Growth of renewable energy leads to new challenges for electric power grid planning and operation. Many renewable energy resources, such as solar and wind, heavily depend on the weather conditions that are inherently uncertain. Such uncertainty is usually revealed progressively over time. Consequently, the grid planning and operation decisions need be adjusted accordingly across multiple stages to achieve optimal efficiency. The multistage decision structure calls for study on multistage grid optimization algorithms that can accommodate the discrete decisions, such as battery charging versus discharging decisions, and scale well with the number of renewable resources, which can go up to tens of thousands. Moreover, several major tripping and disturbance incidences in the past decade have underscored the heightened stability concerns of a power grid with high renewable penetration. In contrast to conventional thermal generators that have large rotating masses to stabilize themselves, renewable resources are typically power electronics-interfaced resources, which lead to lower system inertia, faster grid dynamics, more frequent disturbances, and greater control difficulty. Hence, it is increasingly essential to integrate stability considerations into grid optimization algorithms to enhance reliable power system operation. This research will include open-source implementations of the algorithms developed, which can provide a computational infrastructure and benchmark for assessi