Uncertainty-aware Power System Planning via Gradient Descent

Power system planning models provide important guidance on long-term investment strategies with significant socio-economic impact.

To remain computationally manageable, however, such planning models compromise on the level of complexity with which power system operations and physics are captured.

A common approach in most planning models is to collapse multi-stage power system operational processes into a single stage and, as a result, give up on the ability to account for uncertainty in each operational stage.

In light of newly emerging load patterns and the continuing adoption of weather-dependent stochastic renewable generation, this uncertainty, however, becomes increasingly impactful on operations, and ignoring it has been shown to cause underinvestment in transmission capacity and flexible resources.

In this work, we present a computational approach for power system expansion planning that explicitly considers two-stage day-ahead (DA) and real-time (RT) operational decisions under uncertainty while retaining time-coupling constraints to allow modeling generator ramping and energy storage.

To solve the resulting optimization problem efficiently, we employ a projected stochastic gradient descent algorithm combined with a primal-dual optimization framework and an exponential moving average smoothing strategy to improve convergence stability.

We evaluate the resulting investment decisions within a two-stage DA and RT simulation framework and compare them with a classic expansion planning model that assumes perfect knowledge of renewable generation.

Our experiments show that the proposed framework achieves lower total system costs while ensuring that the implemented technology portfolio achieves set renewable integration targets.