Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving

Authored by Menglian Zheng, Christoph J Meinrenken, Klaus S Lackner

Date Published: 2015

DOI: 10.1016/j.apenergy.2015.02.039

Sponsors: National Institute of Standards and Technology (NIST)

Platforms: Microsoft Visual Basic

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Meeting time-varying peak demand poses a key challenge to the U.S. electricity system. Building-based electricity storage - to enable demand response (DR) without curtailing actual appliance usage - offers potential benefits of lower electricity production cost, higher grid reliability, and more flexibility to integrate renewables. DR tariffs are currently available in the U.S. but building-based storage is still underutilized due to insufficiently understood cost-effectiveness and dispatch strategies. Whether DR schemes can yield a profit for building operators (i.e., reduction in electricity bill that exceeds levelized storage cost) and which particular storage technology yields the highest profit is yet to be answered. This study aims to evaluate the economics of providing peak shaving DR under a realistic tariff (Con Edison, New York), using a range of storage technologies (conventional and advanced batteries, flywheel, magnetic storage, pumped hydro, compressed air, and capacitors). An agent-based stochastic model is used to randomly generate appliance-level demand profiles for an average U.S. household. We first introduce a levelized storage cost model which is based on a total-energy-throughput lifetime. We then develop a storage dispatch strategy which optimizes the storage capacity and the demand limit on the grid. We find that (i) several storage technologies provide profitable DR; (ii) annual profit from such DR can range from 1\% to 39\% of the household's non-DR electricity bill; (iii) allowing occasional breaches of the intended demand limit increases profit; and (iv) a dispatch strategy that accounts for demand variations across seasons increases profit further. We expect that a more advanced dispatch strategy with embedded weather forecasting capability could yield even higher profit. (C) 2015 Elsevier Ltd. All rights reserved.

Tags

Demand response systems Model Prediction Lead-acid-batteries Ion batteries Lithium Programs Lifetime