Online optimization scheduling method for low-carbon multi-energy coupling system based on lyapunov optimization

Abstract

This invention discloses an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization, belonging to the field of online optimization scheduling technology for low-carbon energy systems. The method involves constructing a low-carbon multi-energy coupled system architecture; building a virtual energy storage queue to reflect the current operating state of the system based on the dynamic changes in electrical energy, hydrogen energy, and carbon dioxide during system operation; constructing a Lyapunov function based on the virtual energy storage queue to reflect the dynamic changes in the system's energy state; and building a cyclic hydrogen economic operation model based on the Lyapunov drift-penalty function according to the system operating cost index. Under the constraints of system power balance and mass conservation, the low-carbon multi-energy coupled system is continuously optimized online. This invention can achieve coordinated optimization of economic benefits and carbon emission reduction effects, improving the stability, real-time performance, and sustainability of low-carbon integrated energy system operation.

Description

Technical Field

[0001] This invention relates to the field of online optimization scheduling technology for low-carbon energy systems, and in particular to an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization. Background Technology

[0002] Integrated Energy Systems (IES) achieve multi-energy complementarity and tiered energy utilization by unifying the modeling and coordinated scheduling of multiple energy forms, including electricity, heat, gaseous energy, and hydrogen energy, thereby improving overall system energy efficiency and reducing carbon emissions. Building on this, introducing hydrogen energy into the energy conversion and storage stages of IES, through the integrated production and storage of hydrogen from electricity, as well as the comprehensive recovery and utilization of hydrogen byproducts, enables time-shifting and flexible adjustment of energy even with significant fluctuations in renewable energy output, thus enhancing system stability and low-carbon attributes. However, introducing a dynamic utilization mechanism for hydrogen byproducts during the operation of IES significantly increases the coupling degree of energy flow within the system and the complexity of its operating state. On the one hand, the production and utilization of hydrogen byproducts are subject to multiple factors such as changes in system operating conditions, fluctuations in renewable energy, and changes in load demand, exhibiting significant time-varying characteristics and uncertainties. On the other hand, the mutual conversion and coordinated adjustment between multiple energy carriers place higher demands on the real-time performance and stability of the scheduling strategy.

[0003] To address the challenges of enhanced energy flow coupling, volatile operating states, and increased uncertainty in the optimal scheduling of low-carbon integrated energy systems, existing technologies often employ robust optimization, stochastic programming, and chance-constraint methods to improve system reliability under uncertain environments. However, these methods typically rely on statistical descriptions or predictions of future information, making it difficult for their optimization decisions to reflect rapid changes in system operating states in a timely manner. When faced with real-time disturbances and prediction errors, they are prone to degraded scheduling performance, impacting the overall system efficiency and economy. Lyapunov optimization methods based on queue stability theory have gained increasing attention due to their ability to achieve a balance between system stability and performance under stochastic and uncertain environments without relying on precise prediction information, and they offer a new theoretical framework for the online optimal operation of complex integrated energy systems.

[0004] Therefore, considering the dynamic characteristics of hydrogen byproducts and the random fluctuations of renewable energy, there is an urgent need for an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization. This method should be able to make online decisions and coordinate control based on the current operating status of the system without relying on accurate forecast information. While ensuring the safety and stability of system operation, it should also achieve comprehensive optimization of economic benefits and carbon emission reduction targets, thereby supporting the efficient and sustainable operation of low-carbon integrated energy systems. Summary of the Invention

[0005] The purpose of this invention is to propose an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization, comprising:

[0006] Based on the mathematical models of the gas-fired thermal power unit, carbon capture, storage and utilization device, electric-hydrogen coupling system, methanation unit, methanol synthesis unit, ammonia synthesis unit and urea synthesis unit, a low-carbon multi-energy coupling system architecture is constructed.

[0007] Based on the dynamic changes in electrical energy, hydrogen energy, and carbon dioxide during system operation, a virtual energy storage queue is constructed to reflect the current operating status of the system.

[0008] Based on the virtual energy storage queue, a Lyapunov function is constructed to reflect the dynamic changes in the system's energy state. According to the system operating cost index, a circular hydrogen economic operation model based on the Lyapunov drift-penalty function is constructed.

[0009] Continuous online optimization of a low-carbon multi-energy coupled system is performed under the constraints of system power balance and material conservation.

[0010] Furthermore, the virtual energy storage queue is as follows:

[0011] ;

[0012] ;

[0013] ;

[0014] ;

[0015] ;

[0016] ;

[0017] in, , and These represent virtual energy storage queues; , and Used to ensure satisfaction , and Upper limit constraint, , , These represent the maximum prices for electricity, hydrogen, and carbon emission penalties, respectively.

[0018] Furthermore, the cyclic hydrogen economic operation model based on the Lyapunov drift-penalty function is as follows:

[0019] ;

[0020] ;

[0021] ;

[0022] in, and Characterize IES in The operating costs and operating benefits at any given time; Used to describe the degradation costs incurred by battery energy storage systems during operation; This indicates the net CO2 emissions of the system equipment; and They represent the first Operating costs incurred by a thermal power unit during startup and shutdown; Indicates the flow rate of gas supplied by liquefied natural gas; This indicates the amount of nitrogen gas consumed during ammonia synthesis. and These respectively represent the system in Power purchased and sold at any given time; , , and These correspond to electrical load power, hydrogen load demand, non-TPPs natural gas demand, and ammonia demand, respectively. and These represent the production volumes of urea and methanol, respectively. , , , , , , , and These represent the prices of electricity, hydrogen, natural gas, urea, ammonia, methanol, liquefied natural gas, nitrogen, and carbon emission penalties, respectively. Represents the time-domain scheduling of the system operation; Represents a set of thermal power units; and These represent the electrical power of the battery energy storage system under discharge and charging conditions, respectively. and These represent the amount of CO2 captured and released by the CCUS device during operation, respectively. and These represent the hydrogen flow rates of the hydrogen energy storage system in the hydrogen release and hydrogen storage states, respectively. It is used to control the trade-off between energy costs and queue stability.

[0023] Furthermore, continuous online optimization of low-carbon multi-energy coupled systems includes:

[0024] Obtain the initial operating status of the system and set the Lyapunov control parameters. Set the scheduling period to To initialize the system;

[0025] Entering the time slot loop, during each scheduling period, the current operating status of the system and the virtual energy storage queue are observed to obtain real-time information;

[0026] Solve the optimization problem at time t based on the current system state, and determine whether the optimization result satisfies the constraints. If yes, execute the optimal control decision; if no, execute the constraint repair or backup control strategy to obtain a feasible solution that satisfies the system operation constraints, and then execute the optimal control decision.

[0027] Update the system's virtual energy storage queue based on the decision results for the current time period;

[0028] The system time is advanced to the next scheduling period, entering the next time slot cycle, to achieve continuous online optimization scheduling of the integrated energy system.

[0029] The beneficial effects of this invention are as follows:

[0030] This invention ensures the stability of energy storage systems and energy queues while achieving coordinated optimization of economic benefits and carbon emission reduction effects. It effectively overcomes the difficulty of traditional day-ahead or rolling forecast optimization methods in dealing with real-time uncertainties, and improves the stability, real-time performance and sustainability of low-carbon integrated energy system operation. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the overall execution of an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization, as described in this invention.

[0032] Figure 2 This is a schematic diagram of the overall structural framework of a low-carbon integrated energy system based on Lyapunov optimization.

[0033] Figure 3 A schematic diagram of the optimized operating mechanism of Lyapunov.

[0034] Figure 4 This is a graph showing the evolution of the virtual queue offset over time.

[0035] Figure 5 The graph shows the evolution of the economic benefits of a system based on Lyapunov online scheduling.

[0036] Figure 6(a), (b), and (c) are diagrams showing the optimal energy allocation results for electricity, hydrogen, and natural gas in a multi-energy coupled system.

[0037] Figure 7 This is a comparison chart of CO2 emission effects based on Lyapunov optimization.

[0038] Figure 8 This is a diagram showing the stacked area of ​​high-value-added products based on hydrogen energy conversion. Detailed Implementation

[0039] This invention proposes an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0040] Figure 1 This invention illustrates the overall execution flow of an online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization, as proposed in this invention; specifically, it includes:

[0041] I. Based on the mathematical models of the gas-fired cogeneration unit, carbon capture, storage and utilization device, electric-hydrogen coupling system, methanation unit, methanol synthesis unit, ammonia synthesis unit and urea synthesis unit, a low-carbon multi-energy coupling system architecture is constructed.

[0042] (1) Gas-fired cogeneration unit

[0043] To ensure the safety and economic efficiency of the integrated energy system during scheduling, the operating behavior and scheduling constraints of the gas-fired thermal power plant (TPP) were systematically characterized, and its mathematical model is described by equations (1) to (16). The active power output of the gas turbine unit is limited by its technical operating range to avoid exceeding the allowable operating conditions of the equipment and to ensure the long-term stable operation of the unit. Considering the significant nonlinear coupling relationship between fuel consumption and electrical power output of the gas turbine unit, a heat rate function is introduced to characterize the energy conversion characteristics of the unit. To balance model accuracy and solution efficiency, a piecewise linearization method is used to approximate this nonlinear relationship, enabling it to be embedded in a linear or mixed integer optimization framework for efficient solution. During the switching of the unit's operating state, the start-up and shutdown behaviors will introduce additional fixed operating costs and start-up and shutdown costs. To accurately reflect the impact of unit state changes on the economic efficiency of system operation, the above costs were explicitly modeled and combined with the unit's operating state variables to construct a complete cost description model. Meanwhile, in order to comprehensively constrain the operating characteristics of gas turbine units within the scheduling cycle, minimum continuous operating time constraints, minimum continuous shutdown time constraints, output ramp-up and ramp-down rate limits, and start-up / shutdown logic consistency constraints were further introduced, thus forming a complete unit operation constraint system.

[0044] It should be noted that in this implementation scheme, the methanation unit configured in the system can utilize some hydrogen and carbon dioxide resources to synthesize natural gas, thereby replacing the consumption of traditional fossil fuels to a certain extent and reducing the gas turbine unit's dependence on external fuels. The natural gas demand of the gas turbine unit can be calculated based on its heat rate characteristics and quantitatively described by establishing a corresponding fuel consumption model. The natural gas system is responsible for providing the required fuel to the gas turbine unit, realizing dynamic coordination between the fuel supply process and the unit's power generation behavior.

[0045] Furthermore, the fuel consumption level of gas turbine units not only directly determines the system's operating costs but is also a significant factor influencing the system's carbon emission intensity. By constructing cost functions and emission models related to fuel consumption, fuel cost control and carbon emission constraints can be considered synergistically during optimal scheduling, thereby promoting the integrated energy system towards low-carbon and high-efficiency operation.

[0046] ; (1)

[0047] ; (2)

[0048] ; (3)

[0049] ; (4)

[0050] ; (5)

[0051] ; (6)

[0052] ; (7)

[0053] ; (8)

[0054] ; (9)

[0055] ; (10)

[0056] ; (11)

[0057] ;(12)

[0058] ; (13)

[0059] ;(14)

[0060] ; (15)

[0061] ; (16)

[0062] in, This indicates the fuel consumption level of TPPs; , and These are the parameter coefficients that describe the heat rate function of TPPs; A binary decision variable reflecting the operating status of TPPS; and These are auxiliary binary variables used to constrain the startup and shutdown behavior of the generator units, in order to prevent the two states from occurring simultaneously within the same scheduling cycle; Indicates the first The generating capacity of the thermal power units in Taiwan; and Indicates the first The lower and upper limits of the power output of each TPP unit; the adjustable power generation range of a single unit is divided into... Section, No. The power generation capacity corresponding to the segment is Its unit heat rate coefficient is denoted as ; and Used to depict the first Minimum continuous operating time and minimum continuous downtime of the TPP; and They represent the first The unit operating cost incurred by a TPP during startup and shutdown; and They represent the first The maximum ramp rate and maximum descent rate during the TPP power output change process; This parameter represents the total calorific value of natural gas. This indicates the natural gas consumption of TPPs.

[0063] (2) Carbon capture, storage and utilization device

[0064] During the operation of an integrated energy system, gas-fired cogeneration units inevitably generate a certain amount of carbon dioxide emissions during the power generation process. In order to reduce the carbon emission intensity caused by the operation of cogeneration plants and to realize the recycling of carbon resources, a carbon capture, utilization and storage (CCUS) device is configured in the system to uniformly treat the carbon dioxide generated by cogeneration plants and other related processes. The main function of the carbon capture unit is to separate carbon dioxide from the flue gas emitted by power generation equipment and related industrial processes, so as to realize greenhouse gas emission reduction and centralized management of carbon resources. Among the existing carbon capture technology systems, the post-combustion carbon capture scheme has been widely used in engineering practice due to its high technical maturity, strong adaptability, and ability to achieve good coupling with existing cogeneration units. Therefore, post-combustion carbon capture technology is selected to treat carbon emissions in the integrated energy system. Equations (17) to (25) provide a mathematical description of the operating mechanism of the CCUS system, and the system characterizes the coupling relationship between carbon dioxide emissions, capture and energy consumption. Specifically, the carbon dioxide emissions generated by the system at different times are jointly determined by the fuel consumption level and energy conversion characteristics of the gas turbine unit. The carbon capture capacity of the CCUS unit is constrained by its equipment scale and capture efficiency parameters, while the electrical power consumed in the capture process is quantitatively modeled using corresponding energy consumption coefficients. Uncaptured carbon dioxide is considered as the system's residual emissions, reflecting the impact of carbon capture efficiency on the system's emission reduction capacity. Simultaneously, the captured carbon dioxide can serve as an important raw material for downstream industrial synthesis processes, being transported to units such as methanol synthesis, urea production, and methanation, achieving the recycling of carbon resources and overall low-carbon operation of the system.

[0065] To ensure the operational safety of the CCUS device and the feasibility of the system model during the scheduling process, corresponding constraints need to be imposed on its operation. First, the carbon dioxide storage capacity of the carbon sequestration unit at each moment is dynamically updated according to the system operating status and described by equation (21). Second, the storage capacity is limited by the equipment design parameters, and its upper and lower limits are limited by equation (22). In addition, the carbon capture device must meet the maximum allowable value constraints for both the absorption rate and the release rate of carbon dioxide during operation to avoid the equipment operating under high load conditions. The relevant constraint relationships are characterized by equations (23) and (24).

[0066] In the optimized scheduling model, to improve the system's adjustment flexibility under different operating conditions, strict equivalent constraints are not imposed on the storage status of the CCUS storage unit at the start and end of the scheduling cycle, allowing it to autonomously adjust the carbon storage level according to the system's real-time operating needs. Carbon dioxide not further sealed in the storage unit can be transported to downstream resource utilization stages, but this process is not directly controlled in this embodiment.

[0067] ; (17)

[0068] ; (18)

[0069] ; (19)

[0070] ; (20)

[0071] ; (twenty one)

[0072] ; (twenty two)

[0073] ; (twenty three)

[0074] ; (twenty four)

[0075] ; (25)

[0076] in, Used to characterize CO2 emission levels in the CCUS process; and These are used to describe the CO2 emission characteristics of power grids and thermal power units, respectively. and These represent the carbon capture efficiency of the CCUS device and the power consumption per unit capture amount, respectively. , and These correspond to the CO2 consumption requirements in methanol synthesis, urea production, and methanation processes, respectively. Used to indicate the operating status of the carbon sequestration system; and The minimum and maximum storage capacities of the carbon sequestration system are defined respectively; and These are used to constrain the permissible CO2 absorption and release scales during the operation of the CCUS device; This indicates the electricity demand during the carbon capture and utilization process.

[0077] (3) Electro-hydrogen coupling system

[0078] When the multi-energy coupling system is in a state of abundant power supply, the system absorbs excess electricity through a configured electrolysis hydrogen production unit and converts it into hydrogen energy, thereby realizing the transfer and redistribution of electrical energy on both temporal and spatial scales. This process not only alleviates the problem of power curtailment caused by fluctuations in renewable energy output but also provides the system with an efficient energy storage and reuse pathway. The generated hydrogen can be sent to hydrogen storage facilities for centralized storage and used in subsequent scheduling cycles for transportation fuel supply, industrial production raw materials, or power generation, further improving the overall utilization efficiency and operational benefits of the integrated energy system.

[0079] The power subsystem and the hydrogen energy subsystem are tightly coupled through a bidirectional energy exchange channel, and their energy conversion and transfer relationship is described by equations (26) to (30). The relevant constraints cover the conversion relationship between electrical energy and hydrogen energy during the electrolysis hydrogen production process, the energy release conversion mechanism during the hydrogen power generation process, and the power boundary conditions and energy flow direction control requirements of various equipment during operation. Through the above modeling method, the system can uniformly characterize the energy exchange behavior under different operating conditions.

[0080] At the system operation level, when there is a surplus of electricity, the electrolysis hydrogen production unit is prioritized to consume the excess power. When the power load increases or renewable energy output is insufficient, the stored hydrogen can be converted into electricity through devices such as fuel cells and fed back to the grid. By imposing constraints on the upper and lower limits of equipment operating power and energy flow, conflicts between hydrogen production and power generation processes are avoided during the same period, ensuring the safety, stability, and controllability of the electricity-hydrogen coupling system. This provides strong support for the flexible scheduling and low-carbon operation of the integrated energy system.

[0081] ; (26)

[0082] ;(27)

[0083] ; (28)

[0084] ; (29)

[0085] ; (30)

[0086] in, and These represent the electrical power output of the hydrogen power generation equipment and the electrical power consumption of the electrolysis hydrogen production unit, respectively. and These represent the amount of hydrogen consumed by the hydrogen power generation unit and the amount of hydrogen produced by the electrolysis hydrogen production unit, respectively. and These represent the higher calorific value and lower calorific value of hydrogen, respectively. and Characterize the energy conversion performance of electro-hydrogen production equipment and hydrogen power generation equipment, respectively; Used to describe the compressibility characteristics of hydrogen; and To limit the upper limit of hydrogen transmission during the operation of electro-hydrogen production equipment and hydrogen power generation equipment, binary decision variables are introduced. and This is to avoid the simultaneous occurrence of hydrogen production via electricity and hydrogen power generation within the same time period.

[0087] (4) Methanation unit

[0088] To further transform the energy carrier based on the existing electro-hydrogen coupling system, a methanation unit is introduced to complete the conversion from electrical energy to gaseous fuel. This unit, based on the carbon dioxide hydrogenation reaction mechanism, synthesizes methane by catalytically converting hydrogen and carbon dioxide under specific reaction conditions. During the reaction, hydrogen and carbon dioxide are fully contacted within the reactor, and under the action of a nickel-based catalyst, they undergo a chemical reaction in a suitable temperature and pressure environment to produce methane and water as a byproduct, thus completing the conversion of electrical energy to hydrogen energy to natural gas. This methanation process expands the utilization pathways of hydrogen energy and improves the flexibility of the system's energy conversion; it also enables the resource utilization of carbon dioxide captured by the CCUS unit, effectively constructing a carbon cycle channel and enhancing the low-carbon operation characteristics of the integrated energy system.

[0089] In the model description, Equation (31) constrains the hydrogen in the hydrogen storage system to be used as the input resource for the methanation reaction; the operating level of the methanation unit is limited by the system energy balance relationship and the rated capacity of the equipment, and its allowable operating range is limited by Equation (32). The consumption of carbon dioxide is modeled according to the reaction stoichiometry relationship, specifically described by Equation (33). Referring to relevant research results, the ramp-up rate constraint of the methanation unit was not introduced in the modeling process to balance the model solution efficiency and the feasibility of online optimization.

[0090] ; (31)

[0091] ; (32)

[0092] ; (33)

[0093] in, This indicates the amount of hydrogen consumed during the process of converting hydrogen into synthetic natural gas in the system; Used to depict the volume conversion relationship between hydrogen and natural gas; Indicates the output of natural gas; Used to limit the maximum production of natural gas in the methanation process; This indicates the parameters of the natural gas synthesis process.

[0094] (5) Methanol synthesis unit

[0095] In multi-energy coupled systems, a methanol synthesis unit is introduced as an important downstream conversion step to further enhance the utilization of captured carbon dioxide and hydrogen resources. Based on the carbon dioxide hydrogenation to methanol reaction mechanism, this unit converts carbon dioxide and hydrogen into liquid methanol under specific temperature and pressure conditions in a reaction environment containing copper, zinc, or iron-based catalysts, thus realizing the conversion of carbon resources from a gaseous state to a liquid fuel or chemical feedstock.

[0096] To accurately characterize the material transformation relationships and energy consumption characteristics in the methanol production process, a corresponding mathematical model was constructed. Equations (34) to (36) respectively constrain the methanol production level, the carbon dioxide demand during the reaction process, and the power consumption of the synthesis unit. The unit output energy consumption coefficient introduced in the model is calibrated based on experimental data or engineering experience to reflect the energy demand characteristics of methanol synthesis process as the output changes.

[0097] Through the above modeling method, the methanol synthesis unit can form a collaborative operation relationship with the water electrolysis hydrogen production unit, carbon capture unit and energy storage system in the system. Under the optimized scheduling framework, it can realize the rational allocation of resources and the efficient conversion of energy, thereby improving the overall carbon utilization level and operating economy of the integrated energy system.

[0098] ; (34)

[0099] ; (35)

[0100] ; (36)

[0101] in, This indicates the amount of hydrogen consumed during the methane synthesis process; Used to characterize the electrical power consumed during methane synthesis; and Indicates the process coefficient for methanol synthesis; This represents the unit power consumption coefficient in the methanol synthesis process.

[0102] (6) Ammonia synthesis unit

[0103] In the hydrogen resource utilization process, an ammonia synthesis unit is introduced to achieve further large-scale conversion and storage of hydrogen energy. This unit, based on the classic ammonia synthesis reaction mechanism, utilizes iron-based catalysts to promote the chemical reaction between nitrogen and hydrogen under high temperature and high pressure conditions, thereby generating ammonia. To describe the material conversion characteristics and energy consumption patterns in the ammonia synthesis process, a corresponding mathematical model was constructed to quantitatively characterize the hydrogen consumption, nitrogen feedstock requirements, and energy consumption per unit of ammonia production. Through this model, the correspondence between ammonia production and input hydrogen can be clearly defined, providing calculable constraints for system scheduling.

[0104] The generated ammonia can be supplied as an independent chemical product or used as a raw material input for downstream urea synthesis units, thereby forming a multi-path utilization mechanism for hydrogen resources within the system and improving the flexibility and resource allocation efficiency of the integrated energy system under different operating scenarios.

[0105] ; (37)

[0106] ; (38)

[0107] ; (39)

[0108] in, and These represent the ammonia output during the ammonia production process and the amount of hydrogen required for ammonia synthesis, respectively. Used to characterize the power consumption during ammonia synthesis; and This refers to the ammonia synthesis process coefficient; Characterize the unit power consumption characteristics of the ammonia synthesis process;

[0109] (7) Urea synthesis unit

[0110] In the carbon resource utilization stage of the multi-energy coupled system, a urea synthesis unit is introduced to achieve the synergistic conversion of nitrogen, hydrogen, and carbon resources. This process is carried out under high temperature and high pressure, using ammonia produced from the preceding ammonia synthesis unit as the main reactant, and introducing carbon dioxide recovered from the CCUS unit. Urea is generated under the action of a metal oxide catalyst. During the reaction, ammonia and carbon dioxide first undergo an addition reaction under high pressure to produce the intermediate product ammonium bicarbonate, which is then converted into urea through a dehydration reaction. The generated urea product exists in solution form, and after subsequent cooling, purification, and drying processes, it finally forms a solid urea product that can be stored and transported.

[0111] To characterize the material conversion relationships and energy consumption characteristics in the urea synthesis unit, a corresponding mathematical description model was constructed, and the consumption of ammonia, the input demand of carbon dioxide, and the energy consumption level per unit of urea output were quantitatively characterized by equations (40) to (42). Based on this model, the coordinated scheduling and optimized operation between the urea production process and the front-end ammonia synthesis unit and carbon capture unit can be realized, thereby improving the overall carbon resource utilization efficiency and comprehensive economic efficiency of the system.

[0112] ; (40)

[0113] ; (41)

[0114] ; (42)

[0115] in, This indicates the amount of ammonia consumed during the urea production process; Used to characterize the electricity demand in the urea production process; and These are the process parameters for urea synthesis. Used to describe the power consumption coefficient in the urea production process.

[0116] Figure 2 This is a schematic diagram of the overall structural framework of the low-carbon multi-energy coupling system based on Lyapunov optimization of the present invention. The system integrates and configures multiple energy units such as electrical energy, gas energy and hydrogen energy, and introduces functional modules such as hydrogen production, energy conversion and low-carbon utilization to form a comprehensive architecture for multi-energy coordinated operation.

[0117] Second, based on the dynamic changes in electrical energy, hydrogen energy, and carbon dioxide during system operation, a virtual energy storage queue is constructed to reflect the current operating status of the system.

[0118] (1) Electrochemical energy storage system

[0119] For electrochemical energy storage units in integrated energy systems, equations (43) to (48) are used to uniformly model their operating behavior and constraints. By constructing a time-series update equation for the state of charge of the energy storage unit, the energy change process of the energy storage device during the scheduling cycle is characterized, and minimum and maximum limits for the state of charge are set to clarify the effective energy range in which the energy storage system can participate in scheduling. During the operation of the energy storage device, its charging power and discharging power are constrained by the rated capacity and the control strategy, respectively. In order to avoid energy flow conflicts and ensure the safe operation of the equipment, mutual exclusion logic conditions are introduced into the model to coordinate and control the charging and discharging behavior, and prevent the occurrence of non-physical operating states of charging and discharging in parallel during the same period.

[0120] Furthermore, considering the inevitable performance degradation and lifespan loss of electrochemical energy storage devices during frequent charging and discharging, this invention further introduces a depreciation cost function based on power cycle levels into the optimization model to quantify the economic costs incurred by the energy storage system during the scheduling cycle. This modeling approach can more realistically reflect the impact of energy storage participation in scheduling on the long-term economic efficiency of the system while ensuring the system's operational flexibility.

[0121] ; (43)

[0122] ; (44)

[0123] ; (45)

[0124] ; (46)

[0125] ; (47)

[0126] ; (48)

[0127] (2) Hydrogen energy storage system

[0128] The operating characteristics and scheduling constraints of the hydrogen energy storage unit in the system are characterized by equations (49) to (53). The overall modeling approach is consistent with that of the electrochemical energy storage system, but it reflects the unique properties of the hydrogen energy system in terms of energy carrier and physical constraint. The operating state of the hydrogen energy storage device is described by the evolution of the hydrogen storage capacity over time, and its usable storage range is defined by the tank structure and safety design parameters.

[0129] During operation, the hydrogen absorption and release capacity of the hydrogen energy storage system is constrained by the rated processing capacity of the equipment and the operating conditions. Its hydrogen charging and discharging rates must meet the corresponding upper and lower limits. To avoid energy flow conflicts and ensure the physical feasibility of scheduling decisions, mutual exclusion control conditions are introduced into the model to coordinate and restrict hydrogen charging and discharging behaviors, ensuring that only a single operating mode is allowed to occur during any given scheduling period.

[0130] The above modeling method enables the hydrogen energy storage unit to be flexibly adjusted and effectively participate in the scheduling of the integrated energy system while maintaining the safety and consistency of system operation and control.

[0131] ; (49)

[0132] ; (50)

[0133] ; (51)

[0134] ; (52)

[0135] ; (53)

[0136] in, and These represent the state of charge of the BESS and the state of hydrogen storage of the HSS in the CHEM system, respectively. and These are the energy conversion efficiency and unit power attenuation cost coefficient of BESS, respectively. and These are used to limit the upper limit of the charging and discharging power of the BESS device during operation; and The binary decision variable used to constrain BESS from charging and discharging simultaneously within the same time period; and Used to limit the lower and upper limits of BESS battery capacity; and These are used to limit the upper limits of the hydrogen charging and discharging rates of the HSS unit during operation, respectively. and To avoid the HSS from simultaneously performing hydrogen filling and defilling binary decision variables; and Used to define the range of HSS hydrogen storage capacity.

[0137] By introducing virtual energy queues and constraint queues, the dynamic changes and operational constraints of various energy carriers such as electricity, hydrogen, and gas energy in the integrated energy system are uniformly mapped into queue state variables. The virtual energy queue is used to characterize the energy deviation of various energy storage units relative to their target operating range, reflecting the energy balance state of the system at different time scales; the constraint queue is used to characterize the degree to which operational constraints such as power, capacity, and carbon emissions are met, thereby transforming the system's operational constraints into a dynamically trackable queue form.

[0138] By constructing queues, the system can respond to uncertain disturbances in real time based solely on its current operating state, without relying on accurate forecasts of future loads and renewable energy. This provides state input and optimization basis for subsequent online scheduling decisions based on Lyapunov optimization. The tradeoff parameter V is used to adjust the balance between the system's economic objectives and queue stability. Its value range can be set according to system operating requirements and energy storage capacity, ensuring both system stability and the feasibility of the scheduling strategy.

[0139] ; (54)

[0140] ; (55)

[0141] ; (56)

[0142] ; (57)

[0143] ; (58)

[0144] ; (59)

[0145] ; (60)

[0146] in, , and These represent virtual energy storage queues; , and Used to ensure satisfaction , and Upper limit constraint; The maximum value used to control the trade-off between energy cost and queue stability. The range of values ​​is .

[0147] Third, a Lyapunov function is constructed based on a virtual energy storage queue to reflect the dynamic changes in the system's energy state. Based on the system operating cost index, a circular hydrogen economic operation model based on the Lyapunov drift-penalty function is constructed.

[0148] Based on the aforementioned virtual energy queue and operational constraint queue, Lyapunov optimization is introduced to uniformly characterize the energy evolution and economic objectives during system operation. By constructing a drift-penalty function that includes queue drift terms and economic penalty terms, the long-term operational benefits and queue stability objectives of the system are incorporated into the same optimization framework, thereby forming a real-time operational model oriented towards a circular hydrogen economy.

[0149] This optimization model solves the problem at each scheduling moment solely based on the system's current operating state and queue information, without requiring precise forecasts of renewable energy output or load demand in advance. The optimization decision comprehensively considers multiple factors, including hydrogen byproduct synthesis and utilization, grid interaction, energy storage system charging and discharging behavior, conventional unit operation, and carbon emission constraints. It achieves a coordinated balance between system operating benefits, operating costs, and carbon emission reduction targets while ensuring the stable evolution of various energy queues. Through this approach, online optimized scheduling and real-time operation control of the low-carbon integrated energy system are accomplished.

[0150] ; (61)

[0151] ; (62)

[0152] (63)

[0153] Wherein, equation (63) is the Lyapunov drift-penalty function, and Characterize IES in The operating costs and operating benefits at any given time; Used to describe the degradation costs incurred by battery energy storage systems during operation; This indicates the net CO2 emissions of the system equipment; and They represent the first Operating costs incurred by a thermal power unit during startup and shutdown; Indicates the flow rate of gas supplied by liquefied natural gas; This indicates the amount of nitrogen gas consumed during ammonia synthesis. and These respectively represent the system in Power purchased and sold at any given time; , , and These correspond to electrical load power, hydrogen load demand, non-TPPs natural gas demand, and ammonia demand, respectively. and These represent the production volumes of urea and methanol, respectively. , , , , , , , and These represent the prices of electricity, hydrogen, natural gas, urea, ammonia, methanol, liquefied natural gas, nitrogen, and carbon emission penalties, respectively. Represents the time-domain scheduling of the system operation; Represents a set of thermal power units; and These represent the electrical power of the battery energy storage system under discharge and charging conditions, respectively. and These represent the amount of CO2 captured and released by the CCUS device during operation, respectively. and These represent the hydrogen flow rates of the hydrogen energy storage system in the hydrogen release and hydrogen storage states, respectively. , and Represents a virtual energy storage queue; It is used to control the trade-off between energy costs and queue stability.

[0154] IV. Continuous online optimization of low-carbon multi-energy coupled systems under system power balance constraints and mass conservation constraints.

[0155] In the process of system scheduling, in addition to following the operating conditions described by equations (1) to (63), it is also necessary to further satisfy the balance relationship between electrical energy and matter within the system, including power balance constraints and matter conservation constraints, the forms of which are characterized by equations (64) to (66).

[0156] (1) Power balance constraint

[0157] ; (64)

[0158] ; (65)

[0159] ; (66)

[0160] in, and These represent the electrical power output of the photovoltaic power generation unit and the wind power generation unit, respectively. Characterizing the first The active power output of a thermal power unit during operation; and These represent the power levels of the Battery Energy Storage System (BESS) under discharge and charging conditions, respectively. and These represent the power output of the hydrogen power generation device and the energy consumption of the hydrogen electrolysis unit, respectively. , , and These are used to characterize the electrical power consumption required for the carbon capture, utilization and storage (CCUS) process and the synthesis of methanol, urea and ammonia, respectively. Used to limit the maximum transferable electrical power between CHEM and the main grid.

[0161] (2) Material balance constraints: For the production and consumption of hydrogen, natural gas and hydrogen-based by-products such as ammonia, the system operation must meet the corresponding material conservation relationship.

[0162] ; (67)

[0163] ; (68)

[0164] ; (69)

[0165] ; (70)

[0166] ; (71)

[0167] ; (72)

[0168] in, , , and These represent the system's own hydrogen load requirement, and the amount of hydrogen consumed in the natural gas synthesis, ammonia synthesis, and methanation processes, respectively. and These represent the amount of hydrogen consumed by the hydrogen power generation unit during operation and the hydrogen production output generated by the electrolysis hydrogen production unit during the corresponding time period, respectively. and These represent the amount of hydrogen transferred during the hydrogen release and storage processes of the Hydrogen Storage System (HSS), respectively. , , and These represent the production of synthetic natural gas from hydrogen conversion, the external supply of liquefied natural gas, the natural gas demand of non-TPPs, and the natural gas consumption demand of TPPs, respectively. and These represent the ammonia production scale during ammonia synthesis and the ammonia feedstock consumption during urea synthesis, respectively.

[0169] Based on low-carbon technologies and hydrogen-carbon recycling mechanisms, a scheme for the recovery, coupling, and efficient allocation of hydrogen and carbon dioxide in a multi-energy coupling system is established, enabling them to participate in the synergistic synthesis of hydrogen byproducts such as natural gas, ammonia, urea, and methanol, forming a cyclical conversion and synergistic operation mode among multiple energy carriers. Building upon this, and combining the complementary regulation characteristics of electrochemical energy storage systems and hydrogen energy storage systems, an energy storage coordination mechanism is established to address the overall energy balance and operational flexibility of the system, enabling dynamic allocation of different energy forms across time and space scales. Furthermore, to address the fluctuations in renewable energy output and load uncertainty, a virtual energy queue characterizing the system's energy state and a constraint queue reflecting operational constraints are introduced to dynamically depict the system's real-time operating state. Based on the constructed queue model, a Lyapunov drift-penalty optimization framework is established to construct a cyclic hydrogen economic operation model. Without relying on precise prediction information, the optimization control strategy is solved online based on the system's current state, achieving real-time optimized scheduling of the low-carbon integrated energy system, thereby ensuring the system's economy, low-carbon operation, and operational stability under complex and uncertain environments.

[0170] Figure 3 A schematic diagram of the Lyapunov optimization mechanism used in this invention is shown.

[0171] First, the system is initialized by acquiring its initial operating state and observing relevant parameters, while simultaneously setting the Lyapunov control parameters. and initialize the scheduling period to .

[0172] Then, the time-slot loop process begins. Within each scheduling period, step A is executed first to observe the current operating status of the system and the virtual energy storage queue, obtaining real-time information including renewable energy output, load demand, and energy storage status.

[0173] In step B, a time-specific optimization problem is constructed based on the current system state, and the feasibility of the optimization results is assessed. When the solution meets the system operating constraints, the process proceeds to step C, where optimal control decisions are executed, including power scheduling for gas-fired power plants, power allocation for hydrogen production via water electrolysis, charging and discharging strategies for energy storage systems, and production scheduling for chemical products.

[0174] When the solution to the optimization problem does not meet the constraints, a constraint repair or backup control strategy is executed to obtain a feasible solution that meets the system operation constraints, and corresponding control decisions are made accordingly.

[0175] After completing the scheduling decision, proceed to step D, where the system's virtual energy storage queue is updated based on the decision results for the current time period to reflect the dynamic changes in the system's state.

[0176] Finally, in step E, the system time is advanced to the next scheduling period, i.e., ... Then, it returns to the beginning of the time slot cycle and repeats the above process, thereby realizing continuous online optimization scheduling of the integrated energy system.

[0177] Combination Figure 4 It can be seen that when a suitable V is selected, Lyapunov optimal scheduling not only focuses on maximizing economic benefits but also effectively controls deviations in the system's operating state. Specifically, the active power offset and hydrogen energy storage regulation exhibit periodic changes at different time periods, reflecting the dynamic response capabilities of the electric energy storage system and the hydrogen energy storage system under load fluctuations and renewable energy uncertainties. For example, in the early period, F_B and Z_H fluctuate alternately, while R_C gradually rises from an initial -0.3 to 0.2 in the evening, indicating that the system achieves closed-loop control of the offset by orderly adjusting energy storage and synthetic energy output, preventing excessive deviation of energy from the equilibrium state. Simultaneously, the offset gradually stabilizes, confirming the effectiveness of the virtual energy queue and constrained queue in online optimal scheduling. This enables the system to achieve adaptive matching of load and energy supply without precise prediction, thereby ensuring the safe, reliable, and efficient operation of the low-carbon integrated energy system. Overall, Lyapunov optimization scheduling can achieve precise control and rapid convergence of system operation status, enabling various energy storage and energy carriers to work together efficiently under fluctuating loads and uncertain renewable energy conditions, significantly improving the stability and operational reliability of low-carbon integrated energy systems.

[0178] During system operation, the circular hydrogen economic operation model constructed based on the Lyapunov drift-penalty function can effectively schedule the multi-energy synergistic conversion process, thereby achieving dynamic optimization of the system's economic benefits. For example... Figure 5As shown in the economic benefit curve (profile), the system's economic benefits are relatively low in the initial operation phase (periods 0–5), mainly because the system's energy storage has not yet fully released its energy, and the fluctuations in renewable energy generation mean that energy allocation efficiency has not yet reached its peak. As the system enters the mid-term phase (periods 6–13), economic benefits significantly improve. In this phase, the system achieves optimal synergistic utilization of energy carriers by efficiently integrating hydrogen and carbon dioxide resources and participating in the synthesis of methanol, urea, and natural gas. The dynamic charging and discharging of the energy storage system further improves the overall economic efficiency of the system. In the late stage (periods 14–23), economic benefits show some fluctuations, but remain at a high level overall. This reflects that even under the influence of renewable energy fluctuations and load uncertainties, Lyapunov optimization scheduling can still maintain efficient system operation, ensuring stable and continuous optimization of economic benefits. Overall, the online scheduling method based on Lyapunov optimization can achieve rapid response and continuous optimization of system economic benefits under dynamic and multivariate constraints, fully leveraging the synergistic effect of energy storage and the hydrogen-carbon cycle, and significantly improving the operating efficiency and economic efficiency of the low-carbon integrated energy system.

[0179] Through the Figure 6 Analysis of the optimal energy distribution maps (a), (b), and (c) demonstrates that this method achieves efficient coordination and dynamic balance of multiple energy flows (electricity, hydrogen, and gas) over a time scale. The system can intelligently schedule electrochemical and hydrogen energy storage. When renewable energy output fluctuates, surplus electricity is converted into hydrogen for storage via an "electricity-hydrogen" conversion. During peak load periods or when wind and solar power are insufficient, a "hydrogen-electricity" or "hydrogen-gas" conversion is initiated to compensate for the power shortfall. Data in the figures shows that electricity supply and demand remain balanced (the power purchase / sales curve is flat), and the hydrogen storage charging and discharging process is smooth without any sharp power fluctuations. This confirms that the constructed virtual energy queue effectively guides the optimal transfer and buffering of energy across time periods and across different carriers. Specifically, the energy management strategy demonstrates three core capabilities: First, the ability to match supply and demand in real time, successfully mitigating the impact of wind and solar power fluctuations and load uncertainties on the main grid; second, the ability to optimize multi-energy complementarity, enabling the processes of hydrogen production by electricity, hydrogen power generation, and hydrogen gasification to closely coordinate with primary energy output and energy storage operations, thereby improving the overall energy efficiency of the system; and third, the ability to economically dispatch energy storage, maximizing the value of energy over time by coordinating energy storage devices with different response speeds and storage periods.

[0180] pass Figure 7The comparison between raw CO2 emissions and net CO2 emissions provides a clear visual comparison of the system's emission characteristics at different operating stages. The results show that after introducing low-carbon technologies and the CCUS device, the power plant's net emissions are significantly lower than the raw emissions and remain relatively stable across all operating stages. This indicates that the carbon capture and recycling mechanism can effectively reduce carbon dioxide emissions and improve the system's environmental performance. The overall comparison highlights the power plant's high-efficiency emission reduction capabilities under low-carbon optimized scheduling, providing technical support for the sustainable operation of low-carbon integrated energy systems.

[0181] Figure 8 The graph showing the proportion of hydrogen byproduct production in each operating phase visually illustrates the dynamic proportions of methanol, urea, ammonia, and hydrogen in the total output, with urea accounting for 0%. Analysis indicates that natural gas dominates the entire operating cycle, while the production proportions of methanol, urea, and ammonia, as byproducts, dynamically change across different operating phases. For example, methanol production is higher in certain periods, while ammonia and urea production remain relatively stable, demonstrating the system's flexible control capabilities under the synergistic conversion of multiple energy sources and byproducts. Furthermore, the system can optimize the allocation and dynamic control of various products based on load fluctuations and the uncertainty of renewable energy generation, through the synergistic effect of energy storage and natural gas dispatch. This allows the production ratios of natural gas and byproducts to rapidly respond to real-time operating conditions. This not only ensures high energy efficiency but also enhances the system's adaptability and economic efficiency under different operating conditions. Overall, this design fully demonstrates the ability of a low-carbon integrated energy system to synergistically optimize the production of natural gas as the main energy source and byproducts, providing reliable technical support for the efficient operation of multiple energy sources and byproducts.

[0182] In summary, the online optimization scheduling method proposed in this invention enables low-carbon multi-energy coupled systems to dynamically coordinate the generation of energy storage systems, natural gas, and chemical by-products without the need for precise prediction, achieving comprehensive optimization of economy, operational stability, and low carbon emissions. Operational results show that the system maintains high economic returns across all time periods, effectively controls the output deviations of energy storage and natural gas, and achieves net carbon dioxide emissions significantly lower than the original emission levels. The production ratios of methanol, urea, ammonia, and natural gas can be flexibly adjusted according to the operating conditions, demonstrating the high efficiency and sustainability of multi-energy and multi-product synergistic conversion. This fully showcases the efficient, reliable, and flexible operation capabilities of Lyapunov optimization in the online scheduling of low-carbon integrated energy systems.

Claims

1. An online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization, characterized in that, include: Based on the mathematical models of the gas-fired thermal power unit, carbon capture, storage and utilization device, electric-hydrogen coupling system, methanation unit, methanol synthesis unit, ammonia synthesis unit and urea synthesis unit, a low-carbon multi-energy coupling system architecture is constructed. Based on the dynamic changes in electrical energy, hydrogen energy, and carbon dioxide during system operation, a virtual energy storage queue is constructed to reflect the current operating status of the system. Based on the virtual energy storage queue, a Lyapunov function is constructed to reflect the dynamic changes in the system's energy state. According to the system operating cost index, a circular hydrogen economic operation model based on the Lyapunov drift-penalty function is constructed. Continuous online optimization of a low-carbon multi-energy coupled system is performed under the constraints of system power balance and material conservation.

2. The online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization according to claim 1, characterized in that, The virtual energy storage queue is: ； ； ； ； ； ； in, , and These represent virtual energy storage queues; , and Used to ensure satisfaction , and Upper limit constraint, , , These represent the maximum prices for electricity, hydrogen, and carbon emission penalties, respectively.

3. The online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization according to claim 2, characterized in that, The cyclic hydrogen economy operation model based on the Lyapunov drift-penalty function is as follows: ； ； ； in, and Characterize IES in The operating costs and operating benefits at any given time; Used to describe the degradation costs incurred by battery energy storage systems during operation; This indicates the net CO2 emissions of the system equipment; and They represent the first Operating costs incurred by a thermal power unit during startup and shutdown; Indicates the flow rate of gas supplied by liquefied natural gas; This indicates the amount of nitrogen gas consumed during ammonia synthesis. and These respectively represent the system in Power purchased and sold at any given time; , , and These correspond to electrical load power, hydrogen load demand, non-TPPs natural gas demand, and ammonia demand, respectively. and These represent the production volumes of urea and methanol, respectively. , , , , , , , and These represent the prices of electricity, hydrogen, natural gas, urea, ammonia, methanol, liquefied natural gas, nitrogen, and carbon emission penalties, respectively. Represents the time-domain scheduling of the system operation; Represents a set of thermal power units; and These represent the electrical power of the battery energy storage system under discharge and charging conditions, respectively. and These represent the amount of CO2 captured and released by the CCUS device during operation, respectively. and These represent the hydrogen flow rates of the hydrogen energy storage system in the hydrogen release and hydrogen storage states, respectively. It is used to control the trade-off between energy costs and queue stability.

4. The online optimization scheduling method for low-carbon multi-energy coupled systems based on Lyapunov optimization according to claim 3, characterized in that, Continuous online optimization of low-carbon multi-energy coupled systems includes: Obtain the initial operating status of the system and set the Lyapunov control parameters. Set the scheduling period to To initialize the system; Entering the time slot loop, during each scheduling period, the current operating status of the system and the virtual energy storage queue are observed to obtain real-time information; Solve the optimization problem at time t based on the current system state, and determine whether the optimization result satisfies the constraints. If yes, execute the optimal control decision; if no, execute the constraint repair or backup control strategy to obtain a feasible solution that satisfies the system operation constraints, and then execute the optimal control decision. Update the system's virtual energy storage queue based on the decision results for the current time period; The system time is advanced to the next scheduling period, entering the next time slot cycle, to achieve continuous online optimization scheduling of the integrated energy system.

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