A PSO-ANFIS based Hybrid Approach for Short Term PV Power Prediction in Microgrids

Abstract

This paper proposes a hybrid approach based on a combination of particle swarm optimization (PSO) and adaptive neuro-fuzzy inference systems (ANFIS) for one-day-ahead hourly photovoltaic (PV) power generation prediction in microgrids. The increasing penetration of solar PV energy into electric power generation systems imposes important issues to address resulting from its intermittent and uncertain nature. These challenges necessitate an accurate PV power generation forecasting tool for planning efficient operation of power systems and to ensure reliability of supply. In this paper, a combination of PSO and ANFIS is used to develop a PV power prediction model. To demonstrate the effectiveness of the proposed method, it is tested based on practical information of PV power generation data of a real case study microgrid in Beijing. The proposed approach is compared with two other prediction methods. Evaluation of forecasting performance is made with the persistence forecasting method as a reference model, and results are compared with actual scenario. The proposed approach outperformed back propagation neural network and persistence based forecasting methods, demonstrating its favorable accuracy and reliability.

Keywords::

• Microgrids
• photovoltaics
• PV power forecasting
• ANFIS
• particle swarm optimization

Additional information

Notes on contributors

Yordanos Kassa Semero

Yordanos Kassa Semero received his B.Sc. degree in Electrical Engineering from Mekelle University, Mekelle, Ethiopia in 2008, and M.Sc. degree in Electrical Power Engineering from Arba Minch University, Arba Minch, Ethiopia in 2011. He was a lecturer at the Department of Electrical and Computer Engineering of Mettu University, Mettu, Ethiopia from July 2011 to August 2014. Currently, he is pursuing his PhD degree in Electrical Power Systems and Automation at the School of Electrical and Electronic Engineering of North China Electric Power University, Beijing, China, and is also a research staff at the Microgrid R&D Center of Goldwind Science and Technology, Beijing, China. His research interests include distributed generation, microgrid energy management systems, operation and control of microgrids.

Dehua Zheng

Dehua Zheng was born in Guangdong province in China, on November 26, 1955. He graduated from North China Electric Power University, and pursued further study at the University of Manitoba. His employment experience included the Manitoba Hydropower Company, University of Saskatchewan, China National Wind Power Engineering Technology Research center, and Goldwind Science and Technology. He is a Senior Member of IEEE, Registered Senior Electric Engineer of North America and IEC member. As the chief engineer of Goldwind and Etechwin, he devotes to research and development of Chinese microgrid technology.

Jianhua Zhang

Jianhua Zhang received his M.Sc. degree in electrical engineering from North China Electric Power University, Beijing, China, in 1984. He was a Visiting Scholar with the Queen's University, Belfast, U.K., from 1991 to 1992, and was a Multimedia Engineer of Electric Power Training with CORYS T.E.S.S., France, from 1997 to 1998. Currently, he is a Professor and Head of the Transmission and Distribution Research Institute, North China Electric Power University, Beijing. He is also the Consultant Expert of National “973” Planning of the Ministry of Science and Technology. Prof. Zhang is an IET Fellow and a member of several technical committees. His research interests are in power system security assessment, operation and planning, and microgrid.