# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 4 In-Band Sensor Network Requirements  
Equipment required to implement some form of CVR has already been deployed by many utilities. For instance, tap changers at substation transformers; voltage regulators and capacitor banks are commonly deployed infrastructure for a typical service provider. Leveraging this infrastructure, a form of statically configured CVR can be implemented with simple line studies and no additional equipment. However, this \"blind\" or \"open loop\" CVR is rarely effective due to uncertainty and high variability in the ASV, or the voltage actually supplied. A robust \(\overline{\text{DVO}}\) system requires a \"closed loop\" implementation with reliable, real-time feedback of endpoint voltages. Strategically placed sensors with an efficient, real-time communication path are crucial to ensuring that the supplied voltage is maintained within allowable limits at all times.  
In contemporary CVR deployments, accurate, real-time telemetry of ASV levels at distributed service points is not possible or practical. Instead, static calculations of voltage loss or \"line drop\" for specific line types, loads, and distances are often used as estimates or guidelines for establishing the Minimum Supplied Voltage (MSV) to be used in CVR algorithms. The statically calculated MSV parameters suffer from significant drawbacks, including non-real-time updates due to static estimation, inaccuracy due to dynamic load variation, and complexity due to manual calculations. As a result of these difficulties, the efficacy and practicality of broad-scale CVR implementations is quite limited. Utilities are unable to control or trust DVO implementations, and so the economic value and conservation potential of this technology remain unachieved.  
A compelling alternative to static or manually established MSV parameters involves the use of distributed sensors and a low-latency feedback loop between the supply points (at the loads) and the DVO algorithm or actuator (at the substation or inside the network). With this closed-loop architecture, a CVR implementation becomes a \(\overline{\text{DVO}}\) implementation, and can provide a return on equity (ROE) that correlates directly with the number of sensor points deployed in the distribution grid.  
In many cases, the expense of telemetry systems and the overall system management has made deploying an effective system wide \(\overline{\text{DVO}}\) control feedback loop cost-prohibitive. Wireless solutions require ubiquitous deployment and still do not report schematic conditions that would allow for effective command and control. The management associated with wireless systems for \(\overline{\text{DVO}}\) is also a significant issue. A requirement for balancing multiple communications technologies across licensed and unlicensed spectrum causes consternation for utility executives and planners. The complexity of such systems leads to escalating costs of Operations and Maintenance (O&M).  
In contrast, grid-resident communications use the existing infrastructure of the utility, allowing for schematic command and control at far lower costs, and creating a platform for additional applications such as automated metering infrastructure, demand management and fault isolation and resolution. The assurance of a single communication type that covers 100% of the service territory is highly appealing. The best practices for optimizing CVR and achieving \(\overline{\text{DVO}}\) require addressing the critical components and balancing their functionality and cost, as discussed in following sections.