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Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia

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  • Shabbir, Noman
  • Kütt, Lauri
  • Raja, Hadi A.
  • Jawad, Muhammad
  • Allik, Alo
  • Husev, Oleksandr

Abstract

The Baltic countries have good potential for solar photovoltaic (PV) energy generation, as on average 15 hours of sunlight is available in summer. Another potential option is to encourage the construction of nearly zero-energy buildings (NZEBs) according to the EU framework. This study focuses on solar irradiance and energy generation potential in different regions of Estonia as a case study. Techno-economic analysis of possible solutions to use differently rated domestic and commercial PV systems’ feasibility and payback periods are presented. The results illustrate that all PV systems studied in the research are self-sufficient while selling excess energy to the grid with a nominal payback period. Furthermore, for short-term energy management, we developed an efficient deep learning-based forecasting algorithm. Apart from the inherent non-linear nature of solar energy data, what makes forecasting particularly challenging is to efficiently cope with the issue of data regression and random noise. The RNN-LSTM algorithm is chosen for the prediction of solar energy. This is the first comprehensive report that can encourage potential Estonian users to invest in solar PV systems and gain economic benefits. The results presented in this study cover a broader perspective and are more useful keeping in mind the real market situation of the Baltic countries.

Suggested Citation

  • Shabbir, Noman & Kütt, Lauri & Raja, Hadi A. & Jawad, Muhammad & Allik, Alo & Husev, Oleksandr, 2022. "Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222010593
    DOI: 10.1016/j.energy.2022.124156
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    References listed on IDEAS

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    1. Shabbir, Noman & Usman, Muhammad & Jawad, Muhammad & Zafar, Muhammad H. & Iqbal, Muhammad N. & Kütt, Lauri, 2020. "Economic analysis and impact on national grid by domestic photovoltaic system installations in Pakistan," Renewable Energy, Elsevier, vol. 153(C), pages 509-521.
    2. Das, Barun K. & Hasan, Mahmudul & Das, Pronob, 2021. "Impact of storage technologies, temporal resolution, and PV tracking on stand-alone hybrid renewable energy for an Australian remote area application," Renewable Energy, Elsevier, vol. 173(C), pages 362-380.
    3. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
    4. Londo, Marc & Matton, Robin & Usmani, Omar & van Klaveren, Marieke & Tigchelaar, Casper & Brunsting, Suzanne, 2020. "Alternatives for current net metering policy for solar PV in the Netherlands: A comparison of impacts on business case and purchasing behaviour of private homeowners, and on governmental costs," Renewable Energy, Elsevier, vol. 147(P1), pages 903-915.
    5. Nogueira, Carlos Eduardo Camargo & Vidotto, Magno Luiz & Niedzialkoski, Rosana Krauss & de Souza, Samuel Nelson Melegari & Chaves, Luiz Inácio & Edwiges, Thiago & Santos, Darlisson Bentes dos & Wernck, 2014. "Sizing and simulation of a photovoltaic-wind energy system using batteries, applied for a small rural property located in the south of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 151-157.
    6. Celik, Ali Naci, 2006. "Present status of photovoltaic energy in Turkey and life cycle techno-economic analysis of a grid-connected photovoltaic-house," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 370-387, August.
    7. Mahmoud, Marwan M. & Ibrik, Imad H., 2006. "Techno-economic feasibility of energy supply of remote villages in Palestine by PV-systems, diesel generators and electric grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(2), pages 128-138, April.
    8. Mergani A. Khairalla & Xu Ning & Nashat T. AL-Jallad & Musaab O. El-Faroug, 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model," Energies, MDPI, vol. 11(6), pages 1-21, June.
    9. Noman Shabbir & Lauri Kütt & Kamran Daniel & Victor Astapov & Hadi Ashraf Raja & Muhammad Naveed Iqbal & Oleksandr Husev, 2022. "Feasibility Investigation for Residential Battery Sizing Considering EV Charging Demand," Sustainability, MDPI, vol. 14(3), pages 1-13, January.
    10. Omar, Moien A. & Mahmoud, Marwan M., 2018. "Grid connected PV- home systems in Palestine: A review on technical performance, effects and economic feasibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2490-2497.
    11. Wei, Wu & Skye, Harrison M., 2021. "Residential net-zero energy buildings: Review and perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    12. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    13. Opoku, Richard & Obeng, George Y. & Adjei, Eunice A. & Davis, Francis & Akuffo, Fred O., 2020. "Integrated system efficiency in reducing redundancy and promoting residential renewable energy in countries without net-metering: A case study of a SHS in Ghana," Renewable Energy, Elsevier, vol. 155(C), pages 65-78.
    14. Wang, Ran & Feng, Wei & Wang, Lan & Lu, Shilei, 2021. "A comprehensive evaluation of zero energy buildings in cold regions: Actual performance and key technologies of cases from China, the US, and the European Union," Energy, Elsevier, vol. 215(PA).
    15. Ghasemi, Golara & Noorollahi, Younes & Alavi, Hamed & Marzband, Mousa & Shahbazi, Mahmoud, 2019. "Theoretical and technical potential evaluation of solar power generation in Iran," Renewable Energy, Elsevier, vol. 138(C), pages 1250-1261.
    16. Holdermann, Claudius & Kissel, Johannes & Beigel, Jürgen, 2014. "Distributed photovoltaic generation in Brazil: An economic viability analysis of small-scale photovoltaic systems in the residential and commercial sectors," Energy Policy, Elsevier, vol. 67(C), pages 612-617.
    17. Li, Hong Xian & Zhang, Yitao & Li, Yan & Huang, Jiaxin & Costin, Glenn & Zhang, Peng, 2021. "Exploring payback-year based feed-in tariff mechanisms in Australia," Energy Policy, Elsevier, vol. 150(C).
    18. Watts, David & Valdés, Marcelo F. & Jara, Danilo & Watson, Andrea, 2015. "Potential residential PV development in Chile: The effect of Net Metering and Net Billing schemes for grid-connected PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1037-1051.
    19. Guo, Wenwen & Kong, Li & Chow, Tintai & Li, Chunying & Zhu, Qunzhi & Qiu, Zhongzhu & Li, Lin & Wang, Yalin & Riffat, Saffa B., 2020. "Energy performance of photovoltaic (PV) windows under typical climates of China in terms of transmittance and orientation," Energy, Elsevier, vol. 213(C).
    20. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    21. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    22. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
    23. Dufo-López, Rodolfo & Zubi, Ghassan & Fracastoro, Gian Vincenzo, 2012. "Tecno-economic assessment of an off-grid PV-powered community kitchen for developing regions," Applied Energy, Elsevier, vol. 91(1), pages 255-262.
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    3. Laktuka, Krista & Pakere, Ieva & Kalnbalkite, Antra & Zlaugotne, Beate & Blumberga, Dagnija, 2023. "Renewable energy project implementation: Will the Baltic States catch up with the Nordic countries?," Utilities Policy, Elsevier, vol. 82(C).
    4. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.

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