
A markov chain based photovoltaic power simulation method
- 1 Inner MongoliaAgricultural University
* Author to whom correspondence should be addressed.
Abstract
Photovoltaic power generation has volatility and randomness, which affects the safety and reliability of the power system. In order to achieve accurate simulation of photovoltaic output, this paper proposes a photovoltaic power timing simulation method based on rolling sampled Markov chain model. Firstly, establish a photovoltaic output model and analyze the actual output characteristics; Then, based on the first-order Markov chain model, the relationship between adjacent days is considered, and a multistate transition probability matrix is established to construct an annual time series output model; Finally, based on the annual output data of a photovoltaic power station and historical meteorological monitoring data, an example is simulated to verify the effectiveness of the proposed method.
Keywords
Photovoltaic power generation, Markov chain, Time series power simulation, Power system
[1]. LIU Dunnan, LI Qi, QIN Lijuan, ZHAO Jiawei. Evaluation of Grid Accepting Renewable Energy in Multi-Time Scale [J]. Electric Power Construction, 2017, 38(07): 44-50.
[2]. DING Ming, LIN Yujuan, PAN Hao. Probabilistic Production Simulation Considering Time Sequence Characteristics of Load and New Energy [J]. Proceedings of the CSEE, 2022, 206(11): 33-35+39.
[3]. ZUO Liguang, LIU Zhouzhou, PENG Xu. Evaluation of Cross-regional New Energy Consumption in Power Grid Based on Timing Production Simulation [J]. ENERGY AND ENERGY CONSERVATION, 2022, 206(11): 33-35+39..
[4]. LI Yudun, XIE Kaigui. Reliability assessment of power systems containing multiple wind farms [J]. JOURNAL OF EIECTRIC POWER SCIENCE AND TECHNOLOGYE, 2011, 26(01): 73-76+103.
[5]. X. Peng, G. Gao, G. Hu, Y. Guo, J. Cao and J. Zhao. Research on Inter-regional Renewable Energy Accommodation Assessment Method Based on Time Series Production Simulation[C]//2019 IEEE Sustainable Power and Energy Conference, 2019, pp. 2031-2036.
[6]. YU Haifeng, HUANG Jingjie, JIANG Shiyao, et al. The overall energy storage configuration of wind farms considering thes ervice life of electric energy storage [J]. JOURNAL OF EIECTRIC POWER SCIENCE AND TECHNOLOGYE, 2022, 37(04):152-160.
[7]. LIU Chun, QU Jixian, SHI Wenhui. Evaluation Method of Ability of Accommodation Renewable Energy Based on Probabilistic Production Simulation [J]. Proceedings of the CSEE, 2020, 40(10): 3134-3144.
[8]. CHEN Qichao, LI Hui, WANG Shuai, et al. Sequential Operation Simulation Technology for Renewable Energy Accommodation Considering Multi-Energy Complementarity [J]. Electric Power Construction, 2019, 40(07): 18-25.
[9]. MEI Shengwei, WANG Yingying, LIU Feng. Game Theory Planning Model and Analysis of Wind-light-storage Hybrid Power System [J]. Automation of Elecctric Power Systems, 2011, 35(20): 13-19.
[10]. LIU Zhen, ZHENG Wencong. Reliability Evaluation of Microgrid Based on Typical Daily Method [J]. Telecom Power Technology, 2018, 35(03): 11-13.
[11]. YAO Jianfeng, LING Jing, QU Linan, DING Leiming, ZHENG Xiang, GAO Bingtuan. The Construction Method of Typical Scenario Set for Renewable Energy Based on Improved FCM Clustering Algorithm [J]. Power System and Clean Energy, 2019, 35(04): 76-82.
[12]. ZHANG Jun, XU Xiaoyan, HUANG Yongning, CAO Yang, GU Yujia. Optimal proportion study of wind and PV capacity in Ningxia power grid based on time sequence simulation [J]. Power System Protection and Control, 2014, 42(19): 81-86.
[13]. WU Guannan, ZHANG Mingli, XU Jianyuan, et al. Time series production algorithm for evaluating wind power accommodation capacity [J]. Power System Protection and Control, 2017, 45(23): 151-157.
[14]. ZHANG Bingliang, ZHANG Boyi, WU Yaowu, et al. An Approach to Evaluate the Capacity Benefits and Energy Benefits of Wind Farms Based onProbabilistic Product Simulation [J]. Power System Protection and Control, 2013, 46(08): 74-79.
Cite this article
Wang,Z. (2024). A markov chain based photovoltaic power simulation method. Applied and Computational Engineering,65,147-152.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of Urban Intelligence: Machine Learning in Smart City Solutions - CONFSEML 2024
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).