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Scenario-based multi-objective optimization strategy for rural PV-battery systems

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  • Zhi, Yuan
  • Yang, Xudong

Abstract

Increasing the proportion of photovoltaic (PV) electricity in power systems is effective for achieving carbon neutrality. However, PV electricity is unstable and random, and direct connection to the grid reduces power quality and jeopardizes grid security. Herein, a scenario-based optimized sizing and management strategy for a rural PV-battery system is proposed for a village in Central China. The goal of this power system is not only to meet its own electricity demand without other energy supplies, but also to smoothly feed electricity into the grid. In this study, the possibility of applying the optimization strategies and evaluation methods of power plants in the design of rural power systems is explored. The PV power model, battery charging and discharging model, and farmer's energy use model are established respectively, and the constraints are combined with the nodal power balance principle, and then solved by simulated annealing algorithm with economy and grid interaction smoothness as the objective function. For optimal component sizing, this study considered various scenarios with different types of farmers, seasons, and weather to ensure that the performance of the objective function can be satisfied at any time throughout the year. This study proposed the objective function and evaluation index for smooth interaction with the grid for the first time. The simulation results showed that the required PV and battery capacities differed depending on farmer type, owing to different seasonal electrical loads and weather conditions. Compared to traditional PV-battery systems, the proposed system improves the smoothness of interaction with the grid by 87 %, while reducing transformer expansion pressure by approximately 50 kW. Therefore, based on rational capacity selection and optimization strategies, it is possible for a rural power system to become a virtual power plant.

Suggested Citation

  • Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
    • Handle: RePEc:eee:appene:v:345:y:2023:i:c:s0306261923006785
    DOI: 10.1016/j.apenergy.2023.121314
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    as
    1. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    2. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    3. Yu, Zhenyu & Lu, Fei & Zou, Yu & Yang, Xudong, 2022. "Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty," Applied Energy, Elsevier, vol. 321(C).
    4. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    5. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    6. Othman, M.Y. & Hamid, S.A. & Tabook, M.A.S. & Sopian, K. & Roslan, M.H. & Ibarahim, Z., 2016. "Performance analysis of PV/T Combi with water and air heating system: An experimental study," Renewable Energy, Elsevier, vol. 86(C), pages 716-722.
    7. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Khezri, Rahmat & Mahmoudi, Amin & Whaley, David, 2022. "Optimal sizing and comparative analysis of rooftop PV and battery for grid-connected households with all-electric and gas-electricity utility," Energy, Elsevier, vol. 251(C).
    9. Mulleriyawage, U.G.K. & Shen, W.X., 2020. "Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study," Renewable Energy, Elsevier, vol. 160(C), pages 852-864.
    10. Gasser, Jan & Cai, Hanmin & Karagiannopoulos, Stavros & Heer, Philipp & Hug, Gabriela, 2021. "Predictive energy management of residential buildings while self-reporting flexibility envelope," Applied Energy, Elsevier, vol. 288(C).
    11. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    12. Amadeh, Ali & Lee, Zachary E. & Zhang, K. Max, 2022. "Quantifying demand flexibility of building energy systems under uncertainty," Energy, Elsevier, vol. 246(C).
    13. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    14. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2022. "Combining transfer learning and constrained long short-term memory for power generation forecasting of newly-constructed photovoltaic plants," Renewable Energy, Elsevier, vol. 185(C), pages 1062-1077.
    15. Huang, Sen & Ye, Yunyang & Wu, Di & Zuo, Wangda, 2021. "An assessment of power flexibility from commercial building cooling systems in the United States," Energy, Elsevier, vol. 221(C).
    16. Liu, Jiangyang & Liu, Zhongbing & Wu, Yaling & Chen, Xi & Xiao, Hui & Zhang, Ling, 2022. "Impact of climate on photovoltaic battery energy storage system optimization," Renewable Energy, Elsevier, vol. 191(C), pages 625-638.
    17. Fan, Siyuan & Wang, Xiao & Cao, Shengxian & Wang, Yu & Zhang, Yanhui & Liu, Bingzheng, 2022. "A novel model to determine the relationship between dust concentration and energy conversion efficiency of photovoltaic (PV) panels," Energy, Elsevier, vol. 252(C).
    18. Tang, Hong & Wang, Shengwei, 2022. "A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services," Applied Energy, Elsevier, vol. 305(C).
    19. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    20. Hanif, Sarmad & Alam, M.J.E. & Roshan, Kini & Bhatti, Bilal A. & Bedoya, Juan C., 2022. "Multi-service battery energy storage system optimization and control," Applied Energy, Elsevier, vol. 311(C).
    21. Tang, Hong & Wang, Shengwei, 2021. "Energy flexibility quantification of grid-responsive buildings: Energy flexibility index and assessment of their effectiveness for applications," Energy, Elsevier, vol. 221(C).
    22. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
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    1. Song, Hui & Gu, Mingchen & Liu, Chen & Amani, Ali Moradi & Jalili, Mahdi & Meegahapola, Lasantha & Yu, Xinghuo & Dickeson, George, 2023. "Multi-objective battery energy storage optimization for virtual power plant applications," Applied Energy, Elsevier, vol. 352(C).
    2. Rasool, Muhammad Haseeb & Taylan, Onur & Perwez, Usama & Batunlu, Canras, 2023. "Comparative assessment of multi-objective optimization of hybrid energy storage system considering grid balancing," Renewable Energy, Elsevier, vol. 216(C).

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