Nastaran Naseri, Department of Information Systems
University of Cologne, Germany
While integration of higher shares of renewable energy sources in the power industry portfolio improves sustainability, it introduces more uncertainty to the electricity markets. The uncertainty and variability of renewables escalates the need for cost-effective ways to balance supply and demand in real-time. Energy storage systems are considered a viable solution to hedge against the intermittency of supply. However, most prior studies suggest marginal or even negative profitability of batteries when participating in one stage of the electricity market.
Given the physical characteristics of batteries, which make it suitable in multiple market stages, we investigate the profitability of batteries when simultaneously participating in the day-ahead and balancing markets. We formulate a stochastic programming framework to choose optimal market position, optimal bidding strategy, and optimal capacity split between the two markets. Our results show that participation of batteries in multiple stages of the electricity markets generates additional profit for the battery. The optimal strategy is to participate in the day-ahead with full capacity as a seller and with full-capacity in the down-regulation secondary balancing market as a buyer.
I will also briefly introduce Power TAC, which is a platform, an annual competition, and an ongoing research program based on the Competitive Benchmarking model. Annual competitions serve to test policy alternatives as well as ideas about trading strategies embodied in broker agents built by competing teams.
Bio: Nastaran Naseri is a doctoral researcher at the department of Information Systems at the University of Cologne, where she conducts research on smart information systems for sustainable electricity system at Professor Wolfgang Ketter’s Chair for Information Systems for Sustainable Society.
Nastaran holds B.Sc. and M.Sc. in Industrial Engineering from the Ferdowsi University of Mashhad, Iran. She focuses her research on how data science and machine learning can be leveraged to facilitate a sustainable energy and mobility future. In particular, she explores impacts of adding large scale batteries in different stages of the market in terms of efficiency and profitability.
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