Biblio

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks.

As smart meters continue to be deployed around the world collecting unprecedented levels of fine-grained data about consumers, we need to find mechanisms that are fair to both, (1) the electric utility who needs the data to improve their operations, and (2) the consumer who has a valuation of privacy but at the same time benefits from sharing consumption data. In this paper we address this problem by proposing privacy contracts between electric utilities and consumers with the goal of maximizing the social welfare of both. Our mathematical model designs an optimization problem between a population of users that have different valuations on privacy and the costs of operation by the utility. We then show how contracts can change depending on the probability of a privacy breach. This line of research can help inform not only current but also future smart meter collection practices.