# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 7 Smart Deployments  
A logical approach to deploying a Grid Location Aware \(\overline{\text{DVO}}\) system must use a best practice and continuous improvement methodology that allows for addressing current grid challenges as well as addressing the dynamic grid as it changes over time. As with any systems management challenge, all aspects of the solution must be considered including sensor placement, control point management, and system integration & management. A well-designed \(\overline{\text{DVO}}\) system should be simple and economical to deploy. Devices, which exhibit \"grid location awareness\" can be deployed and managed with minimal oversight. Communications should take place directly over the power lines with no requirement for off-grid secondary networks. The key to this simplicity is the ability to communicate on the wire over multiple transformations without repeaters or bypasses. Until recently, this communications ability was thought to be impossible [27]. The result of this novel architecture is a complete end-to-end system of controllable nodes and sensor locations for total \(\overline{\text{DVO}}\) optimization and quality energy delivery. This system architecture is shown in Figure 7. In the figure, the \(\overline{\text{DVO}}\) monitor at the endpoint transmits the current system voltage along with other telemetry data to the substation and in-line equipment. The substation and the in-line equipment are programmed to adjust the system optimum voltage level based on data which provides complete \"grid location awareness\" at the device level. \(\overline{\text{DVO}}\) monitors throughout the system help protect customers from low voltage conditions  
Figure 6: LSTM Memory Cell Overview Design [42]. or brownouts, and feedback for exception conditions is instantaneous due to the in-line, on-grid communications paradigm. Communications with mid-point grid devices provides additional value and resolution. Control of devices such as capacitor banks, regulators, tap-changing transformers, and switches based on real-time voltage feedback is an added benefit and can increase \(\overline{\text{DVO}}\) savings significantly. Key to any successful deployment is the careful measurement of the grid characteristics and the specific factors involved in managing \(\overline{\text{DVO}}\) including circuit length, load types (residential, commercial, industrial and agricultural), and substation architecture. Additionally, once an on-wire or grid-resident communication system is implemented, the concurrent leverage of ML-based optimization and ML-driven \(\overline{\text{DVO}}\) is also enabled. The ideal ML model will have the capabilities to have an ongoing learning behavior from data coming from sensors and endpoints. The reinforcement learning method, such as the TD or MCTS, approach is the best form of implementation that can estimate the best rewarding predictions or actions and continues to learn from events. This provides the ML model ways to monitor the data, behavior of sensors, and provide a grid-wide optimal solution for distribution. It can learn from extreme events, abnormal events, and finding optimal operational functions to a granular level on individual sensors, endpoints, and control devices. The ML model would sit on a centralized computing server were all of the data telemetry is transmitted to and analyzed for evaluation, transmission, and action. Another form of implementation is to establish an edge ML inference models in the sensors, endpoints, and to the mid-point grid devices dedicated for specific type of data and behavior. At the same time, the reinforced model will also monitor activities and learn from grid events as it tries to find the highest reward outcomes for the whole system. The benefit of this implementation is that the edge ML inference models can be different types of supervised-learning models such as the RNN and LSTM.