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Alan Berscheid

dblp:69/8399 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Energy systems and smart grids › power system planning and operation
power system planning
0.112010
Transmission Network Expansion Planning with Simulation Optimization · AAAI 2010
Mathematical optimization › stochastic optimization › noisy optimization
simulation optimization
0.112010
Transmission Network Expansion Planning with Simulation Optimization · AAAI 2010

Methods — techniques the papers use, named apart from their topics

local search · 0.2limited discrepancy search · 0.2black-box simulation · 0.2
YearPublicationVenuePosition
2010 Transmission Network Expansion Planning with Simulation Optimization
abstract
Within the electric power literature the transmission expansion planning problem (TNEP) refers to the problem of how to upgrade an electric power network to meet future demands. As this problem is a complex, non-linear, and non-convex optimization problem, researchers have traditionally focused on approximate models of power flows. Existing approaches are often tightly coupled to the approximation choice. Until recently, these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (e.g. large amounts of limited control, renewable generation) that necessitates new optimization techniques. In this paper, we propose a local search variation of the powerful Limited Discrepancy Search (LDLS) that encapsulates the complexity of power flows in a black box that may be queried for information about the quality of a proposed expansion. This allows the development of a new optimization algorithm that is independent of the underlying power model.
Russell Bent, Alan Berscheid, G. Loren Toole
AAAI2