Energy optimization and control for data centers and smart grids.

摘要

Today's energy systems are in the midst of a transformation driven by the global climate change and increasing demand of energy. The majority of today's energy supply comes from the combustion of fossil fuels, such as coal, petroleum, and natural gas, which not only have limited supply but also cause a lot of greenhouse gas emissions, driving climate changes. Moreover, worldwide energy demand is still growing very fast for enabling economic growth and high quality of life. Therefore, there is an urgent imperative to build a sustainable energy future. Utilizing sustainable energy sources (e.g., wind, solar, and geothermal), deploying energy-efficient technologies, and reducing energy consumption are exemplary ways to achieve such a sustainable future.;The theme of this dissertation is to apply a computational approach to tackle the current energy sustainability challenges in the context of data centers and smart grids. In particular, advanced modeling, optimization, and control techniques are developed in this dissertation to manage these systems so as to improve their energy efficiency, lower their energy cost, and reduce their carbon footprint. The main contributions of this dissertation are the following. First, several novel control algorithms are developed for data center operators to effectively manage their energy usage. These control algorithms take into account important yet largely unexplored issues in practice, such as uncertain workload arrivals and electricity prices. Experimental results based on real-world traces show that one can effectively conserve the energy consumption, lower the energy cost, and increase the renewable energy utilization in these data centers without violating the service-level agreement by leveraging the proposed control algorithms. Second, two frameworks, "Smart Home" and "Smart Neighborhood", for managing distributed energy resources in residential households are proposed. These frameworks integrate all essential components such as smart appliances, storage devices, and on-site renewable generators in the future smart grid. Results in this work reveal that, by utilizing the real-time detailed data about residential customers's behaviors and power system conditions, one can greatly improve the efficiency and sustainability of current electric power systems. Third, an novel market operation strategy for a virtual power plant consisting of multiple distributed energy resources is developed to minimize the imbalance cost when participating into a wholesale electricity market.