The data for PVs and loads are sourced from “Multi-agent reinforcement learning for active voltage control on power distribution networks”, collected from Jan. 1 st, 2012 to Dec. 31 st, 2014 over a 3-minute interval. In this paper, the data is described as follows: 

The recent developments in the field of the Internet of Things (IoT) bring alongside them quite a few advantages. Examples include real-time condition monitoring, remote control and operation and sometimes even remote fault remediation. Still, despite bringing invaluable benefits, IoT-enriched entities inherently suffer from security and privacy issues. This is partially due to the utilization of insecure communication protocols such as the Open Charge Point Protocol (OCPP) 1.6. OCPP 1.6 is an application-layer communication protocol used for managing electric vehicle chargers.

It contains genuine user feedback on electric bikes, gathered through comments shared by customers after purchasing and using an electric vehicle (EV). These comments provide valuable insights into the user experience, highlighting aspects such as performance, convenience, challenges, and satisfaction levels. This information is particularly useful for conducting sentiment analysis to understand customer emotions, preferences, and concerns.

We establish topological and parametric conditions under which phase angles across three identical impedances can be balanced with small-signal stability guarantees when served from three single-phase sources executing active-power frequency droop control. All standard topologies involving Delta and Wye interconnections of sources and loads are examined.