Optimal Sizing and Sitting of Distributed Generations in Power Distribution Networks Using Firefly Algorithm

Jalili, Aref; Taheri, Bahman

doi:10.1007/s40866-020-00081-9

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…Ackley In order to further verify the performance of the ICF-PSO algorithm, compare it with the standard PSO, Bat Algorithm (BA) and Firefly Algorithm (FA) [52,53]. To facilitate quick comparison of performance, setting the maximum number of iterations to 100 is enough, and calculate the average (AVE) and standard deviation (SD) of the calculation results.…”

Section: Function Name Expressionmentioning

confidence: 99%

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Wang

et al. 2021

Sensors

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The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

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Section: Function Name Expressionmentioning

confidence: 99%

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Wang

et al. 2021

Sensors

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“…where the Operating Cost (OC) of PS and CS is defined with reference to Equation (10) and Equation (11), respectively:…”

Section: The Economic Analysismentioning

confidence: 99%

“…Particular attention is also devoted to control strategies to deal with power fluctuation [8], power flexibility in case of changes of the distribution topology [9] or power loss reduction through the optimal placement of the distribution generations [10]. Widespread contributions deal also with hybrid AC/DC microgrids, proposing hierarchical control strategies [11], energy efficiency methods for load management [12] or power flow control for residential hybrid production system [13].…”

Section: Introductionmentioning

confidence: 99%

Energy, Environmental and Economic Performance of an Urban Community Hybrid Distributed Energy System

et al. 2020

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Energy systems face great challenges from both the supply and demand sides. Strong efforts have been devoted to investigate technological solutions aiming at overcoming the problems of fossil fuel depletion and the environmental issues due to the carbon emissions. Hybrid (activated by both renewables and fossil fuels) distributed energy systems can be considered a very effective and promising technology to replace traditional centralized energy systems. As a most peculiar characteristic, they reduce the use of fossil sources and transmission and distribution losses along the main power grid and contribute to electric peak shaving and partial-loads losses reduction. As a direct consequence, the transition from centralized towards hybrid decentralized energy systems leads to a new role for citizens, shifting from a passive energy consumer to active prosumers able to produce energy and distribute energy. Such a complex system needs to be carefully modelled to account for the energy interactions with prosumers, local microgrids and main grids. Thus, the aim of this paper is to investigate the performance of a hybrid distributed energy system serving an urban community and modelled within the framework of agent-based theory. The model is of general validity and estimates (i) the layout of the links along which electricity is distributed among agents in the local microgrid, (ii) electricity exchanged among agents and (iii) electricity exported to the main power grid or imported from it. A scenario analysis has been conducted at varying the distance of connection among prosumers, the installed capacity in the area and the usage of links. The distributed energy system has been compared to a centralized energy system in which the electricity requests of the urban community are satisfied by taking electricity from the main grid. The comparison analysis is carried out from an energy, environmental and economic point of view by evaluating the primary energy saving, avoided carbon dioxide emissions and the simple payback period indices.

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“…The optimal allocation of DG units for a distribution network is presented in [26]. In addition, optimal sizing renewable and dispatchable DGs in distribution networks has been investigated in [27]. The authors in [28] have proposed a new method to deal with the optimization problem of optimal placement and sizing of energy storage systems subject to improve the reliability of hybrid power distribution networks encompassing renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches

et al. 2021

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In this study, a microgrid scheme encompassing photovoltaic panels, an energy storage system, and a diesel generator as a backup supply source is designed, and the optimal placement for installation is suggested. The main purpose of this microgrid is to meet the intrinsic demand without being supplied by the upstream network. Thus, the main objective in the design of the microgrid is to minimize the operational cost of microgrid’s sources subject to satisfy the loads by these sources. Therefore, the considered problem in this study is to determine the optimal size and placement for generation sources simultaneously for a microgrid with the objectives of minimization of cost of generation resources along with mitigation of power losses. In order to deal with uncertainties of PV generation and load forecasting, the lognormal distribution model and Gaussian process quantile regression (GPQR) approaches are employed. In order to solve the optimization problem, the lightning attachment procedure optimization (LAPO) and artificial bee colony (ABC) methods are employed, and the results are compared. The results imply the more effectiveness and priority of the LAPO approach in comparison with ABC in convergence speed and the accuracy of solution-finding.

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Optimal Sizing and Sitting of Distributed Generations in Power Distribution Networks Using Firefly Algorithm

Cited by 15 publications

References 17 publications

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Energy, Environmental and Economic Performance of an Urban Community Hybrid Distributed Energy System

Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches

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