Evaluation of Advanced Operation and Control of Distributed Wind Farms to Support Efficiency and Reliability

Keane, Andrew; Cuffe, Paul; Diskin, Ellen; Brooks, D.; Harrington, Philip; Hearne, Tony; Rylander, Matthew; Fallon, Teresa

doi:10.1109/tste.2012.2200000

Cited by 22 publications

(10 citation statements)

References 17 publications

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“…The DWGs active and reactive power output are decoupled, which is in line with the modern wind turbine generator's reactive power capability [29], [30]. The decision variables' limits, as well as λ curt input parameter, of the MPOPF model, are listed below.…”

Section: A Mpopf Model Input Parametersmentioning

confidence: 99%

Autonomous Curtailment Control in Distributed Generation Planning

Džamarija

Keane

2016

IEEE Trans. Smart Grid

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While enabling prominent growth of renewablebased distributed generation (DG) and matching energy harvesting, nonfirm connection policy requires accompanying active and reactive power dispatch control. Centralized control often implies comprehensive communication infrastructure and notable capital investment. Hence, DG planning models tailored for decentralized management of thermal and voltage constraints are found wanting. This paper presents a DG planning methodology, which successfully incorporates a new decentralized active power curtailment control and also includes decentralized voltage control. It optimizes autonomous control settings for each generator individually, such that the net energy export from the distribution to transmission level, per unit of installed capacity, is maximized. Comparative time series analysis validates the methodology.Index Terms-Distributed generation (DG), network planning, optimal power flow (OPF), power curtailment control.

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Section: A Mpopf Model Input Parametersmentioning

confidence: 99%

Autonomous Curtailment Control in Distributed Generation Planning

Džamarija

Keane

2016

IEEE Trans. Smart Grid

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“…The FACTS devices implementations include Static VAR Compensator (SVC) [7]- [9], Thyristor Controlled Series Capacitor (TCSC) [10], Thyristor Controlled Phase Shifting Transformer (TCPST) [11], Unified Power Flow Control (UPFC) [12], Dynamic Voltage Restorer (DVR) [13] and many others [14]. Some research results also showed that the use of distributed generation could overcome the voltage profile problem and power losses in distribution system, and also claimed to improve the distribution system performances in terms of power quality [13], reliability [15][16], and stability [17]- [18]. This paper presents the investigation results on the use of combination of two FACTS devices, which are a TCPST and a TCSC, to overcome the voltage profile problem.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Thyristor Controlled Phase Shifting Transformer using PSO Algorithm

Suyono

Hasanah

Pranyata³

2018

IJECE

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The increase of power system demand leads to the change in voltage profile, reliability requirement and system robustness against disturbance. The voltage profile can be improved by providing a source of reactive power through the addition of new power plants, capacitor banks, or implementation of Flexible AC Transmission System (FACTS) devices such as Static VAR Compensator (SVC), Unified Power Flow Control (UPFC), Thyristor Controlled Series Capacitor (TCSC), Thyristor Controlled Phase Shifting Transformer (TCPST), and many others. Determination of optimal location and sizing of device injection is paramount to produce the best improvement of voltage profile and power losses reduction. In this paper, optimization of the combined advantages of TCPST and TCSC has been investigated using Particle Swarm Optimization (PSO) algorithm, being applied to the 30-bus system IEEE standard. The effectiveness of the placement and sizing of TCPST-TCSC combination has been compared to the implementation of capacitor banks. The result showed that the combination of TCPST-TCSC resulted in more effective improvement of system power losses condition than the implementation of capacitor banks. The power losses reduction of 46.47% and 42.03% have been obtained using of TCPST-TCSC combination and capacitor banks respectively. The TCPST-TCSC and Capacitor Bank implementations by using PSO algorithm have also been compared with the implementation of Static VAR Compensator (SVC) using Artificial Bee Colony (ABC) Algorithm. The implementation of the TCSC-TCPST compensation with PSO algorithm have gave a better result than using the capacitor bank with PSO algorithm and SVC with the ABC algorithm.

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“…The basic task of Distribution Network Planning (DNP) is to determine the location, capacity of the substation, feeders and Distributed Generations (DGs) at a minimum investment scheme, satisfying load growth demand and the increasing uncertainties brought by uncontrollable DGs in the modern distribution system [1], [2]. The presence of DGs in distribution network can improve the reliability, efficiency and security of the system [3]. However, the output of non-dispatchable DGs, such as wind turbine generation and photovoltaic, are very volatile.…”

Section: Introductionmentioning

confidence: 99%

Active Distribution Network Optimization Based on Mixed Integer Linear Programming

Zong¹,

Hui²,

Zhang³

et al. 2018

IJOEE

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Various distribution network optimization techniques considering Distributed Generations (DGs) are reviewed in this paper, and a new optimization method based on Mixed Integer Linear Programming (MILP) is proposed. The proposed methodology optimizes the capacity of DGs and the optimal feeder line capacity simultaneously by a cost/benefit analysis. Besides, the line loss in distribution network is explicitly analyzed by using four different methods in the paper. For simplicity, the line loss can be appropriately simplified as a quadratic function of difference of voltage phase angle. Then it is further linearized by using different linearization strategies and then compared with the results by using Mixed Integer Nonlinear Programming (MINLP) method. Finally, the proposed active distribution network planning model with selected linearization technique is tested on the IEEE 33-node distribution network system.

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Evaluation of Advanced Operation and Control of Distributed Wind Farms to Support Efficiency and Reliability

Cited by 22 publications

References 17 publications

Autonomous Curtailment Control in Distributed Generation Planning

Autonomous Curtailment Control in Distributed Generation Planning

Optimization of the Thyristor Controlled Phase Shifting Transformer using PSO Algorithm

Active Distribution Network Optimization Based on Mixed Integer Linear Programming

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