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Stochastic Economic–Resilience Management of Combined Cooling, Heat, and Power-Based Microgrids in a Multi-Objective Approach

Author

Listed:
  • Hossein Azarinfar

    (Faculty of Computer and Electrical Engineering, University of Gonabad, St. Ghafari, Gonabad 9691957678, Iran)

  • Mohsen Khosravi

    (Faculty of Computer and Electrical Engineering, University of Gonabad, St. Ghafari, Gonabad 9691957678, Iran)

  • Kiomars Sabzevari

    (Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran 1435661137, Iran)

  • Maciej Dzikuć

    (Faculty of Economics and Management, University of Zielona Góra, Licealna Street 9, 65-417 Zielona Góra, Poland)

Abstract

The primary goal of a microgrid (MG) operator is to provide electricity to consumers while minimizing costs. For this aim, the operator must engage in the cost-effective management of its resource outputs, which can encompass electrical, thermal, or combined cooling, heat and power (CCHP) systems. Conversely, there has been a growing emphasis on enhancing the resilience of MGs in response to low-probability high-impact (LPHI) incidents in recent years. Therefore, MG-associated energy management strategies have to factor in resilience considerations. While resilience improvement activities increase the operational cost, they lead to a reduction in lost load, and subsequently, a decrease in the MG outage costs, making these activities economically viable. This paper focuses on MGs’ energy management with the primary goals of enhancing resilience, minimizing operational costs, and mitigating active power losses as well as environmental pollution. To attain this goal, various means like renewable resources (specifically photovoltaic (PV) and wind turbine (WT) systems), CCHP, and energy storage devices are integrated. Additionally, for reaching the solution, a genetic algorithm (GA) is implemented. MG operation considers the resilience concept, and according to the obtained results, it is observed that the cost of operation and environmental pollution, respectively, experience an increase about 6.31% and 2.8%. However, due to the reduction in outage costs by an average of 13.91% and power losses by 0.5%, the overall cost is diminished about 5.93%. This cost reduction is achieved through increased CCHP generation and a decreased outage duration during emergencies.

Suggested Citation

  • Hossein Azarinfar & Mohsen Khosravi & Kiomars Sabzevari & Maciej Dzikuć, 2024. "Stochastic Economic–Resilience Management of Combined Cooling, Heat, and Power-Based Microgrids in a Multi-Objective Approach," Sustainability, MDPI, vol. 16(3), pages 1-28, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1212-:d:1330705
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    References listed on IDEAS

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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Pascual, Julio & Arcos-Aviles, Diego & Ursúa, Alfredo & Sanchis, Pablo & Marroyo, Luis, 2021. "Energy management for an electro-thermal renewable–based residential microgrid with energy balance forecasting and demand side management," Applied Energy, Elsevier, vol. 295(C).
    3. Wang, Y. & Rousis, A. Oulis & Strbac, G., 2022. "Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids," Applied Energy, Elsevier, vol. 305(C).
    4. Janko, Samantha & Johnson, Nathan G., 2020. "Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks," Applied Energy, Elsevier, vol. 277(C).
    5. Wu, Raphael & Sansavini, Giovanni, 2020. "Integrating reliability and resilience to support the transition from passive distribution grids to islanding microgrids," Applied Energy, Elsevier, vol. 272(C).
    6. Seyedeh Fatemeh Razmi & Leila Torki & Seyed Mohammad Javad Razmi & Ehsan Mohaghegh Dowlatabadi, 2022. "The Indirect Effects of Oil Price on Consumption through Assets," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 236-242.
    7. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    8. Nelson, James & Johnson, Nathan G. & Fahy, Kelsey & Hansen, Timothy A., 2020. "Statistical development of microgrid resilience during islanding operations," Applied Energy, Elsevier, vol. 279(C).
    Full references (including those not matched with items on IDEAS)

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