Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently.

The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of a predictive energy management system (PEMS) for the management of thermal resources and users’ thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional–integral–derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.

In recent years, the penetration of renewable energy sources into the power distribution system has increased significantly. Power generation has evolved from centralized power plants towards a decentralized structure. In this context, microgrids, i.e., small-scale power distribution grids, are being deployed to support distributed generation. Microgrids encounter different challenges as they have to be resilient, reliable, robust, and capable of ensuring power quality and handling intermittent energy sources and new energy usages. This paper focuses on the predictive management of microgrids equipped with a bank of batteries and a fleet of electric vehicles. The reference strategy is rule-based. Two model predictive control (MPC) based strategies - in the first one, all the batteries are managed as a unique (fictitious) battery whereas in the second one, those batteries are managed independently – are discussed and evaluated in simulation. First, the results highlight the benefits of using a predictive strategy when it comes to efficiently manage electricity storage and release in microgrids equipped with electric vehicles. As an interesting result, the strategy proposed to predictively manage the batteries as a unique (fictitious) battery is a litte bit less efficient regarding economical cost and carbon dioxide emissions reduction than the predictive strategy intending to manage the batteries independently but computation time is significantly lower with a large fleet of electric vehicles.