Future Distribution Network Planning With Demand Response Applications

The philosophy in distribution network planning is continuously evolving to ensure an efficient, reliable and cost-effective network design. This is particularly important with the increasing presence of Distributed Generation (DG) and Demand Response (DR) integration at the distribution network. Thus, there is a need to develop distribution network modelling tool so that the associated impacts and benefits of such integration can be properly assessed and quantified. In light of this, this thesis presents a fractal-based approach to generate a large number of consumer settlements for low voltage distribution networks. Subsequently, branching rate and minimum spanning tree concepts have been applied to connect the load points and create the network for low voltage and medium voltage, respectively. The Particle Swarm Optimization (PSO) technique was then utilized to determine the optimum rating and placement of transformers, DG and capacitors. The developed simulation tool allows the modelling and planning of distribution network to be carried out in a systematic way. In addition, a total of 10,000 network case studies have been performed to assess the network performance under the influence of demand response and solar PV penetration levels. Three different demand response strategies have been considered in this work, namely, consumer response to their own demand profile, consumer response to PV generation profile and the consumer optimized demand response facilitated by smart grid application. Methodology for generating optimum DR pattern for 2,000 individual consumers have also been proposed and implemented with the aim to improve network load factor. These comprehensive analysis of the benefits of DR would enable a more meaningful and robust conclusion to be made. The findings show that DR application at consumer level can greatly facilitate the integration of solar PV systems. The DR benefits include reduced network losses and increased network asset utilization levels. Last but not least, this research work has filed a patent for the invention of Internet-of-Things based remote demand response and energy monitoring system that could be used as an enabler for demand response application in the actual environment

Content-access QoS in peer-to-peer networks using a fast MDS erasure code

This paper describes an enhancement of content access Quality of Service in peer to peer (P2P) networks. The main idea is to use an erasure code to distribute the information over the peers. This distribution increases the users’ choice on disseminated encoded data and therefore statistically enhances the overall throughput of the transfer. A performance evaluation based on an original model using the results of a measurement campaign of sequential and parallel downloads in a real P2P network over Internet is presented. Based on a bandwidth distribution, statistical content-access QoS are guaranteed in function of both the content replication level in the network and the file dissemination strategies. A simple application in the context of media streaming is proposed. Finally, the constraints on the erasure code related to the proposed system are analysed and a new fast MDS erasure code is proposed, implemented and evaluated

Evaluating distributed cognitive resources for wayfinding in a desktop virtual environment.

As 3D interfaces, and in particular virtual environments, become increasingly realistic there is a need to investigate the location and configuration of information resources, as distributed in the humancomputer system, to support any required activities. It is important for the designer of 3D interfaces to be aware of information resource availability and distribution when considering issues such as cognitive load on the user. This paper explores how a model of distributed resources can support the design of alternative aids to virtual environment wayfinding with varying levels of cognitive load. The wayfinding aids have been implemented and evaluated in a desktop virtual environment

Evaluating Resilience of Electricity Distribution Networks via A Modification of Generalized Benders Decomposition Method

This paper presents a computational approach to evaluate the resilience of electricity Distribution Networks (DNs) to cyber-physical failures. In our model, we consider an attacker who targets multiple DN components to maximize the loss of the DN operator. We consider two types of operator response: (i) Coordinated emergency response; (ii) Uncoordinated autonomous disconnects, which may lead to cascading failures. To evaluate resilience under response (i), we solve a Bilevel Mixed-Integer Second-Order Cone Program which is computationally challenging due to mixed-integer variables in the inner problem and non-convex constraints. Our solution approach is based on the Generalized Benders Decomposition method, which achieves a reasonable tradeoff between computational time and solution accuracy. Our approach involves modifying the Benders cut based on structural insights on power flow over radial DNs. We evaluate DN resilience under response (ii) by sequentially computing autonomous component disconnects due to operating bound violations resulting from the initial attack and the potential cascading failures. Our approach helps estimate the gain in resilience under response (i), relative to (ii)