Student thesis: Phd


Smart Grid (SG) is an electrical grid enhanced with information and communication technology capabilities, so it can support two-way electricity and communication flows among various entities in the grid. The aim of SG is to make the electricity industry operate more efficiently and to provide electricity in a more secure, reliable and sustainable manner. Automated Meter Reading (AMR) and Smart Electric Vehicle (SEV) charging are two SG applications tipped to play a major role in achieving this aim. The AMR application allows different SG entities to collect users' fine-grained metering data measured by users' Smart Meters (SMs). The SEV charging application allows EVs' charging parameters to be changed depending on the grid's state in return for incentives for the EV owners.However, both applications impose risks on users' privacy. Entities having access to users' fine-grained metering data may use such data to infer individual users' personal habits. In addition, users' private information such as users'/EVs' identities and charging locations could be exposed when EVs are charged. Entities may use such information to learn users' whereabouts, thus breach their privacy.This thesis proposes secure and user privacy-preserving protocols to support AMR and SEV charging in an efficient, scalable and cost-effective manner.First, it investigates both applications. For AMR, (1) it specifies an extensive set of functional requirements taking into account the way liberalised electricity markets work and the interests of all SG entities, (2) it performs a comprehensive threat analysis, based on which, (3) it specifies security and privacy requirements, and (4) it proposes to divide users' data into two types: operational data (used for grid management) and accountable data (used for billing). For SEV charging, (1) it specifies two modes of charging: price-driven mode and price-control-driven mode, and (2) it analyses two use-cases: price-driven roaming SEV charging at home location and price-control-driven roaming SEV charging at home location, by performing threat analysis and specifying sets of functional, security and privacy requirements for each of the two cases.Second, it proposes a novel Decentralized, Efficient, Privacy-preserving and Selective Aggregation (DEP2SA) protocol to allow SG entities to collect users' fine-grained operational metering data while preserving users' privacy. DEP2SA uses the homomorphic Paillier cryptosystem to ensure the confidentiality of the metering data during their transit and data aggregation process. To preserve users' privacy with minimum performance penalty, users' metering data are classified and aggregated accordingly by their respective local gateways based on the users' locations and their contracted suppliers. In this way, authorised SG entities can only receive the aggregated data of users they have contracts with. DEP2SA has been analysed in terms of security, computational and communication overheads, and the results show that it is more secure, efficient and scalable as compared with related work.Third, it proposes a novel suite of five protocols to allow (1) suppliers to collect users accountable metering data, and (2) users (i) to access, manage and control their own metering data and (ii) to switch between electricity tariffs and suppliers, in an efficient and scalable manner. The main ideas are: (i) each SM to have a register, named accounting register, dedicated only for storing the user's accountable data, (ii) this register is updated by design at a low frequency, (iii) the user's supplier has unlimited access to this register, and (iv) the user cancustomise how often this register is updated with new data. The suite has been analysed in terms of security, computational and communication overheads.Fourth, it proposes a novel protocol, known as Roaming Electric Vehicle Charging and Billing, an Anonymous Multi-User (REVCBAMU) protocol, to support the priced-driven roaming SEV charg
Date of Award31 Dec 2015
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorNing Zhang (Supervisor) & Andrew Forsyth (Supervisor)


  • Roaming Electric Vehicle
  • Homomorphic encryption
  • Data Leakage
  • Selective Data Aggregation
  • Smart Electric Vehicle Charging
  • Advanced Metering Infrastructure
  • Automated Meter Reading
  • Smart Grid
  • Data Privacy
  • Security
  • Electric Vehicle

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