Hierarchical Active Power Control of DFIG-Based Wind Farm With Distributed Energy Storage Systems Based on ADMM

Wu, Qiuwei; Guo, Yifei; Rong, Fei

doi:10.1109/tste.2019.2929820

Cited by 47 publications

(24 citation statements)

References 31 publications

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“…Objective 3 The third objective is to minimize the battery degradation caused by charging and discharging of the BESS. According to [35], the ESS degradation cost model can be expressed as,…”

Section: Cost Functionmentioning

confidence: 99%

“…Objective 3 : The third objective is to minimise the battery degradation caused by charging and discharging of the BESS. According to [35], the ESS degradation cost model can be expressed as

B C = \frac{false(P_{ESS}^{ch} + P_{ESS}^{dis} false) μ_{ESS} γ_{ESS} T_{c}}{a_{1} false[a_{2} false(1 - {SOC}^{0} false) + a_{3} false] e^{a_{4} T_{ESS}} E_{R} false(1 - {SOC}^{ref} false)}

…”

Section: Formulation Of Mpc‐sicmentioning

confidence: 99%

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Synthetic inertial control of wind farm with BESS based on model predictive control

Bao

Ding

et al. 2020

IET Renewable Power Generation

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Wind farms (WFs) can provide controlled inertia through synthetic inertial control (SIC) to support system frequency recovery after disturbances. This paper proposes a model predictive control (MPC) based SIC for a WF consisting of wind turbines (WTs) and a battery storage energy system (BESS). In the proposed MPC-SIC, the active power output of the WTs and BESS during the SIC are optimally coordinated in order to avoid over-deceleration of the WTs' rotor, and minimize the loss of extracted wind energy during the SIC and degradation cost of the BESS. The IEEE 39 bus system with a WF consisting of 100 WTs and a BESS is used to validate the performance of the proposed MPC-SIC. Case studies show that, compared with the conventional SIC, the minimum rotor speed among all WTs with MPC-SIC can be improved by 0.08-0.11 p.u., the loss of captured wind energy of WF with MPC-SIC can be reduced by 12%-64% and the degradation cost of the BESS with MPC-SIC can be reduced by 72%-83%. The results proves that with the proposed MPC-SIC, the wind farm can avoid the over-deceleration of the WTs' rotor and reduce the operation cost of the WF by improving the efficiency of wind energy usage and lifetime of the BESS.

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Section: Cost Functionmentioning

confidence: 99%

“…Objective 3 : The third objective is to minimise the battery degradation caused by charging and discharging of the BESS. According to [35], the ESS degradation cost model can be expressed as

B C = \frac{false(P_{ESS}^{ch} + P_{ESS}^{dis} false) μ_{ESS} γ_{ESS} T_{c}}{a_{1} false[a_{2} false(1 - {SOC}^{0} false) + a_{3} false] e^{a_{4} T_{ESS}} E_{R} false(1 - {SOC}^{ref} false)}

…”

Section: Formulation Of Mpc‐sicmentioning

confidence: 99%

Synthetic inertial control of wind farm with BESS based on model predictive control

Bao

Ding

et al. 2020

IET Renewable Power Generation

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“…These functioning modes of DFIG are liable for the three-phase terminal voltage (Vabc), current (Iabc) as well as real and reactive (P, Q) power direction at grid frequency with constant DC link voltage [10]. The covering approaches for Vabc, Iabc and P, Q, DC-link voltage, speed, transient, and dynamic performance are depicted in [11][12][13]. So, it is essential to develop control techniques for DFIG based WECS.…”

Section: Introductionmentioning

confidence: 99%

Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

Bharti

Sarita

Vardhan³

et al. 2021

IET Renewable Power Gen

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This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…By searching in the literature, it is easy to find that the MPC technology has been applied rigorously in the field of wind farm power control and different MPC strategies, such as multispatial-temporal hierarchical MPC [19][20][21], distributed MPC [22][23][24][25], nonlinear MPC [26,27], adaptive MPC [28][29][30], etc., have been gradually developed. Thus, it is essential to provide a summary of the application of the MPC technology to wind power control in wind farms.…”

Section: Introductionmentioning

confidence: 99%

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Li¹,

Hu²,

Yu³

et al. 2021

JEPT

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Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

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Hierarchical Active Power Control of DFIG-Based Wind Farm With Distributed Energy Storage Systems Based on ADMM

Cited by 47 publications

References 31 publications

Synthetic inertial control of wind farm with BESS based on model predictive control

Synthetic inertial control of wind farm with BESS based on model predictive control

Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

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