publication . Article . 2017

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

McKenna, E; Higginson, S; Grunewald, P; Darby, SJ;
Open Access
  • Published: 10 May 2017 Journal: Energy Efficiency, volume 11, issue 7 (issn: 1570-646X, eissn: 1570-6478, Copyright policy)
  • Country: United Kingdom
Abstract
Demand response is receiving increasing interest as a new form of flexibility within low-carbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning ‘bottom-up’ activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between ...
Subjects
free text keywords: General Energy, Conceptual framework, Demand management, Efficient energy use, Sociotechnical system, Demand response, Operations management, Economics, Demand patterns, Demand forecasting, Electric power system, Demand response, Bottom-up, Simulation, Energy Demand Domestic Residential Service expectation Activity Time use Appliance Electricity
Related Organizations
Funded by
RCUK| Measuring and Evaluating Time- and Energy-use Relationships (METER)
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/M024652/1
  • Funding stream: EPSRC
,
EC| RealValue
Project
RealValue
Realising Value from Electricity Markets with Local Smart Electric Thermal Storage Technology
  • Funder: European Commission (EC)
  • Project Code: 646116
  • Funding stream: H2020 | IA
63 references, page 1 of 5

Aerts, D., Minnen, J., Glorieux, I., Wouters, I., & Descamps, F. (2014). A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison. Building and Environment, 75, 67-78. doi:10.1016/j.buildenv.2014.01.021. [OpenAIRE]

Aghaei, J., & Alizadeh, M.-I. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18, 64- 72. doi:10.1016/j.rser.2012.09.019.

Albadi, M. H., & El-Saadany, E. F. (2008). A summary of demand response in electricity markets. Electric Power Systems Research, 78, 1989-1996. doi:10.1016/j.epsr.2008.04.002. [OpenAIRE]

Baborska-Narozny, M., Stevenson, F., & Ziyad, F. J. (2016). User learning and emerging practices in relation to innovative technologies: A case study of domestic photovoltaic systems in the UK. Energy Res. Soc. Sci., 13, 24-37. [OpenAIRE]

Baetens, R., & Saelens, D. (2015). Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour. Journal of Building Performance Simulation, 1- 17. doi:10.1080/19401493.2015.1070203. [OpenAIRE]

Baetens, R., & Saelens, D. (2016). Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour. Journal of Building Performance Simulation, 9, 431-447. doi:10.1080/19401493.2015.1070203.

Baetens, R., Coninck, R. D., Roy, J. V., Verbruggen, B., Driesen, J., Helsen, L., & Saelens, D. (2012). Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation. Applied Energy, 96, 74-83. doi:10.1016/j.apenergy.2011.12.098.

Baetens, R., De Coninck, R., Jorissen, F., Picard, D., Helsen, L., Saelens, D., 2015. Openideas-an open framework for integrated district energy simulations, in: Building Simulation.

Blumsack, S., & Fernandez, A. (2012). Ready or not, here comes the smart grid! Energy, 37, 61-68.

Boardman, B., 1990. Fuel poverty and the greenhouse effect. Glasg. Heatwise Glasg.

Boßmann, T., & Eser, E. J. (2016). Model-based assessment of demand-response measures-A comprehensive literature review. Renewable and Sustainable Energy Reviews, 57, 1637- 1656. doi:10.1016/j.rser.2015.12.031. [OpenAIRE]

Budischak, C., Sewell, D., Thomson, H., Mach, L., Veron, D. E., & Kempton, W. (2013). Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time. Journal of Power Sources, 225, 60-74. doi:10.1016/j.jpowsour.2012.09.054. [OpenAIRE]

Ceseña, E. A. M., Good, N., & Mancarella, P. (2015). Electrical network capacity support from demand side response: Techno-economic assessment of potential business cases for small commercial and residential end-users. Energy Policy, 82, 222-232. doi:10.1016/j.enpol.2015.03.012. [OpenAIRE]

Costa, F., 2013. Can Rationing Affect Long Run Behavior? Evidence from Brazil. Evid. Braz. June 2013.

Darby, S. (2006). Social learning and public policy: Lessons from an energy-conscious village. Energy Policy, 34, 2929-2940.

63 references, page 1 of 5
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue
publication . Article . 2017

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

McKenna, E; Higginson, S; Grunewald, P; Darby, SJ;