A Methodology for Explicit Representation of the Stochastic Demand due to Electric_x000D_ Vehicles in Generation Expansion Planning Problems

Abstract

Generation expansion planning (GEP) problems are solved to find the optimum investment decisions to satisfy the increasing_x000D_ electricity demand. Integration of electric vehicles (EVs) with the capability of charging from the grid will also increase the_x000D_ electricity demand of the grid. Depending on the charging/driving characteristics of users, demand curves for EVs will be shaped_x000D_ and it will be different on each day. Therefore, it is very crucial to represent this stochastic nature of EVs demand in the associated_x000D_ GEP problems. This paper is proposing a methodology to represent EVs demand realistically on GEP models. The proposed_x000D_ methodology starts with generating random demand patterns to demonstrate possibilities for the EVs demand patterns via Monte_x000D_ Carlo Simulation, then using an optimization-based model to select a representative set. Two stage stochastic programming_x000D_ model is proposed for GEP problems and solved to minimize the expected cost over the entire set, the representative set and the_x000D_ average EVs demand. The results show that GEP models with selected demand curves produce more realistic decisions (closer_x000D_ to the solutions obtained by using the entire demand patterns) than the decisions obtained by the models with average EVs_x000D_ demand. In most cases, the models using average EVs demand fail to capture the new peaks generated by EVs, therefore, they_x000D_ suggest less capacity expansion then the required amount. This results in more unmet demand in the system.