Market Power under Auction-Based Energy Pricing
Auctions serve as a primary pricing mechanism
in various market segments of a deregulated power industry.
In day-ahead (DA) energy markets, strategies such as uniform
price, discriminatory, and second-price uniform auctions result in
different price settlements and thus offer different levels of market
power. In this research, we consider a nonzero sum stochastic game
theoretic model and a reinforcement learning (RL)-based solution
framework that allow assessment of market power in DA markets.
Since there are no available methods to obtain exact analytical
solutions of stochastic games, an RL-based approach is utilized,
which offers a computationally viable tool to obtain approximate
solutions. These solutions provide effective bidding strategies for
the DA market participants. The market powers associated with the
bidding strategies are calculated using well-known indices
like Herfindahl–Hirschmann index and Lerner index and two new indices, quantity modulated price index (QMPI) and revenue-based market power index (RMPI), which are developed in this research. The proposed RL-based methodology is tested on a sample network.
- Nanduri, V. and Das, T. K. A Reinforcement Learning Model to Assess Market Power under Auction-Based Energy Pricing. INFORMS Annual Meeting 2005, San Francisco, CA.
- Nanduri, V., Rajan, B., and Das, T. K. Auction Performance Evaluation in Electricity Markets. INFORMS Annual Meeting 2004, Denver, CO.