With more electric buses, the optimal location of charging station plays an important role for bus electrification. This paper proposes a location planning model of electric bus fast-charging ...stations for the electric bus transit system, that takes the bus operation network and the distribution network into account. The model 1) simulates the operation network of electric buses thoroughly to obtain the charging demand of electric buses and 2) takes into account of the absorption capacity of distribution network and other constraints in the siting and capacity determination stage. The objective of the model is to minimize the sum of the construction cost, operation and maintenance costs, travel cost to charging stations, and the cost of power loss for charging stations at established bus terminus. The Affinity Propagation method is adopted to cluster the bus terminuses in order to obtain a preliminary number of charging stations. Subsequently, the Binary Particle Swarm Optimization algorithm is used to optimize the site selection and capacity. Finally, the model is applied to simulate and analyze the bus operation network of a coastal city in South China. The case study shows that the model can effectively optimize the layout of bus charging stations for the city.
•Proposed a charging demand model for electric buses with bus operation schedule.•Bus terminuses clustering with Affinity Propagation (AP) reduces charging stations.•Binary Particle Swarm Optimization (BPSO) for discrete charging site selection.•Examined various departure intervals, on-board battery quantity, and charger power.•Proposed AP-BPSO identifies the lowest investment cost and reduces modelling time.
There exist many multiobjective optimization problems (MOPs) containing a large number of decision variables in real-world applications, which are known as large-scale MOPs. Due to the ...ineffectiveness of existing operators in finding optimal solutions in a huge decision space, some decision variable division-based algorithms have been tailored for improving the search efficiency in solving large-scale MOPs. However, these algorithms will encounter difficulties when solving problems with complicated landscapes, as the decision variable division is likely to be inaccurate and time consuming. In this paper, we propose a competitive swarm optimizer (CSO)-based efficient search for solving large-scale MOPs. The proposed algorithm adopts a new particle updating strategy that suggests a two-stage strategy to update position, which can highly improve the search efficiency. The experimental results on large-scale benchmark MOPs and an application example demonstrate the superiority of the proposed algorithm over several state-of-the-art multiobjective evolutionary algorithms, including problem transformation-based algorithm, decision variable clustering-based algorithm, particle swarm optimization algorithm, and estimation of distribution algorithm.
In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach them in accordance with their cognitive and learning abilities. Inspired from this ...idea, we consider particles in the swarm as mixed-level students and propose a level-based learning swarm optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging in evolutionary computation. At first, a level-based learning strategy is introduced, which separates particles into a number of levels according to their fitness values and treats particles in different levels differently. Then, a new exemplar selection strategy is designed to randomly select two predominant particles from two different higher levels in the current swarm to guide the learning of particles. The cooperation between these two strategies could afford great diversity enhancement for the optimizer. Further, the exploration and exploitation abilities of the optimizer are analyzed both theoretically and empirically in comparison with two popular particle swarm optimizers. Extensive comparisons with several state-of-the-art algorithms on two widely used sets of large-scale benchmark functions confirm the competitive performance of the proposed optimizer in both solution quality and computational efficiency. Finally, comparison experiments on problems with dimensionality increasing from 200 to 2000 further substantiate the good scalability of the developed optimizer.
•We developed new hybrid evolutionary algorithm for solving generator maintenance scheduling problem.•Hybrid optimization method balance overall reliability and economy.•A case study of 32 thermal ...generating units reveal the effectiveness of the hybrid method.
This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.
In this paper, we propose a new particle swarm optimization algorithm incorporating the best human learning strategies for finding the optimum solution, referred to as a Self Regulating Particle ...Swarm Optimization (SRPSO) algorithm. Studies in human cognitive psychology have indicated that the best planners regulate their strategies with respect to the current state and their perception of the best experiences from others. Using these ideas, we propose two learning strategies for the PSO algorithm. The first one uses a self-regulating inertia weight and the second uses the self-perception on the global search direction. The self-regulating inertia weight is employed by the best particle for better exploration and the self-perception of the global search direction is employed by the rest of the particles for intelligent exploitation of the solution space. SRPSO algorithm has been evaluated using the 25 benchmark functions from CEC2005 and a real-world problem for a radar system design. The results have been compared with six state-of-the-art PSO variants like Bare Bones PSO (BBPSO), Comprehensive Learning PSO (CLPSO), etc. The two proposed learning strategies help SRPSO to achieve faster convergence and provide better solutions in most of the problems. Further, a statistical analysis on performance evaluation of the different algorithms on CEC2005 problems indicates that SRPSO is better than other algorithms with a 95% confidence level.
Abstract
The difficulty of accurately inverting the self-potential (SP) source has always been a major factor hindering the wide application of the SP method. Considering that particle swarm ...optimization has poor accuracy when in the face of high-dimensional SP inversion, while the gradient method depends on the selection of the initial solution, we try to combine these two algorithms to prompt the inversion result to jump out of local optimum.
The use of solar energy as a source of clean energy is increasing throughout the world. Therefore, designing higher-quality photovoltaic cells has attracted researches. Several equivalent circuits ...have been proposed for the photovoltaic cell, but it is necessary to note that in order to achieve maximum power point (MPP), finding appropriate circuit model parameters is required. Many methods for finding the optimal parameters have been proposed. In this paper, flexible particle swarm optimization (FPSO) algorithm is proposed to estimate the parameters of PV cell model. In this algorithm, an elimination phase is added to classic PSO. At the beginning of each phase, a certain number of worst particles are deleted and some new particles are replaced in the new search space. Also, the search space of the parameters in each particle is changed based on the value of these parameters. These modifications have enhanced the proposed algorithm performance by adding the ability of global search and also searching in a reasonable space. To highlight the superiority of the FPSO algorithm, this method is used to estimate the parameters of the single diode model, double diode model, and the photovoltaic module. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known optimization methods. Furthermore, to ensure the practical use of the FPSO algorithm, it is validated by three different solar modules such as monocrystalline (SM55) and multi-crystalline (KC200GT) and polycrystalline (SW255). The simulation results show that the proposed algorithm has high performance in terms of accuracy and robustness.
•A novel optimization algorithm called FPSO is presented.•Single diode model, double diode model, and the photovoltaic module are simulated.•The effectiveness of the FPSO is validated by three different solar modules.•The accuracy of the FPSO is compared with different well-known algorithms.
India’s ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating ...impact on the utility distribution system brought about by improper setup of these charging stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality.
•A novel optimal placement strategy of electric vehicles charging station.•A hybrid algorithm based on both GA and PSO.•Voltage profile shown improvement in the lowest p.u. voltage value.•A better performance in terms of quality of solution with lesser number of iterations.•The minimum stress on the distribution system.
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•An optimal design of an off-grid hybrid renewable energy system (HRES) is suggested.•Techno-economic feasibility study of the hybrid PV-wind-battery-diesel energy system.•The effects ...of demand-side management on the energy demand–supply is investigated.•Test the impact of battery technology on the design of HRES through a sensitivity analysis.•Comparison between particle swarm optimization and HOMER software is made.
The growing research interest in hybrid renewable energy systems (HRESs) has been regarded as a natural and yet critical response to address the challenge of rural electrification. Based on a Bibliometric analysis performed by authors, it was concluded that most studies simply adopted supply-side management techniques to perform the design optimization of such a renewable energy system. To further advance those studies, this paper presents a novel approach by integrating demand–supply management (DSM) with particle swarm optimization and applying it to optimally design an off-grid hybrid PV-solar-diesel-battery system for the electrification of residential buildings in arid environments, using a typical dwelling in Adrar, Algeria, as a case study. The proposed HRES is first modelled by an in-house MATLAB code based on a multi-agent system concept and then optimized by minimizing the total net present cost (TNPC), subject to reliability level and renewable energy penetration. After validation against the HOMER software, further techno-economic analyses including sensitivity study are undertaken, considering different battery technologies. By integrating the proposed DSM, the results have shown the following improvements: with RF = 100%, the energy demand and TNPC are reduced by 7% and 18%, respectively, compared to the case of using solely supply-side management. It is found that PV-Li-ion represents the best configuration, with TNPC of $23,427 and cost of energy (COE) of 0.23 $/kWh. However, with lower RF values, the following reductions are achieved: energy consumption (19%) and fuel consumption or CO2 emission (57%), respectively. In contrast, the RF is raised from 15% (without DSM) to 63% (with DSM). It is clear that the optimal configuration consists of wind-diesel, with COE of 0.21 $/kWh, smaller than that obtained with a stand-alone diesel generator system. The outcomes of this work can provide valuable insights into the successful design and deployment of HRES in Algeria and surrounding regions.
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