arXiv subjects

Daehee Kim

Publications and source records attributed to Daehee Kim.

21 records · Page 2Linked to original sources

arXiv2021

An Optimal Energy-Saving Home Energy Management Supporting User Comfort and Electricity Selling with Different Prices

In this study, we investigate the operation of an optimal home energy management system (HEMS) with integrated renewable energy system (RES) and energy storage system (ESS) supporting electricity selling functions. A multi-objective mixed integer nonlinear programming model, including RES, ESS, home appliances and the main grid, is proposed to optimize different and conflicting objectives which are energy cost, user comfort and PAR. The effect of different selling prices on the objectives is also considered in detail. We further develop a formula for the lower bound of energy cost to help residents or engineers quickly choose best parameters of RES and ESS for their homes during the installation process. The performance of our system is verified through extensive simulations under three different scenarios of normal, economic, and smart with different selling prices using real data, and simulation results are compared in terms of daily energy cost, PAR, user's convenience and consecutive waiting time to use appliances. Numerical results clearly show that the economic scenario achieves 51.6% reduction of daily energy cost compared to the normal scenario while sacrificing the user's convenience, PAR, and consecutive waiting time by 49%, 132%, and 1 hour, respectively. On the other hand, the smart scenario shows only slight degradation of user's convenience and PAR by 2% and 18%, respectively while achieving 46.4% reduction of daily energy cost and the same level of consecutive waiting time. Furthermore, our simulation results show that a decrease of selling prices has tiny impacts on PAR and user comfort even though the daily energy cost increases.

eess.SY↗

arXiv2020

MILP-based Imitation Learning for HVAC control

To optimize the operation of a HVAC system with advanced techniques such as artificial neural network, previous studies usually need forecast information in their method. However, the forecast information inevitably contains errors all the time, which degrade the performance of the HVAC operation. Hence, in this study, we propose MILP-based imitation learning method to control a HVAC system without using the forecast information in order to reduce energy cost and maintain thermal comfort at a given level. Our proposed controller is a deep neural network (DNN) trained by using data labeled by a MILP solver with historical data. After training, our controller is used to control the HVAC system with real-time data. For comparison, we also develop a second method named forecast-based MILP which control the HVAC system using the forecast information. The performance of the two methods is verified by using real outdoor temperatures and real day-ahead prices in Detroit city, Michigan, United States. Numerical results clearly show that the performance of the MILP-based imitation learning is better than that of the forecast-based MILP method in terms of hourly power consumption, daily energy cost, and thermal comfort. Moreover, the difference between results of the MILP-based imitation learning method and optimal results is almost negligible. These optimal results are achieved only by using the MILP solver at the end of a day when we have full information on the weather and prices for the day.

cs.LG↗

arXiv2017

Energy dissipation mechanism revealed by spatially resolved Raman thermometry of graphene/hexagonal boron nitride heterostructure devices

Understanding the energy transport by charge carriers and phonons in two-dimensional (2D) van der Waals heterostructures is essential for the development of future energy-efficient 2D nanoelectronics. Here, we performed in situ spatially resolved Raman thermometry on an electrically biased graphene channel and its hBN substrate to study the energy dissipation mechanism in graphene/hBN heterostructures. By comparing the temperature profile along the biased graphene channel with that along the hBN substrate, we found that the thermal boundary resistance between the graphene and hBN was in the range of (1-2) x 10^(-7) m^(2) KW^(-1) from ~100 C to the onset of graphene break-down at ~600 C in air. Consideration of an electro-thermal transport model together with the Raman thermometry conducted in air showed that local doping occurred under a strong electric field played a crucial role in the energy dissipation of the graphene/hBN device up to T ~ 600 C.

cond-mat.mes-hall↗