A Consumer Level Simulation Model For Demand Response Analysis On Smart Grid

With the growing awareness of the need for Smart Grid, various countries are taking initiatives for developing Smart Grid. However, there is limited research on utilizing Smart Grid for Demand-Response (DR). This study advances the current system of DR by creating a Smart Grid Simulator that allows an intuitive demand response analysis. The simulator demonstrates that substantial amount of electric power can be reduced efficiently by selective demand control over Smart Grid. The graphical interface allows generating the electrical usage data and displays both individual and aggregate usage data over time. This research employs U.S. census data for accurate estimate of family and life style as electric power usage is simulated by taking various inputs including the number of houses, family size, work and life patterns, etc. This study explores potential Privacy issues in Smart Grid and suggests data anonymization as a viable solution for preventing them. Moreover, this study proposes directions for future research on electric devices control using Smart Grid Simulator

Developing a Simulation Model for Power Demand Control Analysis and Privacy Protection in Smart Grid

With the growing awareness of the need for Smart Grid, various countries are taking initiatives for developing Smart Grid. However, there is limited research on utilizing Smart Grid for Power Demand Control compared to the other areas such as Smart Grid communication network or renewable energy integration. Therefore, this study attempts to help the current Outage Management System by creating a Simulator that allows an intuitive power demand control analysis. Electrical usage is simulated by taking various inputs including the number of houses, family size, work and life patterns, electrical devices, time, etc. For accurate estimate of family and life style, US census data has been used. The graphical interface allows generating the electrical usage data, then it displays both individual and aggregate usage data over time. Through the two-way communication capability of Smart Grid, the devices can be remotely and dynamically controlled by the provider to meet the power supply condition. The simulator demonstrates that substantial amount of electric power can be reduced efficiently by selective demand control over Smart Grid. Furthermore, this study explores potential privacy issues in Smart Grid and suggests data anonymization as a viable solution. Recommendations for future studies are proposed as well

Towards a Secure Smart Grid Storage Communications Gateway

This research in progress paper describes the role of cyber security measures undertaken in an ICT system for integrating electric storage technologies into the grid. To do so, it defines security requirements for a communications gateway and gives detailed information and hands-on configuration advice on node and communication line security, data storage, coping with backend M2M communications protocols and examines privacy issues. The presented research paves the road for developing secure smart energy communications devices that allow enhancing energy efficiency. The described measures are implemented in an actual gateway device within the HORIZON 2020 project STORY, which aims at developing new ways to use storage and demonstrating these on six different demonstration sites.Comment: 6 pages, 2 figure

Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices open up tremendous opportunities for managing smart city infrastructures. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus of this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared as opposed to sharing data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system is evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed

Smart Meter Privacy with an Energy Harvesting Device and Instantaneous Power Constraints

A smart meter (SM) periodically measures end-user electricity consumption and reports it to a utility provider (UP). Despite the advantages of SMs, their use leads to serious concerns about consumer privacy. In this paper, SM privacy is studied by considering the presence of an energy harvesting device (EHD) as a means of masking the user's input load. The user can satisfy part or all of his/her energy needs from the EHD, and hence, less information can be leaked to the UP via the SM. The EHD is typically equipped with a rechargeable energy storage device, i.e., a battery, whose instantaneous energy content limits the user's capability in covering his/her energy usage. Privacy is measured by the information leaked about the user's real energy consumption when the UP observes the energy requested from the grid, which the SM reads and reports to the UP. The minimum information leakage rate is characterized as a computable information theoretic single-letter expression when the EHD battery capacity is either infinite or zero. Numerical results are presented for a discrete binary input load to illustrate the potential privacy gains from the existence of a storage device.Comment: To be published in IEEE ICC201

Preserving prosumer privacy in a district level smart grid

This study presents the anonymization of consumer data in a district-level smart grid using the k-anonymity approach. The data utilized in this study covers the demographic information and associated energy consumption of consumers. The anonymization process is implemented at the prosumer level, considering their importance in sharing flexibility and distributed generation at the low voltage grid, and the fact that they need to interact with each other and the grid while keeping their data private. The proposed approach is tested under three anonymization scenarios: prosecutor, journalist, and marketer. The smart grid data are investigated mostly under the prosecutor scenario with three risk levels: lowest, medium and highest. The results of the k-anonymity approach are compared to k-map and k-map + k-anonymity. No difference has been found between the three investigated approaches for the selected data set. Since, the aim of the k-anonymity is to not transform the information about any individual record among those k-1 individuals, the recorded type and the number of attributes play a key role in the anonymization process. One of the risks is the using continuous attributes in the anonymization process which may cause the information lose in the anonymization process such as near real-time energy consumptions. Hence we have focused on to anonymization of the consumers' demographic information, rather than their energy consumption