The rising demand for energy requires high investments in network extensions and renewable sources, alongside replacing inefficient systems. Smart maintenance is important in minimizing unscheduled outages, reducing costs, improving network security, and increasing equipment's life expectancy. The vast amount of data collected by sensors and measurements in energy networks makes it hard for humans to detect failures continuously. Thanks to recent breakthroughs in AI, the energy sector has boosted the use of intelligent algorithms in this field. Despite the widespread popularity and great results of machine learning (ML) models in many applications, they are mostly nevertheless considered "black boxes" as understanding their functionality and transparency in real-world applications is challenging. Explainable Artificial Intelligence (XAI) tackles this by making AI systems' decision-making processes transparent and interpretable. This review paper will not only make the roadmap clear but also ensure an in-depth awareness of the challenges, opportunities, and developments associated with this path by presenting two comprehensive taxonomies. Various XAI methods are compared; as an example, our findings show that SHAP offers high trustworthiness but is less suited for real-time use, while LIME provides faster solutions with lower trustworthiness. To the best of the authors' knowledge, this is the first survey that provides an overview of XAI methods for energy systems maintenance (ESM). It addresses challenges like integrating XAI with IoT-powered digital twins, balancing explainability with cybersecurity, and ensuring scalability while proposing solutions to enhance reliability and efficiency.