Day-ahead Trading of Aggregated Energy Flexibility - Full Version

Flexibility of small loads, in particular from Electric Vehicles (EVs), has recently attracted a lot of interest due to their possibility of participating in the energy market and the new commercial potentials. Different from existing work, the aggregation techniques proposed in this paper produce flexible aggregated loads from EVs taking into account technical market requirements. They can be further transformed into the so-called flexible orders and be traded in the day-ahead market by a Balance Responsible Party (BRP). As a result, the BRP can achieve at least 20% cost reduction on average in energy purchase compared to traditional charging based on 2017 real electricity prices from the Danish electricity market.

[1]  Torben Bach Pedersen,et al.  Data management in the MIRABEL smart grid system , 2012, EDBT-ICDT '12.

[2]  Ronald L. Graham,et al.  Concrete Mathematics, a Foundation for Computer Science , 1991, The Mathematical Gazette.

[3]  Prasanta Ghosh,et al.  Optimizing Electric Vehicle Charging With Energy Storage in the Electricity Market , 2013, IEEE Transactions on Smart Grid.

[4]  L. H. Hansen,et al.  Value of flexible consumption in the electricity markets , 2014 .

[5]  Torben Bach Pedersen,et al.  Generation and Evaluation of Flex-Offers from Flexible Electrical Devices , 2017, e-Energy.

[6]  Ronald L. Graham,et al.  Concrete mathematics - a foundation for computer science , 1991 .

[7]  Manuel A. Matos,et al.  Forecasting issues for managing a portfolio of electric vehicles under a smart grid paradigm , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[8]  Luis Baringo,et al.  A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator , 2017 .

[9]  Marina Gonzalez Vaya,et al.  Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets Under Uncertainty , 2015, IEEE Transactions on Power Systems.

[10]  Karsten Emil Capion,et al.  Optimal charging of electric drive vehicles in a market environment , 2011 .

[11]  Anastasios G. Bakirtzis,et al.  Real-Time Charging Management Framework for Electric Vehicle Aggregators in a Market Environment , 2016, IEEE Transactions on Smart Grid.

[12]  Luis Lino Ferreira,et al.  Message-oriented middleware for smart grids , 2015, Comput. Stand. Interfaces.

[13]  Lei Yang,et al.  Risk-Aware Day-Ahead Scheduling and Real-time Dispatch for Electric Vehicle Charging , 2014, IEEE Transactions on Smart Grid.

[14]  Canbing Li,et al.  An Optimized EV Charging Model Considering TOU Price and SOC Curve , 2012, IEEE Transactions on Smart Grid.

[15]  Morten Lind,et al.  Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects , 2016 .

[16]  Tom Holvoet,et al.  Reinforcement Learning of Heuristic EV Fleet Charging in a Day-Ahead Electricity Market , 2015, IEEE Transactions on Smart Grid.

[17]  Torben Bach Pedersen,et al.  Aggregating and Disaggregating Flexibility Objects , 2012, IEEE Transactions on Knowledge and Data Engineering.

[18]  Birgitte Bak-Jensen,et al.  Demand Response Control in Low Voltage Grids for Technical and Commercial Aggregation Services , 2016, IEEE Transactions on Smart Grid.

[19]  Torben Bach Pedersen,et al.  Dependency-based FlexOffers: scalable management of flexible loads with dependencies , 2016, e-Energy.

[20]  Mushfiqur R. Sarker,et al.  Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead Energy and Reserve Markets , 2016, IEEE Transactions on Power Systems.

[21]  Filipe Joel Soares,et al.  Optimized Bidding of a EV Aggregation Agent in the Electricity Market , 2012, IEEE Transactions on Smart Grid.

[22]  David C. Hoaglin,et al.  Some Implementations of the Boxplot , 1989 .

[23]  Jian Zhao,et al.  Risk-Based Day-Ahead Scheduling of Electric Vehicle Aggregator Using Information Gap Decision Theory , 2017, IEEE Transactions on Smart Grid.

[24]  Torben Bach Pedersen,et al.  Measuring and Comparing Energy Flexibilities , 2015, EDBT/ICDT Workshops.

[25]  R. Weron Modeling and Forecasting Electricity Loads and Prices , 2006 .

[26]  Lingfeng Wang,et al.  Optimal Day-Ahead Charging Scheduling of Electric Vehicles Through an Aggregative Game Model , 2018, IEEE Transactions on Smart Grid.