Enhancing the risk-oriented participation of wind power plants in day-ahead, balancing, and hydrogen markets with shared multi-energy storage systems

The increasing demand for renewable energy sources (RES) to address environmental concerns and reduce fossil fuel dependency highlights the need for efficient energy storage and balancing mechanisms to manage RES output uncertainty. However, providing dedicated storage units to RES owners is often infeasible. Additionally, the growing interest in hydrogen utilization complicates optimal decision-making for multi-energy systems. To tackle these challenges, this paper presents a novel bidding strategy enabling wind farms to participate in day-ahead, balancing, and hydrogen markets through shared multi-energy storage (SMES) systems. These SMES, which include both battery and hydrogen storage, offer a cost-effective solution by allowing RES owners to rent storage capacity. By optimizing SMES utilization and wind farm management, we propose an integrated strategy for day-ahead electrical, and real-time balancing markets, and also hydrogen markets. The approach incorporates with uncertainties of wind generation bidding by using conditional value at risk (CVaR) to account for different risk-aversion levels. The Dantzig–Wolfe Decomposition (DWD) method is applied to decentralize the problem, reduce the calculation burden, and enhance the data privacy. The framework is modeled as a mixed-integer linear program (MILP) and solved using CPLEX solver via GAMS software. The results demonstrate the effectiveness of this strategy, offering insights into the risk-oriented market participation of wind power plants with the aid of SMES system, supporting a more sustainable and resilient energy system. The numerical results show that by utilizing a SMES with only batteries, the revenue can be increased by 17.3% and equipping the SMES with hydrogen storage and participating in both markets, leads to 36.9% increment in the revenue of the wind power plant. • Proposing a framework for bidding of wind farm in the DA and balancing markets. • Considering integration of hydrogen storage in the bidding via the concept of SMES. • Modeling of uncertainties through scenarios and utilizing Conditional Value at Risk. • Utilizing Dantzig–Wolfe decomposition technique for solving the problem.