Potential for RTP in Sweden

Transcription

1 Potential for RTP in Sweden Mattias Vesterberg Chandra Kiran Krishnamurthy Umeå University, Sweden Oben Bayrak SLU Umeå, Sweden 37th IAEE, New York June

2 Introduction Real time pricing (RTP) of electricity discussed as an important efficiency enhancing measure in recent literature has many important policy implications: dealing with shortages (US, Australia), climate policy (EU-Sweden) Success of the RTP scheme depends upon load shifting over the day; energy conservation is neither necessary nor theoretically certain. theoretically requires only modest price elasticity

3 Introduction Little empirical evidence regarding consumer response to RTP; some evidence for reductionof total load, not for load shifting (e.g. Alcott (2011)). Benefits of RTP depend upon: energy technology mix (peakversus-base-load), nature of demand (i.e. demand elasticity), market structure (regulated versus deregulated) and context. Understanding of current demand behavior at hourly time scale important for determining baseline i.e. feasibility and incentives.

4 Objectives of the Study To understand consumer demand behavior at hourly time scale for Sweden provide a basis for analyzing incentives for demand shifts over the day understand possible short-run restrictions to demand shifts over the day

5 The Swedish Context De-regulated electricity market, retail competition; consumers choose between fixed and variable price contracts Sweden, Denmark and Norway are an integrated electricity market, with an integrated spot market, the Nordpool Relatively little diversity in generation technologies (Hydro- 45, Nuclear-39); peak capacity low (300 MW) and infrequently used heating and lighting a large share of winter load (~ 70%) Very low (by EU standard) marginal price (0.1 euro), high average household consumption (15000 kwh/year)

6 Data Are from a study commissioned by the Swedish Energy Agency, between 2005 and household holds monitored, in total, for between 1 and 12 months About 46 appliances were metered in each household, at 10- minute intervals Indoor and outdoor temperature were also monitored most households located in one region (Mälardalen) price or contract information unavailable, income and socioeconomic data available

7 Data

8 Our Approach Using socio-economic and end-use consumption data, estimate daily load curves (for February and June) Using estimated load curves and nord pool average spot price, compute cost of servicing total (and end-use-specific) load Compute cost implications of shifting load curves a few hours ahead

9 Load Curves Total load: all working days in February

10 Heating load Load curves.

11 Load curves. End-use specific load curves separately estimated using a SUR framework (across hours) Main points: obvious peak times, flatter-thananticipated indoor temperature, higher home indoor temperature during office hours, heating load drives shape and magnitude of total load, substantial heterogeneity in terms of heating load

12 Cost Effect of load shift Price path on nord pool (average system price, SEK, for variable monthly contract)

13 Cost Effect of load shift.. Cost of servicing an end use and total load: load times average spot price, for that hour Cost changes: computed as cost for servicing shiftedload curve, shifts of different hours are considered. Computations using average price

14 Cost Effect of load shift.. Summary: small cost savings for most households, except for unrealistic shifts. Substantial heterogeneity in cost changes. Overall, cost savings very moderate.

15 Conclusions Using a rich, appliance level sub-daily data set, we document: limited cost savings for residential load shifts load shifting is likely challenging in the short run, even if cost savings are moderate. Results indicate challenges to Sweden s RTP ambitions