EnergyBidSim

This book presents advanced meta-heuristic algorithms and a Multi-Agent System (MAS) for intelligent bidding in the restructured day-ahead energy market. Enhanced versions of Moth Flame Optimizer (OB-MFO) Firefly Algorithm (RFA) and a hybrid WOA-SCA are proposed using opposition-based learning and adaptive techniques showing superior performance on benchmark tests. These algorithms are applied to market bidding scenarios under uncertainty evaluated using metrics like price volatility and market power. A layered MAS framework is also introduced enabling dynamic decision-making with incomplete data. Results on test systems including IEEE-14 bus show improved accuracy and efficiency over traditional methods.