Artificial neural network approach to improve the performance of battery and thermal storage

Salah Dauga; Ibrahim Aldaouab

doi:10.1117/12.2556489

22 April 2020 Artificial neural network approach to improve the performance of battery and thermal storage

Salah Dauga, Ibrahim Aldaouab

Proceedings Volume 11382, Smart Structures and NDE for Industry 4.0, Smart Cities, and Energy Systems; 113820G (2020) https://doi.org/10.1117/12.2556489
Event: SPIE Smart Structures + Nondestructive Evaluation, 2020, Online Only

Abstract

Energy storage technology and its efficient deployment will be increasingly needed to manage intermittent of renewable energy supply. Large scale storage can support a power grid, as with pumped hydroelectric, or small-scale battery systems can be used to support rooftop PV within a single building. This work considers a microgrid which supplies renewable energy (solar PV) to a single building with both electric (battery) and thermal (hot-water) energy storage. The goal is to explore the potential benefit of using artificial neural network with model productive control algorithm to predict the load demand and energy supply in order to lower the cost of storage system to assist in managing renewable power fluctuations, which is appropriate when significant thermal loads are present. The building considered here contains of apartments, and hourly electrical consumption and thermal demand are developed from historical meter data. Renewable energy from a PV array is dispatched to the load or is stored for later use, and the microgrid performance is measured by the renewable energy penetration, renewable curtailment, and system cost over time. Modeling results indicate that predict the load demand supply lower the cost of the energy. Also, increasing renewable energy penetration and decrease renewable energy curtailment.

Conference Presentation

Citation Download Citation

Salah Dauga and Ibrahim Aldaouab "Artificial neural network approach to improve the performance of battery and thermal storage", Proc. SPIE 11382, Smart Structures and NDE for Industry 4.0, Smart Cities, and Energy Systems, 113820G (22 April 2020); https://doi.org/10.1117/12.2556489

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