TY - JOUR
T1 - Optimal multi-timescale demand side scheduling considering dynamic scenarios of electricity demand
AU - Bao, Zhejing
AU - Qiu, Wanrong
AU - Wu, Lei
AU - Zhai, Feng
AU - Xu, Wenjing
AU - Li, Baofeng
AU - Li, Zhijie
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this paper, an optimal multi-timescale demand side scheduling framework, i.e., the combination of week-ahead and day-ahead, for industrial customers is proposed. Different demand side management (DSM) techniques suitable for distinct week-ahead and day-ahead timescales cooperate for achieving the overall optimal demand scheduling in the entire multi-timescale frame. Specifically, in the week-ahead scheduling, a dynamic scenario generation method is proposed to accurately simulate uncertainties of customer electricity demand time-series during the scheduling horizon, which can represent not only the marginal distribution of possible customer loads at each time instant but also the joint distribution among multiple loads at different time instants. In addition, priorities of various DSM techniques accepted by DSM participants and their willingness are also considered, aiming at mitigating impacts on their normal manufacturing process. With actual historical load data of industrial customers from advanced metering infrastructure system, the dynamic scenario generation method is shown to be effective in preserving statistic features of load fluctuations, and the proposed optimal multi-timescale coordinated demand side scheduling model is demonstrated to be an effective DSM approach.
AB - In this paper, an optimal multi-timescale demand side scheduling framework, i.e., the combination of week-ahead and day-ahead, for industrial customers is proposed. Different demand side management (DSM) techniques suitable for distinct week-ahead and day-ahead timescales cooperate for achieving the overall optimal demand scheduling in the entire multi-timescale frame. Specifically, in the week-ahead scheduling, a dynamic scenario generation method is proposed to accurately simulate uncertainties of customer electricity demand time-series during the scheduling horizon, which can represent not only the marginal distribution of possible customer loads at each time instant but also the joint distribution among multiple loads at different time instants. In addition, priorities of various DSM techniques accepted by DSM participants and their willingness are also considered, aiming at mitigating impacts on their normal manufacturing process. With actual historical load data of industrial customers from advanced metering infrastructure system, the dynamic scenario generation method is shown to be effective in preserving statistic features of load fluctuations, and the proposed optimal multi-timescale coordinated demand side scheduling model is demonstrated to be an effective DSM approach.
KW - Demand side management (DSM)
KW - optimization
KW - scenarios
KW - scheduling
KW - uncertainty
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U2 - 10.1109/TSG.2018.2797893
DO - 10.1109/TSG.2018.2797893
M3 - Article
AN - SCOPUS:85040975986
SN - 1949-3053
VL - 10
SP - 2428
EP - 2439
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 3
M1 - 8269337
ER -