A mixed integer programming solution for market clearing and reliability analysis

By Dan Streiffert, Russ Philbrick, A. Ott. IEEE Power Engineering Society General Meeting, 2005

115 citations recorded by the source corpus.

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Economic Consequences of Alternative Solution Methods for Centralized Unit Commitment in Day-Ahead Electricity Markets (2008, background): “Streiffert et al. [ 3 ] demonstrate, however, that recent advances in computing capabilities and improvements in optimization algorithms now allow MIP to be a”
Analyzing valid inequalities of the generation unit commitment problem (2009, background): “Published in 2005, [ 9 ] discusses the tradeoffs between LR and MIP for PJM and a recent presentation, [10], discusses the implementation of MIP in PJM.”
Uncertainty Management in the Unit Commitment Problem (2009, methodology): “The implementation is written as a mixed-integer linear program [7].”
Optimal Self-Scheduling of a Thermal Producer in Short-Term Electricity Markets by MILP (2010, methodology): “The main advantages of MILP over LR are that 1) it can provide a proven global optimal solution, 2) it provides a more accurate measure of optimality, and 3) it provides enhanced modeling capabilities and adaptability [8].”