Open AccessStochastic multi‐objective framework for optimal dynamic planning of interconnected microgrids
Author(s) -
Samadi Gazijahani Farhad,
Salehi Javad
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2017.0278
Subject(s) - mathematical optimization , particle swarm optimization , computer science , fuzzy logic , reliability (semiconductor) , stochastic programming , distributed generation , electric power system , reliability engineering , power (physics) , renewable energy , engineering , mathematics , physics , quantum mechanics , artificial intelligence , electrical engineering
This paper proposes a new stochastic multi‐objective framework for optimal dynamic planning of interconnected microgrids (MGs) under uncertainty from economic, technical, reliability and environmental viewpoints. In the proposed approach, optimal site, size, type, and time of distributed energy resources are determined along with optimal allocation of section switches to partitioning conventional distribution system into a number of interconnected MGs. The uncertainties of the problem are considered using scenario modelling and backward scenario reduction technique is implemented to deal with computational burden. In addition, three different risk averse, risk neutral and risk seeker strategies are defined for distribution network operator. The proposed framework is considered as two unparalleled objective functions which the first objective minimizes the investment cost, operation and maintenance cost, power loss cost and pollutants emission cost and the second objective is defined to minimize energy not supplied in both connected and islanded modes of MGs. Finally, multi objective particle swarm optimization is applied to minimize the proposed bi‐objective functions and subsequently fuzzy satisfying method is accomplished to select the best solution proportional to risk based strategies. Efficiency of the proposed framework is validated on 85‐bus distribution system and obtained results are presented and discussed.