Pricing and trading strategies in networked microgrid systems: A comprehensive review

Abstract

As microgrids evolve from isolated systems into interconnected networks, pricing and trading strategies have emerged as the economic backbone of networked microgrid systems. These strategies are essential to enable energy self-sufficiency, facilitate energy exchanges within the network, and support scalable coordination. However, the development of effective trading approaches remains a complex and challenging task for researchers. To address these challenges, several studies have been proposed in the literature to overcome the complexities of trading in networked microgrids. This article presents a comprehensive comparative review of existing studies on pricing and trading strategies in networked microgrids. The reviewed methods are classified into five major categories: mathematical optimization techniques, market mechanisms, game-theoretic approaches, reinforcement learning methods, and blockchain-based models. Each category is examined in terms of its technical foundations and the application of the respective strategy within networked microgrids. Furthermore, four key evaluation criteria — fairness, privacy, scalability, and computational efficiency — are identified to facilitate a detailed comparative analysis of the studies within each category. This review highlights the strengths and trade-offs of each approach based on these criteria. Finally, the article highlights real-world pilot projects that demonstrate the practical viability of each categorized approach, while also outlining key research gaps that hinder broader implementation of pricing and trading strategies in networked microgrid systems.