Optimum sizing of photovoltaic battery systems incorporating uncertainty through design space approach

Arun, P.; Banerjee, Rangan; Bandyopadhyay, Santanu

doi:10.1016/j.solener.2009.01.003

Cited by 149 publications

(80 citation statements)

References 29 publications

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“…Replacement of the solutions in the memory by the new best solutions is also encouraged. MSA [46] is applied to the stochastic optimization problems such as configuration of appropriate component for a Hybrid Micro-Grid [47]. In this Renewable Energy Integration Problem, a jungle where the monkey resides is the combination of the generators, wind turbines and PV arrays of the load.…”

Section: Monkey Search Structure Learner For Bayesian Networkmentioning

confidence: 99%

Monkey Behavior Based Algorithms - A Survey

Devi¹,

Sathya²

2017

IJISA

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Abstract-Swarm intelligence algorithms (SIA) are bioinspired techniques based on the intelligent behavior of various animals, birds, and insects. SIA are problemindependent and are efficient in solving real world complex optimization problems to arrive at the optimal solutions. Monkey behavior based algorithms are one among the SIAs first proposed in 2007. Since then, several variants such as Monkey search, Monkey algorithm, and Spider Monkey optimization algorithms have been proposed. These algorithms are based on the tree or mountain climbing and food searching behavior of monkeys either individually or in groups. They were designed with various representations, covering different behaviors of monkeys and hybridizing with the efficient operators and features of other SIAs and Genetic algorithm. They were explored for applications in several fields including bioinformatics, civil engineering, electrical engineering, networking, data mining etc. In this survey, we provide a comprehensive overview of monkey behavior based algorithms and their related literatures and discuss useful research directions to provide better insights for swarm intelligence researchers.

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Section: Monkey Search Structure Learner For Bayesian Networkmentioning

confidence: 99%

Monkey Behavior Based Algorithms - A Survey

Devi¹,

Sathya²

2017

IJISA

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“…Energy generators in traditional electrical grids, which primarily consume fossil fuel as their energy source, produce about 41% of the whole world greenhouse gases and have a huge negative effect on the environment [2]. With the appearance of problems with fossil energy depletion and the greenhouse effect, energy generation in smart grids through renewable energy resources, such as wind power, solar power, and geothermal power, has become an attractive alternative and contributes to solving the problem [3][4][5]. Furthermore, in traditional electrical grids, information is delivered in a single direction.…”

Section: Introductionmentioning

confidence: 99%

Game-theoretic energy management with storage capacity optimization in the smart grids

Gao

Liu

Wei

et al. 2018

J. Mod. Power Syst. Clean Energy

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With the development of smart grids, a renewable energy generation system has been introduced into a smart house. The generation system usually supplies a storage system with the capability to store the produced energy for satisfying a user's future demand. In this paper, the main objective is to determine the best strategies of energy consumption and optimal storage capacities for residential users, which are both closely related to the energy cost of the users. Energy management with storage capacity optimization is studied by considering the cost of renewable energy generation, depreciation cost of storage and bidirectional energy trading. To minimize the cost to residential users, the non-cooperative game-theoretic method is employed to formulate the model that combines energy consumption and storage capacity optimization. The distributed algorithm is presented to understand the Nash equilibrium which can guarantee Pareto optimality in terms of minimizing the energy cost. Simulation results show that the proposed game approach can significantly benefit residential users. Furthermore, it also contributes to reducing the peak-to-average ratio (PAR) of overall energy demand.

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“…The CCP method has been widely applied in different disciplines for optimisation under uncertainty [17], but very few studies are reported on using this method for the design of HRES. Arun et al [18] used the CCP approach in the design of a PV-battery system to deal with the uncertainties in the solar radiation. Seeraj et al [19] used this method to find the battery bank size when renewable energy resource availability, ratings and load demand were assumed to be known.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

Kamjoo

Maheri

Dizqah

et al. 2016

International Journal of Electrical Power & Energy Systems

203

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Citation: Kamjoo, Azadeh, Maheri, Alireza, Dizqah, Arash and Putrus, Ghanim (2016) Multiobjective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. International Journal of Electrical Power & Energy Systems, Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

show abstract

Optimum sizing of photovoltaic battery systems incorporating uncertainty through design space approach

Cited by 149 publications

References 29 publications

Monkey Behavior Based Algorithms - A Survey

Monkey Behavior Based Algorithms - A Survey

Game-theoretic energy management with storage capacity optimization in the smart grids

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

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