Abstract
The integration of renewable energy sources (RES) such as solar photovoltaic (PV) and wind turbine (WT) into the power grid poses operational challenges to the system operators due to their uncertain nature. Furthermore, this uncertain nature of RES severely affects the reliability of optimal power flow based energy scheduling algorithms. To deal with this issue, in this paper, a new methodology to solve the dynamic optimal power flow problem (DOPF) considering uncertainty in RES and load demands is proposed. Latin hypercube sampling technique is used to generate the samples for the variables depending upon their level of uncertainty. Robust optimization (RO) algorithm is used to solve the proposed DOPF problem without and with considering uncertainties in 6 different cases to minimize the total cost of power generation. Python Optimization Modeling Objects (Pyomo) framework is used to implement the proposed DOPF model on a modified IEEE 14 bus test system.
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