Multi-objective resilience enhancement program in smart grids during extreme weather conditions

•Proposing Resiliency Response Program as a way to balance risk and benefit.•Optimal rescheduling of DERs and EESs in a grid during extreme weather conditions.•Optimal reconfiguration of distribution system during extreme weather conditions.•Modeling the effect of wind speed in operational planning and reconfiguration. Weather based electrical power outages cover a huge part of consumer interruptions. So, reliable-economical operation of grids during extreme weather conditions is one of challenges for grid operators. In this regard, this paper proposes resilience enhancement programs in order to increase resilience and economic profits in a smart grid. In proposed approach, resilience improvement is done by modeling weather effects on branches outages and then re-scheduling of distributed energy resources and energy storages, load shifting and dynamic reconfiguration of distribution network. In this paper, hourly variation of weather depended failure probabilities are considered. Resilience enhancement programs aim to mitigate effects of events which may cause by extreme weather before fault inception by rescheduling of resources and selecting suitable reconfigurations. Also, reconfiguration isolates damaged parts after fault inception. The objectives in the proposed approach are defined as minimizing operational cost of distribution network and energy not supplied penalty costs from the system operator’s viewpoint, as well as, maximizing benefits of energy resources owners by considering weather conditions. A multi-objective optimization algorithm based on genetic algorithm and epsilon constraint method using fuzzy decision maker is employed to choose the best solution from a provided Pareto optimal set. In order to evaluate performance of proposed resilience enhancement programs and its effect, resilience assessment metrics are studied. Various simulations prove the efficiency of proposed model in compare with traditional grid during extreme weather conditions.