期刊：
International Journal of Global Energy Issues,2023年45(3):261-272 ISSN：0954-7118
作者机构：
[Ming Luo] Department of Architecture and Art, Central South University, Changsha, Hunan, China;[Hui-Hua Xiong] School of Architecture and Urban Planning, Hunan City University, Heshan District, Yiyang, Hunan, China
关键词：
time weighting;green building engineering;emission reduction prediction
摘要：
Aiming at the problems of low accuracy of emission reduction prediction and long time-consuming emission reduction prediction methods of existing green building engineering emission reduction prediction methods, the paper proposes a new time-weighted emission reduction prediction method for green building engineering. First, construct the objective function of the time change of green building emission reduction, and use time weighting to calculate the weight of green building engineering emission reduction forecast. Secondly, the grey model is used to obtain the fitted sequence of emission reductions of green building projects. Finally, the Markov Chain is used to construct the emission reduction prediction function, and the output result of the function is the prediction result. The results of the simulation study show that the prediction accuracy of emission reductions of the method in this paper is maintained above 95%, and the time cost is effectively reduced.
作者机构：
[Peng, Li] Hunan City Univ, Key Lab Energy Monitoring & Edge Comp Smart City, Yiyang 413002, Peoples R China.;[Zabihi, Alireza] Islamic Azad Univ, Dept Elect Engn, Najafabad Branch, Esfahan 8514143131, Iran.;[Shirvani, Hadis; Azimian, Mahdi] Islamic Azad Univ, Dept Elect & Comp Engn, Kashan Branch, Kashan 8715998151, Iran.;[Shahnia, Farhad] Murdoch Univ, Discipline Engn & Energy, Perth, WA 6150, Australia.
通讯机构：
[Shahnia, F ] M;Murdoch Univ, Discipline Engn & Energy, Perth, WA 6150, Australia.
关键词：
multiobjective;private investor;renewable energy sources;robust optimization;Transmission expansion planning;uncertainties
摘要：
Power system restructuring has changed transmission expansion planning (TEP) and caused many complications due to conflicting and contradictory objectives. The transmission capacity expansion would significantly affect the revenue of investor-owned renewable energy sources (RESs). Thus, the investment decisions on merchant RESs must be considered in the TEP studies conducted by the transmission system operator (TSO). In this regard, this paper aims to propose a multi-objective co-planning of investment in transmission networks and merchant RESs with three objective functions: minimizing the investment cost of newly deployed transmission lines, minimizing transmission congestion cost, and minimizing load curtailment in N-1 conditions. Moreover, the TSO guarantees a desirable rate of return for private investors to finance renewable energy projects. Further, a robust optimization (RO) technique is employed to cope with the uncertainties associated with demand and renewable energy production. Also, a posteriori multi-objective optimization algorithm, i.e., the non-dominated sorting genetic algorithm (NSGAII), is applied to solve the advanced optimization problem, followed by the fuzzy min-max method to acquire the final optimal solution. Finally, the IEEE RTS 24-bus test system is utilized to demonstrate the effectiveness and applicability of the suggested approach.
作者机构：
[杨京渝; 罗隆福] School of Electrical & Information Engineering, Hunan University, Changsha;410000, China;[阳同光; 彭丽; 田飞扬] Key Laboratory Energy Monitoring and Edge Computing for Smart City of Hunan Province, Hunan City University, Yiyang;413000, China;[杨京渝] 410000, China<&wdkj&>Key Laboratory Energy Monitoring and Edge Computing for Smart City of Hunan Province, Hunan City University, Yiyang
通讯机构：
[Luo, L.] S;School of Electrical & Information Engineering, China
期刊：
Advances in Transportation Studies,2023年1(Special Issue):111-122 ISSN：1824-5463
通讯作者：
He, C.
作者机构：
[Xiong H.H.] School of Architecture and Urban planning, Hunan City University, Yiyang, 413000, China;Department of Architecture and Art, Central south University, Changsha, 410012, China;[He C.] School of Architecture, Changsha University of Science and Technology, Changsha, 410114, China;[Dissanayake S.] College of Engineering, Kansas State University, Manhattan, KS 66506, United States;[Jiang J.S.] School of Architecture and Urban planning, Hunan City University, Yiyang, 413000, China, Department of Architecture and Art, Central south University, Changsha, 410012, China
通讯机构：
[He, C.] S;School of Architecture, China
关键词：
balanced distribution;fuzzy ant colony algorithm;mixed traffic flow;urban roads
期刊：
Water Supply,2023年23(9):3761–3774. ISSN：1606-9749
通讯作者：
Qian, DY
作者机构：
[Cao, Yanmin] Hunan City Univ, Sch Civil Engn, Yiyang 413000, Hunan, Peoples R China.;[Qian, Dongyue] Tianjin Acad Water Transportat Engn, Minist Transport, Tianjin 300456, Peoples R China.;[Wang, Chongyu] Hunan Water Planning & Design Inst Co Ltd, Changsha 410008, Hunan, Peoples R China.
通讯机构：
[Qian, DY ] T;Tianjin Acad Water Transportat Engn, Minist Transport, Tianjin 300456, Peoples R China.
关键词：
contribution rate;cross city driving force;water quality factor;Xiang River Basin
摘要：
Based on the monthly monitoring data of 16 water quality monitoring stations in the Xiang River Basin from 1990 to 2020, the Mann-Kendall test was used to analyze dissolved oxygen, 5-day biochemical oxygen demand, permanganate index, total phosphorus, ammonia nitrogen, cadmium, arsenic, and hexavalent chromium. The changing trend of nine indicators, including the stepwise regression method, was used to determine the cross city driving force of each water quality index, and the contribution rate (weight) of each driving force was obtained by principal component analysis. The research results show that (1) agriculture in Yongzhou is the main driving force, and its contribution rate is 67.2%; (2) urbanization has a greater impact on the driving process of the water environment in the Xiang River Basin, and its contribution rate is as follows: Changsha (83%) > Hengyang (80.7%) > Pingxiang (63.7%) > Chenzhou (60.9%) > Xiangtan (57.4%) > Zhuzhou (50%) > Loudi (48.5%); (3) the urbanization of Zhuzhou City and Loudi City's urban sewage discharge not only has an impact on the city's water environment, but also drives the water environment in the downstream Xiangtan area. The research results can provide a basis and reference for the study of water environmental governance in the basin.
