Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets

By Stylianos I. Vagropoulos, Anastasios G. Bakirtzis. IEEE Transactions on Power Systems, 2013

447 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ef3a0c85b708dfc382cdf9c110c212032e9ff02f

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In Automotive Engineering

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Citations in the corpus

Bilateral Contract Networks for Peer-to-Peer Energy Trading (2019, unknown): “, [29]).”
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning (2019, background): “In [15] and [16], the EV aggregator was considered to bid in the day-ahead market and offered ancillary services.”
Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics (2014, background): “There have been several proposed optimal control schemes for HVAC systems to minimize their operation costs considering different temperature comfort criteria [11]–[15].”
Network Equilibrium of Coupled Transportation and Power Distribution Systems (2018, background): “The optimal bidding strategy of a price-taker EV aggregator in the short-term energy and regulation markets is studied in [18].”
Day ahead optimization of an electric vehicle fleet providing ancillary services in the Los Angeles Air Force Base vehicle-to-grid demonstration (2018, methodology): “More recent work has focused on methods to directly handle the uncertain input parameters for vehicle-to-grid optimization through the use of stochastic dynamic programming [9], fuzzy optimization [11], robust [14] and stochastic optimization [10, 12].”
Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead Energy and Reserve Markets (2016, methodology): “The models presented in [10] and [11] use simplified market clearing procedures, which has an impact on the potential revenues obtained by the aggregator, and the role of the system operator (SO) is not considered.”
Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets Under Uncertainty (2015, background): “This has been addressed in [16] and [21], whereas only in [21] a derivation of the probabilistic properties of the aggregation based on driving patterns is provided.”