Bi-level multi-objective optimization of multi-market operation for offshore wind farms with energy storage system considering EV participation

In modern power systems characterized by high offshore wind integration, conventional energy storage systems encounter challenges such as restricted capacity and limited economic returns. To address these limitations, a wind-speed-scenario-responsive business framework is proposed, hypothesizing that all demand loads except EV charging/discharging are inelastic. Wind speed datasets are processed using Variational Mode Decomposition and grouped through K-Means clustering to derive four representative operational scenarios. A bi-level multi-objective optimization model is then developed to identify the most effective energy storage systems power allocation plan, incorporating EV participation while aligning the operations of electricity and ancillary service markets. The optimization aims to maximize total revenue, minimize the system frequency stability index, and reduce carbon emissions. Based on the developed framework, combined with the TOPSIS approach, simulation experiments were performed on the IEEE 30-bus system to determine the optimal participation ratios of energy storage units in the electricity market for four wind speed scenarios, labeled 0.72, 0.83, 0.46, and 0.54. Across all four wind-speed scenarios, the proposed strategy enhances system performance by increasing revenues, improving wind power utilization, and maintaining frequency stability. As an illustration, in high wind speed Scenario 1, wind curtailment is reduced by 60.2%, carbon emissions decrease by 1.7%, and total revenue increases by 5.6%. These results verify the adaptability and practical effectiveness of the business strategy.