Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems, 2nd Edition

Topic Information

Dear Colleagues,

This topic is a continuation of the previous successful Topic “Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems” (https://www.mdpi.com/topics/JC36MT351D). With the development of renewable energy technology, the penetration of renewable energy sources into the existing power systems is inevitable. With increasing penetration of renewable energy sources, power/energy management becomes critical and challenging for the successful operation of smart grids and microgrid systems. Smart grids include power generation/consumption equipment, power transmission and distribution networks, and energy storage equipment, including sensor measurement technology, network technology, communication technology, automation and intelligent control technology. For the efficient and effective control of smart grids and microgrid systems, communication among different entities/devices/agents and associated cybersecurity is another vital vector. This Research Topic will collect articles related to the role of the smart grid in integrated energy systems, including stochastic renewable energy sources, and present important findings to overcome the volatile nature of renewables with the help of energy storage and demand response programs. Areas covered in this Research Topic include, but are not limited to, the following:

• Optimal planning/sizing of microgrids/smart grids;
• Integration and control of renewable energy systems with microgrid systems;
• System integrations through static interfaces;
• Reliability aspects in smart grid systems;
• Storage optimization studies in smart microgrid systems;
• Communications in smart grids for effective control implementations;
• Cyber–physical systems in smart grid/microgrid systems;
• Power quality aspects in smart grid systems with high renewable energy penetration;
• Virtual inertia systems;
• Operations of grid under fault conditions.

Prof. Dr. Jun Zeng
Dr. Qian Xiao
Dr. Fei Gao
Prof. Dr. Yiqi Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	1.8	5.1	2020	26 Days	CHF 1200	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Sustainability sustainability	3.3	7.7	2009	19.3 Days	CHF 2400	Submit

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Published Papers (4 papers)

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All Electricity Electronics Energies Sensors Sustainability

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19 pages, 10000 KiB  

Open AccessArticle

Adaptive Line Resistance Estimation and Compensation for Accurate Power Sharing of Droop-Controlled DC Microgrids

by Xiangyu Qin, Zhengyu Lin, Wei Jiang and Hazel Lee

Energies 2025, 18(9), 2183; https://doi.org/10.3390/en18092183 - 24 Apr 2025

Viewed by 399

Abstract

For a DC microgrid with a traditional droop control strategy, achieving accurate power sharing among power converters is challenging due to mismatched line resistance. In a multi-bus DC microgrid system, changes in the power flow can further lead to variation in the equivalent [...] Read more.

For a DC microgrid with a traditional droop control strategy, achieving accurate power sharing among power converters is challenging due to mismatched line resistance. In a multi-bus DC microgrid system, changes in the power flow can further lead to variation in the equivalent line resistance of each power converter. To improve power sharing accuracy, an adaptive line resistance estimation method is proposed in this paper, which can accurately estimate line resistance without additional hardware. The estimated line resistances are then used to compensate the droop coefficient of each power converter to ensure accurate power sharing between power converters. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method for single bus, multi-bus, and ring-bus DC microgrid systems. Full article

(This article belongs to the Topic Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems, 2nd Edition)

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18 pages, 9781 KiB  

Open AccessArticle

Second-Order Ripple Current Suppression Based on Virtual Impedance in the Application of Dynamic Voltage Restorer

by Guoping Huang, Qiao Shi, Wenqing Li, Qing Zhang and Junfeng Liu

Energies 2025, 18(8), 1896; https://doi.org/10.3390/en18081896 - 8 Apr 2025

Viewed by 369

Abstract

In existing two-stage single-phase dynamic voltage restorer (DVR) voltage sag mitigation devices, the output-side power contains a pulsating component at twice the fundamental frequency (2f₀), leading to the presence of second-order ripple currents (SRCs) on the DC input side. This, [...] Read more.

In existing two-stage single-phase dynamic voltage restorer (DVR) voltage sag mitigation devices, the output-side power contains a pulsating component at twice the fundamental frequency (2f₀), leading to the presence of second-order ripple currents (SRCs) on the DC input side. This, to some extent, affects the reliability of the system and has a significant impact on the lifespan of energy storage devices. In this study, the dual-loop control method of the buck/boost converter is combined with the virtual impedance auxiliary control strategy to suppress SRCs. Compared to existing solutions, this method offers the advantages of being fast, stable, and reliable, while the virtual impedance auxiliary control strategy is flexible and easy to implement. The feasibility and stability of this strategy were verified using a 3 kW DVR prototype. When applying the two virtual impedance methods, the second harmonic content was reduced from 39.64% to 1.74% and 1.78%, respectively. The proposed control strategy demonstrates significant effectiveness in suppressing second harmonic currents. Full article

(This article belongs to the Topic Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems, 2nd Edition)

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23 pages, 4124 KiB  

Open AccessArticle

Optimal Scheduling of Electric Vehicles for Peak Load Regulation: A Multi-Time Scale Approach with Comprehensive Evaluation and Feedback

by Fei Xue, Wei Xiong, Jiahao Chen, Yonghai Yi, Zehui Liu, Jun Zeng and Junfeng Liu

Energies 2025, 18(7), 1815; https://doi.org/10.3390/en18071815 - 3 Apr 2025

Viewed by 335

Abstract

With the increasing prevalence of electric vehicles (EVs), optimizing their scheduling for grid peak-shaving has become a focal point of research. This study develops a multi-time-scale optimization model for EV clusters to participate in peak shaving, integrating a comprehensive evaluation and feedback mechanism. [...] Read more.

With the increasing prevalence of electric vehicles (EVs), optimizing their scheduling for grid peak-shaving has become a focal point of research. This study develops a multi-time-scale optimization model for EV clusters to participate in peak shaving, integrating a comprehensive evaluation and feedback mechanism. The innovation of this paper lies in the addition of an evaluation and feedback loop to the multi-time-scale scheduling optimization method for EVs participating in peak shaving, which fully utilizes the scheduling potential of EV clusters and mitigates the impact of uncertainties associated with EV clusters. The multi-time-scale approach mitigates response errors stemming from EV uncertainties. A feedback loop enables the grid to adaptively adjust scheduling commands to match real-time conditions. Simulations on the IEEE 33-node system demonstrate that the proposed model effectively optimizes EV load profiles, reducing the peak-to-valley difference rate from 41.74% to 35.19%. It also enhances response accuracy to peak-shaving instructions and upgrades the peak-shaving evaluation from a C rating to a B rating, ultimately increasing the revenue for aggregators participating in peak shaving. Full article

(This article belongs to the Topic Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems, 2nd Edition)

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24 pages, 3220 KiB  

Open AccessArticle

Optimizing Rural MG’s Performance: A Scenario-Based Approach Using an Improved Multi-Objective Crow Search Algorithm Considering Uncertainty

by Mohammad Hossein Taabodi, Taher Niknam, Seyed Mohammad Sharifhosseini, Habib Asadi Aghajari, Seyyed Mohammad Bornapour, Ehsan Sheybani and Giti Javidi

Energies 2025, 18(2), 294; https://doi.org/10.3390/en18020294 - 10 Jan 2025

Cited by 1 | Viewed by 1104

Abstract

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs’ coordination. In this paper, two distinct models for RMGs are presented. The first [...] Read more.

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs’ coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard Crow Search Algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated. Full article

(This article belongs to the Topic Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems, 2nd Edition)

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