Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

Chen, Peiyuan; Siano, Pierluigi; Bak‐Jensen, Birgitte; Chen, Zhe

doi:10.1109/tste.2010.2044900

Cited by 70 publications

(16 citation statements)

References 22 publications

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“…Gill et al [15] have shown demand curve levelling and studied the problems of locational marginal pricing with the help of DSM and DR programs. Reference [16] has studied the bidding pool-based day-ahead electricity market by using PR and PRDS to manage the congestion management of the transmission network. Some of the research papers have taken bus voltage and line current as random variables by using stochastic optimization (SO) to minimize system losses [17][18].…”

Section: Smart Grids and Demand Side Management With Renewable Energymentioning

confidence: 99%

An Integration of Smart Grids with Demand Side Management and Renewable Energy, A Review

Koul¹,

Tjprc²

2019

IJMPERD

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The scheduling and improvement of the intelligible smart grid (SG) system is a combined part of the revolutionary target of accomplishing the distributed energy generation and transmission. The progression based on the fossil generation to the smart and renewable systems involves incorporation of innovative procedures at the utility as well as consumer end. It is evident from the present scenario that requirement of customer's demand-side methods expanding at present, and also more prospects in this area are to be accomplished. This paper presents the integration of demand side management (DSM), demand response (DR) and renewable energy in SG systems. The existing literature on DR and DSM integration has been discussed by studying current and relevant publications of journals and conferences in this research area.

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Section: Smart Grids and Demand Side Management With Renewable Energymentioning

confidence: 99%

An Integration of Smart Grids with Demand Side Management and Renewable Energy, A Review

Koul¹,

Tjprc²

2019

IJMPERD

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“…Commonly used kernel functions include linear, polynomial, and RBF kernels. An SVM with the following RBF kernel [28] is used for comparison with the proposed WSVM: (4) where is the width of the RBF kernel, which determines the influence area of the SVs over the data space.…”

Section: A Least-square Support Vector Machinementioning

confidence: 99%

Short-Term Wind Power Prediction Using a Wavelet Support Vector Machine

Zeng

Wang

2012

IEEE Trans. Sustain. Energy

110

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This paper proposes a wavelet support vector machine (WSVM)-based model for short-term wind power prediction (WPP). A new wavelet kernel is proposed to improve the generalization ability of the support vector machine (SVM). The proposed kernel has such a general characteristic that some commonly used kernels are its special cases. Simulation studies are carried to validate the proposed model with different prediction schemes by using the data obtained from the National Renewable Energy Laboratory (NREL). Results show that the proposed model with a fixed-step prediction scheme is preferable for short-term WPP in terms of prediction accuracy and computational cost. Moreover, the proposed model is compared with the persistence model and the SVM model with radial basis function (RBF) kernels. Results show that the proposed model not only significantly outperforms the persistence model but is also better than the RBF-SVM in terms of prediction accuracy.

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“…Several authors have applied stochastic optimization algorithms to wind energy problems in recent literature. Chen et al optimized power factors across a series of wind turbines using sequential quadratic programming, accounting for uncertainty in future wind power generation and other factors. Cheng and Zhang proposed a probabilistic optimization algorithm for the wind turbine dispatch problem in a chance‐constrained formulation, where again wind energy output over a finite horizon is considered as the primary source of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Optimal strategies for wind turbine environmental curtailment

Rogers

2020

Wind Energy

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Wind turbine curtailment is oftentimes required as a means to mitigate environmental impacts of a wind energy installation. For example, curtailment may be required to satisfy constraints on the occurrence of shadow flicker on nearby homes or to reduce wildlife fatalities below a certain limit. This paper introduces an optimal curtailment strategy, which seeks to maximize wind plant revenue while meeting imposed environmental constraints by intelligently selecting curtailment times. To formulate the problem, a discrete set of curtailment decision times is defined, and long-term forecast data are used to predict demand-weighted power production at each time.Dynamic programming is used in conjunction with in situ meteorological sensors to compute the probabilistically optimal curtailment decision in real time. By leveraging both forecast data and real-time measurements, the algorithm ensures that the constraint budget is expended strategically, curtailing when revenue is low and saving operating hours for times when revenue is likely to be higher. Through a series of simulation studies involving shadow flicker constraints, algorithm performance is characterized and compared with two simpler approaches: a greedy scheme and a threshold-based scheme. These studies highlight the benefit of the optimal algorithm as well as the performance tradeoffs inherent in the three different solution approaches. Overall, the dynamic programming algorithm is shown to exhibit significant benefits in cases where sufficient meteorological and demand data are available. K E Y W O R D S Curtailment, Optimal Control 1 | INTRODUCTION Over the past several decades, wind energy development in the United States and other developed countries has primarily targeted sparsely populated areas with large wind resource, readily available transmission interconnects, and minimal presence of vulnerable wildlife.Many of these ideal locations for onshore wind have already been successfully developed, and thus, new activities are focusing on locations closer to populated areas. A major challenge that arises during these development activities is community acceptance and adherence to relevant wildlife regulations. Wind farms impact local residences and wildlife through sound emissions, shadow flicker, visual impact, and encroachment on the habitats of birds, bats, and other species. As a result, wind energy developers are spending increasing effort working with communities and permitting authorities to mitigate these impacts and gain community buy-in for development activities. These mitigation steps oftentimes involve curtailing turbine operation at times when they impose negative impacts on the community or wildlife population-this is sometimes deemed "environmental curtailment." For instance, curtailment may be enforced during certain hours of the night to avoid bat fatalities. 1-3 As another example, if a turbine imposes shadow flicker on a residence beyond a mutually agreed accepted limit, the operator may be required to curtail turbine operation when...

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Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

Cited by 70 publications

References 22 publications

An Integration of Smart Grids with Demand Side Management and Renewable Energy, A Review

An Integration of Smart Grids with Demand Side Management and Renewable Energy, A Review

Short-Term Wind Power Prediction Using a Wavelet Support Vector Machine

Optimal strategies for wind turbine environmental curtailment

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