Adaptive Primary Frequency Regulation with Multi-Scenario Prediction of System Dynamics

Increased amount of renewable energy sources, developments in measurements technology and transition to Industry 4.0 concept introduce new challenges (such as volatile frequency dynamics) on the one hand and possibilities for new control concepts on the other. This work is aimed to combine solutions of three problems in modern power systems into one control framework. The challenges are (1) primary frequency regulation in low-inertia girds with high penetration of renewable energy sources; (2) identification of power disturbances and assessment of power grid state; (3) prediction of power grid dynamics for different scenarios that include credible contingencies. Combination of these three tasks yields primary frequency regulation that on the one hand is represented by proportional controller similar to standard droop control. On the other hand, the developed framework calculates optimal control gains for each credible contingency by simulating system dynamics and, when one of the contingencies appears, updates control gains to minimize frequency oscillations. The efficiency of developed framework is demonstrated detailed case study with detailed power system model running in Real Time Digital Simulator (RTDS) and control framework running on connected PC.