The Impact of Artificial Intelligence Technology on the Development of New Energy

Abstract

Artificial intelligence is quietly reshaping the landscape of the new energy sector. This article systematically reviews how Al technology is "taking root" in the field of new energy from national top-level design to specific power generation and dispatch scenarios, and onward to what the future might hold. Let's start with the big picture. According to the national "Implementation Opinions on Promoting the High-Quality Development of Artificial Intelligence +Energy, by 2027, we aim to preliminarily establish an innovation framework integrating energy and Al; by 2030, China's Al technologies and applications in the energy sector are expected to reach world-leading levels. These goals are quite concrete, providing both a timeline and a clear roadmap. When it comes to specific applications, Al's reach has extended into every corner of the new energy landscape: from power forecasting and intelligent operation and maintenance, to grid dispatch and virtual power plants, and on to energy storage optimization-Al's presence can be felt in virtually every link. For instance, using Al for power forecasting can push accuracy above 95%; intelligent O&M has made the concept of "unattended operations" a reality grid dispatch response times have been compressed from five minutes to 90 seconds. These aren't just theoretical they're delivering tangible results: operational efficiency is up, and reliability is more solid. Of course, the path of technological application hasn't been entirely smooth. Right now, there are several tough challenges that can't be ignored. First, there's the technology itself: the "black box" problem with algorithms hasn't been fully resolved, and they can still stumble in extreme scenarios. Second, there are data barriers everyone guards their own turf, and data sharing mechanisms have yet to gain traction. Third, there's energy consumption: large models are power-hungry, which actually runs somewhat counter to our energy-saving and carbon-reduction goals. Beyond these, information security risks and a shortage of interdisciplinary talent are also hurdles that can't be sidestepped. At the end of the day, if we want Al to truly take on the heavy lifting in the new energy sector, these are challenges we have to confront head-on[1]. Taking a longer-term view, the direction is actually quite clear. With continued breakthroughs in trustworthy Al technologies, the deepening integration of digital twins and large models, and the gradual alignment of "computing power" and "electricity" coordination mechanisms, the energy system of the future is likely to move toward "second-level response, autonomous optimization, and panoramic visibility." By then, Al will no longer be just a nice-to-have add-on-it will be embedded naturally into every corner of the energy system, much like water and electricity.