A global optimization framework for joint generation and transmission expansion planning with AC power flow representation

Abstract

The integration of renewable energy generating units, often located in remote regions with limited grid connectivity, has created a pressing need for coordinated generation and transmission expansion planning (G&TEP). However, considering full AC network representation, the co-optimization of generation and transmission poses a challenging nonconvex mixed-integer problem that is prone to locally suboptimal solutions. In this study, we propose a tailored global optimization framework to identify the most cost-effective set of generating units and candidate transmission lines while satisfying operational and investment constraints. The proposed solver employs second-order cone relaxation, further enhanced through a set of relaxation-tightening constraints, along with feasibility-based and optimization-based bound tightening techniques to improve relaxation strength. A salient feature of the solver is the integration of a no-good cut technique, which enables efficient exploration of alternative candidate solutions within the feasible region. As demonstrated by numerical results, this technique is specifically tailored to the G&TEP problem and significantly improves solution quality while reducing the runtime required to achieve global optimality. A comparative performance analysis with state-of-the-art global MINLP solvers demonstrates that the proposed approach achieves tighter optimality gaps faster and exhibits superior flexibility and scalability.