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Evaluating the efficiency of divestiture policy in promoting competitiveness using an analytical method and agent-based computational economics

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  • Rahimiyan, Morteza
  • Rajabi Mashhadi, Habib

Abstract

Choosing a desired policy for divestiture of dominant firms' generation assets has been a challenging task and open question for regulatory authority. To deal with this problem, in this paper, an analytical method and agent-based computational economics (ACE) approach are used for ex-ante analysis of divestiture policy in reducing market power. The analytical method is applied to solve a designed concentration boundary problem, even for situations where the cost data of generators are unknown. The concentration boundary problem is the problem of minimizing or maximizing market concentration subject to operation constraints of the electricity market. It is proved here that the market concentration corresponding to operation condition is certainly viable in an interval calculated by the analytical method. For situations where the cost function of generators is available, the ACE is used to model the electricity market. In ACE, each power producer's profit-maximization problem is solved by the computational approach of Q-learning. The power producer using the Q-learning method learns from past experiences to implicitly identify the market power, and find desired response in competing with the rivals. Both methods are applied in a multi-area power system and effects of different divestiture policies on market behavior are analyzed.

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  • Rahimiyan, Morteza & Rajabi Mashhadi, Habib, 2010. "Evaluating the efficiency of divestiture policy in promoting competitiveness using an analytical method and agent-based computational economics," Energy Policy, Elsevier, vol. 38(3), pages 1588-1595, March.
    • Handle: RePEc:eee:enepol:v:38:y:2010:i:3:p:1588-1595
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    Cited by:

    1. Weigt, H. & Willems, Bert, 2011. "The Effect of Divestitures in the German Electricity Market," Other publications TiSEM 7bbea5b0-7489-416f-8767-d, Tilburg University, School of Economics and Management.
    2. Atallah, Shady S. & Gomez, Miguel & Conrad, Jon & Nyrop, Jan, 2013. "An Agent-Based Computational Bioeconomic Model of Plant Disease Diffusion and Control: Grapevine Leafroll Disease," Working Papers 180085, Cornell University, Department of Applied Economics and Management.
    3. Ibrahim Ari & Muammer Koc, 2019. "Sustainable Financing for Sustainable Development: Agent-Based Modeling of Alternative Financing Models for Clean Energy Investments," Sustainability, MDPI, vol. 11(7), pages 1-34, April.
    4. Min-Ren Yan, 2015. "Project-Based Market Competition and Policy Implications for Sustainable Developments in Building and Construction Sectors," Sustainability, MDPI, vol. 7(11), pages 1-26, November.
    5. Gong, Chengzhu & Yu, Shiwei & Zhu, Kejun & Hailu, Atakelty, 2016. "Evaluating the influence of increasing block tariffs in residential gas sector using agent-based computational economics," Energy Policy, Elsevier, vol. 92(C), pages 334-347.

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