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首页> 外文会议>Institute of Industrial and Systems Engineers Annual Conference and Expo >Simulation and Optimization for Unit Commitment using a Region-based Sampling (RBS) Algorithm: Abstract ID: 790881
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Simulation and Optimization for Unit Commitment using a Region-based Sampling (RBS) Algorithm: Abstract ID: 790881

机译：使用基于地区的采样（RBS）算法的单位承诺进行仿真和优化：Abstract ID：790881

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摘要

Energy demand is a global crisis as climate changes and the population continues to rise. Hence, is it imperative to produce and distribute energy efficiently; this is commonly referred to as the unit commitment problem. Simulation and optimization are separate approaches to this problem that can synchronize with each other to compensate their unique deficiencies such as the uncertainties associated with simulating renewable power generation. In this paper, we propose a new region-based sampling (RBS) algorithm to determine which demand points to consider based on the region's priority within the community along with a microgrid (MG) optimization model for each scenario. A case study was conducted on a synthetic microgrid to assess the performance of this approach. The results show that energy supplied but not used (overgeneration) was reduced by eighty percent between the first and second replication according to the sampling region selected by the RBS algorithm.
机译：能源需求是一个全球危机，因为气候变化，人口仍在继续上升。 因此，它必须有效地生产和分配能量; 这通常被称为单位承诺问题。 仿真和优化是对该问题的单独方法，可以彼此同步，以补偿它们独特的缺陷，例如与模拟可再生发电相关联的不确定性。 在本文中，我们提出了一种基于新的基于区域的采样（RBS）算法，以确定基于该区域在社区内的优先级以及每个场景的MicroGrid（MG）优化模型来确定哪个需求点。 在合成微电网上进行案例研究，以评估这种方法的性能。 结果表明，根据RBS算法选择的采样区域，所提供的能量但未使用（过度）在第一和第二复制之间减少了八十百分比。

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