Volume 55, Number 2, March-April 2021
|Page(s)||899 - 919|
|Published online||06 May 2021|
Multi-objective optimization based optimal sizing & placement of multiple distributed generators for distribution network performance improvement
Department of Electrical Engineering, Pandit Deendayal Petroleum University, Gandhinagar, India
* Corresponding author: email@example.com
Accepted: 21 March 2021
Integration of Distributed Generations (DGs) into radial distribution network (RDN) is an emerging need to explore the benefits of renewable energy sources (RES). Increasing penetration of RES based DGs in RDN without proper planning leads to several operational problems such as excessive energy losses, poor voltage quality and load balancing. Hence, in this work, multi-objective optimization (MOO) problem is formulated by carefully chosen three conflicting objectives such as power loss minimization, enhancement of load balancing index (LBI) and aggregate voltage deviation index (AVDI). Teaching-Learning-Based-Optimization (TLBO) is used to optimize MOO problem considering placement of DGs at multiple locations in RDN satisfying the constraints on bus voltage magnitude, branch flows and DG size. Comprehensive simulation studies have been carried out to obtain optimal performance for 69-nodes RDN with the increasing penetration of DGs at multiple locations. It is shown that determination of optimal sizing of DGs at multiple locations in RDN with MOO results in lesser power losses, improved voltage profiles and better load balancing as compared to placement of single DG in RDN. Performance measures such as spacing and spread indicators are used for characterizing Pareto solutions for MOO problem. Such set of non-dominated solutions obtained from Pareto front during multi-objective TLBO gives proper guidelines to the utility operator about sizing and placement of DGs based on the assigned priorities to the objectives.
Mathematics Subject Classification: 90C29
Key words: Multi-objective optimization / TLBO / distributed generations / radial distribution network
© EDP Sciences, ROADEF, SMAI 2021
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