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Optimal allocation for combined heat and power system with respect to maximum allowable capacity for reduced losses and improved voltage profile and reliability of microgrids considering loading condition

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  • Naderipour, Amirreza
  • Abdul-Malek, Zulkurnain
  • Nowdeh, Saber Arabi
  • Ramachandaramurthy, Vigna K.
  • Kalam, Akhtar
  • Guerrero, Josep M.

Abstract

This paper presents a method that uses particle swarm optimization to select the optimal allocation of a combined heat and power system that considers the maximum allowable capacity with the aim of reducing losses, improving the voltage profile and reliability of microgrids considering networks loading condition. Decision variables are optimal location and capacity of the combined heat and power systems. The location and maximum capacity of the combined heat and power system were specified in a way to reduce losses, improve the voltage profile, reliability improvement as energy not supplied reduction and maintain the operating constraints. The method is applied to 84- and 32-bus standard microgrids. Capability of the proposed method is proved in obtained results which demonstrated a significant enhancement in voltage profile and a decrease in power losses and customer’s energy not supplied as reliability improvement. Minimum microgrid losses can be achieved with considering these constraints. The power loss, minimum voltage and reliability is improved 43.9%, 3,4% and 80.31% for 84 bus network and 72%, 6.2% and 83.6% for 32 us network, respectively by optimal combined heat and power systems allocation. Also, the superiority of the particle swarm optimization is confirmed in comparison with the genetic algorithm.

Suggested Citation

  • Naderipour, Amirreza & Abdul-Malek, Zulkurnain & Nowdeh, Saber Arabi & Ramachandaramurthy, Vigna K. & Kalam, Akhtar & Guerrero, Josep M., 2020. "Optimal allocation for combined heat and power system with respect to maximum allowable capacity for reduced losses and improved voltage profile and reliability of microgrids considering loading condi," Energy, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220302310
    DOI: 10.1016/j.energy.2020.117124
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    3. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
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    5. Masoud Zahedi Vahid & Ziad M. Ali & Ebrahim Seifi Najmi & Abdollah Ahmadi & Foad H. Gandoman & Shady H. E. Abdel Aleem, 2021. "Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm," Energies, MDPI, vol. 14(16), pages 1-25, August.
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    7. Mohana Alanazi & Abdulaziz Alanazi & Ahmad Almadhor & Hafiz Tayyab Rauf, 2023. "An Improved Fick’s Law Algorithm Based on Dynamic Lens-Imaging Learning Strategy for Planning a Hybrid Wind/Battery Energy System in Distribution Network," Mathematics, MDPI, vol. 11(5), pages 1-30, March.
    8. Feng, Li & Liu, Jiajun & Lu, Haitao & Liu, Bingzhi & Chen, Yuning & Wu, Shenyu, 2022. "Robust operation of distribution network based on photovoltaic/wind energy resources in condition of COVID-19 pandemic considering deterministic and probabilistic approaches," Energy, Elsevier, vol. 261(PB).
    9. Mohsen Khalili & Touhid Poursheykh Aliasghari & Ebrahim Seifi Najmi & Almoataz Y. Abdelaziz & A. Abu-Siada & Saber Arabi Nowdeh, 2022. "Optimal Allocation of Distributed Thyristor Controlled Series Compensators in Power System Considering Overload, Voltage, and Losses with Reliability Effect," Energies, MDPI, vol. 15(20), pages 1-25, October.
    10. Masood, Nahid-Al- & Mahmud, Sajjad Uddin & Ansary, Md Nazmuddoha & Deeba, Shohana Rahman, 2022. "Improvement of system strength under high wind penetration: A techno-economic assessment using synchronous condenser and SVC," Energy, Elsevier, vol. 246(C).

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