The increased rate of cyber-attacks on the power system necessitates the need for innovative solutions to ensure its resiliency. This work builds on the advancement in the IoT to provide a practical framework that is able to respond to multiple attacks on a network of interconnected microgrids. This paper provides an IoT-based digital twin (DT) of the cyber-physical system that interacts with the control system to ensure its proper operation. The IoT cloud provision of the energy cyber-physical and the DT are mathematically formulated. Unlike other cybersecurity frameworks in the literature, the proposed one can mitigate an individual as well as coordinated attacks. The framework is tested on a distributed control system and the security measures are implemented using cloud computing. The physical controllers are implemented using single-board computers. The practical results show that the proposed DT is able to mitigate the coordinated false data injection and the denial of service cyber-attacks.

This paper presents the development and application of a real-time testbed for multiagent system interoperability. As utility independent private microgrids are installed constantly, standardized interoperability frameworks are required to define behavioral models of the individual agents for expandability and plug-and-play operation. In this paper, we propose a comprehensive hybrid agent framework combining the foundation for intelligent physical agents (FIPAs), IEC 61850, and data distribution service (DDS) standards. The IEC 61850 logical node concept is extended using FIPA-based agent communication language with application specific attributes and deliberative behavior modeling capability. The DDS middleware is adopted to enable a real-time publisher-subscriber interoperability mechanism between platforms. The proposed multi-agent framework was validated in a laboratory-based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results were demonstrated for both decentralized and distributed control approaches. Secondary and tertiary control levels of a microgrid were demonstrated for decentralized hierarchical control case study. A consensus-based economic dispatch case study was demonstrated as a distributed control example. It was shown that the developed agent platform is industrially applicable for actual smart grid field deployment.

The smart grid energy management with variable renewable energy resources presents many challenges to the grid operation. An optimized solution to manage the available resources is necessary to achieve reliable operation. This paper presents the Hierarchical Distributed Model Predictive Control (HDMPC) to solve the energy management problem in multi-time frame and multi-layer optimization strategy. The HDMPC combines the concept of enabling the optimization over long time-horizon for a centralized Supervisory Management (SM) layer and another short time-horizon during high power variability for a distributed Coordination Management (CM) layer. The information exchange and interoperability between different layers are provided through the data-centric communication approach. The SM (upper layer) works to present the grid operator with certain operational plans and gives the guidelines to the CM (lower layer). The CM has the responsibility to coordinate the relationship between the centralized optimization objectives and the physical power system layer. The proposed HDMPC control was verified both numerically as well as experimentally. The obtained simulation results show that the control strategy proposed here is successful and combines the benefits of both the centralized and distributed control for a global solution of the grid operation problem. The experimental results demonstrate the feasibility of the real-time implementation of the proposed system for deployment to control future smart grid assets.