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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. INDEX TERMS Renewable energy, distributed generation, distribution system planning, uncertainties, Harris hawks optimizer, particle swarm optimization. NOMENCLATURE Acronyms ABC artificial bee colony BB-BC big bang-big crunch method ADS active distribution system BFOA bacterial foraging optimization algorithm BSA backtracking search algorithm BSOA backtracking search optimization algorithm CDF cumulative density functions. CS-GA cuckoo search-generic algorithm DA dragonfly algorithm DG distributed generator FA firefly algorithm The associate editor coordinating the review of this manuscript and approving it for publication was Lin Zhang .

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