Data-driven mean-variability optimization of PV portfolios with automatic differentiation (Papers Track)

Matthias Zech (German Aerospace Center (DLR), Institute of Networked Energy Systems); Lueder von Bremen (German Aerospace Center (DLR), Institute of Networked Energy Systems)

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Power & Energy Climate Finance & Economics Interpretable ML


Increasing PV capacities has a crucial role to reach carbon-neutral energy systems. To promote PV expansion, policy designs have been developed which rely on energy yield maximization to increase the total PV energy supply in energy systems. Focusing on yield maximization, however, ignores negative side-effects such as an increased variability due to similar-orientated PV systems at clustered regions. This can lead to costly ancillary services and thereby reduces the acceptance of renewable energy. This paper suggests to rethink PV portfolio designs by deriving mean-variability hedged PV portfolios with smartly orientated tilt and azimuth angles. Based on a data-driven method inspired from modern portfolio theory, we formulate the problem as a biobjective, non-convex optimization problem which is solved based on automatically differentiating the physical PV conversion model subject to individual tilt and azimuth angles. To illustrate the performance of the proposed method, a case study is designed to derive efficient frontiers in the mean-variability spectrum of Germany's PV portfolio based on representative grid points. The proposed method allows decision-makers to hedge between variability and yield in PV portfolio design decisions. This is the first study highlighting the problem of ignoring variability in PV portfolio expansion schemes and introduces a way to tackle this issue using modern methods inspired by Machine Learning.