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Multi-Agent Based Optimal Operation of Hybrid Energy Sources Coupled with Demand Response Programs

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  • Tope Roseline Olorunfemi

    (Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

  • Nnamdi I. Nwulu

    (Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

Electricity is an indispensable commodity on which both urban and rural regions heavily rely. Rural areas where the main grid cannot reach make use of distributed energy resources (DER), especially renewable energy sources (RES), in an islanded microgrid. Therefore, it is necessary to make sure there is a sufficient power supply to balance the demand and supply curve and meet people’s demands. The work done in this paper aims to minimize the daily operating cost of the hybrid microgrid while incorporating a demand response strategy built on an incentive-based demand response (IBDR) model. Three case studies were constructed and analyzed to derive the best, most reduced daily operational cost. This was achieved using the CPLEX solver embedded in algebraic modeling language in the Advanced Interactive Multidimensional Modeling Systems (AIMMS) software with multi-agent system (MAS); the MAS was used to make sure that the developed intelligent-based agents work independently to achieve an optimal microgrid system. The sensitivity analysis employed established that case study 2 gave the most reduced daily operation cost (USD 119), which represents an 8% reduction in the daily operational cost from case study 1 and a 9% reduction from case study 3. Then, we achieved 17% and 25% reductions, as compared to specific other approaches.

Suggested Citation

  • Tope Roseline Olorunfemi & Nnamdi I. Nwulu, 2021. "Multi-Agent Based Optimal Operation of Hybrid Energy Sources Coupled with Demand Response Programs," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7756-:d:592601
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    References listed on IDEAS

    as
    1. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    2. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    3. Zhanle Wang & Raman Paranjape & Zhikun Chen & Kai Zeng, 2019. "Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing," Energies, MDPI, vol. 12(15), pages 1-15, July.
    4. Jimyung Kang & Jee-Hyong Lee, 2017. "Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers," Energies, MDPI, vol. 10(10), pages 1-17, October.
    5. 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.
    6. Maheshwari, Arpit & Paterakis, Nikolaos G. & Santarelli, Massimo & Gibescu, Madeleine, 2020. "Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model," Applied Energy, Elsevier, vol. 261(C).
    7. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
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