Risk-Based Uncertainty Set Optimization Method for Energy Management of Hybrid AC/DC Microgrids With Uncertain Renewable Generation

Abstract

Uncertainties associated with the increasing penetration of renewable power generation (RPG) in microgrids have introduced numerous challenges to their effective energy management. This paper proposes a novel risk-based uncertainty set optimization method for the energy management of typical hybrid AC/DC microgrids, where RPG outputs are considered as the major uncertainties. The underlying risks of RPG curtailment and load shedding are formulated in detail based on the probabilistic distribution of forecasted RPG values, and are further considered in the objective functions. The proposed model is solved using a piecewise linearization method combined with the quadratic Newton-Gregory interpolating polynomial technique to linearize the variable integration limit terms of the underlying risks, while the Chebyshev consistent linear approximation method is proposed to approximate the non-linear terms of the bi-directional converter conversion efficiency. Finally, the proposed model is reformulated as a mixed integer linear programming problem, and effectively solved using a high-performance solver. The proposed method is applied in simulations of an actual hybrid AC/DC microgrid system in China to demonstrate its effectiveness, good applicability, and robustness in comparison to standard robust optimization methods. The impact of RPG prediction accuracy and electric vehicle battery loss cost on the obtained solutions are further analyzed and discussed.