Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis - Kumar, Sajjan; Mandal, Kamal K.; Chakraborty, Niladri (Applied soft computing, 2019)
Placement of optimally sized distributed generator (DG) units at optimal locations in the radial distribution networks can play a major role in improving the system performance. The maximum economic and technical benefits can be extracted by minimizing various objectives including yearly economic loss which includes installation, operation and maintenance cost, power loss as well as voltage deviation throughout the buses. The present problem is analysed considering these multi-objective frameworks and presents the best compromise solution or Pareto-optimal solution. Several equality and inequality constraints are also considered for the multi-objective optimization problem. In this paper, a novel multi-objective opposition based chaotic differential evolution (MOCDE) algorithm is proposed for solving the multi-objective problem in order to avoid premature convergence. Performance of population based meta-heuristic techniques largely depends on the proper selections of control parameters. It is reported that wrong parameters selection may lead to premature convergence and even stagnation. The proposed technique uses logistic mapping to generate chaotic sequence for control parameters. The proposed algorithm is implemented on IEEE-33 and IEEE-69 bus radial distribution systems for verifying its effectiveness. A comparative analysis with other modern multi-objective algorithms like NSGA-II, SPEA2 and MOPSO is also presented in this work. It is observed that the proposed algorithm can produce better results in terms of power loss and yearly economic loss minimization as well as improvement of voltage profile. •Techno-economic analysis has been done by optimal placement of multiple DGs.•Three objectives (PL, YELwDG and VD) are considered for multi-objective analysis.•A novel Pareto-based MOCDE technique has been proposed to avoid premature convergence.•A comparative analysis with NSGA-II, SPEA2 and MOPSO algorithms is also presented.•IEEE-33 and IEEE-69 bus systems are taken for different case studies.