Heping Qi, Wenyao Sun^*, Yi Zhao, Xiaoyi Qian, Xingyu Jiang

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069603 - 27 December 2025

Abstract Virtual power plant (VPP) integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions, promote the consumption of renewable energy, and improve economic efficiency. In this paper, aiming at the uncertainty of distributed wind power and photovoltaic output, considering the coupling relationship between power, carbon trading, and green card market, the optimal operation model and bidding scheme of VPP in spot market, carbon trading market, and green card market are established. On this basis, through the Shapley value and independent risk contribution theory in cooperative game theory, the quantitative… More > Graphic Abstract

Yong Li, Yuxuan Chen^*, Jiahui He, Guowei He, Chenxi Dai, Jingjing Tong, Wenting Lei

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069257 - 27 December 2025

Abstract Ensuring reliable power supply in urban distribution networks is a complex and critical task. To address the increased demand during extreme scenarios, this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants (VPPs). The proposed strategy improves system flexibility and responsiveness by optimizing the power adjustment of flexible resources. In the proposed strategy, the Gaussian Process Regression (GPR) is firstly employed to determine the adjustable range of aggregated power within the VPP, facilitating an assessment of its potential contribution to power supply support. Then, an optimal dispatch model based on More > Graphic Abstract

Chen Pan, ChangGyoon Lim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3747-3781, 2025, DOI:10.32604/cmes.2025.073389 - 23 December 2025

Abstract Solar forecasting using ground-based sky image offers a promising approach to reduce uncertainty in photovoltaic (PV) power generation. However, existing methods often rely on deterministic predictions that lack diversity, making it difficult to capture the inherently stochastic nature of cloud movement. To address this limitation, we propose a new two-stage probabilistic forecasting framework. In the first stage, we introduce I-GPT, a multiscale physics-constrained generative model for stochastic sky image prediction. Given a sequence of past sky images, I-GPT uses a Transformer-based VQ-VAE. It also incorporates multi-scale physics-informed recurrent units (Multi-scale PhyCell) and dynamically weighted fuses… More >

Hao Wang^*, Wenyue Zhou, Yiping Luo

Energy Engineering, Vol.122, No.9, pp. 3737-3752, 2025, DOI:10.32604/ee.2025.067938 - 26 August 2025

Abstract Currently, renewable energy has been broadly implemented across diverse sectors, particularly evidenced by its substantially higher integration levels in power systems. The large-scale integration of renewable energy resources introduces distinct fault characteristics into power grids, potentially rendering traditional line protection schemes inadequate for transmission lines interfacing with these sources. Therefore, it is imperative to study line protection methods unaffected by the integration of renewable energy resources. After analyzing the propagation process of the fault traveling wave along the transmission line, a non-unit protection based on traveling wave distance measurement is proposed. The core principle of… More >

Linwei Yao^1,*, Xiangning Lin^1,2, Huashen He¹, Jiahui Yang¹

Energy Engineering, Vol.122, No.8, pp. 3285-3308, 2025, DOI:10.32604/ee.2025.066628 - 24 July 2025

Abstract In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant (WIPP) and renewable energy accommodation, an electricity-hydrogen-waste multi-energy system integrated with phase change material (PCM) thermal storage is proposed. First, a thermal energy management framework is constructed, combining PCM thermal storage with the alkaline electrolyzer (AE) waste heat recovery and the heat pump (HP), while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation. Next, a flue gas treatment method based on purification-separation-storage coordination is adopted, achieving spatiotemporal decoupling between waste incineration… More >

Shaowei He, Wenchao Cui^*, Gang Li, Hairun Xu, Xiang Chen, Yu Tai

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 861-886, 2025, DOI:10.32604/cmc.2025.063979 - 09 June 2025

Abstract The Virtual Power Plant (VPP), as an innovative power management architecture, achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources. However, due to significant differences in operational costs and flexibility of various types of generation resources, as well as the volatility and uncertainty of renewable energy sources (such as wind and solar power) and the complex variability of load demand, the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed. To solve this, this paper proposes an intelligent scheduling method for virtual power… More >

Ramesh Kumar^1,2, Abdullah Bin Queyam³, Manish Kumar Singla^1,4,*, Mohamed Louzazni⁵, Mishra Dipak Kumar⁶

Energy Engineering, Vol.122, No.4, pp. 1373-1386, 2025, DOI:10.32604/ee.2025.058058 - 31 March 2025

Abstract Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants, as it directly impacts operational costs and emissions in light of energy transition goals. This study sets itself apart from existing research by applying a novel optimization technique to a basic ideal Rankine cycle, focusing on a specific power plant that has not been previously analyzed. Currently, this cycle operates at 41% efficiency and a steam quality of 76%, constrained by fixed operational parameters. The primary objectives are to increase thermal efficiency beyond 46% and raise steam quality above… More >

Yuqiao Liu¹, Chen Pan¹, YeonJae Oh^2,*, Chang Gyoon Lim^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4593-4629, 2025, DOI:10.32604/cmc.2025.061196 - 06 March 2025

Abstract Virtual Power Plants (VPPs) are integral to modern energy systems, providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data. Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations. We introduce the Memory-Enhanced Autoencoder with Adversarial Training (MemAAE) model to overcome these limitations, designed explicitly for robust anomaly detection in VPP environments. The MemAAE model integrates three principal components: an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors, an adversarial training module that… More >

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Abdul Moiz¹, Malik Shahzaib¹, Abdul Ghafoor Memon¹, Laveet Kumar², Mamdouh El Haj Assad^3,*

Energy Engineering, Vol.121, No.11, pp. 3095-3120, 2024, DOI:10.32604/ee.2024.051214 - 21 October 2024

Abstract To meet the escalating electricity demand and rising fuel costs, along with notable losses in power transmission, exploring alternative solutions is imperative. Gas turbines demonstrate high efficiency under ideal International Organization for Standardization (ISO) conditions but face challenges during summer when ambient temperatures reach 40°C. To enhance performance, the proposal suggests cooling inlet air by 15°C using a vapor absorption chiller (VAC), utilizing residual exhaust gases from a combined cycle power plant (CCPP) to maximize power output. Additionally, diverting a portion of exhaust gases to drive an organic Rankine cycle (ORC) for supplementary power generation… More >