Optimal sizing and allocation of renewable based distribution generation with gravity energy storage considering stochastic nature using particle swarm optimization in radial distribution network

•optimal sizing and allocation of a PV and WT units with gravity energy storage•Sensitivity analysis has been performed to determine the candidate's buses for the placement of DGs.•The stochastic nature of RES (solar and wind), load, and storage unit has been handle using the probabilistic technique.•load flow analysis is performed using a backward-forward sweep algorithm embedded in the probability framework.•GES technology has been compared with the battery storage system. This paper presents an optimal sizing and allocation of a renewable energy resource (RES) based distribution generation (DG) units with gravity energy storage (GES) in the radial distribution network (DN). The optimization technique Constriction Coefficient Particle Swarm Optimization (CPSO) is utilized to reduce the total energy loss, which is subjected to equality and inequality constraints. Different DG parameters are considered and evaluated to reduce energy losses in electricity DN. To reduce search space and computational burden, a sensitivity analysis is performed to determine the candidate buses for the placement of DGs. The stochastic nature of RES (solar and wind), load, and storage unit has been handle using the probabilistic technique. The suitable penetration level is so adjusted as to restrict RES output on a certain fraction of the system load for stability consideration. The load flow analysis is performed using a backward-forward sweep algorithm embedded in the probability framework. The proposed approach has been examined on four different cases on DN consisting of 33 buses and it has been found that a notable reduction in losses with improved voltage profile is obtained by optimal sizing and placing DG units at an appropriate location. Results obtained using the CPSO technique has been validated by comparing it with the Simple Genetic Algorithm (SGA) technique. Further, the results obtained in case 3 using GES technology have compared with the battery storage system.