OPTIMIZATION-BASED CONTROL OF POWER AND ENERGY SYSTEMS

Abstract

In power and energy systems, whether at the unit, distribution, or transmission level, the coordination of all relevant system components remains a paramount consideration. This thesis is dedicated to optimization-based control for achieving that coordination in various power and energy systems. First, as a means for load-current sharing in the absence of a system model, a data-driven control scheme is introduced for multi-stack fuel cell systems, where raw data of trajectories is leveraged for designing control laws. Second, distributed optimization algorithms are developed for islanded microgrids by leveraging the unique characteristics of inverter-based microgrid systems. As performing economic dispatch in advance may not provide a sufficient guarantee of achieving the expected outcomes due to the uncertainty associated with renewable energy sources, economic dispatch and frequency regulation are concurrently handled. This approach makes inter-layer coordination across timescales unnecessary. Third, a novel error signal is designed as a replacement for the area control error; based on this signal, a distributed algorithm is developed to coordinate multiple battery energy storage systems to provide ramping support to conventional generators, thereby improving the transient behavior of automatic generation control systems. Throughout this thesis, various optimization-based control schemes are involved, ranging from data-driven, feedback-based, to those developed through online convex optimization. The effectiveness and potential of the proposed schemes are demonstrated by comprehensive simulation studies.

Date of Award	1 Aug 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Zhengtao Ding (Supervisor) & Alessandra Parisio (Supervisor)