Transactive energy management in modern multi-vectored energy systems: A comprehensive framework

Torto, S.O.G., Pachauri, R.K., Singh, J.G., Tiwari, S. ORCID: https://orcid.org/0000-0002-7278-6714, Malik, H., Jadoun, V.K., & Afthanorhan, A. (2026). Transactive energy management in modern multi-vectored energy systems: A comprehensive framework. Sustainable Futures 12 e102073. 10.1016/j.sftr.2026.102073.

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Abstract

Global projects are mobilizing technologies to fight power generation curtailment and smooth demand by exploiting excess energy via transactive energy management and control. Sharing and transferring energy between microgrids helps manufacturers and businesses create energy autonomously. The transition to Multi-Vector Multi-Agent Energy Systems (MMV-ES) demands a paradigm shift from traditional centralized control to decentralized, market-based coordination. Transactive Energy Management (TEM) has emerged as a key enabler in this context, supporting local flexibility, peer-to-peer (P2P) trading, and integrated energy vectors across distributed assets. This review systematically decomposes and classifies the existing state of TEM from several perspectives: the market topology, the interaction of the agent, game-theoretic models and the real deployment challenges. Moreover, two game-theory formulations (cooperative and non-cooperative) were given special attention and a detailed comparison between Shapley value and Nucleolus was provided as approaches for fair cost allocation. To enhance the adaptability of the market and the overall efficiency of the system, we introduce the Transactive Energy Reformulation Model (TE-RM), a hybrid model combining AI-powered congestion pricing with coalition formation and fairness-based incentives. The comparative tables in this paper summarize TEM and TE-RM's strengths and weaknesses and compare it to the centralized and conventional DSM methodologies. Lastly, key research gaps including scalability, regulatory fit, and AI model interpretability are reviewed, and future directions are proposed for the integration of future advanced technologies (e.g., reinforcement learning, blockchain, IoT) to enable stable, fair and interoperable energy markets.

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