Emerging distributed technologies aren’t just spurring remarkable changes for electricity providers—they’re also empowering energy consumers like never before.
Thomas Edison revealed the world’s first central power plant in downtown Manhattan. Since then, utilities have enjoyed over a hundred years of undisrupted, predictable consumer activity thanks to a one-rate-fits-all model that assumes the lights are always on.
The deregulation of electricity/market-driven pricing throughout the world and the proliferation of the Internet of Things in consumer markets have forced utilities to ramp up connectivity, visibility, and, ultimately, more cost-effective energy systems. Such advancements include the ubiquity of ‘smart’ home appliances, digitally connected devices synced to a homeowner’s utility program that can be optimized to benefit energy providers and individual users. Smart meters offer compelling insight into this shift. Customer needs for electricity vary widely; technologies that respond to individual needs expose the inefficiencies of a standardized load profile — and monthly bill. As utilities increasingly embrace smart meters in consumer households, they in turn have opened windows to implement alternative rate designs.
When energy demand peaks, system costs climb, too. Those increases, in turn require more expensive generation capacity and physical infrastructure. As a result, customers who can’t benefit from alternative rate designs are subject to imbalanced prices and don’t have the ability to manage their usage in a cost-effective way. With these needs and shortcomings in mind, utilities are exploring mechanisms to reduce peak load and thereby provide cost savings to all customers.
Rate design can be a component of an overall demand-side management (DSM) and distributed energy resources (DER) plan that can address system needs and reduce costs while achieving the primary goal of rate design: to effectively balance the costs of providing a service with the revenues recovered from the customers using that service.
Smart meters are racing to eclipse the antiquated rate design of fixed charges and estimated bills. However, you don’t need to throw the baby out with the bathwater. Utilities can optimize rate design with other demand-side management or distributed energy resources (DER) to achieve defined targets. One design in particular, time-variant pricing, communicates to customers when it is less costly to consume energy — more efficiently than ever before.
Time Variant Pricing in Perspective: How One Utility Reduced Peak Load With Time-of-Use
We recently worked with a utility to identify the best option for reducing peak load while maintaining revenue neutrality. The first thing we did was delve into the utility’s business and cost structure, conducting an extensive review of its customer base, load, and technical challenges. Then we conducted detailed analyses to identify load management options.
We determined that out of a set of DSM options including direct load control, demand response (DR), and energy efficiency programs, a time-of-use (TOU) rate would realize the greatest peak load reductions, at the lowest cost, and in the shortest timeframe. This finding was partly driven by the fact that the utility had already installed smart meters for the customers that accounted for a significant share of both peak load and total electricity consumption. After identifying a TOU rate as the preferred mechanism, we analyzed the utility’s data to optimize the on- and off-peak time periods, the price differential for those time periods, the bill impacts for participating customers, and the overall system cost.
Finally, we created a marketing plan and supported the pilot rollout of the TOU program to promote customer participation. Table 1 outlines the results of the analysis — and ultimately, the implemented TOU rate reduced system peak load by six percent while maintaining revenue neutrality.
Table 1: Time-of-use results
Ratio of on-peak to off-peak price
|TOU off-peak discount||
|Estimated on-peak consumption change||
|Estimated off-peak consumption change||
|Estimated revenue impact||
|Potential system peak load reduction (MW)||
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