Stochastic parallel optimization of an islanded microgrid under multiple source–load uncertainties

Abstract

This paper proposes a stochastic parallel optimization approach for an islanded microgrid. The proposed approach solves two of the problems faced by such microgrids, namely, how to mitigate the adverse effects of multiple source–load uncertainties and how to accelerate the computation speed, by decomposing an entire microgrid into several entities, i.e., the utility and the end users. A chance constrained programming-based optimization model is established to address the former problem. By discretizing the continuous variables, the chance constraints of multiple uncertainties can be transformed into deterministic constraints. The second problem is solved by incorporating a new linear penalty function into the analytical target cascading method. With this proposed penalty function, the algorithm converges rapidly and the scheduling results for both the utility and the end users are obtained with low computational cost. A simulation is conducted to illustrate the effectiveness of the proposed approach.