Game Theory and AI-Driven Reinforcement Learning for Blockchain-Enabled Sustainable Renewable Energy Power Systems

The integration of game theory and AI-driven reinforcement learning into blockchain-enabled sustainable renewable energy power systems represents a transformative approach to decentralized energy management, addressing the intermittency of sources like solar and wind while ensuring equitable resource allocation. Game theory models prosumers simultaneous producers and consumers as rational agents in competitive markets, employing Nash equilibria for non-cooperative bidding in double auctions and cooperative mechanisms like Shapley values for fair profit sharing in peer-to-peer networks.Reinforcement learning enhances this framework by enabling agents to learn adaptive bidding strategies through Markov decision processes, where states capture supply-demand dynamics, actions involve price-volume bids, and rewards prioritize net utility and grid independence. Deep actor-critic networks handle uncertainties in renewable generation, outperforming traditional methods by optimizing long-term cumulative rewards in simulated microgrids.Blockchain underpins these interactions with smart contracts for tamper-proof transactions and consensus algorithms that enforce truthful behavior, reducing reliance on centralized utilities and transaction costs by up to 40%. This synergy fosters resilient, carbon-neutral systems, as demonstrated in real-world pilots, achieving 25-55% gains in efficiency and scalability for smart city applications. Ultimately, the approach promotes sustainable energy transitions by balancing strategic incentives with AI-driven adaptability.