An Adaptive Normal Constraint Method for Bi-Objective Optimal Synthesis of Energy Systems

被引:0
作者
Hennen, Maike [1 ]
Voll, Philip [1 ]
Bardow, Andre [1 ]
机构
[1] Rhein Westfal TH Aachen, Inst Tech Thermodynam, D-52062 Aachen, Germany
来源
24TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A AND B | 2014年 / 33卷
关键词
Pareto front Generation; Distributed Energy Supply Systems; Bi-Objective Optimization; MILP; Synthesis and Optimization; MULTIOBJECTIVE OPTIMIZATION;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
A novel approach is proposed for the efficient generation of the Pareto front for bi-objective optimal synthesis of energy systems. To avoid computationally expensive calculations of solutions not relevant to the decision maker, the proposed method adapts the computation of the Pareto front to the part relevant for practical energy systems. The algorithm produces an evenly distributed set of Pareto optimal solutions employing a modified normal constraint method. In contrast to the classical normal constraint method, the algorithm is no more initialized at the - usually computationally most expensive - single-objective optima but uses an aggregated objective function as starting point for an adaptive exploration of the Pareto front. The presented approach is applied to a real-world synthesis problem of a distributed energy supply system. It is shown that the adaptive normal constraint algorithm automatically generates the most relevant part of the Pareto front for the bi-objective optimal synthesis of an energy system computationally more efficient than the weighted sum method or the epsilon-constraint method.
引用
收藏
页码:1279 / 1284
页数:6
相关论文
共 11 条
  • [1] Multi-objective planning of distributed energy resources: A review of the state-of-the-art
    Alarcon-Rodriguez, Arturo
    Ault, Graham
    Galloway, Stuart
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2010, 14 (05) : 1353 - 1366
  • [2] [Anonymous], 2013, VERS
  • [3] Multicriteria optimization of a distributed energy supply system for an industrial area
    Buoro, D.
    Casisi, M.
    De Nardi, A.
    Pinamonti, P.
    Reini, M.
    [J]. ENERGY, 2013, 58 : 128 - 137
  • [4] HAIMES YY, 1971, IEEE T SYST MAN CYB, VSMC1, P296
  • [5] Ismail-Yahaya A., 2002, AIAA20020178
  • [6] Suitable Modeling for Process Flow Sheet Optimization Using the Correct Economic Criterion
    Kasas, M.
    Kravanja, Z.
    Pintaric, Z. Novak
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50 (06) : 3356 - 3370
  • [7] Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation
    Kim, IY
    de Weck, OL
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2006, 31 (02) : 105 - 116
  • [8] McCarl BA, 2011, MCCARL GAMS USER GUI
  • [9] Multi-objective optimization of solar Rankine cycles coupled with reverse osmosis desalination considering economic and life cycle environmental concerns
    Salcedo, R.
    Antipova, E.
    Boer, D.
    Jimenez, L.
    Guillen-Gosalbez, G.
    [J]. DESALINATION, 2012, 286 : 358 - 371
  • [10] Automated superstructure-based synthesis and optimization of distributed energy supply systems
    Voll, Philip
    Klaffke, Carsten
    Hennen, Maike
    Bardow, Andre
    [J]. ENERGY, 2013, 50 : 374 - 388