Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems

• Published in: Energy (Oxford) Vol. 76; pp. 920 - 930
• Main Authors: Sedighizadeh, Mostafa, Esmaili, Masoud, Esmaeili, Mobin
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2014; Elsevier
• Subjects: Algorithms; Allocations; Applied sciences; Direct current; Distributed generation; Distribution system reconfiguration; electric power; Energy; Exact sciences and technology; Fuzzy; greenhouse gas emissions; Hybrid Big Bang-Big Crunch algorithm; hybrids; Multi-objective optimization; mutation; Networks; Optimization; Reconfiguration; system optimization; Distribution system reconfiguration; Hybrid Big Bang-Big Crunch algorithm; Distributed generation; Multi-objective optimization; Electric power production; Power production; Algorithm; Distribution
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2014.09.004

In this paper, a multi-objective framework is proposed for simultaneous optimal network reconfiguration and DG (distributed generation) power allocation. The proposed method encompasses objective functions of power losses, voltage stability, DG cost, and greenhouse gas emissions and it is optimized subject to power system operational and technical constraints. In order to solve the optimization problem, the HBB-BC (Hybrid Big Bang-Big Crunch) algorithm as one of the most recent heuristic tools is modified and employed here by introducing a mutation operator to enhance its exploration capability. To resolve the scaling problem of differently-scaled objective functions, a fuzzy membership is used to bring them into a same scale and then, the fuzzy fitness of the final objective function is utilized to measure the satisfaction level of the obtained solution. The proposed method is tested on balanced and unbalanced test systems and its results are comprehensively compared with previous methods considering different scenarios. According to results, the proposed method not only offers an enhanced exploration capability but also has a better converge rate compared with previous methods. In addition, the simultaneous network reconfiguration and DG power allocation leads to a more optimal result than separately doing tasks of reconfiguration and DG power allocation. •Hybrid Big Bang-Big Crunch algorithm is applied to network reconfiguration problem.•Joint reconfiguration and DG power allocation leads to a more optimal solution.•A mutation operator is used to improve the exploration capability of HBB-BC method.•The HBB-BC has a better convergence rate than the compared algorithms.