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A Simple Metric for Predicting Revenue from Electric Peak‐Shaving and Optimal Battery Sizing
Author(s) -
Fisher Michael,
Whitacre Jay,
Apt Jay
Publication year - 2018
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.201700549
Subject(s) - peaking power plant , sizing , revenue , electricity , energy storage , metric (unit) , battery (electricity) , peak demand , profit (economics) , computer science , value proposition , load shifting , electric utility , mathematical optimization , power (physics) , economics , operations management , electrical engineering , microeconomics , engineering , renewable energy , distributed generation , mathematics , finance , quantum mechanics , art , physics , management , visual arts
Abstract A major use case for behind‐the‐meter (BTM) electricity storage is peak‐shaving for commercial and industrial customers who must pay peak‐demand charges. Quantifying the value proposition for individual customers currently requires an optimization model, the development of which can be costly in human and computing resources. We disclose here a simple econometric model to predict revenue from retail peak‐shaving. Geared toward electric utilities, third‐party storage providers, and consumers, this model eliminates the need to formulate a model in specialized optimization software. The model is based on a predictive metric that is derived from the building's load profile. During model fitting, we discovered that the revenue estimates generated are independent of the power capacity of the battery if the maximum power‐to‐energy ratio of the storage is held constant. This effect can be used to calculate the profit‐maximizing storage size, which we explore in a case study.