Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks

Rapid advancements in smart grid technologies have brought about the proliferation of intelligent and actuating power system components such as distributed generation, storage, and smart appliance units. Capitalizing fully on the potential benefits of these systems for sustainable and economical power generation, management, and delivery is currently a significant challenge due to issues of scalability, intermittency, and heterogeneity of the associated networks. In particular, vertically integrated and centralized power system management is no longer tractable for optimally coordinating these diverse devices at large scale while also accounting for the underlying complex physical grid constraints. To address these challenges, we propose a hierarchical signal processing framework for optimal power flow management whereby the cyber-physical network relationships of the modern grid are leveraged to enable intelligent decision-making by individual devices based on local constraints and external information. Decentralized and distributed techniques based on convex optimization and game theoretic constructs are employed for information exchanges and decision-making at each tier of the proposed framework. It is shown via theoretical and simulation studies that our technique allows for the seamless integration of power components into the grid with low computational and communication overhead while maintaining optimal, sustainable, and feasible grid operations.