An Optimal Distributed MPC Scheme for Automatic Generation Control Under Network Constraints

Traditional Automatic Generation Control (AGC) regulates an area control error (ACE) signal to control deviations in both the tie-line flows and system frequency. This approach can be suboptimal when the generation mix incorporates a high penetration of Renewable Energy Sources (RESs), and can lead to a biased control response under large supply and/or demand disturbance events. This paper proposes an alternative distributed Model Predictive Control (MPC) scheme which regulates the frequency deviations, and enforces thermal limit constraints on the tie-line flow deviations. The proposed State Constraint Distributed Model Predictive Control (SCDMPC) scheme achieves bias-free control and operation at the economically optimal operating point. The methodology is developed by considering system dynamics accounting for the primary generation sources and the tie-line power flow constraints. The new SCDMPC methodology reduces the regulating reserve requirement, enables a self-smoothing response to the supply/demand fluctuations between control areas, and improves the economics of AGC under high RESs penetration conditions.