###### Abstract  
Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also exposes smart grids to various cybersecurity threats and attacks. This article provides a comprehensive exploration of cyberattacks and cybersecurity in smart grids, focusing on critical components and applications. It examines various cyberattack types and their implications on smart grids, backed by real-world case studies and quantitative models. To select optimal cybersecurity options, the study proposes a multi-criteria decision-making (MCDM) approach using the analytical hierarchy process (AHP). Additionally, the integration of artificial intelligence (AI) techniques in smart-grid security is examined, highlighting the potential benefits and challenges. Overall, the findings suggest that \"security effectiveness\" holds the highest importance, followed by \"cost-effectiveness\", \"scalability\", and \"Integration and compatibility\", while other criteria (i.e., \"performance impact\", \"manageability and usability\", \"compliance and regulatory requirements\", \"resilience and redundancy\", \"vendor support and collaboration\", and \"future readiness\") contribute to the evaluation but have relatively lower weights. Alternatives such as \"access control and authentication\" and \"security information and event management\" with high weighted sums are crucial for enhancing cybersecurity in smart grids, while alternatives such as \"compliance and regulatory requirements\" and \"encryption\" have lower weighted sums but still provide value in their respective criteria. We also find that \"deep learning\" emerges as the most effective AI technique for enhancing cybersecurity in smart grids, followed by \"hybrid approaches\", \"Bayesian networks\", \"swarm intelligence\", and \"machine learning\", while \"fuzzy logic\", \"natural language processing\", \"expert systems\", and \"genetic algorithms\" exhibit lower effectiveness in addressing smart-grid cybersecurity. The article discusses the benefits and drawbacks of MCDM-AHP, proposes enhancements for its use in smart-grid cybersecurity, and suggests exploring alternative MCDM techniques for evaluating security options in smart grids. The approach aids decision-makers in the smart-grid field to make informed cybersecurity choices and optimize resource allocation.  
a 2023 20  
of cyberattacks on smart grids, a comprehensive understanding of their components, vulnerabilities, and potential impacts becomes imperative. This research endeavor seeks to address this pressing concern by delving into multifaceted dimensions. It aims to unravel the intricate interplay between smart-grid components and cyber vulnerabilities, analyze the diverse spectrum of cyber threats and their short-term and long-term consequences, investigate cascading effects on grid components, draw insights from real-world case studies, and develop quantitative models to assess cyberattack impacts. By exploring the techno-economic-safety-social implications of cybersecurity measures and delving into the application of artificial intelligence (AI) techniques, this study aspires to contribute valuable insights to the field of smart-grid cybersecurity, ultimately fortifying the resilience of critical energy infrastructure.