Renewable energy sources has recently been receiving more attention due to cost competitiveness and environmental sustainability. Due to the investment cost of renewable power generation systems, it is important to operate the systems near their maximum output power point, especially for wind and solar PV generation systems. In addition, since wind and solar PV power resources are intermittent, accurate prediction and modeling of wind speed and solar insolation are necessary. Furthermore, to have a more reliable power supply, renewable power generation systems are usually interconnected with the power grid.
Smart Grid is a recently developed set of technologies employing information, communication, and automation to deploy an integrated power grid with smart power generation, transmission, distribution to end users. Smart Grid emphasizes automation, safety, and the close cooperation between the users (demand response) and suppliers to improve the operating effectiveness of power systems, to enhance power quality and to solidify grid reliability. Moreover, Smart Grid integrated with smart meters, EV charging stations and home (building) energy management systems are key to enabling the related concept of a "Smart and Connected City".
As a result, modeling and controlling the power grid using Smart Grid techniques, such as smart meters, micro-grids, and distribution automation become very important issues. Additionally, effective uses of computational intelligence such as evolutionary optimization, machine learning, neural networks, and fuzzy logic to control and model renewable power generation in a smart-grid would facilitate reliable, efficient, and minimal curtailment.
This Research Topic would like to encourage original contributions regarding recent developments and ideas in Computational Intelligence techniques for Smart Grid systems and renewable power generation and use.
Potential topics include, but are not limited to:
• Modeling of integrated renewable power generation systems.
• Control of renewable power generation systems .
• Prediction of renewable energy generation using machine learning and neuro-fuzzy systems.
• Hybrid systems of computational intelligence in Smart Grid and renewable power generation - systems.
• Sustainability metrics of oil and gas integration with renewable energy generation.
• Optimal energy management systems.
• Optimization of power quality, protection and reliability analysis of power system.
• Demand-Response and Smart Buildings.
• Computational intelligence application for Smart Grid and Smart Cities.
Renewable energy sources has recently been receiving more attention due to cost competitiveness and environmental sustainability. Due to the investment cost of renewable power generation systems, it is important to operate the systems near their maximum output power point, especially for wind and solar PV generation systems. In addition, since wind and solar PV power resources are intermittent, accurate prediction and modeling of wind speed and solar insolation are necessary. Furthermore, to have a more reliable power supply, renewable power generation systems are usually interconnected with the power grid.
Smart Grid is a recently developed set of technologies employing information, communication, and automation to deploy an integrated power grid with smart power generation, transmission, distribution to end users. Smart Grid emphasizes automation, safety, and the close cooperation between the users (demand response) and suppliers to improve the operating effectiveness of power systems, to enhance power quality and to solidify grid reliability. Moreover, Smart Grid integrated with smart meters, EV charging stations and home (building) energy management systems are key to enabling the related concept of a "Smart and Connected City".
As a result, modeling and controlling the power grid using Smart Grid techniques, such as smart meters, micro-grids, and distribution automation become very important issues. Additionally, effective uses of computational intelligence such as evolutionary optimization, machine learning, neural networks, and fuzzy logic to control and model renewable power generation in a smart-grid would facilitate reliable, efficient, and minimal curtailment.
This Research Topic would like to encourage original contributions regarding recent developments and ideas in Computational Intelligence techniques for Smart Grid systems and renewable power generation and use.
Potential topics include, but are not limited to:
• Modeling of integrated renewable power generation systems.
• Control of renewable power generation systems .
• Prediction of renewable energy generation using machine learning and neuro-fuzzy systems.
• Hybrid systems of computational intelligence in Smart Grid and renewable power generation - systems.
• Sustainability metrics of oil and gas integration with renewable energy generation.
• Optimal energy management systems.
• Optimization of power quality, protection and reliability analysis of power system.
• Demand-Response and Smart Buildings.
• Computational intelligence application for Smart Grid and Smart Cities.
