Renewable Energy and Power Management in Smart Transportation
This paper designs a heuristic-based charging scheduler capable of integrating renewable energy for electric vehicles, aiming at reducing power load induced from the large deployment of electric vehicles. Based on the power consumption profile as well as the preemptive charging task model which includes the time constraint on the completion time, a charging schedule is generated as a M ×N allocation table, where M is the number of time slots and N is the number of tasks. Basically, it assigns the task operation to those slots having the smallest power load until the last task allocation, further taking different allocation orders according to slack, operation length, and per-slot power demand. Finally, the peaking task of the peaking slot is iteratively picked to supply renewable energy stored in the battery device. The performance measurement result shows that our scheme can reduce the peak load by up to 37.3 % compared with the Earliest allocation scheme for the given amount of available renewable energy.
Keywordssmart grid charging scheduler preemptive task model renewable energy peak load reduction
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- 1.Gellings, C.W.: The Smart Grid: Enabling Energy Efficiency and Demand Response. CRC Press (2009)Google Scholar
- 2.Spees, K., Lave, L.: Demand Response and Electricity Market Efficiency. The Electricity Journal, 69–85 (2007)Google Scholar
- 3.Sortomme, E., Hindi, M., MacPherson, S., Venkata, S.: Coordinated Charging of Plug-in Hybrid Electric Vehicles to Minimize Distribution System Losses. IEEE Transactions on Smart Grid, 198–205 (2011)Google Scholar
- 4.Tremblay, O., Dessaint, L.: Experimental Validation of a Battery Dynamic Model for EV Applications. World Electric Vehicle Journal 3 (2009)Google Scholar
- 5.Lopes, L., Almeida, P., Silva, A.: Smart Charging Strategies for Electric Vehicles: Enhancing Grid Performance and Maximizing the Use of Variable Renewable Energy Sources. In: Proc. 24th International Battery Hybrid Fuel Cell Electric Vehicle Symposium (2009)Google Scholar
- 8.Ding, J., Somani, A.: A Long-Term Investment Planning Model for Mixed Energy Infrastructure with Renewable Energy. In: IEEE Technologies Conference, pp. 1–10 (2010)Google Scholar
- 10.Palomares-Salas, J., Rosa, J., Ramiro, J., Melgar, J., Aguera, A., Moreno, A.: Comparison of Models for Wind Speed Forecasting. In: Proc. International Conference on Computational Science (2009)Google Scholar
- 11.Caron, S., Kesidis, G.: Incentive-Based Energy Consumption Scheduling Algorithms for the Smart Grid. In: IEEE SmartGridComm (2010)Google Scholar
- 12.Lee, J., Park, G., Kim, H., Jeon, H.: Fast Scheduling Policy for Electric Vehicle Charging Stations in Smart Transportation. In: ACM Research in Applied Computation Symposium, pp. 110–112 (2011)Google Scholar
- 13.Derin, O., Ferrante, A.: Scheduling Energy Consumption with Local Renewable Micro-Generation and Dynamic Electricity Prices. In: First Workshop on Green and Smart Embedded System Technology: Infrastructures, Methods, and Tools (2010)Google Scholar