Probabilistic UC Modeling for PEVs Optimal Operation Planning into the Micro-Grid Environment Using Multi-Object NSGA-II

Smart grids focus on three sensitive areas, including load management, distributed products, as well as control and monitoring. One of the best distributed generation technologies is to consider the production and corresponding load in a region n as a sub-system or micro-grid. In this paper, Probabilistic UC Modeling for PEVs Optimal operation planning into the Micro-Grid Environment Using Multi-Object NSGA-II has been proposed. In our micro-grid model DGs resources include micro-turbine, wind turbine, photovoltaic and electrical storage resources such as batteries and thermal storage and also boiler systems with the presence of plug in electric vehicles. Plug in electric vehicles are an energy storage device that similar to rechargeable batteries, which can be recharged to support network load as well as have energy exchange ability with the global network. Here, random behavior of load demand, wind turbines output and the controllability of electric vehicles are considered as grid uncertainties. The proposed model coding procedure is presented into the multi-objective NSGA-II solution method. However, objective functions consist of unit operating and pollution cost, start and shutdown costs, etc. are based on the Pareto criterion. Finally, model evaluation is validated on a test system and conceptual results have been presented.

Keywords:

Plug in electric vehicle , micro-grid , probabilistic unit commitment , non-dominant sorting genetic algorithm (NSGA-II).