I worked as an Assistant Manager at a Renewable Group, for the past 2.5 years. My role required me to supplement in operations and maintenance of wind farm assets for plants in the Northern Zone of the country, namely Gujarat, Rajasthan, Madhya Pradesh, and Maharashtra. Prior to joining the PGP-DSBA course, I had completed my M. Tech in power Systems and started working as a Trainee in the Greenko group.
India ranks fourth in the world with an installed capacity of 40.788 MW for power generation capacity for wind plants alone. Of this 3.19 MW has been installed and operated by Greenko. Greenko alone contributes to 11% of electricity generation in the wind sector. With the need of the hour and large-scale grid integration of wind-generating resources, wind power energy has become an important concern. Real-time national grid operation is disturbed because of the penetration of high wind power, which in turn affects system reliability. Power output from wind power generators is intermittent and variable. Wind power forecasting is essential to have continuous and reliable energy generation. In order to have a day-ahead and week-ahead generation forecast, the weather parameters to be considered are temperature, pressure, humidity, hub height, and wind speed (predicted by a third party). The effect of all the parameters is extrapolated on wind speed and converted to power output through the power curve.
For performing short-term forecasts, statistical models like autoregressive (AR), autoregressive moving averages (ARMA), and autoregressive integrated moving averages (ARIMA) are quite useful. The accurate forecasting of day-ahead power forecasts for unit commitment helps in proper power scheduling, designing apt power evacuation plans, and improving system operation by reducing operating costs, reducing unserved energy, reduces curtailment while maintaining required levels of safety. The benefit can be summarised as,
1. The financial benefit to the organization in terms of reduced operating costs and reduced curtailments.
2. Operational benefit to the organization in maintaining the healthiness of the assets.
3. National benefit in maintaining grid stability and system reliability.
Accurate wind power forecasting improves energy conversion efficiency and reduces the risk of overload, thereby enabling reliable system operation. The study has currently been carried out on a single plant location. With proper ensemble models built on the base plant, the analysis will further be extended to other plants operated by the company. The overall analysis has proved efficient for scheduling, power evacuation, asset management and finance departments. Through proper analytics, not only can the generated power be forecast, but to prevent financial implications, further study can be carried out on measuring lost energy production, and assessing assets’ health and maintenance needs. Pareto charts will also help in identifying the major alarms causing hindrances in asset operation.
