Changes in regulatory requirements, market structures, and operational technologies have introduced complexities that traditional ratemaking approaches can’t address. Poorly designed rates lead to...
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.
One-hundred cases were created as data inputs to the strategist model. These were calculated to represent a wide variety of possible futures. Monte Carlo methods were used to create these different future cases that represent the uncertainty in key future inputs. To accomplish this, we identified a number of pivotal factors and dimensioned the uncertainty around these factors. Data was provided for the years 2005 to 2023. In addition, we developed data sets for four DR programs as inputs to the model.
The key input variables around which we dimensioned uncertainty are:
• Fuel prices: natural gas, residual oil, distillate oil, and coal;
• Peak demand;
• Energy demand;
• Unit outages; and
• Tie-line capacities.
We included four DRR products as potential resources to meet future system needs, in combination with the full range of supply-side options. The four DRR programs are:
• Interruptible Product: A known amount of load reduction based on a two-hour call period. Customers are paid a capacity payment for the megawatts, with penalties if megawatt reductions are not attained.
• Direct Load Control Product: A known amount of load reduction with 5 to 10 minutes of notification. This is focused on mass market customers. As a result, it has a longer ramp-up time to attain a sizeable amount of megawatt capacity.
• Dispatchable Purchase Transaction: A call option where the model looks at the “marginal system cost” and decides to “take” the DRR offered when that price is less than the marginal system cost. This program can also be classified as a day-ahead pricing program.
• Real-Time Pricing (RTP) Product: This program poses a challenge in that there is no feedback loop built into the model that looks at the marginal hourly cost and the demand for that same hour. As a result, two pricing products are examined:
• One is a peak-period pricing program that produced a reduction in peak demand and little impact on load in other hours. This is similar to a critical peak pricing product, with the overall monthly and annual energy demand largely unaffected.
• The second is a standard RTP program that produces a reduction in peak demand and also an overall energy efficiency effect, resulting in reductions in weekly, monthly, and annual energy demand. This is consistent with the RTP literature.
We then use data from each product design to develop inputs to the strategist model, such that each program could be treated consistently by the model. All dollar values are inflated at a rate of 2.5 percent per year. The following data is supplied for each product for the years 2005 to 2023:
Case Study Results
Results from these analyses include the following:
Uncertainty: In the base case, the overall uncertainty in total system costs for each year (100 cases per year) is quite large across these cases—indicating that the uncertainty in the modest number of variables selected results in a wide range of net system costs for each year in the 20-year planning horizon. On average, the range is