摘要翻译：
能源系统优化模型（ESOM）应以互动的方式应用于揭示刀刃上的解决方案，探索替代的系统配置，并提出在高度不确定性条件下实现政策目标的不同方法。在本文中，我们采用了一种称为建模生成方案(MGA)的现有优化技术，该技术涉及到改变模型结构，以系统地探索接近最优的决策空间。MGA功能被整合到Tools for Energy Model Optimization and Analysis（Temoa）中，这是一个开源框架，还包括一个技术丰富的自下而上的ESOM。在本分析中，Temoa被用来探索一个简化的单一地区能源系统中的替代能源未来，该系统代表美国电力部门和部分轻型运输部门。鉴于数据集的限制，我们更多地强调方法上的方法，而不是具体的结果。
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英文标题：
《Energy Scenario Exploration with Modeling to Generate Alternatives (MGA)》
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作者：
Joseph F. DeCarolis, Samaneh Babaee, Binghui Li, Suyash Kanungo
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最新提交年份：
2019
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分类信息：

一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results.
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PDF链接：
https://arxiv.org/pdf/1912.03788