Optimal Inertia Trading for Frequency Stability in Renewable-Rich Interconnected Grids: A Python–MATLAB Co- Simulation Approach

Abstract As renewable energy sources (RESs) such as wind and solar continue to displace conventional synchronous generation, the power grid is facing a critical challenge: the steady loss of inertia. Lower inertia makes frequency more sensitive to sudden imbalances, leading to deeper nadirs, slower recovery, and greater risk of instability. Conventional load frequency control (LFC) cannot fully address these challenges, and while many studies explore new control strategies, few combine real-world inertia data, market signals, and dynamic optimization into a single framework.This paper introduces a Python–MATLAB co-simulation approach for inertia trading , designed to bridge this gap. Using inertia.csv data from National Grid ESO, we quantify how often outturn inertia falls short of market-provided values and flag unstable periods below 120 GVA·s. Auction results are analyzed to rank providers and reveal the true cost of securing inertia, while Indian renewable datasets track long-term penetration trends at national and state levels.Dynamic simulations in MATLAB/Simulink then evaluate three cases: (i) a conventional nominal-inertia system, (ii) a low-inertia RES-integrated system, and (iii) an optimized system after inertia trading using Grey Wolf Optimization (GWO) . The optimized case shows clear benefits—frequency nadirs improve from − 0.20 Hz to − 0.08 Hz, tie-line deviations are limited to ± 0.05 p.u., and settling time is cut nearly in half.These results suggest that market-based inertia procurement, combined with GWO-optimized dynamic control, offers a practical path to restoring stability in renewable-dominated grids . The proposed framework provides both operators and policymakers with a realistic testbed for designing future inertia markets.