Stochastic Model Predictive Control Based Fast-Slow Coordination Automatic Generation Control

Abstract

The increasing integration of renewable energy sources to power systems brings challenges to the real-time frequency control. This paper proposes a stochastic model predictive control (MPC) based two-timescale automatic generation control (AGC) scheme to coordinate the fast converter interfaced generators (CIG) and slow synchronous generators (SG). In the proposed model, the transmission time delay and dynamic features of heterogeneous generators are formulated, so the control performance can be significantly improved. In each AGC period, the AGC commands of fast CIGs are set as multi-step signals while those of SGs remain as single step, and multiple predicted system states of intra-period are incorporated in the control objective. The prediction error is characterized using Gaussian distribution. Based on the decomposition of predicted states and the exact reformulation of the original chance constraints, the stochastic MPC model can be equivalent transformed into a tractable quadratic program model. Case studies conducted on modified IEEE 118-bus system shows the effectiveness of the proposed method.