On the Cyber-Physical Needs of DER-Based Voltage Control/Optimization Algorithms in Active Distribution Network

With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise algorithmic classification and suitable metrics. It has been identified that classical controller definitions along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for algorithmic performance evaluation; while providing the taxonomy. Furthermore, a majority of these control algorithms operate in multiple time scales, and therefore, algorithmic time decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements while focusing on the architectural subtleties of algorithms is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in the presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Finally, the detailed discussion provided in this paper identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test-bed.

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Published in the IEEE Power & Energy Society Section within IEEE Access

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed.

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