Optimization of Microgrid-Controlled Chiller Plants for Data Center Cooling in the Northeastern United States
Desmond Ondieki Ocharo, Agama Omachi
Abstract
The growing demand for data center services has significantly increased energy consumption, with cooling systems accounting for a substantial portion of operational costs and carbon emissions. This paper reviews the optimization of microgrid-controlled chiller plants for data center cooling in the Northeastern United States, focusing on energy efficiency, reliability, and sustainability. The study examines system configurations integrating distributed energy resources such as solar photovoltaics, battery storage, and backup generators, alongside advanced control algorithms including model predictive control, AI-based methods, and hybrid strategies. Key aspects discussed include energy flow management, load balancing, and demand response coordination, which collectively enhance operational performance. The review also explores optimization techniques ranging from mathematical programming and mixed-integer linear programming to metaheuristic and reinforcement learning approaches, highlighting multi-objective trade-offs among cost, carbon footprint, and system reliability. Case studies and simulation analyses demonstrate the effectiveness of these strategies in improving energy efficiency, reducing peak demand, and supporting grid stability. Finally, emerging trends such as digital twins, predictive maintenance, and IoT-enabled optimization are discussed, along with policy implications and pathways for future research. The findings indicate that microgrid-controlled chiller systems provide a resilient, sustainable, and cost-effective solution for modern data center cooling.