Identifying weather patterns responsible for renewable energy production droughts in India

Energy systems across the globe are evolving to meet climate mitigation targets set by the Paris Agreement. This process requires a rapid reduction on nations’ reliance on fossil fuels and significant uptake of renewable generation (such as wind power, solar power, and hydropower). In parallel to the decarbonisation of the electricity sector, both the heat and transport sectors are electrifying to reduce their carbon intensity. Renewable energy sources are weather-dependent, causing production to vary on timescales from minutes to decades. A consequence of this variability is that there may be periods of low renewable energy production, here termed ‘renewable energy droughts’. This energy security challenge needs to be addressed to provide a consistent power supply and to ensure grid stability. India is chosen here as a study area as a region that already has a large existing proportion of renewable generation (42 GW of wind power, 61 GW of solar power and 51 GW of hydropower were installed as of October 2022) and a region that experiences good sub-seasonal predictability in large-scale patterns.

In this study, we use broad variety of data sources to quantify potential and realised capacity over India from 1979 to 2022 using the ERA5 reanalysis and a range of open source renewable energy installation data. Using gridded estimates of existing installed renewable capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model for each Indian state and at national level. We use this model to identify the timing of historical renewable energy droughts and then discuss potential weaknesses in the existing grid – particularly a lack of complementarity between wind and solar production in north India – and vulnerability to high deficit generation in the winter. The data produced here have all been made open access and the methods could easily be reproduced over any region of interest.

We then consider the weather patterns that could cause the largest renewable energy droughts over India and investigate potential sources of predictability. Existing large-scale daily weather types (based on large-scale wind map clustering) as well as novel patterns created by k-means clustering of more relevant variables for wind and solar power are used to investigate the different weather patterns causing renewable energy droughts. Renewable energy droughts largely occur during the winter season (January and February) and are caused by low seasonal wind speeds in combination with weather patterns bringing high cloud cover. These are mainly winter anticyclones and western disturbances.

Sources of potential sub-seasonal predictability are considered for the largest renewable energy droughts, including the Madden Julian Oscillation and Boreal Summer Intra-Seasonal Oscillation. Although both have a stronger relationship with high energy production days, links between phases of these two modes of variability and renewable energy droughts have been identified. These could help to provide early warnings for conditions that challenge supply security in the future.