IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i11p6759-d829350.html
   My bibliography  Save this article

Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

Author

Listed:
  • Masoud Dashtdar

    (Electrical Engineering Department, Bushehr Branch, Islamic Azad University, Bushehr 7515895496, Iran)

  • Aymen Flah

    (National Engineering School of Gabès, Processes, Energy, Environment and Electrical Systems, University of Gabès, LR18ES34, Medinine 6072, Tunisia)

  • Seyed Mohammad Sadegh Hosseinimoghadam

    (Electrical Engineering Department, Bushehr Branch, Islamic Azad University, Bushehr 7515895496, Iran)

  • Hossam Kotb

    (Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21526, Egypt)

  • Elżbieta Jasińska

    (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

  • Radomir Gono

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 708-00 Ostrava, Czech Republic)

  • Zbigniew Leonowicz

    (Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

  • Michał Jasiński

    (Faculty of Electrical Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland)

Abstract

An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%.

Suggested Citation

  • Masoud Dashtdar & Aymen Flah & Seyed Mohammad Sadegh Hosseinimoghadam & Hossam Kotb & Elżbieta Jasińska & Radomir Gono & Zbigniew Leonowicz & Michał Jasiński, 2022. "Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony," Sustainability, MDPI, vol. 14(11), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6759-:d:829350
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/11/6759/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/11/6759/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kakran, Sandeep & Chanana, Saurabh, 2018. "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 524-535.
    2. Feuerriegel, Stefan & Neumann, Dirk, 2016. "Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications," Energy Policy, Elsevier, vol. 96(C), pages 231-240.
    3. Mohsin, Muhammad & Hanif, Imran & Taghizadeh-Hesary, Farhad & Abbas, Qaiser & Iqbal, Wasim, 2021. "Nexus between energy efficiency and electricity reforms: A DEA-Based way forward for clean power development," Energy Policy, Elsevier, vol. 149(C).
    4. Luis Orlando Polanco Vasquez & Cristian Andrés Carreño Meneses & Alejandro Pizano Martínez & Juana López Redondo & Manuel Pérez García & José Domingo Álvarez Hervás, 2018. "Optimal Energy Management within a Microgrid: A Comparative Study," Energies, MDPI, vol. 11(8), pages 1-22, August.
    5. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    6. Xu, Fangyuan & Wu, Wanli & Zhao, Fei & Zhou, Ya & Wang, Yongjian & Wu, Runji & Zhang, Tao & Wen, Yongchen & Fan, Yiliang & Jiang, Shengli, 2019. "A micro-market module design for university demand-side management using self-crossover genetic algorithms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Kumar, R. Seshu & Raghav, L. Phani & Raju, D. Koteswara & Singh, Arvind R., 2021. "Intelligent demand side management for optimal energy scheduling of grid connected microgrids," Applied Energy, Elsevier, vol. 285(C).
    8. Mohammadi, Sirus & Mozafari, Babak & Solimani, Soodabeh & Niknam, Taher, 2013. "An Adaptive Modified Firefly Optimisation Algorithm based on Hong's Point Estimate Method to optimal operation management in a microgrid with consideration of uncertainties," Energy, Elsevier, vol. 51(C), pages 339-348.
    9. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    10. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
    11. Carlos Roldán-Blay & Vladimiro Miranda & Leonel Carvalho & Carlos Roldán-Porta, 2019. "Optimal Generation Scheduling with Dynamic Profiles for the Sustainable Development of Electricity Grids," Sustainability, MDPI, vol. 11(24), pages 1-26, December.
    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. Yi Zhang & Tian Lan & Wei Hu, 2023. "A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response," Sustainability, MDPI, vol. 15(19), pages 1-22, October.
    2. Priyadharshini Ramu & Sivasankar Gangatharan & Sankar Rangasamy & Lucian Mihet-Popa, 2023. "Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique," Sustainability, MDPI, vol. 15(16), pages 1-28, August.
    3. Tuyen Nguyen-Duc & Linh Hoang-Tuan & Hung Ta-Xuan & Long Do-Van & Hirotaka Takano, 2022. "A Mixed-Integer Programming Approach for Unit Commitment in Micro-Grid with Incentive-Based Demand Response and Battery Energy Storage System," Energies, MDPI, vol. 15(19), pages 1-26, September.

    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. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    2. Hafiz Abdul Muqeet & Rehan Liaqat & Mohsin Jamil & Asharf Ali Khan, 2023. "A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment," Energies, MDPI, vol. 16(1), pages 1-23, January.
    3. José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
    4. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    5. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    6. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    7. Luis Alejandro Arias & Edwin Rivas & Francisco Santamaria & Victor Hernandez, 2018. "A Review and Analysis of Trends Related to Demand Response," Energies, MDPI, vol. 11(7), pages 1-24, June.
    8. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    9. Phani Raghav, L. & Seshu Kumar, R. & Koteswara Raju, D. & Singh, Arvind R., 2022. "Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid," Applied Energy, Elsevier, vol. 306(PB).
    10. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    11. Ali M. Jasim & Basil H. Jasim & Habib Kraiem & Aymen Flah, 2022. "A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    12. Leinauer, Christina & Schott, Paul & Fridgen, Gilbert & Keller, Robert & Ollig, Philipp & Weibelzahl, Martin, 2022. "Obstacles to demand response: Why industrial companies do not adapt their power consumption to volatile power generation," Energy Policy, Elsevier, vol. 165(C).
    13. 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.
    14. 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.
    15. Kumar, Abhishek & Deng, Yan & He, Xiangning & Singh, Arvind R. & Kumar, Praveen & Bansal, R.C. & Bettayeb, M. & Ghenai, C. & Naidoo, R.M., 2023. "Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index," Applied Energy, Elsevier, vol. 339(C).
    16. Cosmo, Valeria Di & O’Hora, Denis, 2017. "Nudging electricity consumption using TOU pricing and feedback: evidence from Irish households," Journal of Economic Psychology, Elsevier, vol. 61(C), pages 1-14.
    17. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    18. Wakiyama, Takako & Zusman, Eric, 2021. "The impact of electricity market reform and subnational climate policy on carbon dioxide emissions across the United States: A path analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    19. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    20. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.

    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:gam:jsusta:v:14:y:2022:i:11:p:6759-:d:829350. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.