Advanced Controls Of Cyber Physical Energy Systems

Cyber system is a fairly important component of the energy systems. The network imperfections can significantly reduce the control performance if not be properly treated together with the physical system during the control designs. In the proposed research, the advanced controls of cyber-physical energy systems are explored in depth. The focus of our research is on two typical energy systems including the large-scale smart grid (e.g. wide-area power system) and the smart microgrid (e.g. shipboard power system and inverter-interfaced AC/DC microgrid). In order to proactively reduce the computation and communication burden of the wide-area power systems (WAPSs), an event/self-triggered control method is developed. Besides, a reinforcement learning method is designed to counteract the unavoidable network imperfections of WAPSs such as communication delay and packet dropout with unknown system dynamics. For smart microgrids, various advanced control techniques, e.g., output constrained control, consensus-based control, neuro network and game theory etc., have been successfully applied to improve their physical performance. The proposed control algorithms have been tested through extensive simulations including the real-time simulation, the power-hardware-in-the-loop simulation and on the hardware testbed. Based on the existing work, further research of microgrids will be conducted to develop the improved control algorithms with cyber uncertainties

Overview of AC microgrid controls with inverter-interfaced generations

Distributed generation (DG) is one of the key components of the emerging microgrid concept that enables renewable energy integration in a distribution network. In DG unit operation, inverters play a vital role in interfacing energy sources with the grid utility. An effective interfacing can successfully be accomplished by operating inverters with effective control techniques. This paper reviews and categorises different control methods (voltage and primary) for improving microgrid power quality, stability and power sharing approaches. In addition, the specific characteristics of microgrids are summarised to distinguish from distribution network control. Moreover, various control approaches including inner-loop controls and primary controls are compared according to their relative advantages and disadvantages. Finally, future research trends for microgrid control are discussed pointing out the research opportunities. This review paper will be a good basis for researchers working in microgrids and for industry to implement the ongoing research improvement in real systems

Cyber Physical Energy Systems Modules for Power Sharing Controllers in Inverter Based Microgrids

The Microgrids (MGs) are an effective way to deal with the smart grid challenges, including service continuity in the event of a grid interruption, and renewable energy integration. The MGs are compounded by multiple distributed generators (DGs), and the main control goals are load demand sharing and voltage and frequency stability. Important research has been reported to cope with the implementation challenges of the MGs including the power sharing control problem, where the use of cybernetic components such as virtual components, and communication systems is a common characteristic. The use of these cybernetic components to control complex physical systems generates new modeling challenges in order to achieve an adequate balance between complexity and accuracy in the MG model. The standardization problem of the cyber-physical MG models is addressed in this work, using a cyber-physical energy systems (CPES) modeling methodology to build integrated modules, and define the communication architectures that each power sharing control strategy requires in an AC-MG. Based on these modules, the control designer can identify the signals and components that eventually require a time delay analysis, communication requirements evaluation, and cyber-attacks’ prevention strategies. Similarly, the modules of each strategy allow for analyzing the potential advantages and drawbacks of each power sharing control technique from a cyber physical perspective

Modeling, Simulation and Decentralized Control of Islanded Microgrids

Modeling, Simulation and Decentralized Control of Islanded Microgrids by Farideh Doost Mohammadi This thesis develops a comprehensive modular state-space model of microgrids containing inverter-based Distributed Energy Resources (DERs). The model is validated and then used for small signal stability enhancement and voltage and frequency control. State space models of various microgrid elements are first derived, which allow for the inclusion of any possible elements such as current controlled inverters that are missing in the literature. Then a complete state space model is obtained to complement the models that are available in the literature and whose objectives are system analysis only as compared to the purpose of this work which is stability enhancement and control design. Specifically,;1. Small signal stability is enhanced by adding current controlled inverters to the microgrid. 2. Decentralized secondary frequency and voltage control techniques are proposed.;For secondary frequency control purposes, at first, the control strategies of different kinds of inverters and storage devices are described. Then, a novel solution is introduced for islanded microgrids by decomposing the system into virtual control areas.;For the secondary voltage control an Average Consensus Algorithm (ACA) is used and applied on a network of agents which has been chosen optimally based on the required connectivity. The main purpose of the ACA is to keep the average voltage of all the buses at a desired level during islanding. Then another control strategy is proposed to improve the voltage profile. While the average voltage is kept fixed by the voltage controlled inverters, this voltage profile smoothness is obtained by dedicating zones to current controlled inverters and defining their responsibilities based on the location of the loads