Single-Site Forecasts for 130 Photovoltaic Systems at Distribution System Operator Level, Using a Hybrid-Physical Approach, to Improve Grid-Integration and Enable Future Smart-Grid Operation

Abstract

With rising shares of photovoltaic (PV) generated electricity in the power grids, PV power forecasting is rapidly gaining importance for energy suppliers and grid operators. But specifically, the distribution system operators (DSOs) require detailed forecasts at higher spatial resolutions, to manage grid congestions and avoid voltage band violations, which are local phenomena. In view of future smart grid operation, the authors propose a forecasting scheme using numerical weather prediction (NWP) data to feed a PV performance model and compute single-site power forecasts. The intraday outcomes of this rather conventional forecasting scheme are combined with a supervised-learning model, based on recurrent neural networks (type: long short-term memory) which is fed by the 15 min measurement data of the two previous days for each PV system. This approach accounts for different local effects, not covered by the NWP-based physical model, such as partial shading or snow cover. Furthermore, it mitigates the impacts of “imperfect” data on side of the DSOs, used to parametrize the PV performance model. This hybrid-physical approach has been applied to about 130 PV systems, validated over a 2 year period, and yielded a normalized root-mean-square error of 8.3% and 8.7% in 2020 and 2021.