Within the next 5 years, there will be a significant increase in the number of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs). Since these vehicles utilize grid power for charging, this growth could pose potential threats and benefits for the existing power grid. The main challenge is that when a large number of PHEVs/PEVs simultaneously connect to the grid, the overall power system quality and stability can be severely affected due the large amount of power consumption. The addition of PHEVs and PEVs into our society is an exciting view into the future of electric vehicles, but the power grid must be able to support these new vehicles. By smartly controlling their charging process, the utility can utilize the flexible nature of these loads for peak shaving and valley filling to improve the quality of power. Since these vehicles also have the potential to transfer power back into the grid when it is needed (i.e., when the demand for power is high), a more robust power grid, or ‘smart grid’, is needed.
This project deals project with the pros and cons of PHEVs/PEVs by formulating optimization problems that consider power grid stability and customer satisfaction concurrently through proper choice of the cost function and equality and inequality constraints. Two main approaches are then considered for online, real-time solutions of these optimization problems:
- Centralized Approach: In this approach, the information regarding the charging process of vehicles such as SoC, Energy Cost, Time of Stay, Available Power and etc. is sent to a central control unit, which based on power system stability and customer satisfaction allocates the charging powers to the vehicles for each time step. Various algorithms such as Particle Swarm Optimization (PSO), Auction Theory and Estimation of Distributed Algorithms (EDA) are used to deal with the nonlinear and large scale nature of the problem.
- Distributed Approach: In this approach the central unit is eliminated and charging stations cooperatively find the optimal power allocations by exchanging information among each other. This approach is highly efficient in terms of computational and communicational effort, considering that the overall optimization problem is large scale and nonlinear.
To demonstrate our algorithms, we have developed a digital testbed for a large-scale PHEV/PEV parking deck in Matlab/Simulink and Labview. This testbed is able to show a reliable two-way communication system between all the parties involved. This includes vehicles, charging stations, and the Intelligent Energy Management System (iEMS). Communication can occur through both the Internet and the ZigBee network. We then use Monte Carlos simulation to evaluate the effectiveness of our algorithms.
Documentations
- “A Digital Testbed for a PHEV/PEV Enabled Parking Lot in a Smart Grid Environment“, 2012 Innovative Smart Grid Technologies Conference (ISGT 2012), Washington D.C., U.S.A., Jan 18, 2012
- “Performance Optimization of a PHEV/PEV Enabled Municipal Parking Deck in a Smart Grid Environment“, 2011 IEEE Power and Energy Society General Meeting, Detroit, Michigan, U.S.A., July 24, 2011
- “Investigating a Large-scale PHEV/PEV Parking Deck in a Smart Grid Environment“, 43rd North American Power Symposium, Boston, MA, August 4-6, 2011
- “Microgrid Planning and Operation: Solar Energy and Wind Energy“, 2010 IEEE Power and Energy Society General Meeting, Minneapolis, Minnesota, U.S.A. July 25, 2010
- “ An Intelligent Energy Management System for Charging PHEVs Considering Demand Response,” 2010 FREEDM Annual Conference, Tallahassee, Florida, U.S.A, May 18, 2010
