Low-bandwidth routing and electrical power networks

Cook, Doug; Faber, Vance; Marathe, Madhav V.; Srinivasan, Aravind; Sussmann, Yoram J.

doi:10.1007/bfb0055088

Cited by 3 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Let and respectively denote the number of vertices and edges in the network § . In [CF+97], it was shown that (0/1-VERSION, MAX-#CONTRACTS) is NP -hard; also, unless NP R ā b° C°…”

Section: Description and Discussion Of Algorithmsmentioning

confidence: 99%

“…We next discuss an approximation algorithm of [CF+97]. This has proven performance only when all source vertices 3 I 5 are the same; however, this algorithm extends naturally to the multi-source case which we work on.…”

Section: Description and Discussion Of Algorithmsmentioning

confidence: 99%

“…: i.e., we will reset all the ¹ 6 5 0 to . A deterministic version of this result based on pessimistic estimators, is also provided in [CF+97].…”

Section: Description and Discussion Of Algorithmsmentioning

confidence: 99%

“…Implementing the randomized rounding procedure requires extra care. The pessimistic estimator approach of [CF+97] works with very low probabilities, and requires significant, repeated re-scaling in practice. Thus we focus on the randomized version of the algorithm of [CF+97].…”

Section: Implementation Detailsmentioning

confidence: 99%

See 3 more Smart Citations

Experimental Analysis of Algorithms for Bilateral-Contract Clearing Mechanisms Arising in Deregulated Power Industry

Barrett

Cook

Hicks

et al. 2001

Algorithm Engineering

Self Cite

View full text Add to dashboard Cite

We consider the bilateral contract satisfaction problem arising from electrical power networks due to the proposed deregulation of the electric utility industry in the USA. Given a network § and a set (or multiset) of pairs of vertices (contracts) with associated demand functions that reflect the amount of flow which needs to be sent between the pairs of vertices, the goal is to find the maximum number of simultaneously satisfiable contracts. Four different algorithms for bilateral contract satisfaction problems are considered: (i) SMALLEST-FIRST HEURISTIC (ii) LARGEST-FIRST HEURISTIC (iii) RANDOM-ORDER HEURISTIC and (iv) ILP-RANDOMIZED ROUNDING: an integer linear programming based approximation algorithm with proven performance guarantee in restricted cases. The main focus of the paper is to study how the heuristics performed in fairly realistic settings. For this purpose we used an approximate electrical power network from Colorado.From our preliminary analysis, it appears that although the first three heuristic algorithms do not have a worst-case guarantee, they outperform the theoretically better ILP-RANDOMIZED ROUNDING algorithm. We also tested the algorithm on four types of scenarios that are likely to occur in a deregulated marketplace. The empirical results show that the networks that are adequate in regulated marketplace might not be adequate for satisfying all the bilateral contracts in a deregulated industry. Our studies indicate that simple clearing mechanisms that are currently in use in many of the power markets are computationally fast and near-optimal in their use of network capacity; also, a linear programming upper bound on the quality of any solution, turns out to be a very good bound in practice.Basic and Applied Simulation Sciences, (D-2), and Computer and Computational Science (CCS-3),

show abstract

“…Let and respectively denote the number of vertices and edges in the network § . In [CF+97], it was shown that (0/1-VERSION, MAX-#CONTRACTS) is NP -hard; also, unless NP R ā b° C°…”

Section: Description and Discussion Of Algorithmsmentioning

confidence: 99%

Section: Description and Discussion Of Algorithmsmentioning

confidence: 99%

“…: i.e., we will reset all the ¹ 6 5 0 to . A deterministic version of this result based on pessimistic estimators, is also provided in [CF+97].…”

Section: Description and Discussion Of Algorithmsmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

See 2 more Smart Citations