Improved Markov‐chain‐based ultra‐short‐term PV forecasting method for enhancing power system resilience
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...
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| Published in | Journal of engineering (Stevenage, England) Vol. 2021; no. 2; pp. 114 - 124 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
John Wiley & Sons, Inc
01.02.2021
Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2051-3305 2051-3305 |
| DOI | 10.1049/tje2.12015 |
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| Abstract | 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. |
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| AbstractList | 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. Abstract 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. |
| Author | Liang, Liang Zhu, Xueqin Bai, Xiaoyang |
| Author_xml | – sequence: 1 givenname: Xiaoyang orcidid: 0000-0003-3680-461X surname: Bai fullname: Bai, Xiaoyang organization: Harbin Institute of Technology (Shenzhen) – sequence: 2 givenname: Liang surname: Liang fullname: Liang, Liang email: liangl@hit.edu.cn organization: Harbin Institute of Technology (Shenzhen) – sequence: 3 givenname: Xueqin surname: Zhu fullname: Zhu, Xueqin organization: Harbin Institute of Technology (Shenzhen) |
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| Copyright | 2021 The Authors. published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | The awareness capability of output power for renewable resources is essential for enhancing the resilience of power systems. Photovoltaic (PV) forecasting... Abstract The awareness capability of output power for renewable resources is essential for enhancing the resilience of power systems. Photovoltaic (PV)... |
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| SubjectTerms | 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 |
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| Title | Improved Markov‐chain‐based ultra‐short‐term PV forecasting method for enhancing power system resilience |
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