  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 suppliers' 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
rivals' bids, which are typically not available. To address this issue, we
develop an inverse optimization approach for estimating rivals' 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.
