Jalpa Thakkar¹, Siddharth Shankar Mishra², V. Shanmugapriya³, Mohan Kolhe^4,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.078267 - 18 June 2026

Abstract The big challenge in developing wind energy over the past century, which has focused on environmentally friendly production methods to meet the requirements of modern power systems, is the need for holistic architectures that can cope with variability, connection issues, and sector coupling far beyond conventional electricity-only models. This review offers a critically synthesized, process-level overview of progressive wind–hydrogen hybrids, offering a collective view of advancements in electrical layouts, hydrogen-driven conversion routes, and AI-driven control schemes. In contrast to previous surveys that consider these areas in isolation, we provide an explicit examination of the technical… More >

Siying Li¹, Xinyu Feng², Xin Ma², Hui Huang², Zhipeng Wang², Baolian Liu², Zujun Ding², Weihong Ding², Xiaolong Huang², Jie Ji^2,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.078062 - 18 June 2026

Abstract This study introduces a novel Dual-Stage GT-RO-PCC (Game Theory-Robust Optimization-Price Coupling Control) paradigm to address operational challenges in community-integrated energy microgrids (CIEMs) characterized by multi-energy complementarity and distributed generation. By synergizing strategic interaction mechanisms with uncertainty-aware energy management, the proposed framework establishes a tripartite governance structure integrating microgrid operators, user-side aggregators, and shared energy storage operators. The first stage formulates a Stackelberg game-theoretic model to optimize day-ahead electricity/heat pricing strategies through bilevel optimization, incorporating flexible load management modeling with flexible load disaggregation and carbon emission trading mechanisms. The second stage constructs a two-stage stochastic robust… More >

Sravani Parvathareddy^1,*, Abid Yahya¹, Lilian Amuhaya¹, Ravi Samikannu¹, Raymond S. Suglo²

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.079449 - 27 May 2026

Abstract This paper presents a novel framework for the development of a real-time energy management system for mining microgrids, which integrates the benefits of a long short-term memory (LSTM) network and a feedforward neural network (FNN) for the prediction of the load and solar power, and the optimization of the dispatch, respectively, while ensuring the safety of the microgrid through the application of a convex safety filter. In the proposed framework, the LSTM provides probabilistic multi-step forecasts of load and photovoltaic generation, capturing the high volatility characteristic of mining operations with ramp rates up to 5… More > Graphic Abstract

Jawad Hameed^*, Jiefeng Hu, Md Liton Hossain, Syed Islam

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.074998 - 27 May 2026

Abstract This paper presents a dynamic energy management strategy for a community-scale campus hybrid microgrid integrating photovoltaic (PV) generation, aggregated wind power, a proton exchange membrane fuel cell, and battery energy storage to support electric vehicle (EV) charging infrastructure under variable environmental and load conditions. The system configuration is inspired by existing renewable energy installations and planned developments at the Federation University Mt Helen Campus, enabling realistic modeling of aggregated demand and coordinated multi-source operation. To enhance physical realism, power electronic conversion efficiencies and hierarchical control dynamics are incorporated, while the wind subsystem is represented using… More >

Sobhan Manjili¹, Saeid Jafarzadeh Ghoushchi¹, Mohammad Reza Maghami^2,*, Mazlan Mohamed^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076217 - 27 April 2026

Abstract Accurate electricity load forecasting is crucial for optimizing power distribution networks, especially in rapidly growing cities like Tabriz (annual consumption growth of 7.2%). This study presents a hybrid AI framework integrating the Temporal Fusion Transformer (TFT) and XGBoost for residual error correction. The model is trained and evaluated using actual consumption data from Tabriz’s distribution network (2021–2023). Compared to a baseline TFT model, the proposed framework demonstrates a 11.2% reduction in RMSE (from 0.1249 to 0.1109) and a 10.7% decrease in MAE (from 0.0998 to 0.0891). Attention mechanism analysis reveals temperature (importance coefficient = 0.32), More >

Wei Dong¹, Xuqing Feng², Taoxiang Mei², Xiang Li², Zhenzong He^2,3,*

Frontiers in Heat and Mass Transfer, Vol.24, No.1, 2026, DOI:10.32604/fhmt.2026.075846 - 28 February 2026

Abstract The proton exchange membrane fuel cell (PEMFC) and the hydrogen hybrid power system are studied by the fuzzy-PID (FPID) control method and the fuzzy-PID control method by Artificial Bee Colony algorithm (ABC-FPID), respectively. The results reveal that compared with the FPID control method, the temperature overshoot of the PEMFC stack under the ABC-FPID control method is decreased by 0.6%. Moreover, the circulating water flow rate within the full operating envelope (about 3 min) is reduced by 19.46 L, which means the ABC-FPID control method is more effective in regulating the stack temperature. Then, the ABC-FPID… More >

Amel Ksibi^*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073533 - 29 January 2026

Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >

Mohamed Kouki¹, Nahid Osman², Mona Gafar³, Ragab A. El-Sehiemy^4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3783-3811, 2025, DOI:10.32604/cmes.2025.071964 - 23 December 2025

Abstract The growing integration of nondispatchable renewable energy sources (PV, wind) and the need to cut CO2 emissions make energy management crucial. Microgrids provide a framework for RES integration but face challenges from intermittency, fluctuating loads, cost optimization, and uncertainty in real-time balancing. Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation. While stochastic and machine learning methods are used, they struggle with limited data, complex temporal patterns, and scalability. Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption. To address… More >

Rebhi M’hamed^1,*, Himri Youcef^2,3,*, Bouchiba Bousmaha¹, Yaichi Mouaadh¹

Energy Engineering, Vol.122, No.12, pp. 4899-4918, 2025, DOI:10.32604/ee.2025.070806 - 27 November 2025

Abstract Currently, most conventional street lighting systems use a constant light mode throughout the entire night, from sunset to sunrise, which results in high energy consumption and maintenance costs. Furthermore, scientific research predicts that energy consumption for street lighting will increase in the coming years due to growing demand and rising electricity prices. The dimming strategy is a current trend and a key concept in smart street lighting systems. It involves turning on the road lights only when a vehicle or pedestrian is detected; otherwise, the control system reduces the light intensity of the lamps. Power… More > Graphic Abstract

Saeed Asadi¹, Hajar Kazemi Naeini¹, Delaram Hassanlou², Abolhassan Pishahang³, Saeid Aghasoleymani Najafabadi⁴, Abbas Sharifi⁵, Mohsen Ahmadi^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528 - 26 November 2025

Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >