Optimal Balancing and Control of a Dynamic Load Demand in a Grid Connected Hybrid System using Feed–in Tariff Approach

In this paper, we have proposed, designed and simulated a grid connected residential smart home system benefiting from hybrid energy system (HES) for optimal balancing and control of a dynamic load using Feed–in Tariffs (FiT) tariffs by increasing the onsite self–consumption of solar PV system. To a...

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Veröffentlicht in:3C tecnología 2019-05, Vol.8 (1), p.350-365
Hauptverfasser: Qazi, Abdullah, Halepoto, Irfan Ahmed, Kazi, Kamran
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Sprache:eng
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Zusammenfassung:In this paper, we have proposed, designed and simulated a grid connected residential smart home system benefiting from hybrid energy system (HES) for optimal balancing and control of a dynamic load using Feed–in Tariffs (FiT) tariffs by increasing the onsite self–consumption of solar PV system. To achieve the object function, two different PV system configuration scenarios were designed, simulated and analyzed for different case studies. In first scenario, PV system without battery backup storage system (BBSS) under FiT scheme with flat tariffs was investigated for a typical day of winter and summer. In second scenario, PV system with BBSS under FiT scheme with ToU tariffs was investigated again for a typical day of winter and summer. The optimal balancing and dynamic load control can be achieved by increasing the penetration level of solar PV by maximizing the onsite self–consumption of PV system, minimizing the grid electricity import in high ToU tariff periods, charging the battery during low ToU tariffs, discharging the battery during high ToU tariffs and exporting the excessive PV generation in peak PV generation periods especially in summer season. The simulated results suggest that significant revenue can be saved, and optimal balancing and control can be achieved when PV system is configured with BBSS under FiT scheme by utilizing ToU tariffs. The proposed will be more cost effective and optimal control can be achieved if implemented to residential community microgrids by using cooperative game theory approach.
ISSN:2254-4143
2254-4143
DOI:10.17993/3ctecno.2019.specialissue2.350-365