将热电厂用水纳入多目标优化潮流

J. Kravits, J. Kasprzyk, K. Baker, A. Stillwell
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引用次数: 0

摘要

传统上,电力系统的运行是在保持可靠性的同时最小化成本。然而,极端天气和需求事件可能会影响传统的热电发电业务,因为它们依赖于水来冷却。本文提出了一种新的多目标最优潮流(OPF)问题的求解方法,其中成本、取水量和耗水量均为最小。通过该配方,我们为提取和消耗的水分配优化权重,这可以直接纳入现有的OPF配方。我们将此公式与全球制图敏感性分析应用于现实案例研究,首先证明其在极端气候,水文和操作场景下的一般有效性。然后,我们应用全局排序敏感性分析来确定对系统性能影响最大的发电机。通过这个操作场景分析框架,分析人员可以深入了解电力系统中潜在的系统级和组件级漏洞。这样的见解可以为短期运营和长期电力系统规划提供有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incorporating thermoelectric power plant water use into multi-objective optimal power flow
Traditionally, power systems have been operated to minimize cost while maintaining reliability. However, extreme weather and demand events can affect traditional thermoelectric power generation operations due to their reliance on water for cooling. This paper contributes a novel multi-objective formulation of the optimal power flow (OPF) problem where cost, water withdrawal, and water consumption are minimized. Through this formulation, we assign optimization weights to water withdrawn and consumed, which can be directly incorporated into existing OPF formulations. We apply this formulation with a global mapping sensitivity analysis to a realistic case study to first demonstrate its general effectiveness under extreme climatic, hydrologic, and operational scenarios. Then, we apply a global ranking sensitivity analysis to determine the most influential generators for system performance. Through this operational scenario analysis framework, analysts can gain insights into potential system-level and component-level vulnerabilities within power systems. Such insights can be useful for informing both short-term operations as well as long-term power system planning.
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