Transmission network expansion planning considering the generators' contribution to uncertainty accommodation

Abstract

This paper presents an optimization for transmission network expansion planning (TNEP) under uncertainty circumstances. This TNEP model introduces the approach of parameter sets to describe the range that all possible realizations of uncertainties in load and renewable generation can reach. While optimizing the TNEP solution, the output of each generator is modeled as an uncertain variable to linearly respond to changes caused by uncertainties, which is constrained by the extent to which uncertain parameters may change the operational range of generators, and network topology. This paper demonstrates that the robust optimization approach is effective to make the problem with uncertainties tractable by converting it into a deterministic optimization, and with the genetic algorithm, the optimal TNEP solution is derived iteratively. Compared with other robust TNEP results tested on IEEE 24-bus systems, the proposed method produces a least-cost expansion plan without losing robustness. In addition, the contribution that each generator can make to accommodate with every uncertainty is optimally quantified. Effects imposed by different uncertainty levels are analyzed to provide a compromise of the conservativeness of TNEP solutions.