Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization
Ahmed Al-Shafei, Nima Amjady, Hamidreza Zareipour, Yankai Cao
TL;DR
The paper defines Power System Transition Planning (PTSP) as an industry-aligned Multistage Stochastic Programming framework for long-term, geospatially aware power-system transition under uncertainty. It combines a MILP PSTP model with Stochastic Dual Dynamic Programming (SDDP), enhanced by a Markovian representation and HPC, to jointly optimize transition investments, siting, and operational planning across many technologies (e.g., DTR, SSSC, BESS, pumped storage, GCCS, SMR, H2) while enforcing emissions targets. Key contributions include a detailed linearization of transmission-device interactions, a novel battery degradation model, and VoSS evaluation, validated on AESO-aligned test cases AESO-6 and AESO-144, showing scalable performance and near-MILP parity under stochastic solving. The framework provides a practical, scalable tool for policymakers and planners to design low-cost, robust transition pathways toward zero-emission grids, leveraging scenario-based planning and geospatial data to guide infrastructure and technology choices. Overall, PSTP demonstrates how HPC-enabled MSP with accurate network modeling can deliver actionable, defendable transition plans within regulatory and industry practice.
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.