Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

Nhung Nguyen Hong and Yosuke Nakanishi
Additional contact information
Nhung Nguyen Hong: Graduate School of Environment and Energy Engineering, Waseda University, Tokyo 169-8050, Japan
Yosuke Nakanishi: Graduate School of Environment and Energy Engineering, Waseda University, Tokyo 169-8050, Japan

Energies, 2019, vol. 12, issue 5, 1-20

Abstract: Nowadays, the hybrid wind–diesel system is widely used on small islands. However, the operation of these systems faces a major challenge in frequency control due to their small inertia. Furthermore, it is also difficult to maintain the power balance when both wind power and load are uncertain. To solve these problems, energy storage systems (ESS) are usually installed. This paper demonstrates the effectiveness of using ESS to provide Fast Frequency Response (FFR) to ensure that the frequency criteria are met after the sudden loss of a generator. An optimal day-ahead scheduling problem is implemented to simultaneously minimize the operating cost of the system, take full advantage of the available wind power, and ensure that the ESS has enough energy to provide FFR when the wind power and demand are uncertain. The optimization problem is formulated in terms of two-stage chance-constrained programming, and solved using a Modified Sample Average Approximation (MSAA) algorithm—a combination of the traditional Sample Average Approximation (SAA) algorithm and the k-means approach. The proposed method is tested with a realistic islanded power system, and the effects of the ESS size and its response time is analyzed. Results indicate that the proposed model should perform well under real-world conditions.

Keywords: chance-constrained programming; day-ahead scheduling; energy storage system; fast frequency response; wind power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/5/843/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/5/843/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:5:p:843-:d:210840

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().