Stochastic load frequency control of power systems via Gaussian processes

To enhance the safety and efficiency of the power grid system, a finite-horizon chance constrained optimisation problem is formulated to suppress the load frequency deviation resulting from stochastic uncertainties (e.g. wind energies and load disturbances) and to reduce the mechanical power cost, meanwhile maintaining quality specifications. Especially, using a scenario-based approach, Gaussian process models are built to quantify stochastic uncertainties and to evaluate the model cost and constraint functions over the prediction horizon, which yields a tractable stochastic nonlinear model predictive control (SNMPC) framework for handling chance constrained load frequency control problems with Gaussian parametric uncertainties. Comparative study between the GP-SNMPC framework and scenario generation SMPC framework is carried out, which demonstrates that the GP-SNMPC framework is more computationally efficient and delivers a better performance in keeping load frequency balance while maintaining the system constraints.