The Likely Impact of Smart Electricity Meters in Ireland Seán Lyons (with Conor Devitt & Anne Nolan) ESRI/EPA Environmental Economics Seminar, 30 May 2011

2 Contents n n Introduction and approach n n Counterfactual and scenarios n n Some key parameters n n Effects on:- Networks Suppliers Consumers Generation n n Future research

3 Introduction n n Smart metering: meters read remotely with high frequency data collection, allowing time of use tariffs n n ESRI assisted CER in preparing CBA, work funded by Energy Policy Research Centre n n Objective of CBA is to estimate the payoff to society of various scenarios, compared to no action baseline n n Include effects on consumers, networks, suppliers, generation n n Key data from technical trial, consumer behaviour trial and estimates from firms and consultants

4 Approach to CBA n n CBA compares all benefits and costs in a given option to those expected in baseline scenario n n Counterfactual baseline scenario: what would happen if no Smart Metering? n n Assumptions on service characteristics, costs, prices and demand in the future n n Calculate Net Present Value of each option relative to baseline; consider unquantifiables too n n Memo items: effects of each option on quantity of CO 2 and SO 2 emissions

5 Counterfactual n n Existing metering technology retained n n No new time of use tariffs n n Meter replacement programme goes ahead n n New solution for prepaid metering; large rise in households on prepayment tariffs n n Bi-monthly billing continues n n Variant: Monthly billing from 2020 including monthly manual meter reads and bills

6 Scenario Dimensions n n Communications technology (3 options) 1. 1.DLC-RF 2. 2.DLC-GPRS 3. 3.GPRS-only n n Billing frequency (bi-monthly or monthly) n n In-home display or not n n Monthly billing in baseline or not (from 2020)

7 Some Parameters and Assumptions n n Timing: rollout ; evaluation to 2032 n n Discount rate: 4% (Dept of Fin. Guidelines) n n Macroeconomic outlook, incl. growth in connections: ESRI Low Growth Scenario 2010 n n 100% rollout and mandatory ToU charging n n Emission intensities (CO 2 and SO 2 ) n n Customers on Nightsaver tariffs assumed not to respond, along with 15% assumed vacant properties/holiday homes

8 Consumer behaviour trial n n Over 5,000 residential customers included in randomised controlled trial; about 600 SMEs in a separate trial n n 6+ month control period applied for all, then one year treatment period for most (remainder left as controls) n n Treated residents faced one of four time of use tariffs plus an informational treatment: 1) bimonthly billing, 2) #1 plus in-home display, 3) monthly billing or 4) Overall load reduction incentive n n Results Half-hourly demand data Pre-trial, post-trial and leavers socioeconomic surveys

9 Effects on Consumers n n Benefits due to net reduction in average bills as customers cut 24 hour usage and switch to cheaper times of day n n Significant treatment effects, but no additional price effects found in statistical analysis of residential results n n No significant effects found for SMEs

10 Total NPV by Option

NPV breakdown by component

12 Sensitivity tests n n Effects of informational stimuli were sensitive to tariff group in the trial n n Attractiveness of GPRS communications depends heavily upon assumed network charges n n Supplier billing system expenditure and network costs such as cost of meters and IHDs are relatively significant n n Other cost items less important n n Viability not very sensitive to discount rate n n Inclusion or not of SMEs makes little difference to NPV

13 Emissions reductions and non-quantified benefits n n Emissions reductions relative to baseline once meters are fully in place: CO 2 : 80, ,000 tonnes per year (included in NPVs) SO 2 : tonnes per year (not included in NPVs) n n Unquantified benefits from smart grids, microgeneration, electric vehicles, gas/water smart metering, electric vehicles, smart appliances, extra scope for service differentiation and competition

14 Details of NPVs (m) by Option

15 Sensitivity test: discount rate

16 Sensitivity test: tariffs from trial

17 Future research n n Dataset should be made available to researchers, e.g. through ISSDA n n Consumer electricity demand parameters n n Effect of appliance ownership and use on electricity demand n n Segmentation of electricity user types by patterns of use n n Rich data set, so probably many other applications...