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Techno-Economic-Environmental Energy Management of a Micro-Grid: A Mixed-Integer Linear Programming Approach

Author

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  • Seyed Hasan Mirbarati

    (Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran)

  • Najme Heidari

    (Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran)

  • Amirhossein Nikoofard

    (Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran)

  • Mir Sayed Shah Danish

    (Department of Energy Engineering, Faculty of Engineering, Kabul University, Kabul 1006, Afghanistan
    Department of Electrical and Electronics, Faculty of Engineering, University of the Ryukyus, Senbaru, Nishihara 903-0213, Okinawa, Japan)

  • Mahdi Khosravy

    (Cross Labs, Cross Compass Ltd., Tokyo 104-0045, Japan)

Abstract

In recent years, owing to the effect of fossil fuels on global warming, the exhaustion of oil fields, and the lucrative impacts of renewable energy resources (RESs), the penetration of RESs has been increasing significantly in power systems. An effective way to benefit from all RESs advantages is by applying them in microgrid systems (MGS). Furthermore, MGS can ease the way for utilizing a large amount of RESs, if its economic-environmental-technical aspects of it are taken into account. In this regard, this paper proposes an optimal solution for the energy management of a microgrid by considering a comprehensive study. In the proposed methodology, different distributed energy resources such as wind turbines generator (WTG), energy storage (ES), combined heat and power (CHP), rubbish burning agent (RBA), and diesel generators (DG) are modeled. In addition, electric vehicles (EVs) are considered a load with uncertainty. The objective function of the proposed method is to minimize the microgrid’s total cost by considering the microgrid’s emission cost and technical constraints. In this study, the microgrid’s technical, environmental, and economic aspects are investigated. In addition, the optimization problem is converted into a mixed-integer linear programming method by using the proper linearization method. In this paper, the increasing effect of wind energy penetration rate on the total price also has been studied. The simulation results show that by increasing the wind energy penetration rate by up to 30% of total power, the total cost will decrease by up to 30.9%.

Suggested Citation

  • Seyed Hasan Mirbarati & Najme Heidari & Amirhossein Nikoofard & Mir Sayed Shah Danish & Mahdi Khosravy, 2022. "Techno-Economic-Environmental Energy Management of a Micro-Grid: A Mixed-Integer Linear Programming Approach," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15036-:d:972009
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    References listed on IDEAS

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    1. Shayegan-Rad, Ali & Badri, Ali & Zangeneh, Ali, 2017. "Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties," Energy, Elsevier, vol. 121(C), pages 114-125.
    2. Shiduo Jia & Xiaoning Kang, 2022. "Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk," Energies, MDPI, vol. 15(9), pages 1-21, May.
    3. Tan, Bifei & Chen, Haoyong, 2020. "Multi-objective energy management of multiple microgrids under random electric vehicle charging," Energy, Elsevier, vol. 208(C).
    4. Javed, Muhammad Shahzad & Song, Aotian & Ma, Tao, 2019. "Techno-economic assessment of a stand-alone hybrid solar-wind-battery system for a remote island using genetic algorithm," Energy, Elsevier, vol. 176(C), pages 704-717.
    5. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
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