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An assessment of hybrid-energy storage systems in the renewable environments

JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Nkwanyana, Thamsanqa B. Siti, Mukwanga W. Wang, Zenghui Toudjeu, Ignace Mbungu, Nsilulu T. Mulumba, Willy Tshwane Univ Technol Dept Elect Engn Pretoria South Africa Univ South Africa Sch Engn Dept Elect Engn Pretoria South Africa Univ Pedag Natl Sch Engn Dept Phys Kinshasa DEM REP CONGO

Hybrid energy storage systems (HESS) are regarded as combinatorial storage systems growing power storage capacity system in the world. Many researchers have devoted time and attention to studying energy systems, and many outcomes have been obtained and implemented. Despite its significance in expanding renewable energy stations and energy storage for electric vehicles, HESS still faces numerous issues. This study assesses the optimization methods used to address the HESS problem of durability, charging/discharging, increasing temperature, manufacturing cost and HESS lifespan. The battery is needed to improve the reliability of variable renewable energy plants by optimizing power production. However, the fluctuating charge and discharge of the battery energy storage system (BESS) is one factor that negatively impacts its lasting capacity. In recent years, the HESS comprising battery and supercapacitor (SC) has been proposed to improve system efficiency and lengthen HESS lifespan. The SC has a significant density of power and a long-life period but a low energy density. This work will also investigate the HESS topology of BSs and SCs while recognizing the rate power constraint to manage the functioning of HESS. Energy shortages internationally can be solved with the help of renewable energy sources (RES) and well-functioning HESS. The availability, existing situation, significant accomplishments, and future potentials of renewable energy sources internationally have all been summarized in this study.

关键词： Hybrid energy storage systems Real-time energy management Supercapacitor multiobjective evolutionary algorithm Electric vehicle Battery system Renewable energy sources

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Use of Domain Knowledge to Increase the Convergence Rate of evolutionary algorithms for Optimizing the Cost and Resilience of Water Distribution Systems

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2016年第9期142卷 1-12页

作者： Bi, Weiwei Dandy, Graeme C. Maier, Holger R. Univ Adelaide Sch Civil Environm & Min Adelaide SA 5005 Australia Univ Adelaide Environm & Min Engn Adelaide SA 5005 Australia

evolutionary algorithms (EAs) have been used extensively for the optimization of water distribution systems (WDSs) over the last two decades. However, computational efficiency can be a problem, especially when EAs are applied to complex problems that have multiple competing objectives. In order to address this issue, there has been a move toward developing EAs that identify near-optimal solutions within acceptable computational budgets, rather than necessarily identifying globally optimal solutions. This paper contributes to this work by developing and testing a method for identifying high-quality initial populations for multiobjective EAs (MOEAs) for WDS design problems aimed at minimizing cost and maximizing network resilience. This is achieved by considering the relationship between pipe size and distance to the source(s) of water, as well as the relationship between flow velocities and network resilience. The benefit of using the proposed approach compared with randomly generating initial populations in relation to finding near-optimal solutions more efficiently is tested on five WDS optimization case studies of varying complexity with two different MOEAs. The results indicate that there are considerable benefits in using the proposed initialization method in terms of being able to identify near-optimal solutions more quickly. These benefits are independent of MOEA type and are more pronounced for larger problems and smaller computational budgets.

关键词： multiobjective evolutionary algorithm Optimization Initialization method Water distribution system Near-optimal fronts

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Hierarchical Rank Density Genetic algorithm for Radial-Basis Function Neural Network Design

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International Journal of Computational Intelligence and Applications 2003年第3期3卷 213-232页

作者： Gary G. Yen Haiming Lu Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078-503 USA

In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the training performance and network complexity. Instead of producing a single optimal solution, HRDGA provides a set of near-optimal neural networks to the designers so that they can have more flexibility for the final decision-making based on certain preferences. In terms of searching for a near-complete set of candidate networks with high performances, the networks designed by the proposed algorithm prove to be competitive, or even superior, to three other traditional radial-basis function networks for predicting Mackey–Glass chaotic time series.

关键词： Genetic algorithm multiobjective evolutionary algorithm neural network design RBF network hierarchical representation Pareto set

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A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique

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ENERGY CONVERSION AND MANAGEMENT 2021年 237卷 114103-114103页

作者： Shivam, Kumar Tzou, Jong-Chyuan Wu, Shang-Chen Kun Shan Univ Dept Mech Engn 195 Kunda Rd Tainan 710 Taiwan Precis Machinery Res & Dev Ctr PMC 2737th RdTaichung Ind Pk Taichung 40768 Taiwan

This paper proposes a multi-objective predictive energy management strategy based on machine learning technique for residential grid-connected hybrid energy systems. The hybrid system considered in this study comprise three principal components: a photovoltaic array as a renewable energy source, a battery bank as an energy storage system, and residential building as an electric load. The proposed strategy comprises three levels of controls: a logical level to manage the computational load and accuracy, a dual prediction model based on residual causal dilated convolutional networks for energy production and electric load on system, and a multiobjective optimization for efficient trade of energy with the utility grid by battery charge scheduling. The prediction model used in this study can provide one-step ahead photovoltaic energy production and load forecast with sufficient accuracy using a sliding window training technique and can be implemented on an average personal computer. The energy management problem comprises multiple objectives that include minimization of energy bought from utility grid, maximization the battery bank?s state-of-charge and reduction of carbon dioxide emission. The optimization problem is constrained to the maximum allowed carbon dioxide production and battery bank?s state-of-charge limits. The proposed strategy is tested for static and dynamic electricity prices using hourly energy and load data. Simulation results show a high coefficient of determination of 93.08% for energy production predictions and 97.25% for electric load predictions using proposed dual prediction model. The proposed prediction model is benchmarked against na?ve prediction, support vector machine and artificial neural network models using several metrics and shows noticeable improvements in prediction accuracy. Not only the proposed strategy combined with the proposed prediction model can handle over 50% of the total yearly load requirement but also shows a significan

关键词： Energy management strategy Hybrid energy systems Machine learning multiobjective evolutionary algorithm Predictive control Time-series forecasting

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Two-Objective Design of Benchmark Problems of a Water Distribution System via MOEAs: Towards the Best-Known Approximation of the True Pareto Front

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2015年第3期141卷 04014060-04014060页

作者： Wang, Qi Guidolin, Michele Savic, Dragan Kapelan, Zoran Univ Exeter Coll Engn Math & Phys Sci Ctr Water Syst Exeter EX4 4QF Devon England

Various multiobjective evolutionary algorithms (MOEAs) have been applied to solve the optimal design problems of a water distribution system (WDS). Such methods are able to find the near-optimal trade-off between cost and performance benefit in a single run. Previously published work used a number of small benchmark networks and/or a few large, real-world networks to test MOEAs on design problems of WDS. A few studies also focused on the comparison of different MOEAs given a limited computational budget. However, no consistent attempt has been made before to investigate and report the best-known approximation of the true Pareto front (PF) for a set of benchmark problems, and thus there is not a single point of reference. This paper applied 5 state-of-the-art MOEAs, with minimum time invested in parameterization (i.e.,using the recommended settings), to 12 design problems collected from the literature. Three different population sizes were implemented for each MOEA with respect to the scale of each problem. The true PFs for small problems and the best-known PFs for the other problems were obtained. Five MOEAs were complementary to each other on various problems, which implies that no one method was completely superior to the others. The nondominated sorting genetic algorithm-II (NSGA-II), with minimum parameters tuning, remains a good choice as it showed generally the best achievements across all the problems. In addition, a small population size can be used for small and medium problems (in terms of the number of decision variables). However, for intermediate and large problems, different sizes and random seeds are recommended to ensure a wider PF. The publicly available best-known PFs obtained from this work are a good starting point for researchers to test new algorithms and methodologies for WDS analysis.

关键词： Rehabilitation Water distribution systems algorithms Benchmark Two-objective design Water distribution system multiobjective evolutionary algorithm Hybrid algorithm Best-known Pareto front Benchmark problem

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Energy-saving scheduling strategy for variable-speed flexible job-shop problem considering operation-dependent energy consumption

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Qu, Hongquan Tong, Xiaomeng Cai, Maolin Shi, Yan Lan, Xing Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Guangdong Ant Factory Mfg Co Ltd Foshan 528000 Peoples R China

The rapid growth of industrial production signifies its CO2 2 footprint and climate impact, highlighting the demand for efficient and energy-saving production scheduling. Generally, a single production job comprises multiple operations on various machines. Existing studies estimate job energy consumption from a broader perspective, often disregarding the detailed scheduling of operations. This simplification may result in inaccurate evaluations of energy consumption and savings, as energy consumption can differ significantly among operations, with each requiring specific levels of energy on various machines. Proposing a new scheduling framework to replace the classical strategy is challenging yet necessary due to these variations. This study aims to investigate the multiobjective flexible job-shop scheduling problem by considering operation- dependent energy consumption and to improve the accuracy and thoroughness of the energy-consumption evaluation framework. First, a core metamodel for energy assessment is established and a mathematical model is introduced to minimize the manufacturing span and total energy consumption. Subsequently, a dynamic diffusion weight adjustment for a multiobjective evolutionary algorithm based on decomposition (MOEA/DDDWA) is proposed. Operation-dependent energy consumption and processing quality are discussed, and a superior unified energy-saving strategy is designed to facilitate the selection of the processing-speed range, balancing energy consumption and saving. Finally, numerical experiments are conducted based on datasets of various scales, demonstrating that the core metamodel for energy assessment can achieve significant energy savings. Compared with other classical multiobjective optimization algorithms, the MOEA/D-DDWA is more energy efficient.

关键词： Operation-dependent energy consumption Energy-assessment core metamodel multiobjective evolutionary algorithm Variable-speed flexible job-shop Energy-saving scheduling strategy

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A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering

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ADVANCES IN MECHANICAL ENGINEERING 2024年第1期16卷

作者： Lu, Zhe Lin, Shuwen Chen, Jianxiong Gu, Tianqi Xie, Yu Zhao, Zihao Fuzhou Univ Sch Mech Engn & Automat Fuzhou Peoples R China Fuzhou Univ Sch Mech Engn & Automat 2 Wulongjiang North Rd Minhou 350108 Fujian Peoples R China

To enhance the excavator performance considering the digging force and boom lift force under typical working conditions, this paper aims to solve the complex multiobjective optimization of the excavator by proposing a new knowledge-based method. The digging force at multiple key points is utilized to characterize the excavator's performance during the working process. Then, a new optimization model is developed to address the imbalanced optimization quality among subobjectives obtained from the ordinary linear weighted model. The new model incorporates the loss degree relative to the optimal solution of each subobjective, aiming to achieve a more balanced optimization. Knowledge engineering is integrated into the optimization process to improve the optimization quality, utilizing a knowledge base incorporating seven different types of knowledge to store and reuse the information related to optimization. Furthermore, a knowledge-based multiobjective algorithm is proposed to perform the knowledge-guided optimization. Experimental results demonstrate that the proposed knowledge-based method outperforms existing methods, resulting in an average increase of 15.1% in subobjective values.

关键词： Optimization design knowledge engineering multiobjective optimization excavator multiobjective evolutionary algorithm

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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering

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algorithmS 2021年第11期14卷 338页

作者： Lai, Daphne Teck Ching Sato, Yuji Univ Brunei Darussalam Sch Digital Sci BE-1410 Bandar Seri Begawan Brunei Hosei Univ Fac Comp & Informat Sci Tokyo 1848584 Japan Univ Brunei Darussalam Inst Appl Data Analyt BE-1410 Bandar Seri Begawan Brunei

Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

关键词： multiobjective evolutionary algorithm multiobjective genetic algorithm particle swarm optimization evolutionary algorithm data clustering bare bones particle swarm optimization

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An Integrated Location-Inventory Model for Vendor-Managed Inventory in the Retail Supply Chain

An Integrated Location-Inventory Model for Vendor-Managed In...

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第17届工业工程与工程管理国际学术会议(IE&EM2010)

作者： C.L.Hsieh Department of Management Sciences and Decision Making Tamkang University Department of Industrial Management and Enterprise Information Aletheia University

Supply chain management involves multiple and conflicting objectives.A multi-objective optimization procedure which permits a trade-off evaluation for an integrated location-inventory model is initially *** this paper,we propose an integrated model to incorporate inventory control decisions - such as economic order quantity,safety stock and inventory replenishment decisions under vendor-managed inventory collaborative initiativeinto typical facility location models,which are used to solve the distribution network design *** model includes elements of total cost,customer service and flexibility as its ***,a multiobjective evolutionary algorithm is developed to determine the optimal facility location portfolio in order to reach best compromise of these conflicting criteria.A hybrid evolutionary approach is proposed and its scenario analysis is implemented on a real large retail supply chain in Taiwan to investigate the model performance and to illustrate how parameter changes influence its *** preliminary results are described.

关键词： Facility location problem inventory control vendor-managed inventory integrated supply chain model multiobjective evolutionary algorithm

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A Multi-Objective evolutionary algorithm for Extended Vehicle Routing Problem

A Multi-Objective Evolutionary Algorithm for Extended Vehicl...

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2011 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS 2011)

作者： Lingling Wang State Key Laboratory of Software Engineering Wuhan University Wuhan,China Zhiyi Lin Faculty of Computer Guangdong University of Technology Guangzhou,China

The vehicle routing problem(VRP) is a very typical NP-hard combinatorial optimization problem with various applications in *** this paper,a multi-objective evolutionary algorithm is presented for VRP with soft time **... 详细信息

关键词： vehicle routing problem soft time window multiobjective evolutionary algorithm

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