Risk-aware stochastic scheduling of multi-market energy storage systems

Paper: arXiv 2510.27528

Authors: Gabriel D. Patrón, Di Zhang, Lavinia M. P. Ghilardi, Evelin Blom, Maldon Goodridge, Erik Solis, Hamidreza Jahangir, Jorge Angarita, Nandhini Ganesan, Kevin West, Nilay Shah, Calvin Tsay

Abstract

Energy storage promotes the integration of renewables by operating with charge and discharge policies that balance an intermittent power supply. A key challenge in this emerging sector is how to optimize the operation of storage assets given future price uncertainties and the need to recover the costs of project finance while ensuring an attractive return on equity and hedging against downside risk. This study investigates the scheduling of energy storage assets under price uncertainty, with a focus on electricity markets. A two-stage stochastic risk-constrained approach is employed, whereby electricity price trajectories or specific power markets are observed, allowing for recourse in the schedule. Conditional value-at-risk is used to quantify risk in the optimization problems; this allows for explicit specification of a probabilistic risk limit. The proposed approach is tested in an integrated hydrogen system (IHS) and a battery energy storage system (BESS). In the joint design and operation context for the IHS, the risk constraint results in large installed unit capacities, increasing capital cost but enabling more inventory to buffer price uncertainty. In both case studies, there is an operational trade-off between risk and expected reward; this is reflected in higher expected costs (or lower expected profits) with increasing risk aversion. Despite the decrease in expected reward (up to 500$k), both systems exhibit substantial benefits of increasing risk aversion (up to 1.5$mn) with respect to risk-neutral settings. This work provides a general method to address uncertainties in energy storage scheduling, allowing operators to input their level of risk tolerance on asset decisions.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a mathematically solid two-stage stochastic optimization framework using CVaR constraints for multi-market energy storage scheduling. It applies empirical rigor through concrete real-world energy market case studies (BESS and hydrogen systems) with detailed operational and financial evaluation, though novelty is somewhat evolutionary based on established OR methods.

Research Flowchart

  flowchart TD
    A[Research Goal: Optimize multi-market energy storage scheduling under price uncertainty] --> B[Data Inputs: Market price trajectories & physical asset constraints]
    B --> C[Methodology: Two-stage stochastic optimization model with recourse]
    C --> D[Risk Management: Incorporate Conditional Value-at-Risk CVaR constraints]
    D --> E[Case Studies: Integrated Hydrogen System IHS & Battery Storage BESS]
    E --> F[Outcomes: Risk aversion reduces expected profits up to $500k but improves worst-case downside protection up to $1.5M]

Cited References (64)

1. A unified framework for stochastic optimization (2019)
2. Optimization of conditional value-at-risk (2000)
3. Arbitrage strategy of renewable-based microgrids via peer-to-peer energy-trading (2020)
4. A decision model for an electricity retailer with energy storage and virtual bidding under daily and hourly CVaR assessment (2021)
5. Risk-based optimal decision-making strategy of a Power-to-Gas integrated energy-hub for exploitation arbitrage in day-ahead electricity and Natural Gas markets (2022)
6. Risk-aware microgrid operation and participation in the day-ahead electricity market (2024)
7. Integrating process and power grid models for optimal design and demand response operation of giga-scale green hydrogen (2023)
8. Wholesale Electricity Market Data by RTO (2025)

0 of 64 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.