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Center for Nonlinear Studies |
The contribution of renewable resources to the energy portfolio across the world has been steadily increasing over the past few years. Several studies predict the continuation of this trend in the future leading to large scale integration of renewable resources into energy networks. A principal challenge associated with this is the intermittency and non-dispatchability of the renewable sources. This necessitates the need to incorporate faster reserves, storage devices and similar services operating alongside the slow ramping conventional generators in the energy network. To maintain the robustness of such a network, there are proposals to require hourly planning for some resources, and sub-hourly planning for others: an hourly scale may be used for conventional generator production levels and a sub-hourly scale for renewable generator levels and/or storage and transmission network utilization. The talk will present a multiple time scale stochastic programming formulation of the economic dispatch problem and algorithmic frameworks to tackle it. The first approach highlights the difference between hourly and sub-hourly planning of economic dispatch and uses the two-stage Stochastic Decomposition(SD) algorithm. The second framework combines three principal components: optimization, dynamic control and simulation. The conventional generator decisions are obtained iteratively by solving a regularized linear problem in the first stage of SD. For these first stage decisions, a policy for recommending the dispatch decisions is identified using an Approximate Dynamic Programming based controller. A vector auto-regression based simulator is used to provide the sub-hourly wind generation scenarios. The performance of these algorithms was tested on the IEEE model networks and the Illinois network. The insights gained regarding the benefits of sub-hourly planning and role of operating reserves/storage in energy network with high renewable penetration will be presented.
Host: Feng Pan