Having neither precise definition nor a commonly accepted scope, the term “MicroGrid” tends to be used differently across researchers and practitioners alike. The management of energy usage within a microgrid is one of the topics that was handled from numerous perspectives. This study presents systematic literature review (SLR) of research on architectures and energy management techniques for microgrids, providing an aggregated up-to-date catalogue of solutions suggested by the scientific community. The SLR incorporated 45 papers selected according to inclusion/exclusion criteria and defined a priori. The selection process was based on an automated search and covered three known digital libraries. The extraction process covers three main questions. (i) The architectures of microgrids including their components, their bus configuration, and the adopted utility grid policy. (ii) The employed methods to ensure an optimal usage of energy under uncertainty. (iii) The confronted challenges and constraints of the suggested strategies. The findings of this SLR indicate a great diversity of methods and a rich background. Finally, the SLR suggests that future research should take into account the uncertainty aspect relating to energy management rather than the direct use of historical data as it is commonly done in most research papers. A sensitivity analysis should be provided in the latter case.

Smart grid is designed to overcome the current power grid inefficiency, and to compensate the exhaustible fuelfossil based energy by the use of the renewable energy resources. While working in an ON/OFF-grid switching mode, the system work normally in an `' mode that allows the exchange of the energy flow between the provider and the consumer. Thus, when a fault occurred in the grid, the home energy management system switches to the off-grid mode in which the user consumes what it produces, and in the case of insufficiency low-priority loads are shifted. This paper proposes a strategy that manages the local energy during the isolation period and distributes it efficiently among the load in a way that the critical loads get maximum energy security. The strategy consists of a shifting/shedding algorithm that keeps the total household appliances below a defined threshold, allows load flexibility, gives the customer the ability to control the home appliances, and takes account of his preferences.

One of the challenges that smart Microgrid should face is to rely mostly on renewable energy sources and ensure the balance of the usage of heterogeneous energy sources. But, due to its uncertain and intermittent character, the renewable sources cannot be predictable, and the balancing task becomes less evident to apply. In this paper, we propose a new home energy management strategy to ensure a balanced use of multiple energy sources, while respecting the main requirements of the smart Microgrid. Our proposed stochastic model takes into account the decrease of energy bill, the reduction of power consumption during peak hours, the proposal of an effective scheduling of charging/discharging cycles of battery, and the ensuring of the user's comfort. A Markov decision process (MDP) is used to derive the optimal strategy illustrated through simplified simulations.

The microgrid is a new concept of integrating the distributed energy resources (DER) within the grid. The management of the heterogeneous sources of energy presents a challenge, especially as most of the DER are unpredictable. Besides, implementing microgrids should be economically beneficial to the customer; this will raise the challenge of decreasing the costs while ensuring the energy balance. In this paper, we used a stochastic approach based on a model‐free Markov decision process (MDP) to derive the optimal strategy for the home energy management system. The approach aims to decrease the energy bill while taking into account the intermittency of the renewable energy resources (DER) and other constraints. While other proposals charge the battery from the utility energy, making the state of charge (SOC) of the battery a deterministic variable, our work adopts a scenario where the battery is charged from the excess of the generated energy, which makes the SOC a nondeterministic variable affected by the uncertain character of the renewable energy. Therefore, our model considers the randomness at two levels: renewable energy level and battery SOC level. We take into account the complexity of the solution, and we propose a simple strategy that can be implemented easily in microgrids.