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Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
EnergyBidSim: AI-Powered Price Forecasting for Day-Ahead Markets

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

Paperback

Business General

ISBN10: 6208447550
ISBN13: 9786208447557
Publisher: LAP Lambert Academic Publishing
Published: Oct 9 2025
Pages: 268
Weight: 0.80
Height: 0.61 Width: 6.00 Depth: 9.00
Language: English
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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Business General