Optics for Cheap Solar with CPV Cells

s (In the order of sessions) Stochastic Multi-time Scale Algorithm for Economic Dispatch Problems with Intermittent Energy Sources Harsha Gangammanavar Integrated Systems Engineering, The Ohio State University, Columbus, OH 43210, USA Suvrajeet Sen Department of Industrial and Systems Engineering University of Southern California, Los Angeles, CA 90089, USA Session 1 (10:00am-12:00pm, March 21, Thursday) A principal challenge associated with integrating wind and other renewable resources into grid operations is their intermittent nature. Moreover, these sources of energy present sub-hourly fluctuations. One way to mitigate the impact of these fluctuations is to incorporate faster reserves, storage devices etc operating alongside the slower conventional generators in the energy network. To maintain the robustness of the grid, network operations must be planned at different time scales: conventional generators should continue to be planned at hourly intervals due to their operational constraints, whereas renewable generators should be planned and operated at sub-hourly intervals. This is what we consider as a multi-time scale planning problem, under uncertainty. In this regard we present two alternative multi-time scale stochastic programming formulations of the economic dispatch problem. The first one is a myopic model in which we plan for an hour by incorporating the sub-hourly decisions as a follow-on to the hourly decision. The second approach increases the planning period to two hours which allows the flexibility of modifying conventional generator levels at the beginning of the second hour. We develop an algorithmic framework in which the overall model is based on three principal components: stochastic programming, dynamic control and simulation. A stochastic program provides conventional generator decisions through Stochastic Decomposition method. For these decisions, dynamic control of renewables are recommended using Approximate Dynamic Programming. A recourse function corresponding to the generator plan and control decisions is evaluated which is used to iteratively update the stochastic program. Stochastic Decomposition operating at hourly and Approximate Dynamic Programming at sub-hourly time scale combine together to provide the optimal planning and operation decisions. A state-of-the-art forecast system like the NWP also helps in reducing the variability of system operation and hence the overall cost. Owing to the computational difficulties of these systems, only a limited number of forecasts are available. We use vector autoregression to model these ensembles which captures the spatial and temporal correlations. This model is then used to simulate the stochastic time series’ required by the sampling based multi-time scale algorithm. We present computational results on small scale, in-area energy microgrids with the two formulations. We analyze the scalability of the algorithm on a large real size energy network as well.