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Abstract
Global Warming and progression of modern power networks have profoundly changed traditional power grids in terms of fossil fuel consumption and emission of toxic gases. Therefore, auxiliary power plants and ancillary services have been introduced as an effective alternative, to overcome these new challenges in power systems. In this work, the dynamic environmental economic dispatch (DEED) problem, is investigated by considering the plug-in electric vehicles (PEVs), minimizing the fuel cost and greenhouse gas emissions from fossil fuel units. In the mentioned problem, to make it more practical, various operational constraints, including valve-point loading effect (VPLE), ramp rate limits (RRLs) and generation capacity limits are considered. This paper proposes a new multi-objective exchange market algorithm (EMA) based on the non-dominated sorting theory to find the Pareto front. In addition, the impacts of PEVs, as an uncertainty source, on the mentioned problem are analysed in four different charging scenarios. The efficiency of the proposed method has been detailed on three experimental systems and the obtained results are compared with other algorithms in this field. The results show that the maximum percentage reduction in costs for test cases 1 to 3, are about 2.13, 2.69, and 39.48, respectively, and bout 45.96, 48.20 and 44.07, for emission, respectively. The comparative analysis verify the proposed method efficiency, and accuracy in solving the suggested problem.
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