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South Dakota State University

Virtual Inertia Emulation to Improve Dynamic Frequency Stability of Low Inertia Microgrids

Abstract

dc:description.abstract

<p>Due to low inertia and the intermittent nature of photovoltaic systems, dynamic frequency stability issues arise in microgrids with large photovoltaic systems. This limits the maximum amount of photovoltaic systems that can be penetrated in the microgrid. In order to increase the penetration of photovoltaic systems, the dynamic frequency controller, that is faster than the primary frequency controller (governor control) needs to be added in the microgrid system. For dynamic frequency control, inertial response can be provided from the energy storage system (such as battery, ultra-capacitor, photovoltaic system, etc.), which is termed as virtual inertia. A virtual inertia can be defined as the combination of an energy storage system, a power electronics converter and a proper control algorithm that improves the dynamic frequency stability of the microgrid. A virtual inertia supplies or absorbs the active power to and from the energy storage system to improve the dynamic frequency stability. This thesis presents the design and implementation of a hardware prototype of 1 kW virtual inertia in a microgrid with a real diesel generator and a load. For a step change in load, the virtual inertia improved the frequency response of the system from 57.39 Hz to 58.03 Hz. This improvement in frequency response proves the concept of existing proportional derivative based virtual inertia experimentally. With the addition of virtual inertia, the frequency of the system returns to the nominal frequency slower. Once the primary controller (governor control) of the system takes the action to regulate the frequency, virtual inertia no longer needs to add inertia to the system. So the dynamics of the VI needs to be improved so that the frequency returns to nominal frequency faster. This thesis also proposes an online learning controller based virtual inertia using adaptive dynamic programming that learns online and improves the dynamics of the controller of existing VI. The output of this controller supplements the output of the existing proportional derivative controller of virtual inertia. The supplementary controller is trained to increase the dynamics of the outer controller and to bring the system frequency to nominal frequency faster. Due to faster dynamics, the net energy delivered by the VI can be reduced significantly and improve the total possible discharge cycles from the battery. For performance evaluation, the proposed controller was implemented in a microgrid with a photovoltaic system, a diesel generator and a variable load. With the proposed controller, the frequency of the system returned to nominal frequency faster. The net energy delivered by the proposed controller in a photovoltaic diesel generator microgrid was 46.14% of the net energy delivered by the existing virtual inertia. Due to the decrement in the total energy delivered, the total number of possible battery discharge cycles with ADP based VI was 2.17 times of the total number of possible battery discharge cycles from VI.</p>

Degree

thesis:*
Name thesis:degree_name
Master of Science (MS)
Level thesis:degree_level
Thesis - Open Access
Discipline thesis:degree_discipline
Electrical Engineering and Computer Science
Year dc:date.available
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Shrestha, Dipesh
Contributors dc:contributor
  • Reinaldo Tonkoski

Subjects

dc:subject × 7

Rights

dc:rights
Language dc:language
en

Identifiers

dc:identifier.*
Repository record dc:identifier
https://openprairie.sdstate.edu/etd/1115
OAI identifier oai:identifier
oai:openprairie.sdstate.edu:etd-2118

Chain of custody

source
Harvested from
South Dakota State University
Base URL
openprairie.sdstate.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Shrestha, Dipesh. Virtual Inertia Emulation to Improve Dynamic Frequency Stability of Low Inertia Microgrids. Thesis - Open Access thesis, 2016. https://openprairie.sdstate.edu/etd/1115