A novel optimization model for dynamic power grid design and expansion planning considering renewable resources

Abstract Growing demand for electricity has made power grid design and expansion planning one of the main challenges in power industry management. In recent years, reconfiguration of existing power grid along with the adoption of renewable power generation leads to a significant reduction in expansion costs and GHG emissions. This paper offered a novel optimization model to address the design and planning of power grid expansion in a dynamic environment. Besides capacity planning, the model also determines location and time to construct new facilities. This research aims to satisfy demand by considering reduction in net present value of costs and increase in network efficiency. Electricity tariff and cost of load shedding differ according to different power consumers (i.e., residential, commercial, industrial and agricultural). Due to inherent intermittency in renewable energy resources and their subsequent impact on the entire power grid, different scenarios are generated, and the model is solved using the sample average approximation method. Eventually, validation of the proposed model and sensitivity analysis is carried out through a real case study in Iran. Computational results demonstrate the practicality of the stochastic model and show integration of renewable power plants would decrease the transmission and sub-transmission network costs.