optimal scheduling and real-time state-of-charge management of energy storage system for frequency regulation
Clicks: 244
ID: 222123
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
244 views
45 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #183 of 526 articles by views in acs combinatorial science
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 526 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
An energy storage system (ESS) in a power system facilitates tasks such as renewable integration, peak shaving, and the use of ancillary services. Among the various functions of an ESS, this study focused on frequency regulation (or secondary reserve). This paper presents an optimal scheduling algorithm for frequency regulation by an ESS. This algorithm determines the bidding capacity and base point of an ESS in each operational period to achieve the maximum profit within a stable state-of-charge (SOC) range. However, the charging/discharging efficiency of an ESS causes SOC errors whenever the ESS performs frequency regulation. With an increase in SOC errors, the ESS cannot respond to an automatic generation control (AGC) signal. This situation results in low ESS performance scores, and finally, the ESS is disqualified from performing frequency regulation. This paper also presents a real-time SOC management algorithm aimed at solving the SOC error problem in real-time operations. This algorithm compensates for SOC errors by changing the base point of the ESS. The optimal scheduling algorithm is implemented in MATLAB by using the particle swarm optimization (PSO) method. In addition, changes in the SOC when the ESS performs frequency regulation in a real-time operation are confirmed using the PSCAD/EMTDC tool. The simulation results show that the optimal scheduling algorithm manages the SOC more efficiently than a commonly employed planning method. In addition, the proposed real-time SOC management algorithm is confirmed to be capable of performing SOC recovery.
| Reference Key |
yang2016energiesoptimal
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Jin-Sun Yang;Jin-Young Choi;Geon-Ho An;Young-Jun Choi;Myoung-Hoe Kim;Dong-Jun Won |
| Journal | acs combinatorial science |
| Year | 2016 |
| DOI |
10.3390/en9121010
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.