Unified Optimal Regulation Of Transient And Steady State For Micro-Energy Grid Based On Cyber-Physical Integration

With the gradual depletion of traditional fossil energy and the increasingly serious environmental pollution,energy transformation is urgent,and micro-energy grid(MEG)that integrates different energy subsystems plays an important role in energy transformation.The MEG contains multiple forms of energy and different energy subsystems are highly coupled,making its operation more complex.To ensure the stability and economy of MEG,reasonable and effective regulation methods need to be developed to realize the unified optimal regulation of transient and steady state for MEG.Meanwhile,the continuous development of information technology such as cloud computing,Internet of Things,and artificial intelligence has made the MEG develop into a typical Cyber-Physical System(CPS).The CPS technology,as a research technology spanning three fields of sensing,computing and control,provides greater potential for the unified optimal regulation of transient and steady-state of the MEG.In this context,the introduction of CPS technology into the unified regulation of MEG can fully utilize information systems to tract the operating state of physical energy systems,ensuring the stability of MEG while improving its economic operation capability.Therefore,this paper focuses on this topic from steady-state to transient and studies the unified optimal regulation of transient and steady state for MEG based on cyber-physical integration,mainly including the following aspects.(1)This paper proposes a unified optimal regulation framework of transient and steady state for MEG based on cyber-physical integration,which can be used to describe the state transfer process of multi-layer micro-energy devices,energy storage,and load during the optimization process and thus improves system observability.The proposed modeling framework can be extended to other energy systems with the integration of CPS and provides an effective model support to MEG to achieve unified optimization and regulation of transient and steady states.(2)This paper establishes the MEG device state transfer model based on cyber-physical integration and a day-ahead optimization model of MEG considering demand response.By solving the day-ahead optimization model and traction control of device operating state based on the obtained optimized state transfer path,it can make the device state and its transfer process in the best state,and at the same time make the scheduling scheme of the MEG more accurate and reasonable,and reduce the system operation cost.(3)This paper proposes an intra-day rolling optimization strategy for MEG based on cyber-physical integration.The distributed model predictive control(DMPC)based on the idea of multi-agent divides the MEG system into three subsystems: electricity,heat and cooling.Each subsystem can be viewed as an agent with the integration of CPS to achieve the optimization goals of the system through information exchange.Then a cooperative distributed iterative algorithm is proposed for solving the subsystem optimization problem,and each subsystem is solved in a parallel and iterative manner,which reduces the scale and complexity of solving the optimization problem,while the convergence of the optimization algorithm is analyzed.Finally,considering the variable operating conditions of the MEG system,a DMPC strategy is proposed to adaptively adjust the prediction horizon according to the prediction errors of renewable energy and load power.The strategy can take into account the smoothness and prediction accuracy of device output and improve the control performance of DMPC.(4)This paper proposes a unified optimal regulation strategy of transient and steady state for MEG based on cyber-physical integration.Firstly,according to the amount of system frequency deviation,it is divided into four intervals of frequency normal,frequency alert,frequency emergency and frequency collapse.Based on the divided frequency intervals,multiple operating states of the system are defined and the transfer conditions between different operating states are given,according to which the state transfer model of the MEG is established.Secondly,a multi-objective optimization model of MEG is established by considering system frequency deviation,comprehensive operating cost of MEG and user comfort.And a strategy of adaptively adjusting the weight coefficients of different subobjectives according to the different operating states of the system is proposed,which avoids the difficulties in the selection of weight coefficients and the influence of human subjective factors,and achieves the effective balance of different sub-objectives in different operating states of the system.The proposed strategy realizes the traction control of each stage in the whole operation domain based on the objective functions and state transfer conditions in different stages of transient-steady state,and achieves the stable control and optimal scheduling of the system.