Energy optimization for data centers via carbon-aware multi-energy market coordination

AI-driven digitalization is intensifying data center (DC) energy use and emissions, while variable demands and renewables exacerbate supply-demand and carbon allocation-emission imbalances. These challenges are further compounded by existing strategies that often overlook carbon-energy coupling and lack real-time, incentive-compatible coordination across DCs and energy resources. To address these challenges and satisfy online demands, a tiered carbon trading and capture coordination model is proposed, along with a multi-energy market framework integrating energy storage, an electrolyzer and a combined cooling and power unit, to maximize overall benefits and fully absorb renewable energy. Then, an improved stochastic optimization constructs virtual queues and introduces an auxiliary variable to ensure charge/discharge benefits and system stability while decarbonizing DCs without requiring priori information of system random processes. To avoid privacy leakage, a distributed energy clearing method is applied to facilitate low-complexity trading among DCs. Through case studies, the proposed method can reduce carbon emissions and approach the optimal costs while mitigating battery degradation.