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Optimal operation of a multi-energy system considering renewable energy sources stochasticity and impacts of electric vehicles

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Listed:
  • Ata, Mustafa
  • Erenoğlu, Ayşe Kübra
  • Şengör, İbrahim
  • Erdinç, Ozan
  • Taşcıkaraoğlu, Akın
  • Catalão, João P.S.

Abstract

Electrical, heating and cooling energy demands of the end users are increasing day by day. For the sake of using fewer fossil fuels, decreasing the energy costs and gas emissions as well as increasing the efficiency and flexibility of the traditional energy systems, multi-energy systems (MESs) have begun to be used. In this study, a MES structure which also includes renewable-based generation units as suppliers together with combined heating and power (CHP) and heat pumps (HPs) is presented. The proposed MES structure is modelled as a mixed integer linear programming (MILP) problem with the objective of minimizing total gas and electricity costs in daily operation. Furthermore, electric vehicles (EVs) as a new type of electrical load with inherently different characteristics are evaluated considering different end-user types as residential and commercial together with the capability of offering operational flexibility. In order to tackle with the intermittent structure of the renewable energy sources, a scenario oriented stochastic programming concept is taken into account by addressing real radiation, temperature, and wind data. Moreover, actual time-of-use (TOU) tariffs for electricity prices along with the real gas prices are evaluated. The simulation results of the devised model are given for different case studies and the effectiveness of the system is demonstrated via a comparative study. As a result, it is found that the operational costs are decreased nearly 5.49% by integrating only photovoltaic (PV) production according to the case which has no additional sources. Also, a substantial reduction of 13.45% is achieved by considering both PV and wind generation. Moreover, the flexibility is increased with taking EVs into account on the demand side and this leads to a cost reduction of 8.81% even if EVs are integrated to the system as an extra load.

Suggested Citation

  • Ata, Mustafa & Erenoğlu, Ayşe Kübra & Şengör, İbrahim & Erdinç, Ozan & Taşcıkaraoğlu, Akın & Catalão, João P.S., 2019. "Optimal operation of a multi-energy system considering renewable energy sources stochasticity and impacts of electric vehicles," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219315130
    DOI: 10.1016/j.energy.2019.07.171
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    Citations

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    Cited by:

    1. Mittelviefhaus, Moritz & Pareschi, Giacomo & Allan, James & Georges, Gil & Boulouchos, Konstantinos, 2021. "Optimal investment and scheduling of residential multi-energy systems including electric mobility: A cost-effective approach to climate change mitigation," Applied Energy, Elsevier, vol. 301(C).
    2. Naghikhani, Ali & Hosseini, Seyed Mohammad Hassan, 2022. "Optimal thermal and power planning considering economic and environmental issues in peak load management," Energy, Elsevier, vol. 239(PA).
    3. Zhou, Yuekuan, 2023. "Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation," Renewable Energy, Elsevier, vol. 202(C), pages 1324-1341.
    4. Qingyou Yan & Meijuan Zhang & Wei Li & Guangyu Qin, 2020. "Risk Assessment of New Energy Vehicle Supply Chain Based on Variable Weight Theory and Cloud Model: A Case Study in China," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    5. Vahid-Ghavidel, Morteza & Shafie-khah, Miadreza & Javadi, Mohammad S. & Santos, Sérgio F. & Gough, Matthew & Quijano, Darwin A. & Catalao, Joao P.S., 2023. "Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system," Energy, Elsevier, vol. 265(C).
    6. Çiçek, Alper & Şengör, İbrahim & Erenoğlu, Ayşe Kübra & Erdinç, Ozan, 2020. "Decision making mechanism for a smart neighborhood fed by multi-energy systems considering demand response," Energy, Elsevier, vol. 208(C).
    7. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    8. Petkov, Ivalin & Gabrielli, Paolo & Spokaite, Marija, 2021. "The impact of urban district composition on storage technology reliance: trade-offs between thermal storage, batteries, and power-to-hydrogen," Energy, Elsevier, vol. 224(C).
    9. Zhang, Bin & Hu, Weihao & Cao, Di & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach," Applied Energy, Elsevier, vol. 339(C).
    10. Noorollahi, Younes & Golshanfard, Aminabbas & Hashemi-Dezaki, Hamed, 2022. "A scenario-based approach for optimal operation of energy hub under different schemes and structures," Energy, Elsevier, vol. 251(C).

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