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Chongqing Kang

Researcher at Tsinghua University

Publications -  356
Citations -  15542

Chongqing Kang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Electric power system & Wind power. The author has an hindex of 52, co-authored 330 publications receiving 9376 citations. Previous affiliations of Chongqing Kang include University of Hong Kong.

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Increasing the Flexibility of Combined Heat and Power for Wind Power Integration in China: Modeling and Implications

TL;DR: In this article, a linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks.
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Robust Optimization-Based Resilient Distribution Network Planning Against Natural Disasters

TL;DR: Computational studies on the IEEE distribution test systems validate the effectiveness of the RDNP and reveal that distributed generation is critical in increasing the resilience of a distribution system against natural disasters in the form of microgrids.
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Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

TL;DR: The key result is an abrupt 8.8% decrease in global CO2 emissions in the first half of 2020 compared to the same period in 2019, larger than during previous economic downturns or World War II.
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Review and prospect of integrated demand response in the multi-energy system

TL;DR: The state-of-the-art ofIDR in the MESs is reviewed for the first time and the basic concept of IDR and the value analysis are introduced, and the research on IDR inThe MES is summarized.
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Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life

TL;DR: A novel bidding model is incorporated into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets and a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model.