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  • Open Access

    ARTICLE

    Optimal Operation of Virtual Power Plants Based on Revenue Distribution and Risk Contribution

    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

    Optimal Operation of Virtual Power Plants Based on Revenue Distribution and Risk Contribution

  • Open Access

    ARTICLE

    Intelligent Scheduling of Virtual Power Plants Based on Deep Reinforcement Learning

    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 >

  • Open Access

    ARTICLE

    Hybrid Memory-Enhanced Autoencoder with Adversarial Training for Anomaly Detection in Virtual Power Plants

    Yuqiao Liu1, Chen Pan1, YeonJae Oh2,*, Chang Gyoon Lim1,*

    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 >

  • Open Access

    ARTICLE

    Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

    Haoyi Zhong, Yongjiang Zhao, Chang Gyoon Lim*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1757-1781, 2024, DOI:10.32604/cmes.2024.049208 - 20 May 2024

    Abstract This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery (LiB) time series data. As the energy sector increasingly focuses on integrating distributed energy resources, Virtual Power Plants (VPP) have become a vital new framework for energy management. LiBs are key in this context, owing to their high-efficiency energy storage capabilities essential for VPP operations. However, LiBs are prone to various abnormal states like overcharging, over-discharging, and internal short circuits, which impede power transmission efficiency. Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and… More >

  • Open Access

    ARTICLE

    Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

    Jingjing Bai*, Hongyi Zhou, Zheng Xu, Yu Zhong

    Energy Engineering, Vol.120, No.5, pp. 1163-1183, 2023, DOI:10.32604/ee.2023.025553 - 20 February 2023

    Abstract The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants. Besides, the game relationship between transaction subjects needs to be further explored. This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game. Firstly, a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant, considering the energy consumption characteristics of users. Secondly, the utility functions of multiple virtual power plants are analyzed, and a non-cooperative game model is established to explore the… More >

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