Demo Data from SIGIR 2026 / reviewers in the wild / expert
Marie-Benedicte Adjaho
dblp:177/4123
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 100% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Energy systems and smart grids
energy storage |
0.2 | 1 | 2016 | Joint Optimal Design and Operation of Hybrid Energy Storage Systems · IEEE J. Sel. Areas Commun. 2016 |
Energy systems and smart grids › energy storage
hybrid energy storage system |
0.2 | 1 | 2016 | Joint Optimal Design and Operation of Hybrid Energy Storage Systems · IEEE J. Sel. Areas Commun. 2016 |
Mathematical optimization
multi-objective optimization |
0.1 | 1 | 2016 | Joint Optimal Design and Operation of Hybrid Energy Storage Systems · IEEE J. Sel. Areas Commun. 2016 |
Mathematical optimization › multi-objective optimization
pareto front |
0.1 | 1 | 2016 | Joint Optimal Design and Operation of Hybrid Energy Storage Systems · IEEE J. Sel. Areas Commun. 2016 |
Methods — techniques the papers use, named apart from their topics
pareto optimization · 0.5linear programming · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Joint Optimal Design and Operation of Hybrid Energy Storage SystemsabstractThe wide range of performance characteristics of storage technologies motivates the use of a hybrid energy storage system (HESS) that combines the best features of multiple technologies. However, HESS design is complex, in that it involves the choice of storage technologies, the sizing of each storage element, and deciding when to charge and discharge each underlying storage element (operating strategy). We formulate the problem of jointly optimizing the sizing and the operating strategy of an HESS that can be used for a large class of applications and storage technologies. Instead of a single set of storage element sizes, our approach determines the Pareto-optimal frontier of the sizes of the storage elements along with the corresponding optimal operating strategy. Thus, as long as the performance objective of a storage application (such as an off-grid microgrid) can be expressed as a linear combination of the underlying storage sizes, the optimal vector of storage sizes falls somewhere on this frontier. We present two case studies to illustrate our approach, demonstrating that a single storage technology is sometimes inadequate to meet application requirements, unlike an HESS designed using our approach. We also find simple, near-optimal, and practical operating strategies for these case studies, which allows us to gain several new engineering insights. Yashar Ghiassi-Farrokhfal, Catherine Rosenberg, Srinivasan Keshav, Marie-Benedicte Adjaho |
IEEE J. Sel. Areas Commun. | 4 |