2,182 research outputs found

    Evolutionary computation for wind farm layout optimization

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    This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results

    Informed mutation of wind farm layouts to maximise energy harvest

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    Correct placement of turbines in a wind farm is a critical issue in wind farm design optimisation. While traditional "trial and error"-based approaches suffice for small layouts, automated approaches are required for larger wind farms with turbines numbering in the hundreds. In this paper we propose an evolutionary strategy with a novel mutation operator for identifying wind farm layouts that minimise expected velocity deficit due to wake effects. The mutation operator is based on constructing a predictive model of velocity deficits across a layout so that mutations are inherently biased towards better layouts. This makes the operator informed rather than randomised. We perform a comprehensive evaluation of our approach on five challenging simulated scenarios using a simulation approach acceptable to industry [1]. We then compare our algorithm against two baseline approaches including the Turbine Displacement Algorithm [2]. Our results indicate that our informed mutation approach works effectively, with our approach identifying layouts with the lowest aggregate velocity deficits on all five test scenarios

    RNA Interference (RNAi) for plants

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    As we are facing global population development, strategies are required to improve agricultural production in the battle against hunger and poverty. Agricultural biotechnology provides a powerful method in combination of conventional breeding, new innovations and enhanced management of resources which improves the productivity of livestock, aquaculture, and crops. After the finding of RNA interference (RNAi), researchers have made considerable growth in improving this remarkable crop especially in defence technology. RNA interference is a vital plant growth, development and reaction regulator to various types of stresses. This technology leads to higher efficiency and potency of gene silencing, thus becoming the highly promising technology for crop improvements at a rapid rate with some advantages. Nowadays, RNAi has been widely used for the improvement in agricultural biotechnology and seems to be applicable and commercialized in other fields too

    BlockCopy-based operators for evolving efficient wind farm layouts

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    A novel search operator, BlockCopy, is proposed for efficiently solving the wind farm layout optimisation problem. BlockCopy, which can be used either as mutation or a crossover operator, copies patterns of turbines from part of a layout to another part. The target layout may be the same as the source, or a different layout altogether. The rationale behind this is that it is the relative configurations of turbines rather than their individual absolute positions on the layouts that count, and BlockCopy, for the most part, maintains relative configurations. Our evaluation on four benchmark scenarios shows that BlockCopy outperforms two other standard approaches (namely, the turbine displacement algorithm and random perturbation) from the literature. We also evaluate the BlockCopy operator in conjunction with both singlesolution and population-based strategies

    Building a Digital Wind Farm

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    IoTにおけるリソースの最適化

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    Recently, there are more than 9 billion things that connected in the Internet of Things (IoT) and the number is exceed more than 24 billion in 2020. It means that numerous data will be generated because of increasing quickly of the number of things. An infrastructure should be developed to manage the connected things in IoT. Moreover, cloud computing will play important role in terms of data storage and analysis for IoT. Therefore, a cloud broker is considered as an intermediary in the infrastructure for managing the connected things. The cloud broker will find the best deal between clients and service providers. However, there are three problems among cloud broker, clients and service providers that are the response time of the request from clients, the energy consumption of the system and the profit of the cloud broker. The three problems are considered as multi-objective optimization problem to maximize the profit of the broker while minimizing the response time of the request and the energy consumption. A multi-objective particle swarm optimization (MOPSO) is proposed to solve the problem. MOPSO is compared with a non-dominated sorting genetic algorithm-II (NSGA-II) and a random search algorithm to show the performance. Since, there are a lot of data including social media and geographic location, generated in IoT. Coupling social media with geographic location has boosted the worth of understanding the real-world situations. Event detection aims to find more specific topic which represents real-world event. However,identification of unusual and seemingly inconsistent patterns in data, called outliers, is necessary. The problem is how to partition a spatio-temporal domain to find a meaningful local outlier pattern. A k-dimensional (KD) tree partitioning is applied to divide a spatio-temporal domain into sub-cells. The optimal partitioning problem in a spatio-temporal domain has been proven as an NP-complete problem. Therefore, a genetic algorithm is proposed to solve the problem. Moreover, the smart grid is strongly related to IoT technologies. It is enabled by IoT to handle big data and reduce the number of communication protocols. The micro-grid is studied because of micro-grids are part of a larger system that makes the smart grid to become reality. The operation management problem and pollutant emission problem are important problems for the micro-grid system. Thus, reducing the total energy expenses and pollutant emission of micro-grid and improving the renewable energy sources (battery energy storage) are considered together with the operation management of the micro-grid system. A fitness-based modified game particle swarm optimization (FMGPSO) algorithm is proposed to minimize the total costs of operation and pollutant emissions in the microgrid and multi-microgrid system. FMGPSO is compared with A non-dominated sorting genetic algorithm-III (NSGA-III), a multi-objective covariance matrix adaptation evolution strategy (MO-CMAES), and a speed-constrained multi-objective particle swarm optimization (SMPSO) to show the performance.最近では、Internet of Things(IoT)に接続されているものは90億件を超え、2020年には240億件を超えている。それは、物事の数が急速に増えるため、多くのデータが生成されることを意味する。IoTで接続されたものを管理するためのインフラストラクチャを開発する必要がある。さらに、クラウドコンピューティングは、IoTのデータストレージと分析の観点から重要な役割を果たしている。したがって、クラウドブローカーは、接続されたものを管理するためのインフラストラクチャの仲介者とみなされる。クラウドブローカーは、クライアントとサービスプロバイダーの間で最良の取引を見つけるだろう。しかし、クラウドブローカー、クライアントおよびサービスプロバイダーには、クライアントからの要求の応答時間、システムのエネルギー消費、クラウドブローカーのプロセスという3つの問題がある。この3つの問題は、要求の応答時間とエネルギー消費を最小限に抑えながら、ブローカーのプロビジョニングを最大化するための多目的最適化問題とみなされる。この問題を解決するために、多目的粒子群最適化(MOPSO)が提案されている。 MOPSOは、非優性選別遺伝的アルゴリズム-II(NSGA-II)およびランダム探索アルゴリズムと比較され、性能が示される。ソーシャルメディアや地理的な場所など、IoTで生成される多くのデータがあるためである。地理的な場所とソーシャルメディアを結び付けることで、現実の状況を理解する価値が高まっている。イベント検出は、実際のイベントを表すより特定のトピックを見つけることを目指している。しかし、異常値と呼ばれる異常なパターンや一見不整合なパターンの同定が必要である。問題は、時空間ドメインを分割して意味のある局所的な奇妙なパターンを見つける方法である。時空間領域をサブセルに分割するために、k次元(KD)ツリー分割が適用される。時空間領域における最適な分割問題は、NP完全な問題として証明されている。したがって、この問題を解決するための遺伝的アルゴリズムが提案されている。さらに、スマートグリッドはIoT技術と強く関連している。 IoTによって大きなデータを処理し、通信プロトコルの数を減らすことができる。マイクログリッドはスマートグリッドを現実化させるより大きなシステムの一部であるため、マイクログリッドが研究されている。運用管理上の問題や汚染物質排出問題は、マイクログリッドシステムにとって重要な問題である。したがって、マイクログリッドシステムの運用管理とともに、マイクログリッドの総エネルギー費用と汚染物質排出量の削減と再生可能エネルギー源の改善(バッテリエネルギー貯蔵)が考慮されている。マイクログリッドおよびマルチマイクログリッドシステムにおける操作および汚染物質排出の総コストを最小限に抑えるため、MGPSO アルゴリズムが提案されている。 FMGPSOは、非優先ソート遺伝的アルゴリズム-III(NSGA-III)、多目的共分散行列適応進化戦略(MO-CMAES)、および性能を示すために速度が制約された多目的粒子群最適化(SMPSO)と比較される。室蘭工業大学 (Muroran Institute of Technology)博士(工学

    Surrogate-Assisted Evolutionary Algorithms for Wind Farm Layout Optimisation Problem

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    Due to the increasing need for computationally expensive optimisation in many real-world applications, surrogate-assisted evolutionary algorithms have attracted growing attention. In the literature, surrogate-assisted evolutionary approaches have been successful in highly computational expensive optimisation problems. However, surrogates have not been used with the Wind Farm Layout Optimisation Problem (WFLOP) before. In this work, an evolutionary approach using surrogate modelling techniques to reduce the computational cost of the WFLOP is studied. The WFLOP mainly focuses on finding the optimal geographical placement of wind turbines within a wind farm in order to maximise power generation. But evaluating wind farm layout is very computationally expensive. The purpose of using surrogates is to approximate the real evaluation function of an evolutionary algorithm, but the surrogates can be computed more efficiently. The aim of this study is try to discover whether if surrogate-assisted evolutionary approach is effective on the WFLOP. An analytical wake model is used to calculate the velocity deficits in the downstream generated by individual turbines. A set of initial offline experiments was conducted based on a dataset of wind farm layouts sampled from the space of all layouts, using biased random walk. These experiments were designed to discover which features lead to construction of an accurate surrogate model. According to the results of these experiments, polar coordinates (sorted according to distance) as features are selected for learning. A multilayer perceptron (MLP) neural network and a tree-based regression model (M5P) are chosen as the surrogate models to approximate the real fitness function in conjunction with an (mu, lambda) evolutionary strategy. Two previously presented BlockCopy operators are used in the evolutionary strategy. The surrogate models are managed using a modified version of the Pre-selection strategy and the Best strategy. Our evaluation used four benchmark wind farm scenarios with dimensionality ranging from 200 to 1420 dimensions. The evaluation results show that our preliminary MLP and M5P surrogate models did not improve the optimisation results over traditional evolutionary strategies due to scalability issues. The scalability is a known weakness of many surrogate-assisted evolutionary approaches for the reason that most of them are designed for low-dimensionality problems. However, the research should continue on this topic because of its importance to renewable energy

    Finding the niche: A review of market assessment methodologies for rural electrification with small scale wind power

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    The mass roll out of solar PV across the Global South has enabled electricity access for millions of people. In the right context, Small Wind Turbines (SWTs) can be complementary, offering the potential to generate at times of low solar resource (night, monsoon season, winter, etc.) and increasing the proportion of the total energy system that can be manufactured locally. However, many contextual factors critically affect the viability of the technology, such as the extreme variability in the wind resource itself and the local availability of technical support. Therefore, performing a detailed market analysis in each new context is much more important. The Wind Empowerment Market Assessment Methodology (WEMAM) is a multi-scalar, transdisciplinary methodology for identifying the niche contexts where small wind can make a valuable contribution to rural electrification. This paper aims to inform the development of WEMAM with a critical review of existing market assessment methodologies. By breaking down WEMAM into its component parts, reflecting upon its practical applications to date and drawing upon insights from the literature, opportunities where it could continue to evolve are highlighted. Key opportunities include shifting the focus towards development outcomes; creating community archetypes; localised studies in high potential regions; scenario modelling and MCDA ranking of proposed interventions; participatory market mapping; and applying socio-technical transitions theory to understand how the small wind niche can break through into the mainstream
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