caa a22 4500 606233040 CHVBK 20210128101230.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10287-015-0229-y doi (NATIONALLICENCE)springer-10.1007/s10287-015-0229-y A scalable solution framework for stochastic transmission and generation planning problems [Elektronische Daten] [Francisco Munoz, Jean-Paul Watson] Current commercial software tools for transmission and generation investment planning have limited stochastic modeling capabilities. Because of this limitation, electric power utilities generally rely on scenario planning heuristics to identify potentially robust and cost effective investment plans for a broad range of system, economic, and policy conditions. Several research studies have shown that stochastic models perform significantly better than deterministic or heuristic approaches, in terms of overall costs. However, there is a lack of practical solution techniques to solve such models. In this paper we propose a scalable decomposition algorithm to solve stochastic transmission and generation planning problems, respectively considering discrete and continuous decision variables for transmission and generation investments. Given stochasticity restricted to loads and wind, solar, and hydro power output, we develop a simple scenario reduction framework based on a clustering algorithm, to yield a more tractable model. The resulting stochastic optimization model is decomposed on a scenario basis and solved using a variant of the Progressive Hedging (PH) algorithm. We perform numerical experiments using a 240-bus network representation of the Western Electricity Coordinating Council in the US. Although convergence of PH to an optimal solution is not guaranteed for mixed-integer linear optimization models, we find that it is possible to obtain solutions with acceptable optimality gaps for practical applications. Our numerical simulations are performed both on a commodity workstation and on a high-performance cluster. The results indicate that large-scale problems can be solved to a high degree of accuracy in at most 2h of wall clock time. Springer-Verlag Berlin Heidelberg, 2015 Transmission planning nationallicence Generation planning nationallicence Stochastic mixed-integer programming nationallicence Progressive Hedging nationallicence Munoz Francisco Analytics Department, Sandia National Laboratories, MS 1326, P.O. Box 5800, 87185-1326, Albuquerque, NM, USA aut Watson Jean-Paul Analytics Department, Sandia National Laboratories, MS 1326, P.O. Box 5800, 87185-1326, Albuquerque, NM, USA aut Computational Management Science Springer Berlin Heidelberg 12/4(2015-10-01), 491-518 1619-697X 12:4<491 2015 12 10287 https://doi.org/10.1007/s10287-015-0229-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10287-015-0229-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Munoz Francisco Analytics Department, Sandia National Laboratories, MS 1326, P.O. Box 5800, 87185-1326, Albuquerque, NM, USA aut NATIONALLICENCE 700 1- Watson Jean-Paul Analytics Department, Sandia National Laboratories, MS 1326, P.O. Box 5800, 87185-1326, Albuquerque, NM, USA aut NATIONALLICENCE 773 0- Computational Management Science Springer Berlin Heidelberg 12/4(2015-10-01), 491-518 1619-697X 12:4<491 2015 12 10287