3.2.4. Artificial Intelligent Methods

Artificial neural networks are examples of artificial methods. They are considered as stochastic methods, which could be used to solve optimization problems for system having random variables. For MG systems, RESs have a variable nature caused by the weather conditions, which affect the power generation. As example, the authors of [108] presented an expert system for EM in MG systems using neural networks in order to predict the power generation of the installed RESs. The authors of [109] proposed a mathematical model for a smart load management in a standalone MG system. The studied loads were modeled by neural networks, and a predictive control was used to manage the energy according to predicted load variation. The authors of [110] presented an EM system for an MG system connected to the utility grid with the main objective of maximizing the use of renewable energies while minimizing the carbon emission. Two neural networks were used to model the proposed EM system using evolutionary adaptive dynamic programming and learning concepts. For the deployed neural networks, one was used for the management strategy and the other was used to check the optimal system's performance. The authors of [111] used a neural network to control a bidirectional rectifier/inverter. A dynamic programming algorithm was implemented and was trained using back propagation through time. The deployed neural networks showed a high ability to trace rapidly changing reference commands for frequency and voltage and satisfied control requirements for a faulted power system. The neural network controller used in this work was performed and studied under typical vector control conditions. The authors of [112] proposed a Lagrange-programming neural networks method for an efficient control and management of MG system with the main objective to minimize the overall cost of MG. In this work, the load was classified into different categories of controllable load, thermal load, price sensitive load, and critical load, while variable neurons and Lagrange neurons were combined to obtain optimal scheduling of MG operation. Mainly, neural networks can control, optimize, and identify system's parameters in online or offline applications. Unlike the previous approaches, neural networks can solve problems with nonlinear data in largescale MG systems because of their ability to solve the system's stability via self-learning and prediction capabilities [113,114].

MAB control approaches are generally used in MGs because they are decentralized while allowing multiple interacting agents to follow their specified rules and goals and to

perform autonomously dedicated functions [115]. The principal element of MAB methods is the agent, which can be a virtual or physical entity situated in a specified system (e.g., buildings, MG). It is capable of autonomously reacting depending on the changes of the system's environment [42,116]. The authors of [117] applied a comprehensive description about different optimization techniques to EM and a comparison with other techniques was realized including MAB. The authors of [118] presented an EM based on the differential evolution algorithm, developed in JADE (Java Agent Development Environment) for grid outage. The proposed MAB approach showed its efficiency in minimizing the load's uncertainty as well as the generation costs from the intermittent nature of RES generation. The approach also considered the price variation in the utility grid, and the critical loads were considered while selecting the best solution. The authors of [119] proposed a fault-tolerant multi-agent control approach for coordinated energy and comfort management in integrated buildings and MG systems. Several cooperative agents were presented and trained in order to reach a global coordination, to satisfy related constraints, and to meet the system's objectives. The integrated buildings and MG systems were mathematically formulated as a multi-objective optimization problem, which was solved under different operating conditions. Other interesting research works, which have considered the MAB control approaches for EM in MG systems, are presented by the authors of [120–122]. Multi-agent systems offer the opportunity to implement more than basic control. They have three key features, namely reactive, proactive, and social abilities. From their characteristics, the agent technology is promising for the implementation of flexible, scalable, and distributed systems [123,124]. The usage of MAB method is rapidly growing in power systems, especially for EM in MG systems. MABs, combined with system modeling, make the arrangements of MG units autonomously directed making the scheme more intelligent and protective. The deployment of MAB control in the MG system considers each agent as an intelligent unit, which can communicate with their neighboring agents in a collaborative way to determine future control actions to achieve the common objective. The communication with neighboring agents requires the deployment of advanced ICTs in order to benefit from the advantage of such approaches.

Ant Colony Optimization (ACO) is one of the more commonly used methods for EM in MG systems due to its flexibility for specified constraints, low computational time and complexity, and ease of implementation. This classical method is inspired by the behavior of real ants to search for good solutions to a given optimization problem. It is a simple computational agent that converts the optimization problems into the problems of finding the shortest path on a weighted graph. The authors of [125] used an AOM method for EM in demand side management. The authors first designed an EM controller model using multiple knapsack problem and applied an ACO approach to obtain a viable solution for the designed objective function. By simulation. the authors attempted to justify that the ACO works efficiently in terms of electricity bill reduction and the minimization of peakto-average ratio while considering user satisfaction. Another ACO method was developed by the authors of [126], who investigated a combined cost optimization scheme in order to minimize both operational cost and emission levels while satisfying the MG's load demand. The proposed technique was compared with two other techniques, Lagrange and Gradient, to evaluate the proposed method performance. Mainly, other optimization methods based on AI have been used in the literature for EM and optimization problems. Particle Swarm Optimization was presented by the auhors of [127] for EM fuzzy controller design in dual source propelled electric vehicles. A systemic analysis of the power in energy storage was established by a mathematical model of EM problem.

Despite the efficiency of the abovementioned methods, still real-time and predictive control approaches are required for intelligent energy management in smart MG systems.

#### 3.2.5. Other Interesting Approaches

One of the more interesting approaches for EM is proactive control. The principal of this approach is a mixed-integer optimal control problem that can be presented as a

mixed-integer nonlinear programming problem [128]. The problem consists of finding optimal rules for a set of binary and continuous control variables that minimize the future predictable cost of the system over the time horizon. The proactive control is an "operationoriented measures" scheme that makes the system capable of dealing with the unfavorable condition for the system operation. The authors of [129] presented an MG proactive control approach to manage the adverse impacts of extreme windstorms. When alerts were received for the forecasted windstorm, the approach found a conservative schedule of MG with the minimum number of vulnerable branches in service while the total load was served. The conservative schedule ensured the MG normal operation prior to the windstorm while reducing the MG vulnerability at the event arrival. This method increased the benefits for generation reschedule, conservation voltage regulation, network reconfiguration, and optimal parameter settings of droop-controlled units. The authors of [130] discussed unified resilience evaluation and the operational enhancement approach, including a procedure for assessing the impact of severe weather on power systems. The proposed approach aimed to mitigate the cascading effects that may occur during weather emergencies. Another work, presented by the authors of [131], studied the installation of a battery energy storage system with a PV system in a hierarchical trans-active EM approach in order to reduce consumer's electricity bills. A cost-benefit analysis approach was developed for proactive houses which combined PV units and battery storage systems. The developed control algorithm controlled the charge/discharge cycle of the battery based on an economic benefit analysis in real-time electricity rate and battery cost to give an exact idea of returns and yearly savings to consumers on their investment. The performance of this method can be enhanced when a proactive system is managed using predictive approaches. The authors of [101] compared reactive feedback control and Model Predictive Control in terms of energy consumed, energy error, and management effort for a given data center. The work proposed a feedback control strategy based on the data center model in order to optimize the quality of service, the energy consumed, and the management effort. It is perceived from the literature that the concept of proactive control for energy management in MG systems is rarely used. The concept is very interesting for control-based predictive decisions. Due to the development of information and communication technologies, especially microcontrollers, proactive control can be improved in future researches for EM in MG systems. The method is capable of making the system more preferment with the existing disturbances system operation.

Another interesting control approach is the FL. Like neural networks, the FL method is considered as one of the nonlinear techniques that are used for power regulation with power electronics-based converters. This intelligent control consists of a fuzzifier, rule evaluator, and a defuzzifier, while a set of rules known as rule-based and database is considered for the control strategy deployment. Mainly, the FL method is used to control space vector PWM based three-phase rectifier and is used with intelligent techniques-based Droop-Control to manage multiple distributed energy DC-MG systems [132]. For instance, the authors of [133] proposed a voltage control technic using an FL-based centralized controller with gain scheduling control for DC-MG with an electric-double-layer-capacitor as energy storage. A fuzzy-based control strategy, proposed by the authors of [134,135], is capable of determining small voltage and frequency steps regulations to improve the performance of Droop-Control by diminishing the mismatch in the common bus without heavy communication links. This work considered the frequency and voltage as uncoupled variables and then corrected each one separately by considering that the voltage is a local variable and the frequency is a global variable of the system. The proposed fuzzy method changed the frequency and the voltage reference value in the droop equation of the Voltage Sources Inverters to correct its variation. The authors of [102] used FL and a metaheuristic algorithm known as Grey-Wolf optimization to optimize the interconnection between multiple MG systems. The main aims of this method were to minimize both the costs for the generator units and the emission levels of the fossil fuel sources. Several works have studied the use of FL for energy management in MG systems. The authors of [136]

deployed a mode transition strategy to smooth the mode variation and a fuzzy controller was used to determine the operation mode of coupled MG system with 20 different gridconnected and standalone MG systems. The FL was also considered as a deterministic algorithm for frequency and voltage regulation in both primary and secondary control levels and was characterized by low computational cost and easiness of implementation. In the literature, FL is the most deterministic approaches used together with PI controller. Some FL methods can be classified as AI methods.

## **4. Comparison of Control Approaches for MG Systems**

The choice of an EM approach is an essential requirement for the reliable and stable operation for MG system. Depending on the characteristics of the deployed system (e.g., topologies, operation modes, structure), an EM can be selected. However, the deployment of an approach does not signify that the others are not reliable, and the studied constraints and the fixed objective of the control strategy are the main issue in order to identify the utility of the deployed method. In the rest of this section, the advantages and the disadvantages of different control techniques are presented (see Table 4).


**Table 4.** Brief comparison of control approaches.


**Table 4.** *Cont.*

A good approach must consider the stochastic nature of different control parameters, the installation cost, the components lifetime, the distributed resources, and the reliable and safety operation of the MG system. In fact, the deployment of an EM control strategy requires the classification of the whole system into different levels, while each level should operate by coordinating with the other levels from the sources (e.g., maximum power point tracking) to the end consumers, which can be a local consumer or a neighboring MG consumer. Nowadays, smart components are installed for each source and for each MG system, which can cooperate between them due to the new ICTs. Especially, the actual inverters can execute different control strategies from the source power regulation to the interconnectivity to the utility grid or to the neighboring MG. In addition, the inverters can be installed for a large scale of MG systems, creating a cluster of data and electricity exchange, while these inverters could be connected to the internet in order to store the historic data in the cloud. Mainly, the main objective function for each inverter is ensuring continuous power supply to the consumers without considering the lifetime of the battery storage system or the cost of electricity. In this context, the development of an EM control strategy that considers the electricity price variation and minimizes the battery C/D cycle is required. These two issues allow the maximization of the system profitability by minimizing the electricity bill and avoiding a frequent replacement of battery storage in a MG system. The main idea is to develop an intelligent and predictive control strategy that can optimally control the distributed resources in the MG by considering multiple constraints and objective functions at the same time.
