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.
How flexible do we need to be? Using electricity systems models to identify optimal designs for flexible carbon capture storage system for gas-fired power plants
Strategic Spatial Load Shifting and Market Efficiency
Laboratory for Information and Decision Systems, MIT（麻省理工学院信息与决策系统实验室）；MIT Energy Initiative, MIT（麻省理工学院能源倡议）；Department of Electrical and Computer Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校电气与计算机工程系）
Battery Operations in Electricity Markets: Strategic Behavior and Distortions
Yale School of Management（耶鲁管理学院）；Columbia University, Graduate School of Business（哥伦比亚大学商学院）；Columbia University, Earth and Environmental Engineering（哥伦比亚大学地球与环境工程系）
Enhancing Profit and CO2 Mitigation: Commercial Direct Air Capture Design and Operation with Power Market Volatility
Columbia University（哥伦比亚大学）；Princeton University（普林斯顿大学）；Energy Systems Modelling Analytics Limited（能源系统建模分析有限公司）；Lamont-Doherty Earth Observatory, Columbia University（哥伦比亚大学拉蒙特-多尔蒂地球观测站）；Carbon Direct（碳直接）
