Smoothing of Wind Farm Output by Prediction and Supervisory-Control-Unit-Based FESS

Islam, Farzana; Al‐Durra, Ahmed; Muyeen, S. M.

doi:10.1109/tste.2013.2256944

Cited by 60 publications

(35 citation statements)

References 33 publications

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“…An artificial neutral network that depends on a wind speed predictor has been designed in [65]. In it, the supervisory control unit (SCU) helps in appropriate management to store energy in a low capacity FESS.…”

Section: Flywheel Energy Storage Systemmentioning

confidence: 99%

Intermittent Smoothing Approaches for Wind Power Output: A Review

Jabir

Illias

Raza³

et al. 2017

Energies

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Abstract:Wind energy is one of the most common types of renewable energy resource. Due to its sustainability and environmental benefits, it is an emerging source for electric power generation. Rapid and random changes of wind speed makes it an irregular and inconsistent power source when connected to the grid, causing different technical problems in protection, power quality and generation dispatch control. Due to these problems, effective intermittent smoothing approaches for wind power output are crucially needed to minimize such problems. This paper reviews various intermittent smoothing approaches used in smoothing the output power fluctuations caused by wind energy. Problems associated with the inclusion of wind energy resources to grid are also briefly reviewed. From this review, it has been found that battery energy storage system is the most suitable and effective smoothing approach, provided that an effective control strategy is available for optimal utilization of battery energy system. This paper further demonstrates different control strategies built for battery energy storage system to obtain the smooth output wind power.

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Section: Flywheel Energy Storage Systemmentioning

confidence: 99%

Intermittent Smoothing Approaches for Wind Power Output: A Review

Jabir

Illias

Raza³

et al. 2017

Energies

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“…The voltage v in Equation (5) is determined by the switching states of the converter and the DC link voltage ( ), as given in Equation (8).…”

Section: Discrete-time Model Of Convertermentioning

confidence: 99%

“…Short-term ESSs such as superconducting magnetic energy storage (SMES) systems [6], electrical double-layer capacitors (EDLCs) [7], and flywheel energy storage systems (FESSs) [8][9][10] as well as long-term ESSs such as battery energy storage systems (BESSs) [11,12] are applied to microgrid control. ESSs can also be used to control the power flow at point of common coupling in the grid-connected mode as well as to regulate the frequency and voltage of a microgrid in the islanded mode.…”

Section: Introductionmentioning

confidence: 99%

Application of Model Predictive Control to BESS for Microgrid Control

Nguyen¹,

Yoo²,

Kim³

2015

Energies

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Battery energy storage systems (BESSs) have been widely used for microgrid control. Generally, BESS control systems are based on proportional-integral (PI) control techniques with the outer and inner control loops based on PI regulators. Recently, model predictive control (MPC) has attracted attention for application to future energy processing and control systems because it can easily deal with multivariable cases, system constraints, and nonlinearities. This study considers the application of MPC-based BESSs to microgrid control. Two types of MPC are presented in this study: MPC based on predictive power control (PPC) and MPC based on PI control in the outer and predictive current control (PCC) in the inner control loops. In particular, the effective application of MPC for microgrids with multiple BESSs should be considered because of the differences in their control performance. In this study, microgrids with two BESSs based on two MPC techniques are considered as an example. The control performance of the MPC used for the control microgrid is compared to that of the PI control. The proposed control strategy is investigated through simulations using MATLAB/Simulink software. The simulation results show that the response time, power and voltage ripples, and frequency spectrum could be improved significantly by using MPC.

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“…Previously, researchers have focused on the operational control technology for single type ESSs [11][12][13] and the control performance optimization for hybrid ESSs [14][15][16], improving the storage system optimization configuration to enhance the performance of wind power access systems [17][18][19][20]. Thus, many studies have investigated large-scale ESSs, but the problem of coordinating control for multiple ESSs has not been addressed previously.…”

Section: Introductionmentioning

confidence: 99%

A quasi-automated generation control strategy for multiple energy storage systems to optimize low-carbon benefits

Yan

Zheng

et al. 2015

J. Mod. Power Syst. Clean Energy

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Integrating a battery energy storage system (ESS) with a large wind farm can smooth the intermittent power obtained from the wind farm, but the smoothing function will not be achieved if multiple ESSs are used to smooth the fluctuations in individual wind power plants in a distributed pattern. Therefore, this study focuses on the development of a control strategy to optimize the use of multiple ESSs to accelerate the adoption of wind energy resources. This paper proposes a quasi-automated generation control (QAGC) strategy to coordinate multiple ESSs, which responds to the grid dispatch demand rather than smoothing out the intermittent power from individual wind farms. The aims of QAGC are to ensure that multiple ESSs provide a service that is as balanced as possible, so more wind power systems at various scales can be accepted by the grid, as well maximizing the low-carbon benefits of ESSs. The effectiveness of QAGC is demonstrated by using data from an actual gigawatt scale cluster of wind plants.

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Smoothing of Wind Farm Output by Prediction and Supervisory-Control-Unit-Based FESS

Cited by 60 publications

References 33 publications

Intermittent Smoothing Approaches for Wind Power Output: A Review

Intermittent Smoothing Approaches for Wind Power Output: A Review

Application of Model Predictive Control to BESS for Microgrid Control

A quasi-automated generation control strategy for multiple energy storage systems to optimize low-carbon benefits

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