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Using Data to Weigh the Pros and Cons of Performance-Based Rates

Utilities are relying more on data to minimize the risk of taking on new rate structures.

Performance-based regulation (PBR) has been in the news, with Nevada approving legislation calling for a PBR process. Hawaii just approved a Phase 1 of a ground-breaking PBR initiative, deciding what “performance-based rates, tariffs, and other regulatory mechanisms were most critical for the island state’s efforts to reach 100 percent renewable energy by 2045”[1]

PBR is a departure from the traditional cost of service regulation (COSR), where utility revenues are set based on the revenue requirement that must be collected for the utility to recover its costs and earn a reasonable return. The Regulatory Assistance Project defines PBR as “a regulatory framework to connect goals, targets, and measures to utility performance or executive compensation.” According to Utility Dive’s State of Electric Utility 2019, 22% of respondents have COSR with a mix of performance-based regulation.

PBRs come in different flavors. There is the multiyear rate plan (MRP) with a PBR adjustment mechanism, which provides utilities with a pre-determined annual increase in incremental revenues to cover costs over the term of a PBR plan, usually a 5-year time frame. If the utility is able to innovate and reduce its costs (operations, or “opex”, and capital, or “capex”), it would get to keep the savings. Another flavor is the performance incentive mechanism (PIM), which are performance metrics with targets and incentives that encourage utility management to focus time and resources to outperform in new areas outside of the utility’s core business operations, like increased renewable integration, energy efficiency, and system peak reduction.

It’s rarely discussed, but behind the scenes, data and analytics play a large part in shaping the PBR agenda. The process begins with setting policy goals and defining desired outcomes. After that, several other areas are guided by complex data decisions:

  • Deciding whether to pursue a PBR agenda. Take the go/no go decision for an MRP plan with a PBR adjustment mechanism. Utilities must predict the capital and operating expenditures needed for the next five years and run existing COSR models against PBR models using formulas acceptable to regulators. This requires a highly complex understanding of variables that pertain to the risk involved, and sit in many areas across the value chain.
  • Establishing performance metrics. Performance metrics will be unique to each PIM. With an outcome like reliability, performance metrics are already well defined in SAIDI and SAIFI, but performance metrics may be more difficult to define and even harder to measure for new policy outcomes that are important to regulators.

Peak reduction to benefit the customer, society and the utility is one of the more complex PIMs. Regulators leave it to utilities to decide what mechanisms to deploy. Time-of-use rates (TOU) may help reduce peaks but will require investment in AMI to enable. That will require a cost/benefit analysis. Utilities will want to compare the cost/benefits with other potential strategies, like rates for electric vehicle off-peak charging.

  • Setting targets. Take another example: a PIM to increase the penetration of behind-the-meter resources. An anonymous utility had to develop a credible forecast of how much distributed generation (DG) and energy storage would be added to the system based on state incentives and the utility’s ability to interconnect these resources. They worked with the customer DG integration team to obtain data on projects in the interconnection queue to establish a baseline forecast, and then set targets well above that. In another case, a utility surveyed their customers on satisfaction with the distributed energy resource (DER) interconnection process and used that as a baseline.

PBRs will rely heavily on collaboration among business units, informed by forecast data and analytics.  The data must be consistent. Stakeholders will demand that the models and methodology are credible and transparent. Once a PBR is put in place, it will be up to business intelligence drawing from utility data sources to produce annual reports that demonstrate utility performance to the regulators.

[1] Interest in PBRs has waxed and waned since the 1990s; now PBRs are on the upswing. There are current initiatives in Massachusetts, Minnesota, Hawaii, Rhode Island, Illinois, and Michigan. There is PBR activity in thirteen states according to K.K. DuVivier, a professor of energy law at the University of Denver’s Sturm College of Law in “Xcel-backed Colorado bill prompts debate over the vision for utilities’ future”, Energy News, Allen Best, April 23, 2019.

This article first appeared on the Utility Analytics Institute website.

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