Smart Metering Pilot Project Results

Available online at www.sciencedirect.com

ScienceDirect Energy Procedia 61 (2014) 2176 – 2179

The 6th International Conference on Applied Energy – ICAE2014

Smart Metering Pilot Project Results Uldis Bariss, Lelde Timma, Dagnija Blumberga Institute of Energy Systems and Environment, Riga Technical University, Kronvalda boulvard 1, Riga, LV1010, Latvia

Abstract This paper analyzes the results of a quasi-experimental design with 500 households in a target group (with smart meters) and 500 – in a control group (without smart meters) in Latvia. Lorenz curves are used to quantify the reduction in electricity consumption and to study the re-distribution of energy allocation due to roll-out of smart meters. Results on energy distribution for the target group suggest that the reduction of electricity consumption play a minor role on energy equity in a short-term, but effect is growing over last months of the study. These results could be explained by the hypothesis that investments in energy efficient lighting and home appliances take time. © by Elsevier Ltd. This an open Ltd. access article under the CC BY-NC-ND license ©2014 2014Published The Authors. Published by isElsevier (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the Organizing Committee of ICAE2014

Keywords: electricity consumption, energy efficiency, households, Lorenz curve, smart meters.

1. Introduction In the EU the Directive 2009/72/EC on common rules for the internal market in electricity promotes the implementation of intelligent metering systems until 2020, because costs and benefits for these devices are assessed positively [1]. Additionally, Directive 2012/27/EU on energy efficiency has set binding targets for the savings of primary energy resources – 1.5% annually [2]. These initiatives highlight debates over energy equity in the context of climate and energy efficiency. There are a number of pilot projects and research work exploring potential energy savings by providing better information and feedback on consumption to households. Reviews suggest that these savings are in a range of 5-15% depending on a type of feedback [3,4,5]. Lorenz curves and Gini index are used in economics to assess the equality of income distribution between various groups. These tools can also be applied to studies on the equality of energy distribution. Prior to this study, Jacobsona et al. used Lorenz curves and Gini index to analyze electricity availability and consumption in households, addressing debate over fair energy distribution [6]. We extend the application of Lorenz curves to quantify reduction in electricity consumption and to study re-distribution of energy allocation due to roll-out of smart meters in households.

1876-6102 © 2014 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICAE2014 doi:10.1016/j.egypro.2014.12.103

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Nomenclature Ci

electricity consumption in household i

ci

= Ci / Cg or the share of electricity consumption of household i in the case study group h

ci

= Ci / Hi or electricity consumption per household i

Cg

sum of electricity consumption within the case study group

cgh

sum of electricity consumption in households I (∑cih)

Hi

household i

hi

= Hi / Hg or household i share in the case study group

Hg

sum of the households within the case study

2. Methodology 2.1. Case study group A quasi-experimental design was used, with 500 households in a target group (with smart meters) and 500 – in a control group (without smart meters). Selection steps: (1) 20000 participants were chosen,(2) telephone interviews were conducted with households randomly assigned to a target or control group, (3) the target group was asked for a consent to install a smart meter. Both groups where equally split based on electricity consumption: below 250, 400, 700, 1500, 1900 and above 1900 kWh per month. Based on Eurostat data 176.8 kWh per month were average electricity consumption in household in Latvia while 323.2 kWh per month – in the EU-28 in 2012 [7]. The group with smart meters receives monthly bills (based on actual consumption data) and has online access to consumption data (based on 5 minute integration period).The group without smart meters is observed based on monthly self-reading of electricity consumption. The case study was started in April 2013 and data for this paper are collected up to October 2013. To the target and control group, the utility company provided information on electricity consumption for the same time period in the year 2012. 2.2. Lorenz curve The indices of the Lorenz curve undergo a ranked distribution and are expressed as the cumulative percentage [6]. The distribution of electricity consumption is given as a function of number of households. Initially the density function f(cih) and the cumulative density function F(cih) where defined, where cih are households sorted based on their electricity consumption per month. The cumulative density function in continuous and discrete form, respectively, is given in Equation (1).

³

F (cih )

cih

0

f (c h )dc h

i

¦h

(1)

n

n 1

The first moment distribution function F1(cih) gives the cumulative share of electricity consumption within the case study group, see Equation (2) for continuous and discrete form, respectively.

F1 (cih )

1 c gh

³

cih

0

c h f (c h )dc h

1 c gh

i

¦h c n 1

h n n

(2)

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Uldis Bariss et al. / Energy Procedia 61 (2014) 2176 – 2179

Using continuous forms in Equations (1) and (2) the slope of Lorenz curve L(hi) is – see Equation (3). L(hi )

dF1 (cih ) dF (cih )

º ª1 h d « h ³0ci c h f (c h )dc h » »¼ «¬ cg

d ª ³0ci f (c h )dc h º «¬ »¼ h

cih

(3)

cgh

The relation for the equal distribution of 45°line is given as Equation (4).

ci hi

Ci / C g

1Ÿ

Hi / H g

Ci Hi

Cg Hg

Ÿ cih

(4)

c gh

When the share of electricity consumption of household cih equals to this household i share in the case study group, then electricity consumption must be equalized across the case study group and equal to electricity consumption per household (cih= cgh). The mathematical interpretation adopted from Groot [8]. 3. Results and discussion The roll-out of smart meters reduces electricity use by providing feedback about actual consumption and monthly bill, but energy reduction differs among consumption groups as shown in Table 1. Table 1. Electricity consumption changes in the target and control group between corresponding periods of 2013 and 2012

Factor analyzed

Electricity consumption in household, kWh per month < 250

< 400

< 700

< 1500

< 1900

> 1900

Total consumption

Target group

− 18%

− 4%

− 20%

− 25%

− 34%

− 20%

− 24%

Control group

− 3%

+ 2%

− 2%

− 5%

− 3%

− 8%

− 4%

Difference

− 15%

− 6%

− 18%

− 20%

− 31%

− 12%

− 20%

(a)100%

(b)100%

Cumulative electricty consmuption shares, %

Cumulative electricty consmuption shares, %

The average consumption of the target group in comparison with the control group fell by 20% year on year. These electricity savings in percentage terms are 5-10% over the range of the findings by other pilot studies [3,4,5].A possible explanation may be that consumers over 400 kWh per month represent middle and high income households that are more inclined to adopt efficiency measures (investments in efficient lighting and home appliances). 15% reduction in monthly consumption below 250 kWh requires further analysis, since this group present slow income households who cannot afford higher efficiency devices.

80% 60% 40% 20% 0% 0 1 2 3 4 5 all groups group groups groups groups groups groups 04.-10. 2013

04.-10. 2012

45°

80% 60% 40% 20% 0% 0 1 2 3 4 5 all groups group groups groups groups groups groups 10.2013

10.2012

45°

Fig. 1. Lorenz curves of electricity consumption for the customers with smart meters (a) from April to October; (b) for October.

Uldis Bariss et al. / Energy Procedia 61 (2014) 2176 – 2179

Results on energy distribution for the target group suggest that the reduction of electricity consumption play a minor role on energy equity in a short-term (Fig.1 a), but effect is growing over last months of the study (Fig.1 b). These results could be explained by the hypothesis that investments in energy efficient lighting and home appliances take time. Further research is needed to capture long-term effects. 4. Conclusions Electricity consumption reduces when smart meters are installed and feedback information on actual electricity consumption is available for households. The results are above the range of savings demonstrated in other studies that could be attributable to the limited accuracy of customer self-read consumption and relatively short study period. Energy distribution analysis using Lorenz curves shows that short-term effects on energy equity are minor. Further research is needed to capture long-term effects. References [1] Directive 2009/72/EC of the European Parliament and of the Council of 13 July 2009 concerning common rules for the internal market in electricity and repealing Directive 2003/54/EC, OJ L 211, 14.8.2009, p. 55–93. [2] Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency, amending Directives 2009/125/EC and 2010/30/EU and repealing Directives 2004/8/EC and 2006/32/EC, OJL315, 14.11.2012, p. 1. [3] Sarah, D., 2006. The Effectiveness of Feedback on Energy Consumption: a Review for DEFRA of the Literature on Metering, Billing and Direct Displays. Environmental Change Institute, University of Oxford. [4] Ehrhardt-Martinez, K., Donnelly, K.A., Laitner, J.P., 2010. Advanced Metering Initiatives and Residential Feedback Programs: A Meta-Review for Household Electricity-Saving Opportunities. Report E105. American Council for an Energy - Efficient Economy, Washington, D.C. [5] Vaasa EET, 2011. The Potential of Smart Meter Enabled Programs to Increase Energy and Systems Efficiency: A Mass Pilot Comparison—Empower Demand. Report for European Smart Metering Industry Group. [6] Jacobson, A., Milman, A.D., Kammen, D.M., 2005.Letting the (energy) Gini out of the bottle: Lorenz curves of cumulative electricity consumption and Gini coefficients as metrics of energy distribution and equity. Energy Policy, 33, p. 1825-1832. [7] Eurostat, 2014, Statistics. accessed on 20.02.2014. [8] Groot, L., 2010. Carbon Lorenz curves. Resource and Energy Economics, 32, p.45-64

Uldis Bariss

Uldis Bariss is studying for PhD in Riga Technical University, Institute of Energy Systems and Environment. The main research area is smart metering and energy efficiency potential from feedback on energy consumption combined with electricity market prices. Lelde Timma Doctoral Researcher at Institute of Energy Systems and Environment. Her main research areas: sustainable development, renewable energy and artificial intelligence. She studied in Sweden, Switzerland and Lithuania; defended a double-diploma degree in 2013. She is the co-author of > 40 publications and participated in > 20 international scientific conferences. Dagnija Blumberga

The Professor and Director of Institute of Energy Systems and Environment. Doctor Habilitus Thesis "Analysis of Energy Efficiency from Environmental, Economical and Management Aspects" prepared in Royal Institute of Technology (KTH), Stockholm. She participated in numerous international projects on energy and environment and is the coauthor of > 200 publications and 14 monographs.

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