Secondary frequency control of islanded microgrid considering wind and solar stochastics

TL;DR

This paper introduces a model predictive control method for secondary frequency regulation in islanded microgrids, effectively managing wind and solar power variability to improve frequency stability.

Contribution

It develops a stochastic MPC approach with Kalman filtering and real-time power constraints, prioritizing renewable sources for frequency control.

Findings

Enhanced frequency recovery speed

Reduced frequency deviation

Effective handling of wind and solar fluctuations

Abstract

This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reduce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios.