Performance analysis of blockchain-based smart grid with Ethereum and Hyperledger implementations

Abstract. Smart grids lay the foundation for future communities. Smart homes, smart buildings, smart streets, and smart offices are built when intelligent devices piles on intelligent devices. To reach the maximum capacity, they all must be supported by an intelligent power supply. For optimal and real-time electricity consumption, monitoring and trading, blockchain possess number of potential benefits in its application to electricity infrastructure. A comprehensive system architecture of blockchain-based smart grid is proposed and peer-to-peer (P2P) energy trading is implemented between Distribution System Operators (DSO), Local energy providers and Consumers. This thesis presents a virtual smart grid equipped with smart contracts capable of virtual activities like market payment function and the comparison and the performance of the blockchain-based smart grid by using Ethereum and Hyperledger Fabric-based implementations. The challenges faced during the implementation of blockchain protocols are discussed and evaluation in the light of finding sustainable solutions to develop secure and reliable smart grid operations, is the major objective of the thesis

Realising Team-Working in the Field: An Agent-based Approach

Multi-agent systems technology is applied to enable co-operation between mobile workers in the field, minimising user intervention and increasing reachability. A component-based approach is taken to simplify the management of deployed co-operation services. A Personal Assistant running on a mobile device is introduced to show how an intelligent and autonomous agent can increase the utility of users during workforce co-operation processes. Finally, a real world trial of the technology by network installation and maintenance engineers in the UK is described. Some technical issues revealed during the trial are discussed, as is the impact of the technology on the business process

Simulation of complex environments:the Fuzzy Cognitive Agent

The world is becoming increasingly competitive by the action of liberalised national and global markets. In parallel these markets have become increasingly complex making it difficult for participants to optimise their trading actions. In response, many differing computer simulation techniques have been investigated to develop either a deeper understanding of these evolving markets or to create effective system support tools. In this paper we report our efforts to develop a novel simulation platform using fuzzy cognitive agents (FCA). Our approach encapsulates fuzzy cognitive maps (FCM) generated on the Matlab Simulink platform within commercially available agent software. We firstly present our implementation of Matlab Simulink FCMs and then show how such FCMs can be integrated within a conceptual FCA architecture. Finally we report on our efforts to realise an FCA by the integration of a Matlab Simulink based FCM with the Jack Intelligent Agent Toolkit

Embedded Trusted Monitoring and Management Modules for Smart Solar Panels

This paper investigates developing a prototype of smart solar panels. This architecture consists of a panel monitoring module and the central management unit. The monitoring module is to be embedded inside each PV panel making it secure to transfer the trusted data via Wi-Fi to the central Management unit (which can accommodate an array of PV panels in an installation). This module is required for data storage and provides the ability to upload secure data to the cloud. This platform presents the ability to securely manage large numbers of rooftop solar panels in a distributed ledger by implementing block chain algorithm. For achieving this purpose, Module 400 is envisaged to be turned into a Blockchain node as it provides the infrastructure to implement this technology

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

Is today's architecture about real space, virtual space or what?

Nowadays digital technologies and information and telecommunication technologies are widely used in every aspect of our lives. This article focuses on the digital technologies and their effect on the place-making activities. First an overview of the digital technologies for the creation, occupancy and management of a building is given. Secondly, the concepts of space and virtual space are discussed. Through these discussions, the concept of places and its virtual alternatives and recombination the use of space are described. Finally some concluding remarks are made on whether today’s place making activities about real space or it extends beyond that

An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange