Quantifying the predictability of renewable energy data for improving power systems decision-making

Published: March 24, 2023Decision-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.Sahand Karimi-Arpanahi, S. Ali Pourmousavi, and Nariman Mahdav

Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy

The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.Sahand Karimi-Arpanahi, S. Ali Pourmousav

Impact of high penetration of PV generation on frequency and voltage in a distribution feeder

This paper presents an evaluation of the impact of various levels of photovoltaic (PV) power penetration in a distribution feeder connected to a simplified grid model (SGM). PV generation is implemented in second-by-second iterations with power output based on actual solar radiation and air temperature data. High penetration levels of intermittent PV generation (15% and 30%) are employed in a feeder-configured microgrid to evaluate grid frequency and voltage characteristics. In this study, only governor droop control is included in the proposed SGM without the secondary control action (known as load frequency control). Two different grid models (fast and slow grid), PV generation configurations (concentrated and distributed), and PV penetration levels (15% and 30%) are considered in the simulation studies. Simulation results indicate the impact of the aforementioned parameters on the system frequency and voltage. Results also reveal that distributed PVs in a wide geographical area with different weather regime have less impact on the frequency and voltage

A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market

Available online 3 January 2024. OnlinePublThe South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps. We propose using quantile regression as an ensemble tool for probabilistic forecasting, with our combined forecasts achieving superior results compared to all constituent models. Within our ensemble framework, we demonstrate that averaging models with varying training-length periods leads to a more adaptive model and increased prediction accuracy. The applicability of the final model is evaluated by comparing our median forecasts with the point forecasts available from the Australian NEM operator, with our model outperforming these NEM forecasts by a significant margin.Cameron Cornell, Nam Trong Dinh, S. Ali Pourmousav