Hierarchical decentralized optimal control and reconfiguration of networked microgrids in the power distribution system

The digital era has brought revolutionary automation technology in various science and engineering domains.
Integration of large-scale sustainable Distributed Energy Resources (DERs) in the power distribution network has led to the decentralization of power generation, contrary to the conventional power grid. Supplementary information and communication technology, decentralized digital economic structures, and data-driven learning-based technology have transformed distribution networks as a system of systems in recent decades. Thus the ecosystem surrounding the electricity consumer is getting socially, economically, and politically complex. There is a strong requirement for a technology framework that can offer operational and managerial independence to the geographically distributed consumer base. In the context of power distribution network operation, the need for decentralized hierarchical control structures is dictated by the rapid integration of sustainable DERs. To tackle the contemporary problems of climate change, 100\% sustainable generation-based power grid operations can be a milestone toward carbon neutral society. The nature of such power networks entails a variety of properties including variability in a generation, bidirectional power flow, and nonlinear network dynamics due to complex generation and load mix. The key operational challenge is the coexistence of large-scale DERs to achieve stable and accurate load power sharing while regulating the voltage and frequency in the network to the nominal values. The technology solution needs to be scalable by reducing the dependency on the communication network, robust against the measurement noise, and adaptive to the changing network dynamics. Furthermore, to enhance the overall resiliency of the operation, network dynamics have to be systematically studied and optimal network reconfiguration methodology has to be devised. The vision of the dissertation work is to formulate a hierarchical decentralized control structure to accommodate three-level research objectives. Firstly, at the DER level, considering the low \textit{X/R} and unbalanced nature of the distribution network, appropriate cascaded primary control loops are designed. A unified control architecture is proposed for stable multiple DER power sharing, achieving ride-through capability, and maintaining the network voltage and frequency close to nominal values. The unified control architecture is devised through a systematic definition of steady-state operating modes and the interaction among hierarchical entities in the grid. Secondly, at the microgrid level, a decentralized predictive optimal constrained secondary control framework to maintain the nominal voltage and frequency is formulated. The proposed strategy is built on a first-order model of the primary controller and local/global measurements-based state estimation, facilitating the deployability to grid edge devices. The framework is further extended to incorporate a data-driven approach when model parameters are not available. Lastly, at the network level, detailed network dynamics are modeled as a real-time environment by incorporating primary, and secondary control and protection functions. The reinforcement learning agent is designed by utilizing an extended q-routing methodology, which interacts with the environment through event-driven communication and performs optimal network reconfiguration during events in the environment. The ultimate purpose of this dissertation work is to bring value to engaged stakeholders in the process of achieving 100% sustainable power grid. There exists a gap between the aforementioned hierarchical technology solutions and business delivery models. This gap is addressed in chapter 7 by fostering a market for resiliency services through the Energy-as-a-Service model. The regulatory framework and ownership agreements are yet to evolve to support the delivery model acceptable to the involved stakeholders. Sophisticated technology aggregation and cost structure must be achieved through systematic economic analysis to maximize the revenue for technology and service providers. The author, lastly and most importantly, emphasizes the need for sustainable business model innovation in coordination with the big technology players, new players such as startups including aggregators, and utilities to position themselves in the market.