권순걸 교수

This study proposes the demand-side power procurement problem to optimally reduce consumer's energy cost. The motivation stems from pressing issues on an increase of energy cost in an industrial section. From an energy consumer's perspective, there exists an opportunity to reduce energy cost by adjusting purchase and consumption of energy in response to time-varying electricity price while utilizing renewable energy, which is called demand response. In this case, energy storage can be used to mi

Data centers are among the fastest growing industries in the United States economy in terms of IT service and usage of data. However, data centers consume an excessive amount of energy that contributes to huge energy costs and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions; thus, they face challenges in energy cost savings and making a positive impact on the environment. Therefore, data centers have been striving to: 1) improve

We consider a system where inelastic demand for electric power is met from three sources: 1) the grid; 2) in-house renewables such as solar panels; and 3) an in-house energy storage device. In our setting, energy demand, renewable power supply, and cost for grid power are all time-varying and stochastic. Furthermore, there are limits and inefficiency associated with charging and discharging the energy storage device. We formulate the storage operation problem as a dynamic program with parameters

To make manufacturing systems more energy cost-efficient, significant research proposes adopting energy--aware production scheduling with on-site renewable energy generation systems and battery energy storage systems. Depending on the different system configurations and various practical scenarios, the effect of those proposals would vary on energy efficiency and time-related performance of the system because of the complex dynamics and trade-offs in the manufacturing process. This paper propose

We consider a system of multiple parallel single-server queues where servers are heterogeneous with resources of different capacities and can be powered on or off while running at different speeds when they are powered on. In addition, we assume that application requests are heterogeneous with different workload distributions and resource requirements and the arrival, rates of request are time-varying. Managing such a heterogeneous, transient, and non-stationary system is a tremendous challenge.