Advertisement

Energy Efficiency

, Volume 7, Issue 3, pp 547–558 | Cite as

Assessing the potential of residential demand response systems to assist in the integration of local renewable energy generation

Original Article

Abstract

Mass market demand response programmes may be utilised to assist bulk power network management of fluctuations in output from renewable generation systems. The use of actuated systems may delay the timing at which the technique becomes useful because of the need for the deployment of hardware and software architecture in households. In contrast, demand response systems based only on information exchange between the grid operator and the consumer has the potential for rapid uptake. The extent to which a notional demand response system could maximise the use of local wind generation was evaluated using a half-hourly dataset of electricity exported and imported to and from the grid to a community serviced by a private wire distribution network fed by a 750 kW wind farm. Constraints were modelled to provide an estimate of the proportion of electricity export that could be utilised by the community. The constraints considered were the duration over which the export period occurred, its timing with respect to occupant activity and the availability of dispatchable loads. These constraints reduced the proportion of export that could be utilised by the community creating in effect a maximum addressable opportunity that was found to be 35 % of the original total of electricity exported. This proportion is likely to be further reduced by a number of factors, for instance, demand and generation forecasting errors and longitudinal consumer fatigue.

Keywords

Smart grid Distributed generation Electricity supply Demand response Affordance 

Notes

Acknowledgments

The authors wish to acknowledge the assistance of the Findhorn Community for their support of this research investigation. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement N° 314742.

References

  1. Aghaei, J., & Alizadeh, M. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18, 64–72.CrossRefGoogle Scholar
  2. Awerbuch S. (2004). Restructuring our electricity networks to promote decarbonisation, Tyndall Centre Working Paper 49, March.Google Scholar
  3. Barton, J., Huang, S., Infield, D., Leach, M., Ogunkunle, D., Torriti, J., et al. (2013). The evolution of electricity demand and the role for demand side participation in buildings and transport. Energy Policy, 52, 85–102.CrossRefGoogle Scholar
  4. Boait P.J., Rylatt R.M. and Wright A. (2007). Exergy-based control of electricity demand and microgeneration. Applied Energy 84, 239–253.Google Scholar
  5. Cappers, P., Mills, A., Goldman, C., Wiser, R., & Eto, J. H. (2012). An assessment of the role mass market demand response could play in contributing to the management of variable generation integration issues. Energy Policy, 48, 420–429.CrossRefGoogle Scholar
  6. Chicco, G., & Mancarella, P. (2009). Distributed multi-generation: A comprehensive view. Renewable and Sustainable Energy Reviews, 13, 535–551.CrossRefGoogle Scholar
  7. Coll-Mayor, D., Paget, M., & Lightner, E. (2007). Future intelligent power grids: Analysis of the vision in the European Union and the United States. Energy Policy, 35, 2453–2465.CrossRefGoogle Scholar
  8. Corne D.W., Reynolds A.P., Galloway S., Owens E.H. and Peacock A.D., Short term wind speed forecasting with evolved neural networks, GECCO’13, July 6–10, 2013, Amsterdam, The Netherlands.Google Scholar
  9. Darby, S. J., & McKenna, E. (2012). Social implications of residential demand response in cool temperate climates. Energy Policy, 49, 759–769.CrossRefGoogle Scholar
  10. De Giorgi, M., Ficarella, A., & Tarantino, M. (2011). Error analysis of short term wind power prediction models. Applied Energy, 88(4), 1298–1311.CrossRefGoogle Scholar
  11. Elexon Ltd. (1997). Electricity user load profiles by profile class. UK: UKERC.Google Scholar
  12. Faruqui, A., & Sergici, S. (2010). Household response to dynamic pricing of electricity: A survey of 15 experiments. Journal of Regulatory Economics, 38, 193–225.CrossRefGoogle Scholar
  13. Federal Energy Regulation Commission (FERC) (2012). Assessment of demand response and advanced metering. FERC, Washington, DCGoogle Scholar
  14. Gibson, J. J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  15. Gottwalt, G., Ketter, W., Block, C., Collins, J., & Weinhardt, C. (2011). Demand side management—A simulation of household behavior under variable prices. Energy Policy, 39, 8163–8174.CrossRefGoogle Scholar
  16. Gyamfi, S., & Krumdieck, S. (2011). Price, environment and security: exploring multi-modal motivation in voluntary residential peak demand response. Energy Policy, 39, 2993–3004.CrossRefGoogle Scholar
  17. Gynther, L., Mikkonen, I., & Smits, A. (2012). Evaluation of European energy behavioural change programmes. Energy Efficiency, 5, 67–82.CrossRefGoogle Scholar
  18. Hargreaves, T., Nye, M., & Burgess, J. (2013). Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term. Energy Policy, 52, 126–134.CrossRefGoogle Scholar
  19. Harmonised European Time Use Survey (HETUS) (2013). https://www.h2.scb.se/tus/tus/, accessed March.
  20. Lovins A.B.., Datta E.K., Feiler T., Rábago K.R>, Swisher J.N., Lehmann A. and Wicker K. (2002). Small is profitable: The hidden economic benefits of making electrical resources the right size, Rocky Mountain Institute.Google Scholar
  21. Marantes C., Currie R. and Openshaw D. (2011). Low carbon living—A learning journey, CIRED, 6–9 June, Frankfurt, Germany.Google Scholar
  22. Marnay C. (2008). Microgrids and heterogeneous power quality and reliability, International Journal of Distributed Energy Resources. 44.Google Scholar
  23. Newborough, M., & Augood, P. (1999). Demand-side management opportunities for the UK domestic sector. IEE Proceedings-Generation Transmission and Distribution, 3, 283–293.CrossRefGoogle Scholar
  24. ORIGIN (2013). Project description, http://www.origin-concept.eu/, accessed March.
  25. Rohjans, S., Uslar, M., Bleiker, M., Gonzalez, J., Specht, M., Suding, T. and Weidelt, T. (2010). Survey of smart grid standardization studies and recommendations, IEEEGoogle Scholar
  26. Sinden, G. (2007). Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand. Energy Policy, 35(1), 112–127.CrossRefGoogle Scholar
  27. Stoffregen, T. A. (2000). Affordances and events. Ecological Psychology, 12(1), 1–28.CrossRefGoogle Scholar
  28. Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426.CrossRefGoogle Scholar
  29. Torriti, J. (2012). Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy. Energy, 44, 576–583.CrossRefGoogle Scholar
  30. Wang, X., Guo, P., & Huang, X. (2011). A review of wind power forecasting models. Energy Procedia, 12, 770–778.CrossRefGoogle Scholar
  31. Wolsink, M. (2012). The research agenda on social acceptance of distributed generation in smart grids: Renewable as common pool resources. Renewable and Sustainable Energy Reviews, 16, 822–835.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Institute of Infrastructure and the Environment, School of Built EnvironmentHeriot Watt UniversityEdinburghUK

Personalised recommendations