Deep learning based optimal energy management framework for community energy storage system

This paper proposes a deep learning-based integrated framework for multiple cooperative households to achieve optimal energy distribution. The corresponding energy generation and consumption problems are formulated by a long short-term memory algorithm is combined with an optimization algorithm to produce an optimal solution. In this study, a PV-community energy storage system (CESS) integrated is considered where the scheduling decision of the CESS and utility grid can be subsequently achieved through formulated constraints. The test results demonstrate the efficacy and robustness of the proposed system that achieves superior performance on effective renewable energy usages of maximum 31.74% in a home environment. & COPY; 2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper proposes a deep learning-based integrated framework for multiple cooperative households to achieve optimal energy distribution. The energy generation and consumption problems are formulated using a long short-term memory algorithm combined with an optimization algorithm to produce an optimal solution. The proposed system, which considers a PV-community energy storage system (CESS), achieves superior performance on effective renewable energy usages of maximum 31.74% in a home environment.