Abstract
Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and islanded modes. Energy storage systems play a critical role in maintaining the frequency and voltage stability of an islanded microgrid. As a result, several energy management systems techniques have been proposed. This paper introduces a microgrid system, an overview of local control in a microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We designed a microgrid consisting of renewable sources, Li-ion batteries, the main grid as a backup system, and AC/DC loads. The proposed system control was based on supplying loads as efficiently as possible using renewable energy sources and monitoring the battery’s state of charge. The simulation results using MATLAB Simulink demonstrate the performance of the three proposed microgrid stability strategies (PID, artificial neural network, and fuzzy logic). The comparison results confirmed the viability and effectiveness of the proposed technique for energy management in a microgrid which is based on fuzzy logic controllers.
Highlights
Smart grid principles that are fundamentally new will be required in future power systems
The primary goal of a microgrid system is to meet load demand by prioritizing the energy produced by renewable sources over energy supplied by auxiliary sources, such as those powered by diesel [1]
We provide an overview of the control techniques used for energy management, voltage, and frequency control in the microgrid used in this paper
Summary
Smart grid principles that are fundamentally new will be required in future power systems. An energy management system is required in a microgrid microgrids’ stability and safety, only microgrid (MG) construction and control approaches for DG are required [5]. A leader controls each entity separately based on local data and communication with other local controls for global control This control method has the advantage of boosting the microgrid’s reliability by making all units independent from each another and responsible for their voltage and frequency management. In MG applications, the section shows how to use local control for energy management in a hybrid microgrid to keep voltage and frequency within acceptable limits. According to the above discussion, this paper proposes advanced microgrid modeling, a decentralized approach for smart energy management, and efficient voltage control using smart techniques based on intelligent algorithms.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
