Chenbei LuTsinghua University | TH · Institute for Interdisciplinary Information Sciences
Chenbei Lu
Doctor of Philosophy
CS Ph.D. student. Now a visiting student researcher at the Department of Computing and Mathematical Sciences, Caltech.
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27
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Publications
Publications (27)
Renewable energy penetration increases the power grid's operational uncertainty, threatening the economic effectiveness and reliability of the grid. In this paper, we examine how uncertainty affects unit commitment (UC), a classical electricity market procedure. Stochastic programming has helped handle uncertainty for UC and performed well with dis...
The integration of distributed energy resources, particularly wind energy, presents both opportunities and challenges for the modern electrical grid. On the supply side, wind farms frequently encounter penalties due to wind power’s intermittency and variability. The incorporation of energy storage systems can mitigate these penalties through real-t...
Flexible resources are increasingly significant for the reliable operation of power grids due to the high penetration of renewable energy. Thermostatically controlled loads (TCLs) are one of the common flexible resources, whose control has been extensively studied. Yet, much can be improved. We investigate the scheduling of TCLs facing uncertain te...
The high penetration of renewable energy brings significant uncertainty to the power grids. Taking economic dispatch (ED) as an example, the inaccurate prediction of renewable energy generations dramatically increases the dispatch cost and risks the power grid's reliable operation. The accurate distribution knowledge of the renewable generations en...
The smart grid benefits and suffers from smart meter data. Proper use of massive data can improve energy services but may raise privacy concerns. For example, user energy consumption profiling, a classic method, can identify energy consumption patterns based on the collected load profiles from users. Thus, the privacy of these individual load profi...
The high penetration of renewable energy increases the price volatility between the day-ahead (DA) and real-time (RT) markets, with heightened power system operational risks. Virtual bidding, a rising financial instrument, allows financial entities without energy-generating capacity or demand to arbitrage between the DA and RT markets, which can in...
Price competition among electric vehicle (EV) charging stations is as fierce as the competition among gas stations. Nash equilibrium (NE) is a solution concept that can characterize a competition’s efficient and stable state. However, calculation of the equilibrium is often time-consuming and requires complete information on the charging stations....
Conventional wisdom to improve economic dispatch effectiveness is to design the load forecasting method as accurately as possible. However, this approach can be problematic due to the temporal and spatial correlations between system cost and load prediction errors. This observation motivates us to jointly treat the two forms of correlations by adop...
The increasing number of electric vehicles (EVs) on the road brings both opportunities and challenges to the power system. For the EV charging stations (EVCSs), it is often difficult to conduct effective operations due to the incomplete information in EVs’ departure times and the opacity of their preference information. To tackle this challenge, we...
The Internet of Things (IoT) enables reliable and fast data collection and transmission, providing key infrastructure for power generation, distribution, and control in the smart grid. This IoT-enabled smart grid tackles challenges brought by renewable penetration in new ways: Accurate and real-time information allows for the application of artific...
Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensi...
Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensi...
Conventional wisdom to improve the effectiveness of economic dispatch is to design the load forecasting method as accurately as possible. However, this approach can be problematic due to the temporal and spatial correlations between system cost and load prediction errors. This motivates us to adopt the notion of end-to-end machine learning and to p...