Advanced Strategies for Smart Grid Reliability and Energy Optimization

Topic Information

Dear Colleagues,

We are delighted to invite submissions to the thematic collection “Advanced Strategies for Smart Grid Reliability and Energy Optimization”. This topic is dedicated to advancing scholarly discourse on the methodologies and technologies that enhance the reliability, operational efficiency, and energy optimization of smart grid systems. In an era marked by the accelerated adoption of renewable energy sources and the growing complexity of power distribution networks, it is imperative to explore innovative strategies that can address the multifaceted challenges facing modern energy infrastructures. We welcome original research articles and comprehensive reviews that focus on intelligent control systems, data-driven analytics, predictive maintenance, adaptive demand response mechanisms, and the integration of advanced energy storage solutions. Contributions from interdisciplinary perspectives that link electrical engineering, computational modeling, and energy policy are particularly encouraged to drive a deeper understanding of smart grid modernization. Topics of interest encompass, but are not limited to, grid optimization algorithms, advancements in smart metering technology, and the modeling of hybrid energy systems to enhance system resilience and sustainability.

We look forward to receiving your contributions.

Dr. Shuangqi Li
Dr. Alexis Pengfei Zhao
Dr. Yichen Shen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	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
Eng eng	2.4	3.2	2020	19.7 Days	CHF 1400	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Smart Cities smartcities	5.5	14.7	2018	26.8 Days	CHF 2000	Submit
Vehicles vehicles	2.2	5.3	2019	22.1 Days	CHF 1600	Submit

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Published Papers (4 papers)

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All Applied Sciences Electronics Energies Eng Sensors Smart Cities Vehicles

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26 pages, 796 KiB  

Open AccessArticle

Distributionally Robust Optimal Scheduling for Integrated Energy System Based on Dynamic Hydrogen Blending Strategy

by Yixiao Xiao, Qianhua Xiao, Keyu Wang, Xiaohui Yang and Yan Zhang

Appl. Sci. 2025, 15(13), 7560; https://doi.org/10.3390/app15137560 - 5 Jul 2025

Viewed by 165

Abstract

To mitigate challenges arising from renewable energy volatility and multi-energy load uncertainty, this paper introduces a dynamic hydrogen blending (DHB) strategy for an integrated energy system. The strategy is categorized into Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB), based on the [...] Read more.

To mitigate challenges arising from renewable energy volatility and multi-energy load uncertainty, this paper introduces a dynamic hydrogen blending (DHB) strategy for an integrated energy system. The strategy is categorized into Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB), based on the temporal variations in the hydrogen blending ratio. To evaluate the regulatory effect of DHB on uncertainty, a data-driven distributionally robust optimization method is employed in the day-ahead stage to manage system uncertainties. Subsequently, a hierarchical model predictive control framework is designed for the intraday stage to track the day-ahead robust scheduling outcomes. Experimental results indicate that the optimized CHB ratio exhibits step characteristics, closely resembling the THB configuration. In terms of cost-effectiveness, CHB reduces the day-ahead scheduling cost by 0.87% compared to traditional fixed hydrogen blending schemes. THB effectively simplifies model complexity while maintaining a scheduling performance comparable to CHB. Regarding tracking performance, intraday dynamic hydrogen blending further reduces upper- and lower-layer tracking errors by 4.25% and 2.37%, respectively. Furthermore, THB demonstrates its advantage in short-term energy regulation, effectively reducing tracking errors propagated from the upper layer MPC to the lower layer, resulting in a 2.43% reduction in the lower-layer model’s tracking errors. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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48 pages, 986 KiB  

Open AccessArticle

A Systematic Mapping Study on Automatic Control Systems of Multi-Port dc/dc Power Converters

by Diego Vargas, Leonardo Ortega, Julio C. Caiza and Danny S. Guamán

Energies 2025, 18(13), 3445; https://doi.org/10.3390/en18133445 - 30 Jun 2025

Viewed by 236

Abstract

In the ongoing transition to renewable energy sources, power converters have become indispensable. Their prevalence is increasing, enabling efficient energy conversion, enhancing reliability and stability, and optimizing power extraction from renewable sources. Multi-port dc/dc power converters are widely used because they offer advantages [...] Read more.

In the ongoing transition to renewable energy sources, power converters have become indispensable. Their prevalence is increasing, enabling efficient energy conversion, enhancing reliability and stability, and optimizing power extraction from renewable sources. Multi-port dc/dc power converters are widely used because they offer advantages in managing multiple sources and loads. However, designing an automatic control system for these converters presents a challenge due to their complexity. Many configurations for multi-port dc/dc power converters have been proposed, featuring diverse combinations of controllers, modulation techniques, and topologies tailored to specific applications. The body of knowledge on these configurations has grown. Yet, papers have been published according to the authors’ areas of specialization, thus generating a scattered and unorganized body of knowledge and making it difficult to discern research trends and open challenges. Previous studies have attempted to organize knowledge about these configurations, but they have not established a systematic mapping process that follows a rigorous and objective methodology. This paper conducts a systematic mapping study on Automatic Control Systems of multi-port dc/dc power converters. Our study analyzed 122 papers from the 777 papers found around the topic to find and organize the body of knowledge on topology, controller, efficiency, number of elements, modulation technique, and practical applications. This systematic mapping provides a foundational framework for researchers, aiming to inspire further exploration and the development of innovative controller systems in multi-port dc/dc power converters. We found the application of machine learning techniques in dc/dc power converters constitutes an open challenge in these devices. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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24 pages, 2094 KiB  

Open AccessArticle

Optimizing Hybrid Renewable Energy Systems for Isolated Applications: A Modified Smell Agent Approach

by Manal Drici, Mourad Houabes, Ahmed Tijani Salawudeen and Mebarek Bahri

Eng 2025, 6(6), 120; https://doi.org/10.3390/eng6060120 - 1 Jun 2025

Viewed by 1066

Abstract

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm [...] Read more.

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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26 pages, 5608 KiB  

Open AccessArticle

Natural Gas Consumption Forecasting Model Based on Feature Optimization and Incremental Long Short-Term Memory

by Huilong Wang, Xianjun Gao, Ying Zhang and Yuanwei Yang

Sensors 2025, 25(10), 3079; https://doi.org/10.3390/s25103079 - 13 May 2025

Viewed by 484

Abstract

Natural gas, as a vital component of the global energy structure, is widely utilized as an important strategic resource and essential commodity in various fields, including military applications, urban power generation and heating, and manufacturing. Therefore, accurately assessing energy consumption to ensure a [...] Read more.

Natural gas, as a vital component of the global energy structure, is widely utilized as an important strategic resource and essential commodity in various fields, including military applications, urban power generation and heating, and manufacturing. Therefore, accurately assessing energy consumption to ensure a reliable supply for both military and civilian use has become crucial. Traditional methods have attempted to leverage long-range features guided by prior knowledge (such as seasonal data, weather, and holiday data). However, they often fail to analyze the reasonable correlations among these features. This paper proposes a natural gas consumption forecasting model based on feature optimization and incremental LSTM. The proposed method enhances the robustness and generalization capability of the model at the data level by combining Gaussian Mixture Models to handle missing and anomalous data through modeling and sampling. Subsequently, a weakly supervised cascade network for feature selection is designed to enable the model to adaptively select features based on prior knowledge. Finally, an incremental learning-based regression difference loss is introduced to promote the model’s understanding of the coupled relationships within the data distribution. The proposed method demonstrates exceptional performance in daily urban gas load forecasting for Wuhan over the period from 2011 to 2024. Specifically, it achieves notably low average prediction errors of 0.0556 and 0.0392 on the top 10 heating and non-heating days, respectively. These results highlight the model’s strong generalization capability and its potential for reliable deployment across diverse gas consumption forecasting tasks within real-world deep learning applications. Full article

(This article belongs to the Topic Advanced Strategies for Smart Grid Reliability and Energy Optimization)

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