Stochastic Programming-Based Bounding of Expected Production Costs for Multiarea Electric Power System
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DOI: 10.1287/opre.47.6.836
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References listed on IDEAS
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Cited by:
- Munoz, F.D. & Hobbs, B.F. & Watson, J.-P., 2016. "New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints," European Journal of Operational Research, Elsevier, vol. 248(3), pages 888-898.
- Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2020. "Cost minimization of large-scale infrastructure for electricity generation and transmission," Omega, Elsevier, vol. 96(C).
- Francisco Munoz & Jean-Paul Watson, 2015. "A scalable solution framework for stochastic transmission and generation planning problems," Computational Management Science, Springer, vol. 12(4), pages 491-518, October.
- De Jonghe, C. & Hobbs, B. F. & Belmans, R., 2011. "Integrating short-term demand response into long-term investment planning," Cambridge Working Papers in Economics 1132, Faculty of Economics, University of Cambridge.
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Keywords
stochastic programming; application of the distribution problem to electric power systems; natural resources; energy; calculation of expected electric power generation costs;All these keywords.
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