3篇
您的检索式：作者名="Shunjiang Lin"
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| 1 | Distributionally Robust Optimal Dispatch of Offshore Wind Farm Cluster Connected by VSC-MTDC Considering Wind Speed Correlation显示文摘Multi-terminal voltage source converter-based highvoltage direct current(VSC-MTDC)transmission technology has become an important mode for connecting adjacent offshore wind farms(OWFs)to power systems.Optimal dispatch of an OWF cluster connected by the VSC-MTDC can improve economic operation under the uncertainty of wind speeds.A two-stage distributionally robust optimal dispatch(DROD)model for the OWF cluster connected by VSC-MTDC is established.The first stage in this model optimizes the unit commitment of wind turbines to minimize mechanical loss cost of units under the worst joint probability distribution(JPD)of wind speeds,while the second stage searches for the worst JPD of wind speeds in the ambiguity set(AS)and optimizes active power output of wind turbines to minimize the penalty cost of the generation deviation and active power loss cost of the system.Based on the Kullback–Leibler(KL)divergence distance,a data-driven AS is constructed to describe the uncertainty of wind speed,considering the correlation between wind speeds of adjacent OWFs in the cluster by their joint PD.The original solution of the two-stage DROD model is transformed into the alternating iterative solution of the master problem and the sub-problem by the column-and-constraint generation(C&CG)algorithm,and the master problem is decomposed into a mixedinteger linear programming and a continuous second-order cone programming by the generalized Benders decomposition method to improve calculation efficiency.Finally,case studies on an actual OWF cluster with three OWFs demonstrate the correctness and efficiency of the proposed model and algorithm. | Xiangyong Feng Shunjiang Lin Wanbin Liu Weikun Liang Mingbo Liu | 2023 | CSEE Journal of Power and Energy Systems2023，9，3： | 2 |
| 2 | An Uncertain Optimal Energy Flow Model for CCHP Campus Microgrid Using Parameterized Probability Boxes显示文摘Due to the uncertain fluctuations of renewable energy and load power, the state variables such as bus voltages and pipeline mass flows in the combined cooling, heating, and power campus microgrid(CCHP-CMG) may exceed the secure operation limits. In this paper, an optimal energy flow(OEF) model for a CCHP-CMG using parameterized probability boxes(p-boxes) is proposed to describe the higher-order uncertainty of renewables and loads. In the model, chance constraints are used to describe the secure operation limits of the state variable p-boxes, and variance constraints are introduced to reduce their random fluctuation ranges. To solve this model, the chance and variance constraints are transformed into the constraints of interval cumulants(ICs) of state variables based on the p-efficient point theory and interval Cornish-Fisher expansion. With the relationship between the ICs of state variables and node power, and using the affine interval arithmetic method, the original optimization model is finally transformed into a deterministic nonlinear programming model. It can be solved by the CONOPT solver in GAMS software to obtain the optimal operation point of a CCHP-CMG that satisfies the secure operation requirements considering the higher-order uncertainty of renewables and loads. Case study on a CCHP-CMG demonstrates the correctness and effectiveness of the proposed OEF model. | Shunjiang Lin Xuan Sheng Yuquan Xie Yanghua Liu Mingbo Liu | 2023 | Journal of Modern Power Systems and Clean Energy2023，11，5： | 0 |
| 3 | Expert consensus on prevention and cardiopulmonary resuscitation for cardiac arrest in COVID-19显示文摘Background:Cardiopulmonary resuscitation(CPR)strategies in COVID-19 patients differ from those in patients suffering from cardiogenic cardiac arrest.During CPR,both healthcare and non-healthcare workers who provide resuscitation are at risk of infection.The Working Group for Expert Consensus on Prevention and Cardiopulmonary Resuscitation for Cardiac Arrest in COVID-19 has developed this Chinese Expert Consensus to guide clinical practice of CPR in COVID-19 patients.Main recommendations:1)A medical team should be assigned to evaluate severe and critical COVID-19 for early monitoring of cardiac-arrest warning signs.2)Psychological counseling and treatment are highly recommended,since sympathetic and vagal abnormalities induced by psychological stress from the COVID-19 pandemic can induce cardiac arrest.3)Healthcare workers should wear personal protective equipment(PPE).4)Mouth-to-mouth ventilation should be avoided on patients suspected of having or diagnosed with COVID-19.5)Hands-only chest compression and mechanical chest compression are recommended.6)Tracheal-intubation procedures should be optimized and tracheal-intubation strategies should be implemented early.7)CPR should be provided for 20-30 min.8)Various factors should be taken into consideration such as the interests of patients and family members,ethics,transmission risks,and laws and regulations governing infectious disease control.Changes in management:The following changes or modifications to CPR strategy in COVID-19 patients are proposed:1)Healthcare workers should wear PPE.2)Hands-only chest compression and mechanical chest compression can be implemented to reduce or avoid the spread of viruses by aerosols.3)Both the benefits to patients and the risk of infection should be considered.4)Hhealthcare workers should be fully aware of and trained in CPR strategies and procedures specifically for patients with COVID-19. | Wei Song Jie Wei Xiangdong Jian Deren Wang Yanhong Ouyang Yuanshui Liu Xianjin Du Ying Chen Yingqi Zhang Heping Xu Shuming Xianyu Qiong Ning Xiang Li Xiaotong Han Feng Zhan Tao Yu Wenteng Chen Jun Zhang Wenwei Cai Sheng’ang Zhou Shengyang Yi Yu Cao Xiaobei Chen Shunjiang Xu Zong’an Liang Duohu Wu Fen Ai Zhong Wang Qingyi Meng Yuhong Mi Sisen Zhang Rongjia Yang Shouchun Yan Wenbin Han Yong Lin Chuanyun Qian Wenwu Zhang Yan Xiong Jun Lv Baochi Liu Yan Cao Xiaojun He Xuelian Sun Yufang Cao Tian’en Zhou | 2021 | Asian Pacific Journal of Tropical Medicine2021，14，6： | 0 |
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