A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems. (1st February 2018)

• Record Type: Journal Article
• Authors: Quadri, Imran Ahmad; Bhowmick, S.; Joshi, D.
• Is Part Of: Applied energy. Volume 211(2018)
• Page Start: 1245
• Page End: 1260
• Publication Date: 2018-02-01
• Subjects: Distributed Generation (DG) -- Optimization -- Multi-objective function -- Comprehensive teaching-learning optimization -- TLBO; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.11.108
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Presents a parameter independent intelligent optimization technique. Proposed technique is suitable for both continuous and discrete variables. Optimization technique is validated through mathematical benchmark functions. Proposed technique used to optimally place energy resources in distribution systems. Performance improvement of distribution systems with distributed energy resources. Abstract: Distributed generation (DG) is a better alternative to meet power demand near the load centers than centralized power generation. Optimal placement and sizing of DGs plays a crucial role in improving the performance of distribution systems in terms of network loss reduction, voltage profile improvement, reliability of power supply and stability issues. This paper presents a comprehensive teaching learning-based optimization (CTLBO) technique for the optimal allocation of DGs in radial distribution systems to improve network loss reduction, voltage profile and annual energy savings. The proposed technique can handle mixed integer variables, is parameter independent and possesses immunity to local extrema trappings. The effectiveness of the proposed method is first validated on standard mathematical benchmark functions. It is observed to have better convergence characteristics than teaching learning-based optimization (TLBO) and quasi-oppositional teaching learning-based optimization (QOTLBO). Subsequently, it is applied to optimal DG allocation in IEEE 33-bus, 69-bus and 118-bus radial distribution test systems. Both single and multi-objective formulations are considered. In addition, the selection of the optimal number of DGs in the distribution networks is also investigated and case studies are carried out. Results demonstrate that optimal allocation of DGs using the proposed technique results in marked improvement in the performance of distribution systems over TLBO and QOTLBO. The applicability of the proposed technique for DG allocation in distribution systems with practical load profiles results in further improvement in annual energy loss reduction and cost savings.