One of the biggest challenges in current power system operation is caused by the massive integration of distributed energy resources (DERs), which have high volatility generations. In the meantime, communication and control technologies are significantly improved to provide direct interaction between agents and customers, such as in peer-to-peer frameworks. In addition, the recent developments in monitoring, sensor networks, and advanced metering infrastructure (AMI) greatly enhance the variety, volume, and speed of measurement data in electricity transmission and distribution networks. By harnessing these technologies, the application of big data, artificial intelligence, and machine learning methods are possibly implemented to overcome the challenges from massive DERs integration in power systems. However, these technologies require high capital to operate, which can inflict financial loss if applied without the appropriate strategy.
To overcome the risk of financial loss and increase DER participation, DERs operation should be considering the economic aspect. In this case, peer-to-peer energy trading, demand response, DER aggregated market, and so forth, bring new perspectives in the DERs operation with respect to the economic aspect. At the same time, to bring optimal participation of market agents, the electricity markets require forecasting of the electricity market dynamics, modeling market agents’ behavior, and data-driven bidding strategies. Therefore, this Research Topic aims to bring a platform in which the authors can collaborate to explore and improve the market-based DERs operation and its multiple interconnections with other related fields to support power system transformation.
The topics of interest include, but are not limited to:
• Big data application for the market strategy improvement
• Artificial intelligence and machine learning methods application in market-based DER operation
• Novel frameworks for market-based DER operation
• Novel frameworks for data-driven bidding strategies
• Novel frameworks for autonomous decision-making
• Novel market modeling and regulations for DER integration
• Analyzing the impact of market-based DER operation towards power system.
One of the biggest challenges in current power system operation is caused by the massive integration of distributed energy resources (DERs), which have high volatility generations. In the meantime, communication and control technologies are significantly improved to provide direct interaction between agents and customers, such as in peer-to-peer frameworks. In addition, the recent developments in monitoring, sensor networks, and advanced metering infrastructure (AMI) greatly enhance the variety, volume, and speed of measurement data in electricity transmission and distribution networks. By harnessing these technologies, the application of big data, artificial intelligence, and machine learning methods are possibly implemented to overcome the challenges from massive DERs integration in power systems. However, these technologies require high capital to operate, which can inflict financial loss if applied without the appropriate strategy.
To overcome the risk of financial loss and increase DER participation, DERs operation should be considering the economic aspect. In this case, peer-to-peer energy trading, demand response, DER aggregated market, and so forth, bring new perspectives in the DERs operation with respect to the economic aspect. At the same time, to bring optimal participation of market agents, the electricity markets require forecasting of the electricity market dynamics, modeling market agents’ behavior, and data-driven bidding strategies. Therefore, this Research Topic aims to bring a platform in which the authors can collaborate to explore and improve the market-based DERs operation and its multiple interconnections with other related fields to support power system transformation.
The topics of interest include, but are not limited to:
• Big data application for the market strategy improvement
• Artificial intelligence and machine learning methods application in market-based DER operation
• Novel frameworks for market-based DER operation
• Novel frameworks for data-driven bidding strategies
• Novel frameworks for autonomous decision-making
• Novel market modeling and regulations for DER integration
• Analyzing the impact of market-based DER operation towards power system.
