Distributed coordinated control for voltage regulation in active distribution networks based on robust model predictive control

Abstract

As the integration of renewable energy sources increases, the uncertainty of wind power and photovoltaics bring new challenges to the voltage control problem of active distribution networks (ADNs). To address these challenges, this paper proposes a double-time-scale distributed voltage control strategy for ADNs based on robust model predictive control (RMPC), which considers the coordination between multiple voltage regulation devices. In the slow-time-scale control (STC), on-load Tap changers (OLTC), step voltage regulators (SVR), and capacitor banks (CBs) are optimized to minimize long-term voltage deviations and reduce tap operations of these traditional regulation devices. On this basis, in the fast-time-scale control (FTC), the active and reactive power outputs of distributed generators (DGs) are further optimized based on RMPC to regulate the fast voltage fluctuations while considering the uncertainty of DG outputs. The RMPC model is formulated as a minimum–maximum convex optimization problem, which is transformed into a quadratic programming problem. Moreover, by equivalently processing of adjacent control areas in ADNs, the distribution network model established based on voltage sensitivity method is decomposed to accelerate the solving process. The effectiveness of the proposed double-time-scale distributed RMPC voltage control scheme has been verified in a modified Italia 54-bus system. Results demonstrate that, compared to conventional deterministic centralized control, the proposed scheme achieves 63% reduction for the maximum voltage deviation.