Least-Cost Structuring of 24/7 Carbon-Free Electricity Procurements

TL;DR

This paper presents a probabilistic framework for constructing cost-effective renewable energy portfolios that meet specific hourly carbon-free performance targets, using simulations and probability constraints.

Contribution

It introduces a novel probabilistic approach to optimize renewable procurement portfolios for 24/7 carbon-free electricity with performance guarantees.

Findings

The framework effectively balances cost and performance constraints.

Simulation results demonstrate the approach's practicality with real-world data.

Different methods for managing multiple loads are compared.

Abstract

We consider the construction of renewable portfolios targeting specified carbon-free (CFE) hourly performance scores. We work in a probabilistic framework that uses a collection of simulation scenarios and imposes probability constraints on achieving the desired CFE score. In our approach there is a fixed set of available CFE generators and a given load customer who seeks to minimize annual procurement costs. We illustrate results using a realistic dataset of jointly calibrated solar and wind assets, and compare different approaches to handling multiple loads.