We present a prototype of a decentralized power trading system based on the use of distributed ledger technology. This sort of efficient, decentralized marketplace is needed to empower prosumers and make them first-class members of a smart, decentralized power grid in order to drive further renewable energy adoption. Unlike the bulk of previous work in this field, we focus on private permissioned distributed ledgers rather than conventional blockchains. The proposed solution is entirely independent of cryptocurrency, with an explicit design capability of being adapted piecemeal without any fundamental changes to the present regulatory environment. To be economical, efficient, and scalable, our prototype is based on a lean, Corda-based private permissioned distributed ledger. It allows for instant, automatic bidding on and trading of ‘power promises’ and the robust implementation of short-term, small-scale liquid electrical power futures. We demonstrate that the prototype performs well and presents several clear advantages over existing solutions based on conventional blockchains. Therefore, the proposed approach represents a promising, robust solution to the smart grid decentralized power trading problem.

Power quality disturbances (PQD) have a negative impact on power quality-sensitive equipment, often resulting in great financial losses. To prevent these losses, besides detecting a PQD on time, it is important to classify it, so that appropriate recovery procedures are employed. The majority of research employs machine learning model PQD classifiers on manually extracted features from simulated or real-world signals. This paper presents an end-to-end approach that circumvents the manual feature extraction and uses signals generated from mathematical voltage sag type formulas. We developed a configurable voltage sag generator that was used to form training and validation datasets. Based on the synthetic three-phase voltage signals, we trained several end-to-end LSTM classifiers that classify voltage sags according to ABC classification. The best-performing model achieved an accuracy of over 90% in the real-world dataset.

Distributed ledger technology (DLT) and blockchain-based energy trading solutions are often labeled as resource-hungry, excessive power consumers which consequently create a significant carbon footprint. Using this approach to energy trading, without simultaneously minimizing energy consumption, defeats the purpose of using such trading solutions to encourage renewable energy production and may even cancel out any ecological benefits. This paper demonstrates that it is possible to create a DLT-based energy trading system which provides security, transparency, autonomy, scalability, and decentralization, provided by using a DLT, but with significantly lower penalties than other previously known solutions. This is best illustrated through the fact that the carbon footprint per transaction of the solution presented in this paper is 0.19 grams of CO ₂ compared to 20 grams of CO ₂ created by single transaction of similar complexity on the Ethereum blockchain. This makes per-transaction carbon footprint of our system approximate 100 times smaller than that of the most commonly used Ethereum blockchain. We present the architecture and features of the proposed platform, as well as a thorough analysis of its performance, including power consumption and estimated carbon footprint. All experiments are done on a dedicated Beowulf cluster comprised of general-purpose computers. The cluster mimics a microgrid environment and presents a testing ground for real-world performance and power consumption analysis of a system used for trading energy predominantly produced from prosumers and their renewable sources.

Animated three-dimensional (3D) models are an essential part of interactive content available in products of present day movie and gaming industries. Time required for manual development of animated 3D models is a limiting factor in many of their practical applications. In order to minimize the production time of animated 3D models and, thus, lower costs, there is a need for automation of the animation process. This paper presents an approach to the programmatic addition of animations to automatically rigged avatars stored using the industry-standard FBX file format. The presented solution is developed using the C++ programming language and the Autodesk FBX Software Development Kit (SDK). It allows significantly higher degree of automation and programmability than other standard approaches, based on using standard 3D modelling and animation software, such as Autodesk 3Ds Max or Maya.