A dynamic fuzzy interactive approach for DG expansion planning Majid Esmi Jahromi a,⇑,1 , Mehdi Ehsan b , Abbas Fattahi Meyabadi c a Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran b Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran c Department of Electrical Engineering, Hamadan University of Technology, Hamadan, Iran article info Article history: Received 5 September 2011 Received in revised form 1 June 2012 Accepted 2 June 2012 Available online 15 July 2012 Keywords: Multi objective optimization Distribution network Dynamic planning Fuzzy interactive model Chaotic Local Search Modified HBMO algorithm abstract This paper presents a dynamic multi objective model for distribution network expansion, considering the distributed generators (DGs) and network reinforcements. The proposed model simultaneously optimizes three objective functions namely, total cost, emission cost and technical satisfaction (voltage profile) by finding the optimal schemes of timing, sizing, placement and DG technologies in a long term planning period (dynamic planning). The importance of each objective function can be changed in the interactive steps. The calculation algorithm is based on Chaotic Local Search with Modified Honey Bee Mating Opti- mization (CLSMHBMO). The effectiveness of the proposed model and the calculation method are demon- strated through different studies and comparative analysis on an actual distribution network. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Distributed generations (DGs) are defined as the small power re- sources located closely to load points and connected to the radial distribution network or inside the facilities of the great customers [1,2]. The role of DG units have been increased in the last decade by providing different benefits like cost reduction, reliability of sup- ply, ancillary services, emission reduction, postponement of the transmission and distribution expansion for DG-owner (DGO) and DNO [3–5]. These aspects make the DG expansion planning more technical, beneficial and attractive for system agents. DGs espe- cially the ones based on the renewable energy technologies are becoming more popular as they address climate change and energy security issues [6,7]. The traditional method for improving the eco- nomical and technical performance of a distribution network is investing in network components [8]. In some market models, the DNO is authorized to install DG units in his territory along with net- work reinforcements and in some cases the DNOs are unbundled from DG ownership while it is done by non-DNO entities [9]. 1.1. Literature addressing In distribution network expansion planning, different models have been proposed in the literature addressing which consider different objective functions, including technical (voltage profile [10,11] and its stability [12,13]), economical (network investment deferral [14,15] and active loss reduction [16]) and environmental (emission reduction [17]) issues. One way of treating with multi objective problems is converting them into a single objective mod- el [18–20]. This method may deprive the planner in order to have a set of solutions and to do tradeoff analysis. The Pareto optimal front has been widely used in multi objective applications to over- come the problem [21–26]. This may lead the planner to do trade- off analysis for a set of solutions and objective functions. However, there are some shortcomings associated to the reported multi objective models of DG-owned DNO. For example they are static and all investments are designed to be done at the beginning of the planning horizon to meet the load at the end of the planning period [27–29]. Also they do not simultaneously consider the net- work and DG investment and just use one of their planning options like DG units or network reinforcement [30,31]. In calculation method applications different calculation algo- rithms for single and multi objective applications have been ad- dressed including particle swarm optimization (PSO) [13], genetic algorithm (GA) [32], evolutionary algorithm (EA) [33], immune algorithm (IA), etc. 0142-0615/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2012.06.017 ⇑ Corresponding author. Address: Avenue No. 10 (Farshad Chahar Setad), Emam Khomeini Street, Jahrom, 74166-13778 Fars, Iran. Tel.: +98 912 6068897. E-mail addresses: m.esmi@srbiau.ac.ir, majid_esmi@yahoo.com (M. Esmi Jahro- mi), ehsan@sharif.ac.ir (M. Ehsan), fattahi@profs.hut.ac.ir (A. Fattahi Meyabadi). 1 Address: 8 Sherringham Bend, Cockburn, Western Australia 6164, Australia. Tel.: +61 419 973 185. Electrical Power and Energy Systems 43 (2012) 1094–1105 Contents lists available at SciVerse ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes