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A Comprehensive Review on Evolution of Energy Management Systems in Microgrids: From Traditional Optimization to AI-Driven and Decentralized Architectures

Sinchana P.*, Ankaliki S. G.**, Manjula S. Sureban***, Rangappa Batakurki****

*-**** Shri Dharmasthala Manjunatheshwara College of Engineering and Technology, Dharwad, Karnataka, India.

Periodicity:April - June'2025

Abstract

The transformation of energy management systems (EMS) in microgrids has evolved from traditional optimization techniques to intelligent, decentralized architectures driven by artificial intelligence (AI). This review work highlights the limitations of conventional programming methods such as linear, nonlinear, and mixed-integer models in handling the variability of renewable sources. Modern AI techniques, including deep reinforcement learning, bidirectional long short- term memory networks, and fuzzy logic controllers, provide real-time adaptability and predictive accuracy. Additionally, blockchain-based peer-to-peer (P2P) energy trading introduces secure, transparent, and autonomous coordination among distributed agents. Through a comparative analysis, this work underscores the benefits, challenges, and integration potential of these approaches, advocating for hybrid AI and optimization frameworks to enable resilient, efficient, and user-centric microgrid operations.

Keywords

Energy Management, Microgrids, Power Resilience, Energy Optimization, Blockchain, Grid Stability.

How to Cite this Article?

Sinchana, P., Ankaliki, S. G., Sureban, M. S. and Batakurki, R. (2025). A Comprehensive Review on Evolution of Energy Management Systems in Microgrids: From Traditional Optimization to AI-Driven and Decentralized Architectures. i-manager’s Journal on Power Systems Engineering, 13(1), 23-33.

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	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	15	15	200	15
Pdf & Online	35	35	400	25

A Comprehensive Review on Evolution of Energy Management Systems in Microgrids: From Traditional Optimization to AI-Driven and Decentralized Architectures

Abstract

Keywords

How to Cite this Article?

References

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