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A distributionally ambiguous two-stage stochastic approach for investment in renewable generation
Published online by Cambridge University Press: 12 May 2022
Abstract
The optimal expansion of a power system with reduced carbon footprint entails dealing with uncertainty about the distribution of the random variables involved in the decision process. Optimisation under ambiguity sets provides a mechanism to suitably deal with such a setting. For two-stage stochastic linear programs, we propose a new model that is between the optimistic and pessimistic paradigms in distributionally robust stochastic optimisation. When using Wasserstein balls as ambiguity sets, the resulting optimisation problem has nonsmooth convex constraints depending on the number of scenarios and a bilinear objective function. We propose a decomposition method along scenarios that converges to a solution, provided a global optimisation solver for bilinear programs with polyhedral feasible sets is available. The solution procedure is applied to a case study on expansion of energy generation that takes into account sustainability goals for 2050 in Europe, under uncertain future market conditions.
Keywords
MSC classification
Primary:
90C15: Stochastic programming
Secondary:
65K05: Mathematical programming methods
- Type
- Papers
- Information
- European Journal of Applied Mathematics , Volume 34 , Special Issue 3: The Mathematics in Renewable Energy special issue , June 2023 , pp. 484 - 504
- Copyright
- © The Author(s), 2022. Published by Cambridge University Press
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