| dc.contributor | 統計系 | |
| dc.creator (作者) | 洪英超 | |
| dc.creator (作者) | Hung, Ying-Chao | |
| dc.creator (作者) | Michailidis, George | |
| dc.date (日期) | 2018-06 | |
| dc.date.accessioned | 21-Nov-2018 16:30:26 (UTC+8) | - |
| dc.date.available | 21-Nov-2018 16:30:26 (UTC+8) | - |
| dc.date.issued (上傳時間) | 21-Nov-2018 16:30:26 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/120978 | - |
| dc.description.abstract (摘要) | Time-of-Use (TOU) pricing is an important strategy for electricity providers to manage supply and hence making the grid more efficient and for consumers to manage their costs. In this paper, we discuss a general stochastic modeling framework for consumer’s power demand based on which the TOU contract characteristics can be selected, so as to minimize the mean electricity price paid by the customer. We exploit the characteristics of power demand observed in real grids to propose to model it during homogeneous peak periods as a constant level with fluctuations described by a scaled fractional Brownian motion. We analyze the exceedance process over pre-specified thresholds and use this information for formulating an optimization problem to determine the key features of the TOU contract. Due to the analytical intractability of certain expressions with the exception of short-range dependence fluctuations, the solution of the posited optimization problem requires using techniques such as Monte Carlo simulation and numerical search. The methodology for two pricing schemes is illustrated using real data. | en_US |
| dc.format.extent | 108 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | IEEE Transactions on Smart Grid, | |
| dc.subject (關鍵詞) | Pricing; Power demand; Contracts; Stochastic processes; Brownian motion; Optimization; Load modeling | en_US |
| dc.title (題名) | Modeling and Optimization of Time-of-Use Electricity Pricing Systems | en_US |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1109/TSG.2018.2850326 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/TSG.2018.2850326 | |
