Networked hybrid AC/DC microgrids with nonlinear conversion losses

Accurate conversion loss models are the keys to guaranteeing 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. The simulation results of 9 case studies, from single MG to networked MGs, under different scalability.