# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 1 Introduction
### Terminology  
The area of \"voltage optimization\" and \"volt/var optimization\" is overloaded with seemingly conflicting terminology. For example, in [14], several terms are used and defined in specific ways, whereas in other references (e.g., [15; 16; 17] and others) some terms may be slightly redefined, used differently, or conflated. As a result, we specifically define here what we mean by \"dynamic voltage optimization\" (\(\overline{\text{DVO}}\) ) and the relationship of this approach to other, similar approaches, all of which use various methods to optimize the transfer of power from the distribution grid to the end-user. As described succinctly in [15] and codified in [18], \"[c]controlling voltage is a fundamental task of distribution networks\". In fact, this problem reduces to a multi-point blind estimation where the accuracy of the voltage supplied to arbitrary end-nodes must remain within a small tolerance of a target value, regardless of various disruptive influences. Since voltage generally decreases along feeder lines due to various factors including distance, load characteristics, and equipment conditions, the logical approach to resolving the blind estimation problem is to raise substation voltage to counteract aggregate \"line drop\".] Conservation Voltage Reduction (CVR), which may also be known as Conservation Voltage Regulation or Controlled Voltage Reduction [14; 15; 16; 19; 20] is the longstanding and well-known practice of controlling distribution voltage levels in accordance with existing standards [18]. Techniques associated with CVR have been explored in North America since the 1980s [14]. A superset of CVR is often described as volt/var optimization (\"VVO\") and focuses on a sort of coordinated, distributed power-factor optimization using controllable reactive sources (e.g., capacitor banks). These approaches modulate distribution grid voltage and/or grid-resident subsystems to effect the optimization process. The target of these implementations is to produce meter voltages to the \"bottom half\" of the ANSI recommendations (114-120 V) at the far-end of the feeder, rather than maintaining substation voltages which result in \"top half\" ANSI recommendations at the meter (120-126 V). An issue in all cases is the uniformity of voltage along the feeder or the \"first/last customer\" problem. Distributed Voltage Optimization (\"DVO\") appears in a few references (e.g., [15]) as an alternative approach to endpoint correction which regulates voltage at the boundary between distribution grid and consumer by modulating local power factor. Some references describe end-to-end (substation-to-consumer) CVR in terms of \"Line Drop Compensation\" (LDC) whereas approaches which are not end-to-end (e.g., require in-grid compensating systems) in terms of \"Voltage Spread Reduction\" (VSR), which \"flattens\" the voltage profile along the feeder, providing more uniform delivered voltage. Similar terminology is used in a broad review published by the US Dept. of Energy [21]. In this review, the purpose of CVR is to reduce the voltage as low as possible during peak and non-peak times, whereas voltage optimization (\"VO\") flattens the load curve and optimizes voltage to meet utility objectives (e.g., minimizing loss, optimizing reactive power losses, etc.). This study also refers to volt/var compensation (\"VVC\") as the ability to control remote in-grid devices to achieve volt/var targets based on sensor inputs, which is similar to the \"VVO\" concepts of other references. In the aggregate, [21] concurs that CVR depends on data from end-of-line sensors to control in-line regulators and other grid-resident devices. In the studies examined by [21], the effectiveness of the optimization process differs based on type of energy service provider, with investor-owned and municipal utilities producing a roughly 2.5% voltage efficiency, and cooperative providers producing a 3.5% voltage efficiency.