TWC: Small: Privacy Preserving Cooperation among Microgrids for Efficient Load Management on the Grid

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Smart grid integrates sensors and communication infrastructure into the existing power grid to enable operational intelligence. The concept of microgrid is emerging in conjunction with the smart grid wherein small segments of the grid can be isolated into self-sufficient islands to feed their own demand load with their local energy, e.g., wind, solar. To date, microgrids begin to develop cooperative models for further improving the performance of global and local load management, such as global/local load balancing, energy exchange, and power transmission network topology design/upgrade with the integration of microgrids. However, all the cooperation among microgrids requests them to explicitly share their local sensitive grid operational information for global performance optimization, and thus compromises the privacy of microgrids. Then, microgrids' privacy concerns would impede the development and implementation of the cooperative models such that significant benefits via microgrids' cooperation on the power grid may not be available. This project tackles the privacy concerns in such cooperation, and enables microgrids to efficiently manage their local loads as well as facilitate the main grid to manipulate the global load with limited disclosure.This project proposes a suite of novel privacy preserving cooperative models/techniques for distributed microgrids to efficiently advance load management on the power grid. Provable privacy/security is ensured in the end-to-end process of cooperation, including privately analyzing data collected from different microgrids and privately implementing the schemes/solutions derived from the cooperative models, by composing cryptographic primitives with the secure multiparty computation (SMC) theory and/or imposing defined rigorous privacy notions. Ensuring privacy protection with rigorous standards will allow data to be collected and used in ways that were prohibitive earlier due to the privacy concerns, and then improve both operational efficiency and user acceptance. Load management via privacy preserving cooperation further optimally allocates distributed energy and minimizes the transmission and storage costs in a more secure, reliable and efficient smart grid infrastructure. This project also integrates research and education by exciting undergraduates to join the Science, Technology, Engineering and Math (STEM) research.