IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v182y2016icp496-507.html
   My bibliography  Save this article

Collaboration protocols for sustainable wind energy distribution networks

Author

Listed:
  • Jahanpour, Ehsan
  • Ko, Hoo Sang
  • Nof, Shimon Y.

Abstract

Wind energy has attracted more attentions in recent decades as a green and sustainable electricity generation resource with little or no pollution. Fluctuations in output of wind turbines and their dependency on environmental conditions, however, limit their penetration into power grids. This research proposes a collaboration platform to overcome these challenges by collaboration among communities in the energy distribution network. First, a second order trigonometric regression model is applied to forecast energy demands, and a multiple linear regression model is used to predict energy output of a wind farm. Based on this information, communities in the network can initiate the collaboration process through the proposed platform, which is controlled by two collaboration protocols: Demand and Capacity Sharing Protocol (DCSP) and Best Matching Protocol (BMP). The protocols are applied to optimize communities' profit through matching the communities with excessive capacity with the ones with energy shortage so that they can create a sustainable distribution network. A simulation of three communities with three wind farms is conducted to measure the impact of the platform. The results show the sustainability of the energy network can be achieved by reduced demand loss and holding cost and improved collaborative capacity.

Suggested Citation

  • Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
  • Handle: RePEc:eee:proeco:v:182:y:2016:i:c:p:496-507
    DOI: 10.1016/j.ijpe.2016.09.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527316302407
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2016.09.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kucukali, Serhat & Baris, Kemal, 2010. "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach," Energy Policy, Elsevier, vol. 38(5), pages 2438-2445, May.
    2. Velásquez, J.D. & Nof, S.Y., 2009. "Best-matching protocols for assembly in e-work networks," International Journal of Production Economics, Elsevier, vol. 122(1), pages 508-516, November.
    3. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    4. Lund, Henrik & Kempton, Willett, 2008. "Integration of renewable energy into the transport and electricity sectors through V2G," Energy Policy, Elsevier, vol. 36(9), pages 3578-3587, September.
    5. Sang Ko, Hoo & Nof, Shimon Y., 2012. "Design and application of task administration protocols for collaborative production and service systems," International Journal of Production Economics, Elsevier, vol. 135(1), pages 177-189.
    6. Lund, Henrik, 2005. "Large-scale integration of wind power into different energy systems," Energy, Elsevier, vol. 30(13), pages 2402-2412.
    7. Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
    8. Moghaddam, Mohsen & Nof, Shimon Y., 2014. "Combined demand and capacity sharing with best matching decisions in enterprise collaboration," International Journal of Production Economics, Elsevier, vol. 148(C), pages 93-109.
    9. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    10. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    11. Haeran Cho & Yannig Goude & Xavier Brossat & Qiwei Yao, 2013. "Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 7-21, March.
    12. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    13. Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
    14. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    15. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
    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. Yilmaz, Ibrahim & Yoon, Sang Won, 2020. "Dynamic-distributed decisions and sharing protocol for fair resource sharing in collaborative network," International Journal of Production Economics, Elsevier, vol. 226(C).
    2. Hiromasa Ijuin & Satoshi Yamada & Tetsuo Yamada & Masato Takanokura & Masayuki Matsui, 2022. "Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo," Energies, MDPI, vol. 15(13), pages 1-23, June.
    3. Tang, Ou & Rehme, Jakob, 2017. "An investigation of renewable certificates policy in Swedish electricity industry using an integrated system dynamics model," International Journal of Production Economics, Elsevier, vol. 194(C), pages 200-213.

    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. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    2. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    3. Liu, Wen & Hu, Weihao & Lund, Henrik & Chen, Zhe, 2013. "Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China," Applied Energy, Elsevier, vol. 104(C), pages 445-456.
    4. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    5. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    6. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. Mingotti, Nicola & Lillo Rodríguez, Rosa Elvira & Romo, Juan, 2015. "A Random Walk Test for Functional Time Series," DES - Working Papers. Statistics and Econometrics. WS ws1506, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    9. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    10. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    11. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    12. Lenzen, Manfred & McBain, Bonnie & Trainer, Ted & Jütte, Silke & Rey-Lescure, Olivier & Huang, Jing, 2016. "Simulating low-carbon electricity supply for Australia," Applied Energy, Elsevier, vol. 179(C), pages 553-564.
    13. Brenda López Cabrera & Franziska Schulz, 2017. "Forecasting Generalized Quantiles of Electricity Demand: A Functional Data Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 127-136, January.
    14. Le, Ngoc Anh & Bhattacharyya, Subhes C., 2011. "Integration of wind power into the British system in 2020," Energy, Elsevier, vol. 36(10), pages 5975-5983.
    15. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
    16. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    17. Bellekom, Sandra & Benders, René & Pelgröm, Steef & Moll, Henk, 2012. "Electric cars and wind energy: Two problems, one solution? A study to combine wind energy and electric cars in 2020 in The Netherlands," Energy, Elsevier, vol. 45(1), pages 859-866.
    18. Mirlatifi, A.M. & Egelioglu, F. & Atikol, U., 2015. "An econometric model for annual peak demand for small utilities," Energy, Elsevier, vol. 89(C), pages 35-44.
    19. Wang, Bo & Deng, Nana & Li, Haoxiang & Zhao, Wenhui & Liu, Jie & Wang, Zhaohua, 2021. "Effect and mechanism of monetary incentives and moral suasion on residential peak-hour electricity usage," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    20. Ma, Tao & Østergaard, Poul Alberg & Lund, Henrik & Yang, Hongxing & Lu, Lin, 2014. "An energy system model for Hong Kong in 2020," Energy, Elsevier, vol. 68(C), pages 301-310.

    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:eee:proeco:v:182:y:2016:i:c:p:496-507. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.