
Munther A. Dahleh
Biography
Biography
Munther A. Dahleh received his B.S. in Electrical Engineering from TAMU in 1983, Ph.D. degree from Rice University, Houston, TX, in 1987 in Electrical and Computer Engineering. Since then, he has been with the Department of Electrical Engineering and Computer Science (EECS), MIT, Cambridge, MA, where he is now the William A. Coolidge Professor of EECS. He is also a faculty affiliate of the Sloan School of Management. He is the founding director of the MIT Institute for Data, Systems, and Society (IDSS). He serves on multiple advisory boards including AI advisory board for Samsung and Ikigai. He is the author of the recent book: Data, Systems, and Society: Harnessing AI for Societal Good, Cambridge University Press, April 2025.
Prof. Dahleh Leads a research group that focuses on Decisions Under Uncertainty. He is interested in Networked Systems, information design, and decision theory with applications to Social and Economic Networks, financial networks, Transportation Networks, Neural Networks, agriculture, and the Power Grid. He is also interested in causal learning (machine learning, reinforcement learning) for the purpose of intervention and control. His recent work focused on understanding the economics of data as well as deriving a foundational theory for data markets. Prof. Dahleh is a leader in online education focusing on advanced data science topics as well as professional education in machine learning and AI.
Prof. Dahleh is a four time recipient of the George S. Axelby best paper award for papers published in the IEEE Transactions on Automatic Control. He is also a recipient of the Donald P. Eckman award for the best control engineer under 35. He is a fellow of IEEE and IFAC.
Efficient EV Integration for Grid Storage: A Market-Based Approach
Abstract:
A key challenge in increasing renewable energy penetration is the limited utility-scale storage capacity of the power grid. Transportation electrification offers a promising solution, as idle electric vehicles (EVs) can provide battery storage services to the grid. This concept, known as EV-power grid integration, has the potential to significantly advance decarbonization efforts in both the electricity and transportation sectors. Additionally, flexible EV charging can help mitigate distribution network capacity risks.
However, ineffective scheduling of EV charging can paradoxically lead to higher operational costs and exacerbate capacity constraints. This issue arises form the inherent randomness in EV usage patterns and the strategic behavior of EV users.
To address these challenges, we propose a market-based solution for energy storage management. Our mechanism allows the system operator to efficiently integrate strategic EV fleets with unpredictable usage patterns, leveraging them as storage assets to meet EV demand, reduce costs, and maintain grid flexibility. We develop an efficient scheduling method for EV charging and discharging, enabling user schedule adjustments to alleviate capacity constraints and achieve cost savings. We present computational results that demonstrate the effectiveness of this market-driven scheduling framework in enhancing the integration of time-flexible EVs for grid storage.