Power system feasibility and flexibility analysis for enlarging renewable exploitation under high uncertainties

• Main Author: Weng, Yu
• Other Authors: Hung Dinh Nguyen
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/166745
• Institution: Nanyang Technological University

Global climate changes and the consequent extreme weather are at the bottom of the recent energy crisis over the world, thus pushing the transition to more green resources and electric vehicles (EVs). At the same time, the increasing level of uncertainties induced by the high penetration of renewables and EVs exposes power grids to unprecedented risks of insecure operations. Typically, under stressed conditions, contingencies or sudden changes in power injections may place the systems close to the physical limits and lead to outages. Such progress in a power grid is generally complex and difficult to assess and manage due to a great deal of nonlinearity in the network's behaviors. In addition, the system’s properties and induced physical limits under high uncertainties and in the presence of multiple authorities are still not sufficiently understood. The real-world challenges of incorporating a large share of renewables while ensuring safe operations of power systems need to be effectively addressed to ease the transition to decarbonization. Therefore, this thesis focuses on developing new systematic approaches for enhancing resource exploitation through the feasibility and flexibility analysis of power systems with high renewables to combat climate change. To this end, this thesis conducts a series of research, ranging from system analysis, control design, to the coordination of power systems under steady-state conditions with high uncertainties. The proposed approaches are developed for distribution networks based on algebraic topology techniques by leveraging the underlying physics and recent development in Machine Learning. Mathematical proofs of resulting solvability and feasibility certificates are provided. The established framework can be further extended to mesh transmission systems to cover a wide range of network topologies. The use cases involve a single device, such as an energy storage system, to a hierarchical multi-area energy market to optimize daily operations, thereby allowing individuals and system operators to procure energy services and benefits under safe conditions. In particular, Gaussian Process-based optimal voltage controls and fixed-point theorem-based techniques are developed to regulate the nodal voltages and maintain the steady-state stability of power systems. Their applicability and robustness to system conditions and network topology are verified and demonstrated in various test cases that host high renewables. Next, a framework for feasibility and flexibility analysis is established for both peer-to-peer energy trading and Transmission System Operator-Distribution System Operator (TSO-DSO) coordination to promote renewable usage while ensuring system safety. Specifically, admissible renewable injection regions that ensure operational compliance are constructed to facilitate individual participators and also assist system operators in being aware of vulnerable sub-systems. These ``safe’’ renewable injection ranges and stability margins are computationally efficient to compute and become handy to guarantee the system's seamless operation under high penetration of intermittent renewable resources. The proposed works, therefore, contribute to smoothing the transition to clean energy and a sustainable future.