A flexibility-based multi-objective model for contingency-constrained transmission expansion planning incorporating large-scale hydrogen/compressed-air energy storage systems and wind/solar farms

The remarkable arrival of uncertainties given by the fluctuations in the output of renewable energy sources (RESs) calls for a more flexible power grid. In confronting this issue, large-scale energy storage systems play a vital role in achieving higher flexibility. In this regard, the authors of this study present here a new multi objective model for contingency-constrained transmission expansion planning that incorporates large-scale hydrogen/compressed-air energy storage systems and wind/solar farms to simultaneously boost both supply demand-related flexibility (SDFX) and grid-related flexibility (GDFX). The proposed model is formulated as a non-convex mixed-integer nonlinear master-slave optimization problem. The planning objectives, modeled through the master problem, are intended to minimize the investment and operation costs, while maximizing the SDFX enhancement metric minus the GDFX degradation metric. The slave problem, however, aims to maximize the community welfare function (CIWF) and microgrid welfare function (MIWF) under both normal and N-1 operating conditions. To solve the resulting non-convex mixed-integer nonlinear master-slave optimization problem, a potent symphony orchestra search algorithm (SOSA)-empowered with a multi-computational-step, multi-dimensional, multiple-homogeneous structure-is employed to allow better diversification and exploration. Following the determination of optimal solutions, a conservative-based fuzzy satisfying approach is applied to characterize the best trade-off solution that suits cost-effectiveness and flexibility requirements. The newly developed model was implemented on the IEEE 24-bus and IEEE 118-bus test grids, and the simulation results proved its effectiveness and practicality.