Variable Renewable Energy Sources; Integration Cost; Artificial Intelligence; Machine Learning; Value Creation; Electricity; Climate Change; Digitization; Smart Grid; Load Forecasting; Demand Response; Predictive Maintenance;
The power sector is increasingly relying on variable renewable energy sources (VRE) whose share in energy production is expected to further increase. A key challenge for adopting these energy sources is their high integration costs.Artificial intelligence (AI) solutions and data-intensive technologies are already used in different parts of the electricity value chain and, due to the growing complexity and data generation potential of the future smart grid, have the potential to create significant value in the system. However, different uncertainties or lack of understanding about its impact often hinder the commitment of decision makers to invest in AI and data intensive technologies, also in the energy sector.While previous work has outlined a number of ways AI solutions can be used in the power sector, the goal of this article is to consider the value creation potential of AI in terms of managing VRE integration costs. The authors use an economic model of variable renewable integration cost from the literature to present a systematic review of how AI can decrease substantial integration costs.The authors review a number of use cases and discuss challenges estimating the value creation of AI solutions in the power sector.