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

By Menglian Zheng, C. Meinrenken, Klaus Lackner. 2015

175 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e169e2b61ec9d3f91d1a42a3a38cd898fdef1c08

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In Automotive Engineering

1. Ming Zhou
2. Jianhui Wang
3. Gengyin Li
4. Sibo Nan

In Electrical and Electronic Engineering

1. Ming Zhou
2. Hongbin Sun
3. Qinglai Guo
4. Jianhui Wang
5. Zhengshuo Li
6. Gengyin Li
7. Sibo Nan
8. Zhaoyu Wang

Citations in the corpus

• Optimal residential community demand response scheduling in smart grid (2018, background): “[19] focuses on household distributed energy storage dispatch strategies, and Ref.”
• Value streams in microgrids: A literature review (2016, unknown): “While the economic feasibility is not directly considered in this study, a similar methodology to analyze the economic benefit of batteries is used in [30] and [31], where separate load-shifting and peak-shaving oriented algorithms are presented.”
• Storage-like devices in load leveling: Complementarity constraints and a new and exact relaxation method (2015, background): “Specifically, in [12], the problem of frequency regulation using battery storage is studied, while in [13,14], load-leveling using distributed storage in distribution grids is concerned.”