NDLI: Power factor-based scheduling of distributed battery energy storage units optimally allocated in bulk power systems for mitigating marginal losses

Content Provider	IET Digital Library
Author	Moeini, Ali Kamwa, Innocent Montigny, Martin de
Abstract	This study addresses the problem of multi-objective optimal allocation and management of multiple battery energy storage (BES) units. The multi-objective genetic algorithm is first used to find the so-called Pareto front while minimisation of power losses and the total installed capacity of the BES units are simultaneous objective functions. A number of solutions are chosen and developed over one year to find the best schedule for BES utilisation taking into account the power factor (PF) of the charge and discharge modes. Results of studies on the IEEE reliability test system 1996 confirm the existence of an optimal solution for loss reduction. Optimal tuning of the charge and discharge PFs has also proven effective for marginal loss reduction and saving energy every day of the year. Finally, it is shown that the power loss would decrease, even during charge hours, if PFs of BESs are optimally tuned.
Starting Page	1304
Ending Page	1311
Page Count	8
ISSN	17518687
Volume Number	10
e-ISSN	17518695
Issue Number	Issue 5, Apr (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/10/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2015.0462
Journal	IET Generation, Transmission & Distribution
Publisher Date	2016-04-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	BES Units Bulk Power System Distributed Battery Energy Storage Units Energy Storage Genetic Algorithm IEEE Reliability Test System 1996 Marginal Loss Mitigation Multiobjective Genetic Algorithm Multiobjective Optimal Allocation Multiple Battery Energy Storage Optimisation Technique Pareto Distribution Pareto Front Power Factor Power Factor-based Scheduling Power Generation Reliability Power Generation Scheduling Power Loss Minimisation Power System Economics Power System Managemen Power System Operation Probability Theory Reliability Secondary Cell Statistics Stochastic Linearised SCUC
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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