Safe and Private Forward-Trading Platform for Transactive Microgrids
January 2021·
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Scott Eisele
Taha Eghtesad
Keegan Campanelli
Prakhar Agrawal
Aron Laszka
Abhishek Dubey
Abstract
Power grids are evolving at an unprecedented pace due to the rapid growth of distributed energy resources (DER) in communities. These resources are very different from traditional power sources, as they are located closer to loads and thus can significantly reduce transmission losses and carbon emissions. However, their intermittent and variable nature often results in spikes in the overall demand on distribution system operators (DSO). To manage these challenges, there has been a surge of interest in building decentralized control schemes, where a pool of DERs combined with energy storage devices can exchange energy locally to smooth fluctuations in net demand. Building a decentralized market for transactive microgrids is challenging, because even though a decentralized system provides resilience, it also must satisfy requirements such as privacy, efficiency, safety, and security, which are often in conflict with each other. As such, existing implementations of decentralized markets often focus on resilience and safety but compromise on privacy. In this article, we describe our platform, called TRANSAX, which enables participants to trade in an energy futures market, which improves efficiency by finding feasible matches for energy trades, enabling DSOs to plan their energy needs better. TRANSAX provides privacy to participants by anonymizing their trading activity using a distributed mixing service, while also enforcing constraints that limit trading activity based on safety requirements, such as keeping planned energy flow below line capacity. We show that TRANSAX can satisfy the seemingly conflicting requirements of efficiency, safety, and privacy. We also provide an analysis of how much trading efficiency is lost. Trading efficiency is improved through the problem formulation, which accounts for temporal flexibility, and system efficiency is improved using a hybrid-solver architecture. Finally, we describe a testbed to run experiments and demonstrate its performance using simulation results.
Type
Venue
ACM Transactions on Cyber-Physical Systems, 5(1), 8:1–8:29
Authors

Ph.D. 2026
Taha Eghtesad joined the Applied Artificial Intelligence Lab as a Ph.D. student in Fall 2018 and received his Ph.D.
in 2026 under the supervision of Dr. Aron Laszka. He received a B.S. degree in Computer Engineering, with a minor in
Software Engineering, from Shahid Beheshti University, Tehran, Iran. His research lies at the intersection of
machine learning, game theory, cyber-physical systems, and systems security. In particular, his Ph.D. research
focused on using artificial intelligence and machine learning to make cyber-physical systems more secure.

Associate Professor
Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.
His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.
His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.