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 v...
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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 sortinggeneticalgorithm-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.
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 t...
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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 sortinggeneticalgorithm-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.
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. H...
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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 sortinggeneticalgorithm-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.
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 w...
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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 sortinggeneticalgorithm-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
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 techni...
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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.
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 spee...
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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 sortinggeneticalgorithm-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.
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 issue...
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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 sortinggeneticalgorithm-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.
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 ineffic...
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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 geneticalgorithm 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-iialgorithm presents better solutions than the other two algorithms on average.
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 qualit...
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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 sortinggeneticalgorithm-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.
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 t...
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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 sortinggeneticalgorithm-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.
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