Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty

• Published in: Energy (Oxford) Vol. 103; pp. 86 - 99
• Main Authors: Esmaeili, Mobin, Sedighizadeh, Mostafa, Esmaili, Masoud
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2016
• Subjects: algorithms; Allocations; decision making; Distributed generation; Distribution management; Distribution system reconfiguration; electric power; gas emissions; Loads fuzzy modeling; Mathematical models; Multi-objective Hybrid Big Bang-Big Crunch algorithm; Multi-objective optimization; Networks; Optimization; Pareto optimal solution; Pareto optimality; pollutants; system optimization; Uncertainty; Distribution system reconfiguration; Multi-objective Hybrid Big Bang-Big Crunch algorithm; Pareto optimal solution; Distributed generation; Multi-objective optimization; Loads fuzzy modeling
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2016.02.152

In this paper, a multi-objective framework is proposed for simultaneous network reconfiguration and power allocation of DGs (Distributed Generations) in distribution networks. The optimization problem has objective functions of minimizing power losses, operation cost, and pollutant gas emissions as well as maximizing the voltage stability index subject to different power system constraints. The uncertainty of loads is modeled using the TFN (Triangular Fuzzy Number) technique. A novel solution method called MOHBB-BC (Multi-objective Hybrid Big Bang-Big Crunch) is implemented to solve the optimization problem. The MOHBB-BC derives a set of non-dominated Pareto solutions and accumulates them in a retention called Archive. The diversity of Pareto solutions conserved by applying a crowding distance operator and afterwards, the ‘best compromised’ Pareto solution is selected using a fuzzy decision maker. The proposed method is tested on two test systems of 33-bus and 25-bus in different cases including unbalanced three-phase loads. Results obtained from test cases elaborate that the MOHBB-BC results in more diversified Pareto solutions implying a better exploration capability even with a higher fitness. In addition, considering load uncertainty leads to a more realistic solution than deterministic loads but with higher level of power losses. •Pareto solutions of MOHBB-BC have higher quality and more diversified than MOPSO.•Load uncertainty leads to more realistic solution but with 4.5% more losses.•Mutation in HBB-BC makes it have better exploration and speed than fuzzy BA and HAS.