Transitioning our aging power grid to a smart grid brings a number of benefits including increased efficiency and sustainability as renewable resources (e.g., solar and wind) are incorporated into the grid. Additionally, it will also improve reliability and resiliency as smart sensors will be deployed throughout the grid to detect, respond, and adapt to events such as faults and failures. Finally, through smart meters and other Internet-of-Things devices in smart homes, the smart grid will also bring economical benefits to both power producers and consumers, as producers can introduce real-time pricing to reduce peak power consumption, and home automation systems of consumers can adapt accordingly while satisfying the needs and constraints of the homeowners.
We are currently pursuing a number of parallel subprojects within this area:
- We are investigating appropriate formalisms and algorithms for the elicitation and scheduling problem of home automation systems. Home automation systems need to intelligently elicit preferences and constraints from homeowners regarding their use of smart devices as exhaustive elicitation is not feasible. Such systems also need to schedule the devices in an efficient manner while optimizing certain objectives (e.g., minimizing the monetary cost to the homeowner, minimizing the peak power demand) while satisfying the preferences and constraints elicited.
- We are working towards transitioning off-the-shelf scheduling and coordination algorithms towards a working prototype of a home automation system. The goal is to deploy this prototype system in our microgrid at NMSU. Such a system can also be a testbed for the algorithms developed above. We also have a dataset that we curated for this problem.
- We are also participating in PowerTAC – a trading agent competition, where an autonomous agent plays the role of a broker in an energy marketplace. The goal of the agent is to buy energy from wholesalers and sell them to consumers in such a way that maximizes their profit.
- Huiping Cao, New Mexico State University
- Ferdinando Fioretto, Georgia Institute of Technology
- Christopher Kiekintveld, University of Texas, El Paso
- Tiep Le, New Mexico State University
- Enrico Pontelli, New Mexico State University
- Satishkumar Ranade, New Mexico State University
- Tran Cao Son, New Mexico State University
- Atena M. Tabakhi, Washington University in St. Louis
- Long Tran-Thanh, University of Southampton
RI: Small: Collaborative Research: Preference Elicitation and Device Scheduling for Smart Homes.
National Science Foundation (2018 – 2021).
iCREDITS: Interdisciplinary Center of Research Excellence in Design of Intelligent Technologies for Smartgrids.
National Science Foundation (2014 – 2019).
- Moinul Morshed Porag Chowdhury, Christopher Kiekintveld, Tran Cao Son, and William Yeoh. “Bidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search.” In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 166-172, 2018.
- Tiep Le, Atena M. Tabakhi, Long Tran-Thanh, William Yeoh, and Tran Cao Son. “Preference Elicitation with Interdependency and User Bother Cost.” In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 1459-1467, 2018.
- Atena M. Tabakhi, Tiep Le, Ferdinando Fioretto, and William Yeoh. “Preference Elicitation for DCOPs.” In Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP), pages 278-296, 2017.
- Ferdinando Fioretto, William Yeoh, and Enrico Pontelli. “A Multiagent System Approach to Scheduling Devices in Smart Homes.” In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 981-989, 2017.
- Ferdinando Fioretto, William Yeoh, Enrico Pontelli, Ye Ma, and Satishkumar Ranade. “A Distributed Constraint Optimization (DCOP) Approach to the Economic Dispatch with Demand Response.” In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 999-1007, 2017.