Model Predictive Control Strategies in Microgrids: A Concise Revisit

Abstract

The world is rapidly integrating renewable energy resources into the existing grid systems. However, the unpredictable nature of renewables and uncertain load profiles cause issues such as poor power quality, lower system reliability, complex power management, battery degradation, high operating costs, and lower efficiency. Microgrids can help smart grid technology overcome several problems associated with renewable energy integration. Distant locations can obtain electricity without building extensive transmission infrastructure, cutting development costs, or transmission losses. The intermittent nature of renewable energy sources contributes to microgrid problems such as poor power quality, decreased reliability, and high operating costs. Model predictive control (MPC) is an effective method to address challenging industrial and scientific issues. Advancements in MPC that accept different system constraints have solved multiple concerns in uncertain microgrid systems. MPC applied to three hierarchal control layers in a microgrid resolves the problems of power quality, power sharing, energy management, and economic optimization. This study demonstrates that MPC microgrid control is suitable for low-cost operation, improved management, and reliable control. The shortcomings of recent model predictive control techniques for microgrids are reviewed, and future research directions for MPC microgrids are identified.INDEX TERMS Microgrids, renewable energy resources, model predictive control, power quality enhancement, energy management system, hybrid energy storage system, demand side management, demand response, distributed systems

Possibilities, Challenges, and Future Opportunities of Microgrids: A Review

“…One of the critical challenges of microgrid control is to ensure that the microgrid operates stably and efficiently, even in the presence of uncertainty and disturbances. This operation uses advanced control algorithms, such as model predictive control (MPC) and robust control [60].…”

“…Robust control is another approach to ensure microgrids' stability and efficiency in the presence of uncertainty and disturbances. Robust control algorithms are insensitive to slight variations in system parameters and are able to cope with unexpected disruptions [60]. Another critical aspect of microgrid control is the integration of renewable energy sources, such as solar and wind power, into the microgrid.…”

Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach

“…That is why the optimal allocation of DGs has been of keen interest to the researchers. If the losses of the system increase, which will be a critical issue in DISCOS, inappropriate allocation of DGs would cause more problems on the supply and demand side [27][28][29].…”

Design and Analysis of Input Capacitor in DC–DC Boost Converter for Photovoltaic-Based Systems

“…Now input resistance (R i ) can be easily changed by changing the duty cycle to match or equate optimal impedance at ambient conditions. There are a lot of methods for maximum power point tracker algorithm such as neural network, fuzzy logic control, ripple correlation control, a biological swarm chasing algorithm, hill climbing, conductance incremental, perturbation and observe (P&O), and look up Table [18,19]. To choose a suitable photovoltaic converter, some parameters should be considered.…”