The project is a joint effort by UConn, City of Hartford (the capital city of Connecticut), Town of Windham, and CCAT (Connecticut Center for Advanced Technology) to design resilient, sustainable, highly efficient yet affordable community microgrids, and optimize their performance for both daily and emergency operations. This objective will be achieved via building Microgrid Cloud, an Internet-of-Things-based integrative sensing, communication, computing and control framework, to perform continuous monitoring, analytics and optimization of community microgrid operations. The proposed platform will be deployed in Hartford and Windham to serve as a regional research test bed with higher microgrid reliability, reduced energy costs, improved environmental performance, and increased community income through enhanced energy efficiency.
Key technologies involve an IoT-enabled real-time data streaming, processing, storage and feedback architecture, scalable and dynamic resource management techniques for supporting multi-latency real-time data analytics, novel optimization techniques on community microgrid operation, and a hybrid programmable communication infrastructure for interconnecting community microgrid. As preparatory work, the community microgrid managers in CT municipalities will work with us to identify the requirements of daily and emergency operations, summarize the drawbacks of exiting solutions and techniques.
1. Design an IoT-enabled real-time data streaming, processing, storage and feedback architecture to collect asynchronous data in real-time from heterogeneous data sources in microgrids (distributed energy sources, hardware devices, microgrid utility information, etc.), and integrate them with information from outside the grid to support continuous monitoring, analytics and optimization on microgrid operation while satisfying their timing constraints.
2. Design mixed integer nonlinear optimization methods to optimize the operation of community microgrid, particularly addressing the uncertainty and dynamicity from intermittent renewable energy sources.
3. Design a hybrid programmable communication infrastructure and customized SDN-based techniques to meet the needs of highly resilient community microgrid (e.g., delay guarantee, automatic failure recovery, and communication speed control).
4. Collaborate with multiple municipalities to implement, deploy and customize the platform for multi-time-scale microgrid monitoring and analysis.
5. Summarize best practices and lessons learned. Develop a plan for incorporating other infrastructures (e.g., transportation, water, food) into the platform.
Key Performance Indicators (KPIs):
Energy service restoration time for infrastructures by 80%
Reduce peak energy loads by 10%
Increase use of renewable/sustainable energy feedstocks by 10%
Increase use of distributed generation by 10%
Increase use of energy storage, demand response, and peak load management by 10%
Reduce CO2 emissions by 10%
Energy cost reduction by 30%
1. Economic and cost data from municipalities.
2. Energy metering data
3. Online questionnaire to citizens
4. Community surveys.
Replicability, Scalability, and Sustainability:
1. Modular and hierarchical design that is scalable and reusable.
2. Using widely adopted standards (e.g., Openflow) and open source platform (e.g., Apache Kafka, Spark, Hadoop, HBase) whenever applicable.
3. Cloud-based data analytics platform enables the scalable usage of computing resources for both daily and emergence operations.
4. Certification of technology and interfaces whenever applicable.
1. A scalable, integrated sensing, data analytics, control, and optimization platform for smart communities
2. Enable aggregated local energy sources and storages for demand response and energy saving
3. Reduced energy outage and interruption risks
4. Significant carbon footprint reduction by support high penetration level of renewable energy
Team Information: Team lead:
Peng Zhang, [email protected]