A storage system is a key component of a microgrid. Over the last few years, research has been undertaken to determine optimal management of microgrid resources. Battery storage has a significant impact on the total operational cost as the lifetime of the battery reduces during charging and discharging cycles. In this paper, we propose optimal energy management of a community microgrid in which the cost function includes the degradation cost of the battery and a dynamic penalty to reflect the true operational cost. Particle swarm optimisation (PSO) is used to determine the battery control actions for real-time energy management. Several case studies are presented to demonstrate the effectiveness of the proposed framework in which the new cost function reduces electricity cost by up to 44.50% compared to a baseline method and 37.16% compared to another existing approach.

Energy management of community microgrids considering degradation cost of battery / Hossain, Md Alamgir; Pota, Hemanshu Roy; Squartini, Stefano; Zaman, Forhad; Muttaqi, Kashem M.. - In: JOURNAL OF ENERGY STORAGE. - ISSN 2352-152X. - ELETTRONICO. - 22:(2018), pp. 257-269. [10.1016/j.est.2018.12.021]

Energy management of community microgrids considering degradation cost of battery

Squartini, Stefano;
2018-01-01

Abstract

A storage system is a key component of a microgrid. Over the last few years, research has been undertaken to determine optimal management of microgrid resources. Battery storage has a significant impact on the total operational cost as the lifetime of the battery reduces during charging and discharging cycles. In this paper, we propose optimal energy management of a community microgrid in which the cost function includes the degradation cost of the battery and a dynamic penalty to reflect the true operational cost. Particle swarm optimisation (PSO) is used to determine the battery control actions for real-time energy management. Several case studies are presented to demonstrate the effectiveness of the proposed framework in which the new cost function reduces electricity cost by up to 44.50% compared to a baseline method and 37.16% compared to another existing approach.
2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/264612
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