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Power Quality and Hosting Capacity in the Microgrids
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Power Quality and Hosting Capacity in the Microgrids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 25 March 2025 | Viewed by 5014

Special Issue Editor

Prof. Dr. Wajiha Shireen

E-Mail Website
Guest Editor
Department of Engineering Technology & Electrical and Computer Engineering, Cullen College of Engineering, University of Houston, Houston, TX 77204-4021, USA
Interests: power quality issues; design and development of active power filters; DSP-based control for utility interactive inverters and AC motor drives; converters for distributed energy sources; fuel cells, solar/wind energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microgrids, and the integration of distributed energy resource (DER) units in general, introduce a number of operational challenges that need to be addressed in the design of control and protection systems in a power grid. High power quality (PQ) is an essential requirement for all power grids, as poor PQ may result in equipment malfunction, overconsumption, or even early failure. Due to their unique characteristics, microgrids and RES-based power systems are particularly susceptible to PQ disturbances.

The topics of interest for this publication include, but are not limited to, power quality and possible solutions related to the following:

  • Integration of DERs;
  • Energy storage;
  • Harmonic distortion;
  • Frequency deviation;
  • Voltage unbalance;
  • Microgrid control and protection.

Prof. Dr. Wajiha Shireen
Guest Editor

Manuscript Submission Information

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Keywords

  • integration of DERs
  • energy storage
  • harmonic distortion
  • frequency deviation
  • voltage unbalance
  • microgrid control and protection

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Published Papers (6 papers)

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Research

23 pages, 4257 KiB  
Article
Characterisation of Harmonic Resonance Phenomenon of Multi-Parallel PV Inverter Systems: Modelling and Analysis
by Kasun Peiris, Sean Elphick, Jason David and Duane Robinson
Energies 2025, 18(2), 443; https://doi.org/10.3390/en18020443 - 20 Jan 2025
Viewed by 516
Abstract
Solar PV inverters require output filters to reduce unwanted harmonics in their output, where LCL filters are a more economical choice than larger inductance-only filters. A drawback of these filters is that they can introduce power quality disturbances, especially at higher frequencies (above [...] Read more.
Solar PV inverters require output filters to reduce unwanted harmonics in their output, where LCL filters are a more economical choice than larger inductance-only filters. A drawback of these filters is that they can introduce power quality disturbances, especially at higher frequencies (above 2 kHz). This paper investigates and characterises the resonance phenomenon introduced by different filter types, i.e., LC or LCL, and identifies their behavioural change when combined with multiple parallel grid-tied PV inverter systems. MATLAB/Simulink modelling aspects of PV inverter systems related to resonance phenomenon are presented, including establishing resonance at a specific frequency where potentially large variations in the parameter selection across manufacturers may exist. In addition, a method is developed to establish output filter frequency response through measurements, which is used to develop validated solar PV harmonic models for high-frequency analysis. The low-frequency harmonic models can be used up to the resonant frequency where the current flowing through the filter capacitor is insignificant compared to the current flowing into the electricity network. Full article
(This article belongs to the Special Issue Power Quality and Hosting Capacity in the Microgrids)
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21 pages, 23995 KiB  
Article
A Hybrid Dual-Axis Solar Tracking System: Combining Light-Sensing and Time-Based GPS for Optimal Energy Efficiency
by Muhammad Hammas, Hassen Fituri, Ali Shour, Ashraf Ali Khan, Usman Ali Khan and Shehab Ahmed
Energies 2025, 18(1), 217; https://doi.org/10.3390/en18010217 - 6 Jan 2025
Viewed by 662
Abstract
Fixed solar panels face significant energy loss as they cannot consistently capture optimal sunlight. Because of that, the overall efficiency of the PV panel will be reduced, and the installation requires larger land space to generate appropriate power; this stems from the use [...] Read more.
Fixed solar panels face significant energy loss as they cannot consistently capture optimal sunlight. Because of that, the overall efficiency of the PV panel will be reduced, and the installation requires larger land space to generate appropriate power; this stems from the use of a dual-axis solar tracking system, which can significantly increase overall energy production. The system is based on the combination of two approaches to precisely track the sunlight: first, using multiple LDRs (light-dependent resistors) as photo sensors to track the position of the sun by balancing the resistivity using a proportional integral deprival (PID) controller, and the second approach using the time-based control for cloudy days when sunlight is diffused, getting the time GPS coordinates and time to calculate the accurate position of the sun by determining the azimuth and altitude angle. This dual system significantly improves energy production by 33.23% compared to fixed systems and eliminates errors during shaded conditions while reducing unnecessary energy use from continuous GPS activation. The prototype uses two linear actuators for both angles and a 100-watt solar panel mounted on the dual-axis platform. Full article
(This article belongs to the Special Issue Power Quality and Hosting Capacity in the Microgrids)
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21 pages, 2455 KiB  
Article
Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data
by Dilan C. Hangawatta, Ameen Gargoom and Abbas Z. Kouzani
Energies 2025, 18(1), 128; https://doi.org/10.3390/en18010128 - 31 Dec 2024
Viewed by 441
Abstract
Accurate electrical phase identification (PI) is essential for efficient grid management, yet existing research predominantly focuses on high-frequency smart meter data, not adequately addressing phase identification with low sampling rates using energy consumption data. This study addresses this gap by proposing a novel [...] Read more.
Accurate electrical phase identification (PI) is essential for efficient grid management, yet existing research predominantly focuses on high-frequency smart meter data, not adequately addressing phase identification with low sampling rates using energy consumption data. This study addresses this gap by proposing a novel method that employs a fully connected neural network (FCNN) to predict household phases from energy consumption data. The research utilizes the IEEE European Low Voltage Testing Feeder dataset, which includes one-minute energy consumption readings for 55 households over a full day. The methodology involves data cleaning, preprocessing, and feature extraction through recursive feature elimination (RFE), along with splitting the data into training and testing sets. To enhance performance, training data are augmented using a generative adversarial network (GAN), achieving an accuracy of 91.81% via 10-fold cross-validation. Additional experiments assess the model’s performance across extended sampling intervals of 5, 10, 15, and 30 min. The proposed model demonstrates superior performance compared to existing classification, clustering, and AI methods, highlighting its robustness and adaptability to varying sampling durations and providing valuable insights for improving grid management strategies. Full article
(This article belongs to the Special Issue Power Quality and Hosting Capacity in the Microgrids)
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27 pages, 12057 KiB  
Article
Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control
by Jude Suchithra, Amin Rajabi and Duane A. Robinson
Energies 2024, 17(20), 5037; https://doi.org/10.3390/en17205037 - 10 Oct 2024
Cited by 1 | Viewed by 912
Abstract
Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased [...] Read more.
Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study. Full article
(This article belongs to the Special Issue Power Quality and Hosting Capacity in the Microgrids)
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13 pages, 2662 KiB  
Article
Improved Model-Free Predictive Control of a Three-Phase Inverter
by Muhammad Nauman and Wajiha Shireen
Energies 2024, 17(15), 3761; https://doi.org/10.3390/en17153761 - 30 Jul 2024
Cited by 1 | Viewed by 1003
Abstract
Model predictive control (MPC) performance depends on the accuracy of the system model. Moreover, the optimization algorithm of MPC requires numerous online computations. These inherent limitations of MPC hinder its application in power electronics systems. This paper proposes a two-part solution for these [...] Read more.
Model predictive control (MPC) performance depends on the accuracy of the system model. Moreover, the optimization algorithm of MPC requires numerous online computations. These inherent limitations of MPC hinder its application in power electronics systems. This paper proposes a two-part solution for these challenges for a three-phase inverter with an output LC filter. The first part of the control scheme is a linear and modified model-free approach based on the auto-regressive structure (ARX) with exogenous input. The second part is the computationally efficient optimization algorithm based on the active set method to solve the optimization problem of the MFPC. The objective of the control scheme is to regulate the output voltages of the inverter in the presence of constraints. The constraints are the maximum admissible filter current and optimal duty cycle to avoid any damage to the system. To validate the performance of the proposed control scheme, simulations and hardware-in-loop (HIL) real-time investigations have been performed, comparing the results of the proposed approach with the model-based predictive control. The results showcase the computational efficiency and effectiveness of the MFPC approach, demonstrating its potential for overcoming the limitations of traditional MPC in power electronics systems. Full article
(This article belongs to the Special Issue Power Quality and Hosting Capacity in the Microgrids)
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24 pages, 14052 KiB  
Article
Multivariable Control-Based dq Decoupling in Voltage and Current Control Loops for Enhanced Transient Response and Power Delivery in Microgrids
by Mandarapu Srikanth, Yellapragada Venkata Pavan Kumar, Challa Pradeep Reddy and Rammohan Mallipeddi
Energies 2024, 17(15), 3689; https://doi.org/10.3390/en17153689 - 26 Jul 2024
Cited by 1 | Viewed by 844
Abstract
Being multivariable in nature, voltage and current control loops have controllers in the forward and cross-coupling paths. Most methods discussed in the literature focus on tuning the controllers in the forward paths to reduce the dq coupling. A modified pole-zero cancellation (MPZC) technique [...] Read more.
Being multivariable in nature, voltage and current control loops have controllers in the forward and cross-coupling paths. Most methods discussed in the literature focus on tuning the controllers in the forward paths to reduce the dq coupling. A modified pole-zero cancellation (MPZC) technique has recently been discussed, which uses the concepts of pole-zero cancellation and particle swarm optimization to effectively tune the forward path controllers. However, given the fixed gains in the cross-coupling paths, it is not possible to realize a superior transient response from this technique. Therefore, to achieve enhanced vector control of VSIs under transient conditions, this paper proposes a hybrid MPZC (HMPZC) method, which incorporates multivariable control along with the MPZC technique for both voltage/current control loops. In the proposed HMPZC method, the MPZC method is used to tune the forward path controllers, and multivariable control-based PI controllers are assigned in the cross-coupling paths of dq-axes loops rather than fixed gains. In this paper, these multivariable control-based PI controllers are designed using direct synthesis method-based internal model control (IMC). From the simulation results, it is verified that the proposed HMPZC method has reduced the coupling between the d- and q-axes loops of the current/voltage, leading to the improved transient response and power delivery capability of VSIs. Full article
(This article belongs to the Special Issue Power Quality and Hosting Capacity in the Microgrids)
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