About this Research Topic

Background

In modern power grids, due to the increasingly high penetration of renewable energy sources, their lower-inertia, uncertainty, and intermittency have made it more and more difficult to maintain secure and stable system operations. Fortunately, recent advancements in information and communication technologies have largely helped strengthen the capability of today’s power grids in online situational awareness and decision making against potential risky/insecure conditions. In particular, the widespread deployment of various advanced measurement devices, e.g., synchronous phasor measurement units and smart meters, has significantly enhanced the grids’ visibility and observability in various complicated operational scenarios. With massive measured information available, emerging big data analytics and advanced machine learning techniques can be introduced into power system operation, which would augment the intelligence level of system-wide situational awareness and decision making. As a result, this could accelerate the evolvement of modern power systems toward smart grids from a digitalized data-driven perspective.

In recent years, while various enlightening data analytics and machine learning solutions have been developed for power system intelligent situational awareness and decision making, some significant challenges remain to be tackled:
• Vulnerability to practical defective data acquisition conditions
• Scarcity of risky/insecure scenarios in learning data preparation
• Lack of interpretability of data-driven solutions
• Data privacy and security issues during data analytics
• Insufficient adaptability to time-varying operating conditions; etc.

If the potential of various advanced data analytics and machine learning techniques is fully unlocked, more powerful data-driven solutions for online situational awareness and decision making can be figured out to address these challenges and further enhance smart grid operation. Therefore, this Research Topic focuses on soliciting innovative works that present recent advances and new trends in data-driven intelligent situational awareness and decision making for smart grid operation.

The Research Topic welcomes research efforts related to data-driven situational awareness and decision making at all levels of modern power systems: bulk power grids, distribution networks, microgrids, etc. Specific topics of interest include but are not limited to:
• Data cleansing and analytics in modern power systems
• Data-driven power component condition monitoring
• Data privacy-preserving energy management and operation
• Data security enhancement against power system cyber attacks
• Data-driven renewable generation/load forecasting
• Data-driven power system dynamics modeling
• Data-driven power system dynamic stability/security assessment
• Power system fault/event detection based on data analytics
• Data-driven power system situational awareness and visualization
• Data-driven power system risk hedging and stability control
• Reinforcement learning for power system decision making

Keywords: Data analytics, Machine learning, Smart grid, Situational awareness, Decision making, Power system risk hedging, Control and optimization

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.

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