Integrated Investment and Policy Planning for Power Systems via Differentiable Scenario Generation
This work addresses the challenge of integrating policy-driven demand changes into power system planning, offering a novel optimization framework for energy planners.
The paper introduces a gradient-based method for co-optimizing power system capacity planning and policy investments, using differentiable scenario generation to compute gradients through generative models. Numerical experiments with a diffusion model show feasibility, though no specific performance numbers are reported.
We formulate a method to co-optimize power system capacity planning decisions and policy investments that shape electricity load patterns. To this end, we leverage a gradient-based solution technique that enables the efficient solution of operation-aware planning models. To compute gradients with respect to the conditions that define daily electricity demand profiles, we introduce and formalize the concept of differentiable scenario generation and show that generative machine learning models satisfy the mathematical requirements needed to compute consistent gradients. We demonstrate the feasibility of the proposed approach through numerical experiments using a diffusion model-based scenario generator and a stylized generation and capacity expansion planning model.