IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v222y2024ics0960148123018359.html
   My bibliography  Save this article

Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid

Author

Listed:
  • Chakraborty, Amit
  • Ray, Saheli

Abstract

This article proposes an approach to optimize energy management (EM) in a microgrid (MG) with a battery storage system (BSS) and distributed generation. The primary objective is to address the challenge of cost-effective and environmental-friendly operation of the microgrid, with a specific focus on minimizing operational cost and emission through optimal BSS utilization in the daily schedule. Balancing economic and environmental factors through optimization poses a complex problem, which is addressed here by applying the slime mould algorithm (SMA) along with the weighted sum and fuzzy decision-making method. Three scenarios are considered to demonstrate the MG's EM. In Scenario A, SMA outperforms differential evolution (DE), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA) by reducing operational cost by 17.59 %, 8.19 %, and 9.84 %, respectively, and achieves better emission reduction with reductions of 4.93 %, 15.74 %, and 9.91 % compared to DE, SCA, and GOA, respectively. SMA also dominates DE, SCA, and GOA in other scenarios, producing superior Pareto-optimal solutions. Additionally, comparing the Pareto-optimal results achieved with SMA, Scenario C outperforms Scenarios A and B by achieving reductions of 25.47 % and 35.20 % in operational cost, respectively, along with a corresponding 6.4 % and 36.26 % decrease in emission, highlighting the superior performance of Scenario C.

Suggested Citation

  • Chakraborty, Amit & Ray, Saheli, 2024. "Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s0960148123018359
    DOI: 10.1016/j.renene.2023.119920
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123018359
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119920?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    2. Hirsch, Adam & Parag, Yael & Guerrero, Josep, 2018. "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 402-411.
    3. Kothalanka Kameswara Pavan Kumar & Nirmala Soren & Abdul Latif & Dulal Chandra Das & S. M. Suhail Hussain & Ahmed Al-Durra & Taha Selim Ustun, 2022. "Day-Ahead DSM-Integrated Hybrid-Power-Management-Incorporated CEED of Solar Thermal/Wind/Wave/BESS System Using HFPSO," Sustainability, MDPI, vol. 14(3), pages 1-24, January.
    4. Chakraborty, Amit & Ray, Saheli, 2023. "Operational cost minimization of a microgrid with optimum battery energy storage system and plug-in-hybrid electric vehicle charging impact using slime mould algorithm," Energy, Elsevier, vol. 278(PA).
    5. Baziar, Aliasghar & Kavousi-Fard, Abdollah, 2013. "Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices," Renewable Energy, Elsevier, vol. 59(C), pages 158-166.
    6. Sharma, Sharmistha & Bhattacharjee, Subhadeep & Bhattacharya, Aniruddha, 2018. "Probabilistic operation cost minimization of Micro-Grid," Energy, Elsevier, vol. 148(C), pages 1116-1139.
    7. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    8. Hernández, J.C. & Sanchez-Sutil, F. & Muñoz-Rodríguez, F.J. & Baier, C.R., 2020. "Optimal sizing and management strategy for PV household-prosumers with self-consumption/sufficiency enhancement and provision of frequency containment reserve," Applied Energy, Elsevier, vol. 277(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuan, Zhi & Li, Ji, 2024. "Photovoltaic-penetrated power distribution networks’ resiliency-oriented day-ahead scheduling equipped with power-to-hydrogen systems: A risk-driven decision framework," Energy, Elsevier, vol. 299(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ho-Sung Ryu & Mun-Kyeom Kim, 2020. "Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty," Energies, MDPI, vol. 13(22), pages 1-25, November.
    2. Solanke, Tirupati U. & Khatua, Pradeep K. & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao, 2021. "Control and management of a multilevel electric vehicles infrastructure integrated with distributed resources: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    4. Javidsharifi, Mahshid & Niknam, Taher & Aghaei, Jamshid & Mokryani, Geev, 2018. "Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices," Applied Energy, Elsevier, vol. 216(C), pages 367-381.
    5. Deihimi, Ali & Keshavarz Zahed, Babak & Iravani, Reza, 2016. "An interactive operation management of a micro-grid with multiple distributed generations using multi-objective uniform water cycle algorithm," Energy, Elsevier, vol. 106(C), pages 482-509.
    6. de la Hoz, Jordi & Martín, Helena & Alonso, Alex & Carolina Luna, Adriana & Matas, José & Vasquez, Juan C. & Guerrero, Josep M., 2019. "Regulatory-framework-embedded energy management system for microgrids: The case study of the Spanish self-consumption scheme," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Rabiee, Abdorreza & Sadeghi, Mohammad & Aghaeic, Jamshid & Heidari, Alireza, 2016. "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 721-739.
    8. Incheol Shin, 2020. "Approximation Algorithm-Based Prosumer Scheduling for Microgrids," Energies, MDPI, vol. 13(21), pages 1-16, November.
    9. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2018. "Microgrids energy management systems: A critical review on methods, solutions, and prospects," Applied Energy, Elsevier, vol. 222(C), pages 1033-1055.
    10. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    11. Chakraborty, Amit & Ray, Saheli, 2023. "Operational cost minimization of a microgrid with optimum battery energy storage system and plug-in-hybrid electric vehicle charging impact using slime mould algorithm," Energy, Elsevier, vol. 278(PA).
    12. Abdi, Hamdi & Beigvand, Soheil Derafshi & Scala, Massimo La, 2017. "A review of optimal power flow studies applied to smart grids and microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 742-766.
    13. Salah K. ElSayed & Sattam Al Otaibi & Yasser Ahmed & Essam Hendawi & Nagy I. Elkalashy & Ayman Hoballah, 2021. "Probabilistic Modeling and Equilibrium Optimizer Solving for Energy Management of Renewable Micro-Grids Incorporating Storage Devices," Energies, MDPI, vol. 14(5), pages 1-24, March.
    14. Faridnia, N. & Habibi, D. & Lachowicz, S. & Kavousifard, A., 2019. "Optimal scheduling in a microgrid with a tidal generation," Energy, Elsevier, vol. 171(C), pages 435-443.
    15. Kasaei, Mohammad Javad & Gandomkar, Majid & Nikoukar, Javad, 2017. "Optimal management of renewable energy sources by virtual power plant," Renewable Energy, Elsevier, vol. 114(PB), pages 1180-1188.
    16. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    18. Rosales-Asensio, Enrique & de Simón-Martín, Miguel & Borge-Diez, David & Blanes-Peiró, Jorge Juan & Colmenar-Santos, Antonio, 2019. "Microgrids with energy storage systems as a means to increase power resilience: An application to office buildings," Energy, Elsevier, vol. 172(C), pages 1005-1015.
    19. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    20. Ikeda, Shintaro & Ooka, Ryozo, 2015. "Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system," Applied Energy, Elsevier, vol. 151(C), pages 192-205.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:222:y:2024:i:c:s0960148123018359. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.