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Title: Energy storage arbitrage under day-ahead and real-time price uncertainty

Abstract

Electricity markets must match real-time supply and demand of electricity. With increasing penetration of renewable resources, it is important that this balancing is done effectively, considering the high uncertainty of wind and solar energy. Storing electrical energy can make the grid more reliable and efficient and energy storage is proposed as a complement to highly variable renewable energy sources. However, for investments in energy storage to increase, participating in the market must become economically viable for owners. This paper proposes a stochastic formulation of a storage owner’s arbitrage profit maximization problem under uncertainty in day-ahead (DA) and real-time (RT) market prices. The proposed model helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage. Finally, case study results on realistic market price data show that the novel stochastic bidding approach does significantly better than the deterministic benchmark.

Authors:
ORCiD logo [1]; ORCiD logo [2];  [2];  [2];  [2]
  1. Argonne National Lab. (ANL), Lemont, IL (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
Argonne National Laboratory - Laboratory Directed Research and Development (LDRD); USDOE
OSTI Identifier:
1358239
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: PP; Journal Issue: 99; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; battery storage; plants; energy storage; markets; price arbitrage

Citation Formats

Krishnamurthy, Dheepak, Uckun, Canan, Zhou, Zhi, Thimmapuram, Prakash, and Botterud, Audun. Energy storage arbitrage under day-ahead and real-time price uncertainty. United States: N. p., 2017. Web. doi:10.1109/TPWRS.2017.2685347.
Krishnamurthy, Dheepak, Uckun, Canan, Zhou, Zhi, Thimmapuram, Prakash, & Botterud, Audun. Energy storage arbitrage under day-ahead and real-time price uncertainty. United States. doi:10.1109/TPWRS.2017.2685347.
Krishnamurthy, Dheepak, Uckun, Canan, Zhou, Zhi, Thimmapuram, Prakash, and Botterud, Audun. Tue . "Energy storage arbitrage under day-ahead and real-time price uncertainty". United States. doi:10.1109/TPWRS.2017.2685347. https://www.osti.gov/servlets/purl/1358239.
@article{osti_1358239,
title = {Energy storage arbitrage under day-ahead and real-time price uncertainty},
author = {Krishnamurthy, Dheepak and Uckun, Canan and Zhou, Zhi and Thimmapuram, Prakash and Botterud, Audun},
abstractNote = {Electricity markets must match real-time supply and demand of electricity. With increasing penetration of renewable resources, it is important that this balancing is done effectively, considering the high uncertainty of wind and solar energy. Storing electrical energy can make the grid more reliable and efficient and energy storage is proposed as a complement to highly variable renewable energy sources. However, for investments in energy storage to increase, participating in the market must become economically viable for owners. This paper proposes a stochastic formulation of a storage owner’s arbitrage profit maximization problem under uncertainty in day-ahead (DA) and real-time (RT) market prices. The proposed model helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage. Finally, case study results on realistic market price data show that the novel stochastic bidding approach does significantly better than the deterministic benchmark.},
doi = {10.1109/TPWRS.2017.2685347},
journal = {IEEE Transactions on Power Systems},
number = 99,
volume = PP,
place = {United States},
year = {Tue Apr 04 00:00:00 EDT 2017},
month = {Tue Apr 04 00:00:00 EDT 2017}
}

Journal Article:
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Cited by: 1 work
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