Designing efficient demand management policies for charging Plug-in Hybrid Electrical Vehicles (PHEVs) and Plug-in Electrical Vehicles (PEVs) is becoming a vital issue as increasing numbers of these vehicles are being introduced to the power grid. 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 large number of PHEVs/PEVs simultaneously connect to the grid, the overall power system quality and stability could be severely affected due the large amount of power consumption. On the other hand, by smartly controlling the charging process, the utility can utilize the flexible nature of these loads for peak shaving and valley filling to improve the quality of power.
Conventionally, optimal managing of the charging process requires gathering data in a single node and performing a central optimization. However, as the scale of the problem increases to consider thousands of charging stations distributed over a vast geographical area, the central approach would suffer from vulnerability to single node/link failures as well as computational scalability. In this project, the central demand management unit is eliminated and the global optimal power allocation under all local and global constraints is reached by peer-to-peer coordination of charging stations. This approach is highly efficient in terms of computational and communicational effort, considering that the overall demand management problem is large scale and nonlinear. Moreover, using distributed approach, the demand management system gains robustness against single link/node failures.
Publications:
[1] N. Rahbari-Asr and M.-Y. Chow, “Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks,” IEEE Transactions on Industrial Informatics, vol. 10, no. 3, pp. 1907–1916, 2014.
[2] N. Rahbari-asr, M.-Y. Chow, Z. Yang, and J. Chen, “Network Cooperative Pricing Control System for Large-Scale Optimal Charging of PHEVs / PEVs,” in IECON 2013-39th Annual Conference on IEEE Industrial Electronics Society, 2013, pp. 6148–6153.
