Convergence Bidding Leveraging AI and Optimization | Amir Mousavi | Smart Grid Seminar

Abstract: In the competitive landscape of energy markets, convergence bidding has become an essential strategy for enhancing market efficiency. This presentation explores the principles and benefits of convergence bidding, focusing on its role in aligning prices between Day-Ahead Markets (DAM) and Real-Time Markets (RTM).
Gridmatic, a leader in energy trading, utilizes cutting-edge AI and optimization techniques to implement convergence bidding strategies across all markets. By forecasting future market prices and making data-driven bids and offers, Gridmatic optimizes participation in convergence bidding to take advantage of arbitrage opportunities across markets while reducing risk and maximizing returns. Attendees will gain insights into the convergence bidding concept, AI models, optimization algorithms, and operational strategies that drive our success in energy trading.
Bio: Amir has over two decades of experience in the energy industry leadership role, including developing, integrating, leading, and improving customized software applications and technical solutions. He has worked at some of the world’s largest and most modern power and energy systems, leveraging his deep expertise in power systems, electricity markets, energy trading, and software engineering. His previous roles included senior principal engineer at Equilibrium Energy, principal engineer at Shell, OATI, and Siemens, and senior engineer at ABB, Alstom, and LCG Consulting. He holds a Ph.D. in electrical engineering from the University of Toronto.