Time of use (TOU) electricity pricing study

Transcription

1 Time of use (TOU) electricity pricing study Colin Smithies, Rob Lawson, Paul Thorsnes Motivation is a technological innovation: Smart meters Standard residential meters Don t have a clock Have to be read manually Usually once a month or every other month Smart meters Have a clock Transmit readings wirelessly E.g., half hourly Smart meters provide market opportunities Retail prices that vary by time of day Energy management Diversification, e.g., send gas & water usage with electricity readings 1

2 Why vary retail price over the day? Because production cost (and wholesale price) varies over the day Suppose that retail prices do not vary by time of day Wholesale prices affect generator (i.e., supplier) decisions Which generators to use when When to add capacity Wholesale prices do not affect consumer decisions $ MC MC pk P avg MC op D op D pk Q op Q pk Output 2

3 Daily cycles in demand and prices Summer: Week of 13 January 08 Source: Electricity Commission 3

4 Daily cycles in demand and prices Early spring: Week of 14 Sept 08 Source: Electricity Commission 4

5 Household weekday usage cycle icp (All) wkday (All) INFOGRPB 5.00% Sum of reading 4.00% 3.00% 2.00% SAMPLE GROUP INFOGRPB 1.00% 0.00% 00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 time 5

6 How big is the wholesale price relative to the retail price? Source: Contact Energy 6

7 What are the options for pricing? Average-cost pricing: no variation over the day A common option with a standard meter The retailer bears all of the price risk Marginal cost pricing: retail price varies with wholesale price Real-time pricing (RTP) Feasible with a smart meter The customer bears all of the price risk Share the risk Time-of-use (TOU) pricing Peak/Off-peak (e.g., day/night) price differentials Some companies do this with two meters Peak/Shoulder/Off-peak Critical /peak/off-peak 7

8 Our industry connection Mercury Energy Mighty River Power s retail arm Supplies about 350,000 (and growing thanks to Contact) customers (business & residential) mostly in the Auckland area, out of about 1.8 million electricity customers in NZ Metrix MRP s metering company Metrix have been installing smart meters for residential customers Currently ~ working in Auckland area Aiming for all customers by end of 2010 Mercury are interested in learning about the potential impacts of smart meters on retail markets Focus on varying prices over the course of the day Offer a range of pricing plans similar to that offered large commercial customers 8

9 Study Area 9

10 Characteristics of the population of houses Median Mean Std Dev Min Max Capital value $455,000 $471,912 $123,754 $160,000 $1,750,000 Decade built Floor area (m2) Years in house Daily fixed charge $0.33 $0.58 $0.28 $0.33 $1.07 Variable charge ($/kwh) $0.20 $0.19 $0.02 $0.17 $

11 Study set-up Five groups of about 100 households each Control group Information only group 4c price differential: 20.29c pk / 16.29c op 10c price differential: 24.52c pk / op 20c price differential: pk / op Recruiting method Letters sent out to a random sample of 1500/4000 Interested households opt in (can opt out anytime) Assignment to group Random assignment to TOU vs. information only/control Household chooses TOU plan Time frame: 1 August 08 to 31 October 09 Reward: $100 at end of study No householder worse off financially for participating Though they re not told this upfront 11

12 Information provided Each household in each of the four test groups receives A monthly usage report via mail peak and off-peak usage daily peak and off-peak usage monthly Access to usage information via the web A list of Energy Saving Tips Insulation Heating Lighting Kitchen Bathroom Laundry Other appliances 12

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15 Data generated Electricity usage Kilowatt hours at half-hourly intervals 24/7 Similar data available for one year prior to start of the study House characteristics From Quotable Value Household survey (conducted in Sept and Oct 08) Dwelling characteristics/recent alterations Household characteristics Heating characteristics/behaviours Energy behaviours/attitudes Environmental values Weather information is also available Temperature, rainfall, etc. at half-hourly intervals Coastal station and inland station in the study area 15

16 What do we expect to observe? Non-random selection Some households benefit from TOU pricing without changing behaviour shift $ from Mercury to household Load shifting Some households can consciously shift activities to off-peak times Spread and lower the peaks Conservation Some households can take steps to reduce power consumption How households respond may depend on: House characteristics Demographics Values 16

17 Contribution of this study First study of this kind in New Zealand One of a few using half-hourly readings Emphasis on heating rather than cooling Heating is relatively electricity intensive Distinguishes effect of information from that of pricing Attention to householder values Side effect; will be able to determine heating degrees for Auckland 17