Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.

中文翻译：

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绿色电网的排放感知储能调度

减少对碳密集型能源的依赖对于减少电网的碳足迹至关重要。尽管电网越来越多地部署清洁、可再生能源，但仍然使用传统的碳密集型能源来满足电网需求的很大一部分。在本文中，我们研究了使用部署在电网中的储能来减少电网碳排放的问题。虽然储能以前已用于电网优化，例如调峰和平滑间歇性电源，但我们的见解是使用分布式存储使公用事业公司能够减少对效率较低和碳密集度最高的发电厂的依赖，从而减少其总体排放脚印。我们将分布式储能的排放感知调度问题表述为一个优化问题，并使用一种鲁棒的优化方法，该方法非常适合处理负载预测中的不确定性，尤其是在存在间歇性可再生能源（如太阳能和风能）的情况下。我们使用最先进的神经网络负载预测技术和来自具有 1,341 个家庭的配电网的真实负载轨迹来评估我们的方法。我们的结果表明，每年的碳排放量减少了 50 万公斤以上——相当于我们的电网排放量减少了 23.3%。我们使用最先进的神经网络负载预测技术和来自具有 1,341 个家庭的配电网的真实负载轨迹来评估我们的方法。我们的结果表明，每年的碳排放量减少了 50 万公斤以上——相当于我们的电网排放量减少了 23.3%。我们使用最先进的神经网络负载预测技术和来自具有 1,341 个家庭的配电网的真实负载轨迹来评估我们的方法。我们的结果表明，每年的碳排放量减少了 50 万公斤以上——相当于我们的电网排放量减少了 23.3%。