Optimal Multi-Timescale Demand Side Scheduling Considering Dynamic Scenarios of Electricity Demand. Bao, Z., Qiu, W., Wu, L., Zhai, F., Xu, W., Li, B., & Li, Z. IEEE Transactions on Smart Grid, 10(3):2428–2439, May, 2019. Conference Name: IEEE Transactions on Smart Griddoi abstract bibtex 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.
@article{bao_optimal_2019,
title = {Optimal {Multi}-{Timescale} {Demand} {Side} {Scheduling} {Considering} {Dynamic} {Scenarios} of {Electricity} {Demand}},
volume = {10},
issn = {1949-3061},
doi = {10.1109/TSG.2018.2797893},
abstract = {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.},
number = {3},
journal = {IEEE Transactions on Smart Grid},
author = {Bao, Z. and Qiu, W. and Wu, L. and Zhai, F. and Xu, W. and Li, B. and Li, Z.},
month = may,
year = {2019},
note = {Conference Name: IEEE Transactions on Smart Grid},
keywords = {DSM techniques, Demand side management (DSM), Dynamic scheduling, Gaussian distribution, Job shop scheduling, Optimal scheduling, Uncertainty, customer electricity demand time-series, day-ahead timescales, demand side management, demand side management techniques, dynamic scenario generation method, industrial customers, multitimescale demand side scheduling framework, optimal multitimescale coordinated demand side scheduling model, optimization, power meters, scenarios, scheduling, time series, uncertainty, week-ahead scheduling},
pages = {2428--2439},
}
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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. 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