23-06-2026, 16:24 | view 👀:0 | 🙍 oneddl | redaktor: book24h | Rating👍:

Reinforcement Learning for Energy Markets Foundations, Algorithms, and Applied Intelligence in Modern Power Systems [#1019946]

Name: Reinforcement Learning for Energy Markets Foundations, Algorithms, and Applied Intelligence in Modern Power Systems
Author: book24h

In an era where energy systems face unprecedented volatility, shifting demand, and a growing push for sustainable solutions, this book breaks ground by applying cutting-edge machine-learning techniques to real-world energy markets. Bringing together the theory of reinforcement learning with practical, market-level applications, it offers a clear roadmap for how intelligent agents can navigate complex trading environments, optimizing storage, bidding, and grid interaction to maximize profit while ensuring efficiency and stability.
Inside, you'll find:A comprehensive overview of energy-market dynamics: supply and demand cycles, spot and ancillary markets, price and demand volatility, and regulatory constraints.An accessible yet rigorous treatment of reinforcement learning fundamentals, including Markov Decision Processes (MDPs), policy gradient methods, safe and constrained learning, tailored specifically for energy trading and grid operations.Realistic case studies illustrating how AI-driven agents can manage battery storage, forecast demand, and bid strategically in day-ahead or real-time markets.Discussion of risk, safety, and ethical considerations, how learning-based systems must respect physical limitations, regulatory frameworks, and environmental impact while pursuing economic goals.Guidance for implementation: from data preparation and model selection to simulation environments and evaluation metrics, enabling researchers, energy professionals, and developers to build and deploy their own RL-powered strategies.Whether you're a researcher exploring applications of artificial intelligence, an energy-market analyst seeking innovative tools, or an engineer building the next generation of smart grid technologies, this book bridges the gap between academic theory and practical deployment. By harnessing reinforcement learning, it shows how energy trading and management can evolve into a dynamic, adaptive, and efficient system, paving the way for smarter energy markets everywhere.

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Reinforcement Learning for Energy Markets Foundations, Algorithms, and Applied Intelligence in Modern Power Systems [#1019946]

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