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Comparative PSO optimisation of Microgrid Management Models in Island Operation to Minimise Cost

ENERGIES 2024年第16期17卷 3901页

作者： Zigman, Dubravko Tvoric, Stjepan Lonic, Manuel Zagreb Univ Appl Sci Dept Elect Engn Konavoska 2 Zagreb 10000 Croatia

The rapid progress in renewable energy sources and the increasing complexity of energy distribution networks have highlighted the need for efficient and intelligent energy management systems. This paper presents a comparative analysis of two optimisation algorithms, P and M70, used for the optimal control of the operation of microgrids in islanded mode. The main objective is to minimise production costs while ensuring a reliable energy supply. Algorithm P prioritises the use of photovoltaic (PV) and battery storage and operates the diesel generator at minimum capacity to reduce fuel consumption and maximise the use of renewable energy sources. Algorithm M70, on the other hand, uses a heuristic approach to adaptively manage energy resources in real time. In this study, the performance of both algorithms is evaluated through simulation in different operating scenarios. The results show that both algorithms significantly improve the efficiency of the microgrid, with the M70 algorithm showing better adaptability and cost efficiency in dynamic environments. This research contributes to ongoing efforts to develop robust and scalable energy management systems for future smart grids.

关键词： microgrid energy management optimisation algorithms predictive control heuristic approach renewable energy islanding cost minimisation smart grids

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Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components

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JOURNAL OF CLEANER PRODUCTION 2021年 315卷 128039-128039页

作者： Liang, Guoxi Panahi, Fatemeh Ahmed, Ali Najah Ehteram, Mohammad Band, Shahab S. Elshafie, Ahmed Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Univ Kashan Fac Nat Resources & Earth Sci Kashan Iran Univ Tenaga Nasional UNITEN Inst Energy Infrastruct IEI Coll Engn Dept Civil Engn Selangor 43000 Malaysia Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates

Solid Waste (SW) is one of the critical challenges of urban life. These SWs are considered environmental pollutants that are a threat to ecology and human health. Predicting SW generation is an essential topic for scholars to better manage SWs. This study investigates the application of optimised ANN models for predicting monthly SW generation in Iran using datasets about seven Iranian megacities. The Archimedes optimisation Algorithm (AOA), Sine Cosine Algorithm (SCA), Particle Swarm optimisation (PSO) technique, and Genetic algorithms (GA) were used for training the ANN model. The enhanced gamma test was used to determine the best input combination. AOA and the gamma test were used concurrently to reduce the time needed for choosing the best input combination. Gross domestic product (GDP), population, household size, and numbers of months were the best input combination set. This best input combination was then inputted into the hybrid and standalone ANN models for predicting monthly SW generation. During the final phase, the outputs of ANN-AOA, ANN-SCA, ANNPSO, ANN-GA, and ANN models were used as inputs for an inclusive multiple model (IMM) in order to enhance model accuracy. The IMM model reduced the training phase root mean square error (RMSE) of ANN-AOA, ANNSCA, ANN-PSO, ANN-GA, and ANN models by 55%, 59%, 68%, 72%, and 73%, respectively. Although ANN-AOA provided higher R2 and lower RMSE values than ANN-PSO, ANN-SCA, ANN-GA and ANN models, the IMM model outperformed ANN-AOA, considering that it integrates the advantages of all models used in the current study. The current study also used the fuzzy reasoning concept for modifying ANN model structures. The results indicated that such ANN models' time requirement was lower than those without fuzzy reasoning concept. The general results of the current study indicate that the ANN-AOA and the fuzzy-reasoning based Inclusive Multiple Model have a high ability for predicting different target variables.

关键词： Solid waste Artificial neural network optimisation algorithms Inclusive multiple model

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Multi-Mode Wave Energy Converter Design optimisation Using an Improved Moth Flame optimisation Algorithm

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ENERGIES 2021年第13期14卷 3737页

作者： Neshat, Mehdi Sergiienko, Nataliia Y. Mirjalili, Seyedali Nezhad, Meysam Majidi Piras, Giuseppe Garcia, Davide Astiaso Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Univ Adelaide Sch Mech Engn Adelaide SA 5001 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Sapienza Univ Rome Dept Astronaut Elect & Energy Engn DIAEE I-00197 Rome Italy Sapienza Univ Rome Dept Planning Design & Technol Architecture I-00197 Rome Italy

Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters' optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth-Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.

关键词： renewable energy systems wave energy converters power take-off bio-inspired meta-heuristics optimisation algorithms Moth Flame optimisation evolutionary algorithms swarm intelligence

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Algorithmic green infrastructure optimisation: Review of artificial intelligence driven approaches for tackling climate change

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SUSTAINABLE CITIES AND SOCIETY 2024年 101卷

作者： Shaamala, Abdulrazzaq Yigitcanlar, Tan Nili, Alireza Nyandega, Dan Queensland Univ Technol Sch Architecture & Built Environm City 4 0 Lab 2 George St Brisbane Qld 4000 Australia Queensland Univ Technol Sch Informat Syst 2 George St Brisbane Qld 4000 Australia

Green infrastructure (GI) is a fundamental building block of our cities. It contributes to the sustainability and vitality of cities by offering various benefits such as greening, cooling, water, air quality, and managing carbon emissions. GI plays an essential role in enhancing overall well-being. The utilisation of artificial intelligence (AI) technologies for GI optimisation is perceived as a powerful approach for cities. A knowledge gap, nevertheless, remains in research on AI-driven GI optimisation for tackling climate change. This study aims to consolidate the comprehension of AI-driven GI optimisation, particularly for tackling climate change. The study methodology adopts the PRISMA protocol to perform a systematic literature review. The review results are analysed from six aspects-i.e., optimisation objectives, objectives categories, indicators, models, GI types, and scales. The findings revealed: (a) GI optimisation was mainly undertaken in the areas of air quality, biodiversity and ecosystem security, energy efficiency, public health, heat islands, and water management;(b) Indicator categories were mainly concentrated on indicators related to GI, indicators related to the objective, and other general/supporting indicators. Based on these findings, a framework was developed to enhance the understanding of the AI-driven GI optimisation process within the realm of climate change.

关键词： Green infrastructure Infrastructure optimisation Climate change Artificial intelligence Machine learning optimisation algorithms

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A Method for the Prediction of Process Parameters for Minimal Distortion in Welded Frame Structures Using a FE-simulation

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Procedia CIRP 2014年 18卷 138-143页

作者： A. Belitzki M. Schweier M.F. Zaeh Institute for Machine Tools and Industrial Management Technische Universitaet Muenchen Boltzmannstr. 15 85748 Garching Germany

Welded frame structures are often subject to unintended distortions due to the thermal joining process. In order to precisely quantify and reduce the distortion of welded frame structures using Finite Element (FE) simulation, a fast and reliable method is required, especially for industrial applications. This paper presents a methodical, simulation based and time optimised framework for the prediction of appropriate process parameters for minimal component distortion of complex welded frame structures by means of e.g. a variation of the process parameters or the weld seam sequences. To achieve a minimal distortion of the final structure, different optimisation algorithms will be used in combination with a database.

关键词： welded frame structures FE-simulation component distortion optimisation algorithms

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Strategic tree placement for urban cooling: A novel optimisation approach for desired microclimate outcomes

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URBAN CLIMATE 2024年 56卷

作者： Shaamala, Abdulrazzaq Yigitcanlar, Tan Nili, Alireza Nyandega, Dan Queensland Univ Technol City 4 0 Lab Sch Architecture & Built Environm 2 George St Brisbane Qld 4000 Australia Queensland Univ Technol Sch Informat Syst 2 George St Brisbane Qld 4000 Australia

Trees are crucial elements for improving urban microclimates by providing cooling through shading, evapotranspiration, and windbreaks. To maximise their cooling effects, it is essential to strategically position the trees in optimal locations. However, research on optimising tree location and its impact on microclimates is limited owing to computational challenges and costs. This study introduces a novel method that employs three optimisation algorithms-i.e., Nondominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm optimisation (PSO), and Ant Colony optimisation (ACO)-to identify the optimal locations for trees in urban environments to enhance urban thermal comfort. The research methodology involves simulating microclimate responses to tree placements optimised by each algorithm and assessing the results based on urban thermal comfort. The results underscore the efficacy of optimised tree locations, demonstrating that optimising tree locations can significantly reduce the Universal Thermal Comfort Index (UTCI) in urban areas. Furthermore, the findings suggest that the clustering of tree canopies has a compounding impact on these cooling benefits in urban areas. Notably, all three algorithms significantly improved UTCI. PSO demonstrated the rapid identification of effective tree configurations. However, ACO provided the most substantial reduction in air temperature, highlighting its potential as an effective tool for urban cooling. While efficient, NSGA-II plateaued earlier, suggesting its utility in scenarios where timely solutions are crucial.

关键词： Green infrastructure optimisation optimisation algorithms Tree location configuration Urban cooling Microclimate simulation Urban planning

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Framework and experimental analysis of generalised surrogate-assisted particle swarm optimisation

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2022年第4期15卷 332-346页

作者： Dai, Rui Jie, Jing Zheng, Hui Zhang, Miao Lu, Yixiao Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Liuhe Rd 318 Hangzhou 310023 Zhejiang Peoples R China

The paper develops a framework of generalised surrogate-assisted particle swarm optimisation (GS-PSO) to solve computationally expensive problems. To ensure the generalisation ability and optimisation accuracy of the algorithm, some researches about the factors of GS-PSO and selection of surrogates have been done. Some statistics indexes such as accuracy, robustness, and scalability are formulated to evaluate six popular metamodels, which can help to choose the proper surrogates for GS-PSO during the optimisation process. A series of simulation experiments are conducted based on some notable benchmark functions. The results show that GS-PSO with RBF is a robust surrogate-assisted algorithm for computationally expensive problems. Meanwhile, a proper combination of optimisers and surrogates can contribute to an improvement of GS-PSO for different optimisation problems.

关键词： SAEAs surrogate models optimisation algorithms performance indexes

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Survey on the use of computational optimisation in UK engineering companies

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CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY 2015年 9卷 57-68页

作者： Tiwari, Ashutosh Hoyos, Paula Noriega Hutabarat, Windo Turner, Chris Ince, Nadir Gan, Xiao-Peng Prajapat, Neha Cranfield Univ Mfg Dept Bedford England Alstom Power Methodol Tools Dept Coventry W Midlands England

The aim of this work is to capture current practices in the use of computational optimisation in UK engineering companies and identify the current challenges and future needs of the companies. To achieve this aim, a survey was conducted from June 2013 to August 2013 with 17 experts and practitioners from power, aerospace and automotive Original Equipment Manufacturers (OEMs), steel manufacturing sector, small-and medium-sized design, manufacturing and consultancy companies, and optimisation software vendors. By focusing on practitioners in industry, this work complements current surveys in optimisation that have mainly focused on published literature. This survey was carried out using a questionnaire administered through face-to-face interviews lasting around 2 h with each participant. The questionnaire covered 5 main topics: (i) state of optimisation in industry, (ii) optimisation problems, (iii) modelling techniques, (iv) optimisation techniques, and (v) challenges faced and future research areas. This survey identified the following challenges that the participant companies are facing in solving optimisation problems: large number of objectives and variables, availability of computing resources, data management and data mining for optimisation workflow, over-constrained problems, too many algorithms with limited help in selection, and cultural issues including training and mindset. The key areas for future research suggested by the participant companies are as follows: handling large number of variables, objectives and constraints particularly when solution robustness is important, reducing the number of iterations and evaluations, helping the users in algorithm selection and business case for optimisation, sharing data between different disciplines for multi-disciplinary optimisation, and supporting the users in model development and post-processing through design space visualisation and data mining. (C) 2015 CIRP.

关键词： Computational optimisation Engineering optimisation optimisation algorithms

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Weighted Factor Multi-Objective Design optimisation of a Reluctance Synchronous Machine

Weighted Factor Multi-Objective Design Optimisation of a Rel...

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IEEE International Electric Machines and Drives Conference

作者： Eduan Howard Maarten J. Kamper Department of Electrical and Electronic Engineering Stellenbosch University

ISBN: (纸本)9781479979424

The relationship between the well known competitive torque density and less competitive power factor of reluctance synchronous machines is investigated. The study consists of a multi-objective design optimisation implementing a weighted factor optimisation technique. The investigation was carried out by implementing a complete machine model in the optimisation, with varying machine pole number and flux barrier number combinations. Determination of the weighting of this specific parameter relationship enables the designer to select an optimum multi-objective relationship during optimisation. A specific machine was selected for analysis, then analysed and manufactured for validation of simulation results.

关键词： AC Motors Multi Objective optimisation optimisation algorithms Finite Element Analysis Synchronous Reluctance Machine Flux Barrier Variable Speed Drive Asymmetric Pole Structure

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A user-centered approach to evolutionary algorithms and their use in industry

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COGENT ENGINEERING 2021年第1期8卷 Article: 1995110页

作者： Vincalek, Jakub Walton, Sean Evans, Ben Swansea Univ Fac Sci & Engn Swansea W Glam Wales

The key contribution of this article to the domain of engineering optimisation is establishing best practices with respect to the development of evolutionary algorithms in the context of design engineering. Despite their various uses, the uptake of evolutionary algorithms in industry remains limited. In order to understand why uptake is low a survey of engineers was undertaken, the results of which are presented here. A total of 23 participants (N = 23) took part in the 3-section mixed methods survey. Reflexive thematic analysis was conducted on the open-ended questions. A common thread throughout participants responses is that there is a question of trust towards evolutionary algorithms within industry. Perhaps surprising is that the key to gaining this trust is not producing good results, but creating algorithms which explain the process they take in reaching a result. Based on this, recommendations have been made to increase their use in industry.

关键词： reflexive thematic analysis human in the loop optimisation algorithms design optimisation

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