Profit-Oriented Planning and Multi-Market Operation Model for Hybrid Energy Storage Systems

2026-05-18

AI Summary

This study addresses the gap in existing research by proposing a bi-level optimization framework that jointly optimizes capacity planning and multi-market bidding strategies for hybrid energy storage systems while accounting for technological heterogeneity and profit maximization. The upper level co-optimizes the capacity configuration of two heterogeneous storage technologies and their coordinated bidding in day-ahead energy and reserve markets as well as real-time balancing markets, while the lower level models the system operator’s market clearing process. Formulated as a mixed-integer linear program and solved efficiently via Benders decomposition, the approach uniquely preserves component-specific technical characteristics, enabling internal power dispatch and differentiated market participation. Results demonstrate that systems with high power-to-energy ratios benefit more from energy arbitrage, whereas those with low ratios achieve higher profitability through reserve provision, significantly enhancing overall revenue and operational flexibility.

Abstract

The increasing penetration of renewable energy necessitates improved power system flexibility, driving the deployment of independent energy storage operators (ESOs). Existing research extensively investigates capacity sizing for price-taker storage systems or the operational coordination of aggregated distributed resources, lacking the joint optimization of capacity planning and multi-market bidding for a price-maker ESO with hybrid energy storage system (HESS) that preserves the technological heterogeneity of the integrated components. We propose a bi-level optimization framework to jointly optimize profit-oriented decisions on capacity and multi-market operation. The upper-level problem determines the optimal capacities of two heterogeneous storage systems while coordinating their bidding across day-ahead joint energy-reserve and real-time balancing markets. The lower-level problems represent market clearing of the system operator (SO). The model is reformulated into a mixed-integer linear program and solved with a Benders' decomposition algorithm. Results demonstrate that the ESO can allocate capacity between energy arbitrage and reserve provision strategically. The system with the high power-to-capacity ratio is used to capture arbitrage profits while the system with low power-to-capacity ratio is used to specialize in reserve markets. There can be internal power transfer between storage systems if there exist grid access constraints. The framework provides differentiated bidding strategies and market participation flexibility for HESS to enhance overall profitability.

Problem

• hybrid energy storage system
• capacity planning
• multi-market operation
• price-maker
• profit-oriented optimization

Innovation

• hybrid energy storage system
• bi-level optimization
• multi-market operation
• profit-oriented planning
• Benders decomposition