Hybrid PSO-GSA for Optimal Distribution Networks Automation Considering Uncertainties

Around the world, automation is transforming work, business, and the economy as one of the Pillars of a smart grid. The vital role behind utilizing the automation system into distribution networks is to achieve grid self-healing and improve the reliability level. Distributed Automation can be achieved via Smart secondary substations at each load point with installed automatic switches that needs large investment. Therefore, this paper provides a long term plan based on a cost/benefit study to define the optimal automation level of Distribution networks. DA planning is categorized as a non-linear Mixed Integer optimization problem with multi-dimensions. Thus, it is not possible to apply numerical algorithms for direct searching of all possible automation scenarios. To solve that problem; Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSO- GSA) technique provides a binary coded procedure to handle all the controlled variables to provide the best solution within the permissible constraints. The effectiveness of the proposed methodology is demonstrated by applying it to Bus 4 of the standard reliability (RBTS) considering uncertainty in Cost Damage Function (CDF), Investment Cost (IC) and Failure Rate (FR). Simulation results indicate a significant reduction in system reliability cost and with comparative results to those in literary studies.