CIMTrade

Abstract

Owing to their stochastic nature, Distributed Energy Resources (DERs) are more suited to participate in short-term or intraday electricity markets. The trading horizon of these markets is shorter than day-ahead markets but longer than flex/regulation markets. Very few of the current works on DER trading in intraday markets satisfactorily model the different aspects of this problem. In this paper, we propose a method to find an optimal strategy for aggregators managing heterogeneous DERs to trade in intraday markets. We model the joint price-volume dynamics present in such markets through two dimensional histograms and use it in decision making. Our strategy also allows for the trades/bids placed earlier to be corrected based on the revised forecasts of demand and generation in the DER pool. We model the optimal bidding problem of aggregators in an intraday market as an MINLP problem. We also present two reformulations of the problem which leverage different aspects of the problem's structure. The first relaxation converts the MINLP to an NLP problem. The second relaxation removes the complex non-linearities in the problem and converts the formulation into a coupled MILP -simple maximization set-up, which is then solved in an iterative fashion. We test the performance of our solutions against commonly used approaches. We use real world intraday market logs from France in our experiments. Our results indicate that the proposed relaxations improve the revenue performance of the aggregator by 14% and 19% relative to the commonly followed trading practices. Also, the performance gap between the baseline and proposed techniques remains robust to changes in subscriber generation volumes. Further, we find that enabling pool interactions within the DER set is helpful. In our studies, such enabling improves the revenue performance of aggregator by 2.5%.