Encouraging demand response for household and building consumers at NTU

As a part of Customer Energy Portal (CEP), the ToU (Time of Use) pricing scheme is soon to be launched among NTU's (Nanyang Technological University) household and building consumers. The Time of Use pricing scheme is a method by which the electricity prices alter according to time of...

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書目詳細資料
主要作者: Dutta, Shreya
其他作者: Gooi Hoay Beng
格式: Theses and Dissertations
語言:English
出版: 2015
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在線閱讀:http://hdl.handle.net/10356/64830
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總結:As a part of Customer Energy Portal (CEP), the ToU (Time of Use) pricing scheme is soon to be launched among NTU's (Nanyang Technological University) household and building consumers. The Time of Use pricing scheme is a method by which the electricity prices alter according to time of day, and hence according to demand. It is predicted that by utilizing this scheme, NTU's peak demand of about 31 MW will be able to be reduced by 1 MW i.e. approximately 3%. Hence it is crucial for NTU to employ some Demand Response (DR) programs which result in rewarding or incentivizing the consumers to shift their loads from high demand (thus, high electricity prices) periods to periods with low electricity price or when system reliability is endangered. This will benefit both consumers and energy providers by lowering the electricity prices and hence the overall operational and generation costs. Demand Response is the modification in electricity consumption by customers from their normal usage pattern with respect to change in electricity price over time. It includes changes in timing for a particular appliance usage, instantaneous demand or reduction in overall consumption. It derives its motivation from day-ahead short term multiple forecasting which must use date, time, temperature as well as anthropological data for training the model. Therefore, the aim of this project is to develop a demand response program in order to set ToU prices for the campus household consumers by incorporating concepts of short term forecasting and smart term data analysis.