Title
Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets
Abstract
We consider the process of bidding by electricity suppliers in a day-ahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliersu0027 bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivalsu0027 bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivalsu0027 production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent prices.
Year
DOI
Venue
2019
10.1109/tsg.2019.2891747
IEEE Transactions on Smart Grid
Keywords
Field
DocType
Electricity supply industry,Cost function,Production,Schedules,Nash equilibrium,Modeling
Mathematical optimization,Market clearing,Electricity,Microeconomics,Schedule,Network congestion,Operator (computer programming),Profit maximization,Nash equilibrium,Bidding,Mathematics
Journal
Volume
Issue
ISSN
10
5
1949-3053
Citations 
PageRank 
References 
1
0.37
0
Authors
4
Name
Order
Citations
PageRank
Ruidi Chen1422.91
ioannis ch paschalidis224125.29
Michael Caramanis37517.56
Panagiotis Andrianesis451.57