Data Security and Privacy in Transactive Energy Markets

Abstract

Innovation in power generation, storage, and information technology have created new opportunities in grid management. Advanced metering equipment and smart grid infrastructure enable energy providers to operate more efficiently and cost effectively through better reporting and prediction. While these improvements to the traditional energy management model are important, opportunities for further gains in economic and energy efficiency have been discovered in distributed systems. The next generation of energy market will be transactive, enabling prosumers, consumers equipped with energy generation or storage devices, to trade energy directly between each other. This capacity will result in increased price efficiency, reduced transmission distances, and better integration of renewable energy sources into the grid. The incentive to modernize the grid is clear, but increased information flow demands heightened security measures to protect consumer safety and trust. Many proposed transactive energy market solutions use distributed ledger technology, or blockchain, along with smart contracts to underpin their energy auctions. Blockchain technology has many desirable security properties that make it suitable for handling trades. However, security gaps remain in the processes not managed by the blockchain. The goals of this thesis are to discover the cybersecurity gaps present in transactive energy market systems, identify the areas most in need of improvement, and propose solutions to some of those areas. A thorough review of the literature led us to identify fourteen cybersecurity threat categories. We selected two processes that were significantly affected by these threats and that had few solutions addressing them. These processes are: energy usage data collection and market anonymity. In addition to the literature review, this thesis contributes secure, privacy-preserving schemes for each of these processes, namely Cyclic Homomorphic Encryption Aggregation (CHEA) and Individually Linkable Pseudonymous Trading Scheme (ILPTS). Both schemes improve security and efficiency by reducing infrastructural requirements and increasing decentralization. Formal analysis found that both solutions successfully achieve their security design goals, while performance simulations found that CHEA performs well compared to similar data aggregation schemes from the literature.