AI数据中心负荷增长下碳监管与动力电池梯次利用储能投资的斯塔克尔伯格-贝叶斯容量市场博弈
A Stackelberg-Bayesian Capacity-Market Game of Carbon Regulation and Second-Life Battery Investment under AI Data-Center Load Growth
AI总结：
针对AI数据中心负荷增长带来的成本与碳排放问题，构建三级斯塔克尔伯格-贝叶斯博弈模型，量化碳税等政策对SLB储能投资及碳减排的影响。
AI中文摘要：
人工智能（AI）数据中心正在推动美国所有独立系统运营商/区域输电组织（ISO/RTO）区域的电力负荷快速增长，这既提高了系统成本，也增加了碳排放量。本研究构建了一个三级斯塔克尔伯格-贝叶斯博弈模型：监管机构（领导者）设定碳罚款与补贴，单一ISO容量市场针对建模为经典发电扩展问题的能源平衡进行出清，技术特定投资者（追随者）在不完全信息下决定容量与运营，最终得到贝叶斯纳什均衡。AI的影响被简约地表示为绿地增量扩展的额外负荷增长因子，用于分离该增长会吸引多少新增容量以及由何种技术填补该容量。在该框架内，我们考虑动力电池梯次利用（SLB）储能与新的/首次使用的储能竞争容量市场收益。我们量化了碳税、可再生能源补贴和SLB补贴如何重塑均衡投资组合、碳排放量和利润。最后基于成本效益和减少的碳排放量对不同情景进行了比较。
英文摘要：
Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both system costs and carbon exposure. This study develops a three-level Stackelberg--Bayesian game in which a regulator (leader) sets carbon penalties and subsidies, a single ISO capacity market clears against an energy balance modeled as a classical generation-expansion problem, and technology-specific investors (followers) decide capacity and operation under incomplete information, yielding a Bayesian Nash equilibrium. The AI impact is captured parsimoniously as an additional load-growth factor on a greenfield-incremental expansion, isolating how much new capacity the growth pulls in and which technology fills it. Within this framework, we consider second-life battery (SLB) storage competing against new/first-life storage for capacity-market revenue. We quantify how a carbon tax, a renewable subsidy, and an SLB subsidy reshape the equilibrium investment mix, carbon emissions, and profit. Different scenarios are compared at the end based on cost-effectiveness and reduced carbon emissions.
