DEREGULATED ELECTRICITY MARKET CALCULATION BASED ON NEUROEVOLUTION ALGORITHM

Ahmed Tiguercha, Ahmed Amine Ladjici, and Mohamed Boudour

Keywords

Deregulated electricity markets, neuroevolution, coevolutionary algorithms, market agents interactions, multi-agent system, strategical behaviour

Abstract

The current paper investigates the use of neuroevolution algorithm to simulate the market agents behaviour in a deregulated day- ahead electricity market. The proposed approach is based on multi-agent system simulation of the electricity market, where different agents compete to maximize their profit from the market transactions. Each market agent is modelled as a population of neural networks co-evolving through a competitive coevolutionary algorithm to produce optimal strategies. Numerical results based on the Algerian power system are used to demonstrate the effectiveness of proposed approach in finding the market equilibrium considering different scenarios.

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