CS can contribute to renewable energy in several ways:
1. Better controls algorithms can reduce capital expenses - better controls mean that wind turbines can respond to load cases (high winds, turbulent winds, wind shear, e.g.) more efficiently and more dynamically. By doing so, wind turbine engineers can reduce the physical materials (steel, fiberglass, etc.) required in the wind turbine, thus lowering the up-front cost of wind power and making it more competitive with other forms of power generation.
2. Better software can reduce operating expenses - People use software to run their power plants, to optimize their staffing, to compare one wind farm against another. Lowering operating expenses is another way to help make renewable power more cost competitive with other forms of power gen.
3. Better forecasting software leads to more optimal energy bidding strategies. In many parts of the world, power generators have to bid how much power they can produce into hour-ahead clearing markets. By more accurately knowing when the resource is going to change quickly, energy traders can maximize their participation in the market and minimize penalities for underproducing to their bid.
That's just scratching the surface. There are tons of opportunities in machine learning,.
With IOT, AI and ML everything which we do now, from switching off lights at home, to on street, to it's brightness, to water the plants, to thrive them at the right sunlight in house, to controlling the flow of these can all be controlled using the system running on very efficient codes. Have a look at below article:
There are many great answers as well on Quora, recommend reading these:
Once you've decided which field of Green Energy you're aiming at, I think it's all about exploring the techs involved and your own innovation. Sky is the limit!