Hardware

Dynamic demand response (DDR) is the process of shifting power consumption towards periods of lower demand based on real-time energy pricing data. It is a flexibility measure utilised in the decarbonisation of the UK’s power system to reduce peak demand. Dynamic time-of-use (dTOU) tariffs, such as Agile Octopus, incentivise DDR by providing half-hourly electricity prices for each day. Through this incentive, households are offered the opportunity to reduce their energy costs by applying DDR to energy-intensive, deferrable loads. This paper presents an open-source, Internet of Things (IoT)-based system designed to automate DDR and streamline its implementation. The system identifies the period of lowest electricity prices and activates a relay during this period each day. For validation, the system was tested over a one-month experiment, which showed that, in a favourable scenario, it could reduce an appliance’s electricity costs by up to 44%. These results highlighted the system’s potential to deliver substantial energy cost savings, while also encouraging households to participate in flexibility measures that alleviate pressure on the National Grid.

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings.

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments.