Stochastic energy management of responsive plug-in electric vehicles characterizing parking lot aggregators

Rapid advances in Plug-in Electrical Vehicle (PEV) technologies are expanding charging infrastructures especially in large shopping centers. Flexibility potential of PEVs can compensate for volatility and intermittency of electricity generation in power systems with high penetration of renewable energy sources. This paper proposes a novel multi-stage stochastic-based structure for Parking Lot Aggregators (PLA) to integrate flexibility potentials of PEVs into power systems. The proposed approach uses three trading floors of a short-term electricity market, including day-ahead, adjustment, and balancing markets, to trade the PEVs' flexibility, from 24 h before the energy delivery time until near real-time, in the electricity market. In order to extract the behavior of the PEVs in Shopping Center Parking Lots (SCPL), a data-driven approach is formulated. The suggested data-driven approach makes it possible to optimize the charging/discharging operation of a huge number of PEVs with low time and computational burdens, which is a clear need for near real-time optimization. Instead of subsidizing the responsive PEVs, the PLA maximizes the profit of the market participants, i.e. PEV owners, through trading integrated flexibility in three floors of the electricity market. The suggested approach not only maximizes the income of the PEV owners but also provides functional flexibility to electricity markets, which is a clear need for future power systems with high penetration of intermittent power. Numerical studies with real data on an SCPL and the Danish sector of the Nordic Electricity Market assures competence of the proposed approach to guarantee power system flexibility against the intermittency of renewable energy sources.