论文标题
使用基于代理的模型为分散电力市场优化碳税
Optimizing carbon tax for decentralized electricity markets using an agent-based model
论文作者
论文摘要
避免人为气候变化的影响需要从化石燃料到低碳技术的过渡。实现这一目标的一种方法是使电网格脱碳。但是，必须在其他领域进行进一步的努力，例如运输和供暖，以进行全碳化。这将由于发电而减少碳排放，还可以通过实现低碳替代品来脱碳，例如汽车和加热。碳税已被证明是帮助这种过渡的有效方法。在本文中，我们使用基于电力市场代理的Model Elecsim来演示如何通过遗传算法方法找到最佳的碳税政策。为此，我们使用NSGA-II遗传算法来最大程度地降低电力混合物的平均电价和相对碳强度。我们证明，可以找到一系列适合不同目标的碳税。我们的结果表明，我们能够将电力成本最小化至\ textsterling10/mwh以下，并且在每种情况下都将碳强度降至零。就最佳碳税策略而言，我们发现，在2020年至2035年之间的策略不断增长。每年的帕累托最佳税收策略至少高于\ textsterling81/tco2。平均碳税策略为\ textsterling240/tco2。
Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. A way to achieve this is to decarbonize the electricity grid. However, further efforts must be made in other fields such as transport and heating for full decarbonization. This would reduce carbon emissions due to electricity generation, and also help to decarbonize other sources such as automotive and heating by enabling a low-carbon alternative. Carbon taxes have been shown to be an efficient way to aid in this transition. In this paper, we demonstrate how to to find optimal carbon tax policies through a genetic algorithm approach, using the electricity market agent-based model ElecSim. To achieve this, we use the NSGA-II genetic algorithm to minimize average electricity price and relative carbon intensity of the electricity mix. We demonstrate that it is possible to find a range of carbon taxes to suit differing objectives. Our results show that we are able to minimize electricity cost to below \textsterling10/MWh as well as carbon intensity to zero in every case. In terms of the optimal carbon tax strategy, we found that an increasing strategy between 2020 and 2035 was preferable. Each of the Pareto-front optimal tax strategies are at least above \textsterling81/tCO2 for every year. The mean carbon tax strategy was \textsterling240/tCO2.
