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A Stochastic Programming Based Scheduling and Dispatch for Smart Grid with Intermittent Wind Power | OMICS International | Abstract
ISSN: 2229-8711

Global Journal of Technology and Optimization
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Research Article

A Stochastic Programming Based Scheduling and Dispatch for Smart Grid with Intermittent Wind Power

Mingsheng Gao*, Jian L, Wei Li and Xiao Yao

College of Internet of Things Engineering, Hohai University, China

Corresponding Author:
Gao M
College of Internet of Things Engineering
Hohai University, China
Tel: +86-25-8378 7955
E-mail: [email protected]

Received Date: December 26, 2015; Accepted Date: January 27, 2016; Published Date: January 29, 2016

Citation:Gao M, Jian L, Li W, Yao X (2016) A Stochastic Programming Based Scheduling and Dispatch for Smart Grid with Intermittent Wind Power. Global J Technol Optim 7:191. doi:10.4172/2229-8711.1000191

Copyright: © 2016 Gao M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


From electricity supply side, power providers’ electricity energy is assumed to comprise two parts: one is procured from the wholesale market/national power grid, and the other is produced by their own wind generations. From electricity demand side, we assume two categories of energy users, namely traditional energy users and opportunistic energy users. Taking a unit time into account, this paper models the profits of power providers as a stochastic programming problem with three system parameters to be determined, i.e., the electricity procurement, the day-ahead price corresponding to traditional energy users and the real-time price corresponding to opportunistic energy users. Our objective is to maximize providers’ profits while preserving the balance between electricity supply and demand. To solve this problem, we first convert the stochastic programming problem into a nonlinear programming problem with constraint conditions; we then solve it using standard nonlinear optimization methods. With our proposed model, power provider can not only maximize the profits, but also can easily achieve the tradeoff between the profits of the power providers and the penetration of wind energy by tuning the system parameters. Numerical results show our proposed method can potentially benefit both power providers and energy users.