As an emerging paradigm in distributed power systems, microgrids provide promising solutions to local renewable energy generation and load demand satisfaction. However, the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling, especially for small-scale microgrids. Instead of deploying stochastic models to cope with such challenges, this paper presents a retroactive approach to real-time energy scheduling, which is prediction-independent and computationally efficient. Extensive case studies were conducted using 3-year-long real-life system data, and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11% on average, which suggests better performance than model predictive control with the cost difference at 30% compared to the perfect dispatch. . His current interests include power system optimization, artificial neural networks, energy system modelling, data analytics, and energy economics for renewable energy and storage systems.Zhao XU received the B.Eng., M.Eng. and Ph.D.interests include demand side, grid integration of wind and solar power, electricity market planning and management, and artificial intelligence (AI) applications.Minghua CHEN received the B.Eng. and M.S. degrees from the