A review of the modern optimization algorithms in smart grid concept applications

Abstract

Optimization algorithms are essential for the efficient design and operation of smart grids (SGs), as they address critical challenges such as resource allocation, load balancing, and the integration of renewable energy sources and others. This chapter focuses on modern optimization techniques and their growing relevance in enhancing SG performance amidst increasing complexities and uncertainties. A systematic review was conducted to explore state-of-the-art algorithms, including heuristic, metaheuristic, and machine learning-based approaches, alongside their applications in various SG scenarios. The chapter provides key insights into current trends, performance benchmarks, and the suitability of different algorithms for specific use cases. The findings reveal the potential of hybrid optimization methods to address multi-objective challenges, such as minimizing operational costs while ensuring grid reliability and sustainability. Based on these insights, the chapter offers strategic recommendations for future research, emphasizing the need for robust, scalable, and adaptive algorithms to support the evolving requirements of SG systems.