IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3063-d371006.html
   My bibliography  Save this article

An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Author

Listed:
  • Krishnamoorthy R

    (Department of Electrical and Electronics Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India)

  • Udhayakumar K

    (Department of Electrical and Electronics Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India)

  • Kannadasan Raju

    (Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Tamil Nadu 602117, India)

  • Rajvikram Madurai Elavarasan

    (Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Tamil Nadu 602117, India)

  • Lucian Mihet-Popa

    (Faculty of Electrical Engineering, Ostfold University College, No-1757 Halden, Norway)

Abstract

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m 2 . The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

Suggested Citation

  • Krishnamoorthy R & Udhayakumar K & Kannadasan Raju & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization," Energies, MDPI, vol. 13(12), pages 1-41, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3063-:d:371006
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3063/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3063/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jianxing Yu & Yiqin Fu & Yang Yu & Shibo Wu & Yuanda Wu & Minjie You & Shuai Guo & Mu Li, 2019. "Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China," Energies, MDPI, vol. 12(15), pages 1-19, July.
    2. Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
    3. Oh, Ki-Yong & Kim, Ji-Young & Lee, Jae-Kyung & Ryu, Moo-Sung & Lee, Jun-Shin, 2012. "An assessment of wind energy potential at the demonstration offshore wind farm in Korea," Energy, Elsevier, vol. 46(1), pages 555-563.
    4. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    5. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
    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. Hendra Hendra & Dhimas Satria & Hernadewita Hernadewita & Yozerizal Yozerizal & Frengki Hardian & Ahmed M. Galal, 2023. "Performance of Generator Translation and Rotation on Stroke Length Drive of the Two-Rod Mechanism in Renewable Energy Power Plant," Sustainability, MDPI, vol. 15(7), pages 1-14, March.
    2. Sachin Kumar & Kumari Sarita & Akanksha Singh S Vardhan & Rajvikram Madurai Elavarasan & R. K. Saket & Narottam Das, 2020. "Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique," Energies, MDPI, vol. 13(21), pages 1-30, October.
    3. Thiyagarajan Rameshkumar & Perumal Chandrasekar & Raju Kannadasan & Venkatraman Thiyagarajan & Mohammed H. Alsharif & James Hyungkwan Kim, 2022. "Electrical and Mechanical Characteristics Assessment of Wind Turbine System Employing Acoustic Sensors and Matrix Converter," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    4. Mohanasundaram Anthony & Valsalal Prasad & Kannadasan Raju & Mohammed H. Alsharif & Zong Woo Geem & Junhee Hong, 2020. "Design of Rotor Blades for Vertical Axis Wind Turbine with Wind Flow Modifier for Low Wind Profile Areas," Sustainability, MDPI, vol. 12(19), pages 1-26, September.
    5. Rajvikram Madurai Elavarasan & Leoponraj Selvamanohar & Kannadasan Raju & Raghavendra Rajan Vijayaraghavan & Ramkumar Subburaj & Mohammad Nurunnabi & Irfan Ahmad Khan & Syed Afridhis & Akshaya Harihar, 2020. "A Holistic Review of the Present and Future Drivers of the Renewable Energy Mix in Maharashtra, State of India," Sustainability, MDPI, vol. 12(16), pages 1-33, August.
    6. Mageswaran Rengasamy & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "The Motivation for Incorporation of Microgrid Technology in Rooftop Solar Photovoltaic Deployment to Enhance Energy Economics," Sustainability, MDPI, vol. 12(24), pages 1-27, December.
    7. Vijayaraja Loganathan & Dhanasekar Ravikumar & Rupa Kesavan & Kanakasri Venkatesan & Raadha Saminathan & Raju Kannadasan & Mahalingam Sudhakaran & Mohammed H. Alsharif & Zong Woo Geem & Junhee Hong, 2022. "A Case Study on Renewable Energy Sources, Power Demand, and Policies in the States of South India—Development of a Thermoelectric Model," Sustainability, MDPI, vol. 14(14), pages 1-29, July.
    8. Yang, Xinrong & Jiang, Xin & Liang, Shijing & Qin, Yingzuo & Ye, Fan & Ye, Bin & Xu, Jiayu & He, Xinyue & Wu, Jie & Dong, Tianyun & Cai, Xitian & Xu, Rongrong & Zeng, Zhenzhong, 2024. "Spatiotemporal variation of power law exponent on the use of wind energy," Applied Energy, Elsevier, vol. 356(C).
    9. Jan K. Kazak & Joanna A. Kamińska & Rafał Madej & Marta Bochenkiewicz, 2020. "Where Renewable Energy Sources Funds are Invested? Spatial Analysis of Energy Production Potential and Public Support," Energies, MDPI, vol. 13(21), pages 1-26, October.
    10. Mani Rajalakshmi & Sankaralingam Chandramohan & Raju Kannadasan & Mohammed H. Alsharif & Mun-Kyeom Kim & Jamel Nebhen, 2021. "Design and Validation of BAT Algorithm-Based Photovoltaic System Using Simplified High Gain Quasi Boost Inverter," Energies, MDPI, vol. 14(4), pages 1-24, February.
    11. Senthilkumar Subramanian & Chandramohan Sankaralingam & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan & Kannadasan Raju & Lucian Mihet-Popa, 2021. "An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III," Sustainability, MDPI, vol. 13(1), pages 1-29, January.
    12. Varadharajan Sankaralingam Sriraja Balaguru & Nesamony Jothi Swaroopan & Kannadasan Raju & Mohammed H. Alsharif & Mun-Kyeom Kim, 2021. "Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors," Sustainability, MDPI, vol. 13(4), pages 1-31, February.
    13. Kanchana Kadirvel & Raju Kannadasan & Mohammed H. Alsharif & Zong Woo Geem, 2023. "Design and Modeling of Modified Interleaved Phase-Shifted Semi-Bridgeless Boost Converter for EV Battery Charging Applications," Sustainability, MDPI, vol. 15(3), pages 1-16, February.
    14. Shoaib Ahmed Khatri & Nayyar Hussain Mirjat & Khanji Harijan & Mohammad Aslam Uqaili & Syed Feroz Shah & Pervez Hameed Shaikh & Laveet Kumar, 2022. "An Overview of the Current Energy Situation of Pakistan and the Way Forward towards Green Energy Implementation," Energies, MDPI, vol. 16(1), pages 1-27, December.
    15. Siddik Shakul Hameed & Ramesh Ramadoss & Kannadasan Raju & GM Shafiullah, 2022. "A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression," Sustainability, MDPI, vol. 14(7), pages 1-29, April.
    16. Govindan, Kannan, 2023. "Pathways to low carbon energy transition through multi criteria assessment of offshore wind energy barriers," Technological Forecasting and Social Change, Elsevier, vol. 187(C).

    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. Varadharajan Sankaralingam Sriraja Balaguru & Nesamony Jothi Swaroopan & Kannadasan Raju & Mohammed H. Alsharif & Mun-Kyeom Kim, 2021. "Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors," Sustainability, MDPI, vol. 13(4), pages 1-31, February.
    2. Siddik Shakul Hameed & Ramesh Ramadoss & Kannadasan Raju & GM Shafiullah, 2022. "A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression," Sustainability, MDPI, vol. 14(7), pages 1-29, April.
    3. Sajid Ali & Choon-Man Jang, 2019. "Selection of Best-Suited Wind Turbines for New Wind Farm Sites Using Techno-Economic and GIS Analysis in South Korea," Energies, MDPI, vol. 12(16), pages 1-22, August.
    4. Jianxing Yu & Yiqin Fu & Yang Yu & Shibo Wu & Yuanda Wu & Minjie You & Shuai Guo & Mu Li, 2019. "Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China," Energies, MDPI, vol. 12(15), pages 1-19, July.
    5. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data," Energies, MDPI, vol. 10(9), pages 1-24, September.
    6. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Determination of the Most Optimal On-Shore Wind Farm Site Location Using a GIS-MCDM Methodology: Evaluating the Case of South Korea," Energies, MDPI, vol. 10(12), pages 1-22, December.
    7. Nam, Woochul & Oh, Ki-Yong & Epureanu, Bogdan I., 2019. "Evolution of the dynamic response and its effects on the serviceability of offshore wind turbines with stochastic loads and soil degradation," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 151-163.
    8. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Study on offshore wind power potential and wind farm optimization in Hong Kong," Applied Energy, Elsevier, vol. 130(C), pages 519-531.
    9. Jin-Young Kim & Hyun-Goo Kim & Yong-Heack Kang, 2017. "Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula," Energies, MDPI, vol. 10(7), pages 1-15, July.
    10. Shih-Chieh Liao & Shih-Chieh Chang & Tsung-Chi Cheng, 2021. "Managing the Volatility Risk of Renewable Energy: Index Insurance for Offshore Wind Farms in Taiwan," Sustainability, MDPI, vol. 13(16), pages 1-27, August.
    11. Stefano Bonnini & Michela Borghesi, 2022. "Relationship between Mental Health and Socio-Economic, Demographic and Environmental Factors in the COVID-19 Lockdown Period—A Multivariate Regression Analysis," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    12. Khandaker Dahirul Islam & Thanansak Theppaya & Fida Ali & Jompob Waewsak & Tanita Suepa & Juntakan Taweekun & Teerawet Titseesang & Kuaanan Techato, 2021. "Wind Energy Analysis in the Coastal Region of Bangladesh," Energies, MDPI, vol. 14(18), pages 1-18, September.
    13. Aiguo Tan & Xiangning Lin & Jinwen Sun & Ran Lyu & Zhengtian Li & Long Peng & Muhammad Shoaib Khalid, 2016. "A Novel DFIG Damping Control for Power System with High Wind Power Penetration," Energies, MDPI, vol. 9(7), pages 1-15, July.
    14. Waewsak, Jompob & Landry, Mathieu & Gagnon, Yves, 2015. "Offshore wind power potential of the Gulf of Thailand," Renewable Energy, Elsevier, vol. 81(C), pages 609-626.
    15. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
    16. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    17. Mohammed H. Alsharif & Jeong Kim & Jin Hong Kim, 2018. "Opportunities and Challenges of Solar and Wind Energy in South Korea: A Review," Sustainability, MDPI, vol. 10(6), pages 1-23, June.
    18. Chang, Tian-Pau & Liu, Feng-Jiao & Ko, Hong-Hsi & Cheng, Shih-Ping & Sun, Li-Chung & Kuo, Shye-Chorng, 2014. "Comparative analysis on power curve models of wind turbine generator in estimating capacity factor," Energy, Elsevier, vol. 73(C), pages 88-95.
    19. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    20. Efthimiou, G.C. & Kumar, P. & Giannissi, S.G. & Feiz, A.A. & Andronopoulos, S., 2019. "Prediction of the wind speed probabilities in the atmospheric surface layer," Renewable Energy, Elsevier, vol. 132(C), pages 921-930.

    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:12:p:3063-:d:371006. 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.