In the rapidly evolving landscape of energy management, a groundbreaking review published in the journal Applied Sciences (Aplicaciones Científicas) is set to reshape how we think about decentralized energy systems and peer-to-peer (P2P) energy trading. Led by Paul Arévalo from the Faculty of Engineering at the University of Cuenca in Ecuador, the study synthesizes findings from 94 high-quality research papers to provide a comprehensive roadmap for advancing distributed energy systems toward scalability, resilience, and sustainability.
The global energy sector is undergoing a profound transformation, driven by the urgent need to transition from traditional, centralized energy systems to decentralized networks dominated by renewable energy sources (RES). As energy demands escalate and environmental imperatives grow, the integration of RES, energy storage technologies, and electric vehicles (EVs) has become crucial. Within this paradigm, P2P energy trading frameworks have emerged as a revolutionary model, empowering prosumers—consumers who also produce energy—to engage directly in energy transactions and fostering the development of collaborative energy communities.
However, as energy systems become more decentralized, they also grow in complexity. The integration of heterogeneous energy resources introduces new challenges, including the variability of RES, the need for real-time coordination across spatially dispersed assets, and the importance of ensuring secure and transparent energy transactions. Multi-Agent Systems (MAS) have been widely recognized as a powerful tool for addressing these challenges, enabling decentralized coordination, scalability, and flexibility in managing energy resources. At the same time, advancements in blockchain technology and smart contracts promise enhanced security and automation for P2P trading, while electric machines are essential in supporting renewable integration and grid flexibility.
Arévalo’s review highlights advancements in six key areas: optimization and modeling, multi-agent systems, simulations, blockchain and smart contracts, robust frameworks, and electric machines. Despite progress, several studies reveal challenges related to scalability, data privacy, computational complexity, and system adaptability, particularly in dynamic and decentralized environments. “Stochastic–robust optimization and multi-agent systems improve decentralized coordination,” Arévalo explains, “while blockchain enhances security and automation in peer-to-peer trading. Simulations validate energy strategies, bridging theory and practice, and electric machines support renewable integration and grid flexibility.”
The synthesis underscores the need for unified frameworks that combine artificial intelligence, predictive control, and secure communication protocols. This review aims to provide a roadmap for advancing distributed energy systems toward scalable, resilient, and sustainable energy solutions. By consolidating findings from 94 high-quality studies across these thematic areas, the review not only identifies key trends and challenges but also highlights the underexplored synergies between these areas.
One of the critical gaps identified in the review is the lack of a holistic approach that integrates the diverse components of decentralized energy systems into a cohesive framework capable of addressing real-world complexities. Previous works have often focused on isolated aspects of these systems, such as optimizing energy allocation in small-scale microgrids or developing theoretical frameworks for blockchain-based P2P trading. However, they have largely failed to address the interdependencies between these components, particularly in large-scale, dynamic environments where socio-economic, regulatory, and technical factors play a significant role.
The integration of electric machines into decentralized systems is another critical yet neglected aspect that can significantly enhance renewable energy integration, grid stability, and operational flexibility. By bridging these knowledge gaps, this work lays the foundation for developing scalable, resilient, and efficient energy management solutions tailored to modern decentralized networks.
The implications for the energy sector are profound. As the world moves towards a more decentralized and renewable energy future, the insights from this review will be instrumental in shaping the development of next-generation energy management systems. The convergence of algorithmic intelligence, secure digital infrastructures, resilient control schemes, and physical assets—such as electric machines—that enable energy flow will be crucial for advancing toward sustainable, self-organizing, and intelligent microgrids.
The review published in Applied Sciences (Aplicaciones Científicas) provides a comprehensive and transparent synthesis of current research trends, confirming that stochastic–robust optimization and multi-agent reinforcement learning are among the most effective strategies for handling uncertainty and enabling scalability in decentralized systems. These approaches facilitate real-time coordination among distributed energy resources and prosumers, particularly under dynamic and volatile conditions.
As the energy sector continues to evolve, the findings from this review will guide researchers, policymakers, and industry leaders in developing innovative solutions that address the complexities of decentralized energy systems. The future of energy management lies in the integration of advanced technologies and unified frameworks that can adapt to the ever-changing landscape of renewable energy and P2P trading. The work of Paul Arévalo and his colleagues is a significant step forward in this direction, paving the way for a more sustainable and resilient energy future.
