Economics at your fingertips  

Using a Modified Erev-Roth Algorithm in an Agent-Based Electricity Market Model

E. Gaivoronskaia and Alexander Tsyplakov
Additional contact information
E. Gaivoronskaia: Novosibirsk State University, Novosibirsk, Russia

Journal of the New Economic Association, 2018, vol. 39, issue 3, 55-83

Abstract: One of the important tools for the analysis and prediction of operation of electricity markets are agent-based models, which simulate the behavior of decentralized agents (for example, producers and buyers), each with its own objectives and means. In these models learning of agents submitting price bids to a wholesale market plays an important role. In the process of repeated interaction an agent adapts to the environment and to the behavior of other agents, learns to predict the results of its own actions. The paper presents a modification of the classical Erev-Roth reinforcement learning algorithm which takes into account the distance between alternatives. The proposed modified algorithm is used to represent agents' learning in an agent-based model of the Russian wholesale electricity market (Siberian pricing zone) within the bounds of the day-ahead market. It is shown that it has some significant advantages as compared to the original algorithm. In particular, the algorithm is naturally interpretable, is robust to the choice of discretization step, is invariant to a shift in payoffs scale. On the whole, the algorithm is more flexible than the original one. When the modified algorithm is used, one observes good coherence between the dynamics of model price and the observable dynamics of the price in the market.

Keywords: agent-based models; wholesale electricity market; day-ahead market; learning algorithms; Erev-Roth algorithm (search for similar items in EconPapers)
JEL-codes: C63 D43 D44 D83 L1 L94 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Journal of the New Economic Association is currently edited by Victor Polterovich and Aleksandr Rubinshtein

More articles in Journal of the New Economic Association from New Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Alexey Tcharykov ().

Page updated 2021-07-09
Handle: RePEc:nea:journl:y:2018:i:39:p:55-83