In the face of increasingly frequent extreme weather events, the energy sector is grappling with the challenges posed by distributed photovoltaic (PV) fluctuations. These fluctuations, caused by sudden changes in sunlight due to weather conditions, can significantly impact the balance of power systems, leading to issues like solar power curtailment and even load shedding. However, a groundbreaking study led by Caiqi Zhou from the National Power Dispatching and Control Center in Beijing, China, offers a promising solution to mitigate these risks.
The research, published in ‘Zhongguo dianli’ (China Electric Power), introduces a multi-level rolling warning method for distributed PV fluctuations. This innovative approach, based on interval analysis theory, aims to provide real-time warnings of potential harm from PV fluctuations, enabling power systems to better prepare and respond to these events.
At the heart of this method is the establishment of warning levels and thresholds. “We clarify the power control mechanism for handling distributed PV fluctuations and determine the range of fluctuations that can be controlled by different power control measures,” explains Zhou. This involves calculating the probabilities of each warning level based on the probability density of PV fluctuations, allowing for a more nuanced understanding of the risks involved.
The method also takes into account the differences in forecasting accuracy of PV fluctuations at different time scales. By periodically adjusting the warning results, it achieves a rolling warning system that can adapt to changing conditions. This adaptability is crucial for the energy sector, where the ability to anticipate and respond to fluctuations can significantly enhance grid stability and reliability.
The commercial impacts of this research are substantial. By providing a more accurate and timely warning system, power companies can better manage their resources, reduce the need for solar power curtailment, and minimize the risk of load shedding. This not only improves the efficiency of power systems but also enhances the reliability of renewable energy sources, making them a more attractive option for investors and consumers alike.
The case study results presented in the research are particularly compelling. The method was able to determine the thresholds for each warning range while providing warning results for different system operating conditions and PV fluctuation events. Moreover, the root mean square error of the warning results obtained with this method compared to those of the Monte Carlo method was only 1.6718%, verifying its effectiveness and applicability.
As the energy sector continues to evolve, the need for advanced warning systems like the one proposed by Zhou and his team will only grow. This research not only shapes future developments in the field but also paves the way for a more resilient and efficient power grid. By embracing such innovative solutions, the energy sector can better navigate the challenges posed by extreme weather events and ensure a stable and reliable power supply for all.
