Renewable energy forecasting is critical for integrating variable renewable energy resources like wind and solar power into the grid. Wind and solar power are heavily dependent on weather conditions, and thus their energy generation patterns are quite erratic. This creates problems not only for grid operators who must account for these frequent fluctuations in their load management schedules, but also for power generators who have to pay hefty charges for deviation from scheduled energy generation.
Accurate renewable energy forecasts help generators plan their operations better. Meanwhile, for grid operators, forecasts help them predict the ramp up and down in generation and they can then manage load accordingly. This helps in reducing fuel costs, improving system reliability, and minimising energy curtailment.
For a particular region or state, renewable energy forecasting can be carried out in a centralised as well as a decentralised manner. Many in the industry believe that centralised forecasting is more suitable for ensuring overall grid stability as it provides energy forecasts for all the projects in a specific region. Meanwhile, decentralised forecasts provide only plant-level information. Thus, for grid operators, centralised forecasts would offer better consistency owing to the use of a single technique as well as less uncertainty. However, even in centralised forecasts, it is vital to use a mix of techniques to improve accuracy and prevent errors.
Key challenges and the way forward
With the increasing penetration of variable renewable energy into the grid, it is becoming quite critical to incorporate advanced forecasting and scheduling techniques to ensure grid stability. However, there are still many issues that need to be addressed before proper forecasting tools can be deployed. First, there remains some level of uncertainty in accurate weather forecasting that needs to be taken care of as solar and wind energy generation have a significant dependence on the weather.
Second, refined regulatory frameworks must be designed that take grid balancing and ancillary services into consideration. Third, grid operators and manpower need to be trained regularly regarding the latest developments in this space and more efforts need to be diverted towards incorporating advanced AI tools for forecasting. Finally, with large volumes of distributed renewable energy assets and electric vehicles being connected to the grid, forecasting and course correction in real time with advanced tools and techniques is playing a vital role in stable grid operations.
*Summarized from ReGlobal.co
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