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

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 so...

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Bibliographic Details
Published inEnergies (Basel) Vol. 12; no. 5; p. 843
Main Authors Nguyen Hong, Nhung, Nakanishi, Yosuke
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 04.03.2019
Subjects
Alternative energy sources
chance-constrained programming
Contingency
day-ahead scheduling
Electric power generation
Energy industry
Energy storage
energy storage system
Expected values
fast frequency response
Frequency dependence
Frequency response
Generators
Industrial plants
Integer programming
Monte Carlo simulation
Operating costs
Optimization
Power plants
Synchronous machines
Wind power
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12050843