Centralized Power Reserve Algorithm of De-loaded Wind Farm for Primary Frequency Regulation

Bubshait, Abdullah; Alsaleem, Abdulhakeem; Simões, Marcelo Godoy

doi:10.1109/ecce.2018.8557561

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…Given that the target power of AGC is 31.07 MW, based on (28) and Table S4, the output power adjustment amount ΔPower is calculated to be 5250 kW. This can further be used to determine the control sequence of control WTs by (20). The calculation results show that all WTs except for the template WTs participate in this power adjustment.…”

Section: Results Of Active Power Distributionmentioning

confidence: 99%

“…Different control strategies have different coordination control results. Some scholars formulated the coordination control problem as multi-objective optimisation problems, which coordinate the power adjustment amount undertaken between WTs by multi-objective optimisation algorithm (MOOPA) [16][17][18][19][20][21][22]. The minimum target power tracking error, the minimum energy consumption cost and the minimum mechanical load of WTs are taken as the multi-objectives to optimise the output power of WTs, which is used to coordinate all WTs as a whole could optimally work at the desired operating states.…”

Section: Introductionmentioning

confidence: 99%

“…Authors took the pitch angle, rotation speed and wind speed of WTs as input, used fuzzy theory to evaluate the WTs' adjustment performance and adopted the ability weight distribution algorithm (AWDA) to coordinate all WTs [28]. The control strategy of [16][17][18][19][20][21][22][23][24][25][26][27][28] is shown in Fig. 1 (below the dotted line).…”

Section: Introductionmentioning

confidence: 99%

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Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

Liu

Wang

Chen

et al. 2020

IET Generation Trans & Dist

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With the real‐time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs’ output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs’ DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF.

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Section: Results Of Active Power Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

Liu

Wang

Chen

et al. 2020

IET Generation Trans & Dist

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“…While it is necessary to consider the results more cautiously due to the use of wake models, which may not capture the effects of changing thrust and/or TSR , as opposed to the high‐fidelity CFD methods used above, other studies have optimized wind farms for frequency regulation, 11–13,46,51,58,73 reducing power gradients, 80 and providing reactive power dispatch 16 using some form of AIC. It is likely that all of these capabilities have great potential with AIC, but future work using high‐fidelity methods that accurately represent wind turbine wake dynamics and their effects on power production are needed.…”

Section: Wake Management Techniquesmentioning

confidence: 99%

Review of wake management techniques for wind turbines

Houck

2021

Wind Energy

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Summary The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested.

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“…In [17]- [19] centralized control architectures are developed to facilitate the provision of PFR from large-scale wind farms (WFs) to the TS. In particular, in these papers, optimization problems are formulated aiming to optimally distribute PFR among the wind turbines of a WF based on different operational objectives, e.g., cost minimization [17], power loss minimization [18], maximization of kinetic energy [19], etc. The concept of virtual power plant (VPP) is introduced in [20] to coordinate the operation of combined wind and photovoltaic (PV) power plants for the provision of frequency ASs to the TS.…”

Section: Introductionmentioning

confidence: 99%

Provision of Primary Frequency Response as Ancillary Service From Active Distribution Networks to the Transmission System

Kontis

Nozal

Mauricio

et al. 2021

IEEE Trans. Smart Grid

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This paper deals with the provision of primary frequency response (PFR) as ancillary service (AS) from active distribution networks (ADNs) to the transmission system (TS). In particular, two methodologies are developed. The first one aims to quantify the PFR capability range of the ADN. This range is defined by determining the range of the aggregated, i.e., equivalent, active power -frequency P (f ) droop curves that can be provided at the point of interconnection (POI) with the TS. The second one targets to optimally control P (f ) droop curves of individual distributed energy resources (DERs), installed in the premises of the ADN, to guarantee specific frequency regulation characteristic at the POI. This frequency regulation characteristic is expressed by means of a P (f ) droop curve. Both methods are tested on two discrete distribution systems. Several test cases are examined to demonstrate their implementation. Additionally, comparisons against conventional approaches and time series simulations are conducted to evaluate the performance of the proposed methods.

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Centralized Power Reserve Algorithm of De-loaded Wind Farm for Primary Frequency Regulation

Cited by 6 publications

References 12 publications

Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

Review of wake management techniques for wind turbines

Provision of Primary Frequency Response as Ancillary Service From Active Distribution Networks to the Transmission System

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