The size growth of modern wind turbines creates challenges in their control system design, particularly due to greater wind variability across larger rotor areas. As modern turbine control systems rely on the availability of accurate wind speed information, the increasing unrepresentativeness of pointwise measurement devices, such as anemometers, necessitates the incorporation of more representative rotor-effective wind speed (REWS) estimation. Classical REWS estimators, based on static power relations, often fail to account for dynamic changes, leading to inaccurate estimation. To overcome these challenges, this paper introduces a power-balance-based REWS estimation framework and splits the estimation problem into two modules: an aerodynamic power estimator and a wind speed estimate solver. Two possible aerodynamic power estimation techniques are discussed based on numerical derivative and state estimation. As state estimator calibration remained a challenge for varying wind turbine sizes, a gain-tailoring method for the performance calibration throughout a range of modern wind turbine sizes has been derived for the state-estimation-based aerodynamic power estimator. Two types of wind speed estimate solvers are analyzed, namely the continuous and iterative single-step methods. From the two modules, the best-performing methods – the state estimation aerodynamic power estimator and iterative single-step wind speed solver – are chosen to form the optimal power balance REWS estimator. The combined optimal estimator is validated through OpenFAST simulations of the National Renewable Energy Laboratory (NREL) 5 MW and IEA 22 MW turbines and compared against a baseline method. The proposed method demonstrates good tracking of the REWS, better noise resilience, and convenient estimator gain calibration across different turbine sizes.