Planning and reliability assessment to integrate distributed automation system into distribution networks utilizing binary hybridPSOandGSAalgorithms 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 (DA) can be achieved via Smart secondary substations at each load point with installed automatic sectionalize switches that need a 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 constrained with system reliability indices limits. DA planning is categorized as a non-linear Mixed Integer optimization problem with multi-dimensions. Hence, it is not possible to apply numerical algorithms to directly search for 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 limits. The effectiveness of the proposed methodology is demonstrated by applying it to RBTS-Bus 4 standard reliability test system considering uncertainty in Cost Damage Function (CDF), Investment Cost (IC), and Failure Rate (FR). Also, this paper study the effect of change in Depreciation ratio (DR), Load Growth (LG) rate besides; peak Load Change (LC). Finally, a reliability assessment using NEPLAN software is done for the optimal decision to enhance the technical analysis. Moreover, Distributed Generations (DG) has a significant impact on the reliability of distribution networks. Simulation results indicate that the system reliability cost is significantly reduced with adequate system reliability level, with comparative results to those in literary studies.