Transactive energy management in modern multi-vectored energy systems
A comprehensive framework
Bibliographic Data
| ID | 24033733 |
|---|---|
| Authors | Stephen Oko Gyan Torto (University of Petroleum and Energy Studies), Rupendra Kumar Pachauri (0000-0002-6109-2565, International University of Business Agriculture and Technology), Jai Govind Singh (0000-0002-0162-3360, Asian Institute of Technology), Shubham Tiwari (0000-0002-7278-6714, International Institute for Applied Systems Analysis), Hasmat Malik (University of Technology Malaysia, corresponding author), Vinay Kumar Jadoun (0000-0002-3373-8613, Manipal Academy of Higher Education, corresponding author), Asyraf Afthanorhan (0000-0002-8817-9062, Sultan Zainal Abidin University) |
| Year | 2026 |
| Volume | 12 |
| Pages | 102073 |
| Publication date | 2026-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Futures (JOURNAL) |
| Journal identifiers | ISSN: 2666-1888 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.sftr.2026.102073 |
| OpenAlex | W7202250660 |
| Language | EN |
| References cited | 80 |
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.
Distributed generation · Enabling · Energy management · Energy transition · Interoperability · Interpretability · Key (lock) · Shapley Value · Transactive memory · Electric Power System Optimization · Integrated Energy Systems Optimization · Smart Grid Energy Management
| Highly cited | No |
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