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Accommodating renewable generation through an aggregator-focused method for inducing demand side response from electricity consumers

Accommodating renewable generation through an aggregator-focused method for inducing demand side response from electricity consumers

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The ability to influence electricity demand from domestic and small business consumers, so that it can be matched to intermittent renewable generation and distribution network constraints is a key capability of a smart grid. This involves signalling to consumers to indicate when electricity use is desirable or undesirable. However, simply signalling a time-dependent price does not always achieve the required demand response and can result in unstable system behaviour. The authors propose a demand response scheme, in which an aggregator mediates between the consumer and the market and provides a signal to a ‘smart home’ control unit that manages the consumer's appliances, using a novel method for reconciliation of the consumer's needs and preferences with the incentives supplied by the signal. This method involves random allocation of demand within timeslots acceptable to the consumer with a bias depending on the signal provided. By simulating a population of domestic consumers using heat pumps and electric vehicles with properties consistent with UK national statistics, the authors show the method allows total demand to be predicted and shaped in a way that can simultaneously match renewable generation and satisfy network constraints, leading to benefits from reduced use of peaking plant and avoided network reinforcement.

References

    1. 1)
      • 24. Energy Saving Trust: ‘Measurement of domestic hot water consumption in dwellings’, available at http://www.bsria.co.uk/news/est-water/, accessed February 2013.
    2. 2)
      • 14. Giordano, V., Fulli, G.: ‘A business case for smart grid technologies: a systemic perspective’, Energy Policy, 2012, 40, pp. 252259.
    3. 3)
      • 31. General Motors UK Ltd.: ‘Vauxhall Ampera specification 2012’, available at http://www.vauxhall.co.uk/vehicles/vauxhall-range/cars/ampera/index.html, accessed February 2013.
    4. 4)
      • 28. Department for Communities and Local Government: ‘English housing survey headline report 2010–11’, available at http://www.communities.gov.uk/publications/corporate/statistics/ehs201011headlinereport, accessed February 2013.
    5. 5)
      • 25. Department for Communities and Local Government: ‘Domestic building services compliance guide’, 2010.
    6. 6)
      • 4. Department of Energy and Climate Change: ‘Energy consumption in the UK – domestic data tables’, available at http://www.decc.gov.uk/en/content/cms/statistics/publications/ecuk/ecuk.aspx, accessed February 2013.
    7. 7)
      • 7. Faruqui, A., Sergici, S.: ‘Household response to dynamic pricing of electricity: a survey of 15 experiments’, J. Regul. Econ., 2010, 38, pp. 193225 (doi: 10.1007/s11149-010-9127-y).
    8. 8)
      • 8. Boait, P., Rylatt, R.M., Wright, A.: ‘Exergy-based control of electricity demand and microgeneration’, Appl. Energy, 2007, 84, pp. 239253 (doi: 10.1016/j.apenergy.2006.09.001).
    9. 9)
      • 33. Department for Communities and Local Government: ‘English housing survey housing stock summary statistics tables (2009) pp. SST6.2’, available at http://www.communities.gov.uk/documents/statistics/xls/1937429.xls, accessed February 2013.
    10. 10)
      • 11. Mohensian-Rad, A., Leon-Garcia, A.: ‘Optimal residential load control with price prediction in real-time electricity pricing environments’, IEEE Trans. Smart Grid, 2010, 1, (2), pp. 120132 (doi: 10.1109/TSG.2010.2055903).
    11. 11)
      • 13. Rastegar, M., Fotuhi-Firuzabad, M., Aminifar, F.: ‘Load commitment in a smart home’, Appl. Energy, 2012, 96, pp. 4554doi:10.1016/j.apenergy.2012.01.056 (doi: 10.1016/j.apenergy.2012.01.056).
    12. 12)
      • 3. Gross, R., Heptonstall, P., Anderson, D., Green, T., Leach, M., Skea, J.: ‘The costs and impacts of intermittency: an assessment of the evidence on the costs and impacts of intermittent generation on the British electricity network’ (UK Energy Research Centre, 2006).
    13. 13)
      • 32. Gill, P.E., Murray, W., Wright, M.H.: ‘Practical optimization’ (Academic Press, 1981).
    14. 14)
      • 1. Department of Energy and Climate Change: ‘The UK Renewable Energy Roadmap’, available at http://www.decc.gov.uk/assets/decc/11/meeting-energy-demand/renewable-energy/2167-uk-renewable-energy-roadmap.pdf, accessed February 2013.
    15. 15)
      • 22. Office for National Statistics: ‘Families and households 2001–2011’, available at http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-248983, accessed February 2013.
    16. 16)
      • 19. Hippert, H.S., Pedreira, C.E., Souza, R.C.: ‘Neural networks for short-term load forecasting: a review and evaluation’, IEEE Trans. Power Syst., 2001, 16, (1), pp. 4455 (doi: 10.1109/59.910780).
    17. 17)
      • 2. Department of Energy and Climate Change: ‘2050 pathways analysis’, available at https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/68821/2050-pathways-analysis-response-pt1.pdf, accessed February 2013.
    18. 18)
      • 5. Pudjianto, D., Djapic, P., Aunedi, M., et al: ‘Smart control for minimizing distribution network reinforcement cost due to electrification’, Energy Policy, 2013, 52, pp. 7684 (doi: 10.1016/j.enpol.2012.05.021).
    19. 19)
      • 18. Boait, P.J., Fan, D., Stafford, A.: ‘Performance and control of domestic ground-source heat pumps in retrofit installations’, Energy Build., 2011, 43, pp. 19681976 (doi: 10.1016/j.enbuild.2011.04.003).
    20. 20)
      • 23. CIBSE: ‘Current CIBSE TRY/DSY hourly weather data set – Birmingham 2010’, available at http://www.cibseknowledgeportal.co.uk/weather-data, accessed February 2013.
    21. 21)
      • 10. Papadaskalopoulos, D., Strbac, G.: ‘Decentralised agent-based participation of load appliances in electricity pool markets’. Proc. 21st Int. Conf. Electricity Distribution (CIRED) Frankfurt, June 2011, paper 1049.
    22. 22)
      • 27. Stokes, M.: ‘Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis’. PhD thesis, De Montfort University, 2005.
    23. 23)
      • 12. Ramchurn, S.D., Vytelingum, P., Rogers, A., Jennings, N.R.: ‘Agent-based control for decentralised demand side management in the smart grid’. Proc. 10th Int. Conf. Autonomous Agents and Multiagent Systems (AAMAS 2011), Taipei, May 2011, pp. 512.
    24. 24)
      • 30. Nissan Motor GB Ltd: ‘Nissan leaf specification 2012’, available at http://www.nissan.co.uk/#vehicles/electric-vehicles/electric-leaf/leaf, accessed February 2013.
    25. 25)
      • 34. Rotering, N., Ilic, M.: ‘Optimal charge control of plug-in hybrid electric vehicles in deregulated electricity markets’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 10211029 (doi: 10.1109/TPWRS.2010.2086083).
    26. 26)
      • 16. Strbac, G.: ‘Demand side management: benefits and challenges’, Energy Policy, 2008, 36, pp. 44194426 (doi: 10.1016/j.enpol.2008.09.030).
    27. 27)
      • 17. Glass, J., Dainty, A.R., Gibb, A.: ‘New build: materials, techniques, skills and innovation’, Energy Policy, 2008, 36, pp. 45344538 (doi: 10.1016/j.enpol.2008.09.016).
    28. 28)
      • 9. Roscoe, A., Ault, G.: ‘Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response’, IET Renew. Power Gener., 2010, 4, (4), pp. 369382 (doi: 10.1049/iet-rpg.2009.0212).
    29. 29)
      • 26. Energy Savings Trust: ‘Getting warmer: a field trial of heat pumps’ (Energy Saving Trust, 2010).
    30. 30)
      • 20. Stokes, M., Rylatt, M., Lomas, K.: ‘A simple model of domestic lighting demand’, Energy Build., 2004, 36, pp. 103116 (doi: 10.1016/j.enbuild.2003.10.007).
    31. 31)
      • 21. Boait, P., Stafford, A.: ‘Electrical load characteristics of domestic heat pumps and scope for demand side management’. Proc. 21st Int. Conf. Electricity Distribution (CIRED) Frankfurt, June 2011, paper 0125.
    32. 32)
      • 29. Department for Transport: ‘National travel survey 2010’, available at http://www.dft.gov.uk/statistics/releases/national-travel-survey-2010, accessed February 2013.
    33. 33)
      • 6. Hargreaves, T., Nye, M., Burgess, J.: ‘Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term’, Energy Policy, 2013, 52, pp. 126134doi.org/10.1016/j.enpol.2012.03.027 (doi: 10.1016/j.enpol.2012.03.027).
    34. 34)
      • 15. Vaze, P., Tindale, S.: ‘Repowering communities – small scale solutions to large scale energy problems’ (Earthscan, 2011).
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