Optimization based methods for unit commitment: Lagrangian relaxation versus general mixed integer programming
By X. Guan, Qiaozhu Zhai, Alex Papalexopoulos. IEEE Power Engineering Society General Meeting, 2003
159 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/c503a186e36f87a7e2dbc26dc442e87024fa2c40
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Mohammad Shahidehpour
- Zuyi Li
- Lei Wu
- Jianhui Wang
- Shmuel S. Oren
- Mohammad E. Khodayar
- Salar Fattahi
- Morteza Ashraphijuo
Citations in the corpus
- Hourly Coordination of Electric Vehicle Operation and Volatile Wind Power Generation in SCUC (2012, background): “Unlike the grid-controlled scheme, the increase in PEV storage capacity may not reduce the grid operation cost in the consumer-controlled scheme.”
- An overview on mathematical programming approaches for the deterministic unit commitment problem in hydro valleys (2017, background): “For further papers dealing with LR for the general UC problem, the reader is referred to [9,40,61–63,79,89].”
- MIP Reformulation for Max-Min Problems in Two-Stage Robust SCUC (2017, background): “It is noted that both (MP) and (SP) are NP-hard problems [21], [22].”
- Economic Consequences of Alternative Solution Methods for Centralized Unit Commitment in Day-Ahead Electricity Markets (2008, background): “Guan et al. [ 8 ] state, for example, that the 1%–2% duality gaps achieved with LR were sufficient for monopoly utilities but that the development of competitive markets in which generators compete to provide their products has increased the need for more accurate unit commitment solutions.”
- Conic relaxations of the unit commitment problem (2017, background): “The UC problem is hard due to its nonconvex nature and its large instances are computationally challenging to solve [1].”
- Quantum computing for energy systems optimization: Challenges and opportunities (2019, background): “It has been proven that UC is not only NP-hard but also NP-complete, so it is impossible to develop an algorithm with polynomial computation time to solve it [57].”