Advanced Visualizations for Smart Grid Data Analytics

The complexities of modernizing the grid at a time of increasingly frequent and severe weather events have forced electric power utilities to focus attention on public safety power shutoff (PSPS)-related decision-making to ensure that wildfire ignition risk is minimized while uninterrupted power to consumers is maximized. PSPS-related decision-making is challenging and requires synthesizing, tracking, and determining the impact of dynamic, real-time and uncertain weather-related data streams on a complex, multi-faceted grid system. Entrusted only to a small cadre of senior managers, not enough to adequately respond to the increasingly frequent and severe environmental threats to the grid, utilities need effective decision support tools and training to facilitate PSPS decision-making and grow the pool of qualified decision makers, respectively. The project team proposes to develop a complementary decision and training support system to address these challenges.In Phases I and II of this SBIR, the project team employed a user-centered design (UCD) process to research, design, develop, and demonstrate a PSPS human machine interface (HMI) prototype that supports real-time wildfire risk management for emergency grid managers. Specifically, the project team worked with an engaged electric utility partner to analyze current PSPS HMI needs and design and develop an integrated PSPS HMI with multi-factor sorting and color-coding to help prioritize attention, linking across displays to relate disparate information, easy access to detailed information on-demand, and intuitive cues to facilitate anticipation and understanding of future conditions. The PSPS HMI was iteratively matured and developed into an interactive prototype, validated in an experiment, and enhanced with initial overlays from an innovative Active Decision Visualization of Impact Critical Elements (ADVICE) concept, which allows users to explore and automatically integrate critical decision data on a decision surface and see intuitive visual representations of the impact of those integrated data on PSPS decisions, thereby externalizing the decision-making process and improving decision transparency and qualityWith continued engagement and enthusiastic endorsement from the same utility partner for this Phase IIB effort, the project team proposes to logically build on the foundational work of Phases I and II to develop a decision support system (DSS) and complementary training support system (TSS) that innovatively applies ADVICE to real-time PSPS decision-making and revolutionizes PSPS decision training by making a once invisible decision-making process visible and learnable. The DSS and TSS will be iteratively improved through machine learning, informed by feedback from domain experts, access to historical grid data, and inputs from user actions, culminating in a matured and validated DSS along with an innovative, effective, and efficient TSS that supports and improves preparation for and execution of PSPS decision-making.The DSS and TSS for PSPS-related decision-making developed in Phase IIB will lead to better, more transparent, consistent, and defensible emergency grid management decision-making. In turn, the wider benefits will be a more reliable energy service with fewer interruptions, reduced consumer and business costs associated with power outages and system failures and increased public safety from well-managed power grids.