  Traditional electrical power grids have long suffered from operational
unreliability, instability, inflexibility, and inefficiency. Smart grids (or
smart energy systems) continue to transform the energy sector with emerging
technologies, renewable energy sources, and other trends. Artificial
intelligence (AI) is being applied to smart energy systems to process massive
and complex data in this sector and make smart and timely decisions. However,
the lack of explainability and governability of AI is a major concern for
stakeholders hindering a fast uptake of AI in the energy sector. This paper
provides a review of AI explainability and governance in smart energy systems.
We collect 3,568 relevant papers from the Scopus database, automatically
discover 15 parameters or themes for AI governance in energy and elaborate the
research landscape by reviewing over 150 papers and providing temporal
progressions of the research. The methodology for discovering parameters or
themes is based on "deep journalism", our data-driven deep learning-based big
data analytics approach to automatically discover and analyse cross-sectional
multi-perspective information to enable better decision-making and develop
better instruments for governance. The findings show that research on AI
explainability in energy systems is segmented and narrowly focussed on a few AI
traits and energy system problems. This paper deepens our knowledge of AI
governance in energy and is expected to help governments, industry, academics,
energy prosumers, and other stakeholders to understand the landscape of AI in
the energy sector, leading to better design, operations, utilisation, and risk
management of energy systems.
