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

2025-10-31

AI Summary

This paper addresses the stochastic scheduling optimization of energy storage systems (ESS) under electricity price uncertainty and renewable generation volatility across multiple markets. We propose a risk-constrained two-stage stochastic optimization framework that integrates Conditional Value-at-Risk (CVaR) to quantify tail risk, incorporates dynamic electricity price forecasting, and jointly models coupled operations across energy, ancillary services, and hydrogen markets—enabling coordinated dispatch of hybrid battery-hydrogen storage assets. Our key contribution is the first application of a CVaR-based risk limit mechanism to multi-market ESS coordination, explicitly capturing how risk-aversion preferences shape optimal dispatch policies. Numerical experiments demonstrate that increasing risk aversion slightly reduces expected revenue but significantly enhances system robustness, energy buffering capability, and stability against extreme price shocks.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Energy storage can promote the integration of renewables by operating with charge and discharge policies that balance an intermittent power supply. This study investigates the scheduling of energy storage assets under energy 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 tail risk in the optimization problems; this allows for the 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 larger installed unit capacities, increasing capital cost but enabling more energy inventory to buffer price uncertainty. As shown 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 levels of risk aversion. Despite the decrease in expected reward, both systems exhibit substantial benefits of increasing risk aversion. 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.

Problem

• Optimizing energy storage scheduling under electricity price uncertainty
• Managing risk-reward tradeoffs in multi-market energy storage operations
• Quantifying financial risks in renewable energy storage system dispatch

Innovation

• Two-stage stochastic risk-constrained scheduling approach
• Conditional value-at-risk quantifies tail risk
• Integrated hydrogen and battery storage systems tested