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The data center of tomorrow is characterized as one containing a dense aggregation of commodity computing, networking and storage hardware mounted in industry standard racks. In fact, the data center is a computer. The walls of the data center are akin to the walls of the chassis in today’s computer system. The new slim rack mounted systems and blade servers enable reduction in the footprint of today’s data center by 66%. While maximizing computing per unit area, this compaction leads to extremely high power density and high cost associated with removal of the dissipated heat. Today’s approach of cooling the entire data center to a constant temperature sampled at a single location, irrespective of the distributed utilization, is too energy inefficient. We propose a smart cooling system that provides localized cooling when and where needed and works in conjunction with a compute workload allocator to distribute compute workloads in the most energy efficient state. This paper shows a vision and construction of this intelligent data center that uses a combination of modeling, metrology and control to provision the air conditioning resources and workload distribution. A variable cooling system comprising variable capacity computer room air conditioning units, variable air moving devices, adjustable vents, etc. are used to dynamically allocate air conditioning resources where and when needed. A distributed metrology layer is used to sense environment variables like temperature and pressure, and power. The data center energy manager redistributes the compute workloads based on the most energy efficient availability of cooling resources and vice versa. The distributed control layer is no longer associated with any single localized temperature measurement but based on parameters calculated from an aggregation of sensors. The compute resources not in use are put on “standby” thereby providing added savings.

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

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