Optimizing distributed generation parameters through economic feasibility assessment. (1st March 2016)

• Record Type: Journal Article
• Authors: Muttaqi, K.M.; Le, An D.T.; Aghaei, J.; Mahboubi-Moghaddam, E.; Negnevitsky, M.; Ledwich, G.
• Is Part Of: Applied energy. Volume 165(2016)
• Page Start: 893
• Page End: 903
• Publication Date: 2016-03-01
• Subjects: Distributed generation -- Optimal placement -- Optimal size -- Supply quality -- Supply reliability -- Energy losses; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.01.006
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: A cost based DG sizing and placement problem is presented. The model simultaneously optimizes three objectives: quality, reliability and cost. An economic approach is implemented to evaluate the system reliability. A PSO algorithm is proposed to solve the optimization problem. Abstract: To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.