Towards Pareto-optimal energy management in integrated energy systems: A multi-agent and multi-objective deep reinforcement learning approach

Dou, Jiaming; Wang, Xiaojun; Liu, Zhao; Sun, Qingkai; Wang, Xihao; He, Jinghan

doi:10.1016/j.ijepes.2024.110022

International Journal of Electrical Power & Energy Systems

2024

DOI: 10.1016/j.ijepes.2024.110022

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Towards Pareto-optimal energy management in integrated energy systems: A multi-agent and multi-objective deep reinforcement learning approach

Jiaming Dou,

Xiaojun Wang,

Zhao Liu

et al.

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Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

Abbas,

Wu,

Ali

2024

IET Generation Trans & Dist

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This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

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Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

Abbas,

Wu,

Ali

2024

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

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Towards Pareto-optimal energy management in integrated energy systems: A multi-agent and multi-objective deep reinforcement learning approach

Cited by 1 publication

References 35 publications

Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

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