Navigating uncertainties in power system reliability and renewable energy integration: strategies and methodologies
Creators: name: Chen, Zhetao
Publisher: name: No Publisher Supplied
Year: 2024

description: This dissertation undertakes an extensive investigation into the reliability analysis and optimization of power network systems amidst uncertainties, emphasizing the incorporation of novel strategies in energy market operations. Contemporary power networks encounter a myriad of uncertainties, from the variability of renewable energy sources and transmission infrastructure vulnerabilities, such as line and tower failures, to unpredictable demand fluctuations. These uncertainties compromise the network's reliability and complicate its design, adversely affecting the energy market's operational efficiency. Despite significant research in this field, much of the existing literature concentrates on traditional aspects of network reliability and optimization algorithms, often overlooking critical factors relevant to current power network dynamics and energy market complexities. This dissertation aims to fill these gaps by offering a thorough analysis of power network reliability and design optimization under uncertainties, with a specific emphasis on innovative energy market operation strategies. It is divided into two main sections: (1) a detailed examination of reliability analysis and the optimal design of power networks under uncertainties, and (2) an investigation into the optimal energy scheduling in the energy market amidst uncertainties.The first section of this dissertation introduces the Eigenvector Dimension Reduction (EDR) method for reliability analysis in the face of time-dependent load uncertainties. This novel approach offers a more efficient alternative to traditional Monte Carlo simulations, enabling precise probability-based evaluations of system reliability without leading to unnecessary system expansion. The EDR method is then utilized for the reliability-based design optimization (RBDO) of distributed generators' (DGs) placement to enhance power transfer quality and minimize losses under uncertain conditions. A modified binary particle swarm optimization (B-PSO) algorithm is also introduced, showcasing its effectiveness in optimally locating DGs to improve both system reliability and operational efficiency.The second section delves into the integration of credit-based scheduling in energy market operations, particularly targeting the intermittency issues of renewable energy sources. It presents a pioneering framework for day-ahead market scheduling that incorporates a credit score system for renewable power producers, established through Bayesian inference, to enhance both the operational efficiency and economic benefits of market transactions. Additionally, a Stackelberg game-theoretic model is introduced for strategic imbalance pricing and capacity bidding within a credit score-driven energy market, aimed at improving market efficiency and aligning with renewable energy targets. The dissertation also outlines a bidding capacity strategy for a state-of-the-art hybrid charging station for electric buses, which acts as a power supplier in the market. This strategy is crafted to navigate the challenges of renewable energy intermittency and price volatility, offering innovative methods for integrating renewable sources into the market and maintaining economic sustainability.In summary, this dissertation makes a substantial contribution to the fields of reliability analysis and system optimization in power networks, introducing novel solutions for the integration of renewable energy sources into energy market operations. The research presented provides critical insights for policymakers, system operators, and market participants, with the goal of improving the reliability, efficiency, and sustainability of future power systems.