Integrated Investment and Policy Planning for Power Systems via Differentiable Scenario Generation

TL;DR

This paper presents a gradient-based method for integrated power system planning and policy investment, utilizing differentiable scenario generation with machine learning models like diffusion models.

Contribution

It introduces a novel differentiable scenario generation framework enabling co-optimization of capacity planning and policy decisions in power systems.

Findings

Feasibility demonstrated with diffusion model-based scenario generator.

Efficient solution of operation-aware planning models achieved.

Gradient computation enabled by formalizing differentiable scenario generation.

Abstract

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.