Trip Chain Information Model Based Stochastic Energy Management System for Microgrid

Forecasting the demand-supply and achieving a balance between the two is challenging for power engineers to integrate intermittent renewable energy resources, energy storage systems, and plug-in electric vehicles in the emerging grid. Plug-in electric vehicles with bidirectional capacity and an effective energy management system can solve numerous power quality problems and cost-related issues. The most challenging part faced by the decision-makers is the stochastic behavior of plug-in electric vehicles and renewable energy sources. This paper utilizes the vehicle-to-grid capacity of a plug-in electric vehicle to minimize the operating cost of microgrid and plug-in electric vehicles (PEV) charging cost. To tackle to uncertain behavior of PEV, the concept of trip chain information base modeling has been applied. An additional benefit of stochastic modeling has been achieved by the day ahead and real-time scheduling of PEV in Intra hour periods. The results are compared for four cases: benchmark model, deterministic framework, stochastic framework without trip chain modeling of PEV, and stochastic framework with trip chain modeling of PEV.