McGee Young, Recurve
As more solar power is added to the grid, how can utilities make better use of the time value of behind-the-meter interventions while also adjusting to the impact of location-specific supply constraints?
In this paper, to be presented at IEPEC 2019, McGee Young offers an overview of several methodologies for employing interval-level data for estimating time- and location-dependent savings from energy efficiency interventions.
The increased penetration of advanced metering infrastructure is leading to a transition away from traditional billing analysis methods that use monthly data and toward the adoption of methods that leverage finer time intervals of energy use. The availability of consumption data at hourly and even smaller time intervals is increasing pressure on evaluators to develop standard protocols for measuring the impacts of efficiency programs at finer levels of time and place. This makes sense given the new challenges confronting the utility industry.
The first key shift is being seen in the time valuation of energy efficiency. Increasing availability of rooftop solar is driving large changes in the time-dependent valuation of energy efficiency: the shifts in hourly avoided costs as measured by cost effectiveness tests is profound. In regions with high solar penetration, avoided costs plummet to near zero at midday in summer and surge in late evenings as solar goes offline. These large shifts in hourly avoided costs drives the need to rethink the methodologies for calculating hourly load shapes of both discrete efficiency measures as well as the load shapes of “whole building” interventions.
The second key shift is in the focus of energy efficiency as both a time- and location-dependent resource. The ability to measure the impacts of energy efficiency interventions by discrete locations, and at specific times of peak demand, increases the potential utility of energy efficiency to address time-dependent supply constraints of specific local capacity area substations.
This paper presents an overview of several methodologies for employing interval-level data for estimating time- and location-dependent savings from energy efficiency interventions. The straightforward use cases employing randomized control trials, the more challenging methods using quasi-experimental designs, and site-specific approaches for larger, one-off interventions are discussed. The paper concludes with recommendations for moving toward an industry consensus for a protocol for time-dependent savings estimation.
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