1. T. Ding, W. Jia, M. Shahidehpour, O. Han, Y. Sun, and Z. Zhang, “Review of optimization methods for energy hub planning, operation, trading, and control,” IEEE Transactions on Sustainable Energy, vol. 13, no. 3, pp. 1802–1818, 2022.
2. S. Harichandan, S. K. Kar, R. Bansal, and S. K. Mishra, “Achieving sustainable development goals through adoption of hydrogen fuel cell vehicles in india: An empirical analysis,” International Journal of Hydrogen Energy, vol. 48, no. 12, pp. 4845–4859, 2023.
3. M. Roustai, M. Rayati, A. Sheikhi, and A. Ranjbar, “A scenario-based optimization of smart energy hub operation in a stochastic environment using conditional-value-at-risk,” Sustainable cities and society, vol. 39, pp. 309–316, 2018.
4. N. Voropai, E. Ukolova, D. Gerasimov, K. Suslov, P. Lombardi, and P. Komarnicki, “A study on cost-effectiveness of energy supply based on the energy hub concept,” in 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), pp. 1–4, IEEE, 2019.
5. S. Cheng, R. Wang, J. Xu, and Z. Wei, “Multi-time scale coordinated optimization of an energy hub in the integrated energy system with multi-type energy storage systems,” Sustainable Energy Technologies and Assessments, vol. 47, p. 101327, 2021.
6. M. A. Mohamed, A. Almalaq, E. M. Awwad, M. A. El-Meligy, M. Sharaf, and Z. M. Ali, “An effective energy management approach within a smart island considering water-energy hub,” IEEE Transactions on Industry Applications, 2020.
7. T. Ma, J. Wu, L. Hao, and D. Li, “Energy flow matrix modeling and optimal operation analysis of multi energy systems based on graph theory,” Applied Thermal Engineering, vol. 146, pp. 648–663, 2019.
8. A. Heidari, S. Mortazavi, and R. Bansal, “Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies,” Applied Energy, vol. 261, p. 114393, 2020.
9. E. Shahrabi, S. M. Hakimi, A. Hasankhani, G. Derakhshan, and B. Abdi, “Developing optimal energy management of energy hub in the presence of stochastic renewable energy resources,” Sustainable Energy, Grids and Networks, vol. 26, p. 100428, 2021.
10. Y. Luo, X. Zhang, D. Yang, and Q. Sun, “Emission trading based optimal scheduling strategy of energy hub with energy storage and integrated electric vehicles,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 2, pp. 267–275, 2020.
11. P. Li, W. Sheng, Q. Duan, Z. Li, C. Zhu, and X. Zhang, “A lyapunov optimization-based energy management strategy for energy hub with energy router,” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4860–4870, 2020.
12. D. Wu, J. Bai, W. Wei, L. Chen, and S. Mei, “Optimal bidding and scheduling of aa-caes based energy hub considering cascaded consumption of heat,” Energy, vol. 233, p. 121133, 2021.
13. M. Jalili, M. Sedighizadeh, and A. S. Fini, “Optimal operation of the coastal energy hub considering seawater desalination and compressed air energy storage system,” Thermal Science and Engineering Progress, vol. 25, p. 101020, 2021.
14. J. Bai, W. Wei, L. Chen, and S. Mei, “Rolling-horizon dispatch of advanced adiabatic compressed air energy storage based energy hub via data-driven stochastic dynamic programming,” Energy Conversion and Management, vol. 243, p. 114322, 2021.
15. S. S. Gougheri, M. Dehghani, A. Nikoofard, H. Jahangir, and M. A. Golkar, “Economic assessment of multi-operator virtual power plants in electricity market: A game theory-based approach,” Sustainable Energy Technologies and Assessments, vol. 53, p. 102733, 2022.
16. S. S. Gougheri, H. Jahangir, M. A. Golkar, and A. Moshari, “Unit commitment with price demand response based on game theory approach,” in 2019 International Power System Conference (PSC), pp. 234–240, IEEE, 2019.
17. A. V. Olympios, N. Le Brun, S. Acha, N. Shah, and C. N. Markides, “Stochastic real-time operation control of a combined heat and power (chp) system under uncertainty,” Energy Conversion and Management, vol. 216, p. 112916, 2020.
18. S. Sadeghi, H. Jahangir, B. Vatandoust, M. A. Golkar, A. Ahmadian, and A. Elkamel, “Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach,” International Journal of Electrical Power & Energy Systems, vol. 127, p. 106646, 2021.
19. Y. Li, K. Li, Z. Yang, Y. Yu, R. Xu, and M. Yang, “Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach,” Journal of Cleaner Production, vol. 330, p. 129840, 2022.
20. G. Mavrotas, “Generation of efficient solutions in multiobjective mathematical programming problems using gams. effective implementation of the ε-constraint method,” Lecturer, Laboratory of Industrial and Energy Economics, School of Chemical Engineering. National Technical University of Athens, 2007.
21. “Raw weather data - renewables.ninja.” https://www.renewables.ninja/news/raw-weather-data. Accessed: 29-Dec-2022.
22. “Home | world weather information service.” https://worldweather.wmo.int/en/home.html. Accessed: 29-Dec-2022.
23. “Igmc english > home..” https://www.igmc.ir/en. Accessed: 29-Dec-2022.
24. http://www.ieso.ca/power-data.
25. “National household travel survey.” https://nhts.ornl.gov/. Accessed: 05-May-2020.
26. S. S. Gougheri, H. Jahangir, M. A. Golkar, A. Ahmadian, and M. A. Golkar, “Optimal participation of a virtual power plant in electricity market considering renewable energy: A deep learning-based approach,” Sustainable Energy, Grids and Networks, vol. 26, p. 100448, 2021.