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

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 load...

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Veröffentlicht in:Energy (Oxford) 2020-04, Vol.196, p.117124, Article 117124
Hauptverfasser: Naderipour, Amirreza, Abdul-Malek, Zulkurnain, Nowdeh, Saber Arabi, Ramachandaramurthy, Vigna K., Kalam, Akhtar, Guerrero, Josep M.
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Sprache:eng
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Zusammenfassung: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.
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2020.117124