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Yang, M., Zhang, H., Yu, X., Seklouli, A.S., Bouras, A. and Ouzrout, Y. (2025) A Composite Photovoltaic Power Prediction Optimization Model Based on Nonlinear Meteorological Factors Analysis and Hybrid Deep Learning Framework. International Journal of Electrical Power & Energy Systems, 169, Article 110660.
https://doi.org/10.1016/j.ijepes.2025.110660

has been cited by the following article:

• TITLE: Green Electricity Product Solution in the Background of Electricity Market

  AUTHORS: Maoqing Shi

  KEYWORDS: Green Electricity, Electricity Market, Load Clustering, Photovoltaic Power Generation Forecasting, Value-Driven Solutions

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.16 No.3, March 17, 2026

  ABSTRACT: In the development of a new power system dominated by green energy, green electricity has become a standardized commodity circulating nationwide through market mechanisms. The role of green electricity has evolved from being a dependent entity reliant on grid-guaranteed procurement to becoming a value-driven competitor driven by price signals and electricity demand. This paper proposes a green electricity product solution designed to provide reliable value assessment models and feasible implementation plans for users with diverse renewable energy needs. By modeling current internal power generation and consumption data within enterprises, the solution enables efficient communication between both parties during preliminary discussions. Based on digitalized models, green electricity service providers can deliver comprehensive solutions that are environmentally sustainable, economically viable, and highly efficient through policy analysis and technical evaluation.