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.
References
[1]
Wang, D., Tian, H., Luo, Z., et al. (2025) Practices and Reflections on Renewable Energy Participating in Electricity Market. Automation of Electric Power Systems, 49, 1-7.
[2]
Jiang, X., Cui, Y. and Ma, Y. (2025) Recommendation Method for Retail Electricity Packages Based on Weighted Interval-Valued Intuitionistic Hesitant Fuzzy Sets. 2025 5th IEEE International Conference on Energy Engineering and Power Systems (EEPS), Shenzhen, 31 October 2025-2 November 2025, 1047-1053. https://doi.org/10.1109/eeps68057.2025.11351882
[3]
Xu, L., Zhou, C., Sheng, J., Li, J. and Xu, Y. (2025) Analysis of Distributed PV Harmonic Characteristics Based on Harmonic Coupling Admittance Matrix Model. 2025 8th Asia Conference on Energy and Electrical Engineering (ACEEE), Qingdao, 25-27 July 2025, 478-482. https://doi.org/10.1109/aceee66257.2025.11148731
[4]
Li, D., Xu, J., Liu, S. and AL-Wesabi, I. (2025) Impacts of Government Subsidies and Risk Aversion on an Electricity Plant’s Energy Transition and the Optimal Electricity Sales Decisions under Renewable Electricity Production Uncertainty. Energy Economics, 152, Article 109041. https://doi.org/10.1016/j.eneco.2025.109041
[5]
Mantilla, N., Oviedo-Cepeda, J.C., Toquica, D., Agbossou, K. and Henao, N. (2025) A Market-Clearing Mechanism Based on Hierarchical-Spectral Clustering with Multiple Similarity Measures for Flexibility Spot Markets. Sustainable Energy, Grids and Networks, 44, Article 101983. https://doi.org/10.1016/j.segan.2025.101983
[6]
Wang, L., Cao, Z. and Li, P. (2024) Research on the Mechanism of Leader-Follower Product Sales Competition Based on Market Access Restrictions and Forward Contract Considerations. Heliyon, 10, e32372. https://doi.org/10.1016/j.heliyon.2024.e32372
[7]
Tian, X., Yuan, B., Zheng, P., Mei, Y. and Zhang, C. (2026) Standardized Modeling and Prediction of Multi-Type Loads for Shared Energy Storage Scheduling in Customized Park Applications. Electric Power Systems Research, 250, Article 112180. https://doi.org/10.1016/j.epsr.2025.112180
[8]
Priya, E. and Preetha Roselyn, J. (2024) A Multi-Layer Constrained Spectral K-Embedded Clustering Methodology Approach for Intelligent Partitioning of Power Grid to Enhance Resiliency in Transmission Networks. E-Prime—Advances in Electrical Engineering, Electronics and Energy, 10, Article 100813.
[9]
Huang, Y., Yao, Z. and Xu, Q. (2024) Classification Model of Electricity Consumption Behavior Based on Sparse Denoising Autoencoder Feature Dimensionality Reduction and Spectral Clustering. International Journal of Electrical Power & Energy Systems, 158, Article 109960. https://doi.org/10.1016/j.ijepes.2024.109960
[10]
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
[11]
Chandel, S.S., Gupta, A., Chandel, R. and Tajjour, S. (2023) Review of Deep Learning Techniques for Power Generation Prediction of Industrial Solar Photovoltaic Plants. Solar Compass, 8, Article 100061. https://doi.org/10.1016/j.solcom.2023.100061
[12]
Chen, W., Wu, Y., Pei, T., Lin, J. and Yuan, G. (2026) Predictive Maintenance Strategy for Photovoltaic Power Systems: Collaborative Optimization of Transformer-Based Lifetime Prediction and Opposition-Based Learning HHO Algorithm. Energy Engineering, 123, 1-10. https://doi.org/10.32604/ee.2025.070905
[13]
Yu, H., Chen, S., Chu, Y., Li, M., Ding, Y., Cui, R., et al. (2024) Self-Attention Mechanism to Enhance the Generalizability of Data-Driven Time-Series Prediction: A Case Study of Intra-Hour Power Forecasting of Urban Distributed Photovoltaic Systems. Applied Energy, 374, Article 124007. https://doi.org/10.1016/j.apenergy.2024.124007
[14]
Michael, N.E., Bansal, R.C., Ismail, A.A.A., Elnady, A. and Hasan, S. (2024) A Cohesive Structure of Bi-Directional Long-Short-Term Memory (BiLSTM)-GRU for Predicting Hourly Solar Radiation. Renewable Energy, 222, Article 119943. https://doi.org/10.1016/j.renene.2024.119943
[15]
Feng, J. and Li, Z. (2026) Distributed Multi-Community-Collaborative Method for Multi-Site Behind-the-Meter Solar Disaggregation for Heterogeneous Communities. IEEE Transactions on Smart Grid, 1. https://doi.org/10.1109/TSG.2026.3660316
[16]
Di Piazza, M.C., Luna, M., Tona, G.L. and Di Piazza, A. (2017) Energy Management of Hybrid PV-Storage Systems for Improved Grid Integration. 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Milan, 6-9 June 2017, 1-5. https://doi.org/10.1109/eeeic.2017.7977539
