Abstract
With the integration of plug-in hybrid electric vehicles (PHEVs) and renewable energy resources into the power system, the environmental-economic dispatch of conventional power generation units takes new dimensions due to several ineluctable uncertain factors. This study presents a probabilistic optimisation framework for the emission, economic dispatch of thermal power generation units, considering the stochastic charging demand of PHEVs, intermittent wind power generation and uncertain load demand. To characterise the uncertainty in the output variables, the point estimate method and Nataf transformation are employed, considering the correlation between wind power plants. To find Pareto optimal solutions concerning minimisation of the emission and fuel cost, heuristic fitness sharing mechanisms and a meta-heuristic algorithm are proposed. Monte Carlo simulation together with state-of-the-art optimisation techniques (NSGA-II and SPEA-2) are employed in a quantitative performance appraisal to scrutinise the effectiveness of the proposed method in the uncertainty quantification and multi-objective optimisation.
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