IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v30y2005i10p1759-1779.html
   My bibliography  Save this article

Impacts of temporal precision in optimisation modelling of micro-Combined Heat and Power

Author

Listed:
  • Hawkes, Adam
  • Leach, Matthew

Abstract

When modelling the environmental and economic aspects of meeting a given heat and power demand with a combination of combined heat and power (CHP) and grid power, it is common to use a coarse temporal precision such as 1-h demand blocks in heat and power demand data. This may be appropriate for larger applications where demand is reasonably smooth, but becomes questionable for applications where demand exhibits substantial volatility such as for a single residential dwelling—an important potential market for the commercialisation of small-scale fuel cells and other micro-CHP. Choice of temporal precision is also influenced by the relative ease in obtaining coarse data, their compatibility with available energy price data, and avoidance of computational overheads when data sets expand. The thesis of this paper is that use of such coarse temporal precision leads to averaging effects that result in misleading environmental and economic outcomes for cost-optimal micro-CHP systems. Much finer temporal precision is required to capture adequately the specific characteristics of residential energy demand and the technical qualities of solid oxide fuel cell and stirling engine micro-CHP systems. This thesis is generally supported by the results of analysis, which shows that in some cases optimal design generation capacity of the CHP system is reduced by more than half between analyses using 1-h precision and 5-min precision energy demand data. When optimal dispatch of given generator and boiler capacities is considered, the quantities of energy delivered by the various components of the energy provision system (i.e. generation from CHP, heat from CHP, heat from an additional boiler, electricity from grid) varied by up to 40% between precisions analysed. Total CO2 emissions reduction is overestimated by up to 40% by the analyses completed using coarse demand data for a given micro-CHP generator capacity. The economic difference is also significant at up to 8% of lifetime costs for a given micro-CHP generator capacity.

Suggested Citation

  • Hawkes, Adam & Leach, Matthew, 2005. "Impacts of temporal precision in optimisation modelling of micro-Combined Heat and Power," Energy, Elsevier, vol. 30(10), pages 1759-1779.
    • Handle: RePEc:eee:energy:v:30:y:2005:i:10:p:1759-1779
    DOI: 10.1016/j.energy.2004.11.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054420400475X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2004.11.012?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. Michalik, G. & Khan, M.E. & Bonwick, W.J. & Mielczarski, W., 1997. "Structural modelling of energy demand in the residential sector: 2. The use of linguistic variables to include uncertainty of customers' behaviour," Energy, Elsevier, vol. 22(10), pages 949-958.
    2. Michalik, G. & Khan, M.E. & Bonwick, W.J. & Mielczarski, W., 1997. "Structural modelling of energy demand in the residential sector: 1. Development of structural models," Energy, Elsevier, vol. 22(10), pages 937-947.
    Full references (including those not matched with items on IDEAS)

    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. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    2. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    3. Egelioglu, F. & Mohamad, A.A. & Guven, H., 2001. "Economic variables and electricity consumption in Northern Cyprus," Energy, Elsevier, vol. 26(4), pages 355-362.
    4. 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.
    5. Doukas, Haris, 2013. "Modelling of linguistic variables in multicriteria energy policy support," European Journal of Operational Research, Elsevier, vol. 227(2), pages 227-238.
    6. Hayn, Marian & Bertsch, Valentin & Zander, Anne & Nickel, Stefan & Fichtner, Wolf, 2016. "The impact of electricity tariffs on residential demand side flexibility," Working Paper Series in Production and Energy 14, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    7. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    8. Shimoda, Yoshiyuki & Asahi, Takahiro & Taniguchi, Ayako & Mizuno, Minoru, 2007. "Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model," Energy, Elsevier, vol. 32(9), pages 1617-1633.
    9. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    10. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    11. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    12. Beccali, M. & Cellura, M. & Lo Brano, V. & Marvuglia, A., 2008. "Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2040-2065, October.
    13. Clinch, J. Peter & Healy, John D., 2003. "Valuing improvements in comfort from domestic energy-efficiency retrofits using a trade-off simulation model," Energy Economics, Elsevier, vol. 25(5), pages 565-583, September.

    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:energy:v:30:y:2005:i:10:p:1759-1779. 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.journals.elsevier.com/energy .

    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.