Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Cong Liu Project Name: power-grid-sim Division: DIS Project title: Novel Power System Operations Methods for Wind-powered Plug-in Hybrid Electric Vehicles Associated funding: LDRD, DOE-OE Other Systems: Science: In this project, the objective is to propose a novel power system operations method, namely a new stochastic unit commitment and contingency analysis, which can address the uncertainty and variability brought by wind and electric vehicles, while maintaining the stability and reliability of the system. The proposed method will make use of a unified high-performance parallel computing framework for solving the stochastic unit commitment problem as well as implementing contingency analysis. These capabilities can be used by system operators to manage the grid. It can also be applied in planning studies to assess the impact of high penetration of wind and PHEVs on the power system. Project description: The new tasks will be separated into two subtasks. 1.we will test the contingency analysis algorithm on 50000 buses power system which represents the eastern interconnection in north America. The biggest challenge of contingency analysis for bulk power systems is the limited computational capability for power flow optimization. Due to the nature of power flow computation, a thorough contingency analysis usually has to confront a brute-force enumeration of contingency scenarios. The number of scenarios is astronomically increased if multiple contingencies are taken into account at the same time (N-k). In addition, the extremely expanded scale of power systems also raises the computational cost for each single power flow run. Last but not least, there has been a need for near-real-time contingency analysis since off-line studies cannot reflect the accurate system conditions due to the mismatch between the real operating states and presumed conditions. However, taking advantage of modern monitoring techniques such as phasor meas urement units, contingency analysis tends to be implemented in real time. This kind of on-line contingency analysis calls for more advanced algorithms to achieve reasonable accuracy in a timely manner. 2. Stochastic optimization problems are suitable for high-performance parallel computing architectures since a large fraction of the variables of the problem can be decoupled between uncertainty realizations or scenarios. In the unit commitment problem, the number of coupling variables is proportional to the number of power units to be committed and the scheduling horizon. In the particular case of wind power generation, the number of units can increase dramatically. This loosely coupled structure can be decomposed at the model level (outside the optimization solver). Model-level decomposition techniques such as Lagrangean relaxation and Benders decomposition are easy to implement through a parallel computing way. in this year, we will test a algorithm by using Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 400000 Q1: 100000 Q2: 100000 Q3: 100000 Q4: 100000 Justification: Thank You, The LCRC Accounts System