Multi-objective chance constrained programming model for operational-planning of V2G integrated microgrid

Abstract

This paper presents a novel chance constrained programming model for the operational-planning of a microgrid containing renewable energy sources like wind and solar, and vehicle to grid (V2G) storage. The model investigates the economic implications of the V2G storage on the microgrid operation. The microgrids consisting of intermittent renewable energy sources always need additional backup to mitigate the fluctuating power. This additional backup results in increased investment and operational costs. The V2G storage in such a situation can act as energy buffer and can supply the microgrid with balancing services at low costs. However the availability of the power from the V2G storage is also intermittent in nature because of the random plug-in pattern of electric vehicles. In this paper a microgrid containing a wind, PV and diesel generation and energy storage (V2G/flxed) is connected to an external grid. The external grid has the problem of capacity shortage. Because of the intermittent energy sources, a novel multi-objective stochastic chance constrained programming model is developed for the operational-planning of microgrid. The multiple objectives include the minimization the total net present cost and the operating hours of the diesel generation subject to constraints. The time series data for wind speed, solar radiation, V2G plug-in pattern and load fluctuation is estimated by using the Metropolis-Hastings algorithm, which is a Markov chain Monte Carlo Method. Due to the conflicting objectives, the non-dominant solutions are sorted out and presented using the Pareto front. The simulation results show confidence levels of provision of power by V2G storage and renewable energy sources. The results further show substantial economic implications of V2G storage on microgrid operation in terms of minimization of the total net present cost.