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Energy hubs integrating renewable energy sources and demand response programs for cost-effective operations

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  • Hu, Yuhan
  • Jin, Yang

Abstract

The integration of renewable energies for responding to the energy demand has increased, and due to the complexity of this demand, the role of an optimized energy hub is emphasized. This study is conducted to address this challenge by considering the uncertainties caused by variable wind speed, solar radiation, and energy market prices. A scenario-based modeling approach is incorporated with an innovative probabilistic optimization framework to achieve an optimum balance between energy demand and supply. The model is designed to respond to the electrical and cooling loads by integrating different renewable energy reservoirs, energy storage systems, and dual-sector demand response programs. The primary objective of the model is to optimize the energy flow and cost-efficiency across multiple scenarios, which is conducted via a modified Emperor Penguin Colony algorithm. Results reveal that utilizing renewable energies considerably reduces operational and electricity costs and enhances profitability. The scenario analysis exhibits an increased income and reduced operation cost by about 1.66 % and 11.81 % in Case 2 compared to Case 1. In Case 3, these values reach 3.39 % and 3.51 % compared to Case 2. These results highlight the benefits of reducing uncertainty in terms of both income growth and cost reduction.

Suggested Citation

  • Hu, Yuhan & Jin, Yang, 2025. "Energy hubs integrating renewable energy sources and demand response programs for cost-effective operations," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225029135
    DOI: 10.1016/j.energy.2025.137271
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    1. Gao, Fang, 2026. "Forecast-driven economic co-transition of renewable-rich urban energy hubs with eco-friendly transport-based storage systems under political–social welfare in future-ready smart cities," Renewable Energy, Elsevier, vol. 269(C).
    2. Hong, Kaijie & Li, Zheng & Fu, Kang & Zhang, Guo & Zhou, Ya, 2026. "Risk-aware modeling of active distribution grids for sustainable solar hosting and congestion relief with virtual multi energy assets and demand flexibility," Renewable Energy, Elsevier, vol. 262(C).
    3. Yang, Yilong & Fan, Shuai & Xiao, Jucheng & Huang, Renke & He, Guangyu, 2025. "Aggregation and packaging of distributed energy resources: A time-coupled flexibility-demand-oriented method," Energy, Elsevier, vol. 334(C).

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