# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 1 Introduction
### Motivation  
Reducing demand on electricity generation and distribution resources subject to the constraint of minimizing customer inconvenience is a well-known conundrum. One useful general approach to these competing requirements is the concept of Dynamic Voltage Optimization (\(\overline{\text{DVO}}\) ) [17; 19]. Approaches such as CVR, LDC, VSR, and DVO which are similar to \(\overline{\text{DVO}}\) have been proven useful in numerous field deployments and small-scale trials. Similarly, a form of \(\overline{\text{DVO}}\) has been explored in other field trials [17]. In short, by carefully controlling the voltage supplied to the distribution system, significant savings have been demonstrated, and 1:1 power savings [23; 24] have been realized.  
Although approaches similar to \(\overline{\text{DVO}}\) have demonstrated clear potential, many pilot programs have experienced limited success due to absence of real-time feedback [25; 26]. Without real-time feedback, such implementations typically introduce a safety margin on the lower end of the distribution voltage to ensure regulatory compliance, and to protect customers from under-voltage conditions.  
Field trials of systems based on statistical modeling using incomplete data have produced favorable outcomes, such as pioneered by companies including DVI Grid Solutions ([[https://dvigridsolutions.com](https://dvigridsolutions.com)]([https://dvigridsolutions.com](https://dvigridsolutions.com))). However, each of these approaches require some form of feedback from the power grid, and so suffer from incomplete or sparse data due to limited line issues, cost of deployment, and related complexities.  
By implementing real-time feedback and leveraging deployed grid subsystems to report voltage levels, a real-time \(\overline{\text{DVO}}\) system can be realized and savings can be maximized to any given ambient condition. Approaches have been suggested which may provide a partial solution [27], but the inclusion of ML techniques in concert with the enhanced data-gathering approaches have not been clearly articulated. ML techniques have been shown to improve performance in various non-traditional applications such as network communication [28; 29] where data collection can be ongoing, and the real-time leverage available via the volume of temporal data may exceed its utility for storage. As a result, the combination of ML techniques with an on-wire sensor system has the potential to create multiple disruptive application vectors in this space, including substantial promise in the implementation of \(\overline{\text{DVO}}\).  
The following sections of this paper describe the background and functional definition of CVR along with a detailed discussion of the benefits of CVR implementations, as embodied in case studies from major service providers. In addition, the paper introduces a novel and enhanced \(\overline{\text{DVO}}\) architecture based on grid-resident sensors with in-band communication and ML-directed optimization.  
These capabilities provide various aspects of \"grid awareness\", and in addition to enabling \(\overline{\text{DVO}}\) may provide enhancements to technical, functional, and business processes that extend beyond the \(\overline{\text{DVO}}\) application [30]. These improvements are described with appropriate operational justification and presented in the context of an ML-enhanced, on-wire sensor system for the distribution grid.