Open AccessImproved Markov‐chain‐based ultra‐short‐term PV forecasting method for enhancing power system resilience
Author(s) -
Bai Xiaoyang,
Liang Liang,
Zhu Xueqin
Publication year - 2021
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/tje2.12015
Subject(s) - photovoltaic system , computer science , markov chain , cluster analysis , interval (graph theory) , resilience (materials science) , reliability engineering , power (physics) , renewable energy , term (time) , moment (physics) , mathematical optimization , engineering , mathematics , artificial intelligence , electrical engineering , machine learning , physics , classical mechanics , combinatorics , quantum mechanics , thermodynamics
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.