Defining CPS Challenges in a Sustainable Electricity Grid

Cyber-Physical Systems (CPS) are characterized as complex distributed systems exhibiting substantial uncertainty due to interactions with the physical world. Today's electric grids are often described as CPS because a portfolio of distributed supplies must be dispatched in real-time to match uncontrolled, uncertain demand while adhering to constraints imposed by the intervening transmission and distribution network. With the increased control complexity required by deep penetration of fluctuating renewable supplies, the grid becomes more profoundly a CPS and needs to be addressed as a system. In this evolving CPS, a large fraction of supply is under-actuated, a substantial portion of demand needs to become dispatch able, interactions among distributed elements are no longer unidirectional, and operating requirements of elements are more dynamic. To more sharply define these CPS challenges, we obtain a yearlong, detailed measurement of the real-time blend of supplies on the primary California grid dispatched to meet current demand and then scale the solar and wind assets, preserving uncontrolled weather effects, to a level of penetration associated with California's 2050 GHG targets. In this representation of a future sustainable grid, we assess the impact of demand shaping, storage, and agility on the reconstituted supply portfolio, characterize resulting duration curves and ramping, and investigate the distributed control and management regime. We articulate new operational and market opportunities and challenges that may materialize from intermittent periods of abundance and scarcity in the overall energy network. We find that in a sustainable grid, lulls in renewable production during winter are more critical than peaks in demand during summer, capacity for load shifting and energy storage are more valuable as renewables penetration increases, and that grid balancing requires integrated management of supply and demand resources.

[1]  Alec Brooks,et al.  Demand Dispatch - Using Real-Time Control of Demand to help Balance Generation and Load , 2010 .

[2]  B. Roberts,et al.  Capturing grid power , 2009, IEEE Power and Energy Magazine.

[3]  J. Kiviluoma,et al.  Global potential for wind-generated electricity , 2009, Proceedings of the National Academy of Sciences.

[4]  Prashanth Mohan,et al.  Design and Evaluation of an Energy Agile Computing Cluster , 2012 .

[5]  Kamin Whitehouse,et al.  The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.

[6]  David E. Culler,et al.  Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control , 2012, Proceedings of the IEEE.

[7]  Steven D. Czajkowski Focusing on Demand Side Management in the Future of the Electric Grid , 2010 .

[8]  Randy H. Katz,et al.  NapSAC: design and implementation of a power-proportional web cluster , 2010, CCRV.

[9]  T Joseph Lui,et al.  Get Smart , 2010, IEEE Power and Energy Magazine.

[10]  David J. C. MacKay Sustainable Energy - Without the Hot Air , 2008 .

[11]  Tajana Rosing,et al.  Utilizing green energy prediction to schedule mixed batch and service jobs in data centers , 2011, OPSR.

[12]  Bruno Sinopoli,et al.  A Cyber–Physical Systems Approach to Data Center Modeling and Control for Energy Efficiency , 2012, Proceedings of the IEEE.

[13]  Jay Taneja,et al.  Towards Cooperative Grids: Sensor/Actuator Networks for Renewables Integration , 2010, 2010 First IEEE International Conference on Smart Grid Communications.