Summary:
Government policies for energy efficiency in transportation systems are likely to 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-favourable 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 favour 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 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.
This work-in-progress 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 day-ahead 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 FLA is explicitly captured. The corresponding
sensitivity of expected profits is analysed with an efficient frontier. As a result, this model is intended to obtain the optimal PEV charging schedule and according FLA market bids,
subject to energy demand requirements for transportation of the final customers. Once the methodology is confirmed, stylized examples and fully fledged case studies can be calculated
with the here presented model.
Registration date: 03/04/2013
IIT-13-018A
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