-hydrogen-carbon coupled multi-energy system optimal operation objective function, the above-mentioned various constraints and the objective function are integrated into a collaborative optimization model, and the annual collaborative optimal operation scheme is obtained by solving, thereby providing operable technical support for the planning and design and operation management of a new energy large base electric-hydrogen-carbon integrated project.

Description

Technical Field

[0001] This invention relates to the field of new energy multi-energy complementarity and energy system optimization scheduling technology, and in particular to an optimized operation method for an electric-hydrogen-carbon coupled multi-energy system based on energy flow-material flow coordination. Background Technology

[0002] In recent years, with the continuous advancement of the "dual carbon" target, my country's installed capacity of renewable energy such as wind power and photovoltaics has grown rapidly, and multiple large-scale "wind-solar-thermal-storage integrated" new energy base projects have been launched. While large-scale new energy integration brings clean power to the power system, it also introduces significant volatility and intermittency, leading to a series of operational problems such as voltage fluctuations, frequency deviations, and power flow reversals.

[0003] To mitigate the impact of high-proportion renewable energy integration on the power system, it is necessary to introduce flexible resources from the power source side, such as gas turbines, pumped storage, and electrochemical energy storage, while also proactively implementing demand-side response and load management from the load side. However, in large renewable energy base areas like desert and wasteland far from load centers, load levels are limited and the load structure is dominated by electrical loads. Traditional methods for improving the allocation of flexible resources suffer from high costs, low utilization rates, and insufficient environmental friendliness.

[0004] With the maturation of key technologies such as water electrolysis for hydrogen production, hydrogen energy storage, fuel cells, and carbon capture and carbon-based chemical utilization, more and more research and engineering practices are focusing on the synergistic development and integrated operation of electricity-hydrogen-carbon coupled systems. By configuring hydrogen production units and hydrogen energy storage systems in new energy bases, hydrogen can be produced using off-peak electricity prices or surplus electricity, converting some intermittent electrical energy into dispatchable hydrogen energy and realizing energy transfer across time periods. Furthermore, by configuring carbon capture devices and coupling them with carbon-based chemical plants such as methanol, methane, and urea plants, some carbon dioxide can be converted into high-value-added chemical products, reducing carbon emissions and improving overall economic efficiency.

[0005] Existing research has made a series of advances in modeling electro-hydrogen coupling or electro-hydrogen-carbon coupling systems, but the following shortcomings still exist: (1) Most studies only focus on the energy balance of the power system, and do not give enough consideration to the material conservation relationship between hydrogen and carbon-based chemical products, and lack unified modeling and collaborative analysis of energy flow and material flow. (2) Some work only establishes optimization models for a typical day or short-term operation scenario, making it difficult to evaluate the annual operation benefits and new energy consumption potential of multi-energy systems on an 8760-hour scale; (3) In dealing with multi-network constraints, highly aggregated or single-node approximate models are generally used, which do not fully characterize the power flow characteristics and capacity constraints between the power grid, hydrogen pipeline network and potential heat network, and make it difficult to accurately assess the bottleneck locations of key equipment and transmission and distribution channels. (4) The objective functions are mostly based on minimizing costs or local economic indicators, failing to systematically take into account the capacity for new energy absorption, carbon emission reduction effect and chemical co-production benefits.

[0006] Therefore, how to uniformly characterize the energy flow and material flow in a multi-node electricity-hydrogen-carbon coupling system, while taking into account both annual economic efficiency and low carbon emissions, and achieve synergistic optimization and comprehensive benefit improvement of new energy power, hydrogen production, hydrogen storage, carbon capture and carbon-based chemical co-production, remains a major problem that urgently needs to be solved. Summary of the Invention

[0007] This invention provides an optimized operation method for an electric-hydrogen-carbon coupled multi-energy system based on the synergy of energy flow and material flow, in order to overcome the above-mentioned technical problems.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: An optimized operation method for an electric-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination, comprising the following steps: S1. Construct a collaborative operation framework for an electricity-hydrogen-carbon coupled multi-energy system, wherein the electricity-hydrogen-carbon coupled multi-energy system includes at least: an electricity subsystem, a hydrogen subsystem, and a carbon capture and carbon-based chemical utilization subsystem; S2. Based on the aforementioned collaborative operation framework, construct energy-material flow balance constraints, network power flow and capacity constraints, and equipment operation logic constraints respectively; S3. Construct an objective function for optimizing the operation of the electric-hydrogen-carbon coupled multi-energy system with the goal of maximizing economic benefits throughout the year; S4. The objective function for optimizing the operation of the electric-hydrogen-carbon coupled multi-energy system is integrated with energy flow-mass flow constraints, network power flow and capacity constraints, and equipment operation logic constraints into a collaborative optimization model; Hourly data for the entire year or representative time-series data obtained through typical daily clustering and scenario reduction are used as the optimization time scale, substituted into the collaborative optimization model, and the solver is called to solve the collaborative optimization model to obtain the optimal collaborative operation scheme for each time period of the entire year. The energy-mass flow balance constraints include power balance constraints of the power subsystem, renewable energy output constraints, mass balance constraints of the hydrogen subsystem, operational constraints of the electrolytic hydrogen production and hydrogen energy storage unit, and carbon flow balance constraints of the carbon capture and carbon utilization subsystem. The network power flow and capacity constraints include DC power flow constraints of the power grid, hydrogen pipeline transmission and distribution capacity constraints, and power flow capacity constraints of multiple network couplings of optional thermal subsystems. The equipment operation logic constraints include unit start-up and shutdown constraints and equipment output limit constraints.

[0009] Furthermore, on an hourly scale, let... t For time period index, n For node indexing, then in any time period t and nodes n The generalized form of the power balance constraint of the power subsystem is expressed as: (1) in, For node n, the first g The power generation unit at time t Those who have made contributions To purchase power from the external power grid, and They are respectively from nodes m To node n and by nodes n Active power of the line to node m Let n be the electrical load power at node n. The power consumed in the electrolytic hydrogen production at node n. Power consumed by the electric heating and electro-heat conversion units. Electricity for chemical synthesis; Furthermore, the renewable energy output constraint is expressed as: (2) (3) in, and These represent the time periods for wind power and solar power, respectively. t and nodes n The amount of abandoned power; and These represent the time periods for wind power and solar power, respectively. t and nodes n The power; , They are nodes n Wind power and photovoltaic installed capacity, , This is a normalized output curve obtained based on historical resource data. Furthermore, on an hourly scale, in any given time period t and nodes n The mass balance constraint of the hydrogen subsystem is expressed as: (4) in, For nodes n Uppere The hydrogen flow rate produced by the Taiwanese electrolyzer at time t For nodes n Hydrogen storage device at time t Hydrogen release flow rate; and The hydrogen pipeline is located at the node. m With nodes n Inter-transport flow rate; This represents the terminal hydrogen load flow rate. The hydrogen flow rate consumed by the chemical synthesis unit. The hydrogen charging flow rate for the hydrogen storage device.

[0010] Furthermore, the operational constraints of the electrolytic hydrogen production and hydrogen energy storage unit include: electro-hydrogen energy conversion constraints, hydrogen energy storage state recursion constraints, energy storage equipment rated limit constraints, and monthly energy storage closed-loop boundary constraints. The electro-hydrogen energy conversion constraint is used to characterize the conversion ratio between the input electrical power of electrolysis and the output hydrogen flow rate, and is expressed in a time-by-time summary form for a single electrolyzer as follows: (5) in, Indicates the hydrogen production rate; For the efficiency of the electrolytic cell, Input electrical power to the electrolytic cell; Set a recursive constraint on the hydrogen storage inventory status for each scheduling period, expressed as: (6) in, and For a moment t and t +1 node n Hydrogen energy storage, and These are the hydrogen charging and dehydrogenation efficiencies, respectively. Based on equation (6), the recursive constraint for the total hydrogen storage in the natural day dimension is obtained, expressed as: (7) in, and The first d End of the day and the d -1 Day End Node n hydrogen energy storage capacity, , These are the hydrogen charging and dehydrogenation efficiencies, respectively. The rated limit constraints for energy storage equipment include upper and lower limits for hydrogen storage capacity, upper limit for hydrogen charging flow rate in a single time period, and upper limit for hydrogen discharging flow rate in a single time period, which are expressed as follows: (8) (9) (10) in, and These represent the lower and upper limits of the hydrogen storage tank capacity, respectively. Indicates the maximum hydrogen charging rate; Indicates the maximum hydrogen release rate; Set a closed-loop boundary constraint for the monthly hydrogen energy storage level, that is, at any node n The requirement is that the hydrogen storage at the end of each natural month must be equal to the initial storage at the beginning of the month.

[0011] Furthermore, at any given time period t The carbon flow balance constraint of the carbon capture and utilization subsystem in the overall system general form is expressed as: (11) in, For untreated carbon dioxide emissions from combustion sources, For carbon capture devices at time t The amount of carbon dioxide captured. This represents the amount of carbon dioxide consumed in the synthesis of carbon-based products such as methanol, methane, and urea at that moment. This represents the amount of carbon dioxide offset through carbon allowance trading at that moment. For system time t Net emissions.

[0012] Furthermore, the DC power flow constraints of the power grid include: (12) (13) in, For the line l At any moment t active power, These are the phase angles of the nodes at both ends of the line. For line susceptance, This represents the maximum line capacity. The hydrogen pipeline distribution capacity constraints include: Hydrogen pipeline flow capacity constraint, used to limit the bidirectional hydrogen pipeline flow rate to not exceed the pipeline's rated transport capacity in a single time period, is expressed as: (14) in, This is the upper limit of the hydrogen pipeline capacity.

[0013] Furthermore, the objective function for optimizing the operation of the electricity-hydrogen-carbon coupled multi-energy system, constructed with the goal of maximizing annual economic benefits, can be summarized as follows: (15) in, For electricity purchase price, For the first k Unit revenue of chemical products For the corresponding output, For the first u Fuel cost coefficient, To correspond to fuel consumption power, This is the carbon emission cost coefficient. This is the power curtailment penalty coefficient. For a moment t The amount of abandoned electricity.

[0014] The present invention has the following beneficial effects: First, this invention constructs a unified modeling framework for energy flow and material flow in an electric-hydrogen-carbon coupled multi-energy system, which characterizes the physical and chemical processes of various devices from three dimensions: electric power, hydrogen flow rate and carbon dioxide flow rate, and realizes unified modeling of key devices in the multi-energy system. Second, this invention proposes a multi-network collaborative constraint method based on node power balance, hydrogen material balance and carbon flow balance, which unifies power grid flow constraints, hydrogen pipeline transmission and distribution constraints and energy storage device operation constraints into a single optimization framework, significantly improving the model's ability to characterize the coupling characteristics of multiple networks. Third, this invention constructs an optimized operation model with the goal of maximizing economic benefits throughout the year. It comprehensively considers electricity sales revenue, hydrogen and carbon-based product revenue, electricity purchase and fuel costs, carbon emission costs, curtailment penalty costs, and operation and maintenance costs, and can quantitatively evaluate the comprehensive benefits of the multi-energy system under 8760 hours of full-time operation. Fourth, the optimized operation method provided by this invention can output detailed equipment output plans, energy and material flow distribution, and key operation indicators for each time period and node, providing operable technical support for the planning, design, and operation management of the integrated electricity-hydrogen-carbon project of the new energy base. Fifth, this invention can optionally introduce investment decision variables such as the installed capacity of wind power, photovoltaic and thermal power units, as well as hydrogen transportation infrastructure, incorporate the annualized cost of capacity into the objective function, and support unified modeling of various hydrogen transportation modes such as pipeline hydrogen transportation and tanker / ship transportation, so as to realize the integrated collaborative optimization of "planning-operation" on the power supply side and the hydrogen transportation side, and provide a quantitative basis for the selection of new energy base schemes and phased construction. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of an optimized operation method for an electric-hydrogen-carbon coupled multi-energy system based on energy flow-material flow coordination, as described in this invention. Figure 2 This is a schematic diagram illustrating the synergistic conversion relationship between electrical energy flow, hydrogen energy flow, and carbon-based material flow in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] To address the shortcomings of existing technologies and the need for optimization, this embodiment provides an optimized operation method for an electricity-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination. This method focuses on the structural optimization, energy and mass flow modeling, dynamic operational constraints, and market responsiveness of a multi-energy system composed of a multi-node power grid, hydrogen network, and carbon capture and utilization devices in a typical scenario of a large-scale new energy base. Figure 1 and Figure 2 As shown, the specific steps include: S1. Construct a collaborative operation framework for an electricity-hydrogen-carbon coupled multi-energy system, wherein the electricity-hydrogen-carbon coupled multi-energy system includes at least: an electricity subsystem, a hydrogen subsystem, and a carbon capture and carbon-based chemical utilization subsystem; Specifically, this embodiment comprehensively analyzes the system structure and energy / matter flow paths of the new energy base, clarifies the flow direction and operational interaction between each energy unit and matter unit, and thus constructs a system as follows: Figure 2 The framework shown is a collaborative operation framework for an electricity-hydrogen-carbon coupled multi-energy system. This framework supports the configuration of wind turbines, photovoltaic power plants, coal-fired or gas-fired power plants, combined heat and power (CHP) units, water electrolysis hydrogen production units, hydrogen energy storage units, thermal energy storage units, battery energy storage units, carbon capture units, and carbon-based chemical plants such as ammonia synthesis, methanol, and methane at each node, achieving unified modeling and collaborative optimization of electrical energy flow, hydrogen energy flow, and carbon-based material flow.

[0019] In this embodiment, the main energy flow and material flow paths and coupling relationships within the electro-hydrogen-carbon coupled multi-energy system include: (1) In terms of power subsystem, wind power and photovoltaic power generation are the main renewable power sources. After being inverted and connected to the grid, they directly supply the power load. They can also provide power for equipment such as electrolytic hydrogen production devices, electric boilers, battery energy storage and carbon capture, so as to realize the multi-path utilization and consumption of renewable energy. (2) Regarding the hydrogen subsystem, the water electrolysis device uses electricity to produce hydrogen. On the one hand, the hydrogen can be compressed and sent to the hydrogen energy storage device or the hydrogen pipeline / tank truck system to shift peaks and balance in time and space; on the other hand, it can be sent to gas turbines or fuel cells to perform "hydrogen-to-electricity" conversion and participate in the peak shaving and backup of the power system. (3) In terms of carbon capture and carbon-based chemical utilization subsystem, the flue gas generated by thermal power units and CHP units captures carbon dioxide through carbon capture devices. The captured CO2 can be directly used to synthesize carbon-based chemical products such as ammonia, methanol, and methane. It can also be supplemented by purchasing CO2 when the demand is insufficient. The uncaptured CO2 is emitted into the atmosphere and the emission amount is included in the carbon emission cost. Specifically, this embodiment implements multi-network transmission and distribution. The power grid serves as an external energy interaction platform, enabling interconnection and mutual supply of electrical energy between nodes. Hydrogen can be continuously transported through fixed pipelines or transported in batches via tank trucks / ships. Different hydrogen transportation methods differ in terms of capacity constraints and economics, and can be flexibly configured according to the scenario. Depending on the actual situation, a district heating network can also be introduced to achieve multi-energy synergy of cooling, heating, electricity, and hydrogen.

[0020] Specifically, on a time scale, the optimization method in this embodiment supports modeling and solving hourly operating data for 8760 hours throughout the year, covering the analysis of multi-dimensional operating characteristics such as energy and material flow, power / flow balance and economic benefits among wind power, photovoltaic power, thermal power, electrolytic hydrogen production, hydrogen energy storage, carbon capture, carbon-based chemical plants, power grid and hydrogen grid. S2. Based on the aforementioned collaborative operation framework, construct energy-material flow balance constraints, network power flow and capacity constraints, and equipment operation logic constraints respectively; Specifically, this embodiment employs a hierarchical modeling and constraint integration approach to systematically express the power output, energy / mass flow, and dynamic coupling relationships of various types of equipment. The energy-mass flow balance constraints include power balance constraints for the power subsystem, renewable energy output constraints, mass balance constraints for the hydrogen subsystem, operational constraints for the electrolytic hydrogen production and hydrogen storage units, and carbon flow balance constraints for the carbon capture and utilization subsystem. The network power flow and capacity constraints include DC power flow constraints for the power grid, hydrogen pipeline transmission and distribution capacity constraints, and multi-network coupled power flow capacity constraints for the optional thermal subsystem. The equipment operation logic constraints include unit start-up and shutdown constraints and equipment output limit constraints.

[0021] In a specific embodiment, at the time-by-time scale, let t For time period index, n For node indexing, then in any time period t and nodes n The generalized form of the power balance constraint of the power subsystem is expressed as: (1) in, For node n, the first g The power generation unit at time t Those who have made contributions To purchase power from the external power grid, and They are respectively from nodes m To node n and by nodes n The active power of the line to node m, Let n be the electrical load power at node n. The power consumed in the electrolytic hydrogen production at node n. Power consumed by the electric heating and electro-heat conversion units. This refers to the electrical power used in chemical synthesis.

[0022] Specifically, in this embodiment, to clearly characterize the energy income and expenditure of all subdivided power generation and consumption equipment within the system, each power item in the above general formula is decomposed. The power balance constraint corresponding to the subdivided equipment can be expressed as: (2) in, and Representing nodes respectively n During the period t The grid-connected power of wind and solar power, Indicates the output of the thermal power unit. This indicates the generating capacity of the CHP unit. Indicates the battery's energy storage and discharge power. This indicates the output of the hydrogen power generation device. This indicates the power flow calculated to the node via the power grid. n Net received power; For hydrogen production power by water electrolysis, The power consumption of the carbon capture device. and These represent the compression / processing power of hydrogen and carbon dioxide, respectively. For the electrical power used in chemical synthesis plants, For the electrical power consumption of the air separation nitrogen production unit, Battery charging power, For the power consumption of the electric boiler, This represents the electrical load power at the node.

[0023] To reflect the coupling relationship between wind and solar power output, installed capacity, and resource conditions, this embodiment uses the "installed capacity × normalized output curve" method to describe the available power of renewable energy sources.

[0024] set up , They are nodes n Wind power and photovoltaic installed capacity, , To establish a linear coupling relationship between the wind and solar installed capacity planning variables and the hourly available output based on the normalized output curve obtained from historical resource data, the renewable energy output constraint is expressed as: (3) (4) in, and These represent the time periods for wind power and solar power, respectively. t and nodes n The amount of abandoned power; and These represent the time periods for wind power and solar power, respectively. t and nodes n The power.

[0025] Specifically, the hydrogen subsystem includes stages such as hydrogen electrolysis, hydrogen energy storage, hydrogen-to-electricity conversion, and hydrogen delivery. This embodiment establishes a hydrogen mass conservation constraint on a daily scale and links it to variables such as electrolysis power, hydrogen for chemical use, and hydrogen-to-electricity conversion on an hourly scale.

[0026] In a specific embodiment, on an hourly scale, at any given time period t and nodes n The mass balance constraint of the hydrogen subsystem is expressed as: (5) in, For nodes n Upper e Taipower's electrolytic cell at all times t Hydrogen production flow rate, For nodes n Hydrogen storage device at time t Hydrogen release flow rate; and The hydrogen pipeline is located at the node. m With nodes n Inter-transport flow rate; This represents the terminal hydrogen load flow rate. The hydrogen flow rate consumed by the chemical synthesis unit. The hydrogen charging flow rate for the hydrogen storage device; To achieve long-term total quantity control based on the natural day period, the hydrogen flow rate for all time periods of a single day is aggregated. The mass balance constraint of the hydrogen subsystem at the natural day aggregation scale is expressed as: (6) in, d Indexed by natural day, For nodes n During the period t Hydrogen production capacity, , The first d Daily hydrogen charging and discharging volume, Hydrogen quantity for chemical synthesis The amount of hydrogen used for hydrogen-to-electricity conversion. For the first d Daily hydrogen load service volume, To be converted to nodes via the hydrogen network n Daily net input; In a specific embodiment, the operational constraints of the electrolytic hydrogen production and hydrogen energy storage unit include: electro-hydrogen energy conversion constraints, hydrogen energy storage state recursion constraints, energy storage equipment rated limit constraints, and monthly energy storage closed-loop boundary constraints. The electro-hydrogen energy conversion constraint is used to characterize the conversion ratio between the input electrical power of electrolysis and the output hydrogen flow rate, and is expressed in a time-by-time summary form for a single electrolyzer as follows: (7) In the formula, This represents the hydrogen production rate of a single electrolyzer. For the efficiency of the electrolytic cell, Input electrical power to the electrolytic cell; Equation (7) uses a single electrolyzer as the statistical unit to reflect the basic electro-hydrogen conversion logic; to adapt to the aggregation calculation caliber of the embodiment which takes nodes as the whole, the hydrogen production of a single electrolyzer is summarized to obtain the overall refined conversion relationship of the node, corresponding to the following constraints: (8) in, The hydrogen production coefficient per unit electrolysis power; Represents a node n All electrolyzers during the time period t The hydrogen production rate; Represents the node n The power of all electrolytic cells; Specifically, Equation (7) and Equation (8) have the same physical conversion core, only the statistical granularity is different. Both quantify the scale of the system's electrical energy to hydrogen energy production.

[0027] After calculating the hydrogen production, the produced hydrogen can be stored in a hydrogen storage device. Therefore, a recursive constraint on the hydrogen storage capacity status is further set for each scheduling period to characterize the dynamic change of the hydrogen storage capacity at adjacent minimum scheduling times, expressed as: (9) in, and For a moment t and t +1 node n Hydrogen energy storage, and These are the hydrogen charging and dehydrogenation efficiencies, respectively. Equation (9) is applicable to hourly fine-sequence scheduling; to meet the total balance control requirements at the natural day scale, the hydrogen charging and discharging flows throughout the entire day are accumulated and aggregated to obtain the recursive constraint of the total hydrogen storage volume at the natural day scale, expressed as: (10) in, and The first d End of the day and the d -1 Day End Node n hydrogen energy storage capacity, , These are the hydrogen charging and dehydrogenation efficiencies, respectively. Specifically, Equations (9) and (10) are recursive equations with different time granularities under the same hydrogen storage conservation logic, respectively adapted to short-term fine simulation and long-term daily total amount calculation.

[0028] The above two types of energy storage recursive equations only describe the changes in stock volume. It is also necessary to limit the equipment operation to not exceed rated operating conditions. Therefore, corresponding rated limit constraints are set for energy storage equipment, including upper and lower limits for hydrogen storage capacity, upper limit for hydrogen charging flow rate in a single time period, and upper limit for hydrogen discharging flow rate in a single time period, expressed as follows: (11) (12) (13) in, and These represent the lower and upper limits of the hydrogen storage tank capacity, respectively. Indicates the maximum hydrogen charging rate; Indicates the maximum hydrogen release rate; Specifically, equations (11), (12), and (13) are hard inequality boundaries. Regardless of whether equation (9) is used for time-by-time recursion or equation (10) is used for daily total recursion, all hydrogen storage-related variables must simultaneously satisfy these three sets of limit constraints at any time and on any natural day.

[0029] Specifically, relying solely on equations (9) and (10) for long-term monthly calculations would cause a continuous shift in the hydrogen storage value, disrupting the consistency of the simulation benchmark for each month throughout the year. Therefore, this embodiment sets a closed-loop boundary constraint for the monthly hydrogen storage level, that is, at any node... n The requirement is that the hydrogen storage at the end of each natural month is equal to the initial storage at the beginning of the month. This boundary condition is used as an upper-level cycle control rule in conjunction with Equations (9) and (10) to eliminate the cumulative deviation of the storage in long-term simulation, thereby ensuring that the optimization results throughout the year achieve physical closed loop between each month. Specifically, in an electric-hydrogen-carbon coupled multi-energy system, thermal power units and CHP units are the main sources of carbon dioxide emissions. In this embodiment, at any given time period... t The carbon flow balance constraint of the carbon capture and utilization subsystem in the overall system general form is expressed as: (14) in, For untreated carbon dioxide emissions from combustion sources, For carbon capture devices at time t The amount of carbon dioxide captured. This represents the amount of carbon dioxide consumed in the synthesis of carbon-based products such as methanol, methane, and urea at that moment. This represents the amount of carbon dioxide offset through carbon allowance trading at that moment. For system time t Net emissions; Equation (14) represents the macroscopic carbon balance constraint covering the entire system, including carbon capture, carbon chemical utilization, and carbon quota trading. To refine the characterization of the on-site diversion path after the original carbon dioxide production of thermal power units and CHP units, after removing the carbon chemical and carbon trading links, a simplified carbon flow balance constraint describing only the carbon emission distribution relationship of the units is obtained, as shown in Equation (15): (15) in, For an electric-hydrogen-carbon coupled multi-energy system in a time period t The original CO2 emissions, The amount of CO2 captured by the carbon capture device; This refers to the amount of CO2 directly emitted into the atmosphere. The calculation formula is: (16) in, and These are the unit power generation emission coefficients for thermal power and CHP units, respectively; To utilize the captured CO2 in downstream chemical synthesis, this embodiment further includes the following: (17) in, The amount of CO2 purchased from external sources. This refers to the total CO2 consumption required for chemical plants to synthesize products such as ammonia, methanol, and methane.

[0030] Specifically, this embodiment can achieve full-chain modeling of carbon flow from "original emission – capture – emission – purchase – utilization" by combining equations (16) and (17), and introduce carbon purchase cost and direct emission penalty into the objective function to achieve economic constraints on carbon emission behavior.

[0031] In a specific embodiment, the DC power flow constraint of the power grid includes: (18) (19) in, For the line l At any moment t active power, These are the phase angles of the nodes at both ends of the line. For line susceptance, This represents the maximum line capacity. The hydrogen pipeline distribution capacity constraints include: Hydrogen pipeline flow capacity constraint, used to limit the bidirectional hydrogen pipeline flow rate to not exceed the pipeline's rated transport capacity in a single time period, is expressed as: (20) in, This is the upper limit of the hydrogen pipeline capacity.

[0032] Specifically, at the hydrogen distribution level, hydrogen can be transferred between nodes via pipeline or tanker / ship transport. To differentiate the contribution of these two transport channels to the node's hydrogen supply, a daily net input constraint for multi-mode hydrogen distribution is further set, decomposing the total daily net hydrogen input at each node into two parts: pipeline transport and tanker land transport, expressed as: (twenty one) in, and These represent the conversion to nodes via pipeline and tanker truck, respectively. n Daily net input; Among them, pipeline transport flow is constrained by the maximum transport capacity of the pipeline, while tanker transport volume is determined by the loading capacity of a single vehicle and the frequency of departures. By setting capacity and cost parameters for different transport modes, the impact of various infrastructure options such as "pipeline only," "tank / ship only," and "pipeline + tanker coexistence" on the economic operation of the system can be flexibly evaluated.

[0033] In a specific embodiment, when the system is configured with a district heating network and a thermal storage device, this embodiment adds optional thermal subsystem and multi-network coupling constraints. These optional thermal subsystem and multi-network coupling constraints include thermal power balance constraints, thermal storage time-series operation constraints, and heat network flow capacity constraints. The thermal power balance constraints and thermal storage time-series operation constraints characterize the heat flow relationship between the CHP unit, electric boiler, and thermal storage unit. At the same time, combined with the power flow equations of the power grid, hydrogen network, and thermal network, the power and medium flow of each network branch are uniformly limited to not exceeding the rated transmission capacity of the equipment, thus completing the coordinated operation and management of the energy of the electric-hydrogen-thermal multi-network.

[0034] In a specific embodiment, in order to ensure the safe and stable operation of key equipment such as thermal power units, CHP units, and electrolytic hydrogen production units, this embodiment sets unit start-up and shutdown constraints. By introducing start-up and shutdown binary variables, the start-up and shutdown process of the units is characterized, and the maximum number of start-ups and shutdowns per day, the minimum start-up and shutdown time, and the start-up energy consumption constraints are set.

[0035] Specifically, for thermal power units, let u(t) represent the binary variable of the unit's operating state during time period t, and v(t) represent the start-stop switching variable. Then, by using linear constraints, the changes of v(t) and u(t) in adjacent time periods are correlated, and further... Setting an upper limit limits the frequency of start-ups and shutdowns, thus avoiding reduced efficiency and equipment wear caused by frequent start-ups and shutdowns.

[0036] In this embodiment, for electrolytic hydrogen production units and some chemical units, equipment output limits (minimum stable output) can be set according to process characteristics, and start-up energy consumption and shutdown time constraints can be set to reflect the characteristics of limited start-up and shutdown of the unit in actual operation, and to ensure that the optimization scheme is feasible in engineering.

[0037] In this embodiment, an ancillary services model and power constraints can also be constructed to measure the ability of an electricity-hydrogen-carbon coupled multi-energy system to participate in ancillary services and market transactions: To achieve flexible adjustment and improved economic efficiency of multi-energy systems, this embodiment characterizes the system's market participation capabilities from two dimensions: the power sector and the hydrogen sector. The power sector includes ancillary services such as peak shaving (both upward and downward adjustments), reserve capacity, and demand response; the hydrogen sector includes hydrogen supply services for industrial users, transportation, or synthetic fuel intermediates. By comprehensively constraining factors such as available reserve capacity, ramp-up capability, storage SOC, and hydrogen reserves, the available capacity for upward or downward adjustments in each time period is determined, and the revenue from ancillary services and hydrogen supply is calculated based on a market price model.

[0038] S3. Construct an objective function for optimizing the operation of the electric-hydrogen-carbon coupled multi-energy system with the goal of maximizing economic benefits throughout the year; In a specific embodiment, the objective function for optimizing the operation of the electricity-hydrogen-carbon coupled multi-energy system, which aims to maximize annual economic benefits, is expressed in the following general form: (twenty two) in, For electricity purchase price, For the first k Unit revenue of chemical products For the corresponding output, For the first u Fuel cost coefficient, To correspond to fuel consumption power, This is the carbon emission cost coefficient. This is the power curtailment penalty coefficient. For a moment t The amount of abandoned power; In this embodiment, to adapt to the needs of full-dimensional economic accounting simulation, the revenue and expenditure items are further refined based on the general framework of Equation (12), and the economic indicators of the entire industrial chain, such as hydrogen sales service revenue, power auxiliary service revenue, annualized equipment investment cost, hydrogen storage and transportation cost, water cost, and load shedding penalty, are expanded to obtain the annual comprehensive benefit objective function in the form of complete item expansion: (twenty three) in, , , These are revenues from the sale of chemical products, revenues from hydrogen auxiliary services, and revenues from electricity auxiliary services. , , , , , , These are fuel costs, carbon emission costs, water resource costs, curtailment penalties, load shedding penalties, investment costs, and transmission costs.

[0039] Specifically, in another alternative embodiment, the objective function can also be set to minimize the overall cost.

[0040] Specifically, the revenue from synthetic products is obtained by multiplying the annual output of synthetic ammonia, methanol, methane, etc., by the corresponding product prices and summing them up; the revenue from hydrogen services is obtained by multiplying the annual hydrogen supply to various hydrogen loads by the hydrogen service price and summing them up; the revenue from electricity and ancillary services is determined by the electricity sold, the electricity purchased, and the ability to participate in the ancillary services market, along with the corresponding prices; and the revenue from various cost items is obtained by multiplying the corresponding power / flow / installed capacity variables by the unit cost coefficient and summing them up.

[0041] S4. Integrate the objective function of the electric-hydrogen-carbon coupled multi-energy system with energy flow-mass flow constraints, network power flow and capacity constraints, and equipment operation logic constraints into a collaborative optimization model; use 8760 hours of hourly data throughout the year or representative time-series data obtained through typical daily clustering and scenario reduction as the optimization time scale, substitute it into the collaborative optimization model, and call a mixed integer linear programming or mixed integer quadratic programming solver to solve the collaborative optimization model to obtain the electric power, hydrogen flow, carbon flow, and collaborative optimal operation scheme of multi-energy equipment for each node in the collaborative operation framework for each period of the year. The aforementioned collaborative optimization model, a mixed-integer linear programming model, takes a multi-node topology and an 8760-hour or equivalent representative scenario as a basis. It simultaneously considers the installed capacity of wind power, photovoltaic, and thermal power units, as well as the channel capacity of various hydrogen transportation methods such as hydrogen pipelines and tanker / ship transport, as decision variables. The corresponding annualized investment and transportation costs are explicitly included in the objective function, thus forming an integrated "planning-operation" collaborative optimization model that balances capacity planning and operational scheduling. Solving this model automatically outputs the optimal operating trajectory and annual economic-environmental comprehensive evaluation indicators for the multi-energy system under different hydrogen transmission modes and installed capacity schemes. This information can be used to guide the scheme comparison and rolling optimization of integrated electricity-hydrogen-carbon projects in large-scale new energy bases.

[0042] Specifically, in this embodiment, the objective function, along with energy-mass flow balance constraints, network power flow and capacity constraints, and equipment operation logic constraints, constitutes a mixed-integer linear programming model, which is then solved using a mature mathematical programming solver (such as Gurobi). Given wind power and photovoltaic resource curves, electrical load, hydrogen load, and heat load forecast data, the optimal solution output by the solver should include at least the following: (1) Optimal configuration scheme of wind power, photovoltaic and thermal power installed capacity at each node; (2) Hourly balance results of electrical energy flow, hydrogen energy flow, thermal energy flow and carbon-based material flow and equipment output plan; (3) The annual output and revenue of synthetic ammonia, methanol, methane and hydrogen services, as well as the cost breakdown of fuel, carbon purchase, water use, abandoned electricity, load shedding, installed capacity investment and hydrogen transportation, multi-network power flow distribution, and total annual profit of the system; (4) Key operating indicators such as energy storage SOC, load shedding level, carbon emission level, hydrogen storage, carbon capture, and line utilization rate provide quantitative basis for engineering design and operation strategy optimization.

[0043] In summary, this embodiment addresses the operational realities and optimization needs of the electricity-hydrogen-carbon coupled multi-energy system in a large-scale new energy base. It proposes a collaborative optimization operation method based on real physical constraints, energy flow-material flow collaborative modeling, and market rule coupling. This method integrates various equipment and subsystems, including wind power, photovoltaic power, thermal power, electrolytic hydrogen production, hydrogen storage, carbon capture and utilization, and multi-network transmission and distribution, into a unified optimization scheduling framework. This significantly improves the economy, flexibility, and low-carbon characteristics of the multi-energy system, providing strong technical support for the engineering demonstration of large-scale new energy bases and the planning of regional integrated energy systems.

[0044] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the operation of an electro-hydrogen-carbon coupled multi-energy system based on the synergy of energy flow and material flow, characterized in that, The specific steps include: S1. Construct a collaborative operation framework for an electricity-hydrogen-carbon coupled multi-energy system, wherein the electricity-hydrogen-carbon coupled multi-energy system includes at least: an electric subsystem, a hydrogen subsystem, and a carbon capture and carbon-based chemical utilization subsystem; S2. Based on the aforementioned collaborative operation framework, construct energy-material flow balance constraints, network power flow and capacity constraints, and equipment operation logic constraints respectively; S3. Construct an objective function for optimizing the operation of the electric-hydrogen-carbon coupled multi-energy system with the goal of maximizing economic benefits throughout the year; S4. The objective function for optimizing the operation of the electric-hydrogen-carbon coupled multi-energy system is integrated with energy flow-mass flow constraints, network power flow and capacity constraints, and equipment operation logic constraints into a collaborative optimization model; Hourly data for the entire year or representative time-series data obtained through typical daily clustering and scenario reduction are used as the optimization time scale, substituted into the collaborative optimization model, and the solver is called to solve the collaborative optimization model to obtain the optimal collaborative operation scheme for each time period of the entire year.

2. The optimized operation method for an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination according to claim 1, characterized in that, The energy-mass flow balance constraints include power balance constraints of the power subsystem, renewable energy output constraints, mass balance constraints of the hydrogen subsystem, operational constraints of the electrolytic hydrogen production and hydrogen energy storage unit, and carbon flow balance constraints of the carbon capture and carbon utilization subsystem. The network power flow and capacity constraints include DC power flow constraints of the power grid, hydrogen pipeline transmission and distribution capacity constraints, and power flow capacity constraints of multiple network couplings of optional thermal subsystems. The equipment operation logic constraints include unit start-up and shutdown constraints and equipment output limit constraints.

3. The optimized operation method for an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow synergy according to claim 2, characterized in that, On an hourly scale, let t For time period index, n For node indexing, then in any time period t and nodes n The generalized form of the power balance constraint of the power subsystem is expressed as: (1) in, For node n, the first g The power generation unit at time t Those who have made contributions To purchase power from the external power grid, and They are respectively from nodes m To node n and by nodes n Active power of the line to node m Let n be the electrical load power at node n. The power consumed in the electrolytic hydrogen production at node n. Power consumed by the electric heating and electro-heat conversion units. This refers to the electrical power used in chemical synthesis.

4. The optimized operation method for an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow synergy according to claim 3, characterized in that, The renewable energy output constraint is expressed as: (2) (3) in, and These represent the time periods for wind power and solar power, respectively. t and nodes n The amount of abandoned power; and These represent the time periods for wind power and solar power, respectively. t and nodes n The power; , They are nodes n Wind power and photovoltaic installed capacity, , This is a normalized output curve obtained based on historical resource data.

5. The optimized operation method for an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination according to claim 4, characterized in that, At an hourly scale, in any time period t and nodes n The mass balance constraint of the hydrogen subsystem is expressed as: (4) in, For nodes n Upper e The hydrogen flow rate produced by the Taiwanese electrolyzer at time t For nodes n Hydrogen storage device at time t Hydrogen release flow rate; and The hydrogen pipeline is located at the node. m With nodes n Inter-transport flow rate; This represents the terminal hydrogen load flow rate. The hydrogen flow rate consumed by the chemical synthesis unit. The hydrogen charging flow rate for the hydrogen storage device.

6. The optimized operation method for an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination according to claim 5, characterized in that, The operational constraints of the electrolytic hydrogen production and hydrogen energy storage unit include: electro-hydrogen energy conversion constraints, hydrogen energy storage state recursion constraints, energy storage equipment rated limit constraints, and monthly energy storage closed-loop boundary constraints. The electro-hydrogen energy conversion constraint is used to characterize the conversion ratio between the input electrical power of electrolysis and the output hydrogen flow rate, and is expressed in a time-by-time summary form for a single electrolyzer as follows: (5) in, Indicates the hydrogen production rate; For the efficiency of the electrolytic cell, Input electrical power to the electrolytic cell; Set a recursive constraint on the hydrogen storage inventory status for each scheduling period, expressed as: (6) in, and For a moment t and t +1 node n Hydrogen energy storage, and These are the hydrogen charging and dehydrogenation efficiencies, respectively. Based on equation (6), the recursive constraint for the total hydrogen storage in the natural day dimension is obtained, expressed as: (7) in, and The first d End of the day and the d -1 Day End Node n hydrogen energy storage capacity, , These are the hydrogen charging and dehydrogenation efficiencies, respectively. The rated limit constraints for energy storage equipment include upper and lower limits for hydrogen storage capacity, upper limit for hydrogen charging flow rate in a single time period, and upper limit for hydrogen discharging flow rate in a single time period, which are expressed as follows: (8) (9) (10) in, and These represent the lower and upper limits of the hydrogen storage tank capacity, respectively. Indicates the maximum hydrogen charging rate; Indicates the maximum hydrogen release rate; Set a closed-loop boundary constraint for the monthly hydrogen energy storage level, that is, at any node n The requirement is that the hydrogen storage at the end of each natural month must be equal to the initial storage at the beginning of the month.

7. The method for optimizing the operation of an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination according to claim 6, characterized in that, At any time period t The carbon flow balance constraint of the carbon capture and utilization subsystem in the overall system general form is expressed as: (11) in, For untreated carbon dioxide emissions from combustion sources, For carbon capture devices at time t The amount of carbon dioxide captured. This represents the amount of carbon dioxide consumed in the synthesis of carbon-based products such as methanol, methane, and urea at that moment. This represents the amount of carbon dioxide offset through carbon allowance trading at that moment. For system time t Net emissions.

8. The optimized operation method for an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination according to claim 7, characterized in that, The DC power flow constraints of the power grid include: (12) (13) in, For the line l At any moment t active power, These are the phase angles of the nodes at both ends of the line. For line susceptance, This represents the maximum line capacity. The hydrogen pipeline distribution capacity constraints include: Hydrogen pipeline flow capacity constraint, used to limit the bidirectional hydrogen pipeline flow rate to not exceed the pipeline's rated transport capacity in a single time period, is expressed as: (14) in, This is the upper limit of the hydrogen pipeline capacity.

9. The optimized operation method for an electro-hydrogen-carbon coupled multi-energy system based on energy flow-mass flow coordination according to claim 8, characterized in that, The objective function for optimizing the operation of an electricity-hydrogen-carbon coupled multi-energy system, constructed with the goal of maximizing annual economic benefits, can be summarized as follows: (15) in, For electricity purchase price, For the first k Unit revenue of chemical products For the corresponding output, For the first u Fuel cost coefficient, To correspond to fuel consumption power, This is the carbon emission cost coefficient. This is the power curtailment penalty coefficient. For a moment t The amount of abandoned electricity.

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