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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

    The Study of Long-Term Trading Revenue Distribution Models in Wind-Photovoltaic-Thermal Complementary Systems Based on the Improved Shapley Value Method

    Dongfeng Yang, Ruirui Zhang, Chuang Liu*, Guoliang Bian

    Energy Engineering, Vol.122, No.7, pp. 2673-2694, 2025, DOI:10.32604/ee.2025.062154 - 27 June 2025

    Abstract Under the current long-term electricity market mechanism, new energy and thermal power face issues such as deviation assessment and compression of generation space. The profitability of market players is limited. Simultaneously, the cooperation model among various energy sources will have a direct impact on the alliance’s revenue and the equity of income distribution within the alliance. Therefore, integrating new energy with thermal power units into an integrated multi-energy complementary system to participate in the long-term electricity market holds significant potential. To simulate and evaluate the benefits and internal distribution methods of a multi-energy complementary system… More >

  • Open Access

    ARTICLE

    Application of Grey Model and Neural Network in Financial Revenue Forecast

    Yifu Sheng1, Jianjun Zhang1,*, Wenwu Tan1, Jiang Wu1, Haijun Lin1, Guang Sun2, Peng Guo3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4043-4059, 2021, DOI:10.32604/cmc.2021.019900 - 24 August 2021

    Abstract There are many influencing factors of fiscal revenue, and traditional forecasting methods cannot handle the feature dimensions well, which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend. The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso. It can reduce the dimensionality of the original data, make separate predictions for each explanatory variable, and then use neural networks to make multivariate predictions,… More >

  • Open Access

    ARTICLE

    Wind Power Revenue Potential: Simulation for Finland

    Sakarias Paaso*, Ali Khosravi

    Energy Engineering, Vol.118, No.4, pp. 1111-1133, 2021, DOI:10.32604/EE.2021.014949 - 31 May 2021

    Abstract Potential revenue from wind power generation is an important factor to be considered when planning a wind power investment. In the future, that may become even more important because it is known that wind power generation tends to push electricity wholesale prices lower. Consequently, it is possible that if a region has plenty of installed wind power capacity, revenue per generated unit of electricity is lower there than could be assumed by looking at the mean electricity wholesale price. In this paper, we compare 17 different locations in Finland in terms of revenue from wind More >

  • Open Access

    ARTICLE

    Resource Management in Cloud Computing with Optimal Pricing Policies

    Haiyang Zhang1, Guolong Chen2, Xianwei Li2,3,*

    Computer Systems Science and Engineering, Vol.34, No.4, pp. 249-254, 2019, DOI:10.32604/csse.2019.34.249

    Abstract As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue More >

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