Integrating Renewables: Curtailment, Forecasting and the 175 GW Target
- inspiri_admin
- Nov 11, 2020
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India’s renewable capacity ambition collides with a hard operational reality: variability. Why better forecasting, storage and market design — not more capacity alone — determine how much clean energy the grid actually delivers.
Every megawatt of renewable capacity only counts if the grid can absorb its output. As solar and wind shares grow, operators face the same equation from opposite ends: too much injection at midday, too little at the evening peak. Curtailment — renewable energy that could have been generated but the grid could not take — is the visible symptom of that gap.
Curtailment is a forecasting problem
A significant share of curtailment traces back to forecast error. When the day-ahead renewable forecast overshoots actual generation, conventional units were already backed down and the surplus has nowhere to go. When it undershoots, expensive fast-response reserves fill the gap. Shrinking the forecast error band directly reduces both.
- Plant-level and portfolio-level generation forecasting
- Ramp alerts ahead of cloud-cover or wind-lull events
- Curtailment analytics to identify systemic, not random, losses
- Hybrid project planning informed by observed curtailment patterns
Markets as the balancing instrument
The RTM has quietly become the grid’s shock absorber for renewable variability: sellers with surplus green power can find buyers in half-hourly sessions instead of spilling energy. Our work with renewable utilities focuses on making those market decisions data-driven — when to sell, when to hold, and how to position a hybrid portfolio across DAM and RTM.
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