Journal of Green Energy Research and Innovation
Quarterly

A Multi-Objective Framework for Smart Energy Hubs: Leveraging Compressed Air Storage and Demand Response

Document Type : Research article

Authors

Pouria Hajiamoosha 1
Abdollah Rastgou 1
Hadi Afshar 2

1 Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

2 Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.

https://doi.org/10.61186/jgeri.2.2.1
Abstract
In this paper, a multi-carrier energy hub that can generate and deliver electricity, heating, and cooling ‎energy from different sources, such as wind, solar, fuel cells, batteries, and compressed air is ‎proposed. The intelligent energy hub can also participate in electrical and thermal demand response, ‎which aims to reduce peak demand and enhance overall system efficiency. The scheduling problem is ‎a mixed-integer linear programming problem that seeks to minimize the system cost and carbon ‎dioxide emissions. To obtain optimal solutions that strike a balance between cost, emissions, and ‎decision maker's preferences, an augmented epsilon-constraint min-max fuzzy method is employed. ‎The proposed strategy's advantages are demonstrated through a case study, where it is compared with ‎other methods. The results show that the proposed approach effectively reduces the cost and ‎emissions of the smart energy hub while improving the load shape and energy hub efficiency. ‎Moreover, the results showed that the integration of compressed air systems and demand response ‎programs enhances the performance of the smart energy hub, making it more flexible and reliable. ‎The GAMS software is employed for the modeling and resolution of the scheduling issue.‎

Graphical Abstract

A Multi-Objective Framework for Smart Energy Hubs: Leveraging Compressed Air Storage and Demand Response

Highlights

❖ A multi-carrier energy hub generates and delivers electricity, heating, and cooling from diverse sources.
❖ The hub participates in demand response to reduce peak demand and enhance system efficiency.
❖ We use a mixed-integer linear programming approach to minimize costs and carbon emissions.
❖ Case study results demonstrate significant cost and emission reductions while improving energy hub efficiency.

Keywords

Energy hub
Multi-objective programming
Storage devices
Optimization

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

Credit Authorship Contribution Statement

Pouria Hajiamoosha: Formal analysis, Methodology, Software, Roles/Writing - original draft, Writingreview & editing. Abdollah Rastgou: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization. Hadi Afshar: Formal analysis, Software, Writing-review & editing.

Bibliography
Pouria Hajiamoosha  is a dedicated researcher in the field of electrical power engineering, with a focus on optimization and the management of microgrids and energy systems. He holds a Master’s degree in Electrical Power Engineering from the Islamic Azad University, Science and Research Branch of Kermanshah, as well as a Bachelor’s degree in Electrical and Electronics Engineering from the Islamic Azad University.

Abdollah Rastgou holds a Bachelor's, Master's, and Ph.D. degree in Power Electrical Engineering. He completed his Bachelor's degree at Tabriz University and went on to earn both his Master's and Ph.D. degrees with honors from Kurdistan University in Sanandaj. Dr. Rastgou has a keen interest in power system planning, bi-level planning, and renewable resource planning. His academic journey reflects a strong commitment to advancing the field of electrical engineering, particularly in optimizing power systems for sustainability and efficiency. 

Hadi Afshar received the B.Sc. and the M.Sc. degrees in electrical engineering from the Semnan University, Semnan, Iran, in 2010 and 2013, respectively. His research interests include smart grids, renewable energy systems, energy management, power system operation, optimization, and planning.

 

Citation
P. Hajiamoosha, A. Rastgou, and H. Afshar," A Multi-Objective Framework for Smart Energy Hubs: Leveraging Compressed Air Storage and Demand Response," Journal of Green Energy Research and Innovation, vol. 2, no. 2, pp. 1-25, 2025.

  1. T. Ding, W. Jia, et al., "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. P. Hajiamoosha, A. Rastgou, S. Bahramara, and S. M. Bagher Sadati, "Stochastic Energy Management in a Renewable Energy-Based Microgrid Considering Demand Response Program," International Journal of Electrical Power & Energy Systems, vol. 129, 106791, 2021.
  3. A. Rastgou, "Distribution Network Expansion Planning: An Updated Review of Current Methods and New Challenges," Renewable and Sustainable Energy Reviews, vol. 189, 114062, 2024.
  4. K. Gholami, S. Karimi, and A. Rastgou, "Fuzzy Risk-Based Framework for Scheduling of Energy Storage Systems in Photovoltaic-Rich Networks," Journal of Energy Storage, vol. 52, 104902, 2022.
  5. A. A. Eladl, M. I. El-Afifi, M. M. El-Saadawi, and B. E. Sedhom, "A Review on Energy Hubs: Models, Methods, Classification, Applications, and Future Trends," Alexandria Engineering Journal, vol. 68, pp. 315–342, 2023.
  6. T. Ha, Y. Xue, et al., "Optimal Operation of Energy Hub Based Micro-Energy Network with Integration of Renewables and Energy Storages," Journal of Modern Power Systems and Clean Energy, vol. 10, no. 1, pp. 100–108, 2022.
  7. A. Zhou, Z. Ma, S. Zou, J. Zhang, and Y. Guo, "Distributed Energy Management of Double-Side Multienergy Systems Via Sub-Gradient Averaging Consensus," IEEE Transactions on Smart Grid, vol. 14, no. 2, pp. 979–995, 2023.
  8. A. Rastgou, J. Moshtagh, and S. Bahramara, "Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm," Journal of Operation and Automation in Power Engineering, vol. 6, no. 1, pp. 111-125, 2018.
  9. M. Jadidbonab, E. Babaei, and B. Mohammadi-ivatloo, "CVaR-Constrained Scheduling Strategy for Smart Multi Carrier Energy Hub Considering Demand Response and Compressed Air Energy Storage," Energy, vol. 174, pp. 1238–1250, 2019.
  10. M. Rastegar, M. Fotuhi-Firuzabad, and M. Lehtonen, "Home Load Management in a Residential Energy Hub," Electric Power Systems Research, vol. 119, pp. 322–328, 2015.
  11. A. Hussain, S. M. Arif, M. Aslam, and S. D. A. Shah, "Optimal Siting and Sizing of Tri-Generation Equipment for Developing an Autonomous Community Microgrid Considering Uncertainties," Sustainable Cities and Society, vol. 32, pp. 318–330, 2017.
  12. F. Jabari, S. Nojavan, and B. Mohammadi Ivatloo, "Designing and Optimizing a Novel Advanced Adiabatic Compressed Air Energy Storage and Air Source Heat Pump Based Μ-Combined Cooling, Heating and Power System," Energy, vol. 116, pp. 64–77, 2016.
  13. X. Chen, G. Gong, Z. Wan, C. Zhang, and Z. Tu, "Performance Study of a Dual Power Source Residential CCHP System Based on PEMFC and PTSC," Energy Conversion and Management, vol. 119, pp. 163–176, 2016.
  14. M. Geidl, G. Koeppel, et al., "Energy Hubs for the Future," IEEE Power and Energy Magazine, vol. 5, no. 1, pp. 24–30, 2007.
  15. M. Geidl, and G. Andersson, "Optimal Coupling of Energy Infrastructures," 2007 IEEE Lausanne Power Tech, 2007.
  16. S. Bahrami, and F. Safe, "A Financial Approach to Evaluate an Optimized Combined Cooling, Heat and Power System," Energy and Power Engineering, vol. 05, no. 05, pp. 352–362, 2013.
  17. D. Xu, Q. Wu, et al., "Distributed Multi-Energy Operation of Coupled Electricity, Heating, and Natural Gas Networks," IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 2457–2469, 2020.
  18. M. Rastegar, and M. Fotuhi-Firuzabad, "Load Management in a Residential Energy Hub with Renewable Distributed Energy Resources," Energy and Buildings, vol. 107, pp. 234–242, 2015.
  19. M. C. Bozchalui, S. A. Hashmi, H. Hassen, C. A. Canizares, and K. Bhattacharya, "Optimal Operation of Residential Energy Hubs in Smart Grids," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1755–1766, 2012.
  20. M. Moeini-Aghtaie, A. Abbaspour, M. Fotuhi-Firuzabad, and P. Dehghanian, "Optimized Probabilistic PHEVs Demand Management in the Context of Energy Hubs," IEEE Transactions on Power Delivery, vol. 30, no. 2, pp. 996–1006, 2015.
  21. S. Zheng, Y. Sun, et al., "Incentive-Based Integrated Demand Response for Multiple Energy Carriers Considering Behavioral Coupling Effect of Consumers," IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3231–3245, 2020.
  22. M. Majidi, and K. Zare, "Integration of Smart Energy Hubs in Distribution Networks Under Uncertainties and Demand Response Concept," IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 566–574, 2019.
  23. M. Mazidi, A. Zakariazadeh, S. Jadid, and P. Siano, "Integrated Scheduling of Renewable Generation and Demand Response Programs in a Microgrid," Energy Conversion and Management, vol. 86, pp. 1118–1127, 2014.
  24. S. Nojavan, and H. A. Aalami, "Stochastic Energy Procurement of Large Electricity Consumer Considering Photovoltaic, Wind-Turbine, Micro-Turbines, Energy Storage System in the Presence of Demand Response Program," Energy Conversion and Management, vol. 103, pp. 1008–1018, 2015.
  25. P. Mancarella, and G. Chicco, "Real-Time Demand Response from Energy Shifting in Distributed Multi-Generation," IEEE Transactions on Smart Grid, vol. 4, no. 4, pp. 1928–1938, 2013.
  26. B. Yan, S. Xue, Y. Li, J. Duan, and M. Zeng, "Gas-Fired Combined Cooling, Heating and Power (CCHP) in Beijing: A Techno-Economic Analysis," Renewable and Sustainable Energy Reviews, vol. 63, pp. 118–131, 2016.
  27. K. Saberi, H. Pashaei-Didani, R. Nourollahi, K. Zare, and S. Nojavan, "Optimal Performance of CCHP Based Microgrid Considering Environmental Issue in the Presence of Real Time Demand Response," Sustainable Cities and Society, vol. 45, pp. 596–606, 2019.
  28. D. K. Critz, S. Busche, and S. Connors, "Power Systems Balancing with High Penetration Renewables: The Potential of Demand Response in Hawaii," Energy Conversion and Management, vol. 76, pp. 609–619, 2013.
  29. L. Guo, W. Liu, J. Cai, B. Hong, and C. Wang, "A Two-Stage Optimal Planning and Design Method for Combined Cooling, Heat and Power Microgrid System," Energy Conversion and Management, vol. 74, pp. 433–445, 2013.
  30. Z. Tan, L. Ju, et al., "The Optimization Model for Multi-Type Customers Assisting Wind Power Consumptive Considering Uncertainty and Demand Response Based on Robust Stochastic Theory," Energy Conversion and Management, vol. 105, pp. 1070–1081, 2015.
  31. H. Aalami, M. P. Moghaddam, and G. Yousefi, "Demand Response Modeling Considering Interruptible/Curtailable Loads and Capacity Market Programs," Applied Energy, vol. 87, no. 1, pp. 243–250, 2010.
  32. E. Drury, P. Denholm, and R. Sioshansi, "The Value of Compressed Air Energy Storage in Energy and Reserve Markets," Energy, vol. 36, no. 8, pp. 4959–4973, 2011.
  33. R. Rezaeipour, and A. Zahedi, "Multi-Objective Based Economic Operation and Environmental Performance of PV-Based Large Industrial Consumer," Solar Energy, vol. 157, pp. 227–235, 2017.
  34. S. Nojavan, M. Majidi, and N. N. Esfetanaj, "An Efficient Cost-Reliability Optimization Model for Optimal Siting and Sizing of Energy Storage System in a Microgrid in the Presence of Responsible Load Management," Energy, vol. 139, pp. 89–97, 2017.
  35. S. Nojavan, M. Majidi, A. Najafi-Ghalelou, M. Ghahramani, and K. Zare, "A Cost-Emission Model for Fuel Cell/PV/Battery Hybrid Energy System in the Presence of Demand Response Program: Ε-Constraint Method and Fuzzy Satisfying Approach," Energy Conversion and Management, vol. 138, pp. 383–392, 2017.
Journal of Green Energy Research and Innovation
Volume 2, Issue 2
Spring 2025
Pages 1-25

  • Receive Date 05 March 2025
  • Revise Date 11 March 2025
  • Accept Date 15 March 2025
  • Publish Date 01 June 2025

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