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Two-phase multi-objective evolutionary approach for short-term optimal thermal generation scheduling in electric power systems.

机译：电力系统短期最优热发电调度的两阶段多目标进化方法。

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摘要

The task of short-term optimal thermal generation scheduling can be cast in the form of a multi-objective optimization problem. The goal is to determine an optimal operating strategy to operate power plants, in such a way that certain objective functions related to economic and environmental issues, as well as transmission losses are minimized, under typical system and operating constraints. Due to the problem's inherent complexity, and the large number of associated constraints, standard multi-objective optimization algorithms fail to yield optimal solutions.;In this dissertation, a novel, two-phase multi-objective evolutionary approach is proposed to address the short-term optimal thermal generation scheduling problem. The objective functions, which are based on operation cost, emission and transmission losses, are minimized simultaneously.;During the first phase of this approach, hourly optimal dispatches for each period are obtained separately, by minimizing the operation cost, emission and transmission losses simultaneously. The constraints applied to this phase are the power balance, spinning reserve and power generation limits. Three well known multi-objective evolutionary algorithms, NSGA-II, SPEA-2 and AMOSA, are modified, and several new features are added. This hourly schedule phase also includes a repair scheme that is used to meet the constraint requirements of power generation limits for each unit as well as balancing load with generation. The new approach leads to a set of highly optimal solutions with guaranteed feasibility. This phase is applied separately to each hour long period.;In the second phase, the minimum up/down time and ramp up/down rate constraints are considered, and another improved version of the three multi-objective evolutionary algorithms, are used again to obtain a set of Pareto-optimal schedules for the integral interval of time (24 hours). During this phase, the hourly optimal schedules that are obtained from the first phase are used as inputs.;A bi-objective version of the problem, as well as a three-objective version that includes transmission losses as an objective, are studied. Simulation results on four test systems indicate that even though NSGA-II achieved the best performance for the two-objective model, the improved AMOSA, with new features of crossover, mutation and diversity preservation, outperformed NSGA-II and SPEA-2 for the three-objective model. It is also shown that the proposed approach is effective in addressing the multi-objective generation dispatch problem, obtaining a set of optimal solutions that account for trade-offs between multiple objectives. This feature allows much greater flexibility in decision-making. Since all the solutions are non-dominated, the choice of a final 24-hour schedule depends on the plant operator's preference and practical operating conditions. The proposed two-phase evolutionary approach also provides general frame work for some other multi-objective problems relating to power generation as well as in other real world applications.
机译：短期最优火力发电调度的任务可以以多目标优化问题的形式进行。目的是确定运行电厂的最佳运行策略，以使在典型的系统和运行约束下，与经济和环境问题以及传输损耗相关的某些目标功能最小化。由于问题的内在复杂性和相关约束的大量存在，标准的多目标优化算法无法产生最优解。本文提出了一种新颖的两阶段多目标进化方法来解决该问题。最优发电计划问题。同时将基于运营成本，排放和传输损失的目标函数最小化；在此方法的第一阶段，通过同时最小化运营成本，排放和传输损失，分别获得每个时段的每小时最佳调度。应用于此阶段的约束是功率平衡，旋转储备和发电限制。修改了三种众所周知的多目标进化算法NSGA-II，SPEA-2和AMOSA，并添加了几个新功能。该每小时计划阶段还包括一个修复方案，该方案可用于满足每个单元的发电限制的约束要求以及平衡负载与发电的需求。新方法导致了一组具有最优可行性的高度优化解决方案。此阶段分别应用于每个小时长的时段。在第二阶段，考虑了最小上/下时间和斜坡上/下速率限制，并再次使用三种多目标进化算法的另一个改进版本获得完整时间间隔（24小时）的一组帕累托最优计划。在此阶段中，将从第一阶段获得的每小时最佳计划用作输入。研究了该问题的双目标版本以及以传输损耗为目标的三目标版本。在四个测试系统上的仿真结果表明，即使NSGA-II在两目标模型上获得了最佳性能，但改进的AMOSA具有交叉，突变和多样性保留的新功能，在三个方面均优于NSGA-II和SPEA-2目标模型。还表明，所提出的方法有效地解决了多目标发电调度问题，获得了解决多个目标之间权衡问题的一组最佳解决方案。此功能使决策具有更大的灵活性。由于所有解决方案都不占主导地位，因此最终24小时计划的选择取决于工厂运营商的偏好和实际操作条件。提出的两阶段进化方法还为与发电以及其他实际应用有关的其他一些多目标问题提供了通用框架。

著录项

  • 作者

    Li, Dapeng.;

  • 作者单位

    Kansas State University.;

  • 授予单位 Kansas State University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 139 p.
  • 总页数 139
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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