Advanced Strategies for Smart Grid Reliability and Energy Optimization

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 62

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)

► Show Figures

Figure 1

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 223

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 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)

► Show Figures

Figure 1

Submit your Abstract

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Eng eng	-	2.1	2020	21.5 Days	CHF 1200	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Smart Cities smartcities	7.0	11.2	2018	28.4 Days	CHF 2000	Submit
Vehicles vehicles	2.4	4.1	2019	19.9 Days	CHF 1600	Submit

Topic Menu

Topic Editors

Advanced Strategies for Smart Grid Reliability and Energy Optimization

Topic Information

Keywords

Participating Journals

Published Papers (2 papers)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI