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A Mechanism for Electric Vehicles to Support the Power Grid of the Future

This project built an automated demand-response mechanism for a fleet of plug-in electric vehicles. The charging schedules generated can ensure that a new demand peak is not created and can reduce consumer cost by over 30% when compared to standard charging, which could also increase peak demand by 3.5%.
Year Started: 
2012

This project developed a mathematical optimization model for home energy management systems to efficiently shift electricity load when a plug-in electric vehicle and battery storage unit are added. The analysis showed that the consumer can benefit economically by shifting electricity use away from higher-priced periods. The researchers' simulation findings enhance the motivation for more intelligent electricity management in the residential sector. 

The proposed Optimal Load Shifting model and algorithm are readily implementable in industrial practice. The model has a modular design that is flexible and can be configured to accommodate added capabilities. The model model as developed consists of regular load shifting, thermal control, battery electricity storage system and automated windows. The model is capable of performing quickly, which is very important for a home energy management system operating in a real-time dynamic environment.

The researchers also developed a smart grid valuation framework that can be used to interpret the results produced by the model with respect to the efficiency of smart appliances and their respective prices. The framework is centered on four key questions: (1) which smart appliance provides the greatest overall savings?; (2) which smart appliance provides the greatest incremental savings?; (3) which smart appliance has the highest benefit/cost ratio?; (4) what incentives do smart appliances provide for behavioral changes? The project studied the model's benefits, the major concerns associated with its use in the real world, and possible future areas of investigation.

Further research is needed to apply the model to additional data sets that represent greater diversity in terms of pricing schemes, climate conditions and consumer preferences.

Publications: A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles IEEE Transactions on Smart Grid 4(4), 1772-1779 Taheri, N., Entriken, R., Ye, Y. (2013)

A Benefit-Cost Framework for Optimal Load Shifting Proceedings of 2014 Grid of the Future Symposium, CIGRE US National Committee Skorupski, R., Hu, L., Entriken, R., Ye, Y. (2014)

A Mathematical Formulation for Optimal Load Shifting of Electricity Demand Working paper