Optimization of wind farm micro-siting for complex terrain using greedy algorithm

Song, Mengxuan; Chen, Kepi; He, Zhimin; Zhang, X.

doi:10.1016/j.energy.2014.01.082

Cited by 43 publications

(30 citation statements)

References 19 publications

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“…The time to completion is 18 þ 11 þ 6 ¼ 35 h for this case. The greedy algorithm is a short-sighted optimisation solution, and may not always find the global optimum, but its general success and simplicity makes it very attractive for a wide range of applications such as financial markets [26], and the layout of wind arrays [10]. To apply the greedy algorithm to our case study, we first need to consider the average theoretical tidal power per unit area, over a tidal cycle, which is given by…”

Section: The Greedy Algorithmmentioning

confidence: 99%

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Optimal phasing of the European tidal stream resource using the greedy algorithm with penalty function

Neill¹,

Hashemi²,

Lewis³

2015

Renewable Energies Offshore

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a b s t r a c tThe regular periodicity of astronomical tides allows their accurate prediction, and so it should be possible to determine how best to optimise the future distribution of arrays of tidal energy devices for any shelf sea region. By considering together the magnitude and phase of tidal currents over a shelf sea region, maximum aggregated power generation, with minimal periods of low generation, can be deduced. Here, we make use of the greedy algorithm to optimise future exploitation of the tidal stream resource over the northwest European shelf seas, a region which contains a world-leading tidal energy resource. We also apply a penalty function to the greedy algorithm, favouring the selection of future hypothetical sites where power generation would be out-of-phase with previously developed sites. Our results demonstrate that the Pentland Firth and Channel Islands would be optimal sites for parallel development for relatively low numbers of arrays, with important contributions from the Irish Sea for larger scale exploitation. Although there is minimal phase diversity between European tidal stream sites to deliver firm power generation, it is possible that the vertical tide could contribute to such baseload through the parallel development of lagoons or impoundments.

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Section: The Greedy Algorithmmentioning

confidence: 99%

“…Ref. [10]], to optimise the aggregation of power from diverse tidal stream sites by maximising net power generation, while minimising periods when instantaneous power generation is below a threshold, by applying a penalty function.…”

Section: Introductionmentioning

confidence: 99%

Optimal phasing of the European tidal stream resource using the greedy algorithm with penalty function

Neill¹,

Hashemi²,

Lewis³

2015

Renewable Energies Offshore

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“…3. Table 2 Except for the seasonal features of wind, the effects of wakes, obstacles, and terrain inevitably affected the accuracy of wind measurements and the quality of wind power output [25,27,28]. Fig.…”

Section: Wind Data Descriptionmentioning

confidence: 99%

Improved wind prediction based on the Lorenz system

et al. 2015

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a b s t r a c tAtmospheric disturbance is a complex nonlinear process. The Lorenz system was seen as a classical model to reveal essential characteristics of nonlinear systems. It has further improved people's understanding of the evolution of the climate system. Different from traditional studies working on improving the numerical methods for wind prediction, dynamic characteristics of the atmospheric system are fully considered here. This paper proposed the concept of the Lorenz Comprehensive Disturbance Flow (LCDF) and defined the perturbation formula for wind prediction. The Lorenz disturbance has significant influence on wind forecasting, which is proved by using wind data from the Sotavento wind farm. That is to say, the change process of atmospheric motion around the wind farm is more ideally described based on the Lorenz system. This research has important theoretical value in developing nonlinear systems and plays a great role on wind prediction and wind resource exploitation.

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“…Despite these efforts, reducing the computational cost of wake evaluations while maintaining accuracy during the optimization process remains a challenge. Hence, subsequent work [10][11][12] has focused on better integration of wake modeling and optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming

et al. 2016

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Optimization of wind farm micro-siting for complex terrain using greedy algorithm

Cited by 43 publications

References 19 publications

Optimal phasing of the European tidal stream resource using the greedy algorithm with penalty function

Optimal phasing of the European tidal stream resource using the greedy algorithm with penalty function

Improved wind prediction based on the Lorenz system

Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming

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