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Consideration of small signal stability in multi-objective DS reconfiguration in the presence of distributed generation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第1期11卷 236-245页

作者： Shukla, Jyoti Das, Biswarup Pant, Vinay Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India

This study presents a methodology for distribution system (DS) reconfiguration in the presence of distributed generations with objectives of minimising real power loss, switching operations as well as maximising the voltage stability margin while maintaining the constraints of bus voltage, branch current carrying capacity and radiality of DS. Furthermore, small signal stability of the system has also been considered in the formulated reconfiguration problem. To obtain the pareto-optimal solutions of this constrained multi-objective optimisation problem, knee point-driven evolutionary algorithm, is applied. In contrast to the non-dominated sorting genetic algorithm-ii (NSGA-ii)-based approach, preference is given to the knee points among non-dominated solutions in selection and tournament mating. Therefore, it maintains better balance between the convergence of the method and the diversity in the population. The method has been tested on IEEE 33-bus, 69-bus and 119-bus radial DSs to demonstrate its feasibility and effectiveness. The obtained results have also been compared with those obtained by the multi-objective NSGA-ii-based method.

关键词： distributed power generation power system stability Pareto optimisation genetic algorithms distribution networks small signal stability multiobjective DS reconfiguration distributed generation distribution system real power loss minimization switching operation voltage stability margin maximization branch current carrying capacity formulated reconfiguration problem Pareto-optimal solution constrained multiobjective optimisation problem knee point-driven 20 evolutionary algorithm nondominated sorting genetic algorithm-ii IEEE 33-bus radial DS 69-bus radial DS 119-bus radial DS multiobjective NSGA-ii-based method

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Customer-oriented multi-objective optimization on a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 3787-3805页

作者： Zhou, Tong Zhang, Shuai Zhang, Dongping Chan, Verner Yang, Sihan Chen, Mengjiao Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Shenzhen Bepsun Ind Commerce Syst Co Ltd Shenzhen Peoples R China

With the increasing demand for express delivery and enhancement of sustainable logistics, the collaborative multidepot delivery based on electric vehicles has gradually attracted the attention of logistics industry. However, most of the existing studies assumed that the products required by different customers could be delivered from any homogeneous depot, ignoring the limitations in facilities and environment of depots in reality. Thus, this study proposed a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows and battery swapping, which not only involves the multi-heterogeneous-depot to meet different customer demands, but also considers the constraints of mixed time windows to ensure timely delivery. Furthermore, a customer-oriented multi-objective optimization model minimizing both travel costs and time window penalty costs is proposed to effectively improve both delivery efficiency and customer satisfaction. To solve this model, an extended non-dominated sorting genetic algorithm-ii is proposed. This combines a new coding scheme, a new initial population generation method, three crossover operators, three mutation operators, and a particular local search strategy to improve the performance of the algorithm. Experiments were conducted to verify the effectiveness of the proposed algorithm in solving the proposed model.

关键词： Electric vehicle routing problem multi-objective optimization collaborative multi-heterogeneous-depot mixed time windows nondominated sorting genetic algorithm-ii

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Parametric study and multi-objective optimization in single-point incremental forming of extra deep drawing steel sheets

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2016年第5期230卷 825-837页

作者： Kurra, Suresh Nasih, H. R. Regalla, Srinivasaprakash Gupta, Amit Kumar BITS Pilani Hyderabad Campus Dept Mech Engn Hyderabad 500078 Andhra Prades India

Single-point incremental forming is the most economical process to make the sheet metal prototypes and low volume production without any dedicated dies and simple tooling. The surface finish of the parts produced in this process gets affected by various process parameters. To get the proper quality of parts for functional applications, it is important to understand the effect of various process parameters on part quality. Another drawback with this process is long processing time, which also gets affected by different process parameters. Thus, the first objective of this article is to study the effect of various process parameters on surface roughness and manufacturing time. Second objective is to carry out the multi-objective optimization to get optimum process parameters. For this, detailed experiments are conducted using Box-Behnken design. The effects of step depth, tool diameter, wall angle, feed rate and lubricant type on surface roughness and processing time have been investigated. Based on experimental data, mathematical models have been developed for both the response variables, namely, surface roughness and manufacturing time. Since the response variables are mutually exclusive in nature, multi-objective optimization algorithm (non-dominated sorting genetic algorithm-ii) has been used to get optimum process parameters. The Pareto front obtained from this algorithm helps the manufacturing engineer to select optimum process parameters in single-point incremental forming process.

关键词： Incremental forming Box-Behnken design surface roughness manufacturing time multi-objective optimization nondominated sorting genetic algorithm-ii

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Optimisation of double-sided linear switched reluctance motor for mass and force ripple minimisation

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第4期13卷 509-517页

作者： Soltanpour, Mohammad Reza Abdollahi, Hassan Masoudi, Siamak Shahid Sattari Aeronaut Univ Sci & Technol Dept Elect Engn Tehran Iran Islamic Azad Univ Abhar Branch Dept Tech & Engn Abhar Iran

Linear switched reluctance motors (LSRMs) are attractive machines for industrial applications. Their structure is simple, robust, and low cost. Despite these advantages, LSRMs suffer from inherent high force ripples which induce noise and vibration problems. The force performance of these motors is largely influenced by their geometry. Therefore, the dimensions must be optimised to improve the force performance. The aim of the present work was to determine the optimum dimensions of a double-sided LSRM using non-dominated sorting genetic algorithm-ii and multi-objective seeker optimisation algorithm along with a three-dimensional (3D) finite element analysis. The analysis and modelling results are presented, and a laboratory prototype is built. The results revealed that the optimised motor has a higher average force, lower force ripple, and lower total mass

关键词： finite element analysis linear motors reluctance motors genetic algorithms force ripple minimisation inherent high force ripples double-sided LSRM multiobjective seeker optimisation algorithm three-dimensional finite element analysis nondominated sorting genetic algorithm-ii double-sided linear switched reluctance motor mass ripple minimisation

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A hybrid approach for multi-weapon production planning with large-dimensional multi-objective in defense manufacturing

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2014年第2期228卷 302-316页

作者： Zhou, Yu Jiang, Jiang Yang, Zhen-yu Tan, Yue-jin Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

Multi-weapon production planning contains multi-objective combinatorial optimization and decision-making problems with the NP-hard and large-dimensional natures, which are difficult to be attacked by one single technique successfully. A four-stage hybrid approach is proposed to solve this problem. In the first stage, the multi-weapon production planning problem is formulated with 2N (N > 5) objectives based on operational capability requirements and expected downside risk measure. In the second stage, the formulation addressed is converted into a bi-objective optimization model using goal programming. In the third stage, an algorithm DENS based on differential evolution and nondominated sorting genetic algorithm-ii is developed to obtain the Pareto set. Finally, the multiple attribute decision-making method technique for order preference by similarity to ideal solution is employed to acquire the compromise solution from the Pareto set. A case study is given to demonstrate the effectiveness of the proposed approach. The concrete advantages of goal programming, DENS, and technique for order preference by similarity to ideal solution are also validated in this case. This approach can support the weapon production planning in defense manufacturing and is also applicable to solve the multi-level and multi-objective problem in other manufacturing fields.

关键词： Production planning multi-objective goal programming differential evolution nondominated sorting genetic algorithm-ii technique for order preference by similarity to ideal solution

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Optimal recovery strategy of DERs integrated distribution network based on scheduling rationality

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IET RENEWABLE POWER GENERATION 2020年第18期14卷 3888-3896页

作者： Wang, Jun Zheng, Xiaodong Tai, Nengling Liu, Yu Yang, Zengli Wang, Jing Tu, Qi Shanghai Jiao Tong Univ Minist Educ Key Lab Control Power Transmiss & Convers Shanghai Peoples R China Shanghai Tech Univ Sch Informat Sci & Technol Shanghai Peoples R China State Grid Hubei Elect Power Co Wuhan Hubei Peoples R China State Grid Shanghai Elect Power Co Shanghai Peoples R China

In order to better schedule distributed energy resources (DERs) to improve the recovery ability of the distribution network, the optimal recovery strategy is proposed in this study. The strategy can be applied to speed up the recovery process and reduce the power shortage of shedding loads in the distribution network when a power outage occurs. The scheduling coefficient is defined to quantify the rationality of each scheduling resource. Based on this index, this study establishes an optimal scheduling model, which consists of three objectives. The non-dominated sorting genetic algorithm-ii is applied to search this multi-objective optimal recovery solution in this study. Then, the modified IEEE 39-node system is used as the case study to verify the feasibility of the proposed model and the test results prove the effectiveness and efficiency of the proposed model.

关键词： distributed power generation genetic algorithms scheduling distribution networks load shedding optimal recovery strategy scheduling rationality distributed energy resources scheduling recovery ability recovery process power shortage power outage scheduling coefficient scheduling resource optimal scheduling model multiobjective optimal recovery solution DER integrated distribution network nondominated sorting genetic algorithm-ii

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Unbalance mitigation by optimal placement of static transfer switches in low voltage distribution feeders

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第20期14卷 4612-4621页

作者： Heidari-Akhijahani, Adel Safdarian, Amir Lehtonen, Matti Sharif Univ Technol Elect Engn Dept Tehran *** Iran Aalto Univ Dept Elect Engn & Automat Espoo Finland

With the rapid proliferation of residential rooftop photovoltaic (PV) systems, current and voltage unbalance issues have become a matter of great concern in low voltage (LV) distribution feeders. To overcome the issues, this study proposes a model to optimally rephase customers and PVs among the three phases via static transfer switches (STSs). The optimal STS placement is also considered in the model to achieve a cost effective solution with optimum number and location of STSs. The objective is to minimise total energy losses caused by current unbalance, minimise the number of STSs, and keep voltage unbalance along the feeder within the acceptable range. The model is solved via a non-dominated sorting genetic algorithm-ii (NSGA-ii) which provides a Pareto front. A fuzzy decision-making approach is then applied to choose the final solution among the Pareto front points. The proposed model is simulated on the IEEE 123-Node Test Feeder. The simulations are conducted in MATLAB where the COM interface capability is used to call OpenDSS to evaluate NSGA-ii populations. According to the achieved results, the proposed model can effectively and affordably apply STSs to mitigate unbalance issues in LV feeders hosting high penetration of rooftop PVs.

关键词： genetic algorithms Pareto optimisation building integrated photovoltaics fuzzy set theory decision making power generation economics power distribution economics nondominated sorting genetic algorithm-ii fuzzy decision-making approach IEEE 123-node test feeder LV feeders static transfer switches low voltage distribution feeders residential rooftop photovoltaic systems optimal STS placement cost effective solution NSGA-ii Pareto front MATLAB OpenDSS software

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Multiobjective model for solving resource-leveling problem with discounted cash flows

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第6期25卷 2009-2030页

作者： Abadi, Niloofar Nikoofal Sahl Bagheri, Mohsen Assadi, Mohammad Sadjad Univ Technol Dept Ind Engn Mashhad Iran

Nowadays, executers are struggling to improve the economic and scheduling situation of projects. Construction scheduling techniques often produce schedules that cause undesirable resource fluctuations that are inefficient and costly to implement on site. The objective of the resource-leveling problem is to reduce resource fluctuation related costs (hiring and firing costs) without violating the project deadline. In this article, minimizing the discounted costs of resource fluctuations and minimizing the project makespan are considered in a multiobjective model. The problem is formulated as an integer nonlinear programming model, and since the optimization problem is NP-hard, we propose multiobjective evolutionary algorithms, namely nondominated sorting genetic algorithm-ii (NSGA-ii), strength Pareto evolutionary algorithm-ii (SPEA-ii), and multiobjective particle swarm optimization (MOPSO) to solve our suggested model. To evaluate the performance of the algorithms, experimental performance analysis on various instances is presented. Furthermore, in order to study the performance of these algorithms, three criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm. To validate the results obtained for the suggested model, we compared the results of the first objective function with a well-tuned genetic algorithm and differential algorithm, and we also compared the makespan results with one of the popular algorithms for the resource constraints project scheduling problem. Finally, we can observe that the NSGA-ii algorithm presents better solutions than the other two algorithms on average.

关键词： scheduling resource leveling net present value serial scheduling scheme nondominated sorting genetic algorithm-ii strength Pareto evolutionary algorithm-ii multiobjective particle swarm optimization

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Multi-objective hybrid decomposition and local dominance based meter placement for distribution system state estimation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第20期14卷 4416-4425页

作者： Chintala, Bhanu Prasad Vinod Kumar, D. M. Natl Inst Technol Warangal Dept Elect Engn Warangal Andhra Pradesh India

The study proposes a new hybrid multi-objective evolutionary optimisation algorithm based on decomposition and local dominance for meter placement in distribution system state estimation. The evenly distributed qualitative and diverse solutions on the Pareto front are required for a decision-maker for selecting a final optimal solution. Such a Pareto front can be achieved by obtaining the balance between convergence and diversity of multi-objective optimisation algorithm. Therefore, the proposed method combined dominance and decomposition techniques, modelled meter placement as a constrained combinatorial multi-objective optimisation. The meter placement is designed as a trade-off between three objectives that are minimising the cost of the meters, average relative percentage error (ARPE) of voltage magnitude and ARPE of voltage angle. As the meter placement problem is a combinatorial optimisation, the binomial distribution-based Monte Carlo method is utilised to initialise the population, which aims to improve the diversity, as a consequence it improves the convergence, which is a by-product of this method. The results of the proposed method are compared with multi-objective evolutionary algorithm based on decomposition, non-dominated sorting genetic algorithm-ii and with multi-objective hybrid particle swarm optimisation-krill herd algorithm, multi-objective hybrid estimation of distribution algorithm-interior point method and demonstrated on PG&E 69-bus distribution system and Practical Indian 85-bus distribution system.

关键词： Monte Carlo methods genetic algorithms Pareto optimisation particle swarm optimisation power system state estimation average relative percentage error constrained combinatorial multiobjective optimisation modelled meter placement combined dominance optimal solution Pareto front evenly distributed qualitative solutions hybrid multiobjective evolutionary optimisation algorithm distribution system state estimation local dominance based meter placement multiobjective hybrid decomposition Indian 85-bus distribution system PG& E 69-bus distribution system distribution algorithm-interior point method multiobjective hybrid estimation multiobjective hybrid particle swarm optimisation-krill herd algorithm nondominated sorting genetic algorithm-ii binomial distribution-based Monte Carlo method combinatorial optimisation meter placement problem ARPE

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Multi-objective design method for construction of multi-microgrid systems in active distribution networks

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IET SMART GRID 2020年第3期3卷 331-341页

作者： Moghateli, Fereshteh Taher, Seyed Abbas Karimi, Ali Shahidehpour, Mohammad Univ Kashan Dept Elect Engn Kashan Iran IIT Dept Elect & Comp Engn Chicago IL 60616 USA

One of the important issues in the planning stage of active distribution networks (ADNs) is the optimal design of microgrids (MGs). The design, as a multi-MG system, is comprehensively investigated in this study. In this way, the allocation of energy storage systems (ESSs) and partitioning of ADN are simultaneously performed in order to minimise the cost and maximise the self-adequacy and the reliability considering the uncertainty of load and renewable energy resources. In this study, two approaches are considered. In approach I, the cost, reliability and self-adequacy objectives are taken into account whereas, in approach ii, a new probabilistic index representing the ratio of load to storage capacity is also added to mentioned objectives. The proposed multi-objective problem is solved with non-dominated sorting genetic algorithm-ii (NSGA-ii) as a well-known algorithm based on a probabilistic approach using the Monte-Carlo simulation method (MCSM) and in each approach, several Pareto optimal solutions are evaluated. To simulate and validate the effectiveness of the proposed method, two benchmark distribution networks (the 33-bus and the 119-bus) are used.

关键词： energy storage distributed power generation Pareto optimisation genetic algorithms Monte Carlo methods power distribution faults multiobjective design method active distribution networks planning stage ADNs optimal design multiMG system energy storage systems renewable energy resources reliability self-adequacy objectives storage capacity multiobjective problem nondominated sorting genetic algorithm-ii NSGA-ii probabilistic approach Monte-Carlo simulation method Pareto optimal solutions benchmark distribution networks multimicrogrid systems construction

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