Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers

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.

Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid

“…The ANFIS control structure utilizes the Sugeno-type fuzzy systems [22]. The structure consists of a collection of units organized into five interconnected layers [23], [24] that are tuned automatically by the hybrid learning algorithm.…”

“…The structure consists of a collection of units organized into five interconnected layers [23], [24] that are tuned automatically by the hybrid learning algorithm. The significance of each layer and detailed operation of the 2-input-1-output ANFIS structure is presented in [22]. The flowchart of Figure 4 shows the ANFIS-PID algorithm.…”

“…The system advisor model [20] presented a platform to access technical details of actual microgrid components, while Kumar [21] introduced battery charging and discharging dynamics. Research by Sumarmad et al [22] proposed proportional plus integral plus derivative (PID), fuzzy logic control (FLC), and artificial neural network (ANN) algorithms for voltage regulation in a hybrid microgrid system. The MPPT controller is based on the P&O method.…”

A New Type-3 Fuzzy PID for Energy Management in Microgrids

“…The developed genetic algorithm (GA) by the use of nondominated sorting approach is suggested in [11], to design a frequency regulator. The application of FLSs in designing of voltage controller is studied in [12]. The particle swarm optimization (PSO) is used in [13] for optimization, and it is examined on an inverter-based MG.…”

A brief review on hardware structure of AC electric spring

“…For different energy storage devices, the compensation range of ES is also different. The use of energy management system [19] and use of controller such as PID, Fuzzy, and ANN with ES is used to reduce voltage fluctuation [24].…”