We introduce a mathematical formulation of energy storage systems into a generation capacity expansion framework to evaluate the role of energy storage in the decarbonization of distributed power systems. The modeling framework accounts for dynamic charging/discharging efficiencies and maximum cycling powers as well as cycle and calendar degradation of a Li-ion battery system. Results from a single node case study indicate that incorporating the dynamic efficiencies and cycling powers of batteries in the generation planning problem does not significantly change the optimal generation portfolio, while adding substantial computational burden. In contrast, accounting for battery degradation leads to substantially different generation expansion outcomes, especially in deep decarbonization scenarios with larger energy storage capacities. Under the assumptions used in this study, we find that battery energy storage is economically viable for 2020 only under strict CO$_2$ emission constraints. In contrast, given the projected technology advances and corresponding cost reductions, battery energy storage exhibits an attractive option to enable deep decarbonization in 2050.

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规划低碳分布式电力系统：评估储能的作用

我们将储能系统的数学公式引入发电容量扩展框架，以评估储能在分布式电力系统脱碳中的作用。建模框架考虑了锂离子电池系统的动态充电/放电效率和最大循环功率以及循环和日历退化。单节点案例研究的结果表明，将电池的动态效率和循环功率纳入发电规划问题不会显着改变最佳发电组合，同时会增加大量计算负担。相比之下，考虑到电池退化会导致发电扩张结果大不相同，尤其是在具有更大能量存储容量的深度脱碳场景中。根据本研究中使用的假设，我们发现只有在严格的 CO$_2$ 排放限制下，电池储能在 2020 年才具有经济可行性。相比之下，鉴于预计的技术进步和相应的成本降低，电池储能展示了一个有吸引力的选择，可以在 2050 年实现深度脱碳。