A Probabilistic Approach Considering Contingency Parameters for Peak Load Demand Forecasting

被引：1

作者：

Shabbir, Md Nasmus Sakib Khan ^{[1
]}

Ali, Mohammad Zawad ^{[1
]}

Liang, Xiaodong ^{[1
]}

Chowdhury, Muhammad Sifatul Alain ^{[1
]}

机构：

[1] Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NF A1B 3X9, Canada

来源：

CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING-REVUE CANADIENNE DE GENIE ELECTRIQUE ET INFORMATIQUE | 2018年 / 41卷 / 04期

关键词：

Bayesian network; contingency parameters; generation planning; load forecasting; Monte Carlo simulation; ELECTRIC-LOAD; REGRESSION-MODEL; POWER-SYSTEMS; ENERGY; HORIZONS;

D O I：

10.1109/CJECE.2018.2876820

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate load forecasting is a critical step for power system generation planning. Contingency parameters of the system and their dynamic characteristics should be taken into account for the purpose of load forecasting. In this paper, a probabilistic load forecasting algorithm considering contingency parameters is developed for the peak load forecasting. Using the chi-square distribution test and historical data, the probabilistic distribution of contingency parameters can be determined. In a case study, the Monte Carlo simulation is run to forecast load demand and generation scenarios of Bangladesh based on the developed adaptive algorithm and the calculated probabilistic distribution. The influence of contingency parameters is evaluated using a Bayesian network in a sensitivity study.

引用

页码：224 / 233

页数：10

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