About this Research Topic
The unlimited exploitation and utilization of fossil energy accelerates the excessive emission of carbon dioxide to exacerbate global warming. Hence, the development and utilization of alternative low carbon energy is becoming very promising. As a result, many optimization problems have been formulated and solved for saving fossil fuel cost and reducing energy waste in the power system and final energy use applications. However, most of the formulating problems display non-convex, multi-modal, non-smooth, or mixed integer features, posing significant challenges to system operators and energy users. Population-based search engines and evolutionary algorithms have some obvious advantages, such as imposing no restrictions on the problem formulation as well as conceptual and computational simplicity. Thus, evolutionary algorithms are promising, powerful tools for efficiently solving complicated problems involving smart grid or energy system scheduling to reduce carbon emissions.
This Research Topic intends to bring together the state-of-the-art evolutionary algorithms in dealing with the emerging problems in the field of complex modern power and energy systems. The submissions are encouraged to focus on smart grid scheduling with integration of new participants such as renewable generations, plug-in electric vehicles, distribution generations and energy storages, multiple time-special energy reductions, and other energy optimisation topics.
1. Optimal smart grid scheduling and integration with renewable energy generations
2. Energy management, intelligent coordination and control of electric vehicles/ships
3. Low carbon-based building energy management systems
4. Charging and discharging strategies for energy storage battery systems
5. Internal and whole scale management for single and hybrid energy storage systems
6. Low carbon energy reduction strategies for food and chemical process industry
7. Energy reduction strategies for energy intensive manufacturing processes
8. Parameters identification for photovoltaic models and PEM fuel cells
9. Thermodynamic optimization for heat exchanger design and Organic Rankine Cycle
10. Unit commitment, economic dispatch and optimal power flow
11. Low carbon power generation, transmission and utilization
This Research Topic intends to bring together the state-of-the-art evolutionary algorithms in dealing with the emerging problems in the field of complex modern power and energy systems. The submissions are encouraged to focus on smart grid scheduling with integration of new participants such as renewable generations, plug-in electric vehicles, distribution generations and energy storages, multiple time-special energy reductions, and other energy optimisation topics.
1. Optimal smart grid scheduling and integration with renewable energy generations
2. Energy management, intelligent coordination and control of electric vehicles/ships
3. Low carbon-based building energy management systems
4. Charging and discharging strategies for energy storage battery systems
5. Internal and whole scale management for single and hybrid energy storage systems
6. Low carbon energy reduction strategies for food and chemical process industry
7. Energy reduction strategies for energy intensive manufacturing processes
8. Parameters identification for photovoltaic models and PEM fuel cells
9. Thermodynamic optimization for heat exchanger design and Organic Rankine Cycle
10. Unit commitment, economic dispatch and optimal power flow
11. Low carbon power generation, transmission and utilization
Keywords: Renewable Energy, Energy Storage System, Battery, Artificial Intelligence, Electric Vehicles
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.
