More Efficient Energy Management for Networked Hybrid AC/DC Microgrids With Multivariable Nonlinear Conversion Losses

Abstract

Accurate conversion loss models are the keys to guarantee the more efficient operation of networked hybrid ac/dc microgrids (N-ac/dc-MGs). A two-stage stochastic unit commitment (UC) problem is proposed to improve the operational efficiency of N-ac/dc-MGs under uncertain renewable energy generation output and loads. The nonlinear power losses of ac/dc converters and dc/dc converters under different operating modes are formulated as novel multivariate nonlinear functions of both power and voltage. These functions are approximated by linear surrogate models and adopted by the dynamic optimal power flow (OPF), where the voltage and power of the converters can be optimized considering the physical laws among voltages of converters. Embedding the dynamic OPF problem as mixed-integer recourse, a two-stage stochastic programming problem is formulated for the day-ahead operation of N-ac/dc-MGs. To reduce the computational cost, a finite iteration convergent Benders decomposition algorithm is proposed to solve the UC problem. Case studies are performed on hybrid ac/dc MGs and N-ac/dc-MGs. Simulation results reveal that using more accurate conversion loss models, the output commands, especially the dc bus voltage, from the energy management systems will lead to more efficient system operation, and thus, save energy.