About this Research Topic
This Research Topic is Volume II of a series. The previous volume, which has attracted over 10k views can be found here: Distributed Learning, Optimization, and Control Methods for Future Power Grids
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 grids. 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 grid, 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. To be able to efficiently operate such complex large-scale systems, new sets of control and optimization tools should be developed.
Since centralized controllers often suffer from serious computation, communication, and robustness issues for power systems with many controllable devices, distributed learning, optimization, and control methods are perhaps the only viable strategies for such systems. The goal is to develop computationally efficient and distributed solutions to improve the operation and economics of networked systems with a high proportion of renewable energy. “Distributed learning, optimization, and control methods for the future power grids, Volume II” is a timely Research Topic in Frontiers in Energy Research for those who hope to publish their original works to address the critical areas of distributed learning, control, and high-performance optimization techniques for future power grids.
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
• design of communication protocols/networks to support distributed control/optimization for power grids
• network topology design for future power grids
• distributed energy resources and virtual power plants
• distributed state estimation for power systems
• distributed control of smart power electronics
• 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 grids. 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 grid, 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. To be able to efficiently operate such complex large-scale systems, new sets of control and optimization tools should be developed.
Since centralized controllers often suffer from serious computation, communication, and robustness issues for power systems with many controllable devices, distributed learning, optimization, and control methods are perhaps the only viable strategies for such systems. The goal is to develop computationally efficient and distributed solutions to improve the operation and economics of networked systems with a high proportion of renewable energy. “Distributed learning, optimization, and control methods for the future power grids, Volume II” is a timely Research Topic in Frontiers in Energy Research for those who hope to publish their original works to address the critical areas of distributed learning, control, and high-performance optimization techniques for future power grids.
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
• design of communication protocols/networks to support distributed control/optimization for power grids
• network topology design for future power grids
• distributed energy resources and virtual power plants
• distributed state estimation for power systems
• distributed control of smart power electronics
• 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
Keywords: distributed learning, distributed optimization, distributed control, data analytics, power grids, demand response
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
