Forecasting Uncertainty Parameters of Virtual Power Plants Using Decision Tree Algorithm

Abstract

Renewable energy sources (RESs) are increasingly used to meet the world’s growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R²), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ANN and ARIMA algorithms.

Keywords:

• forecasting
• uncertainty parameters
• renewable energy sources
• distributed generators
• machine learning
• decision tree algorithm
• ANN algorithm
• ARIMA algorithm
• statistical metrics

Additional information

Notes on contributors

Raji Krishna

Raji Krishna is currently pursuing his PhD in the School of Electrical Engineering, Vellore Institute of Technology Chennai campus, Chennai, India. He received his B.Tech degree in Electrical and Electronics Engineering from Jawaharlal Nehru Technological University, Kakinada, India, in 2012 and M.Tech degree in power systems and automation from GITAM University, Visakhapatnam, India, in 2015. He is working on optimal energy management of hybrid microgrids. His current research interests are power systems optimization, power, and energy management, and application of deep learning, machine learning, artificial intelligence, and soft computing techniques for power system problems.

Hemamalini Sathish

Hemamalini Sathish (Senior Member of IEEE) is currently a Professor in the School of Electrical Engineering, Vellore Institute of Technology Chennai campus, Chennai, India. She received her Ph.D. degree in power systems from the National Institute of Technology, Tiruchirappalli, India, in 2011. She has about 28 years of teaching and research experience. Dr. Hemamalini is a senior member of IEEE as well as a Lifetime Member of the Indian Society for Technical Education. Her current research interests include power system optimization, renewable energy, microgrids, power electronics applications in power systems, reliability, Artificial Intelligence - Machine Learning Applications, protection in microgrids, and electric vehicles.

Ning Zhou

Ning Zhou (Senior Member of IEEE) is currently an associate professor with the Electrical and Computer Engineering Department at Binghamton University. In 2005, he received his Ph.D. in Electrical Engineering with a minor in statistics from the University of Wyoming. From 2005 to 2013, Dr. Zhou worked as a power system engineer at the Pacific Northwest National Laboratory. Dr. Zhou is a senior member of the IEEE Power and Energy Society (PES). He is an associated editor of IET Power Generation Transmission and Distribution. His research interests include power system dynamics and statistical signal processing.