Paper: arXiv 2312.07733

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical concepts like probabilistic constraints, quantile approximations, and SLSQP optimization, indicating high complexity. It is empirically rigorous, featuring realistic data calibration, scenario-based backtesting, and multi-load case studies that simulate real-world procurement structuring.

Research Flowchart

  flowchart TD
  A["Research Goal: Minimize annual cost of 24/7 CFE procurement"]
  B["Key Inputs: Available CFE generators; Hourly load & renewable data"]
  C["Methodology: Probabilistic framework with CFE score constraints"]
  D["Computational Process: Stochastic optimization across multiple scenarios"]
  E["Outcomes: Optimal renewable portfolio; Cost comparison across strategies"]
  A --> B
  B --> C
  C --> D
  D --> E