There are many challenges and opportunities in areas such as net zero, internet of things, big data, machine learning, and smart grid, particularly concerning distributed learning, optimization, decision-making, and control. New energy resources are distributed in nature, and there are demands in distributed control and resource optimization for energy and power systems. Recent advances in distributed networks along with the development of complex and large-scale subsystems have significantly incentivized coordination and cooperation over multi-agent systems. Acknowledging the role of network communication in the decision-making, many distributed algorithms have been developed for distributed machine learning, optimization, and differential games, where certain control perspectives, such as consensus, adaptation, and time-varying topologies or parameters, are intrinsically aligned. Motivated by the interplay among optimization, control and learning, a revisit of typical control methods may offer deeper insights into how these algorithms can be refined in terms of their design and convergence. This talk will cover recent activities carried out by the speaker’s group in distributed algorithms, rooted in control theory. Topics include distributed time-varying optimization of multi-agent systems, cooperative and competitive machine learning over networks, with a particular focus on resource allocation, load forecasting, and day-ahead bidding in smart energy and power systems, federated learning algorithms focusing on data heterogeneity and their application on load forecasting and load profile identification.