Multi-objective Adaptive Guided Differential Evolution for Multi-objective Optimal Power Flow Incorporating Wind-Solar-Small Hydro-Tidal Energy Sources

Kahraman H. T., Duman S.

in: Differential Evolution: From Theory to Practice , B. Vinoth KumarDiego OlivaP. N. Suganthan, Editor, Springer, London/Berlin , Singapore, pp.341-365, 2022

  • Publication Type: Book Chapter / Chapter Research Book
  • Publication Date: 2022
  • Publisher: Springer, London/Berlin 
  • City: Singapore
  • Page Numbers: pp.341-365
  • Editors: B. Vinoth KumarDiego OlivaP. N. Suganthan, Editor
  • Karadeniz Technical University Affiliated: Yes


Electric power systems are large-scale, nonlinear, and complex structures. In recent years, power systems have become more complex with the integration of renewable energy sources (RESs) into the system, and this has brought planning problems. One of these planning problems is the optimal power flow (OPF), which optimizes the determined objective function within the equality and inequality limits. In this study, it is aimed to solve the multi-objective optimal power flow (MOOPF) involving RESs such as wind, solar, small hydro, and tidal systems by using the multi-objective adaptive guided differential evolution (MOAGDE) algorithm. The proposed algorithm was applied to optimize various objective functions contemporaneously, and the performance of the algorithm was evaluated by comparing it with the results of other multi-objective optimization algorithms in the literature. Simulation studies were carried out in the IEEE 30-bus test system, in which traditional fossil fueled generating units were replaced with RESs. The results from simulation studies indicate that the MOAGDE algorithm has better results compared to the multi-/many-objective ant lion optimizer (MALO), multi-/many-objective dragon fly (MODA), and omni-optimizer (OMNI) optimization algorithms.