摘要翻译：
在2015年的《巴黎协定》中，决定到2050年将能源部门的CO2排放量降至零，并将全球平均气温升幅限制在工业化前水平之上1.5摄氏度。这种承诺只有在基于可变的可再生技术的实际无二氧化碳发电的情况下才有可能。从历史上看，关于可再生能源的主要批评点是由天气依赖性驱动的可变性。Power-to-X系统将多余的电力转换为其他储存的能量供以后使用，可以在抵消可再生电力生产的可变性方面发挥重要作用。然而，为了做到这一点，这些系统必须进行适当的调度，以确保它们由低碳技术供电。在这篇论文中，我们介绍了一个图形化的方法，以最小化碳排放和电力成本，在日前市场上调度电力到X工厂。这种图形化的方法易于实现，并且可以直观地向涉众解释。在利用四个不同国家的历史价格和CO2强度进行的仿真研究中，我们发现随着调度期限的增加，价格和CO2强度有下降的趋势。当每年需要越来越多的满负荷小时时，这种影响就会减弱。此外，研究优化价格或CO2强度之间的权衡，表明这确实是一种权衡：不可能同时获得最低价格和CO2强度。
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英文标题：
《A graphical approach to carbon-efficient spot market scheduling for
  Power-to-X applications》
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作者：
Neeraj Bokde, Bo Tranberg, Gorm Bruun Andresen
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  In the Paris agreement of 2015, it was decided to reduce the CO2 emissions of the energy sector to zero by 2050 and to restrict the global mean temperature increase to 1.5 degree Celcius above the pre-industrial level. Such commitments are possible only with practically CO2-free power generation based on variable renewable technologies. Historically, the main point of criticism regarding renewable power is the variability driven by weather dependence. Power-to-X systems, which convert excess power to other stores of energy for later use, can play an important role in offsetting the variability of renewable power production. In order to do so, however, these systems have to be scheduled properly to ensure they are being powered by low-carbon technologies. In this paper, we introduce a graphical approach for scheduling power-to-X plants in the day-ahead market by minimizing carbon emissions and electricity costs. This graphical approach is simple to implement and intuitively explain to stakeholders. In a simulation study using historical prices and CO2 intensity for four different countries, we find that the price and CO2 intensity tends to decrease with increasing scheduling horizon. The effect diminishes when requiring an increasing amount of full load hours per year. Additionally, investigating the trade-off between optimizing for price or CO2 intensity shows that it is indeed a trade-off: it is not possible to obtain the lowest price and CO2 intensity at the same time.
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PDF链接：
https://arxiv.org/pdf/2009.03160