A Simple Metric for Predicting Revenue from Electric Peak‐Shaving and Optimal Battery Sizing. Issue 4 (5th February 2018)

• Record Type: Journal Article
• Authors: Fisher, Michael; Whitacre, Jay; Apt, Jay
• Is Part Of: Energy technology. Volume 6:Issue 4(2018:Apr.)
• Page Start: 649
• Page End: 657
• Publication Date: 2018-02-05
• Subjects: batteries -- energy storage -- peak shifting -- revenue prediction -- techno-economic analysis; Energy development -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1002/ente.201700549
• Languages: English
• ISSNs: 2194-4288

Abstract

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. Abstract : What′s the charge? We present a simple econometric model of revenue from peak‐shaving that eliminates the need for an optimization model for battery storage of electricity generated from renewable sources. A novel metric derived from the building's load profile is used for prediction. The model can also be used to calculate the profit‐maximizing storage size.