Assessing uncertainty in the timing of energy use during cost-optimal distributed energy technology selection and sizing. (December 2020)

• Record Type: Journal Article
• Authors: Kwasnik, Ted; Elgqvist, Emma; Anderson, Kate
• Is Part Of: Renewable energy focus. Volume 35(2020)
• Page Start: 122
• Page End: 131
• Publication Date: 2020-12
• Subjects: Renewable energy sources -- Periodicals; Solar energy -- Periodicals; 333.79405
• DOI: 10.1016/j.ref.2020.09.002
• Languages: English
• ISSNs: 1755-0084

Abstract

Highlights: Simulated loads effectively identify where solar PV and storage are uneconomical. Using simulated loads instead of metered data overestimates optimal storage size. Simulated loads yield storage only 46% of time when meter data would include solar PV. Matching annual peak load time or monthly peak magnitude improves sizing accuracy. Abstract : This paper empirically derives uncertainty ranges in cost-optimal solar PV and storage sizing by comparing results from the REopt Lite optimization platform from metered data and a set of simulated Department of Energy Commercial Reference Building (CRB) profiles at 65 sites. We find load profile shape alone does not explain a site's optimal configurations (i.e., PV, Storage, PV and Storage, No System). Still, load profile shape does introduce uncertainty to optimal PV and storage capacities. Across all cases where PV is part of an optimal configuration, we find the average ratio of power capacities derived from metered loads to capacities derived from CRB profiles to be 0.97 (and as high as 1463), where 1 would be a perfect match in system size. For storage, the ratio is 1.6 (and as high as 42). We also assess how, in the absence of complete metered data, a CRB profile can be selected that would be expected to yield the most similar solar PV and storage capacities. From those metrics that can be available from billing data (i.e., peak demand, monthly load totals), we find that uncertainty is most reduced by selecting the CRB's with an annual peak occurring at the most similar time, or those with the lowest average root mean square error (RMSE) among monthly peak loads. This research can help improve the implementation and interpretation of results derived from simulated load profiles and is an important next step in advancing smart grid solutions.