This work proposes the use of blockchain technology to create a Decentralized Transactive Energy platform for Peer-to-Peer Energy Trading without authorized third-party agents. The distributed double auction mechanism is used in the proposed model as it enables every peer in the system to become an auctioneer, where the blockchain component will eliminate the single point of attack and guarantee the legal actions of all peers with the secure process of transactions. The main contribution of the paper is the combined use of Demand Response with a Decentralized Network. By considering optimal prosumer scheduling in the system, both preferences and needs of the peers in the network will be maximized. Experimental testing of the proposed strategy shows quick convergence of the decentralized auction, loss minimization caused by long transmission, improved security, and efficiency of the system.

The present-day advances in technologies provide the opportunities to pave a road from conventional power systems towards smart grids. As a result, smart grid features enable us to analyze the electricity usage data and identify electricity consumption patterns. This paper provides an analysis of half-hourly electricity consumption in domestic regions of the UK using clustering methods. To decrease the data dimensions and make it convenient to work with, unsupervised clustering methods such as k-means and Self-Organizing Maps are used for load profiling. The households are divided into several types and clusters, depending on the number of bedrooms and their daily electricity consumption patterns. Clustering is performed every day for different seasons providing intra-daily and seasonal variations. Probabilistic Neural Network is implemented to train the labeled dataset based on the clusters which identify load profile classes. The paper provides an investigation of the interconnection between house types and profile classes.