Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Niknami, Ahmad; Tolou Askari, Mohammad; Amir Ahmadi, Meysam; Babaei Nik, Majid; Samiei Moghaddam, Mahmoud

doi:10.1016/j.clet.2024.100763

Cleaner Engineering and Technology

2024

DOI: 10.1016/j.clet.2024.100763

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Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Ahmad Niknami,

Mohammad Tolou Askari,

Meysam Amir Ahmadi

et al.

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Cited by 3 publications

References 26 publications

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Multiple microgrids with electric vehicle charging in a hybrid GJO-PCGAN approach for energy management

Rangasamy,

Prakash,

Sakhare

et al. 2025

Electr Eng

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Multiple microgrids with electric vehicle charging in a hybrid GJO-PCGAN approach for energy management

Rangasamy,

Prakash,

Sakhare

et al. 2025

Electr Eng

View full text Add to dashboard Cite

Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency

Arévalo,

Ochoa-Correa,

Villa-Ávila

2024

Electronics

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Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.

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Microgrid Optimization Strategy for Charging and Swapping Power Stations with New Energy Based on Multi-Agent Reinforcement Learning

Sun,

Duan,

Yang

2024

Sustainability

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Aiming at the coordinated control of charging and swapping loads in complex environments, this research proposes an optimization strategy for microgrids with new energy charging and swapping stations based on adaptive multi-agent reinforcement learning. First, a microgrid model including charging and swapping loads, photovoltaic power generation, and wind power generation was constructed, and the Markov decision process was used to characterize the stochastic characteristics of new energy power generation, including charging and swapping loads. The deep relationship between uncertainty factors and charging and swapping laws was explored, and an adaptive multi-agent deep reinforcement learning method was used to optimize the random action selection process, improve the convergence speed of the coordinated optimization model, and realize coordinated control of multiple charging and swapping loads. Finally, through the analysis of different scenarios, the effectiveness of the proposed adaptive multi-agent reinforcement learning model for coordinated control of charging and swapping loads was verified. The results show that the proposed method has a faster convergence speed and can effectively optimize the charging process of charging and swapping loads, reducing power fluctuations of the newly connected energy grid.

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Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Cited by 3 publications

References 26 publications

Multiple microgrids with electric vehicle charging in a hybrid GJO-PCGAN approach for energy management

Multiple microgrids with electric vehicle charging in a hybrid GJO-PCGAN approach for energy management

Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency

Microgrid Optimization Strategy for Charging and Swapping Power Stations with New Energy Based on Multi-Agent Reinforcement Learning

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