Two-stage framework for optimizing the operation of remote off-grid power systems under uncertainties.

• A novel hybridization scheme using both stochastic and deterministic techniques. • Achieves an optimum operational trajectory under forecast uncertainties. • Simplified and computationally efficient problem formulation. • Reduced dependence on frequently updated forecasts. • Proof-of-concept is depicted on a remote off-grid PV-Diesel-Battery power system. With the precipitated integration of intermittent renewable sources, operation optimization employed with uncertainty management strategies is imperative to ensure a reliable, cost-effective, and secure energy supply for the remote off-grid power systems. This paper presents a two-stage operation optimization process embedded in a model predictive control framework. The first stage decision variables are derived in a deterministic optimization framework to achieve minimum operational costs and emissions. The second stage uses a stochastic optimization framework to refine the first stage decision variables to achieve a feasible operation considering several candidate scenarios in a computationally inexpensive manner. Several measures are integrated to the proposed framework in order to address the operational requirements pertaining to remote off-grid power systems. The effectiveness of the proposed framework is demonstrated through numerical experiments for an isolated remote power system in Northern Canada for both summer and winter seasons. Quality of the obtained results as well as the computation efficiency of the overall framework has been verified compared to the existing energy management techniques. The overall result confirms the applicability of the proposed method in achieving a cost-effective and environmentally friendly operational trajectory while effectively accounting for the underlying uncertainties with a reduced computational burden. [ABSTRACT FROM AUTHOR]