IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2505-d358834.html
   My bibliography  Save this article

Multi-Objective Sizing Optimization of a Grid-Connected Solar–Wind Hybrid System Using Climate Classification: A Case Study of Four Locations in Southern Taiwan

Author

Listed:
  • Kumar Shivam

    (Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang Dist., Tainan City 710, Taiwan)

  • Jong-Chyuan Tzou

    (Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang Dist., Tainan City 710, Taiwan)

  • Shang-Chen Wu

    (Department of Mechanical Engineering, Kun Shan University, No.195, Kunda Rd., Yongkang Dist., Tainan City 710, Taiwan)

Abstract

Increased concerns over global warming and air pollution has pushed governments to consider renewable energy as an alternative to meet the required energy demands of countries. Many government policies are deployed in Taiwan to promote solar and wind energy to cope with air pollution and self-dependency for energy generation. However, the residential sector contribution is not significant despite higher feed-in tariff rates set by government. This study analyzes wind and solar power availability of four different locations of southern Taiwan, based on the Köppen–Geiger climate classification system. The solar–wind hybrid system (SWHS) considered in this study consists of multi-crystalline photovoltaic (PV) modules, vertical wind turbines, inverters and batteries. Global reanalysis weather data and a climate-based electricity load profile at a 1-h resolution was used for the simulation. A general framework for multi-objective optimization using this simulation technique is proposed for solar–wind hybrid system, considering the feed-in tariff regulations, environmental regulations and installation area constraints of Taiwan. The hourly load profile is selected using a climate classification system. A decomposition-based differential evolutionary algorithm is used for finding the optimal Pareto set of two economic objectives and one environmental objective with maximum installation area and maximum PV capacity constraints. Two types of buildings are chosen for analysis at four climate locations. Analysis of Pareto sets revealed that the photovoltaic modules are economic options for a grid-connected mode at all four locations, whereas solar–wind hybrid systems are more environmentally friendly. A method of finding the fitness index for the Pareto front sets and a balanced strategy for choosing the optimal configuration is proposed. The proposed balanced strategy provides savings to users—up to 49% for urban residential buildings and up to 32% for rural residential buildings with respect to buildings without a hybrid energy system (HES)—while keeping carbon dioxide (CO 2 ) emissions lower than 50% for the total project lifecycle time of 20 years. The case study reveals that for all four locations and two building types an HES system comprising a 15 kW photovoltaic system and a small capacity battery bank provides the optimal balance between economic and environmental objectives.

Suggested Citation

  • Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Objective Sizing Optimization of a Grid-Connected Solar–Wind Hybrid System Using Climate Classification: A Case Study of Four Locations in Southern Taiwan," Energies, MDPI, vol. 13(10), pages 1-30, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2505-:d:358834
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2505/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2505/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing Xiao & Jing-Jing Li & Xi-Xi Hong & Min-Mei Huang & Xiao-Min Hu & Yong Tang & Chang-Qin Huang, 2018. "An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization," Complexity, Hindawi, vol. 2018, pages 1-16, May.
    2. Katheryn Donado & Loraine Navarro & Christian G. Quintero M. & Mauricio Pardo, 2019. "HYRES: A Multi-Objective Optimization Tool for Proper Configuration of Renewable Hybrid Energy Systems," Energies, MDPI, vol. 13(1), pages 1-20, December.
    3. Ali Saleh Aziz & Mohammad Faridun Naim Tajuddin & Mohd Rafi Adzman & Makbul A. M. Ramli & Saad Mekhilef, 2019. "Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy," Sustainability, MDPI, vol. 11(3), pages 1-26, January.
    4. Mengjun Ming & Rui Wang & Yabing Zha & Tao Zhang, 2017. "Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(5), pages 1-15, May.
    5. Mehdi Taebnia & Marko Heikkilä & Janne Mäkinen & Jenni Kiukkonen-Kivioja & Jouko Pakanen & Jarek Kurnitski, 2020. "A Qualitative Control Approach to Reduce Energy Costs of Hybrid Energy Systems: Utilizing Energy Price and Weather Data," Energies, MDPI, vol. 13(6), pages 1-17, March.
    6. Arnau González & Jordi-Roger Riba & Antoni Rius, 2015. "Optimal Sizing of a Hybrid Grid-Connected Photovoltaic–Wind–Biomass Power System," Sustainability, MDPI, vol. 7(9), pages 1-20, September.
    7. Al-Attar Ali Mohamed & Shimaa Ali & Salem Alkhalaf & Tomonobu Senjyu & Ashraf M. Hemeida, 2019. "Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm," Sustainability, MDPI, vol. 11(23), pages 1-20, November.
    8. Maheri, Alireza, 2014. "Multi-objective design optimisation of standalone hybrid wind-PV-diesel systems under uncertainties," Renewable Energy, Elsevier, vol. 66(C), pages 650-661.
    9. González, Arnau & Riba, Jordi-Roger & Rius, Antoni & Puig, Rita, 2015. "Optimal sizing of a hybrid grid-connected photovoltaic and wind power system," Applied Energy, Elsevier, vol. 154(C), pages 752-762.
    10. Paolo Conti & Giovanni Lutzemberger & Eva Schito & Davide Poli & Daniele Testi, 2019. "Multi-Objective Optimization of Off-Grid Hybrid Renewable Energy Systems in Buildings with Prior Design-Variable Screening," Energies, MDPI, vol. 12(15), pages 1-25, August.
    11. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    12. Mazzeo, Domenico & Oliveti, Giuseppe & Baglivo, Cristina & Congedo, Paolo M., 2018. "Energy reliability-constrained method for the multi-objective optimization of a photovoltaic-wind hybrid system with battery storage," Energy, Elsevier, vol. 156(C), pages 688-708.
    13. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Houssem Rafik Al-Hana Bouchekara & Mohammad Shoaib Shahriar & Muhammad Sharjeel Javaid & Yusuf Abubakar Sha’aban & Makbul Anwari Muhammad Ramli, 2021. "Multi-Objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin," Energies, MDPI, vol. 14(5), pages 1-24, February.
    2. Ludmil Stoyanov & Ivan Bachev & Zahari Zarkov & Vladimir Lazarov & Gilles Notton, 2021. "Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes," Energies, MDPI, vol. 14(11), pages 1-28, May.
    3. Yan Yang & Qingyu Wei & Shanke Liu & Liang Zhao, 2022. "Distribution Strategy Optimization of Standalone Hybrid WT/PV System Based on Different Solar and Wind Resources for Rural Applications," Energies, MDPI, vol. 15(14), pages 1-21, July.
    4. Antonio Manuel Gómez-Orellana & Juan Carlos Fernández & Manuel Dorado-Moreno & Pedro Antonio Gutiérrez & César Hervás-Martínez, 2021. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux," Energies, MDPI, vol. 14(2), pages 1-33, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Katheryn Donado & Loraine Navarro & Christian G. Quintero M. & Mauricio Pardo, 2019. "HYRES: A Multi-Objective Optimization Tool for Proper Configuration of Renewable Hybrid Energy Systems," Energies, MDPI, vol. 13(1), pages 1-20, December.
    2. Adefarati, T. & Bansal, R.C. & Bettayeb, M. & Naidoo, R., 2022. "Technical, economic, and environmental assessment of the distribution power system with the application of renewable energy technologies," Renewable Energy, Elsevier, vol. 199(C), pages 278-297.
    3. Sadeghi, Delnia & Hesami Naghshbandy, Ali & Bahramara, Salah, 2020. "Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization," Energy, Elsevier, vol. 209(C).
    4. Bhatt, Ankit & Sharma, M.P. & Saini, R.P., 2016. "Feasibility and sensitivity analysis of an off-grid micro hydro–photovoltaic–biomass and biogas–diesel–battery hybrid energy system for a remote area in Uttarakhand state, India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 53-69.
    5. Rajbongshi, Rumi & Borgohain, Devashree & Mahapatra, Sadhan, 2017. "Optimization of PV-biomass-diesel and grid base hybrid energy systems for rural electrification by using HOMER," Energy, Elsevier, vol. 126(C), pages 461-474.
    6. Hayes, Liam & Stocks, Matthew & Blakers, Andrew, 2021. "Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis," Energy, Elsevier, vol. 229(C).
    7. Sun, Wei & Harrison, Gareth P., 2019. "Wind-solar complementarity and effective use of distribution network capacity," Applied Energy, Elsevier, vol. 247(C), pages 89-101.
    8. Talaat, M. & Farahat, M.A. & Elkholy, M.H., 2019. "Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies," Energy, Elsevier, vol. 170(C), pages 668-682.
    9. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    10. Wen, Shuli & Lan, Hai & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun & Cheng, Peng, 2016. "Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system," Applied Energy, Elsevier, vol. 175(C), pages 158-167.
    11. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    12. Duchaud, Jean-Laurent & Notton, Gilles & Darras, Christophe & Voyant, Cyril, 2019. "Multi-Objective Particle Swarm optimal sizing of a renewable hybrid power plant with storage," Renewable Energy, Elsevier, vol. 131(C), pages 1156-1167.
    13. Hu, Jing & Harmsen, Robert & Crijns-Graus, Wina & Worrell, Ernst, 2019. "Geographical optimization of variable renewable energy capacity in China using modern portfolio theory," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Becker, Raik & Thrän, Daniela, 2017. "Completion of wind turbine data sets for wind integration studies applying random forests and k-nearest neighbors," Applied Energy, Elsevier, vol. 208(C), pages 252-262.
    15. Bosch, Jonathan & Staffell, Iain & Hawkes, Adam D., 2018. "Temporally explicit and spatially resolved global offshore wind energy potentials," Energy, Elsevier, vol. 163(C), pages 766-781.
    16. Luis M. Abadie & Nestor Goicoechea, 2021. "Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain," Energies, MDPI, vol. 14(12), pages 1-24, June.
    17. Ihsan, Abbas & Jeppesen, Matthew & Brear, Michael J., 2019. "Impact of demand response on the optimal, techno-economic performance of a hybrid, renewable energy power plant," Applied Energy, Elsevier, vol. 238(C), pages 972-984.
    18. Alexander N. Kozlov & Nikita V. Tomin & Denis N. Sidorov & Electo E. S. Lora & Victor G. Kurbatsky, 2020. "Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques," Energies, MDPI, vol. 13(10), pages 1-20, May.
    19. Bertsiou, M. & Feloni, E. & Karpouzos, D. & Baltas, E., 2018. "Water management and electricity output of a Hybrid Renewable Energy System (HRES) in Fournoi Island in Aegean Sea," Renewable Energy, Elsevier, vol. 118(C), pages 790-798.
    20. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2016. "Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building," Applied Energy, Elsevier, vol. 171(C), pages 153-171.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2505-:d:358834. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.