EnergyBidSim: AI Powered Price Forecasting for Day Ahead Markets
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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.
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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.
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