| Autor: |
Berger, Mathias, Radu, David, Dubois, Antoine, Pandžić, Hrvoje, Dvorkin, Yury, Louveaux, Quentin, Ernst, Damien |
| Zdroj: |
Optimization Letters; Apr2022, Vol. 16 Issue 3, p877-907, 31p |
| Abstrakt: |
This paper studies the problem of siting renewable power generation assets using large amounts of climatological data while accounting for their spatiotemporal complementarity. The problem is cast as a combinatorial optimisation problem selecting a pre-specified number of sites so as to minimise the number of simultaneous low electricity production events that they experience relative to a pre-specified reference production level. It is shown that the resulting model is closely related to submodular optimisation and can be interpreted as generalising the well-known maximum coverage problem. Both deterministic and randomised algorithms are discussed, including greedy, local search and relaxation-based heuristics as well as combinations of these algorithms. The usefulness of the model and methods is illustrated by a realistic case study inspired by the problem of siting onshore wind power plants in Europe, resulting in instances featuring over ten thousand candidate locations and ten years of hourly-sampled meteorological data. The proposed solution methods are benchmarked against a state-of-the-art mixed-integer programming solver and several algorithms are found to consistently produce better solutions at a fraction of the computational cost. The physical nature of solutions provided by the model is also investigated, and all deployment patterns are found to be unable to supply a constant share of the electricity demand at all times. Finally, a cross-validation analysis shows that, except for an edge case, the model can successfully and reliably identify deployment patterns that perform well on previously unseen climatological data from historical data spanning a small number of weather years. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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