Opportunities and Developmental Dilemmas Brought by AI Intervention in the Construction of Renewable Energy Microgrids

Renewable energy-based microgrids are viewed as a fundamental solution to the energy transition. However, throughout operation they are under double strain of production change and lack of economic efficiency. Based on the public statistics and existing research literature, this study studies the application performance of artificial intelligence in the fields of prediction, scheduling, operation and maintenance and power trading. The operating data of the existing project reveals that, under certain conditions, standard artificial intelligence can reduce prediction mistakes, minimise unplanned downtime and boost trade profits. But the scale of these benefits is strongly dependent on the availability of high-quality data and local optimisation, and limited by external institutional obstacles including data silos, the absence of market regulations, and unclear legal responsibilities and rights. The existing power system has not set up a proper institutional basis. Simply deploying technology is hard to overcome the current obstacles without the backing of a well-matched system. When combining numerous local optimisations, it might cause system concerns, such as over-limit grid power flow and voltage instability. The research shows that the future development direction should be to limit the quest of single technical indicators and to move toward the coordinated evolution of technology, data and system, promoting microgrids from demonstration and verification to large-scale commercial implementations.