Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Yang, Kyoungboo; Cho, Kyung‐Ho

doi:10.3390/en12234403

Cited by 27 publications

(19 citation statements)

References 43 publications

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“…Therefore, parameter tuning is also a vital part of the controller design process. At present, the most widely studied parameter optimization methods are intelligent optimization algorithms, including particle swarm optimization (PSO) [19], genetic algorithm (GA) [20], and simulated annealing (SA) [21]. However, these algorithms have poor robustness and can only obtain the optimal parameters of the controller under certain operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

et al. 2021

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To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.

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Section: Introductionmentioning

confidence: 99%

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

et al. 2021

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“…Thus, feasible comparisons of these newer works with that of Mosetti et al [3] are limited. Other works adopted identical conditions to those proposed by Mosetti et al and used genetic algorithms [7][8][9][10], particle swarm optimisation [11], mixed integer programming [12,13] and simulated annealing [14], for example.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem

Manikowski

Walker

Craven

2021

JMSE

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Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

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“…It was shown that innovative solutions dealing with turbines, such as a lubrication system for hollow roller bearings, can lead to increased energy efficiency and system output [17,18]. Different algorithms have been employed to find the optimal layout of wind farm and placement of wind turbines for different regions [19,20]. Furthermore, impacts of climate change on wind energy resources have been evaluated using different GCMs and also regional climate models for different areas all over the world.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Framework to Investigate Wind Energy Sustainability Considering Climate Change Impacts

et al. 2020

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Wind power is a key element for future renewable energy resources and plays a vital role in sustainable development. Global warming and future climate conditions are going to impact many atmospheric, oceanic, and earth systems. In this study, impacts of climate change on wind power resources under future climatic conditions are evaluated for the Persian Gulf to explore the sustainability of this kind of energy for present and future developments. To that end, three regional climate models obtained from coordinated regional downscaling experiment (CRODEX), including daily simulations of near-surface wind speeds for a 20-year period in the present and future, were considered. Prior to computing the wind power at turbine hub-height, historical simulations of CORDEX were evaluated versus ERA-Interim wind outputs to determine the accuracy of the regional climate models. An attempt was made to build an ensemble model from available models by assigning weights to the models based on their merits. Subsequently, the wind power at the turbine hub-height was computed for historical and future periods to detect the impacts of climate change. Some points with a relatively high energy potential were selected as energy hotspots for further investigations. The results revealed that the mean annual wind power over the study area changed remarkably, which is of great importance for sustainable developments. Moreover, the results of the directional investigations showed roughly the same directional distribution for the future period as the past.

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Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Cited by 27 publications

References 43 publications

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem

An Ensemble Framework to Investigate Wind Energy Sustainability Considering Climate Change Impacts

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