In recent years, renewable energy has developed rapidly around the world, making important contributions to low-carbon sustainable development. However, renewable energy such as wind and photovoltaic energy are highly random and intermittent, which brings great challenges to the operation and control of power networks. The traditional method is to build a large number of peak-shaving power stations and arrange a large number of energy storage devices to stabilize the fluctuations of renewable energy, and the cost may be prohibitive to be sustainable. In the future power network, advanced sensors, actuators, and communication equipment will be deployed on various systems such as generators, power lines, substations, transformers, distributed energy resources, air conditioners, and electric vehicles. A challengeable issue arises on how to manage these hundreds of millions of active endpoints.
Recently, distributed learning, optimization, and control methods for networked systems have received growing attention. The goal is to develop computationally efficient and distributed solutions to improve the operation and economics of networked systems. Distributed learning, optimization, and control methods for managing hundreds of millions of active endpoints in the future power network are expected to enable a stable and economic operation of the power network with a high proportion of new energy. “Distributed learning, optimization, and control methods for the future power networks” is a timely Research Topic in Frontiers in Energy Research for those who hope to publish their original works about distributed learning, optimization, and control methods in future power systems.
Topics of interest include, but are not limited to:
• multi-agent reinforcement learning for control and optimization in power systems
• distributed optimization and smart decisions in power systems
• data analytics for electrical energy systems
• demand response solution with distributed learning
• advanced energy management and economic dispatch
• demand forecast modeling and energy planning
• resilient control and analysis for power systems
• distributed energy resources and virtual power plants
• distributed state estimation for power systems
• distributed control of smart power electronics
• distributed optimization for grid-interactive buildings and communities
• distributed management of transactive energy systems and prosumer-based markets
• distributed intelligence for networked microgrids and autonomous energy systems
• edge intelligence for power systems and energy internet
In recent years, renewable energy has developed rapidly around the world, making important contributions to low-carbon sustainable development. However, renewable energy such as wind and photovoltaic energy are highly random and intermittent, which brings great challenges to the operation and control of power networks. The traditional method is to build a large number of peak-shaving power stations and arrange a large number of energy storage devices to stabilize the fluctuations of renewable energy, and the cost may be prohibitive to be sustainable. In the future power network, advanced sensors, actuators, and communication equipment will be deployed on various systems such as generators, power lines, substations, transformers, distributed energy resources, air conditioners, and electric vehicles. A challengeable issue arises on how to manage these hundreds of millions of active endpoints.
Recently, distributed learning, optimization, and control methods for networked systems have received growing attention. The goal is to develop computationally efficient and distributed solutions to improve the operation and economics of networked systems. Distributed learning, optimization, and control methods for managing hundreds of millions of active endpoints in the future power network are expected to enable a stable and economic operation of the power network with a high proportion of new energy. “Distributed learning, optimization, and control methods for the future power networks” is a timely Research Topic in Frontiers in Energy Research for those who hope to publish their original works about distributed learning, optimization, and control methods in future power systems.
Topics of interest include, but are not limited to:
• multi-agent reinforcement learning for control and optimization in power systems
• distributed optimization and smart decisions in power systems
• data analytics for electrical energy systems
• demand response solution with distributed learning
• advanced energy management and economic dispatch
• demand forecast modeling and energy planning
• resilient control and analysis for power systems
• distributed energy resources and virtual power plants
• distributed state estimation for power systems
• distributed control of smart power electronics
• distributed optimization for grid-interactive buildings and communities
• distributed management of transactive energy systems and prosumer-based markets
• distributed intelligence for networked microgrids and autonomous energy systems
• edge intelligence for power systems and energy internet
