A COMBINED PROBABILISTIC TIME-SERIES MODEL FOR WIND DIESEL SYSTEMS SIMULATION

被引:10
|
作者
MANWELL, JF
MCGOWAN, JG
机构
[1] Renewable Energy Research Laboratory, Mechanical Engineering Department, University of Massachusetts, Amherst
关键词
D O I
10.1016/0038-092X(94)90127-N
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This article describes a new simulation model for wind/diesel systems. It involves a combined time series and statistical approach to estimate the fuel use of diesel generators. In addition to provision for modeling non-identical diesels, the model allows the inclusion of multiple, non-identical wind turbines whose output may or may not be correlated. Three diesel dispatching strategies are provided. One assumes no storage, and when storage is employed, either a peak shaving or cycle charge control option can be used. The storage module uses a flexible battery model specially designed for time series simulation codes. A key assumption for the main analytical model is that, within each time step, the load and wind power are assumed to be normally distributed. The mean net load is the mean load less the mean wind power and its variance is found from the variance of the load and the wind power. A loss of load probability is used to find the maximum and minimum anticipated values of the net load. In addition to summarizing the overall analytical model, this article presents the results of a number of simulations demonstrating the performance prediction (diesel fuel usage) capabilities of the model. For one of these cases (a no storage system), the results show excellent correlation between the model and actual data. Other cases summarized show that the use of the model greatly facilitates the integration of storage into the control scheme, and gives the fuel saving potential for several different wind/diesel system configurations.
引用
收藏
页码:481 / 490
页数:10
相关论文
共 50 条
  • [31] TIME-SERIES MODEL FOR VEHICLE SPEEDS
    MAHALEL, D
    HAKKERT, AS
    [J]. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 1985, 19 (03) : 217 - 225
  • [32] How to Trust Generative Probabilistic Models for Time-Series Data?
    Piatkowski, Nico
    Posch, Peter N.
    Krause, Miguel
    [J]. LEARNING AND INTELLIGENT OPTIMIZATION, LION 15, 2021, 12931 : 283 - 298
  • [33] CONVERGENCE OF A NONLINEAR TIME-SERIES MODEL
    PHILLIPS, CB
    [J]. ECONOMETRIC THEORY, 1995, 11 (04) : 808 - 809
  • [34] A clustering model for time-series forecasting
    Coric, Rebeka
    Dumic, Mateja
    Jelic, Slobodan
    [J]. 2019 42ND INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2019, : 1105 - 1109
  • [35] TESTING THE ADEQUACY OF A TIME-SERIES MODEL
    GODFREY, LG
    [J]. BIOMETRIKA, 1979, 66 (01) : 67 - 72
  • [36] On a mixture GARCH time-series model
    Zhang, Zhiqiang
    Li, Wai Keung
    Yuen, Kam Chuen
    [J]. JOURNAL OF TIME SERIES ANALYSIS, 2006, 27 (04) : 577 - 597
  • [37] Probabilistic properties of neuron spiking time-series obtained in vivo
    Bershadskii, A
    Dremencov, E
    Fukayama, D
    Yadid, G
    [J]. EUROPEAN PHYSICAL JOURNAL B, 2001, 24 (03): : 409 - 413
  • [38] Probabilistic properties of neuron spiking time-series obtained in vivo
    A. Bershadskii
    E. Dremencov
    D. Fukayama
    G. Yadid
    [J]. The European Physical Journal B - Condensed Matter and Complex Systems, 2001, 24 : 409 - 413
  • [39] A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
    Jung, Seungjae
    Kim, Kyung-Min
    Kwak, Hanock
    Park, Young-Jin
    [J]. NEURIPS WORKSHOPS, 2020, 2020, 137 : 98 - 105
  • [40] Creating Probabilistic Databases from Imprecise Time-Series Data
    Sathe, Saket
    Jeung, Hoyoung
    Aberer, Karl
    [J]. IEEE 27TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2011), 2011, : 327 - 338