Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy

Wan, Chunqiu; Wang, Jun; Yang, Geng; Gu, Huajie; Zhang, Xing

doi:10.1016/j.renene.2012.04.052

Cited by 124 publications

(46 citation statements)

References 12 publications

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“…Clearly these ideal wind cases are quite simple compared with the real wind, and they do not need much consideration in wind modelling. The same ideal wind cases were used in many following studies which mainly aimed at developing various optimization methods, including GA [4], particle swarm optimization [5], extended pattern search [6], and so on.…”

Section: Introductionmentioning

confidence: 99%

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Feng

Shen

2015

Energies

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Abstract:Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint distributions of wind speed and wind direction, which is based on the parameters of sector-wise Weibull distributions and interpolations between direction sectors. It is applied to the wind measurement data at Horns Rev and three different joint distributions are obtained, which all fit the measurement data quite well in terms of the coefficient of determination . Then, the best of these joint distributions is used in the layout optimization of the Horns Rev 1 wind farm and the choice of bin sizes for wind speed and wind direction is also investigated. It is found that the choice of bin size for wind direction is especially critical for layout optimization and the recommended choice of bin sizes for wind speed and wind direction is finally presented.

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Section: Introductionmentioning

confidence: 99%

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Feng

Shen

2015

Energies

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“…The economic dispatch problem in this paper is optimized by a Gaussian particle swarm optimization algorithm with differential evolution local search (DLGPSO) [12,13]. The DLGPSO optimizer offers a self-tuning capability based on Gaussian distribution so as to avoid the parameter sensitivity of the original PSO algorithm.…”

Section: B Synchronous Inertia Constrained Economic Dispatchmentioning

confidence: 99%

“…Meanwhile, synchronous condensers are scheduled to keep the adequacy of synchronous inertia. The proposed economic dispatch algorithm schedules wind reserve and synchronous condensers simultaneously with conventional FCAS from synchronous generators.The economic dispatch problem in this paper is optimized by a Gaussian particle swarm optimization algorithm with differential evolution local search (DLGPSO) [12,13]. The DLGPSO optimizer offers a self-tuning capability based on Gaussian distribution so as to avoid the parameter sensitivity of the original PSO algorithm.…”

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confidence: 99%

Minimum Synchronous Inertia Requirement of Renewable Power Systems

Yan

Saha

2018

IEEE Trans. Power Syst.

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151

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Abstract-The installed capacity of wind generation and photovoltaics (PV) in many countries is going to dominate generation fleets in a bid to meet growing renewable energy targets. Synchronous inertia has never been problematic as there was more available than needed, but it is being significantly reduced due to the increasing integration of non-synchronous renewable generation. When the low bidding priced generation of wind and PV becomes considerably large, conventional economic dispatch algorithms can result in less online synchronous inertia and put power system security at risk. However, the compromise of power system security due to synchronous inertia shortage is not well studied in literature. This paper develops a synchronous inertia constrained economic dispatch algorithm to satisfy the minimum required synchronous inertia of frequency control. Synchronous condensers and wind reserve are economically allocated to alleviate any shortage of synchronous inertia and frequency control ancillary services (FCAS). A Gaussian particle swarm optimization algorithm is introduced to simultaneously co-optimize the dispatch of synchronous generators and their FCAS, wind reserve and synchronous condensers.Index Terms-Synchronous inertia, economic dispatch, wind turbine, renewable energy, power system.  NOMENCLATURE   CtOverall power system operational cost in the economic dispatch cycle t in terms of net load   [2] are also crucial, the most basic and challenging problem lies in the electricity supply side.Economic dispatch algorithms proposed in literature have focused on the improvement of algorithm performance [3,4] and consideration of more operational constraints [5], such as spinning reserve and generator ramp rates. They did not dynamically evaluate the synchronous inertia adequacy of a dispatch result in terms of N-1 contingency [3][4][5]. This is due to the fact that synchronous inertia adequacy is not a problem for a conventional power system with limited non-synchronous generation.However, synchronous inertia is being materially reduced due to ongoing displacement of synchronous generators by asynchronous ones, such as wind turbine generators and photovoltaic (PV) panels. Electronic inverters used in wind turbines and PV panels lack the synchronous inertia that can help an RPS survive a major disturbance.If comprised of less online synchronous inertia, an RPS may routinely suffer a rapid rate of change of frequency (RoCoF) and a large frequency deviation following a disturbance. A great RoCoF of 6 Hz/s was recorded in the South Australian blackout on September 28, 2016 which was caused by the "lightness" of the South Australian power system [6]. The instantaneous penetration level of wind and PV generation was over 50% before the blackout in South Australia and only three thermal power plants were dispatched [6].This paper therefore proposes a synchronous inertia constrained economic dispatch to keep the minimum amount of synchronous inertia online. The proposed algorithm introduces a feedback loop...

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“…Another successful population-based metaheuristic algorithm applied to the WFDO problem is the Particle Swarm Optimization (PSO) algorithm [184,192,193,201,203,212,243,264,269,353], which was developed by Eberhart and Kennedy in 1995 [345]. The PSO algorithm is inspired by the social behavior of fish schooling and bird flocking.…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

et al. 2014

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This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research. OPEN ACCESSEnergies 2014, 7 6931

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Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy

Cited by 124 publications

References 12 publications

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction

Minimum Synchronous Inertia Requirement of Renewable Power Systems

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

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