Combining Data-Driven and Model-Driven Approaches for Optimal Distributed Control of Standalone Microgrid

Abstract

This paper focuses on the comprehensive restoration of both voltage and frequency in a standalone microgrid (SAMG). In a SAMG, the power balance is achieved through traditional methods such as droop control for power sharing among distributed generators (DGs). However, when such microgrids (MGs) are subjected to perturbations coming from stochastic renewables, the frequency and voltage parameters deviate from their specified values. In this paper, a novel hybrid-type consensus-based distributed controller is proposed for voltage and frequency restoration. Data-based communication is ensured among the DGs for controlling voltage and frequency parameters. Different parameters such as voltage, frequency, and active and reactive power converge successfully to their nominal values using the proposed algorithms, thereby ensuring smooth operation of inverter-dominated DGs. Additionally, the machine-learning-based long short-term memory (LSTM) algorithm is implemented for renewable power forecasting using historical data from the proposed location for visualising the insolation profile. The effectiveness of our approach is demonstrated through a SAMG, which consists of four inverters, showing that the proposed approach can improve system stability, increase efficiency and reliability, and reduce costs compared to traditional methods. The complete study is performed in Python and MATLAB environments. Our results highlight the potential of data-driven approaches to revolutionise power system operation and control.