Japan's electricity demand is projected to grow by up to 40% from the 2019 level in 2050, if the wider use of generative artificial intelligence spurs the construction of more data centers, an industry organization said Wednesday. The Organization for Cross-Regional Coordination of Transmission Operators, which coordinates electricity supply and demand across Japan, warned in a report that supply shortages may occur even if nuclear power reactors and aging thermal power plants are rebuilt. The organization, which comprises power utilities nationwide, suggested several scenarios for electricity supply and demand in 2040 and 2050. According to the report, power demand is estimated to rise to between 900 billion and 1.1 trillion kilowatt-hours in 2040 and between 950 billion and 1.25 trillion kilowatt-hours in 2050, higher than the 2019 demand of 880 billion kilowatt-hours. Even if power companies make significant progress in replacing their nuclear and thermal power plants with newer models, the country's electricity supply is expected to fall short of demand by up to 23 million kilowatts in 2050.

Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.

Across the globe, energy systems are changing, creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, large fossil fuelled power stations are being replaced by increasing levels of widely distributed wind and solar generation. This renewable power is clean and free at the point of use but it cannot always be relied upon. To date National Grid has managed this intermittency by keeping polluting power stations online to make up the difference but Artificial Intelligence offers an alternative approach. What's needed is a smart grid which can integrate renewable energy efficiently at scale without having to keep polluting power stations online to manage intermittency.