Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load

TLDR

A scenario‑based robust energy management method accounting for the worst‑case amount of renewable generation and load is developed. The economic robust model maximizes total exchange cost while minimizing social benefits cost, models RG and load uncertainty with interval predictions, employs Taguchi orthogonal arrays to generate worst‑case scenarios, and uses a simple OA‑based search strategy to solve the optimization problem, also examining price effects. Optimizing the worst‑case scenario yields a robust energy management solution that withstands most realizations of the uncertain set, as confirmed by Monte Carlo verification and numerical case studies on a typical microgrid.

Abstract

A scenario-based robust energy management method accounting for the worst-case amount of renewable generation (RG) and load is developed in this paper. The economic and robust model is formulated to maximize the total exchange cost while getting the minimum social benefits cost at the same time. Uncertainty of RG and load is described as an uncertain set produced by interval prediction. Then, the Taguchi's orthogonal array (OA) testing method is used to provide possible testing scenarios. A simple, but practical, search strategy based on OA is designed for solving the optimization problem. By optimizing the worst-case scenario, the energy management solution of the proposed model is robust against most of the possible realizations of the modeled uncertain set by Monte Carlo verification. Numerical cases on the typical microgrid system show the effectiveness of the model and solution strategy. In addition, the influence of exchange electricity price and other parameters are also discussed in the cases.