A risk-averse optimal dispatch scheme for data centers considering carbon emission and water constraints based on enhanced ADMM

• Proposes a novel water footprint model integrating temperature effects and tiered pricing for precise DC water consumption evaluation. • Develops a CVaR-based risk-averse scheme managing workload, PV, and temperature uncertainties to reduce extreme-scenario losses. • Enhances ADMM with step-size control to accelerate convergence while maintaining optimality gap in distributed DC dispatch. • Addresses spatio-temporal challenges by incorporating time-difference effects in large-scale networks and water-carbon constraints for eco-economic efficiency. • Validates water reduction under penalty pricing via workload shifting, balancing operational costs and environmental sustainability. With the background of global climate change, the significance of energy utilization considering environmentally friendly is widely recognized. As a heavy load user in the power system, the role of data center (DC) in environment protection needs to be considered in the operation of the power system. This paper proposes a distributed risk-averse optimal dispatch scheme for DCs considering carbon emission and water restriction. Firstly, a model for the consumption of cooling water was established based on the equation of heat conduction, and the utilization of water resources in data centers was restricted by imposing an excess penalty price; Furthermore, the conditional value at risk (CVaR) method is introduced to manage the risks arising from diverse uncertainties, including workload, power of photovoltaic and temperature in the proposed scheme. To improve computational performance and protect the privacy between the DCs, the improved multiplier based alternating direction method of multipliers (M−ADMM) distributed solution method is developed to solve the dispatch model for DCs, and consider the time difference between photovoltaics and temperature caused by geographical longitude location. Numerical results illustrate the benefits for environment protection and economic, risk management ability, and computational performance of the proposed scheme.