New study in @iScience_CP with #PyPSA-Eur combining modelling-to-generate-alternatives (MGA) with global sensitivity analysis (GSA) to show many ways to design cost-effective renewable electricity systems with robustness to uncertain technology costs.
doi.org/10.1016/j.isci…
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doi.org/10.1016/j.isci…
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This paper with @nworbmot extends a previous study using MGA for renewable electricity system planning.
We added technology cost projections as parametric uncertainty to the structural exploration of near-optimal solutions.
https://twitter.com/fneum_/status/1308728435787866113?s=20
We added technology cost projections as parametric uncertainty to the structural exploration of near-optimal solutions.
Like a previous study by @timtroendle, we used multi-fidelity surrogate modelling techniques using sparse polynomial chaos expansions and low-discrepancy sampling to manage over 50,000 optimisation runs effectively.
https://twitter.com/timtroendle/status/1294320932509622273?s=20
That approach allowed us to merge results from one simpler and another more detailed model. One covers 37 regions at 4-hourly and the other 128 regions at 2-hourly resolution over a full year.
Results and visualisations made possible with the applied methodology include ...
... the distributions of system cost, generation, storage, and transmission expansion in least-cost solutions.
... the distributions of system cost, generation, storage, and transmission expansion in least-cost solutions.
... the sensitivity of capacities towards their own technology cost with uncertainty bands for the influence of the remaining cost parameters.
Note the varying width of uncertainty bands, the low uncertainty for transmission expansion needs, and the shape of the battery plot!
Note the varying width of uncertainty bands, the low uncertainty for transmission expansion needs, and the shape of the battery plot!
... sensitivity indices attributing output variance in capacities build and system cost to individual technology costs.
These show that the uncertainty of *electricity* system cost is largely driven by the investment cost of wind.
These show that the uncertainty of *electricity* system cost is largely driven by the investment cost of wind.
... fuzzy near-optimal cones identifying feasible alternatives common to all, few or no cost samples.
... the probabilistic near-optimal feasible space in two technology dimensions simultaneously (cross-section of a near-optimal cone for given epsilon). These outline constraints around extremising the capacity expansion of combinations of two technologies simultaneously.
The robust finding of our study is that there is consistent investment flexibility in shaping fully renewable power systems, even without availing of the many flexibility options offered through sector coupling (electricity-only) and perfect technology cost foresight.
This opens the floor to discussions about social trade-offs and navigating around issues, such as public opposition toward wind turbines or transmission lines. We offer a method to present a wide spectrum of expansion options that are feasible and within a reasonable cost range.
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