A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm

• Published in: Energy (Oxford) Vol. 102; pp. 199 - 215
• Main Authors: Ahmadigorji, Masoud, Amjady, Nima
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2016
• Subjects: Algorithms; BCSSO (Binary chaotic shark smell optimization); Chaos theory; Distributed generation; Distribution management; Distribution network; Investments; Mathematical models; Multiyear expansion planning; Networks; Optimization; Reinforcement; Reliability; BCSSO (Binary chaotic shark smell optimization); Distributed generation; Multiyear expansion planning; Reliability; Distribution network
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2016.02.088

In this paper, a new model for MEPDN (multiyear expansion planning of distribution networks) is proposed. By solving this model, the optimal expansion scheme of primary (i.e. medium voltage) distribution network including the reinforcement pattern of primary feeders as well as location and size of DG (distributed generators) during an ascertained planning period is determined. Furthermore, the time-based feature of proposed model allows it to specify the investments/reinforcements time (i.e. year). Moreover, a minimum load shedding-based analytical approach for optimizing the network's reliability is introduced. The associated objective function of proposed model is minimizing the total investment and operation costs. To solve the formulated MEPDN model as a complex multi-dimensional optimization problem, a new evolutionary algorithm-based solution method called BCSSO (Binary Chaotic Shark Smell Optimization) is presented. The effectiveness of the proposed MEPDN model and solution approach is illustrated by applying them on two widely-used test cases including 12-bus and 33-bus distribution network and comparing the acquired results with the results of other solution methods. •A multiyear expansion planning model for distribution network is presented.•A new evolutionary algorithm-based solution approach is proposed.•A minimum load shedding-based analytical method for EENS minimization is suggested.•The efficacy of the proposed solution approach is broadly investigated.