IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v149y2015icp392-403.html
   My bibliography  Save this article

Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment

Author

Listed:
  • Murphy, M.D.
  • O’Mahony, M.J.
  • Upton, J.

Abstract

The objective of this study was to assess the benefits of introducing a demand side management optimisation controller to a cold thermal storage ice bank. This controller consisted of an ice bank model, an air temperature forecast model and an optimisation algorithm. The financial and grid utilisation benefits produced by implementation of this controller over the current state of the art in ice bank load shifting control was tested in a day ahead real time electricity pricing forecast environment. This hypothetical real time electricity price was based on the cost of electricity in the Irish wholesale market. Multiple ice bank charge levels were simulated in order to quantify the performance of two control methods for varying operating conditions. First, the “standard controller” was based on the current modus operandi for ice bank systems where ice was generated for food cooling at night when the off-peak electricity tariff is available (00:00–08:00h). Second, the “upgraded controller” was developed as a bespoke Demand Side Management control system for food refrigeration in a future electricity pricing environment. It consisted of a dual function load shifting optimisation algorithm, an ice bank model, and a predictive air temperature model. A preliminary study was also carried out to test the robustness of the controller’s performance in an uncertain real time electricity pricing forecast scenario. Both economic and grid management benefits were found by simulating the operation of the cold thermal storage load shifting controller in a forecasted day ahead real time electricity pricing environment. The energy savings achieved for 100% ice bank charge were low, but as the desired charge level was reduced to 75%, 50% and 25% the savings potential increased. The introduction of uncertain real time electricity pricing forecasts nullified any cost savings made by the load-shifting controller in comparison to the current state of the art.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:149:y:2015:i:c:p:392-403
    DOI: 10.1016/j.apenergy.2015.03.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915002901
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.03.006?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. Bartusch, Cajsa & Alvehag, Karin, 2014. "Further exploring the potential of residential demand response programs in electricity distribution," Applied Energy, Elsevier, vol. 125(C), pages 39-59.
    2. Finn, P. & Fitzpatrick, C. & Connolly, D. & Leahy, M. & Relihan, L., 2011. "Facilitation of renewable electricity using price based appliance control in Ireland’s electricity market," Energy, Elsevier, vol. 36(5), pages 2952-2960.
    3. 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.
    4. 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.
    5. Roulston, M.S. & Kaplan, D.T. & Hardenberg, J. & Smith, L.A., 2003. "Using medium-range weather forcasts to improve the value of wind energy production," Renewable Energy, Elsevier, vol. 28(4), pages 585-602.
    6. Ericson, Torgeir, 2011. "Households' self-selection of dynamic electricity tariffs," Applied Energy, Elsevier, vol. 88(7), pages 2541-2547, July.
    7. 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.
    8. 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.
    9. Foley, Aoife & Tyther, Barry & Calnan, Patrick & Ó Gallachóir, Brian, 2013. "Impacts of Electric Vehicle charging under electricity market operations," Applied Energy, Elsevier, vol. 101(C), pages 93-102.
    10. Sreedharan, P. & Miller, D. & Price, S. & Woo, C.K., 2012. "Avoided cost estimation and cost-effectiveness of permanent load shifting in California," Applied Energy, Elsevier, vol. 96(C), pages 115-121.
    11. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    12. Andersson, M. & Karlsson, B., 1996. "Cost-effective incentives for cooperation between participants in the electricity market," Applied Energy, Elsevier, vol. 54(4), pages 301-313, 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. Hao, Ling & Wei, Mingshan & Xu, Fei & Yang, Xiaochen & Meng, Jia & Song, Panpan & Min, Yong, 2020. "Study of operation strategies for integrating ice-storage district cooling systems into power dispatch for large-scale hydropower utilization," Applied Energy, Elsevier, vol. 261(C).
    2. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
    3. Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    4. Philip Shine & John Upton & Paria Sefeedpari & Michael D. Murphy, 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses," Energies, MDPI, vol. 13(5), pages 1-25, March.
    5. Daniele Testi & Paolo Conti & Eva Schito & Luca Urbanucci & Francesco D’Ettorre, 2019. "Synthesis and Optimal Operation of Smart Microgrids Serving a Cluster of Buildings on a Campus with Centralized and Distributed Hybrid Renewable Energy Units," Energies, MDPI, vol. 12(4), pages 1-17, February.
    6. Zhou, Xiaoyan & Zhang, Ying & Ma, Xun & Li, Guoliang & Wang, Yunfeng & Hu, Chengzhi & Liang, Junyu & Li, Ming, 2022. "Performance characteristics of photovoltaic cold storage under composite control of maximum power tracking and constant voltage per frequency," Applied Energy, Elsevier, vol. 305(C).
    7. Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1-29, November.
    8. Ayman Esmat & Julio Usaola & Mª Ángeles Moreno, 2018. "A Decentralized Local Flexibility Market Considering the Uncertainty of Demand," Energies, MDPI, vol. 11(8), pages 1-32, August.
    9. Shine, P. & Scully, T. & Upton, J. & Shalloo, L. & Murphy, M.D., 2018. "Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis," Applied Energy, Elsevier, vol. 210(C), pages 529-537.
    10. Michael D. Murphy & Paul D. O’Sullivan & Guilherme Carrilho da Graça & Adam O’Donovan, 2021. "Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods," Energies, MDPI, vol. 14(4), pages 1-24, February.
    11. Nikolaos Kolokas & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization," Energies, MDPI, vol. 14(11), pages 1-36, May.
    12. Jannesari, Hamid & Abdollahi, Naeim, 2017. "Experimental and numerical study of thin ring and annular fin effects on improving the ice formation in ice-on-coil thermal storage systems," Applied Energy, Elsevier, vol. 189(C), pages 369-384.
    13. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    14. Breen, M. & Murphy, M.D. & Upton, J., 2019. "Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms," Applied Energy, Elsevier, vol. 242(C), pages 1697-1711.
    15. Xuan Vien Nguyen, 2021. "Fabrication and Performance Evaluation of Cold Thermal Energy Storage Tanks Operating in Water Chiller Air Conditioning System," Energies, MDPI, vol. 14(14), pages 1-16, July.
    16. Jarvinen, J. & Goldsworthy, M. & White, S. & Pudney, P. & Belusko, M. & Bruno, F., 2021. "Evaluating the utility of passive thermal storage as an energy storage system on the Australian energy market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    17. Sundar Raj Thangavelu & Inam Ullah Nutkani & Chia Meng Hwee & Aung Myat & Ashwin Khambadkone, 2015. "Integrated Electrical and Thermal Grid Facility - Testing of Future Microgrid Technologies," Energies, MDPI, vol. 8(9), pages 1-24, September.
    18. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.

    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. 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.
    2. 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.
    3. 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.
    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. Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1-29, November.
    6. 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.
    7. Ally, Clint & Bahadoorsingh, Sanjay & Singh, Arvind & Sharma, Chandrabhan, 2015. "A review and technical assessment integrating wind energy into an island power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 863-874.
    8. Dong, Jun & Jiang, Yuzheng & Liu, Dongran & Dou, Xihao & Liu, Yao & Peng, Shicheng, 2022. "Promoting dynamic pricing implementation considering policy incentives and electricity retailers’ behaviors: An evolutionary game model based on prospect theory," Energy Policy, Elsevier, vol. 167(C).
    9. Kim, Kyungah & Choi, Jihye & Lee, Jihee & Lee, Jongsu & Kim, Junghun, 2023. "Public preferences and increasing acceptance of time-varying electricity pricing for demand side management in South Korea," Energy Economics, Elsevier, vol. 119(C).
    10. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2019. "Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid," Energy, Elsevier, vol. 167(C), pages 312-324.
    11. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    12. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    13. Burns, Kelly & Mountain, Bruce, 2021. "Do households respond to Time-Of-Use tariffs? Evidence from Australia," Energy Economics, Elsevier, vol. 95(C).
    14. 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.
    15. Sun, Chuanwang, 2015. "An empirical case study about the reform of tiered pricing for household electricity in China," Applied Energy, Elsevier, vol. 160(C), pages 383-389.
    16. Sreedharan, P. & Farbes, J. & Cutter, E. & Woo, C.K. & Wang, J., 2016. "Microgrid and renewable generation integration: University of California, San Diego," Applied Energy, Elsevier, vol. 169(C), pages 709-720.
    17. Kernan, R. & Liu, X. & McLoone, S. & Fox, B., 2017. "Demand side management of an urban water supply using wholesale electricity price," Applied Energy, Elsevier, vol. 189(C), pages 395-402.
    18. Nezamoddini, Nasim & Wang, Yong, 2017. "Real-time electricity pricing for industrial customers: Survey and case studies in the United States," Applied Energy, Elsevier, vol. 195(C), pages 1023-1037.
    19. 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.
    20. Buckley, Penelope, 2020. "Prices, information and nudges for residential electricity conservation: A meta-analysis," Ecological Economics, Elsevier, vol. 172(C).

    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:149:y:2015:i:c:p:392-403. 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.