上海大学学报(自然科学版) ›› 2018, Vol. 24 ›› Issue (5): 807-818.doi: 10.12066/j.issn.1007-2861.1850

• 研究论文 • 上一篇    下一篇

燃料价格及电力需求不确定下电力生产决策的鲁棒优化

汪建1(), 王挺1,2, 刘惠霞3   

  1. 1. 上海大学 管理学院, 上海 200444
    2. 庆应义塾大学 管理工程系, 横滨 2238522, 日本
    3. 戴尔 (中国) 有限公司上海分公司, 上海 200050
  • 收稿日期:2016-09-26 出版日期:2018-10-30 发布日期:2018-10-26
  • 通讯作者: 汪建 E-mail:jwang@t.shu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(71572104);教育部博士点新教师基金资助项目(20123108120030);教育部哲学社会科学研究重大课题攻关资助项目(13JZD025)

Robust optimization of production quantity decisions with uncertainty in fuel price and electric power demand

WANG Jian1(), WANG Ting1,2, LIU Huixia3   

  1. 1. School of Management, Shanghai University, Shanghai 200444, China
    2. Department of Administration Engineering, Keio Univeristy, Yokohama 2238522, Japan
    3. Dell (China) Co., Ltd., Shanghai Branch, Shanghai 200050, China
  • Received:2016-09-26 Online:2018-10-30 Published:2018-10-26
  • Contact: WANG Jian E-mail:jwang@t.shu.edu.cn

摘要:

考虑了发电侧燃料价格和需求侧电力需求的不确定性, 建立了以发电成本最低为目标的电力生产决策的椭球鲁棒优化模型, 设计了基于MATLAB的SeDuMi算法进行求解. 数值实验结果表明: 当不确定性因素的扰动方向一致时, 总成本随着参数不确定范围的增大呈近似线性增大; 当不确定性因素的扰动方向不一致时, 总成本随着参数的波动加剧而增大. 研究分析了燃料价格和电力需求这两个参数的不确定范围对总成本的影响显著性. 结果发现, 随着参数变动范围的差异, 燃料价格和电力需求的不确定性对于电力生产决策影响的显著性也不一样. 研究结论为不确定性电力供应链的决策提供了理论支持以及相关的方法.

关键词: 鲁棒优化, 椭球扰动, 生产决策, 燃料价格, 电力需求

Abstract:

A robust optimization model is established for decisions of electric power production quantity. This model examines impact on the production decisions from two important variables, i.e., fuel price and electric power demand. A new algorithm is developed with SeDuMi in MATLAB. Several suggestions are given based on data analysis. The total production cost of electric power may increase with an increasing uncertainty range of these two variables. If the disturbance directions of the uncertainties in these two variables change in the same direction, the total production cost increases linearly with the increasing uncertainty range. If the disturbance directions of the uncertainties in these two variables change inconsistently, the total production cost increases with increasing volatility. The results show that the changing range of these two variables can also affect the significant level of the relationship between these two variables and the production decisions. This study supports the electric power production quantity decisions under uncertain fuel price and electric power demand.

Key words: robust optimization, ellipsoidal disturbance, production quantity decision, fuel price, electric power demand

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