A review of uncertainty management approaches for active distribution system planning

The growing expansion of distribution systems propelled by high penetration of renewable energy sources, growth of electric vehicles, the integration of energy storage systems, and the implementation of demand-side management strategies have introduced uncertainties related to generation fluctuations, and consumption patterns. These uncertainties are to be modeled precisely and effectively to achieve flawless embedding of the distributed energy resources into the distribution grid. This paper begins by highlighting the growing importance of distributed energy resources in active distribution network planning and then provides a comprehensive overview for methods used in the literature to characterize uncertain parameters, specifically through probability distributions, distributionally robust representations, and robust uncertainty sets. The study principally aims to critically analyze the prevailing techniques for modeling and optimization of active distribution planning problems with high penetration of distributed energy resources under growing uncertainties in physical space, such as intermittency in renewable energy production, demand variation, network topology changes, variability in electric vehicle charging load, and customer attitude towards demand response. Various methodologies, including probabilistic methods, possibilistic methods, stochastic optimization, robust optimization, information gap decision theory, and hybrid approaches, are thoroughly assessed and compared. This comprehensive and comparative assessment aids researchers in selecting the most appropriate uncertainty modeling technique in accordance with the characteristics of uncertain variables and the specific planning problem being addressed. Furthermore this study also examines the implications of cyber space uncertainties in active distribution network planning.