Optimizing economic/environmental dispatch with wind and thermal units

A. Al-Awami, E. Sortomme, M. El-Sharkawi
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引用次数: 40

Abstract

In this paper, the economic/environmental dispatch for a smart grid with wind and thermal units is formulated. The formulation takes into account the stochastic nature of wind power output and the imbalance charges due to the mismatch between the actual and scheduled wind power outputs. Because minimizing the operating cost of thermal and wind units, and minimizing the emissions of thermal units are two conflicting objectives, multi-objective optimization (MOO) technique is used. With MOO, a set of solutions that are optimal in the Pareto sense is identified. An enhanced multi-objective particle swarm optimization (MO-PSO) is proposed to search for the set of Pareto-optimal solutions. The effect of different system conditions on the Pareto-optimal solutions is investigated. These system conditions include load level and different imbalance cost coefficients. Test results show the effectiveness of the proposed technique in identifying the set of Pareto optimal solutions. This technique is an important tool that system operators require in order to operate the grid with high penetration of wind power more efficiently while maintaining emissions within restricted limits.
优化风电和热电机组的经济/环境调度
本文提出了风电和热电机组智能电网的经济/环境调度方案。该公式考虑了风电输出的随机性和由于实际风电输出与计划风电输出不匹配而产生的不平衡电荷。由于热电机组和风力机组的运行成本最小化和热电机组的排放最小化是两个相互冲突的目标,因此采用了多目标优化(MOO)技术。使用MOO,可以确定一组在帕累托意义上最优的解决方案。提出了一种改进的多目标粒子群优化算法(MO-PSO)来搜索pareto最优解集。研究了不同系统条件对pareto最优解的影响。这些系统条件包括负荷水平和不同的不平衡成本系数。测试结果表明,该方法在识别Pareto最优解集方面是有效的。该技术是系统运营商需要的重要工具,以便更有效地运行具有高风力渗透的电网,同时将排放保持在限制范围内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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