Optimal planning of renewable energy-based DG units (RE-DGs) in active distribution systems (ADSs) has many positive technical and economical implications and aim to increase the overall system performance. The optimal allocation and sizing of RE-DGs, particularly photovoltaic (PV) and wind turbine (WT), is still a challenging task due to the stochastic behavior of renewable resources. This paper proposed a novel methodology to solve the problem of RES-DGs planning optimization based on improved Harris Hawks Optimizer (HHO) using Particle Swarm Optimization (PSO). The uncertainties associated with the intermittent behaviour of PV and WT output powers are considered using appropriate probability distribution functions. The optimization problem is formulated as a non-linear constrained optimization problem with multiple objectives, where power loss reduction, voltage improvement, system stability, and yearly economic saving have been taken as the optimization objectives taken into account various operational constraints. The proposed methodology, namely HHO-PSO, has validated on three test systems; standard IEEE 33 bus and 69 bus systems and 94 bus practical distribution system located in Portuguese. The obtained results reveal that the HHO-PSO provide better solutions and maximizes the techno-economic benefits of the distribution systems for all considered cases and scenarios. Furthermore, simulation results are evaluated by comparing to those well-known approaches reported in the recent literature.

This paper proposes a combination of phasor particle swarm optimization (PPSO) and a gravitational search algorithm, namely a hybrid PPSOGSA algorithm, for optimal power flow (OPF) in power systems with an integrated wind turbine (WT) and solar photovoltaic (PV) generators. The OPF formulation includes the forecasted active power generation of WT and PV as dependent variables, whereas the voltage magnitude at WT and PV buses is considered as control (decision) variables. Forecasting the output power of WT and PV generators is based on the real-time measurements and the probabilistic models of wind speed and solar irradiance. The proposed OPF approach and the solution method are verified on the IEEE 30-bus test system. The robustness and efficiency of the proposed PPSOGSA algorithm in solving the OPF problem are evaluated by comparing with 20 well-established metaheuristic optimization methods under the same system data, control variables, and constraints. The statistical features of the OPF results are estimated by using the Monte Carlo method.