Real-time deterministic power flow control through dispatch of distributed energy resources

Integration of intermittent renewable resources and mass electrification of heat and transport into the existing electricity network, with limited network asset reinforcement requires incorporation of intelligence in form of active management of flexible resources within different sections of the distribution network. A hierarchical multi-level control framework is proposed for this purpose which incorporates the appropriate optimisation and control strategies at different levels. In particular a novel deterministic control algorithm for controlling power flows at the community cell level has been developed and presented in this paper. This algorithm incorporates robustness to communication and device failure and is easily expandable to an arbitrary number of devices. The simulation results presented in this paper show that the effectiveness of the proposed control technique depends on distributed energy resources flexibility and storage capacity

Optimal multistage PMU placement for wide-area monitoring

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Analysis and Design of Privacy-Enhancing Information Sharing Systems

Recent technological advancements have enabled the collection of large amounts of personal data of individuals at an ever-increasing rate. Service providers, organisations and governments can collect or otherwise acquire rich information about individuals’ everyday lives and habits from big data-silos, enabling profiling and micro-targeting such as in political elections. Therefore, it is important to analyse systems that allow the collection and information sharing between users and to design secure and privacy enhancing solutions. This thesis contains two parts. The aim of the first part is to investigate in detail the effects of the collateral information collection of third-party applications on Facebook. The aim of the second part is to analyse in detail the security and privacy issues of car sharing systems and to design a secure and privacy-preserving solution. In the first part, we present a detailed multi-faceted study on the collateral information collection privacy issues of Facebook applications; providers of third-party applications on Facebook exploit the interdependency between users and their friends. The goal is to (i) study the existence of the problem, (ii) investigate whether Facebook users are concerned about the issue, quantify its (iii) likelihood and (iv) impact of collateral information collection affecting users, (v) identify whether collateral information collection is an issue for the protection of the personal data of Facebook users under the legal framework, and (vi) we propose solutions that aim to solve the problem of collateral information collection. In order to investigate the views of the users, we designed a questionnaire and collected the responses of participants. Employing real data from the Facebook third-party applications ecosystem, we compute the likelihood of collateral information collection affecting users and quantify its significance evaluating the amount of attributes collected by such applications. To investigate whether collateral information collection is an issue in terms of users’ privacy we analysed the legal framework in light of the General Data Protection Regulation. To provide countermeasures, we propose a privacy dashboard extension that implements privacy scoring computations to enhance transparency towards collateral information collection