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Day-Ahead Renewable Energy Commitment with Battery Energy Storage.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Day-Ahead Renewable Energy Commitment with Battery Energy Storage./
Author:	Sawaqed, Sara.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	71 p.
Notes:	Source: Masters Abstracts International, Volume: 84-01.
Contained By:	Masters Abstracts International84-01.
Subject:	Industrial engineering. -
ISBN:	9798834056423

Day-Ahead Renewable Energy Commitment with Battery Energy Storage.
Sawaqed, Sara.

Day-Ahead Renewable Energy Commitment with Battery Energy Storage. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 71 p.

Source: Masters Abstracts International, Volume: 84-01.

Thesis (M.S.)--State University of New York at Binghamton, 2022.

This item must not be sold to any third party vendors.

Electricity generation via renewable energy sources has expanded rapidly in recent years due to increasing interest in environmental sustainability. Nowadays, renewable energy sources are becoming a major part of the electricity generation portfolio as they become technically and economically competitive. Battery Energy Storage (BES) has been introduced and integrated to dispatch electricity generated by renewable energy sources to efficiently meet electricity demand. This research proposes a numerical analysis method to investigate and evaluate the value of using the BES considered for day-ahead renewable energy commitment based on both deterministic and stochastic optimization models. Due to the uncertainty in generation by renewable energy sources and time-varying electricity prices in the market, determining the optimal day-ahead commitment is considered a complicated problem. Therefore, a two-stage stochastic optimization model is proposed and formulated to optimize day-ahead commitments in response to variability and uncertainty. The value of the stochastic solution (VSS) is evaluated and measured in this study to show the effectiveness of using the stochastic optimization model over the deterministic model. The numerical results show that the profits for the deterministic and the stochastic models increased by 3% and 5%, respectively, when the BES is integrated and used. The VSS approach shows that the stochastic optimization model outperforms the deterministic model by 20%.

ISBN: 9798834056423Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Battery energy storage

Day-Ahead Renewable Energy Commitment with Battery Energy Storage.
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245 1 0 $a Day-Ahead Renewable Energy Commitment with Battery Energy Storage.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 71 p.
500 $a Source: Masters Abstracts International, Volume: 84-01.
500 $a Advisor: Yoon, Sang Won.
502 $a Thesis (M.S.)--State University of New York at Binghamton, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Electricity generation via renewable energy sources has expanded rapidly in recent years due to increasing interest in environmental sustainability. Nowadays, renewable energy sources are becoming a major part of the electricity generation portfolio as they become technically and economically competitive. Battery Energy Storage (BES) has been introduced and integrated to dispatch electricity generated by renewable energy sources to efficiently meet electricity demand. This research proposes a numerical analysis method to investigate and evaluate the value of using the BES considered for day-ahead renewable energy commitment based on both deterministic and stochastic optimization models. Due to the uncertainty in generation by renewable energy sources and time-varying electricity prices in the market, determining the optimal day-ahead commitment is considered a complicated problem. Therefore, a two-stage stochastic optimization model is proposed and formulated to optimize day-ahead commitments in response to variability and uncertainty. The value of the stochastic solution (VSS) is evaluated and measured in this study to show the effectiveness of using the stochastic optimization model over the deterministic model. The numerical results show that the profits for the deterministic and the stochastic models increased by 3% and 5%, respectively, when the BES is integrated and used. The VSS approach shows that the stochastic optimization model outperforms the deterministic model by 20%.
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653 $a Solar power generation
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