A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques

Abstract

The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy market. The main reason to integrate EVs is the growing hazards of climate change due to carbon emissions. Moreover, this paper sheds light on the growing importance of artificial intelligence (AI) in the technological realm and its incorporation into electrical systems with the notion of strengthening existing smart grid technologies and to handle the uncertainties in load management. This paper also delineates the different methodologies to effectively mitigate the probability of facing cyber-attacks and to make the smart grids invulnerable.

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

“…In particular, the implementation of an FLC with respect to a classical ANN-based controller allows for a reduction in the Total Harmonic Distortion index on both grid and load currents below 5%, thus complying with the IEEE-519 standard [56] and being able to reach the maximum possible output power [45]. From this perspective, AI is mainly exploited to optimize energy flows [25,57]. As a general framework, the use of AI in smart grids is needed to harmonize the different loads that can generate demand and, thus, to set up demand response strategies.…”

“…Moreover, Ref. [25] emphasizes the importance of optimal planning and control algorithms for Electric Vehicle Charging The growth in the usage of EVs and electric mobility is driving the market for numerous ESSs [3]. Therefore, electric and electrochemical devices are employed to fully realize electric motion, given their high efficiency and adaptability to different conditions [4].…”

“…The latter remarks are still valid for what concerns the coupling between RESs and EV charging [25]. The importance of protection against cyberattacks is a universal concern that permeates all discussed topics.…”

“…Moreover, Ref. [25] emphasizes the importance of optimal planning and control algorithms for Electric Vehicle Charging Stations, focusing on system configurations, energy management, and advanced control issues. It highlights the potential benefits of hybrid designs and portable energy storage systems for enhancing flexibility and profitability in grid-tied EV charging station networks.…”

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

“…Refs. [33][34][35] introduced an LSTM-based energy management system for MGs, achieving superior performance in predicting power consumption and generation and enhancing the stability and reliability of the MG. GRUs have gained attention in recent years due to their simpler architecture and efficiency in training. Refs.…”