Optimal scheduling of active distribution networks with penetration of PHEV considering congestion and air pollution using DR program

Abstract Congestion is one of the greatest issues in the power market planning which can create different problems for both consumers and producers. With the growth of the tendency to use distributed energy recourses (DER) and energy storages (ES), distribution networks have altered from passive to active. In this paper, the impact of Plug-in Electric Vehicles (PEV) on managing the congestion and air pollution reduction has been assessed with considering demand response program under the uncertainties of renewables. Various DERs and ESs including wind turbine, solar panels, combined heat and power units, PEVs have applied in the proposed modeling with the aim of the congestion and carbon emission alleviation. The uncertainties of renewable resources output modeled by the scenario generation and reduction method, then one of them has been chosen as an estimated scenario to simulate this modeling. Grey wolf optimization algorithm has been proposed to solve this mixed integer non-linear programming model. The results show that our proposed approach has minimized the total operation cost of ADNs along with reduction of congestion and CO2 emission. Furthermore, it improves some technical specification of the system such as voltage profile and power losses. Finally, a case study referring to the modified IEEE 69-bus distribution test system is utilized to proof the efficiency and proficiency of the suggested congestion relief method.