Quantifying the predictability of renewable energy data for improving power systems decision-making - Karimi-Arpanahi, Sahand; Pourmousavi, S. Ali; Mahdavi, Nariman (Patterns (New York, N.Y.), 2023)
Decision-making in the power systems domain often relies on predictions of renewable generation. While sophisticated forecasting methods have been developed to improve the accuracy of such predictions, their accuracy is limited by the inherent predictability of the data used. However, the predictability of time series data cannot be measured by existing prediction techniques. This important measure has been overlooked by researchers and practitioners in the power systems domain. In this paper, we systematically assess the suitability of various predictability measures for renewable generation time series data, revealing the best method and providing instructions for tuning it. Using real-world examples, we then illustrate how predictability could save end users and investors millions of dollars in the electricity sector. [Display omitted] •Effective method found to quantify the predictability of renewable generation data•Some applications of predictability in the power systems domain discussed•Low generation predictability can reduce up to 10% of solar farms’ revenue•Considering predictability in policy design imperative for a reliable power system As energy generation from solar and wind sources is subject to weather variability, short- and long-term decisions in the electricity sector are heavily influenced by their generation forecasts. Consequently, millions of dollars of public and private funds have been spent on developing accurate forecasting tools. In this article we argue that, while these tools are essential, there is a complementary aspect to this problem, namely the inherently limited predictability of renewable energy data. Researchers and practitioners in the energy sector have often overlooked this aspect. Here, we present a reliable method for quantifying the data predictability of renewable energy sources and illustrate its applications through real-world examples, from private investment decisions to public policies. Our findings indicate that considering predictability in power system decisions can save millions of dollars in operation costs, prevent the waste of clean energy, and lower electricity costs. The net-zero energy transition requires more renewable energy in the electricity generation mix. However, these sources have unpredictable generation that poses challenges for the power grid and lowers profits for investors. Currently there is no reliable method for quantifying renewable energy predictability. This paper presents a predictability measure and demonstrates its significance for the energy industry. Our findings indicate that considering predictability in decision-making leads to better policies, higher profits for investors, and lower electricity costs for customers.