@article {28080,
	title = {Optimal Energy Trading with Demand Responses in Cloud Computing Enabled Virtual Power Plant in Smart Grids},
	journal = {IEEE Transactions on Cloud Computing},
	year = {2021},
	month = {10/2021},
	publisher = { IEEE},
	abstract = {The increasing penetration of renewable energy sources and electric vehicles (EVs) poses a significant challenge for the power grid operator in terms of increasing peak load and power quality reduction. Moreover, there is a growing demand for fast charging services in smart grids. Addressing the growing demand from fast charging services is challenging. To overcome this challenge, in this paper, we propose a new computational architecture combining energy trading and demand responses based on cloud computing for managing virtual power plants (VPPs) in smart grids. In the proposed system, EVs can be charged at high charging rates without affecting the operation of the power grid by purchasing energy through the energy trading platform in the cloud. In addition, users with storage devices can sell energy surplus to the market. On the one hand, the energy trading platform can be regarded as an internal market of the VPP that aims to maximize its revenue. The interest of the EV owners, on the other hand, is to minimize the cost for charging. Therefore, we model the interactions between the EV owners and the VPP as a non-cooperative game.},
	doi = {10.1109/TCC.2021.3118563},
	author = {Chung, Hwei-Ming and Maharjan, Sabita and Zhang, Yan and Eliassen, Frank and Strunz, Kai}
}
@article {28072,
	title = {Placement and Routing Optimization for Automated Inspection with UAVs: A Study in Offshore Wind Farm},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {17},
	year = {2021},
	month = {05/2021},
	pages = {3032-3043},
	publisher = { IEEE},
	abstract = {Wind power is a clean and widely deployed alternative to reducing our dependence on fossil fuel power generation. Under this trend, more turbines will be installed in wind farms. However, the inspection of the turbines in an offshore wind farm is a challenging task because of the harsh environment (e.g., rough sea, strong wind, and so on) that leads to high risk for workers who need to work at considerable height. Also, inspecting increasing number of turbines requires long man hours. In this regard, unmanned aerial vehicles (UAVs) can play an important role for automated inspection of the turbines for the operator, thus reducing the inspection time, man hours, and correspondingly the risk for the workers. In this case, the optimal number of UAVs enough to inspect all turbines in the wind farm is a crucial parameter. In addition, finding the optimal path for the UAVs{\textquoteright} routes for inspection is also important and is equally challenging. In this paper, we formulate a placement optimization problem to minimize the number of UAVs in the wind farm and a routing optimization problem to minimize the inspection time. Wind has an impact on the flying range and the flying speed of UAVs, which is taken into account for both problems. The formulated problems are NP-hard. We therefore design heuristic algorithms to find solutions to both problems, and then analyze the complexity of the proposed algorithms. The data of the Walney wind farm are then utilized to evaluate the performance of the proposed algorithms. Simulation results clearly show that the proposed methods can obtain the optimal routing path for UAVs during the inspection.},
	doi = {10.1109/TII.2020.3004816},
	author = {Chung, Hwei-Ming and Maharjan, Sabita and Zhang, Yan and Eliassen, Frank and Strunz, Kai}
}
@article {28069,
	title = {Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grid},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {17},
	year = {2020},
	month = {04/2020},
	publisher = { IEEE},
	abstract = {The power consumption of households has been constantly growing over the years. To cope with this growth, intelligent management of the consumption profile of the households is necessary, such that the households can save the electricity bills, and the stress to the power grid during peak hours can be reduced. However, implementing such a method is challenging due to the existence of randomness in the electricity price and the consumption of the appliances. To address this challenge, we employ a model-free method for the households which works with limited information about the uncertain factors. More specifically, the interactions between households and the power grid can be modeled as a non-cooperative stochastic game, where the electricity price is viewed as a stochastic variable. To search for the Nash equilibrium (NE) of the game, we adopt a method based on distributed deep reinforcement learning. Also, the proposed method can preserve the privacy of the households. We then utilize real-world data from Pecan Street Inc., which contains the power consumption profile of more than 1; 000 households, to evaluate the performance of the proposed method. In average, the results reveal that we can achieve around 12\% reduction on peak-to-average ratio (PAR) and 11\% reduction on load variance. With this approach, the operation cost of the power grid and the electricity cost of the households can be reduced.},
	author = {Chung, Hwei-Ming and Maharjan, Sabita and Zhang, Yan and Eliassen, Frank}
}
@inproceedings {28082,
	title = {Edge Intelligence Empowered UAV s for Automated Wind Farm Monitoring in Smart Grids},
	journal = {IEEE Globecom 2020},
	year = {2020},
	publisher = { IEEE},
	abstract = {With the exploitation of wind power, more turbines will be deployed at remote areas possibly with harsh working conditions (e.g., offshore wind farm). The adverse working environment may lead to massive operating and maintenance costs of turbines. Deploying unmanned aerial vehicles (UAVs) for turbine inspection is considered as a viable alternative to manual inspections. An important objective of automated UAV inspection is to minimize the flight time of the UAVs to inspect all the turbines. A first contribution of this paper is thus formulating an optimization problem to compute the optimal routes for turbine inspection satisfying the above goal. On the other hand, the limited computational capability on UAVs can be used to increase the power generation of wind turbine. Power generation from the turbines can be optimized by controlling the yaw angle of the turbines. Forecasting wind conditions such as wind speed and wind direction is crucial for solving both optimization problems. Therefore, UAVs can utilize their limited computational capability to perform wind forecasting. In this way, UAVs form edge intelligence in offshore wind farm. With the forecasted wind conditions, we design two algorithms to solve the formulated problems, and then evaluate the proposed methods with real-world data. The results reveal that the proposed methods offer an improvement of 44\% of the power generation from the turbine compared to hour-ahead forecasting and 25\% reduction of the flight time of the UAVs compared to the chosen baseline method.},
	doi = {10.1109/GLOBECOM42002.2020.9322269},
	author = {Chung, Hwei-Ming and Maharjan, Sabita and Zhang, Yan and Eliassen, Frank and Yuan, Tingting}
}
@article {28066,
	title = {Intelligent Charging Management of Electric Vehicles Considering Dynamic User Behavior and Renewable Energy: A Stochastic Game Approach},
	journal = { IEEE Transactions on Intelligent Transportation Systems},
	year = {2020},
	month = {07/2020},
	publisher = { IEEE},
	abstract = {Uncoordinated charging of a rapidly growing number of electric vehicles (EVs) and the uncertainty associated with renewable energy resources may constitute a critical issue for the electric mobility (E-Mobility) in the transportation system especially during peak hours. To overcome this dire scenario, we introduce a stochastic game to study the complex interactions between the power grid and charging stations. In this context, existing studies have not taken into account the dynamics of customers{\textquoteright} preference on charging parameters. In reality, however, the choice of the charging parameters may vary over time, as the customers may change their charging preferences. We model this behavior of customers with another stochastic game. Moreover, we define a quality of service (QoS) index to reflect how the charging process influences customers{\textquoteright} choices on charging parameters. We also develop an online algorithm to reach the Nash equilibria for both stochastic games. Then, we utilize real data from the California Independent System Operator (CAISO) to evaluate the performance of our proposed algorithms. The results reveal that the electricity cost with the proposed method can result in a saving of about 20\% compared to the benchmark method, while also yielding a higher QoS in terms of charging and waiting time. Our results can be employed as guidelines for charging service providers to make efficient decisions under uncertainty relative to power generation of renewable energy.},
	doi = {10.1109/TITS.2020.3008279},
	author = {Chung, Hwei-Ming and Maharjan, Sabita and Zhang, Yan and Eliassen, Frank}
}
@inproceedings {28081,
	title = {[PDF] from arxiv.org A study on the characteristics of douyin short videos and implications for edge caching},
	journal = {ACM TURC 2019},
	year = {2019},
	publisher = {ACM},
	abstract = {Douyin, internationally known as TikTok, has become one of the most successful short-video platforms. To maintain its popularity, Douyin has to provide better Quality of Experience (QoE) to its growing user base. Understanding the characteristics of Douyin videos is thus critical to its service improvement and system design. In this paper, we present an initial study on the fundamental characteristics of Douyin videos based on a dataset of over 260 thousand short videos collected across three months. The characteristics of Douyin videos are found to be significantly different from traditional online videos, ranging from video bitrate, size, to popularity. In particular, the distributions of the bitrate and size of videos follow Weibull distribution. We further observe that the most popular Douyin videos follow Zifp{\textquoteright}s law on video popularity, but rest of the videos do not. We also investigate the correlation between popularity metrics used for Douyin videos. It is found that the correlation between the number of views and the number of likes are strong, while other correlations are relatively low. Finally, by using a case study, we demonstrate that the above findings can provide important guidance on designing an efficient edge caching system.},
	author = {Chen, Zhuang and He, Qian and Mao, Zhifei and Chung, Hwei-Ming and Maharjan, Sabita}
}
