Nodal Capacity Expansion Planning with Flexible Large-Scale Load Siting

TL;DR

This paper introduces a stochastic optimization model for power system capacity expansion that explicitly includes large-scale load siting and operational flexibility, leveraging high-performance computing for scalability.

Contribution

It presents a novel co-optimization framework integrating load siting, generation, transmission, and storage expansion with scenario-based flexibility modeling.

Findings

The model effectively assesses system cost and reliability impacts of large-scale load siting.

Scenario parallelization improves computational scalability and solution quality.

Application to real-world test cases demonstrates practical value.

Abstract

We propose explicitly incorporating large-scale load siting into a stochastic nodal power system capacity expansion planning model that concurrently co-optimizes generation, transmission and storage expansion. The potential operational flexibility of some of these large loads is also taken into account by considering them as consisting of a set of tranches with different reliability requirements, which are modeled as a constraint on expected served energy across operational scenarios. We implement our model as a two-stage stochastic mixed-integer optimization problem with cross-scenario expectation constraints. To overcome the challenge of scalability, we build upon existing work to implement this model on a high performance computing platform and exploit scenario parallelization using an augmented Progressive Hedging Algorithm. The algorithm is implemented using the bounding features of mpisppy, which have shown to provide satisfactory provable optimality gaps despite the absence of theoretical guarantees of convergence. We test our approach to assess the value of this proactive planning framework on total system cost and reliability metrics using realistic testcases geographically assigned to San Diego and South Carolina, with datacenter and direct air capture facilities as large loads.