Improved Markov‐chain‐based ultra‐short‐term PV forecasting method for enhancing power system resilience

• Published in: Journal of engineering (Stevenage, England) Vol. 2021; no. 2; pp. 114 - 124
• Main Authors: Bai, Xiaoyang, Liang, Liang, Zhu, Xueqin
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.02.2021; Wiley
• Subjects: Accuracy; Algorithms; Alternative energy; Clustering; Controllability; Energy resources; Energy storage; Forecasting; Markov analysis; Markov chains; Methods; Neural networks; Other topics in statistics; Photovoltaic cells; Power system planning and layout; Predictions; Radiation; Renewable resources; Resilience; Solar power stations and photovoltaic power systems
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/tje2.12015

The awareness capability of output power for renewable resources is essential for enhancing the resilience of power systems. Photovoltaic (PV) forecasting technology is an essential technology for increasing the operation efficiency and controllable resources for power systems after extreme natural events. Conventional Markov chain (MC) methods often ignore the time characteristics and the actual distribution of the PV output power sequence when making PV forecasts. This article proposes improved MC methods of equal quantity and clustering‐based division methods. The methods can consider the interval distributions of the PV output power time series and select an hour as the time interval. As a sequence, the predicted power at the next moment can be closer to the expectation of the output power distributions. Such a method is combined with a similar day algorithm to calculate the forecast result. Case studies were conducted with one‐year operation data from a 25‐MW PV station. The results indicate that the proposed methods can effectively improve the accuracy of prediction results compared with traditional methods.