化工学报 ›› 2014, Vol. 65 ›› Issue (11): 4509-4516.DOI: 10.3969/j.issn.0438-1157.2014.11.042

• 过程系统工程 • 上一篇    下一篇

基于带补偿随机规划的蒸汽动力系统优化设计

盖丽梅1, 孙力2, 刘畅1, 贺高红1   

  1. 1. 大连理工大学精细化工国家重点实验室, 膜科学与技术研究开发中心, 辽宁 大连 116024;
    2. 曼彻斯特大学过程集成中心, 英国 曼彻斯特 M13 9PL
  • 收稿日期:2014-04-18 修回日期:2014-08-19 出版日期:2014-11-05 发布日期:2014-11-05
  • 通讯作者: 孙力
  • 基金资助:

    国家杰出青年科学基金项目(21125628);辽宁省科学技术计划项目(2011224005);中国石油科技创新基金资助(2011D- 5006-0401).

Steam power system optimization design based on stochastic programming with recourse

GAI Limei1, SUN Li2, LIU Chang1, HE Gaohong1   

  1. 1. State Key Laboratory of Fine Chemicals, Research and Development Center of Membrane Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China;
    2. Centre for Process Integration, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, M13 9PL, UK
  • Received:2014-04-18 Revised:2014-08-19 Online:2014-11-05 Published:2014-11-05
  • Supported by:

    supported by the National Science Fund for Distinguished Young Scholars of China (21125628), the Science and Technology Plan Projects of Liaoning Province(2011224005) and China Petroleum Science and Technology Innovation Fund(2011D-5006-0401).

摘要: 在蒸汽动力系统优化设计中,考虑不确定因素的优化策略能避免基于确定性设计策略的保守设计,并能针对不确定因素的实现提出相应的调度调节策略.本研究分析了蒸汽动力系统设计包含的不确定因素的特性及其对蒸汽动力系统优化目标和约束条件的影响.不确定因素的表达分成两类:基于时间变化表达和基于发生概率表达.对基于时间变化表达的因素,转化为多周期问题进行处理;对外部工艺过程变化引起的汽电需求不确定波动等基于发生概率表达的因素,应用随机规划策略,补偿不确定参数的实现可能引起的约束背离.基于本研究建立的多周期带补偿的二阶段随机规划MILP模型,求解蒸汽动力系统结构,同时优化调度调节策略,用调节决策和惩罚不足应对汽电需求等不确定因素的实现,实现系统安全稳定运行和经济效益最优.

关键词: 随机规划, 蒸汽动力系统, 模拟, 优化设计, 过程系统

Abstract: Steam power system optimization design under uncertainty provides schedule plans in the design stage to avoid a conservative design based on deterministic design. The characteristics of uncertainties and their influence on optimization objectives and constraints were analyzed in this work. Uncertain factors were divided into two types. Fluctuations of some variables were expressed by time, and fluctuations of the others were expressed by probabilities. The first type variables caused the design to be a multi-period problem. Fluctuation of the second type variables were compensated based on stochastic programming to deal with constraint violations. A mixed integer linear programming model (MILP) based on multi-cycle stochastic programming with recourse was formulated to obtain optimal system configuration and operating state. Schedule plans were addressed in the design stage to satisfy uncertain steam and power demand.

Key words: stochastic programming, steam power system, simulation, optimal design, process systems

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