Game Theory and Robust Predictive Control for Peer-to-Peer Energy Management: A Pathway to a Low-Carbon Economy

The shift towards decentralized energy systems demands innovative strategies to manage renewable energy integration, optimize resource allocation, and ensure grid stability. This review investigates the application of game theory and robust predictive control as essential tools for decentralized and peer-to-peer energy management. Game theory facilitates strategic decision-making and cooperation among prosumers, distributors, and consumers, enabling efficient energy trading and dynamic resource distribution. Robust predictive control complements this by addressing uncertainties in renewable energy generation and demand, ensuring system stability through adaptive and real-time optimization. By examining recent advancements, this study highlights key methodologies, challenges, and emerging technologies such as blockchain, artificial intelligence, and digital twins, which enhance these approaches. The review also explores their alignment with global sustainability objectives, emphasizing their role in promoting affordable clean energy, reducing emissions, and fostering resilient urban energy infrastructures. A systematic review methodology was employed, analyzing 153 selected articles published in the last five years, filtered from an initial dataset of over 200 results retrieved from ScienceDirect and IEEE Xplore. Practical insights and future directions are provided to guide the implementation of these innovative methodologies in decentralized energy networks.