rather than the resource owner or the distribution operator. Creating data stores of smart inverter measurements as well as access to realtime measurements might be a particularly effective way to monitor grid edge resources such as PV installations. Further, awareness of inverter capabilities (e.g., voltage or frequency ride-through) and realtime operational status data will improve contingency planning and increase situational awareness. Monitoring and sensing grid edge energy storage systems are much more challenging than monitoring PV systems because energy storage systems are normally not visible by remote sensing and are not necessarily dependent on weather conditions. Detecting and monitoring such systems rely heavily on the interconnection registration and smart meter infrastructures. The deployment of smart meters makes it possible to measure energy storage systems separately and independently. Grid edge energy storage systems can also be measured indirectly if additional and detailed sensing data are available. One example is Satellites Others Local Weather Sensors PMU AMI SCADA Inverter µPMU Innovation: Predicting at BA Level Predicting at Substation Level Predicting Behind Each Smart Meter Figure 5. Sensors used in grid edge monitoring. PMU: phasor measurement unit; μPMU: microPMU; SCADA: supervisory control and data acquisition. Actual Image Image Label Detected Image 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Actual ImageImage LabelDetected Image 10 20 30 40 50 60 Actual ImageImage LabelDetected Image 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Actual ImageImage LabelDetected Image 10 20 30 40 50 60 10 20 30 40 50 60 01020304050600 10 20 30 40 50 60 0102030405060 10 20 30 40 50 60 10 20 30 40 50 60 Figure 6. Nonintrusive grid edge PV detection from satellite aerial images based on deep learning. (Source: Figure based on data from Google Earth.) IEEE Electrification Magazine / DECEMBER 2022 15