Data-Driven Distributionally Robust Optimization for Long-Term Contract vs. Spot Allocation Decisions: Application to Electricity Markets

TL;DR

This paper proposes a data-driven distributionally robust optimization framework to improve long-term contract versus spot market decisions in electricity markets, offering better risk management against uncertain outcomes.

Contribution

It introduces a novel DRO approach tailored for electricity market decision-making, comparing it with traditional models to highlight its robustness benefits.

Findings

DRO outperforms risk-neutral models in uncertain scenarios

Wasserstein DRO provides better risk mitigation

The approach enhances decision stability in volatile markets

Abstract

There are numerous industrial settings in which a decision maker must decide whether to enter into long-term contracts to guarantee price (and hence cash flow) stability or to participate in more volatile spot markets. In this paper, we investigate a data-driven distributionally robust optimization (DRO) approach aimed at balancing this tradeoff. Unlike traditional risk-neutral stochastic optimization models that assume the underlying probability distribution generating the data is known, DRO models assume the distribution belongs to a family of possible distributions, thus providing a degree of immunization against unseen and potential worst-case outcomes. We compare and contrast the performance of a risk-neutral model, conditional value-at-risk formulation, and a Wasserstein distributionally robust model to demonstrate the potential benefits of a DRO approach for an ``elasticity-aware'' price-taking decision maker.