LARGE SCALE ACCOMMODATION OF PLUG-IN ELECTRIC VEHICLES IN SMART GRIDS: CHALLENGES AND OPPORTUNITIES IN DEMAND RESPONSE

Abstract

This thesis presents a closed-form statistical expression for residential plug-in electric vehicles’ (PEVs’) unsupervised charging. We illustrate that adding PEVs to households increases daily peak demand quite significantly. Then, we propose a distributed demand response (DR) technique for vehicle-to-grid (V2G) enabled PEVs to employ their demand elasticity. The proposed algorithms succeed to make the peak the same as when there is no PEVs in the system. Moreover, as the electricity markets will become fully liberated soon, we modified our previous algorithm and postulate another algorithm for managing PEVs' charging/discharging to lower electricity costs for retailers. This algorithm uses day-ahead (DA) demand shaping and real-time (RT) demand altering and can save significant costs annually in its interactions with markets. Additionally, since greenhouse gases (GHGs) recently absorbed the world's attention, we captured this concern into our algorithms. We demonstrate that with some incentives and/or regulations, a retailer could help lessen GHGs’ emissions.