Smart Meters, Demand Response and Energy Efficiency

1. Smart Meters, Demand Response and Energy Efficiency GRIDSCHOOL 2010 MARCH 8-12, 2010  RICHMOND, VIRGINIA INSTITUTE OF PUBLIC UTILITIES ARGONNE NATIONAL LABORATORY Rick Hornby Synapse Energy Economics rhornby@synapse-energy.com  617 661 3248 Do not cite or distribute without permission MICHIGAN STATE UNIVERSITY

2. Introduction Investments in smart meter infrastructure (SMI) are typically justified based upon projected savings in distribution service costs, electricity supply costs and sometimes include externalities such as reductions in emissions of greenhouse gases (GHG). The justifications often mention, but rarely quantify, other categories of benefits such as improvements in distribution service reliability. Projected savings in electricity supply costs are based on projected reductions in electric demand (demand response or DR) and electric energy (energy efficiency or EE) that will be enabled by smart meters and the unit $ value of those reductions. This session will address the key issues associated with those projections What is the difference between DR and EE? What are the relative values of DR and EE? How do the differences between Mass Market Customers and Medium to Large C&I Customers affect the ability to achieve DR and EE? Why are projections of DR from mass market customers via dynamic pricing (DP) enabled by smart meters uncertain? Why are projections of EE from mass market customers via feedback enabled by smart meters uncertain?

3. Introduction - Smart Meter Infrastructure

4. DR Versus EE - electricity use varies by time period throughout the year

5. DR Versus EE

6. DR Versus EE

7. II. Relative Values of DR and EE Reductions in electricity use, both demand and energy, translate into direct quantity savings and indirect price mitigation savings. (Customers who reduce receive direct quantity savings, all customers receive indirect price mitigation savings.) Direct quantity savings equal the quantity of reduction demand and energy multiplied by the corresponding prices: Quantity Saving ($) = (demand reduction in Kw* $/kW) +(energy reduction in kWh * $/kWH) Indirect Price Mitigation savings equal the total quantity of demand and energy being used multiplied by the reduction in price due to the reduction in quantity, e.g. Price mitigation saving ($) = (Total demand * reduction in capacity price $/kW) +(total energy * reduction in energy price $/kWh)

8. II. Relative Values of DR and EE – Quantity

9. II. Relative Values of DR and EE – Quantity

10. II. Relative Values of DR and EE – Quantity

11. II. Relative Values of DR and EE – Price Mitigation Reducing demand via “DSM bids” reduces capacity prices (demand can be met at a lower point on the supply curve)

12. II. Relative Values of DR and EE

13. II. Relative Values of DR and EE re Monthly Bills

14. III. Mass Market Customers Have Different Characteristics from Medium to Large C&I Customers

15. III. Mass Market Customers Have Different Characteristics from Medium to Large C&I Customers

16. IV. Why Projections of DR from mass market customers via DP Enabled By Smart Meters are Uncertain DR for Mass Market customers is not new. Many utilities have many years experience offering direct load control (DLC) programs to those customers. Under these programs the customer allows the utility to cycle the operation of certain major loads during critical peak periods, e.g. 5 hours, on a limited number of afternoons each summer, e.g. 12. The loads are typically central air conditioning, water heating and pool pumps. In exchange the customer receives a one-time incentive, e.g. $50, and a programmable controllable thermostat (PCT). DR via DP enabled by the equivalent of Smart Meters is not new. Some utilities and curtailment service providers have been offering this to large C&I customers for several years. What is new is DR from Mass Market customers via DP enabled by Smart Meters. Under these rate offerings customers who elect to reduce their use during these critical peak periods relative to their normal levels will either receive a rebate or avoid paying a premium rate. (DP designed as a rebate is called Critical peak rebate, DP designed as a premium rate is called Critical Peak Pricing).

17. IV. Why Projections of DR from mass market customers via DP Enabled By Smart Meters are Uncertain Uncertainty re the long-term value of avoided capacity due to uncertainty re marginal source of capacity. Electricity use may grow more slowly in the future due to loss of manufacturing and improvements in efficiency. New transmission projects may allow regions with excess existing capacity to serve regions that need new capacity. New renewable capacity will be added to comply with renewable portfolio standards, regardless of need for capacity. The lower the avoided costs of capacity the lower the value to prospective participants. (applies to all DR)

18. IV. Why Projections of DR from mass market customers via DP Enabled By Smart Meters are Uncertain Uncertainty re the percentage of mass market customers who will elect to reduce use during critical peak periods on a sustained basis, year after year, and the magnitude of those reductions. The mass market customers with the best value proposition are those whose demand is high in summer months. That demand is primarily for central air conditioning and pool pumps. In many regions, only about 50 % of mass market customers have that high demand. Of those, 20% to 30% may be already on DLC. Thus, only about 35% of total mass market customers may have a very attractive value proposition.

19. IV. Why Projections of DR from mass market customers via DP Enabled By Smart Meters are Uncertain

20. V. Why Projections of EE from mass market customers via Feedback Enabled By Smart Meters are Uncertain EE from Mass Market customers via feedback is relatively new. 2009 report by the Electric Power Research Institute (EPRI) concludes that “residential electricity use feedback” can be an effective tool but “Further research is necessary on such points as “participation levels, the persistence of feedback effects, the relative value of different types of feedback, dynamic pricing interactions, and distinguishing the effects of feedback among different demographic groups.” Residential Electricity Use Feedback: A Research Synthesis and Economic Framework. EPRI, Palo Alto, CA: 2009. 1016844 (Feedback Research Synthesis). Available at http://www.opower.com) Feedback can be, and is being, provided using monthly usage data from existing meters as well as hourly usage data from new smart meters. It is not yet clear whether feedback based on hourly usage data from new smart meters leads to materially greater EE than feedback from monthly usage data. ACEEE expected to release an evaluation of this approach in 1st Quarter 2010.

21. Contact Synapse Energy Economics 617 661 3248 www.synapse-energy.com Rick Hornby (ext 243) rhornby@synapse-energy.com