# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 5 Byproducts and Challenges
### Byproduct: Grid Mapping  
In addition to assisting with a \(\overline{\text{DVO}}\) implementation, the presence of a robust in-band sensor network enables a form of location or registration (\"Mapping\") of devices which are resident on the electric power distribution network (the \"Grid\"). Thus, the effect of the process described here can be referred to as \"Grid Mapping\" [30; 40].  
The approach to this mapping process depends on the use of \"current profiles\", or characteristic disturbances in electrical current which are generated by grid-resident devices. When suitably processed, analyzed, and categorized, these current profiles can be detected at locations in the grid which may be distant from the location of the device which produced them. The presence of similar current profiles at different grid locations can be used to reveal the presence and approximate location of a grid-resident device or system. Thus, the process is \"Grid Mapping\" via \"Current Profiles\" [40].  
It is well-known that current disturbances persist in grid infrastructure. These disturbances have been used for applications related to fault detection and isolation, grid stabilization, and communications. It is also well-known that the location of grid-resident devices can be performed (inaccurately) via extrinsic methods such as GPS coordinates. The approach described in [30; 40] performs approximate device location by correlation of downstream (specific) current profiles with upstream (aggregate) current profiles to determine the presence (or absence) of the downstream profile in the aggregated upstream location (typically the substation). In this fashion, important logistical information regarding downstream devices e.g., smart meters can be inferred from the collection ofcurrent profiles. This method provides a completely passive approach to \"Grid Mapping\", where no information or excitation is added to the grid.  
Time-synchronous information related to presence/absence of grid-resident devices is an important concept for Smart Grid infrastructure. Present grid implementations do not include device registration or location functions, and particularly not in a passive system architecture which requires no additional excitation. Accurate location of downstream grid-resident devices, including user devices (e.g., electric vehicles) and operator devices (e.g., transformers, smart meters, etc.) is an important aspect of all grid systems, and is a viable component of the Smart Grid.  
Typically, the downstream, or specific, current profile (DCP) is observed from an electric meter or other endpoint device, whereas the upstream, or aggregate, current profile (UCP) is observed from a substation feeder or other infrastructure location. The downstream device (e.g., smart meter) may have nominal capabilities for data acquisition, processing, and storage and does not have access to \"higher order\" information which can be used in \"mapping\" or device registration. The upstream device may have significantly greater capabilities for processing, storage, data acquisition, and situational awareness of grid subsystems and architecture.  
The DCP data is processed, encoded, and parameterized by the downstream device (e.g., smart meter) for efficient transmission to the upstream location(s), where it will be processed and analyzed. Processing at the upstream location(s) may include activities such as auto or cross-correlation involving DCP(s) and UCP(s) for the purpose of determining presence or absence of the DCP in time-synchronous UCP data as well as time-dependent notations for preservation of state and location information for each DCP. The techniques for data compression, the fidelity criteria, and the feeder-resident correlation operations for estimation of device presence/absence, along with processing for geo-spatial and network-architecture indicators are the subjects of ongoing ML-based analysis, which can provide the utility operator with numerous benefits related to situational awareness.