by Admin Admin | Nov 24, 2014 | Announcement
Potential PhD positions are available for highly motivated students with Master’s degree in Electrical Engineering and background in Systems and Control. The project involves using analytical skills to design and implement Distributed Energy Management algorithms for different applications such as renewable energy and energy storage integration, home energy management, microgrid, etc. The students will work with ADAC group members as a team to design intelligent algorithms and implement them in embedded systems.
Requirements:
Interested students with the following requirements are encouraged to apply:
- Master’s Degree in Electrical Engineering
- Strong Background in System and Control
- Good programming skill
- Good communication and social skills
If you are interested, please directly contact Prof. Mo-Yuen Chow at chow@ncsu.edu
by Admin Admin | Nov 14, 2014 | Completed
Project Description
The amount of data collected in Electric Vehicles has been growing fast because we have many more sensors, higher bandwidth communication systems, and cheaper memory to monitor and measure real-time driving range related data and store the data on the vehicles, in connected clouds, etc. This massive amount of data can have different levels of accuracy, resolutions, and relevance in unstructured ways. Big Data technologies have been emerging to address huge, diverse and unstructured data to substantially improve the overall system performance. With proper use of Big Data concepts and techniques, the remaining driving range estimation of the vehicle can be substantially improved.
The range estimation needs the incorporation and synchronization of all standard, real-time and historical data. Usually, the standard and historical data provides an initial prediction of the driving range; and the real-time data updates the estimation during the driving. However, under different conditions, some data are more relevant than others for the range estimation. This data can be historical, standard, or real-time depending on different situations. The big data analytics helps us identify the relevant data and discover its correlation to the remaining driving range estimation.
Publication
- H. Rahimi-Eichi and M.-Y. Chow, “Big-Data Framework for Electric Vehicle Range Estimation,” presented at the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON2014), IEEE, Dallas, TX , 2014.
Link
Samsung Advanced Institute of Technology (SAIT)
by Admin Admin | Nov 13, 2014 | Current
Project Description:
The efficiency, reliability, economics and sustainability of the production and distribution of electricity are the major concerns of all the power utilities and customers. Without large investments in new transmission capacity and generation sources to keep up with the growth in demand and corresponding investment, the existing early-twentieth-century-designed power grids would face a lot of challenges. An innovative and economical way of solving these challenges is the introduction of “microgrid” concept. Microgrids are electricity distribution systems consisting of local loads and distributed energy resources such as distributed generators, storage devices, and responsive demands as cheap spinning reserves. This flexibility of generation and demand available in the microgrids along with the access to renewables, can dramatically improve the power quality and reliability of the power networks at the distribution level. However, economically and intelligently managing these energy resources to improve the power quality, maximize the benefit of the unit owners while ensuring their privacy, requires advanced optimization and control technologies.
The objective of ADAC Distributed Intelligent Energy Management (DIEM) project is to design a robust cooperative distributed energy management framework for the microgrids to coordinate and schedule energy generation, energy storage and energy demand at different levels to achieve global optimal in terms of economic operation while satisfying the quality constraints. The envisioned framework is scalable, robust to communication imperfections (such as time delay, packet losses), and robust to single point of failures.
Because of the distributed nature of the controllable devices (e.g., distributed generations and controllable loads), agent-based system modeling is used by utilizing the graph theory. Assuming a two-way communications among distributed controllable devices, the system can be modeled using adjacency matrix based on the availability of communication link between each pair of agents. Based on the communications network, consensus and gossip based algorithm are applied for the development of the DIEM algorithms.
Publications:
- Z. Zhang and M.-Y. Chow, “Convergence Analysis of the Incremental Cost Consensus Algorithm Under Different Communication Network Topologies in a Smart Grid,” Power Systems, IEEE Transactions on, vol. 27, no. 4, pp. 1761–1768, 2012.
- Z. Zhang, Y. Zhang, and M.-Y. Chow, “Distributed energy management under smart grid plug-and-play operations,” in Power and Energy Society General Meeting (PES), 2013, no. Ic, pp. 1–5.
- N. Rahbari-asr, Y. Zhang, and M. Chow, “Stability Analysis for Cooperative Distributed Generation Dispatch in a Cyber-Physical Environment,” in IECON 2014 – 40th Annual Conference on IEEE Industrial Electronics Society, Dallas, TX, USA, 2014.
- Y. Zhang and M.-Y. Chow, “Hybrid Incremental Cost Consensus Algorithm for Smart Grid Distributed Energy Management under Packet Loss Environment,” in IECON 2014 – 40th Annual Conference on IEEE Industrial Electronics Society, Dallas, TX, USA, 2014.
- N. Rahbari-Asr, Z. Zhang, and M.-Y. Chow, “Consensus-Based Distributed Energy Management with Real-Time Pricing,” in 2013 IEEE Power & Energy Society General Meeting, 2013, pp. 1–5.
- N. Rahbari-Asr, U. Ojha, Z. Zhang, and M.-Y. Chow, “Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart-Grid,” IEEE Transactions on Smart Grid, vol. 5, no. 6, pp. 2836–2845, 2014.
Links:
by Admin Admin | Aug 22, 2014 | Internships
About the Project
Students built a fast-prototyping experimental platform for future battery tests. By implementing the battery monitoring algorithms on this PHEV testbed, the algorithms can be evaluated and optimized and thus make PHEV batteries more reliable.
Documentations
