Capacity Planning and Operational Optimization for Low-Carbon Data Center Integrated Energy System Considering Exergy Efficiency

With the rapid development of the digital economy, the energy consumption and carbon emissions of data centers (DCs) have significantly increased. In recent years, the construction of data center integrated energy systems (DCTES) has emerged as one of the critical trends in energy conservation and emission reduction for DCs under the global net-zero emission initiative. To support the planning and construction of low-carbon DCTES, this paper proposes a multi-objective optimization model for capacity allocation and operational planning of DCTES, integrating energy and economic considerations with a focus on low-carbon performance. Based on the "quality" analysis method of exergy from the second law of thermodynamics, the model proposed comprehensively accounts for the dynamic exergy efficient characteristics of energy conversion devices under varying load conditions, revealing the energy flow distribution characteristics of DC-IES under different objectives. The computational results indicate that compared with the optimization scheme assuming constant equipment efficiency, the scheme considering dynamic equipment efficiency reduces energy loss rate, economic cost, and carbon emissions by 2.6%, 1.9%, and 4.8%, respectively, demonstrating clear advantages. Moreover, compared with the economically optimal scheme, the multi-objective optimization scheme significantly reduces carbon emissions and energy loss rate of the DC-IES by 22.72% and 20.73%, respectively. Furthermore, compared to the scheme scenarios with the minimum exergy loss rate and lowest carbon emissions, the multi-objective optimization scheme reduces economic costs by 54.54% and 60.78%, respectively. Compared with the scheme relying solely on grid electricity supply, the multi-objective optimization scheme that regards the DC as an integrated energy system can reduce carbon emissions by 40. 97%.