Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake

Abstract

Adequately accounting for interactions between Low Carbon Technologies (LCTs) at the building level and the overarching energy system means capturing the granularity associated with decentralised heat and power supply in residential buildings. The approach presented here adds novelty in terms of a realistic socioeconomic differentiation by employing dwelling/household archetypes (DHAs) and neighbourhood clusters at the Output Area (OA) level. These archetypes are combined with a mixed integer linear program (MILP), which is used to generate optimum (minimum cost) technology configurations and operation schedules. Even in the baseline case, i.e. without any LCT penetration, a substantial deviation from the standard load profile (SLP) is encountered, suggesting that for some neighbourhoods this profile is not appropriate. With the application of LCTs this effect is much stronger, including more negative residual load, more variability, and higher ramps with increased LCT penetration, and crucially different between neighbourhood clusters. The main policy implication of the study is the importance of understanding electrical load profiles at the neighbourhood level, because of the consequences they have for investment in the overarching energy system (e.g. transmission and distribution infrastructure, centralised generation plant etc.). Further work should focus on attaining a superior socioeconomic differentiation between households.

Developing a municipality typology for modelling decentralised energy systems

“…Depending on indicators such as the demand for space heating and domestic hot water, as well as georeferenced drilling costs for deep geothermal energy, the cluster analysis results in 16 clusters. Clusters and typologies have often been applied at the district scale, in identifying the most cost-effective low carbon energy solution for different types of districts (Hargreaves et al 2017;McKenna et al 2016McKenna et al , 2017aSu et al 2017), as well as at the building scale, for example in the context of residential heat demand studies (McKenna et al 2016(McKenna et al , 2017a. In addition, Marquant et al (2017) present a holistic approach for optimisation of multi-scale distributed energy systems, by employing clusters of similar buildings at the district level.…”

Organizing prosumers into electricity trading communities: Costs to attain electricity transfer limitations and self‐sufficiency goals

“…Community energy storage has been investigated in terms of economic benefits and possibilities to integrate renewable energy sources and demand‐side management . There have also been studies on the interactions that occur on local markets, the different energy carriers in a local energy community or energy hub, and the effects of different load curves on neighborhood clusters …”

“…[25][26][27][28] There have also been studies on the interactions that occur on local markets, [29][30][31] the different energy carriers in a local energy community or energy hub, 32,33 and the effects of different load curves on neighborhood clusters. 34 The present study adds to the understanding of local grouping of residential prosumers of electricity through direct comparisons of investment and hourly operation of PV plus battery systems for (i) individual prosumers and (ii) prosumers acting as part of an electricity trading community, which has not been provided by studies cited above. We look at what benefit, if any, there is for residential prosumers of electricity to join together in an electricity trading community, and we analyze the patterns of trading to the surrounding electricity system for different cases.…”

Quantifying the potential for consumer-oriented policy to reduce European and foreign carbon emissions

“…Many detailed studies exist examining the carbon footprint reductions afforded by various green actions, e.g. shifting diets (Alexander, Brown, Arneth, Finnigan, & Rounsevell, 2016;Coley, Goodliffe, & Macdiarmid, 1998;Hoolohan, Berners-Lee, McKinstry-West, & Hewitt, 2013;Springmann et al, 2016;Stehfest et al, 2009;Tukker et al, 2011;Vranken, Avermaete, Petalios, & Mathijs, 2014;Westhoek et al, 2014), changing transport behaviour (Greening, 2004;International Energy Agency, 1997;Roth & Kåberger, 2002), household purchasing (Bin & Dowlatabadi, 2005;Duarte et al, 2015;Liu, Daily, Ehrlich, & Luck, 2003;Minx et al, 2009;Munksgaard, Pedersen, & Wier, 2000;Zacarias-Farah & Geyer-Allély, 2003) disposal (Beylot, Vaxelaire, & Villeneuve, 2015;Lave, Hendrickson, & McMichael, 1994) and energy use (McKenna, Hofmann, Merkel, Fichtner, & Strachan, 2016) patterns, and so on. There are also many studies which decompose the average per capita carbon footprint in countries into categories in a static way, i.e.…”