An operating control method for an integrated energy system

Abstract

The present invention discloses an operation control method for an integrated energy system, which has the following characteristics and includes the following steps: Step 1, establish a five-dimensional adaptive digital twin model for the whole life cycle of the integrated energy system with multiple spatio-temporal scales based on microscopic mechanisms; Step 2, establish a multi-layer collaborative optimization target system for the integrated energy system; Step 3, based on the multi-layer collaborative optimization target system of the integrated energy system, establish a multi-spatio-temporal scale distributed collaborative control method for the whole life cycle of the integrated energy system of cooling, heating, electricity and gas based on the prediction of the adaptive digital twin model; Step 4, establish a comprehensive evaluation method for multi-granularity indicators of the integrated energy system. Among them, in Step 1, the integrated energy system includes cooling, heating, electricity and gas networks, energy conversion equipment, energy storage equipment, and building loads.

Description

Technical Field

[0001] The present invention relates to the technical field of multi - energy complementary integrated energy systems, and particularly relates to an operation control method for an integrated energy system. Background Art

[0002] Energy shortage, air pollution and global warming are issues that are increasingly being taken seriously worldwide. The multi - energy complementary integrated energy system has become the trend of future social development. The multi - energy integrated energy system of cooling, heating, electricity and gas has multiple relationship couplings and co - exists with multi - scale inertia, which increases the complexity and difficulty of its operation control. There is still a lack of accurate and efficient operation control methods for the multi - energy integrated energy system of cooling, heating, electricity and gas. Summary of the Invention

[0003] The present invention is made to solve the above problems, and aims to provide an operation control method for an integrated energy system.

[0004] The present invention provides an operation control method for an integrated energy system, having the following features, including the following steps: Step 1, establishing a five - dimensional adaptive digital twin model of the whole life cycle of the integrated energy system with multi - space - time scales based on micro - mechanisms; Step 2, establishing a multi - layer collaborative optimization target system for the integrated energy system; Step 3, based on the multi - layer collaborative optimization target system of the integrated energy system, establishing a multi - space - time scale distributed collaborative control method for the whole life cycle of the cooling, heating, electricity and gas integrated energy system predicted by the adaptive digital twin model; Step 4, establishing a multi - index comprehensive evaluation method for the integrated energy system, including a multi - granularity index system, to evaluate the effect of the multi - space - time scale distributed collaborative control. Among them, in Step 1, the integrated energy system includes a cooling, heating, electricity and gas network, energy conversion equipment, energy storage equipment, and building loads. In Step 4, the multi - granularity index system includes at least physical indexes, safety indexes, overall system reliability, resilience, economy, environment and social indexes at the equipment level and subsystem level.

[0005] In the operation control method of the integrated energy system provided by the present invention, the following features may also be included: Among them, in step 1, the five-dimensional adaptive digital twin model of the integrated energy system with multiple spatial and temporal scales in the whole life cycle based on microscopic mechanisms includes subsystem models of cold, heat, electricity, and gas networks, energy conversion devices, and energy storage devices, and specifically includes the following steps: Step 1-1, establish a mechanism model of any one or more of equipment, networks, and building loads, and take energy conversion and storage devices as the center to establish the mechanism models of each subsystem of the integrated energy system; Step 1-2, based on the mechanism models of each subsystem and historical data drive, establish the digital twin models of each subsystem; Step 1-3, when each subsystem is operating, use real-time measurement data to realize the online adaptive coordination and fusion of the digital twin models of each subsystem, and construct the adaptive digital twin model of the whole life cycle of the subsystem; Step 1-4, couple the adaptive digital twin models of the whole life cycle of each subsystem to realize the mass and energy matching of multiple spatial and temporal scales and the cascaded utilization of energy between each subsystem, and construct the five-dimensional digital twin model of the integrated energy system with multiple spatial and temporal scales and self-adaptation in the whole life cycle.

[0006] In the operation control method of the integrated energy system provided by the present invention, the following features may also be included: Among them, the mechanism model of any one or more of equipment, networks, and building loads is based on experimental research. Starting from the microscopic mechanisms of power, heat, flow, heat transfer, or chemical reaction processes, combined with relevant theories, a modular modeling method is used to establish one-dimensional, two-dimensional, or three-dimensional multi-spatial and temporal scale coupled transient mechanism models of energy conversion devices, energy storage devices, and cold, heat, electricity, and gas networks. The multi-spatial and temporal scale coupled transient mechanism model includes at least a physical property library, control equations, a heat delay model, and a device or material performance degradation model, and two-dimensional or three-dimensional transient numerical models are used for key components.

[0007] In the operation control method of the integrated energy system provided by the present invention, the following features may also be included: Among them, in step 1, the five-dimensional scale of the adaptive digital twin model includes three spatial dimensions, a time dimension, and a hierarchical dimension. The hierarchical dimension is the structural level from micro to macro, and is at least divided into the structural levels of equipment components, equipment or networks, subsystems, and the overall system.

[0008] In the operation control method of the integrated energy system provided by the present invention, the following features may also be included: Among them, in step 2, the method for establishing the multi-layer collaborative optimization target system of the integrated energy system is: combining the adaptive digital twin simulation with experimental research, and based on the theories of power, heat, flow, heat transfer, and chemical reactions, constructing the multi-layer collaborative optimization target system of the integrated energy system. The optimization targets include at least the optimization target of minimizing entropy production for cold, heat, and gas networks and cold and heat energy storage, and the dynamic optimization target of maximizing the instantaneous COP for the heat pump energy storage coupling subsystem.

[0009] In the operation control method of the integrated energy system provided by the present invention, it may further have the following characteristics: Among them, step 3 is specifically divided into the following sub-steps: Step 3-1, aiming at the uncertainties of source-load and multi-scale inertia, based on the multi-step prediction, online rolling optimization and feedback correction strategy of the digital twin model of the equipment network in the subsystem, predict and optimize the future dynamic response output of the equipment network; Step 3-2, select a suitable intelligent optimization algorithm to achieve the collaborative optimization control of each equipment network in the subsystem; Step 3-3, conduct collaborative optimization control between subsystems. Based on the adaptive model, dynamic optimization control indicators of multiple coupled subsystems and multi-layer optimization objectives, adopt the Nash optimization and hybrid optimization algorithms, conduct game analysis of multiple subsystems based on the Nash equilibrium theory, adopt the hybrid game, determine the global dynamic Nash equilibrium point, quickly obtain the globally optimal solution with physical significance, establish a non-simultaneous distributed multi-layer dynamic collaborative optimization method, or adopt the analytic hierarchy process and information entropy method to achieve the multi-time-scale distributed collaborative control of the IES overall system.

[0010] In the operation control method of the integrated energy system provided by the present invention, it may further have the following characteristics: Among them, in step 3-1, in the online rolling optimization of the equipment network of the subsystem, based on the key performance parameters of the coupled subsystem, dynamic optimization control indicators and adaptive thresholds, establish a method for adaptively selecting the control time of the equipment network. The dynamic optimization control indicators of the coupled subsystem are different from those of a single equipment network. In step 3-2, the intelligent optimization algorithm is any one of the improved genetic algorithm, hybrid particle swarm algorithm, and machine learning algorithm. In step 3-3, the dynamic optimization control indicators of the multiple coupled subsystems are different from those of a single subsystem.

[0011] Functions and effects of the invention

[0012] According to the operation control method of the integrated energy system involved in the present invention, the specific steps are as follows: Step 1, establish a five-dimensional adaptive digital twin model of the integrated energy system's full life cycle with multi-time scales based on microscopic mechanisms; Step 2, establish a multi-layer collaborative optimization target system for the integrated energy system; Step 3, based on the multi-layer collaborative optimization target system of the integrated energy system, establish a multi-time-scale distributed collaborative control method for the integrated energy system's full life cycle of cooling, heating, electricity, and gas based on the prediction of the adaptive digital twin model; Step 4, establish a multi-index comprehensive evaluation method for the integrated energy system, including a multi-granularity index system to evaluate the effect of the multi-time-scale distributed collaborative control. Among them, in step 1, the integrated energy system includes cooling, heating, electricity, and gas networks, energy conversion equipment, energy storage equipment, and building loads. In step 4, the multi-granularity index system includes at least physical indicators, safety indicators, overall system reliability, resilience, economy, environment, and social indicators at the equipment level and subsystem level.

[0013] Therefore, the comprehensive energy system full-life-cycle multi-space-time scale adaptive digital twin model established based on the mechanism model by the present invention realizes the correlation between microphysical processes and macroscopic performance characteristics, and can reflect the time-varying characteristics of the dynamic coupling changes of the multi-space-time scale dynamic characteristics of equipment, networks or buildings during energy conversion, storage, transmission or chemical reaction processes with the change of working conditions and service time, improving the simulation prediction accuracy and the accuracy of model predictive control.

[0014] In addition, according to the actual operation characteristics of the coupled system, the present invention establishes a method for adaptively selecting the control time of equipment networks, etc., and performs real-time dynamic optimization control, which helps to solve the problem that it is difficult to determine the optimal scheduling period due to the time-varying characteristics of equipment networks and the uncertainty caused by multiple inertia scales.

[0015] Finally, with the energy conversion / storage device as the subsystem center, the present invention uses multi-energy storage decoupling to achieve the decoupling of cold, heat, electricity and gas pipe networks, dynamic collaborative optimization of multi-physical processes, energy quality matching, and energy cascade utilization, and establishes distributed collaborative control based on multi-layer collaborative optimization objectives, which helps to solve the problems of multiple relationship coupling, coexistence of multi-scale inertia, and difficult optimization scheduling in IES, helps to improve the operation control flexibility, energy supply capacity, reliability and safety risk prevention and control ability of IES, and realizes distributed prediction multi-space-time scale collaborative control for the efficient and safe operation of the comprehensive energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the digital twin modeling flowchart of the comprehensive energy system in the embodiment of the present invention;

[0017] Figure 2 is the distributed collaborative control structure diagram of the comprehensive energy system in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the following embodiments will specifically describe a comprehensive energy system operation control method of the present invention in conjunction with the drawings.

[0019] In this embodiment, a comprehensive energy system operation control method is provided.

[0020] The comprehensive energy system operation control method involved in this embodiment includes the following steps:

[0021] Step S1, establish a five-dimensional adaptive digital twin model of the comprehensive energy system full-life-cycle multi-space-time scale based on micro-mechanisms.

[0022] In this embodiment, the integrated energy system includes cold, heat, electricity and gas networks, energy conversion and storage devices, and building loads. The five-dimensional adaptive digital twin model of the integrated energy system with multi-space-time scales in the whole life cycle based on microscopic mechanisms includes subsystem models of cold, heat, electricity and gas networks, energy conversion and storage devices.

[0023] Figure 1 It is the digital twin modeling flowchart of the integrated energy system in the embodiment of the present invention.

[0024] As Figure 1 shown, the specific implementation of step S1 is as follows:

[0025] Step S1-1, establish mechanism models of equipment, networks, building loads, etc. Taking energy conversion and storage devices as the center, establish subsystem mechanism models of the integrated energy system.

[0026] In this embodiment, the mechanism models of equipment, networks, building loads, etc. are based on experimental research. Starting from the microscopic mechanisms of power, heat, flow, heat transfer or chemical reaction processes, combined with relevant theories, a modular modeling method is used to establish one-dimensional, two-dimensional or three-dimensional multi-space-time scale coupled transient mechanism models of energy conversion devices, energy storage devices and cold, heat, electricity and gas networks.

[0027] The multi-space-time scale coupled transient mechanism model includes a physical property library, control equations, a heat delay model, an equipment / material performance degradation model, etc. Two-dimensional / three-dimensional transient numerical models are used for key components.

[0028] Step S1-2, based on the subsystem mechanism models and historical data-driven, establish digital twin models of each subsystem.

[0029] When each subsystem is running, use real-time measurement data to realize online adaptive coordination and fusion of the digital twin models of each subsystem, and construct an adaptive digital twin model of the whole life cycle of the subsystem.

[0030] Step S1-4, couple the adaptive digital twin models of the whole life cycle of each subsystem to realize the mass-energy multi-space-time scale matching and energy cascade utilization between each subsystem, and construct an adaptive multi-space-time scale five-dimensional digital twin model of the integrated energy system in the whole life cycle.

[0031] The five-dimensional scale of the adaptive digital twin model includes three spatial dimensions, a time dimension, and a hierarchical dimension. The hierarchical dimension is the structural level from micro to macro, and is divided into structural levels such as equipment components, equipment or networks, subsystems, and the overall system.

[0032] Step S2, establish a multi-layer collaborative optimization target system for the integrated energy system. The specific method is as follows:

[0033] Combining adaptive digital twin simulation with experimental research, based on theories of electricity, heat, flow, heat transfer, and chemical reactions, a multi-layer collaborative optimization objective system for integrated energy systems is constructed. For example, minimizing entropy production is used as the optimization objective for cold, heat, and gas networks and cold and heat energy storage, and maximizing the instantaneous COP is used as the dynamic optimization objective for the heat pump energy storage coupling subsystem.

[0034] Step S3: Based on the multi-layer collaborative optimization objective system of the integrated energy system, establish a multi-temporal and multi-spatial scale distributed collaborative control method for the entire life cycle of the integrated cold, heat, electricity, and gas energy system based on the adaptive digital twin model prediction.

[0035] Figure 2 It is the structural diagram of the distributed collaborative control of the integrated energy system in the embodiment of the present invention.

[0036] The specific implementation manner of step S3 is as follows:

[0037] Step S3-1: For the uncertainties of source load and multi-scale inertia, based on the multi-step prediction, online rolling optimization, and feedback correction strategies of the digital twin model of the equipment network in the subsystem, predict and optimize the future dynamic response output of the equipment network.

[0038] In online rolling optimization, based on the key performance parameters of the coupling subsystem, dynamic optimization control indicators, and adaptive thresholds, establish a method for adaptively selecting the control time of the equipment network.

[0039] The dynamic optimization control indicators of the coupling subsystem are different from those of a single equipment network.

[0040] Step S3-2: Select a suitable intelligent optimization algorithm to achieve the collaborative optimization control of each equipment network within the subsystem. In this embodiment, the intelligent optimization algorithm uses the improved genetic algorithm among the improved genetic algorithm, hybrid particle swarm algorithm, and machine learning algorithm.

[0041] Step S3-3: Conduct collaborative optimization control among subsystems. Based on the adaptive model, dynamic optimization control indicators of multiple coupling subsystems, and multi-layer optimization objectives, use the Nash optimization and hybrid optimization algorithms. Based on the game analysis of multiple subsystems using the Nash equilibrium theory, adopt a hybrid game to determine the global dynamic Nash equilibrium point, quickly obtain the globally optimal solution with physical significance, establish a non-simultaneous distributed multi-layer dynamic collaborative optimization method, or use the analytic hierarchy process and information entropy method to achieve the multi-temporal and multi-spatial scale distributed collaborative control of the IES overall system.

[0042] Step S4: Establish a multi-index comprehensive evaluation method for the integrated energy system, including a multi-granularity index system, such as physical indicators and safety indicators at the equipment level and subsystem level, and reliability, resilience, economic, environmental, and social indicators of the overall system, to evaluate the effect of the multi-temporal and multi-spatial scale distributed collaborative control.

[0043] Functions and effects of the embodiment

[0044] According to the integrated energy system operation control method involved in this embodiment, the specific steps are as follows: Step 1, establish a five-dimensional adaptive digital twin model of the integrated energy system's full life cycle with multiple spatio-temporal scales based on microscopic mechanisms; Step 2, establish a multi-layer collaborative optimization target system for the integrated energy system; Step 3, based on the multi-layer collaborative optimization target system of the integrated energy system, establish a distributed collaborative control method for the full life cycle of the integrated energy system of heat, cold, electricity, and gas based on the prediction of the adaptive digital twin model; Step 4, establish a comprehensive evaluation method for multiple indicators of the integrated energy system, including a multi-granularity indicator system to evaluate the effect of the distributed collaborative control of multiple spatio-temporal scales. Among them, in Step 1, the integrated energy system includes heat, cold, electricity, and gas networks, energy conversion equipment, energy storage equipment, and building loads. In Step 4, the multi-granularity indicator system includes at least physical indicators, safety indicators, overall system reliability, resilience, economy, environment, and social indicators at the equipment level and subsystem level.

[0045] Therefore, the five-dimensional adaptive digital twin model of the integrated energy system's full life cycle with multiple spatio-temporal scales established based on the mechanism model in this embodiment realizes the correlation between microscopic physical processes and macroscopic performance characteristics, and can reflect the time-varying characteristics of the dynamic coupling changes of the dynamic characteristics of equipment, networks, and buildings at multiple spatio-temporal scales during energy conversion, storage, transmission, or chemical reaction processes with the change of working conditions and service time, improving the simulation prediction accuracy and the accuracy of model predictive control.

[0046] In addition, according to the actual operation characteristics of the coupled system, this embodiment establishes a method for adaptively selecting the control time of equipment networks, etc., for real-time dynamic optimization control, which helps to solve the problem that it is difficult to determine the optimal scheduling period due to the time-varying characteristics of equipment networks and multiple inertia scales and the resulting uncertainties.

[0047] Finally, taking the energy conversion / storage equipment as the subsystem center, this embodiment uses multi-energy storage decoupling to achieve the decoupling of heat, cold, electricity, and gas pipe networks, dynamic collaborative optimization of multiple physical processes, energy quality matching, and energy cascade utilization, and establishes a distributed collaborative control based on multi-layer collaborative optimization objectives, which helps to solve the problems of multiple relationship couplings, coexistence of multi-scale inertia, and difficult optimization scheduling in IES, helps to improve the operation control flexibility, energy supply capacity, reliability, and safety risk prevention and control ability of IES, and realizes distributed prediction multi-spatio-temporal scale collaborative control for the efficient and safe operation of the integrated energy system.

[0048] The above embodiments are preferred cases of the present invention and are not used to limit the protection scope of the present invention.

Claims

1. A method for operating and controlling an integrated energy system, characterized in that It includes the following steps: Step 1, establish a five-dimensional adaptive digital twin model for the whole life cycle of the integrated energy system with multiple spatio-temporal scales based on microscopic mechanisms; Step 2, establish a multi-layer collaborative optimization target system for the integrated energy system; Step 3, based on the multi-layer collaborative optimization target system of the integrated energy system, establish a multi-spatio-temporal scale distributed collaborative control method for the whole life cycle of the integrated energy system of cooling, heating, electricity and gas predicted by the adaptive digital twin model; Step 4, establish a comprehensive evaluation method for multiple indicators of the integrated energy system, including a multi-granularity indicator system, to evaluate the effect of the multi-spatio-temporal scale distributed collaborative control; Among them, in Step 1, the integrated energy system includes a cooling, heating, electricity and gas network, energy conversion equipment, energy storage equipment, and building loads; Step 3 is specifically divided into the following sub-steps: Step 3-1, aiming at the uncertainties of source load and multi-scale inertia, predict and optimize the future dynamic response output of the equipment network based on the multi-step prediction, online rolling optimization and feedback correction strategies of the digital twin model of the equipment network in the subsystem; Step 3-2, select a suitable intelligent optimization algorithm to achieve collaborative optimization control of each equipment network within the subsystem; Step 3-3, perform collaborative optimization control between the subsystems. Based on the adaptive model, multi-coupled subsystem dynamic optimization control indicators, and multi-layer optimization objectives, adopt the Nash optimization and hybrid optimization algorithms, conduct game analysis of multiple subsystems based on the Nash equilibrium theory, adopt mixed game, determine the global dynamic Nash equilibrium point, quickly obtain the globally optimal solution with physical significance, and establish a non-simultaneous distributed multi-layer dynamic collaborative optimization method; In Step 4, the multi-granularity indicator system includes at least physical indicators, safety indicators, overall system reliability, resilience, economy, environment, and social indicators at the equipment level and subsystem level.

2. The operation control method of the integrated energy system according to claim 1, wherein: Among them, In Step 1, the five-dimensional adaptive digital twin model for the whole life cycle of the integrated energy system with multiple spatio-temporal scales based on microscopic mechanisms includes subsystem models of the cooling, heating, electricity and gas network, energy conversion equipment, and energy storage equipment. Specifically, it includes the following steps: Step 1-1, establish a mechanism model of any one or more of the equipment, network, and building loads, and take the energy conversion and storage equipment as the center to establish the mechanism models of each subsystem of the integrated energy system; Step 1-2, based on the mechanism models of each subsystem and historical data driving, establish the digital twin models of each subsystem; Step 1-3, when each subsystem operates, use real-time measurement data to realize online adaptive collaboration and fusion of the digital twin models of each subsystem, and construct an adaptive digital twin model for the whole life cycle of the subsystem; Step 1-4, couple the adaptive digital twin models for the whole life cycle of each subsystem to achieve energy-mass multi-spatio-temporal scale matching and cascaded utilization between the subsystems, and construct an adaptive multi-spatio-temporal scale five-dimensional digital twin model for the whole life cycle of the integrated energy system.

3. The operation control method of the integrated energy system according to claim 2, wherein: Among them, The mechanism model of any one or more of the above-mentioned equipment, network and building load is based on experimental research. Starting from the microscopic mechanism of power, heat, flow, heat transfer or chemical reaction processes, combined with relevant theories, a modular modeling method is adopted to establish a one-dimensional, two-dimensional or three-dimensional multi-space-time scale coupled transient mechanism model of energy conversion equipment, energy storage equipment and cold, heat, electricity and gas networks. The multi-space-time scale coupled transient mechanism model at least includes a physical property library, control equations, a heat delay model, and a device or material performance degradation model. Two-dimensional or three-dimensional transient numerical models are used for key components.

4. The operation control method of the integrated energy system according to claim 1, characterized in that: Among them, In step 1, the five-dimensional scale of the adaptive digital twin model includes three-dimensional space, time dimension and hierarchical dimension. The hierarchical dimension is the structural level from micro to macro, and is at least divided into the structural levels of equipment components, equipment or network, subsystem, and overall system.

5. The operation control method of the integrated energy system according to claim 1, characterized in that: Among them, In step 2, the method for establishing the multi-level collaborative optimization target system of the integrated energy system is: Combining adaptive digital twin simulation with experimental research, and based on power, heat, flow, heat transfer and chemical reaction theories, constructing the multi-level collaborative optimization target system of the integrated energy system. The optimization targets at least include the optimization target of minimizing entropy production for cold, heat and gas networks and cold and heat storage, and the dynamic optimization target of maximizing instantaneous COP for the heat pump energy storage coupling subsystem.

6. The operation control method of the integrated energy system according to claim 1, characterized in that: Among them, In step 3, the analytic hierarchy process and the information entropy method are used to realize the multi-space-time scale distributed collaborative control of the IES overall system.

7. The operation control method of the integrated energy system according to claim 6, characterized in that: Among them, In step 3-1, in the online rolling optimization of the equipment network of the subsystem, based on the key performance parameters of the coupled subsystem, the dynamic optimization control index and the adaptive threshold, a method for adaptively selecting the control time of the equipment network is established. The dynamic optimization control index of the coupled subsystem is different from the dynamic optimization control index of a single equipment network. In step 3-2, the intelligent optimization algorithm is any one of the improved genetic algorithm, the hybrid particle swarm algorithm, and the machine learning algorithm. In step 3-3, the dynamic optimization control index of the multiple coupled subsystems is different from the dynamic optimization control index of a single subsystem.

Images