A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company

作     者：Ahmadi, Abdollah Charwand, Mansour Siano, Pierluigi Nezhad, Ali Esmaeel Sarno, Debora Gitizadeh, Mohsen Raeisi, Fatima 

作者机构：Univ New South Wales Australian Energy Res Inst Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Salerno Dept Ind Engn Fisciano Italy Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran Hormozgan Univ Dept Management Bandarabass Iran 

出 版 物：《ENERGY》 (能源杂志)

年 卷 期：2016年第117卷第Part1期

页      面：1-9页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：Distribution system Distributed generation Interruptible load Stochastic programming 

摘      要：In order to supply the demands of the end users in a competitive market, a distribution company purchases energy from the wholesale market while other options would be in access in the case of possessing distributed generation units and interruptible loads. In this regard, this study presents a two stage stochastic programming model for a distribution company energy acquisition market model to manage the involvement of different electric energy resources characterized by uncertainties with the minimum cost. In particular, the distribution company operations planning over a day-ahead horizon is modeled as a stochastic mathematical optimization, with the objective of minimizing costs. By this, distribution company decisions on grid purchase, owned distributed generation units and interruptible load scheduling are determined. Then, these decisions are considered as boundary constraints to a second step, which deals with distribution company s operations in the hour-ahead market with the objective of minimizing the short-term cost. The uncertainties in spot market prices and wind speed are modeled by means of probability distribution functions of their forecast errors and the roulette wheel mechanism and lattice Monte Carlo simulation are used to generate scenarios. Numerical results show the capability of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.