Evaluation of a data driven stochastic approach to optimize the participation of a wind and storage power plant in day-ahead and reserve markets
作者:
Jose L. Crespo-Vazquez， C. Carrillo.
所属专业方向:
电力市场
摘要:
A more comprehensive participation of renewable generators in the power market is being practiced in
many countries. To add storage capability to these generators is also a major trend nowadays. Decisions
concerning the participation in the power market have to be made several hours in advance, which is a
key challenge for the renewable energy-based generators. In this work, a decision making framework
under uncertainty for a wind and storage power plant participating in day-ahead and reserve markets is
developed. Available wind energy and regulation requirements by the system operator are considered as
uncertain parameters. To maximize the net income of this system under uncertainty, a two-stage convex
stochastic model is developed. In order to create meaningful scenarios to be used in our proposed sto-
chastic model, at first, a Long Short-Term Memory Recurrent Neural Network is designed to generate
forecasts for regulation requirements. Univariate and multivariate clustering based on k-means algo-
rithms are also used to generate influential scenarios from historical data. Several simulation experi-
ments are carried out to evaluate the quality of the proposed stochastic approach using real-world wind
farm data. Simulation result shows the validity and usefulness of the proposed data-driven approaches to
handle the uncertainty in regulation requirements.
关键字:
Stochastic optimization，Convex programming，Influential scenarios，Reserve market，Wind energy
论文“Evaluation of a data driven stochastic approach to optimize theparticipation of a wind and storage power plant in day-ahead and reserve markets”，2018，Energy d0 V- U- k. m
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发表于 2019-3-26 21:50:05
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