Electricity-hydrogen-chemical-storage-heat multi-energy coupling system intelligent regulation and control simulation verification platform and method based on distributed architecture

Abstract

The invention relates to an electricity-hydrogen-chemical-storage-heat multi-energy coupling system intelligent regulation and control simulation verification platform and method based on a distributed architecture, and the platform comprises an energy management and control platform which employs a cross-time-scale layered regulation and control architecture and comprises an optimization scheduling layer and a cooperative control layer; the optimization scheduling layer generates a multi-time scale scheduling instruction from a second level to a day level based on a multi-target dynamic optimization function; the cooperative control layer receives an instruction of the optimization scheduling layer, and executes real-time electricity-hydrogen-storage-heat cooperative frequency and voltage regulation control through a power balance controller, a heat supply network balance controller and a hydrogen transmission balance controller; the distributed simulation system is used for truly simulating dynamic behaviors of the physical system; and the timing system is used for providing a global clock synchronization signal for the energy management and control platform and the distributed simulation system. According to the method, a high-precision, high-real-time and high-reliability simulation verification means is provided for coal-based chemical green low-carbon transformation, the engineering risk can be remarkably reduced, and the flexibility and economical efficiency of the system are improved.

Description

Technical Field

[0001] This invention belongs to the field of energy and chemical system simulation and optimization control technology, and particularly relates to an intelligent control simulation verification platform and method for a multi-energy coupled system based on a distributed architecture of electricity-hydrogen-chemical-storage-thermal energy. Background Technology

[0002] Promoting energy structure adjustment and achieving the "dual carbon" goal is a major national strategic need. However, this process faces dual challenges: on the one hand, high-carbon emissions, represented by coal chemical industry, account for a large proportion of industrial production, with complex processes, numerous and intense carbon emission points, making emission reduction extremely difficult; on the other hand, new energy sources such as wind power and photovoltaics are random and volatile, and large-scale grid connection puts pressure on the stable operation of the power system, while also leading to wind and solar curtailment issues. To address these issues, deeply coupling traditional high-carbon industries such as coal chemical industry with green electricity, green hydrogen, and green heat to build a multi-energy complementary system is considered an effective path for green and low-carbon transformation. Replacing the gray hydrogen produced in the water-gas conversion process of coal chemical industry with green hydrogen can directly reduce carbon emissions at the source of the process; using the by-product oxygen from water electrolysis to produce hydrogen to replace or supplement the high-energy-consuming air separation unit can reduce the overall energy consumption of the system; and introducing green heat generated by electric boilers and heat pumps to replace process steam can further reduce dependence on fossil fuels. This "electricity-hydrogen-chemical-storage-heat" multi-energy synergy model aims to achieve a shift from a single fossil fuel-driven model to a multi-source clean energy synergy model.

[0003] These systems encompass output fluctuations and hydrogen production and storage dynamics at the second and minute levels, to hourly fluctuations in thermal energy supply and storage, and even daily-level or higher chemical production scheduling and market trading activities, exhibiting significant multi-scale and strong coupling characteristics in the time dimension. Simultaneously, complex coupling relationships exist between electricity, hydrogen energy, thermal energy, and chemical material flows. These complex dynamic characteristics across time domains and multiple energy flows place extremely high demands on the system's planning, design, and operational control, requiring real-time accurate simulation, multi-time-scale coordination, and closed-loop decision verification.

[0004] Currently, research on technologies for such complex systems still faces significant limitations. Most existing methods employ independent simulation of subsystems or simple cascade simulations, such as setting fixed energy input boundaries in chemical process simulations or simplifying the dynamic characteristics of chemical processes in energy system optimization. For example, they do not involve dynamic models of chemical processes and thermodynamic systems, making it impossible to achieve full-chain simulation of "electricity-hydrogen-chemical-storage-heat." Using fixed-step-size cascade simulations cannot solve the problem of cross-time-domain synchronization between the second-level dynamics of the power grid and the hour-level slow dynamics of chemical processes, and lacks hardware-in-the-loop verification capabilities. Existing research generally suffers from the following technical bottlenecks:

[0005] 1. Insufficient System Integration Depth: Most studies remain at the level of macroscopic path analysis or steady-state simulation, lacking a system scheme for dynamic and refined coupling design of the entire "electricity-hydrogen-chemical-storage-heat" chain, making it difficult to realistically reflect the interaction of multiple energy flows in transient and dynamic processes. In particular, the lack of a third-order coupled dynamic model of hydrogen pipeline pressure-flow-temperature and a real-time feedback mechanism for the hydrogen-carbon ratio of chemical reactors leads to insufficient simulation accuracy.

[0006] 2. Lack of intelligent regulation capabilities: Existing research does not adequately characterize the uncertainties in the electricity market brought about by a high proportion of new energy sources, nor the impact of fluctuating power on the dynamic response of chemical processes. In particular, it lacks multi-timescale collaborative scheduling and real-time control strategies ranging from second-level and minute-level to hourly / day-level, making it difficult to guarantee the flexibility and robustness of system operation. Existing optimization objectives are mostly statically weighted, and a multi-objective dynamic weight adjustment mechanism for electricity purchase costs, carbon emission intensity, and temperature fluctuations in chemical processes has not been established.

[0007] 3. Limitations of Simulation Verification Platforms: Traditional simulation platforms struggle to address the challenge of cross-time-domain data synchronization caused by significant differences in simulation step sizes across various energy subsystems. The simulation process often neglects computational resource load balancing and fails to effectively integrate hardware-in-the-loop and software-in-the-loop hybrid simulation technologies. This results in insufficient simulation real-time performance, accuracy, and testing support for actual hardware controllers, making it impossible to provide a reliable quantitative verification environment for the system's flexible adjustment capabilities under complex operating conditions. In particular, the lack of a global clock synchronization mechanism based on the PTP (IEEE 1588) protocol makes it difficult to control the virtual-to-real closed-loop delay within 100ms.

[0008] In view of this, it is urgent to break through the existing technological bottlenecks and develop an integrated system and intelligent control simulation platform to realize the scientific configuration of multi-energy coupled systems, the coordinated scheduling of multiple time scales, and the construction of stable control strategies that adapt to fluctuating power sources. Summary of the Invention

[0009] The purpose of this invention is to provide an intelligent control simulation verification platform and method for a multi-energy coupling system based on a distributed architecture, namely, an "electricity-hydrogen-chemical-storage-thermal" system. This overcomes the shortcomings of existing technologies, such as the inability to achieve dynamic coupling simulation of multi-energy and multi-material flows across time scales and the lack of hardware-in-the-loop hybrid verification capabilities, making it difficult to perform closed-loop verification of intelligent control strategies. This invention is applicable to the green and low-carbon transformation of the coal-based chemical industry under the penetration of high proportions of renewable energy, the development of multi-time scale collaborative scheduling strategies, and virtual-real hybrid closed-loop verification.

[0010] This invention provides an intelligent control simulation and verification platform for a multi-energy coupled system based on a distributed architecture, comprising:

[0011] The energy management platform adopts a hierarchical control architecture across time scales, including an optimization scheduling layer and a collaborative control layer. The optimization scheduling layer is used to generate multi-time scale scheduling instructions from the second level to the day level based on a multi-objective dynamic optimization function. The collaborative control layer is used to receive the instructions from the optimization scheduling layer and perform real-time electricity-hydrogen-storage-heat collaborative frequency and voltage regulation control through the power balance controller, the heat network balance controller, and the hydrogen transmission balance controller to ensure the real-time dynamic stability of the system.

[0012] The distributed simulation system includes simulation models of various subsystems such as power grid, thermal power, wind power, photovoltaic, energy storage, hydrogen production and storage, heating and thermal storage, and coal chemical industry. Each simulation model is connected and interacts with data through the power grid control bus, heating network control bus, and hydrogen transmission control bus to realistically simulate the dynamic behavior of the physical system.

[0013] The time synchronization system uses the IEEE 1588 precision time protocol to provide a global clock synchronization signal for the energy management platform and the distributed simulation system, establishes a unified simulation time reference, and achieves microsecond-level synchronization accuracy through a master-slave clock mechanism to ensure the time consistency of data between simulation nodes.

[0014] Furthermore, the optimized scheduling layer, based on a multi-objective dynamic optimization function, comprehensively considers system operational stability, system operational economy, system carbon emission intensity, and fluctuations in key chemical process parameters, and constructs the following mathematical model:

[0015]

[0016] The constraints include:

[0017] Power balance constraints:

[0018]

[0019] Thermodynamic equilibrium constraints:

[0020] Material balance constraints:

[0021] Market constraints: Taking into account both total market transaction volume and system production schedule;

[0022] Equipment operating constraints include: electrolyzer power ramp-up rate, hydrogen storage tank pressure limit, and methanol synthesis tower temperature safety range;

[0023] Among them, the weight coefficients α(t), β(t), and γ(t) adopt an online adjustment mechanism based on the analytic hierarchy process and reinforcement learning, and are adaptively updated according to the new energy prediction error and market electricity price fluctuations to achieve a dynamic trade-off between economy and security.

[0024] The collaborative control layer receives day-ahead, intraday, and real-time instructions from the optimization scheduling layer, and achieves rapid adjustment through three core controllers deployed in the distributed simulation system, wherein:

[0025] The power balance controller adopts a virtual synchronous machine control strategy to adjust the power of electrochemical energy storage and electrolyzer, maintain grid frequency stability, and has a response time of less than 200ms.

[0026] The heat network balance controller is based on model predictive control, which adjusts the output of electric boilers and heat pumps and controls the temperature difference between the supply and return water of the heat network within ±2℃ of the set value.

[0027] The hydrogen supply balance controller adopts pressure-flow cascade PID control, which maintains the hydrogen header pressure within the range of 2.0±0.05MPa by adjusting the opening of the inlet and outlet valves of the hydrogen storage tank, thus ensuring stable feed to the methanol synthesis tower.

[0028] Furthermore, the simulation models of each subsystem are deployed on independent computing nodes, adopting a parallel computing architecture to improve simulation efficiency, and optimizing the allocation of computing resources through a dynamic load balancing strategy.

[0029] Furthermore, the simulation objects of the distributed simulation system include:

[0030] The power generation system uses wind power, photovoltaic, and solar thermal power as the main primary energy sources, supplemented by thermal power and the power grid as regulating and guaranteeing power sources; the wind power and photovoltaic models adopt time series models based on actual output data, and superimpose second-level fluctuation components to simulate real randomness;

[0031] The energy and mass transfer system consists of an electric bus, a thermal bus, and a material flow network, which respectively realize the bidirectional flow and cascade utilization of electrical energy, thermal energy, hydrogen, and oxygen.

[0032] Energy conversion and storage systems include water electrolysis hydrogen production devices, hydrogen storage tanks, oxygen storage devices, electrochemical energy storage systems, thermal storage systems, electric boilers and heat pumps, and coal chemical systems, used to realize energy form conversion and spatiotemporal transfer.

[0033] Furthermore, the power bus connects green electricity, energy storage, the power grid, and thermal power to supply power to hydrogen production, thermal storage, electric boilers, heat pumps, and coal chemical systems, using a three-phase AC power flow model or a DC equivalent model; the thermal bus connects thermal storage systems, electric boilers, heat pumps, the heating portion of thermal power plants, and high- and low-quality waste heat generated by coal chemical systems, realizing the cascade utilization of thermal energy, using a hydraulic-thermal coupling model for the heat network; the material flow network includes hydrogen flow and oxygen flow, using a pressure-flow-temperature third-order coupled dynamic model, with pipeline transmission delay and pressure drop loss calculated according to the actual pipeline topology.

[0034] Furthermore, the coal chemical system simulation includes:

[0035] The dynamic hydrogen-to-carbon ratio model of the water-gas shift reactor is used to calculate the C / H molar ratio at the reactor inlet and outlet in real time, and feed it back to the hydrogen supply balance controller to adjust the green hydrogen replenishment and maintain the stability of the syngas composition.

[0036] An oxygen load substitution model for air separation units is used to establish a nonlinear mapping relationship between the flow rate of oxygen produced by electrolysis and the energy consumption of air separation units. The substitution rate can be continuously adjusted within the range of 0 to 60%.

[0037] A temperature-pressure coupled model for the methanol synthesis tower is used, employing a third-order inertial element to describe the dynamics of the catalyst bed temperature, in order to ensure the authenticity of the control strategy verification.

[0038] Furthermore, the distributed simulation system adopts a hybrid simulation strategy that combines a variable step size adaptive algorithm with an event-driven mechanism. By introducing a simulation time anchor mechanism, it achieves multi-rate data synchronization and bidirectional interpolation alignment between the second-level power grid dynamic model and the daily-level chemical scheduling model.

[0039] Furthermore, the platform supports a hybrid hardware-in-the-loop and software-in-the-loop simulation architecture, which constructs a semi-physical simulation verification environment by connecting the real controller to the virtual model closed loop.

[0040] This invention also provides a simulation verification method for intelligent control of an electro-hydrogen-chemical-storage-thermal multi-energy coupled system using the aforementioned platform, comprising the following steps:

[0041] Step 1: Generate multi-time-scale scheduling instructions through the optimization scheduling layer of the energy management platform and distribute them to each sub-model of the distributed simulation system;

[0042] Step 2: During the closed-loop simulation cycle, the simulation models of each subsystem of the distributed simulation system interact with each other through the power grid control bus, the heating network control bus, and the hydrogen transport control bus. The interaction frequency is adaptively adjusted according to the dynamic characteristics of each system.

[0043] Step 3: Feed back the interactive results generated by the co-simulation, including power, pressure, temperature, flow rate and state variables, to the collaborative control layer of the energy management platform in real time;

[0044] Step 4: Based on the feedback results, the energy management platform adjusts and verifies the control strategy for the electro-hydrogen-chemical-storage-thermal multi-energy coupled system through online rolling optimization and model correction, forming a closed-loop iteration.

[0045] Step 5: When connected to the hardware-in-the-loop, the real controller connects to the simulation closed loop through the standard communication interface. The time synchronization system ensures time synchronization between the virtual model and the physical controller, thus constructing a semi-physical verification environment.

[0046] By employing the above scheme, a simulation verification platform and method for intelligent regulation of a multi-energy coupled system based on a distributed architecture of electricity, hydrogen, chemical, storage, and heat are developed. This platform achieves deep coupling of all elements—electricity, hydrogen, chemical, storage, and heat—through power buses, thermal buses, and a material flow network. It utilizes a variable step-size adaptive algorithm and an event-driven mechanism to solve the cross-time-domain synchronization problem between second-level grid dynamics and daily-level chemical scheduling. A hierarchical architecture for optimized scheduling and collaborative control is constructed to achieve multi-time-scale collaborative regulation. It supports hybrid hardware-in-the-loop and software-in-the-loop simulations, and a virtual-real closed-loop verification environment is built using the IEEE 1588 time synchronization system. This invention provides a high-precision, high-real-time, and high-reliability simulation verification method for the green and low-carbon transformation of coal-based chemical industry, significantly reducing engineering risks and improving system flexibility and economy. Specific technical effects are as follows:

[0047] (1) Systematic innovation: For the first time, the deep dynamic coupling simulation of all elements of "electricity-hydrogen-chemical-storage-heat" was realized. Through the real-time feedback model of hydrogen-carbon ratio, nonlinear mapping of oxygen load substitution and thermal cascade utilization network, the feasibility verification of multi-energy complementarity and low carbonization throughout the entire process from source design to dynamic operation was ensured.

[0048] (2) Improved regulation capability: A hierarchical collaborative optimization scheduling and real-time control architecture is proposed. Combined with dynamic weight multi-objective function and variable step size synchronization mechanism, the whole-chain optimization regulation from long-term economy to short-term stability is realized.

[0049] (3) Engineering application value: The virtual and real hybrid simulation verification environment based on the global clock synchronization mechanism of PTP (IEEE 1588) protocol can control the closed-loop delay within 100ms, which closely approximates the actual control system, providing a reliable basis for system planning, controller parameter tuning and extreme condition testing, and reducing the risk of engineering application.

[0050] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0051] Figure 1 This is an architecture diagram of the intelligent control simulation verification platform for an electro-hydrogen-chemical-storage-thermal multi-energy coupling system based on a distributed architecture according to the present invention;

[0052] Figure 2 This is a diagram of the hierarchical control architecture across time scales in an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of the electro-hydrogen-chemical-storage-thermal multi-energy coupling system in an embodiment of the present invention;

[0054] Figure 4It is the workflow diagram of the intelligent regulation simulation verification platform for the electric-hydrogen-thermal energy storage multi-energy coupling system according to the present invention. Specific Embodiments

[0055] The following combines the drawings and embodiments to further describe in detail the specific embodiments of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0056] Refer Figure 1 As shown, an intelligent regulation simulation verification platform for an electric-hydrogen-thermal energy storage multi-energy coupling system based on a distributed architecture includes:

[0057] (1) An energy management and control platform, adopting a cross-time-scale hierarchical regulation architecture based on a collaborative optimization and distributed control method across time scales. Refer Figure 2 As shown, it includes an optimization scheduling layer and a collaborative control layer, which are used to achieve flexible scheduling on multiple time scales.

[0058] The optimization scheduling layer generates multi-time-scale scheduling instructions from the second level to the daily level based on multi-objective dynamic optimization functions such as power balance, thermal balance, and carbon emissions, and formulates multi-time-scale flexible scheduling strategies from the day-ahead to the real-time to meet the needs of diverse markets.

[0059] The collaborative control layer receives the instructions from the optimization scheduling layer and performs real-time electric-hydrogen-thermal energy storage collaborative frequency modulation and voltage regulation control through a power balance controller, a heat network balance controller, a hydrogen transmission balance controller, etc., to ensure the real-time dynamic stability of the system.

[0060] Specifically, the optimization scheduling layer constructs the following mathematical model based on multi-objective dynamic optimization functions, comprehensively considering system operation stability, system operation economy, system carbon emission intensity, and fluctuations in key parameters of the chemical process:

[0061]

[0062] The constraint conditions include:

[0063] Power balance constraint:

[0064]

[0065] Thermal balance constraint:

[0066] Material balance constraint:

[0067] Market constraint: Comprehensively consider the total market transaction volume and the system production plan.

[0068] Equipment operation constraints: Include the power ramp rate of the electrolyzer, the pressure limit of the hydrogen storage tank, the temperature safety range of the methanol synthesis tower, etc.

[0069] Among them, the weight coefficients α(t), β(t), and γ(t) adopt an online adjustment mechanism based on the analytic hierarchy process (AHP) and reinforcement learning, and are adaptively updated according to the new energy prediction error and market electricity price fluctuations to achieve a dynamic trade-off between economy and security.

[0070] The collaborative control layer receives day-ahead, intraday, and real-time instructions from the optimization scheduling layer, and achieves rapid adjustment through three core controllers deployed in the distributed simulation system.

[0071] Power balance controller: It adopts a virtual synchronous machine (VSG) control strategy to adjust the power of electrochemical energy storage and electrolyzer, maintain grid frequency stability, and has a response time of less than 200ms;

[0072] Heat network balance controller: Based on model predictive control (MPC), it adjusts the output of electric boilers and heat pumps to control the temperature difference between the supply and return water of the heat network within ±2℃ of the set value;

[0073] Hydrogen supply balance controller: It adopts pressure-flow cascade PID control, which maintains the hydrogen header pressure within the range of 2.0±0.05MPa by adjusting the opening of the inlet and outlet valves of the hydrogen storage tank, thus ensuring stable feed to the methanol synthesis tower.

[0074] (2) Distributed simulation system, which includes simulation models of various subsystems such as power grid, thermal power, wind power, photovoltaic, energy storage, hydrogen production and storage, heating and thermal storage, and coal chemical industry. Each model is connected and interacts with data through the power grid control bus, heating network control bus and hydrogen transmission control bus to realistically simulate the dynamic behavior of the physical system. The simulation models of each subsystem are deployed on independent computing nodes, and a parallel computing architecture is adopted to improve simulation efficiency. The allocation of computing resources is optimized through dynamic load balancing strategy.

[0075] Figure 3 This is a schematic diagram of a multi-energy coupled system of electricity, hydrogen, chemical, storage, and heat. The core of the distributed simulation architecture for deep coupling of electricity, hydrogen, chemical, storage, and heat lies in the organic integration of different energy subsystems through multiple bus lines, forming a network for the bidirectional flow of energy and matter. The simulation objects include:

[0076] The power generation system primarily utilizes green electricity such as wind power, photovoltaics, and solar thermal power as primary energy sources, supplemented by thermal power and the power grid (optional) as regulating and backup power sources. The wind power and photovoltaic models employ time series models based on actual output data, superimposed with second-level fluctuation components to simulate real randomness.

[0077] The energy and mass transfer system consists of an electric busbar, a thermal busbar, and a material flow network, which respectively realize the bidirectional flow and cascade utilization of electrical energy, thermal energy, hydrogen, and oxygen.

[0078] Power bus: Connects green power, energy storage, the power grid, and thermal power, and supplies power to hydrogen production, heat storage, electric boilers, heat pumps, and coal chemical systems. It adopts a three-phase alternating current power flow model or a direct current equivalent model.

[0079] Thermal bus: Connects the heat storage system, electric boilers, heat pumps, the heat supply part of thermal power, and the high- and low-quality waste heat generated by the coal chemical system, realizes the cascade utilization of heat energy, and adopts a heat network hydraulic-thermal coupling model.

[0080] Material flow network: mainly includes hydrogen gas flow and oxygen gas flow, adopts a three-order coupling dynamic model of pressure-flow-temperature, and calculates the pipeline transmission delay and pressure drop loss according to the actual pipe network topology.

[0081] Exergy conversion and storage system: includes water electrolysis hydrogen production devices, hydrogen storage tanks, oxygen storage devices, electrochemical energy storage systems, heat storage systems, electric boilers, heat pumps, and coal chemical systems, and is used to realize the conversion of energy forms and the transfer of time and space. The simulation of the coal chemical system includes:

[0082] Dynamic hydrogen-carbon ratio model of water-gas shift reactor: calculates the C / H molar ratio at the inlet and outlet of the reactor in real time, and feeds it back to the hydrogen transportation balance controller to adjust the green hydrogen supplement amount and maintain the stability of the syngas composition.

[0083] Oxygen load substitution model of air separation unit: establishes a non-linear mapping relationship between the electrolysis by-product oxygen flow and the energy consumption of the air separation unit, and the substitution rate can be continuously adjusted within the range of 0-60%.

[0084] Temperature-pressure coupling model of methanol synthesis tower: uses a three-order inertia link to describe the dynamic temperature of the catalyst bed layer to ensure the authenticity of the control strategy verification.

[0085] The distributed simulation system adopts a hybrid simulation strategy combining a variable-step adaptive algorithm and an event-driven mechanism. By introducing a simulation time anchor point mechanism, it realizes multi-rate data synchronization and two-way interpolation alignment between the second-level power grid dynamic model and the daily-level chemical industry scheduling model; the platform supports a hybrid simulation architecture of hardware-in-the-loop and software-in-the-loop, and can connect real controllers to the virtual model closed-loop to build a semi-physical simulation verification environment.

[0086] (3) Time synchronization system, as the basic support of the platform, provides a global clock synchronization signal for the energy management and control platform and the distributed simulation system, and establishes a unified simulation time benchmark, which is the premise for realizing cross-time-domain data synchronization. The time synchronization system uses the IEEE 1588 Precision Time Protocol (PTP) to provide a global clock synchronization signal for the energy management and control platform and the distributed simulation system, establishes a unified simulation time benchmark, and the synchronization accuracy is less than 1 millisecond. The time synchronization system realizes microsecond-level synchronization accuracy through the master-slave clock mechanism to ensure the time consistency of data between simulation nodes.

[0087] Ref. Figure 4 As shown, the platform's operation process is as follows:

[0088] Step 1: Generate multi-time-scale scheduling instructions through the optimization scheduling layer of the energy management platform and distribute them to each sub-model of the distributed simulation system;

[0089] Step 2: During the closed-loop simulation cycle, the simulation models of each subsystem of the distributed simulation system interact with each other through the power grid control bus, the heating network control bus, and the hydrogen transport control bus. The interaction frequency is adaptively adjusted according to the dynamic characteristics of each system.

[0090] Step 3: Feed back the interactive results generated by the co-simulation, including power, pressure, temperature, flow rate and state variables, to the collaborative control layer of the energy management platform in real time;

[0091] Step 4: Based on the feedback results, the energy management platform adjusts and verifies the control strategy for the multi-energy coupled system through online rolling optimization and model correction, forming a closed-loop iteration.

[0092] Step 5: When connected to the hardware-in-the-loop, the real controller connects to the simulation closed loop through a standard communication interface (such as Modbus TCP, OPCUA). The time synchronization system ensures time synchronization between the virtual model and the physical controller, thus building a semi-physical verification environment.

[0093] Based on a standardized cross-time domain communication mechanism, this platform achieves hybrid simulation driven by both time and events, and possesses dynamic load balancing capabilities, ensuring high efficiency and stability in the simulation process. Facing the challenge of significant differences in simulation step sizes among subsystems, the platform employs a variable step size adaptive algorithm and multi-physics dynamic equivalent modeling technology to construct a standardized data interface, thereby seamlessly achieving multi-rate data synchronization from second-level rapid dynamics to day-level long-term planning. Furthermore, the platform adopts a hybrid simulation architecture combining hardware-in-the-loop and software-in-the-loop technologies. Through global clock synchronization, it connects the real controller and the virtual model to the same closed loop, constructing a highly realistic hardware-in-the-loop simulation verification environment. It achieves the following technical effects:

[0094] Systematic Innovation: The platform provides an unprecedented high-precision verification environment for the deep coupling architecture of all elements of "electricity-hydrogen-chemical-storage-thermal". Through dynamic feedback of hydrogen-carbon ratio, nonlinear substitution of oxygen load and thermal cascade utilization network, it ensures the feasibility of multi-energy complementarity and decarbonization from the source design.

[0095] Innovative control capabilities: Hybrid simulation-driven and multi-rate synchronization technologies support a hierarchical system of "optimized scheduling" and "cooperative control". Through dynamic weighted multi-objective functions and VSG / MPC / PID multi-controller collaboration, full-chain optimized control from long-term economic efficiency to short-term stability is achieved, greatly improving the system's flexibility and reliability in dealing with fluctuations.

[0096] Engineering Value Innovation: The virtual-physical hybrid verification environment based on the PTP protocol can control the closed-loop latency to within 100ms, providing a reliable basis for system planning, operation strategy formulation, and controller parameter tuning. This significantly reduces engineering application risks and provides crucial support for the safe, efficient, and low-carbon operation of energy systems. Compared with traditional offline simulation, this platform offers higher verification efficiency and stronger engineering guidance.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A simulation and verification platform for intelligent control of a multi-energy coupled system based on a distributed architecture of electricity, hydrogen, chemical, storage, and heat, characterized in that, include: The energy management platform adopts a layered control architecture that spans multiple time scales, including an optimization scheduling layer and a collaborative control layer. The optimized scheduling layer is used to generate multi-timescale scheduling instructions from the second level to the day level based on a multi-objective dynamic optimization function; the collaborative control layer is used to receive the instructions from the optimized scheduling layer and perform real-time electricity-hydrogen-storage-heat collaborative frequency and voltage regulation control through the power balance controller, the heat network balance controller and the hydrogen transmission balance controller to ensure the real-time dynamic stability of the system. The distributed simulation system includes simulation models of various subsystems such as power grid, thermal power, wind power, photovoltaic, energy storage, hydrogen production and storage, heating and thermal storage, and coal chemical industry. Each simulation model is connected and interacts with data through the power grid control bus, heating network control bus, and hydrogen transmission control bus to realistically simulate the dynamic behavior of the physical system. The time synchronization system uses the IEEE 1588 precision time protocol to provide a global clock synchronization signal for the energy management platform and the distributed simulation system, establishes a unified simulation time reference, and achieves microsecond-level synchronization accuracy through a master-slave clock mechanism to ensure the time consistency of data between simulation nodes.

2. The intelligent control simulation verification platform for a multi-energy coupled system based on a distributed architecture of electro-hydrogen-chemical-storage-thermal energy as described in claim 1, characterized in that, The optimized scheduling layer is based on a multi-objective dynamic optimization function, taking into account system operational stability, system operational economy, system carbon emission intensity, and fluctuations in key parameters of the chemical process, and constructs the following mathematical model: ； The constraints include: Power balance constraints: ； Thermodynamic equilibrium constraints: ; Material balance constraints: ; Market constraints: Taking into account both total market transaction volume and system production schedule; Equipment operating constraints include: electrolyzer power ramp-up rate, hydrogen storage tank pressure limit, and methanol synthesis tower temperature safety range; Among them, the weight coefficients α(t), β(t), and γ(t) adopt an online adjustment mechanism based on the analytic hierarchy process and reinforcement learning, and are adaptively updated according to the new energy prediction error and market electricity price fluctuations to achieve a dynamic trade-off between economy and security. The collaborative control layer receives day-ahead, intraday, and real-time instructions from the optimization scheduling layer, and achieves rapid adjustment through three core controllers deployed in the distributed simulation system, wherein: The power balance controller adopts a virtual synchronous machine control strategy to adjust the power of electrochemical energy storage and electrolyzer, maintain grid frequency stability, and has a response time of less than 200ms. The heat network balance controller is based on model predictive control, which adjusts the output of electric boilers and heat pumps and controls the temperature difference between the supply and return water of the heat network within ±2℃ of the set value. The hydrogen supply balance controller adopts pressure-flow cascade PID control, which maintains the hydrogen header pressure within the range of 2.0±0.05MPa by adjusting the opening of the inlet and outlet valves of the hydrogen storage tank, thus ensuring stable feed to the methanol synthesis tower.

3. The intelligent control simulation verification platform for a multi-energy coupled system based on a distributed architecture of electro-hydrogen-chemical-storage-thermal energy as described in claim 1, characterized in that, The simulation models of each subsystem are deployed on independent computing nodes, and a parallel computing architecture is adopted to improve simulation efficiency. A dynamic load balancing strategy is used to optimize the allocation of computing resources.

4. The intelligent control simulation verification platform for a multi-energy coupled system based on a distributed architecture of electro-hydrogen-chemical-storage-thermal energy as described in claim 3, is characterized in that, The simulation objects of the distributed simulation system include: The power generation system uses wind power, photovoltaic, and solar thermal power as the main primary energy sources, supplemented by thermal power and the power grid as regulating and guaranteeing power sources; the wind power and photovoltaic models adopt time series models based on actual output data, and superimpose second-level fluctuation components to simulate real randomness; The energy and mass transfer system consists of an electric bus, a thermal bus, and a material flow network, which respectively realize the bidirectional flow and cascade utilization of electrical energy, thermal energy, hydrogen, and oxygen. Energy conversion and storage systems include water electrolysis hydrogen production devices, hydrogen storage tanks, oxygen storage devices, electrochemical energy storage systems, thermal storage systems, electric boilers and heat pumps, and coal chemical systems, used to realize energy form conversion and spatiotemporal transfer.

5. The intelligent control simulation verification platform for a multi-energy coupled system based on a distributed architecture of electro-hydrogen-chemical-storage-thermal energy as described in claim 4, characterized in that, The power bus connects green electricity, energy storage, the power grid, and thermal power, supplying power to hydrogen production, thermal storage, electric boilers, heat pumps, and coal chemical systems, using a three-phase AC power flow model or a DC equivalent model. The thermal bus connects thermal storage systems, electric boilers, heat pumps, the heating portion of thermal power plants, and high- and low-quality waste heat generated by coal chemical systems, enabling cascade utilization of thermal energy, using a hydraulic-thermal coupling model for the heat network. The material flow network includes hydrogen and oxygen flows, using a pressure-flow-temperature third-order coupled dynamic model, with pipeline transmission delay and pressure drop losses calculated based on the actual pipeline topology.

6. The intelligent control simulation verification platform for a multi-energy coupled system based on a distributed architecture of electro-hydrogen-chemical-storage-thermal energy as described in claim 4, is characterized in that, The coal chemical system simulation includes: The dynamic hydrogen-to-carbon ratio model of the water-gas shift reactor is used to calculate the C / H molar ratio at the reactor inlet and outlet in real time, and feed it back to the hydrogen supply balance controller to adjust the green hydrogen replenishment and maintain the stability of the syngas composition. An oxygen load substitution model for air separation units is used to establish a nonlinear mapping relationship between the flow rate of oxygen produced by electrolysis and the energy consumption of air separation units. The substitution rate can be continuously adjusted within the range of 0 to 60%. A temperature-pressure coupled model for the methanol synthesis tower is used, employing a third-order inertial element to describe the dynamics of the catalyst bed temperature, in order to ensure the authenticity of the control strategy verification.

7. The intelligent control simulation verification platform for a multi-energy coupled system based on a distributed architecture of electro-hydrogen-chemical-storage-thermal energy as described in claim 1, characterized in that, The distributed simulation system adopts a hybrid simulation strategy that combines a variable step size adaptive algorithm with an event-driven mechanism. By introducing a simulation time anchor mechanism, it achieves multi-rate data synchronization and bidirectional interpolation alignment between the second-level power grid dynamic model and the daily-level chemical scheduling model.

8. The intelligent control simulation verification platform for a multi-energy coupled system based on a distributed architecture of electro-hydrogen-chemical-storage-thermal energy as described in claim 7, is characterized in that, The platform supports a hybrid hardware-in-the-loop and software-in-the-loop simulation architecture, which constructs a semi-physical simulation verification environment by connecting the real controller to the virtual model closed loop.

9. A simulation verification method for intelligent control of an electro-hydrogen-chemical-storage-thermal multi-energy coupled system using the platform described in any one of claims 1 to 8, characterized in that, Includes the following steps: Step 1: Generate multi-time-scale scheduling instructions through the optimization scheduling layer of the energy management platform and distribute them to each sub-model of the distributed simulation system; Step 2: During the closed-loop simulation cycle, the simulation models of each subsystem of the distributed simulation system interact with each other through the power grid control bus, the heating network control bus, and the hydrogen transport control bus. The interaction frequency is adaptively adjusted according to the dynamic characteristics of each system. Step 3: Feed back the interactive results generated by the co-simulation, including power, pressure, temperature, flow rate and state variables, to the collaborative control layer of the energy management platform in real time; Step 4: Based on the feedback results, the energy management platform adjusts and verifies the control strategy for the electro-hydrogen-chemical-storage-thermal multi-energy coupled system through online rolling optimization and model correction, forming a closed-loop iteration. Step 5: When connected to the hardware-in-the-loop, the real controller connects to the simulation closed loop through the standard communication interface. The time synchronization system ensures time synchronization between the virtual model and the physical controller, thus constructing a semi-physical verification environment.

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