Within the broad topical designation of “smart grid,” research in demand response, or demand-side management, focuses on investigating possibilities for electrically powered devices to adapt their power consumption patterns to better match the availability of intermittent renewable energy sources, especially wind. Devices such as battery chargers, heating and cooling systems, and computers can be controlled to change the time, duration, and magnitude of their power consumption while still meeting workload constraints such as deadlines and rate of throughput. This thesis presents a system by which a computer server, or multiple servers in a data center, can estimate the power imbalance on the electrical grid and use that information to dynamically change the power consumption as a service to the grid. Implementation on a testbed demonstrates the system with a hypothetical but realistic usage case scenario of an online video streaming service in which there are workloads with deadlines (high-priority) and workloads without deadlines (low-priority). The testbed is implemented with real servers, estimates the power imbalance from the grid frequency with real-time measurements of the live outlet, and uses a distributed, real-time algorithm to dynamically adjust the power consumption of the servers based on the frequency estimate and the throughput of video transcoder workloads. Analysis of the system explains and justifies multiple design choices, compares the significance of the system in relation to similar publications in the literature, and explores the potential impact of the system.

Approved for public release; distribution is unlimited / To ensure sufficient capacity to handle unexpected demands for electric power, decision makers often over-estimate expeditionary power requirements. Therefore, we often use limited resources inefficiently by purchasing more generators and investing in more renewable energy sources than needed to run power systems on the battlefield. Improvement of the efficiency of expeditionary power units requires better managing of load requirements on the power grids and, where possible, shifting those loads to a more economical time of day. We analyze the performance of a previously developed optimization model for scheduling time-shiftable electrical loads in an expeditionary power grids model in two experiments. One experiment uses model data similar to the original baseline data, in which expected demand and expected renewable production remain constant throughout the day. The second experiment introduces unscheduled demand and realistic fluctuations in the power production and the demand distributions data that more closely reflect actual data. Our major findings show energy grid power production composition affects which uncertain factor(s) influence fuel con-sumption, and uncertainty in the energy grid system does not always increase fuel consumption by a large amount. We also discover that the generators running the most do not always have the best load factor on the grid, even when optimally scheduled. / Lieutenant Commander, United States Navy

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