Bidding behaviors in duopoly electricity markets with aspirant market share goals. (November 2013)

• Record Type: Journal Article
• Authors: Zhou, Zhi; Chan, Wai Kin Victor; Chow, Joe H.
• Other Names: Mustafee Navonil guest-editor.; Taylor Simon J E guest-editor.
• Is Part Of: Simulation. Volume 89:Number 11(2013)
• Page Start: 1369
• Page End: 1387
• Publication Date: 2013-11
• Subjects: Bidding behavior -- agent-based simulation -- electricity market -- collusive behavior -- price war -- cyclical pricing dynamics; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549713499248
• Languages: English
• ISSNs: 0037-5497

Abstract

The deregulation and restructuring of electricity markets have created a variety of challenging research problems. In addition, due to the complexity of electricity markets, most of these research problems are not amenable to analytical methods. Agent-based simulation is an approach for simulating and analyzing complex systems with interacting autonomous agents. In this paper, we use an agent-based approach to study the following emergent problem related to electricity market share and competition: what happens if a market participant tries to reach the following two (sometimes conflicting) goals simultaneously, (1) reaching an aspirant market share goal and (2) maximizing profit? More interestingly, what happens if two such participants are competing with each other? The developed agent-based model allows us to examine how the market share goal and profit maximization goal together influence the bidding behaviors of generation companies (i.e. agents) in a day-ahead electricity auction market. It also reveals that conservative market share goals often lead to a collusive behavior and profit maximization. However, if every participant has an aggressive market-share goal, a price war would result. On the other hand, if agents bear unequal market-share goals (e.g. one aggressive and one conservative), one agent will become more profitable than the other. As a result, if both agents want to maximize profit, they will both bid aggressively, resulting in a price war. Therefore, the agent-based model produces results that may explain some real-world pricing outcomes. In addition, to benchmark our agent-based model and to demonstrate the effect of the market-share goal, we develop an analytical model without the market-share goal and compare its results with those from the agent-based model.