EDBT 2026 Demo / reviewers in the wild / expert
Micha Kahlen
dblp:147/4774
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Multi-agent systems · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems › multi-robot coordination
market-based coordination |
0.2 | 1 | 2015 | Aggregating Electric Cars to Sustainable Virtual Power Plants: The Value of Flexibility in Future Electricity Markets · AAAI 2015 |
Methods — techniques the papers use, named apart from their topics
optimization · 0.4heuristic algorithm · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Aggregating Electric Cars to Sustainable Virtual Power Plants: The Value of Flexibility in Future Electricity MarketsabstractElectric vehicles will play a crucial role in balancing the future electrical grid, which is complicated by many intermittent renewable energy sources. We developed an algorithm that determines for a fleet of electric vehicles, which EV at what price and location to commit to the operating reserve market to either absorb excess capacity or provide electricity during shortages (vehicle-2-grid). The algorithm takes the value of immobility into account by using carsharing fees as a reference point. A virtual power plant autonomously replaces cars that are committed to the operating reserves and are then rented out, with other idle cars to pool the risks of uncertainty. We validate our model with data from a free float carsharing fleet of 500 electric vehicles. An analysis of expected future developments (2015, 2018, and 2022) in operating reserve demand and battery costs yields that the gross profits for a carsharing operator increase between 7-12% with a negligible decrease in car availability (<0.01%). Micha Kahlen, Wolfgang Ketter |
AAAI | 1 |