A Novel Coordination Scheme of Virtual Inertia Control and Digital Protection for Microgrid Dynamic Security Considering High Renewable Energy Penetration

Abstract

With rapid growth of renewable energy sources (RESs) in modern power systems, the microgrids (µGs) have become more susceptible to the disturbances (e.g. large frequency/voltage fluctuations) than the conventional power systems due to decreasing their inertia constant. This low system inertia issue could affect the µGs stability and resiliency in the situation of uncertainties, thus threaten their dynamic security. Hence, preserving µG dynamic security is one of the important challenges, which is addressed in this study. Therefore, this study proposes a novel concept of frequency control incorporating a virtual inertia control-based optimal proportional-integral controller to emulate virtual inertia into the µG control loop, thus stabilising µG frequency during high penetration of RESs. Moreover, the proposed virtual inertia control system is coordinated with digital over/ under frequency protection for enhancement of the frequency stability and preservation of the µG dynamic security because of the high integration level of the RESs. The simulation results of the studied µG are carried out using MATLAB/Simulink ® software to validate the effectiveness of the proposed coordination scheme. Results approved that the proposed coordination scheme can effectively regulate the µG frequency and guarantee robust performance to preserve the dynamic security of µG with high penetration of RESs for different contingencies. Nomenclature velocity of particle i for n iteration c 1 , c 2 acceleration constant rand () Random number between 0 and 1

Applying Virtual Inertia Control Topology to SMES System for Frequency Stability Improvement of Low-Inertia Microgrids Driven by High Renewables

“…Two contrastive severe scenarios with different integration levels of wind/solar farms, industrial/residential load patterns, and control parameters were performed for investigating microgrid frequency response. The results of the proposed SMES approach were compared with traditional ESS-based virtual inertia control presented in References [2][3][4][5][6]21]. To comply with the frequency operating standards (grid codes) of actual power systems (Nordic and AEMC) [27], the acceptable ranges of frequency deviation used in this work are applied as ±1 Hz during the generation/load event, and as ±0.5 Hz during no contingency.…”

“…The significant inherent requirement and constraints (e.g., generation rate constraint (GRC) and governor dead-band imposed by physical system dynamics) were applied in this study based on Reference [5]. Based on References [1][2][3][4][5][6]13,14,21,22,24,25], the dynamic model (depicted in Figure 2) is sufficient for frequency stability study and analysis. The main simulation parameters for the studied microgrid are given in Table 1 [5,14].…”

“…The simulation studies were performed under a MATLAB/Simulink ® 2019 environment. To evaluate the efficacy and robustness of the proposed control method, the obtained results were compared with conventional ESS-based virtual inertia control in References [2][3][4][5][6]21].…”

“…The main contributions of this paper over the existing virtual inertia control techniques are as follows: (1) this is the first paper that proposes an explicit model of the SMES system that can capture the key dynamic characteristics of virtual inertia control topology for enhancing inertia emulation capability, improving frequency stability, and resiliency of the microgrid; (2) the dynamic effects of the proposed SMES-based virtual inertia control are analyzed and validated by the time domain simulation, evaluating a new trade-off between inertia response/emulation and short-term ESS power in regard to frequency stability improvement; (3) compared with the conventional ESS-based virtual inertia control in References [2][3][4][5][6]21], the proposed control strategy shows remarkable performance and stability in terms of high energy/power and fast response, resulting in the lowest frequency deviation and transient excursion. Thus, the lower and short-term power from the ESS could be solved, avoiding system instability, cascading outages, and wide-area power blackouts.…”

Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids

“…In the last few years, there has been a growing interest in the development of microgrids (MGs) for enhancing power system reliability, better power quality and reducing the environmental impacts [1,2]. An MG is a small-scale power system with distinct electrical boundaries with the capability of supplying its loads autonomously when it is islanded from the main grid [3].…”