Huang Tan¹, Haibo Yu¹, Tianyang Chen¹, Hanjun Deng², Yetong Hu^3,*

Energy Engineering, Vol.122, No.5, pp. 1949-1973, 2025, DOI:10.32604/ee.2025.062574 - 25 April 2025

Abstract With the increasing integration of emerging source-load types such as distributed photovoltaics, electric vehicles, and energy storage into distribution networks, the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source, multi-load systems. This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches—primarily focused on economic objectives—insufficient to meet the growing demands for flexible scheduling and dynamic response. To address these challenges, this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational… More >

Sheik Mohideen Shah¹, Meganathan Selvamani¹, Mahesh Thyluru Ramakrishna^2,*, Surbhi Bhatia Khan^3,4,5, Shakila Basheer⁶, Wajdan Al Malwi⁷, Mohammad Tabrez Quasim⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >

Li Wang^1,*, Xiaoyong Wang²

Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

Inna Bilous¹, Dmytro Biriukov¹, Dmytro Karpenko², Tatiana Eutukhova², Oleksandr Novoseltsev^2,*, Volodymyr Voloshchuk¹

Energy Engineering, Vol.121, No.12, pp. 3617-3634, 2024, DOI:10.32604/ee.2024.051684 - 22 November 2024

Abstract This article focuses on the challenges of modeling energy supply systems for buildings, encompassing both methods and tools for simulating thermal regimes and engineering systems within buildings. Enhancing the comfort of living or working in buildings often necessitates increased consumption of energy and material, such as for thermal upgrades, which consequently incurs additional economic costs. It is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions, considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in… More > Graphic Abstract

Yixuan Yu, Yulin Wang^*, Qingcheng Li, Bowen Jiao

Energy Engineering, Vol.121, No.3, pp. 769-788, 2024, DOI:10.32604/ee.2023.044042 - 27 February 2024

Abstract The plug-in hybrid vehicles (PHEV) technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks. Meanwhile, plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies (EMS). Therefore, a series hybrid system is constructed based on a 100-ton mining dump truck in this paper. And inspired by the dynamic programming (DP) algorithm, a predictive equivalent consumption minimization strategy (P-ECMS) based on the DP optimization result is proposed. Based on the optimal control manifold and the SOC reference trajectory obtained by the More >

Ali Sheraz Akram¹, Sagheer Abbas¹, Muhammad Adnan Khan^2,3,5, Atifa Athar⁴, Taher M. Ghazal^5,6, Hussam Al Hamadi^7,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 959-973, 2024, DOI:10.32604/cmc.2023.032216 - 30 January 2024

Abstract Energy management is an inspiring domain in developing of renewable energy sources. However, the growth of decentralized energy production is revealing an increased complexity for power grid managers, inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization, minimize energy costs without affecting production, and minimize environmental effects. Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings, which necessitates energy optimization… More >

Haoliang Yang^*, Yonggang Dong, Zhifang Yang

Energy Engineering, Vol.120, No.12, pp. 2917-2931, 2023, DOI:10.32604/ee.2023.029167 - 29 November 2023

Abstract Reducing the impact of power outages and maintaining the power supply duration must be considered in implementing emergency energy dispatching in micro-networks. This paper studies a new emergency energy treatment method based on the robust optimal method and the industrial park micro-network with the optical energy storage system. After controlling the load input, a control strategy of adjusting and removing is proposed. Rolling optimal theory is applied to emergency energy scheduling based on a robust optimal mathematical model. A weighting factor is introduced into the optimal model to balance the importance of reducing and retaining More >

Ali S. Alghamdi¹, Mohana Alanazi², Abdulaziz Alanazi³, Yazeed Qasaymeh^1,*, Muhammad Zubair^1,4, Ahmed Bilal Awan⁵, M. G. B. Ashiq⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2163-2192, 2023, DOI:10.32604/cmes.2023.029453 - 03 August 2023

Abstract To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat… More >

Oussama Accouche^1,*, Rajan Kumar Gangadhari²

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 151-173, 2023, DOI:10.32604/cmc.2023.038648 - 08 June 2023

Abstract Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand. Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources. In energy-efficient data centers, distributed generation can be used to meet the facility’s overall power needs. This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demand-side load management into account. This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from… More >

Nisha Vasudevan^1,*, Vasudevan Venkatraman², A. Ramkumar¹, T. Muthukumar³, A. Sheela⁴, M. Vetrivel⁵, R. J. Vijaya Saraswathi⁶, F. T. Josh⁷

Energy Engineering, Vol.120, No.8, pp. 1747-1761, 2023, DOI:10.32604/ee.2023.027821 - 05 June 2023

Abstract MigroGrid (MG) has emerged to resolve the growing demand for energy. But because of its inconsistent output, it can result in various power quality (PQ) issues. PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources. Similarly, the employment of nonlinear loads will introduce harmonics into the system and, as a result, cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system. Thus, this research focuses on power quality enhancement in the MG using… More >