Spatial–temporal solar power forecasting for smart grids
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The document discusses a spatial-temporal solar power forecasting method designed for smart grids, addressing challenges posed by increasing solar power integration at the low voltage level. This novel method uses vector auto regression to leverage solar generation data from smart meters and distribution transformers, focusing on 6-hour forecasts. Testing in Évora, Portugal, showed an improvement of 8% to 10% over traditional autoregressive forecasting models.
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Spatial–temporal solar power forecasting for smart grids
- 1. SPATIAL–TEMPORAL SOLAR POWER FORECASTING FOR SMART GRIDS ABSTRACT The solar power penetration in distribution grids is growing fast during the last years, particularly at the low voltage (LV) level, which introduces new challenges when operating distribution grids. Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting. This paper presents a new spatial–temporal forecasting method based on the vector auto regression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers. The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels. This framework has been tested in the smart grid pilot of Évora, Portugal, and using data from 44 micro generation units and 10 MV/LV substations. A benchmark comparison was made with the autoregressive forecasting model (AR—univariate model) leading to an improvement on average between 8% and 10%.