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|Title:||Intelligent decision support system for market participation by load aggregators||Authors:||Gao, Yue||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||Gao, Y. (2018). Intelligent decision support system for market participation by load aggregators. Master's thesis, Nanyang Technological University, Singapore.||Abstract:||Many investigations and research works have been conducted on the National Electricity Market of Singapore (NEMS) by market regulators and researchers. It is well-known for its distinguished maturity and structure. A demand response (DR) program was implemented in April 2016 to further improve its efficiency and competitiveness. With the liberalization and deregulation of the electricity market, it becomes more flexible for demand side to actively participate in the wholesale electricity market. This has changed the way how electricity is traded. In this research, a software-based intelligent decision support system is developed for participation of loads in the demand response program of the wholesale electricity market of NEMS. This work focuses on the study of the Singapore demand response program featuring demand side bidding and incentive payments. The impact of demand side participation on the market is analyzed. The issues associated with the structure and operation of the electricity market are addressed in this research. In addition, DR can be employed in energy management to mitigate the difficulty brought by uncertain demand. Firstly, the current market clearing model (MCM) of Singapore, which is an assessment tool for the simultaneous allocation of energy and ancillary services, is introduced. The principle of transmission loss and nodal prices integrated in the MCM is presented as well. Secondly, the methodology of integration of demand side bidding with the newly introduced requirements is discussed. Finally, a stochastic optimization is proposed for the coordinated operation of generating units and demand response to adequately deal with the uncertain electricity demand. The coordination is accomplished through a twostage stochastic programming model. In the future, the proposed modeling can be utilized for further evaluation on real time demand response (DR) and interruptible load (IL) strategies. The challenges to be addressed are also outlined while the solution is still under testing.||URI:||http://hdl.handle.net/10356/73248||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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