AI如何破解电力“尖峰之困”？

Core Viewpoint - Artificial intelligence (AI) technology is rapidly transitioning from the laboratory to all aspects of the power system, demonstrating significant potential to support high proportions of renewable energy integration and enhance system resilience and regulation capabilities, addressing the long-standing challenge of building power systems "for peak demand" [1][4]. Group 1: System Stability Challenges - The power system faces severe challenges from peak loads, exemplified by the East China grid setting six historical records for electricity load this summer, with the highest load reaching 442 million kilowatts, a year-on-year increase of 4.97% [3]. - The transformation of China's energy structure is marked by rapid growth in renewable energy installations like wind and solar, which introduce inherent volatility and uncertainty, testing the grid's regulation capabilities [3][4]. Group 2: Limitations of Traditional Approaches - Traditional methods of continuously building power sources to meet short-term peak demands have shown limitations, with new installations often remaining underutilized for most of the year [4]. - The integration of large-scale renewable energy into distribution networks is an irreversible trend, and AI technology has significant potential for optimizing grid coordination [4]. Group 3: AI Empowerment in Power Systems - AI is penetrating various aspects of the power system at an unprecedented speed, particularly in weather forecasting, where it can process vast amounts of meteorological data to enhance prediction accuracy [6]. - AI-driven predictions allow renewable energy companies to optimize pricing strategies dynamically and plan maintenance during low production periods, improving operational efficiency and profitability [6]. Group 4: Practical Applications of AI - Case studies show that AI can significantly enhance grid regulation and renewable energy absorption, such as in Shaoxing, where a county-level AI system improved frequency regulation by 48 times and increased renewable energy absorption by 30% [7]. - The development of AI models for real-time decision-making has expanded the factors considered in grid management, improving decision accuracy by 15% [7]. Group 5: Mechanisms for AI Implementation - The successful application of AI in power systems requires the establishment of compatible mechanisms and environments, as current market mechanisms are still maturing [8]. - There is a need for flexible pricing mechanisms that reflect regional load characteristics, which could optimize load distribution and enhance system efficiency [8].