Appraisal of viable optimization techniques for efficient design and control planning of renewable energy-based microgrid systems

Abstract

Due to the rapid increase in energy demand and technological improvements, a noteworthy shift has been witnessed in the energy sector through the implementation of the distributed energy systems concept. At the hub of these changes are renewable energy sources, which have risen to be the substitute solution for meeting the proliferating energy demand while mitigating the effects of climate change and contributing to socioeconomic growth. Contrary to the conventional approach, these distributed energy systems are integrated using microgrid systems, and this sophisticated technological solution necessitates the management of information exchange and power scheduling between the dispersed generating sources, customer loads, and the primary grid infrastructure. Thus, there is a need for improvement in the optimal planning and control of microgrid operation regarding computational efficiency, reliability of the obtained solution, and algorithm robustness. In addition to comparing various MG optimization problem formulations and solution approaches using multiple optimization algorithms, this paper reviews various optimization techniques used in renewable energy-based microgrid systems design and control planning. The paper evaluates particle swam optimization and mixed integer linear programming capacity for microgrid planning, considering computational efficiency, solution dependability, and algorithm resilience while providing credible information on the capabilities of parallel computing for robust optimal of high renewable microgrid design considering uncertainties and energy market dynamics. • Reviewed optimal renewable energy-based microgrid design and planning methods. • Compared the classical and heuristic techniques for MG optimization problem solutions. • Hybridized approach to improve computational efficiency with PSO and MILP algorithms. • Discussed parallel computing capabilities for effective microgrid operation planning.