Energy systems: Enhancing efficiency and reducing risk with CAES, fuel cells, and UPFC

Understanding the components of an integrated renewable power system is important, especially in deregulated power markets. One of the major challenges in integrating renewable sources into existing electrical infrastructure is their inherent variability, especially with wind energy, which can fluctuate very significantly. This study examines how integrating wind farms (WF) with compressed air energy storage (CAES), fuel cells (FC), and unified power flow controllers (UPFC) affects voltage profiles, generation costs, and overall economics of the system within a deregulated electricity market. In support of this research, actual and forecasted wind speed data (AWS, FWS) from two randomly selected locations in India were collected. The study considers surplus and deficit charge rates to assess the cost imbalances that result from a mismatch between AWS and FWS. A different set of optimization algorithms including sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and the artificial gorilla troop optimization algorithm (AGTO) is used for checking on both economic performance and system risks. It explores CAES, fuel cells, and UPFC roles in the hybrid network to address real-time risks posed in this regard within the hybrid network. Value-at-risk (VaR) and conditional value-at-risk (CVaR) techniques have been employed in estimating the system's risks. It is shown that the strategic integration of CAES and fuel cells effectively dampens the cost imbalances that result from differences between AWS and FWS. Besides, the results show that the installation of UPFCs brings about a considerable improvement in system profitability and voltage stability. The modified IEEE 30-bus test system was used to validate the results. It found that the optimization algorithms AGTO can be quite effective in improving system performance and managing economic risks in hybrid energy networks.