Distributed Cooperative Performance Optimization for Large-scale PHEV/PEV Charging
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.
Links
AIS Gene Library Based Real-Time Resource Allocation on Time-Sensitive Large-Scale Multi-Rate Systems
When looking forward towards Intelligent Transportation Systems (ITS), driver warning systems are an integral part of an ITS. There is a need for warning systems that can integrate the information that is currently available in the vehicles with the information about the environment in order to make more informed and accurate decisions. These warning systems should be supported by roadside infrastructures for the acquisition and processing of global/environmental information. In such systems, the roadside infrastructures need to communicate a large amount of time-sensitive data to many of the vehicles. In such a large-scale time-sensitive system, real-time information extraction (e.g. determining the risk for each vehicle) and optimal resource (e.g. bandwidth) allocation are crucial yet computationally demanding.
This project will investigate and develop gene library based real-time information extraction and resource allocation methodology that can be adaptively tuned using the concepts of Artificial Immune System (AIS). This gene library is designed to extract only the relevant information from a vehicle to determine abnormality/risk in vehicle movements at various traffic environments and to provide optimal real-time sampling rate adaptations and emergency interventions based on the information.
Documentations
Links
Control of Dynamic Network based Systems under Conditions of Partial or Missing Information using Stochastic Resonance
Intelligent Network-Based Control
Fig. 1 Intelligent Space via Network-Based Control.
Project Description:
The main objective of the Intelligent Network-Based Control Project is to develope a wireless unmanned vehicle to find its way through a platform with obstacles. It should arrive at the destination specified by a user in a remote location using the shortest amount of time, and avoiding any collisions.
Main Focuses:
- How to recognize the vehicle and obstacles.
- How to generate a path around the obstacles in the shortest amount of time.
- How network delays contribute to control errors.
The vision of the vehicle is established by a wireless network camera placed on top of the platform. The vehicle is battery operated and completely controlled by wireless communication via IP network. Image processing technology, as well as hardware and software implementations are utilized to demonstrate the path generation and path tracking algorithms. Gain Scheduler Control algorithm may also be implemented in this project to compensate for any network delay disturbance.
Fig. 2 Path generation algorithm.
Publications:
Y. Tipsuwan, M.-Y Chow, “Neural Network Middleware for Model Predictive Path Tracking of Networked Mobile Robot over IP Network,” IEEE IECon’03, Roanoke, VA, Nov 2 – Nov 6, 2003.
Y. Tipsuwan, M.-Y. Chow, “An Implementation of a Networked PI Controller over IP Network,” IEEE IECon’03, Roanoke, VA, Nov 2 – Nov 6, 2003.
Y. Tipsuwan, M.-Y. Chow, “On the Gain Scheduling for Networked PI Controller Over IP Network,” 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Port Island, Kobe, Japan, July 20-24, 2003.
Y. Tipsuwan, M.-Y. Chow, “Gain adaptation of mobile robot for compensating QoS deterioration,” Proceedings of IECon’02, Sevilla, Spain, November 5 – 8, 2002.
Y. Tipsuwan, M.-Y. Chow, “Network-Based Controller Adaptation Based On QoS Negotiation and Deterioration,” IECon01, Denver, CO, Nov.28-Dec.02, 2001, pp. 1794 -1799.
Y. Tipsuwan, M.-Y. Chow, “Network-based control adaptation for network QoS variation,” MILCOM 2001, October 28-31, 2001, McLean, VA, pp. 257-261.
M.-Y. Chow, Y. Tipsuwan, “Gain Adaptation of Networked Dc Motor Controllers on QoS Variations,” IEEE Transactions on Industrial Electronics, Vol. 50, no. 5, October, 2003.
Y. Tipsuwan and M.-Y. Chow, “Control Methodologies in Networked Control Systems,” Control Engineering Practice, vol. 11, 2003, pp.1099-1111.
M.-Y. Chow, “Methodologies in Time Sensitive Network-Based Control Systems,” 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Port Island, Kobe, Japan, July 20-24, 2003.
M.-Y. Chow, Y. Tipsuwan, “Real Time Network-Based Control System,” IEEE IECon 2002 Tutorial, Sevilla, Spain, November 5, 2002.
M.-Y. Chow, Y. Tipsuwan, “Network-Based Control Systems: A Tutorial,” Proceedings of IEEE IECon 2001 Tutorial, November 28 – December 2, Denver, CO, pp. 1593 -1602.
