Multi-objective Planning Model for Reconfigured Microgrids with Distribution Generation and Capacitors under Uncertainty Environment

In this paper, a hybrid optimization combination of ant lion optimization (ALO), genetic algorithm and general algebraic modelling system (GAMS) is presented for optimal placement and sizing of distribution generation and capacitor banks in reconfigured microgrids under uncertainty environment. Appropriate probabilistic models are considered to take care of uncertainty in electricity demand and solar irradiance. Various scenarios are investigated for reactive power compensation with and without interaction of renewable energy sources and reconfiguration. The proposed method is tested on IEEE 69-bus test systems with hourly varying load profile. Numerical results shows that the proposed technique provide significant benefits of reduction in power loss, improvement in voltage profile and cost savings.