Optimal Configuration of Standalone Wind–Solar–Storage Complementary Generation System Based on the GA-PSO Algorithm
Abstract
The capacity configuration of the standalone wind–solar–storage complementary power generation system (SWS system) is affected by environmental, climate condition, load and other stochastic factors. This makes the capacity configuration of the SWS system problematic when the capacity configuration method of traditional power generation is used. An optimal configuration method of the SWS system based on the hybrid genetic algorithm and particle swarm optimization (GA-PSO) algorithm is proposed in this study to improve the stability and economy of the SWS system. The constituent elements of investment, maintenance cost and various reliability constraints of the SWS system were also discussed. The optimal configuration of the SWS system based on GA-PSO was explored to achieve the optimization objective, which was to minimize investment and maintenance costs of the SWS system while maintaining power supply reliability. The investment and maintenance costs of the SWS system under different configuration methods were calculated and analyzed on the bases of the monthly mean wind speed, solar radiation and load data of Xiaoertai Village in Zhangbei County of Hebei Province in the last 10 years. Results show that the optimal configuration method based on the GA-PSO algorithm could effectively improve the economy of the system and meet the requirements of system stability. The proposed method shows great potential for guiding the optimal configuration of the SWS system in remote areas.References
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[1] Monaaf DA Al-Falahi, SDG Jayasinghe, and H Enshaei. A review on
recent size optimization methodologies for standalone solar and wind
hybrid renewable energy system. Energy Conversion and Management,
143:252–274, 2017.
[2] Haidong CHEN, Ping ZHUANG, Jiankuang XIA, W Dai, Y Lu, Q Gao,
and T Chen. Optimal power flow of distribution network with distributed
generation based on modified firefly algorithm. Power System Protection
and Control, 44(1):149–154, 2016.
[3] Partha Sarothi Sikder and Nitai Pal. Modeling of an intelligent battery
controller for standalone solar-wind hybrid distributed generation system.
Journal of King Saud University-Engineering Sciences, 2019.
[4] Hassan Fathabadi. Novel standalone hybrid solar/wind/fuel cell/battery
power generation system. Energy, 140:454–465, 2017.
[5] Mehdi Dali, Jamel Belhadj, and Xavier Roboam. Hybrid solar–wind
system with battery storage operating in grid-connected and standalone
mode: control and energy management–experimental investigation.
Energy, 35(6):2587–2595, 2010.
[6] Abdelkader Abbassi, Mohamed Ali Dami, and Mohamed Jemli. A statistical
approach for hybrid energy storage system sizing based on capacity
distributions in an autonomous pv/wind power generation system.
Renewable Energy, 103(Complete):81–93, 2017.
[7] Banu Y. Ekren and Orhan Ekren. Simulation based size optimization
of a pv/wind hybrid energy conversion system with battery storage
under various load and auxiliary energy conditions. Applied Energy,
86(9):1387–1394, 2009.
[8] Claudia Gutiérrez, Miguel Ángel Gaertner, Oscar Perpiñán, Clemente
Gallardo, and Enrique Sánchez. A multi-step scheme for spatial analysis
of solar and photovoltaic production variability and complementarity.
Solar Energy, 158:100–116, 2017.
[9] Jakub Jurasz, Alexandre Beluco, and Fausto A Canales. The impact
of complementarity on power supply reliability of small scale hybrid
energy systems. Energy, 161:737–743, 2018.
[10] Thi Hoai Thu Nguyen, Tomonori Nakayama, and Masayoshi Ishida.
Optimal capacity design of battery and hydrogen system for the dc
grid with photovoltaic power generation based on the rapid estimation
of grid dependency. International Journal of Electrical Power & Energy
Systems, 89(Complete):27–39, 2017.
[11] Kabitri Chattopadhyay, Alexander Kies, Elke Lorenz, Lüder von Bremen,
and Detlev Heinemann. The impact of different pv module configurations
on storage and additional balancing needs for a fully renewable
european power system. Renewable energy, 113:176–189,
2017.
[12] H. E. Jun, Changhong Deng, X. U. Qiushi, Cuilin Liu, and Hua Pan.
Optimal configuration of distributed generation system containing wind
pv battery power sources based on equivalent credible capacity theory.
Power System Technology, 37(12):3317–3324, 2013.
[13] Zhu Lan, Yan Zheng, Yang Xiu, F Yang, and CHEN Jie. Optimal configuration
of battery capacity in microgrid composed of wind power and
photovoltaic generation with energy storage. Power System Technology,
36(12):26–31, 2012.
[14] Lin Xu, Xinbo Ruan, Chengxiong Mao, Buhan Zhang, and Yi Luo. An
improved optimal sizing method for wind-solar-battery hybrid power
system. IEEE transactions on Sustainable Energy, 4(3):774–785,
2013.
[15] Tao Ma, Hongxing Yang, Lin Lu, and Jinqing Peng. Technical feasibility
study on a standalone hybrid solar-wind system with pumped hydro
storage for a remote island in hong kong. Renewable Energy, 69(3):7–
15, 2014.
[16] Daywes Pinheiro Neto, Elder Geraldo Domingues, António Paulo
Coimbra, Aníbal Traça De Almeida, Aylton José Alves, and Wesley
Pacheco Calixto. Portfolio optimization of renewable energy assets:
Hydro, wind, and photovoltaic energy in the regulated market in brazil.
Energy Economics, 64:238–250, 2017.
[17] Lanre Olatomiwa. Optimal configuration assessments of hybrid renewable
power supply for rural healthcare facilities. Energy Reports,
2:141–146, 2016.
[18] Amir Ahadi, Sang Kyun Kang, and Jang Ho Lee. A novel approach for
optimal combinations of wind, pv, and energy storage system in dieselfree
isolated communities. Applied Energy, 170:101–115, 2016.
[19] Ahmed M. A. Haidar, Priscilla N. John, and Mohd Shawal. Optimal
configuration assessment of renewable energy in malaysia. Renewable
Energy, 36(2):881–888, 2011.
[20] Fahad Iqbal and Anwar Shahzad Siddiqui. Optimal configuration analysis
for a campus microgrid—a case study. Protection and Control of
Modern Power Systems, 2(1):23, 2017.
[21] YH Liang, YQ Zhu, and Xin Wang. Optimal configuration of micro-grid
power supply based on levelized cost of electricity analysis. Southern
Power System Technology, 10(2):56–61, 2016.
[22] Shuang Han, Lu-na Zhang, Yong-qian Liu, Hao Zhang, Jie Yan, Li Li,
Xiao-hui Lei, and Xu Wang. Quantitative evaluation method for the
complementarity of wind–solar–hydro power and optimization of wind–
solar ratio. Applied energy, 236:973–984, 2019.
Published
2019-12-28
How to Cite
SUN, Qian et al.
Optimal Configuration of Standalone Wind–Solar–Storage Complementary Generation System Based on the GA-PSO Algorithm.
Journal of Power Technologies, [S.l.], v. 99, n. 4, p. 231–236, dec. 2019.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1617>. Date accessed: 22 dec. 2024.
Issue
Section
Energy Engineering and Technology
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