## 2 Energy Management and Review of Surveys
### Challenging Issues  
Based on the literature, the most important challenging issues in EM of MGs are categorized as follows:  
* Optimization of the large-scale problems of MMGs [8].
* Diverse consumers [8].
* Cyber-secure transactions [8].
* Multi-carrier energy interactions [1,8].
* Multi-energy trading mechanisms [8].
* Forecast both power and load demand in MGs [9].
* The time-varying topologies of MGs [9].
* Modeling the spatial correlation between different renewable power generation sources [10].
* Appropriate modeling of uncertain parameters such as renewable resources, load demand, and electricity price [10,14].
* Modeling the active power trading and sharing in IMMGs [10].
* Applied research on xEVs for worldwide deployments, including the EVs speed, the required standards, the connectivity of the EVs with variable-source-based MGs, and the departure and arrival pattern of xEVs [14].
* Variable market prices [14].
* Need for addressing some well-known problems in traditional power systems in MGs, such as the profit-based unit commitment problem (PBUCP) [40]
* Further investigation of the cost-effective EM techniques for SMG networks [17].
* MG controls at the global MG level: islanding detection, re-synchronization with the upstream network, power quality management, protection from internal faults, and planning new MGs [11].  
* MG controls at MG components level: need the critical ESS components for periodic maintenance, minimizing generation fluctuations, communication between nodes and control agents, and limitations in plug-and-play capabilities of different components [11].
* Highly automated and intelligent control systems [11].
* Using machine learning to handle the large amount of front-end data produced by MGs and ESSs [11].
* Manufacturing efficient super-capacitors, batteries, and fuel cells [11; 12].
* Customer confidentiality regulations [12].
* Reliability studies in islanded modes [12].
* Management of communication systems [12].
* Demand response integration [12].
* Further investigations on the effect of the conventional grid on greenhouse gas emissions [12].
* Minimizing the operational cost [15; 16].
* Maintaining or improving reliability and stability [15; 16].
* Reducing power system fluctuations (voltage or frequency) [15].
* Monitoring: the need for real-time semantic features for EM, optimal sensor location strategies, and an occupant detection system [18].
* Analysis: the need for semi-supervised approaches for classification of the loads, consumers, multivariate forecasting time series approaches, and fault detection methods [18].
* Management and decision making: the need for dynamic, adaptive, and distributed control schemes, and smart real-time energy consumption scheduling [18].
* Maximizing the utilization of green sources [16].
* Reduce the use of utility power [16].
* Maximizing the system efficiency [16].
* Different optimization issues in the operation of multi-carrier energy systems, such as integrated optimal power and gas flow (IOPGF) problems [41; 42].
* Modeling multiple contingencies [1].
* Load curtailment costs related to different operating conditions [1].
* Constraint modeling, such as topology and stability [1].
* Modeling the impact of the market practice on reactive power costs [1].
* Applying novel and comprehensive methods mainly based on heuristic algorithms [1].
* Comprehensive analysis software [1].
* New objective functions linked to the storage devices allocation problems [1].
* Issues and challenges for implementing an interoperable smart microgrid (ISM), including: alerts and alarms, bandwidth, channel analysis, data fluctuations, data privacy/security, data rate, distance coverage, energy efficiency, latency, link budget, link failures, network topology, node placement, power consumption, receiver sensitivity, scalability, spectrum usage, standards applicability, system cost, system migration, technology access, throughput, and typical framework [34].