Distributionally robust optimization with multiple time scales: valuation of a thermal power plant
- PDF / 1,842,154 Bytes
- 29 Pages / 439.37 x 666.142 pts Page_size
- 3 Downloads / 187 Views
Distributionally robust optimization with multiple time scales: valuation of a thermal power plant Wim van Ackooij1 Georg Ch. Pflug2,3
· Debora Daniela Escobar2 · Martin Glanzer2
·
Received: 9 April 2019 / Accepted: 15 October 2019 © The Author(s) 2019
Abstract The valuation of a real option is preferably done with the inclusion of uncertainties in the model, since the value depends on future costs and revenues, which are not perfectly known today. The usual value of the option is defined as the maximal expected (discounted) profit one may achieve under optimal management of the operation. However, also this approach has its limitations, since quite often the models for costs and revenues are subject to model error. Under a prudent valuation, the possible model error should be incorporated into the calculation. In this paper, we consider the valuation of a power plant under ambiguity of probability models for costs and revenues. The valuation is done by stochastic dynamic programming and on top of it, we use a dynamic ambiguity model for obtaining the prudent minimax valuation. For the valuation of the power plant under model ambiguity we introduce a distance based on the Wasserstein distance. Another highlight of this paper is the multiscale approach, since decision stages are defined on a weekly basis, while the random costs and revenues appear on a much finer scale. The idea of bridging stochastic processes is used to link the weekly decision scale with the finer simulation scale. The applicability of the introduced concepts is broad and not limited to the motivating valuation problem. Keywords Model ambiguity · Distributionally robust decision making · Multistage stochastic optimization · Multiscale stochastic optimization · Dynamic programming · Wasserstein distance · Nested distance
B
Martin Glanzer [email protected]
1
OSIRIS, EDF R&D, 7 Boulevard Gaspard Monge, 91120 Palaiseau, France
2
Department of Statistics and Operations Research, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Wien, Austria
3
International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
123
W. van Ackooij et al.
1 Introduction Since the deregulation of the energy market, the question of how to determine the value of a power plant can be asked. The traditional approach of valuing it within a given portfolio of other assets in a coordinated way against one’s customer load is one possibility. A second approach is to adopt the ideas of real option pricing in finance. In the first case one ends up with models resembling unit commitment (e.g., van Ackooij et al. 2018) but at a long time scale. Although the actual operation of the power plant can be presented in great detail, it will be harder to incorporate other features in the model. This will typically be the case for uncertainty, where one ends up with multi-stage mixed-integer programs which are not easily solved. One can also argue that it is unreasonable to model the system as fully coordinated. In contrast, when modelling the power p
Data Loading...
