Experience with time-of-use pricing programs shows that a large majority of low-income customers will benefit from dynamic prices. In fact, not making such prices available to these customers...
AMI/Demand Response: Getting It Right the First Time
Each DR portfolio will have a different set of AMI needs, based on overall technology infrastructure.
and this estimate may vary by the number of customers impacted and the characteristics of different systems.
Total System Cost: Overall, the incorporation of DRR results in some reduction in the average total system cost net present value (NPV) in all three scenarios with DRR (callable DRR, DRR with CPP, and DRR with standard RTP). In the scenario with the standard RTP program, savings are about 3.5 times those in the scenario with the critical peak pricing program, and similarly, savings in the scenario with the critical peak pricing program are approximately twelve times those with only the callable DRR programs.
Incremental System Cost: As the system being studied is a very large system, it is meaningful to look at the incremental costs of meeting energy demand, as opposed to a percentage of the total system cost. On average, the savings in incremental costs due to DRR (year on year) are 10 percent for the scenario with peak pricing and 23 percent for the scenario with standard RTP. For the scenario with the standard RTP program there is a range of savings from -73 percent to +320 percent, and in 53 percent of the cases the incremental costs in the callable DRR scenario are less than or equal to those in the base scenario. In a few cases the DRR provides large reductions in incremental costs.
Overall, this case study shows that a Monte Carlo approach, coupled with a resource planning model, can address the value of DRR given uncertainties in future outcomes for key variables, and can also assess the impact DRR has on reducing the costs associated with low-probability, high-consequence events. In this case study, the addition of DRR to the resource plan reduces the costs associated with extreme events, and it reduces the NPV of total system costs over the planning horizon. This is an important finding. It can be compared to being paid to buy life insurance. Not only does DR reduce the expected or mean net system costs of meeting load growth, but it also greatly reduces the impacts of adverse events by between $1 billion and $2.5 billion—a considerable sum and a sizeable reduction in risks to ratepayers.
The outcomes of this case study are illustrative of both a method for assessing DR portfolios along supply-side portfolios, and the potential magnitude of the benefits. The role of DR resources is important for both cost effectiveness and risk management. As a result, any AMI structure developed should align with the development of DR resources.
As a final note, the total DRR capacity across all the DR options is approximately 15 percent of system-peak demand in 2015. A large DRR capability initially was viewed as appropriate for this case study. As the results section indicates, this level of DRR capability was found to be an overbuild for this system, i.e., DRR values of between 7 and 10 percent of total system peak would probably have been more appropriate for this system. This indicates that any resource will have diminishing returns at some level and, as with any