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Distributed algorithms have been proposed as options to scale control propositions to the massive number of intelligent energy devices, sub-systems, and distributed energy resources being integrated into the electricity grid. Distributed algorithms rely on the communication network for exchanging information. Failures in the communication network can jeopardize distributed decision-making and in the worst-case scenario can lead to system-level stability problems. This paper proposes a communication-failure-resilient architecture for distributed operation and control in smart grids with hybrid producer/consumer (prosumer) agents. We describe the relations between system-wide performance and communication failures and identify topological conditions on the cyber-physical network, under which prosumers can perform key operating tasks, such as distributed frequency regulation through an imperfect communication network. [ABSTRACT FROM AUTHOR]

This paper proposes an architectural solution to enhance the resilience of distributed decision-making and control in microgrids and smart grids with respect to communication delays. The paper develops a resource allocation framework to optimally place the multiple-input multiple-output (MIMO) communication technology on critical channels to reduce the communication bottle neck and expedite the convergence speed of distributed online optimization algorithms, which require convergence in the cyber network before the solution can be implemented on the physical grid. The paper shows that optimal placement of the MIMO technology can decrease communication transmission delays, which in turn improves the reliability of power system operational protocols, such as distributed frequency regulation. Illustrative case studies using data from real-world power systems show that only upgrading the critical communication channels is sufficient to achieve system-wide performance improvement. • Proposing an architectural solution to enhance the resilience of distributed optimization in smart grids with respect to communication delays. • Determining an upper bound on the communication delays under different MIMO communication architectures. • Developing a resource allocation algorithm to optimally place the MIMO technology and to increase the resilience of communication networks. [ABSTRACT FROM AUTHOR]