IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v146y2015icp141-149.html
   My bibliography  Save this article

Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources

Author

Listed:
  • Rajeev, T.
  • Ashok, S.

Abstract

Demand-side management programs such as load shifting utilise the flexibility in the load consumption pattern of consumers to enable the effective capacity utilisation of renewable energy sources. The locational and temporal characteristics of renewable energy sources cause forecasting and operational challenges in the implementation of such a renewable energy program. In this paper, a dynamic load-shifting program using real-time data in a cloud computing framework is proposed to address the aforementioned issues. A new dynamic renewable factor is proposed to facilitate on-time incentive based load shifting program. The effectiveness of the dynamic load-shifting program was evaluated using simulated case studies. The case study indicates that PV energy utilisation at the consumer side is increased by 18% by the application of the proposed load-shifting program. The study result in Kerala, India, consisting of more than 7.5 million domestic consumers, indicates that demand reduction of 250–300MW at times of peak demand can be achieved by using load shifting in the domestic sector.

Suggested Citation

  • Rajeev, T. & Ashok, S., 2015. "Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources," Applied Energy, Elsevier, vol. 146(C), pages 141-149.
    • Handle: RePEc:eee:appene:v:146:y:2015:i:c:p:141-149
    DOI: 10.1016/j.apenergy.2015.02.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915001877
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.02.014?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. Kremers, Enrique & González de Durana, José Marı´a & Barambones, Oscar, 2013. "Emergent synchronisation properties of a refrigerator demand side management system," Applied Energy, Elsevier, vol. 101(C), pages 709-717.
    2. Valenzuela, Jorge & Thimmapuram, Prakash R. & Kim, Jinho, 2012. "Modeling and simulation of consumer response to dynamic pricing with enabled technologies," Applied Energy, Elsevier, vol. 96(C), pages 122-132.
    3. Markovic, Dragan S. & Zivkovic, Dejan & Branovic, Irina & Popovic, Ranko & Cvetkovic, Dragan, 2013. "Smart power grid and cloud computing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 566-577.
    4. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    5. Schroeder, Andreas, 2011. "Modeling storage and demand management in power distribution grids," Applied Energy, Elsevier, vol. 88(12), pages 4700-4712.
    6. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    7. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    8. Masa-Bote, D. & Castillo-Cagigal, M. & Matallanas, E. & Caamaño-Martín, E. & Gutiérrez, A. & Monasterio-Huelín, F. & Jiménez-Leube, J., 2014. "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, Elsevier, vol. 125(C), pages 103-113.
    9. Finn, P. & O’Connell, M. & Fitzpatrick, C., 2013. "Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction," Applied Energy, Elsevier, vol. 101(C), pages 678-685.
    10. Middelberg, Arno & Zhang, Jiangfeng & Xia, Xiaohua, 2009. "An optimal control model for load shifting - With application in the energy management of a colliery," Applied Energy, Elsevier, vol. 86(7-8), pages 1266-1273, July.
    11. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods," Energy, Elsevier, vol. 36(7), pages 3968-3978.
    12. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    13. Ashok, S. & Banerjee, R., 2000. "Load-management applications for the industrial sector," Applied Energy, Elsevier, vol. 66(2), pages 105-111, June.
    14. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    15. Moura, Pedro S. & de Almeida, Aníbal T., 2010. "The role of demand-side management in the grid integration of wind power," Applied Energy, Elsevier, vol. 87(8), pages 2581-2588, August.
    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. Hurtado, L.A. & Rhodes, J.D. & Nguyen, P.H. & Kamphuis, I.G. & Webber, M.E., 2017. "Quantifying demand flexibility based on structural thermal storage and comfort management of non-residential buildings: A comparison between hot and cold climate zones," Applied Energy, Elsevier, vol. 195(C), pages 1047-1054.
    2. Zhou, Kaile & Yang, Shanlin, 2015. "A framework of service-oriented operation model of China׳s power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 719-725.
    3. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    4. Lejun Ma & Huan Wang & Baohong Lu & Changjun Qi, 2018. "Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    5. Ahmed Al-Shafei & Hamidreza Zareipour & Yankai Cao, 2022. "High-Performance and Parallel Computing Techniques Review: Applications, Challenges and Potentials to Support Net-Zero Transition of Future Grids," Energies, MDPI, vol. 15(22), pages 1-58, November.
    6. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    7. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Chen, Kaixuan & Lin, Jin & Song, Yonghua, 2019. "Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model," Applied Energy, Elsevier, vol. 242(C), pages 1121-1133.
    9. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    10. De Coninck, Roel & Helsen, Lieve, 2016. "Quantification of flexibility in buildings by cost curves – Methodology and application," Applied Energy, Elsevier, vol. 162(C), pages 653-665.
    11. Xiang, Liu, 2020. "Energy emergency supply chain collaboration optimization with group consensus through reinforcement learning considering non-cooperative behaviours," Energy, Elsevier, vol. 210(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. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    2. Stötzer, Martin & Hauer, Ines & Richter, Marc & Styczynski, Zbigniew A., 2015. "Potential of demand side integration to maximize use of renewable energy sources in Germany," Applied Energy, Elsevier, vol. 146(C), pages 344-352.
    3. Loganthurai, P. & Rajasekaran, V. & Gnanambal, K., 2016. "Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand," Energy, Elsevier, vol. 107(C), pages 419-430.
    4. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    5. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
    6. Finn, P. & O’Connell, M. & Fitzpatrick, C., 2013. "Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction," Applied Energy, Elsevier, vol. 101(C), pages 678-685.
    7. Alasseri, Rajeev & Tripathi, Ashish & Joji Rao, T. & Sreekanth, K.J., 2017. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 617-635.
    8. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    9. Jin, Hongyang & Li, Zhengshuo & Sun, Hongbin & Guo, Qinglai & Chen, Runze & Wang, Bin, 2017. "A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment," Applied Energy, Elsevier, vol. 206(C), pages 1364-1378.
    10. Murphy, M.D. & O’Mahony, M.J. & Upton, J., 2015. "Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment," Applied Energy, Elsevier, vol. 149(C), pages 392-403.
    11. Dashti, Reza & Afsharnia, Saeed & Ghasemi, Hassan, 2010. "A new long term load management model for asset governance of electrical distribution systems," Applied Energy, Elsevier, vol. 87(12), pages 3661-3667, December.
    12. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.
    13. van Staden, Adam Jacobus & Zhang, Jiangfeng & Xia, Xiaohua, 2011. "A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges," Applied Energy, Elsevier, vol. 88(12), pages 4785-4794.
    14. Stenner, Karen & Frederiks, Elisha R. & Hobman, Elizabeth V. & Cook, Stephanie, 2017. "Willingness to participate in direct load control: The role of consumer distrust," Applied Energy, Elsevier, vol. 189(C), pages 76-88.
    15. Moreno, Blanca & García-Álvarez, María Teresa & Ramos, Carmen & Fernández-Vázquez, Esteban, 2014. "A General Maximum Entropy Econometric approach to model industrial electricity prices in Spain: A challenge for the competitiveness," Applied Energy, Elsevier, vol. 135(C), pages 815-824.
    16. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
    17. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    18. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    19. Summerbell, Daniel L. & Khripko, Diana & Barlow, Claire & Hesselbach, Jens, 2017. "Cost and carbon reductions from industrial demand-side management: Study of potential savings at a cement plant," Applied Energy, Elsevier, vol. 197(C), pages 100-113.
    20. Upton, J. & Murphy, M. & Shalloo, L. & Groot Koerkamp, P.W.G. & De Boer, I.J.M., 2015. "Assessing the impact of changes in the electricity price structure on dairy farm energy costs," Applied Energy, Elsevier, vol. 137(C), pages 1-8.

    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:appene:v:146:y:2015:i:c:p:141-149. 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/wps/find/journaldescription.cws_home/405891/description#description .

    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.