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Two-stage stochastic framework for energy hubs planning considering demand response programs

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  • Mansouri, Seyed Amir
  • Ahmarinejad, Amir
  • Javadi, Mohammad Sadegh
  • Catalão, João P.S.

Abstract

The integrated use of electricity and natural gas has captured great attention over recent years, mainly due to the high efficiency and economic considerations. According to the energy hub design and operation, which allows using different energy carriers, it has turned into a critical topic. This paper develops a two-stage stochastic model for energy hub planning and operation. The uncertainties of the problem have arisen from the electric, heating, and cooling load demand forecasts, besides the intermittent output of the solar photovoltaic (PV) system. The scenarios of the uncertain parameters are generated using the Monte-Carlo simulation (MCS), and the backward scenario reduction technique is used to alleviate the number of generated scenarios. Furthermore, this paper investigates the effectiveness of demand response programs (DRPs). The presented model includes two stages, where at the first stage, the optimal energy hub design is carried out utilizing the particle swarm optimization (PSO) algorithm. In this respect, the capacity of the candidate assets has been considered continuous, enabling the planning entity to precisely design the energy hub. The problem of the optimal energy hub operation is introduced at the second stage of the model formulated as mixed-integer non-linear programming (MINLP). The proposed framework is simulated using a typical energy hub to verify its effectiveness and efficiency.

Suggested Citation

  • Mansouri, Seyed Amir & Ahmarinejad, Amir & Javadi, Mohammad Sadegh & Catalão, João P.S., 2020. "Two-stage stochastic framework for energy hubs planning considering demand response programs," Energy, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220312317
    DOI: 10.1016/j.energy.2020.118124
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    References listed on IDEAS

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    1. Gutiérrez-Alcaraz, G. & Galván, E. & González-Cabrera, N. & Javadi, M.S., 2015. "Renewable energy resources short-term scheduling and dynamic network reconfiguration for sustainable energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 256-264.
    2. Khalilpour, Rajab & Vassallo, Anthony, 2016. "Planning and operation scheduling of PV-battery systems: A novel methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 194-208.
    3. Yi, Ji Hyun & Ko, Woong & Park, Jong-Keun & Park, Hyeongon, 2018. "Impact of carbon emission constraint on design of small scale multi-energy system," Energy, Elsevier, vol. 161(C), pages 792-808.
    4. Javadi, Mohammad Sadegh & Razavi, Seyed-Ehsan & Ahmadi, Abdollah & Siano, Pierluigi, 2019. "A novel approach for distant wind farm interconnection: Iran South-West wind farms integration," Renewable Energy, Elsevier, vol. 140(C), pages 737-750.
    5. Mohamed Lotfi & Mohammad Javadi & Gerardo J. Osório & Cláudio Monteiro & João P. S. Catalão, 2020. "A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation," Energies, MDPI, vol. 13(1), pages 1-19, January.
    6. Ramli, Makbul A.M. & Bouchekara, H.R.E.H. & Alghamdi, Abdulsalam S., 2018. "Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 400-411.
    7. Amiri, S. & Honarvar, M. & sadegheih, A., 2018. "Providing an integrated Model for Planning and Scheduling Energy Hubs and preventive maintenance," Energy, Elsevier, vol. 163(C), pages 1093-1114.
    8. Chen, Cong & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Simab, Mohsen & Javadi, Mohammad Sadegh & Nezhad, Ali Esmaeel, 2018. "Multi-objective programming of pumped-hydro-thermal scheduling problem using normal boundary intersection and VIKOR," Energy, Elsevier, vol. 143(C), pages 854-866.
    10. Li, Sheng & Sui, Jun & Jin, Hongguang & Zheng, Jianjiao, 2013. "Full chain energy performance for a combined cooling, heating and power system running with methanol and solar energy," Applied Energy, Elsevier, vol. 112(C), pages 673-681.
    11. Wang, Yi & Zhang, Ning & Zhuo, Zhenyu & Kang, Chongqing & Kirschen, Daniel, 2018. "Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch," Applied Energy, Elsevier, vol. 210(C), pages 1141-1150.
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