Multi-horizon stochastic programming
- 734 Downloads
Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject to significant uncertainty, which has to be taken into account. This combination of uncertainties on two different time scales creates problems for the traditional multistage stochastic-programming formulation of the problem due to the exponential growth in model size. In this paper, we present an alternative formulation of the problem that combines the two time scales, using what we call a multi-horizon approach, and illustrate it on a stylized optimization model. We show that the new approach drastically reduces the model size compared to the traditional formulation and present two real-life applications from energy planning.
KeywordsStochastic programming Multistage Energy planning Scenario tree construction
The research presented in this paper has been supported by the project ‘Energy Efficiency and Risk Management in Public Buildings’ (EnRiMa), funded by the European Commission via the 7th Framework Programme (FP7), project number 260041. Part of the presented work also builds on research performed in the Ramona project (The Research Council of Norway, project number 175967) on production assurance and security of supply for natural gas transport.
- Christiansen DS, Wallace SW (1998) Option theory and modeling under uncertainty. Ann Oper Res 82: 59–82Google Scholar
- De Jonghe C, Hobbs B, Belmans R (2011) Integrating short-term demand response into long-term investment planning, Cambridge working papers in economics, vol 1132. Faculty of Economics, University of Cambridge, CambridgeGoogle Scholar
- Fleten S-E, Jørgensen T, Wallace SW (1998) Real options and managerial flexibility. Telektronikk 94(3/4):62–66Google Scholar
- Groissböck M, Stadler M, Edlinger T, Siddiqui A, Heydari S, Perea E (2011) The first step for implementing a stochastic based energy management system at campus Pinkafeld. Technical Report C-2011-1, Center for Energy and innovative Technologies, Hofamt Priel, AustriaGoogle Scholar
- Hellemo L, Midthun K, Tomasgard A, Werner A (2013) Multi-stage stochastic programming for natural gas infrastructure design with a production perspective. In: Gassmann, HI, Wallace, SW, Ziemba, WT (eds) Stochastic programming: applications in finance, energy, planning and logistics, World Scientific Series in Finance. World Scientific, SingaporeGoogle Scholar
- King AJ, Wallace SW, Lium A-G, Crainic TG (2012) Service network design, chapter 5, Springer series in operations research and financial engineering. Springer. doi: 10.1007/978-0-387-87817-1_5
- Midthun KT (2007) Optimization models for liberalized natural gas markets. PhD thesis, Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, NorwayGoogle Scholar
- Myklebust J (2010) Techno-economic modelling of value chains based on natural gas—with consideration of CO2 emissions. PhD thesis, Department of Industrial Economics and Technology Management, Norwegian University of Science and TechnologyGoogle Scholar
- Pérez-Valdés G, Kaut M, Nørstebø V, Midthun K (2012) Stochastic MIP modeling of a natural gas-powered industrial park. Energy Procedia 26:74–81. doi: 10.1016/j.egypro.2012.06.012. Proceedings of the 2nd Trondheim Gas Technology Conference
- Römisch W (2009) Scenario reduction techniques in stochastic programming. In: Stochastic Algorithms: Foundations and Applications. Lecture Notes in Computer Science, vol 5792, pp 1–14. Springer, BerlinGoogle Scholar