Generation Expansion Planning with Upstream Supply Chain Constraints on Materials, Manufacturing, and Deployment

TL;DR

This paper tackles the misalignment between generation expansion planning and upstream supply chain constraints by introducing SC-GEP, a two-module, multi-stage MILP framework that explicitly models material flow, lead times, and field availability alongside generation decisions. It employs Nested Benders Decomposition to solve the large-scale problem, enabling endogenized investment, operation, and penalty costs under material and deployment bottlenecks. The Maryland case study demonstrates that upstream frictions shift technology choices, delay deployments, and elevate costs, with distinct reliability implications under low and high demand. The work underscores the practical importance of integrating upstream constraints into long-term planning to ensure feasible, reliable, and cost-effective energy transitions.

Abstract

Rising electricity demand underscores the need for secure and reliable generation expansion planning that accounts for upstream supply chain constraints. Traditional models often overlook limitations in materials, manufacturing capacity, lead times for deployment, and field availability, which can delay availability of planned resources and thus to threaten system reliability. This paper introduces a multi-stage supply chain-constrained generation expansion planning (SC-GEP) model that optimizes long-term investments while capturing material availability, production limits, spatial and temporal constraints, and material reuse from retired assets. A decomposition algorithm efficiently solves the resulting MILP. A Maryland case study shows that supply chain constraints shift technology choices, amplify deployment delays caused by lead times, and prompt earlier investment in shorter lead-time, low-material-intensity options. In the low-demand scenario, supply chain constraints raise investment costs by $1.2 billion. Under high demand, persistent generation and reserve shortfalls emerge, underscoring the need to integrate upstream constraints into long-term planning.

Figures (11)

• Figure 1: Overview of upstream and downstream components in generation expansion planning
• Figure 2: Spatial and temporal representation for maryland power system.
• Figure 3: Material intensity (tonnes of materials per MW of installed capacity) for selected products across technologies.
• Figure 4: Operational capacity over the modeling horizon for baseline Low and High scenarios. Stacked bars represent technology-specific capacity, with Low baseline on the left in each year. Lines show net peak load, indicating system demand.
• Figure 5: Optimization Results for Status Variables: (a) Planning Capacity, (b) Built Capacity, and (c) Retirement Capacity. Stacked bars represent technology-specific capacity, with Low baseline on the left in each year.