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首页> 外文期刊>IAENG Internaitonal journal of computer science >Development of a Semi-definite Programming Weighted Sum Based Approach for Solving Stochastic Multi-objective Economic Dispatch Problems Incorporating CHP Units
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Development of a Semi-definite Programming Weighted Sum Based Approach for Solving Stochastic Multi-objective Economic Dispatch Problems Incorporating CHP Units

机译：基于半确定规划加权和的求解CHP单元的随机多目标经济调度问题的方法

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摘要

This paper has presented a weighted sum based semidefinite programming (SDP) optimization technique for solving stochastic multi-objective economic dispatch (MOED) model that incorporates Combined Heat and Power (CHP) units. The stochastic multi-objective model was transformed into its deterministic equivalent through their expectation, with the assumption that involved random variables are normally distributed. The multi-objective problem was recast in matrix form as a SDP relaxation problem and subsequently solved with a MATLAB programming suite. The system inequality and equality constraints uncertainty were entered into YALMIP, which is a linear matrix inequality parser. Simulations were performed on modified IEEE 6 and 20 units' networks with 2 CHP units. The efficiency of the proposed method is determined by investigating reformulated problems in stochastic and deterministic models on power dispatch. The standard weighted sum method is utilized in generating the Pareto-optimal solution between two objectives' functions. An optimal selection of control weight selection k_1 parameter that provides a better convergence property and moderately good extent of the Pareto distributions was empirically determined. The proposed SDP method performed well in accuracy of results and providing lower operational cost in the Pareto set produced.
机译：本文提出了一种基于加权和的半定规划（SDP）优化技术，用于求解结合了热电联产（CHP）单元的随机多目标经济调度（MOED）模型。假设所涉及的随机变量是正态分布的，则随机多目标模型通过期望将其转化为确定性等价模型。将多目标问题以SDP松弛问题的形式重现为矩阵形式，随后使用MATLAB编程套件进行了解决。将系统不等式和等式约束不确定性输入到YALMIP中，它是线性矩阵不等式解析器。在带有2个CHP单元的改进的IEEE 6和20单元的网络上进行了仿真。通过研究随机和确定性电力调度模型中的重构问题来确定所提出方法的效率。标准加权和方法用于生成两个目标函数之间的帕累托最优解。根据经验确定控制权重选择k_1参数的最佳选择，该参数可提供更好的收敛性和适度的帕累托分布范围。所提出的SDP方法在结果准确性方面表现良好，并在生产的帕累托集中提供了较低的运营成本。

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