Modelling uncertainty in renewable generation entry to deregulated electricity market

This paper presents a stochastic representation of generation expansion planning approach considering renewable generation entry in a competitive electricity market. The Markov Chain Monte Carlo (MCMC) simulation method has been applied to consider load uncertainty, wind volatility and generation cost co-efficient. The Metropolis-Hastings (M-H) sampling algorithm has been introduced for reducing the computational burden of a large number of planning scenarios. The proposed model has been applied to the IEEE-RTS 24-bus test system. Simulation results have been presented showing the probability distribution of economic, system reliability and congestion indices. This study is aiming to improve the planning efficiency of renewable generation entry into the electricity market.

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