The optimal scheduling of energy systems is important in ensuring the balance of energy supply and demand. Quantitative comparative studies of the development status

hot spots and trends in this field at home and abroad for more than 30 years

the research of energy systems optimal scheduling was analyzed in the CNKI and WoS databases from 1990—2022 by CiteSpace software. The results show that this field is in the conventional scientific stage but the literature has a high growth rate

the domestic literature growth rate is faster than the international and the inter-institutional exchange is close; the foreign hotspots are optimization algorithms

uncertainty and stability control for microgrids

dynamic scheduling with energy storage for integrated energy system (IES)

as well as reinforcement learning and game theory for scheduling technique; the domestic hotspot trends are algorithms

dynamic optimization/bilayer optimization/time-sharing tariffs

multi-intelligent bodies

demand side management and deep learning for microgrids

as well as uncertainty

energy hubs

electricity to gas

integrated demand response

data driven

carbon trading

carbon capture and reinforcement learning for IES. The results show heuristic algorithms and deep learning techniques are expected to achieve a paradigm shift in future large-scale energy systems.