Sanjit Brahma^*, Ranjay Das

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.075245 - 27 March 2026

Abstract A wind-turbine power system is often challenged by voltage instability, reactive power imbalance, and limited fault ride-through capability under grid disturbances. Doubly Fed Induction Generator based wind farms, owing to their partial coupling with the grid, are particularly vulnerable to voltage dips and excessive reactive power absorption during fault events. This study proposes an adaptive control strategy based on Model Reference Adaptive Control integrated with stator flux-oriented vector control to regulate active and reactive power of a DFIG-based wind farm connected to a standard IEEE 9-bus power system under fault conditions. The proposed control scheme… More > Graphic Abstract

Mahboobeh Attaei^1,2, Maria Vieira¹, Cinthia Maia Pederneiras^3,4,*, Filipa Clara Coimbra¹, David Bastos¹, Rosário Veiga³

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.074246 - 27 March 2026

Abstract The construction industry is a significant contributor to global CO₂ emissions, and urgent innovation is needed to mitigate its environmental impact. This paper provides a comprehensive review of scalable approaches for CO₂ uptake in construction materials, including the injection of CO₂ into fresh concrete, the CO₂ curing of precast concrete, and the use of ceramics as CO₂ sinks. Among these three approaches, CO₂ curing methods for concrete represent the most advanced and widely adopted strategies within industrial practice, with substantial research supporting their effectiveness and scalability. The comparison of carbonation mineralisation across three distinct material groups reveals that… More > Graphic Abstract

Zemin Liang, Songyu Gao, Qi Yao^*

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072877 - 27 March 2026

Abstract For mixed-integer programming (MIP) problems in new power systems with uncertainties, existing studies tend to address uncertainty modeling or MIP solution methods in isolation. They overlook core bottlenecks arising from their coupling, such as variable dimension explosion, disrupted constraint separability, and conflicts in solution logic. To address this gap, this paper focuses on the coupling effects between the two and systematically conducts three aspects of work: first, the paper summarizes the uncertainty optimization methods suitable for addressing uncertainty-related issues in power systems, along with their respective advantages and disadvantages. It also clarifies the specific forms… More >

Wulfran Fendzi Mbasso^1,2, Idriss Dagal³, Manish Kumar Singla^4,5,*, Muhammad Suhail Shaikh⁶, Aseel Smerat⁷, Abdullah Mohammed Al Fatais^8,9, Ali Saeed Almufih^8,9, Rabia Emhamed Al Mamlookol^10,11

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.072751 - 27 March 2026

Abstract Photovoltaic (PV) systems in the field operate under complex, uncertain conditions rapid irradiance ramps, partial shading, temperature swings, surface soiling, and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality. We propose a drift-aware, power-quality-constrained MPPT framework that co-optimizes MPPT, PLL, and current-loop gains under stochastic frequency drift, while enforcing IEEE-519 limits (per-order I_h/I_L and TDD) during optimization. Unlike energy-only or THD-only methods, the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting. Operating scenarios are calibrated to… More > Graphic Abstract

Xu Liu^*, Hongsheng Su

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072246 - 27 March 2026

Abstract Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems, as well as optimized scheduling that addresses the variability of wind and solar energy, to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy. This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage, utilizing information gap decision theory (IGDT). A model is constructed that integrates the synergistic functions of carbon capture and storage (CCS), power-to-gas (P2G), and gas turbine units through electrical coupling.… More >

Yang Li¹, Xianfu Gong¹, Sifan Chen¹, Yi Lei^2,*, Donghui Zhang², Yue Xing²

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072189 - 27 March 2026

Abstract This paper develops an innovative computational model for assessing the Carbon Emission Factor (CEF) of provincial power systems that incorporates inter-provincial electricity transfers and hybrid generation portfolios combining conventional and renewable sources. A key contribution lies in evaluating how deep regulation of thermal power plants influence the carbon intensity of coal-fired generation and coal-fired generation together with high penetration renewables. Furthermore, the study quantitatively analyzes the role of renewable energy consumption and the prospective application of Carbon Capture and Storage (CCS) in reducing system-wide CEF. Based on this framework, the paper proposes phased carbon emission… More > Graphic Abstract

Fengchao Chen^*, Aoqi Mei, Zheng Liu, Ruhao Wu, Qiwei Li

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.072188 - 27 March 2026

Abstract With the high proportion of new energy access, the traditional fault self-healing mechanism of the distribution network is challenged. Aiming at the demand for fast recovery of new distribution network faults, this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm. Firstly, the graph theory adjacency matrix is used to characterize the topology of the distribution network, and the dynamic positioning of new energy nodes is realized. Secondly, based on the output model and load characteristic model of wind, photovoltaic, and energy storage,… More >

Ho Hai Quan^1,2, Le Huu Binh^1,*, Nguyen Dinh Hoa Cuong³, Le Duc Huy⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075549 - 12 March 2026

Abstract Wireless Sensor Networks (WSNs) play a crucial role in numerous Internet of Things (IoT) applications and next-generation communication systems, yet they continue to face challenges in balancing energy efficiency and reliable connectivity. This study proposes SAC-HTC (Soft Actor-Critic-based High-performance Topology Control), a deep reinforcement learning (DRL) method based on the Actor-Critic framework, implemented within a Software Defined Wireless Sensor Network (SDWSN) architecture. In this approach, sensor nodes periodically transmit state information, including coordinates, node degree, transmission power, and neighbor lists, to a centralized controller. The controller acts as the reinforcement learning (RL) agent, with the… More >

Omar Almomani^1,*, Mahran Al-Zyoud¹, Ahmad Adel Abu-Shareha², Ammar Almomani^3,4,*, Said A. Salloum⁵, Khaled Mohammad Alomari⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075465 - 12 March 2026

Abstract In Wireless Sensor Networks (WSNs), survivability is a crucial issue that is greatly impacted by energy efficiency. Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints in WSNs. This paper presents an Adaptive Enhanced Grey Wolf Optimizer (AEGWO) for energy-efficient cluster head (CH) selection that mitigates the exploration–exploitation imbalance, preserves population diversity, and avoids premature convergence inherent in baseline GWO. The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation, a hybrid velocity-momentum update based on the dynamics of PSO, and… More >

Hamad Ali Abosaq¹, Jarallah Alqahtani^1,*, Fahad Masood², Alanoud Al Mazroa³, Muhammad Asad Khan⁴, Akm Bahalul Haque⁵

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075250 - 12 March 2026

Abstract The technological advancement of the vehicular Internet of Things (IoT) has revolutionized Intelligent Transportation Systems (ITS) into next-generation ITS. The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment. The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient, mobility-aware, and secure system for distributed intelligence. This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning (DRL-FL) approach to design an energy-efficient and threat-resilient ITS. In this approach, a Policy Proximal Optimization (PPO)-based DRL agent is first employed for adaptive client selection. Second, More >