Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization

TL;DR

This paper proposes an industry-aligned, comprehensive framework for long-term power system transition planning that addresses large-scale stochastic optimization challenges using advanced computational methods.

Contribution

It introduces a novel, broadly applicable framework for power system transition planning that incorporates uncertainty and leverages high-performance computing for tractability.

Findings

Framework effectively models long-term power transition planning.

Demonstrated scalability on realistic Alberta power system case.

Utilizes Stochastic Dual Dynamic Programming for large-scale problems.

Abstract

This work introduces the category of Power System Transition Planning optimization problem. It aims to shift power systems to emissions-free networks efficiently. Unlike comparable work, the framework presented here broadly applies to the industry's decision-making process. It defines a field-appropriate functional boundary focused on the economic efficiency of power systems. Namely, while imposing a wide range of planning factors in the decision space, the model maintains the structure and depth of conventional power system planning under uncertainty, which leads to a large-scale multistage stochastic programming formulation that encounters intractability in real-life cases. Thus, the framework simultaneously invokes high-performance computing defaultism. In this comprehensive exposition, we present a guideline model, comparing its scope to existing formulations, supported by a fully detailed example problem, showcasing the analytical value of the solution gained in a small test case. Then, the framework's viability for realistic applications is demonstrated by solving an extensive test case based on a realistic planning construct consistent with Alberta's power system practices for long-term planning studies. The framework resorts to Stochastic Dual Dynamic Programming as a decomposition method to achieve tractability, leveraging High-Performance Computing and parallel computation.