| Title | (Invited Paper) Applying VLSI EDA to Energy Distribution System Design |
| Author | *Sani Nassif, Gi-Joon Nam, Jerry Hayes (IBM Austin Research Laboratory, U.S.A.), Sani Fakhouri (University of California, Irvine, U.S.A.) |
| Page | pp. 91 - 96 |
| Keyword | EDA, Energy distribution network, Simulation, Optimization |
| Abstract | Energy distribution networks refer to that part of the electricity network that delivers power to homes and business. It is reported that significant amounts of energy are being wasted simply due to inefficiencies in this network. Further, this domain is rapidly changing with new types of loads such as electric vehicles or the spread of new types of energy sources such as photo-voltaic and wind. In this paper, we demonstrate a comprehensive design automation capability for energy distribution networks leading to much more flexible yet effective system. The new system's capabilities include power load distribution and transfers, equipment upgrading, geospatial-aware network optimization, outage identification, contingency planning and loss analysis/reduction. These features are enabled by advanced simulation, analysis and optimization engines that are adapted from those available in the traditional VLSI design automation area. The paper will conclude with potential future research directions that require further innovations in energy distribution networks. |
| Title | (Invited Paper) A Model-Based Design of Cyber-Physical Energy Systems |
| Author | Mohammad Abdullah Al Faruque, *Fereidoun Ahourai (University of California, Irvine, U.S.A.) |
| Page | pp. 97 - 104 |
| Keyword | Model-based design, Cyber-physical systems, cyber-physical energy system, gridlab-d, co-simulation |
| Abstract | Cyber-Physical Energy Systems (CPES) are an amalgamation of both power gird technology, and the intelligent communication and co-ordination between the supply and the demand side through distributed embedded computing. Through this combination, CPES are intended to deliver power efficiently, reliably, and economically. The design and development work needed to either implement a new power grid network or upgrade a traditional power grid to a CPES-compliant one is both challenging and time consuming due to the heterogeneous nature of the associated components/subsystems. The Model Based Design (MBD) methodology has been widely seen as a promising solution to address the associated design challenges of creating a CPES. In this paper, we demonstrate a MBD method and its associated tool for the purpose of designing and validating various control algorithms for a residential microgrid. Our presented co-simulation engine GridMat is a MATLAB/Simulink toolbox; the purpose of it is to co-simulate the power systems modeled in GridLAB-D as well as the control algorithms that are modeled in Simulink. We have presented various use cases to demonstrate how different levels of control algorithms may be developed, simulated, debugged, and analyzed by using our GridMat toolbox for a residential microgrid. |
| Title | (Invited Paper) The Data Center as a Grid Load Stabilizer |
| Author | Hao Chen, Michael C. Caramanis, *Ayse K. Coskun (Boston University, U.S.A.) |
| Page | pp. 105 - 112 |
| Keyword | demand response, regulation service, data center energy management, power market |
| Abstract | To accommodate the increasing presence of volatile
and intermittent renewable energy sources in power generation,
independent system operators (ISO) offer opportunities for demand
side regulation service (RS) so as to stabilize the grid load.
These power market features allow the demand side to earn monetary
credits by modulating its power consumption dynamically
following an RS signal broadcast by ISO. This paper studies the
capacities and benefits of a major potential demand side, the data
center, to provide RS. We propose a dynamic control policy that
modulates the data center power consumption in response to ISO
requests by leveraging server power capping techniques and various
server power states. Results demonstrate that using our
policy, data centers can provide fast reserves in quantities that
are substantial proportions (around 50%) of their average energy
consumption, with no major deterioration in quality of service
(QoS). By doing so, data centers decrease their energy costs
around 50%, while providing the ISOs and the society in general
with cost effective demand side reserves that render massive renewable
generation adoption affordable. |
| Slides |