Incorporating Mobile Energy Resources in Optimal Power Flow Models Considering Geographical and Road Network Data

Mobile energy resources (MERs) are becoming an increasingly popular asset in modern power systems with several potential benefits. While the incorporation of MERs in optimal power flow (OPF) models can enhance both reliability and resilience of the system, they add additional levels of complexity to the optimization problem. To properly determine optimal dispatch of MERs, electrical power grid constraints must be incorporated with and associated to those posed by geography and road networks. In this paper, a mixed-integer programming (MIP) model is proposed which incorporates MER dispatch into OPF. The computational implementation using Python utilizes real-world geographical and road network data to provide an optimal dispatch of MERs in the OPF solution considering actual driving and dispatching times. The implemented model is demonstrated and validated using a 24-bus test system in Portugal. A day-ahead operation planning scenario is considered with overloading and loss of renewable generation, showcasing how load shedding can be mitigated through proper dispatch of MERs. Several scenarios are tested, including the definition of critical loads, varying individual MER capacities and numbers, and MER dispatch origins or depots. Finally, an N-1 contingency analysis is performed to study the effect the different MER dispatch scenarios on overall system reliability.