Abstract It is expected that government policies for energy efficiency in transportation systems will clear the way for alternative propulsion technologies, such as Plug-in Electric Vehicles (PEVs), to become widespread in automotive industry sales. However, integrating PEVs in electric power systems (EPSs), such that system-favorable charging schedules are facilitated, still poses regulatory and technical challenges for the entire spectrum of stakeholders, from policy makers to regulated distribution system operators and competitive fleet owners. To favor an EPS in question, i.e. a collection of producers, consumers represented by retailers/load aggregators that meet in the electricity market as well as network operators, a combination of competitive market prices as well as regulated use-of-system charges should govern the PEV charging. However, the value proposition, i.e. the value adding services that a Flexible Load Aggregator (FLA) is theoretically bringing to the EPS via participating in electricity markets with a contracted fleet of PEVs under Direct Load Control (DLC), remains unclear to this point. Abstract This paper presents a methodology to approximate the economic impact of using a PEV fleet’s aggregated battery as a resource in electricity markets, ignoring all network aspects. A stochastic profit optimization of the FLA’s self-scheduling is formulated with price taker participation in dayahead energy and ancillary service markets for capacity. Uncertainty in market prices as well as energy demand is addressed. Using the Conditional Value-at-Risk (CVaR) methodology, risk aversion of the 22 FLA is explicitly captured. The corresponding sensitivity of expected profits is analyzed with an efficient frontier. As a result, we obtain the optimal PEV charging schedule and according FLA market bids, subject to energy demand requirements for transportation of the final customers.
Keywords: plug-in electric vehicles, electricity markets, stochastic programming.
9th International Conference on Computational Management Science, 18-20 April 2012, Imperial College London. Londres, Reino Unido
Published: April 2012.