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Efficient optimisation of wind power under acoustic constraints

Baldwin Dumortier 1, 2 Emmanuel Vincent 1, 2 Madalina Deaconu 3, 4 Patrice Cornu 5 
1 PAROLE - Analysis, perception and recognition of speech
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
3 TOSCA - TO Simulate and CAlibrate stochastic models
CRISAM - Inria Sophia Antipolis - Méditerranée , IECL - Institut Élie Cartan de Lorraine : UMR7502
Abstract : Great attention is currently paid to protecting residential areas from the noise pollution due to wind turbines. A family of acoustic constraints was developed to assess it. Maximizing electricity production under these constraints is difficult as they rapidly evolve with weather conditions and background noise. Today, this problem is addressed by computing a curtailment plan involving fixed operating modes, but the chosen modes are often suboptimal. In this article, we show that this problem can be expressed as a non-linear knapsack problem and we solve it using an efficient branch-and-bound (B&B) algorithm that converges asymptotically to the global optimum. The algorithm is initialised with a greedy heuristic that iteratively downgrades the turbines with the best acoustical to electricity loss ratio. The solution is then refined using a depth-first search strategy and a bounding stage based on a continuous relaxation problem solved with an adapted gradient algorithm. The results are evaluated using data from 28 real wind farms.
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Submitted on : Sunday, November 6, 2016 - 3:50:23 PM
Last modification on : Saturday, May 7, 2022 - 3:10:50 AM
Long-term archiving on: : Tuesday, February 7, 2017 - 12:38:40 PM


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  • HAL Id : hal-01393125, version 1


Baldwin Dumortier, Emmanuel Vincent, Madalina Deaconu, Patrice Cornu. Efficient optimisation of wind power under acoustic constraints. 2016. ⟨hal-01393125⟩



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