摘要：
In the context of energy saving and carbon emission reduction, the electric vehicle (EV) has been identified as a promising alternative to traditional fossil fuel-driven vehicles. Due to a different refueling manner and driving characteristic, the introduction of EVs to the current logistics system can make a significant impact on the vehicle routing and the associated operation costs. Based on the traveling salesman problem, this paper proposes a new optimal EV route model considering the fast-charging and regular-charging under the time-of-use price in the electricity market. The proposed model aims to minimize the total distribution costs of the EV route while satisfying the constraints of battery capacity, charging time and delivery/pickup demands, and the impact of vehicle loading on the unit electricity consumption per mile. To solve the proposed model, this paper then develops a learnable partheno-genetic algorithm with integration of expert knowledge about EV charging station and customer selection. A comprehensive numerical test is conducted on the 36-node and 112-node systems, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm.
作者机构：
[王建城; 刘光远; 杨洪明; 苏盛] College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan Province, China;[盛小勇] Taizhou Power Supply Company, State Grid Jiangsu Electric Power Co. Ltd., Taizhou, Jiangsu Province, China
关键词：
覆冰;广义极值分布;广义帕雷托分布;重现期
摘要：
针对轻、重覆冰区极值覆冰的统计特性有显著差异、可能适用不同极值估计方法的问题,利用湖南省轻覆冰区的永州、双峰和重覆冰区的衡山气象站近50 a 导线覆冰逐日冰厚资料,应用基于广义极值分布(generalized extreme value, GEV)的年极值抽样法和基于广义帕雷托分布(generalized Pareto distribution,GPD)的跨阈法(peak over threshold,POT)和独立风暴法(method of independent storm,MIS)进行了50 a重现期覆冰极值估计。结合K-S 检验、卡方(χ~2)检验及极值分布参数和极值覆冰估计值的对比分析表明,在有足够观测数据的前提下,基于GEV 分布的年极值抽样法估计的轻重覆冰区极值覆冰精度均优于基于GPD 分布的2种方法。此外,轻覆冰区极值覆冰服从极值III 型分布而重覆冰区极值覆冰服从极值II 型分布,因GEV分布涵盖这2种极值分型,在有足够的覆冰观测数据的条件下,建议采用GEV 分布估计多年期覆冰极值。
期刊：
Proceedings of the 5th IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, DRPT 2015,2015年:2764-2768
作者机构：
[杨鑫; 杨洪明; 张俊; 罗捷; 吴俊明] Hunan Province Key Laboratory of Smart Grids Operation and Control (Changsha University of Science and Technology), Changsha, 410114, China
通讯机构：
[Wu, J.] H;Hunan Province Key Laboratory of Smart Grids Operation and Control (Changsha University of Science and Technology), Changsha, China
摘要：
Vehicle routing problem is a classic combinational optimization problem, which has been attracting research attentions in logistics and optimization area. Conventional static vehicle routing problem assumes the logistics information is accurate and timely, and does not take into account the uncertainties, which is therefore inadequate during practical applications. In this paper, a vehicle initial routing optimization model considering uncertainties is proposed, the vehicle capacity, customer time-window, and the maximum travelling distance as well as the road capacity are considered. In the cyber-physical logistics system background, a routing adjustment model is proposed to minimize the total distribution cost considering the road congestion, and the static and dynamic models are proposed for traffic information transmission network to quantitatively analyse the impact of the traffic information transmission delay on the vehicle routing optimization. The learnable genetic algorithm is adopted to solve the initial routing optimization model and the routing adjustment model. The simulation results have verified its effectiveness.
关键词：
Distributed primal-dual sub-gradient algorithm;extreme learning machine;Gumbel-Copula;optimal dispatch;virtual power plant
摘要：
To implement the optimal dispatch of distributed energy resources (DER) in the virtual power plant (VPP), a distributed optimal dispatch method based on ELM (Extreme Learning Machine) transformation is proposed. The joint distribution of maximum available outputs of multiple wind turbines in the VPP is firstly modeled with the Gumbel-Copula function. A VPP optimal dispatch model is then formulated to achieve maximum utilization of renewable energy generation, which can take into account the constraints of electric power network and DERs. Based on the Gumbel-Copula joint distribution, the nonlinear functional relationship between the wind power cost and wind turbine output is approximated using ELM. The approximated functional relationship is then transformed into a set of equality constraints, which can be easily integrated with the optimal dispatch model. To solve the optimal dispatch problem, a distributed primal-dual sub-gradient algorithm is proposed to determine the operational strategies of DERs via local decision making and limited communication between neighbors. Finally, case studies based on the 15-node and the 118-node virtual power plant prove that the proposed method is effective and can achieve identical performance as the centralized dispatch approach.
作者：
Hongming Yang;Dangqiang Zhang;Ke Meng;Mingyong Lai;Zhao Yang Dong
期刊：
Intelligent Systems, Control and Automation: Science and Engineering,2014年72:139-160 ISSN：2213-8986
通讯作者：
Yang, H.
作者机构：
[Zhao Yang Dong; Yang H.] School of Electrical Engineering and Information, Changsha University of Science and Technology, Changsha 410114, China;[Meng K.; Dong Z.Y.] Centre for Intelligent Electricity Networks, The University of Newcastle, NSW 2308, Australia;[Lai M.] Key Laboratory of Logistics Information and Simulation Technology, Human University, Changsha 410082, China
通讯机构：
[Yang, H.] S;School of Electrical Engineering and Information, Changsha University of Science and Technology, Changsha 410114, China
关键词：
Combined cooling heating and power system;Copula function;Economic scheduling;Global descent method;Renewable energy;Tradable green certificate
关键词：
copula function;emissions trading;global descent algorithm;multi-network combined cooling heating and power system;optimal scheduling;renewable energy
摘要：
A multi-network combined cooling heating and power (CCHP) system is composed of different energy resources and customer demand, which are connected by electricity network and heating/cooling pipe network. In this paper, the joint probability distribution of available power generation by multiple wind turbines is established based on Copula function and marginal prob-ability distribution of wind speed. The optimal scheduling model for multi-network CCHP system is proposed to reduce greenhouse gas emissions and maximize renewable energy utilization, meanwhile considering the impacts of emission trading scheme on fossil-fired units and security operation constraints of electricity network and heating/cooling pipe network. After that, sampling average approximation, function smoothing and global descent algorithm are employed in order to address the calculation of non-smooth and non-convex scheduling optimization problem. The global descent algorithm continuously updates the local optimal solutions to find global optimal solutions. Finally, one modified 15-bus system is used to analyze the impacts of joint probability distribution, sampling number and emission trading scheme on the scheduling results, which verify the effectiveness of the proposed model and solving algorithm.
作者机构：
College of Electrical and Information Engineering, Hunan University, Changsha;410082, China;College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha;410004, China;Hunan COMBINE Distributed Energy Technology Co. Ltd., Changsha
通讯机构：
[Wang, J.] C;College of Electrical and Information Engineering, Hunan University, Changsha, China
作者机构：
[吴俊明; 杨洪明] Hunan Province Key Laboratory of Smart Grids Operation and Control, Changsha University of Science and Technology, Changsha 410114, China;[刘保平] Lishui Power Supply Company of State Grid, Lishui 323000, China;[易德鑫] Jinjiang Electric Power Co., Ltd., Jinjiang 362200, China;[Yang H.-Z.] Hunan Zhongtian Engineering Supervision Co., Ltd., Changsha 410007, China
通讯机构：
[Yang, H.-M.] H;Hunan Province Key Laboratory of Smart Grids Operation and Control, Changsha University of Science and Technology, Changsha 410114, China
作者机构：
[Y.X.Zhang; Z.Y.Dong; H.M.Yang] School of Electrical & Information Engineering, The University of Sydney;[Y.X.Zhang; Z.Y.Dong; H.M.Yang] School of Electrical and Information Engineering, Changsha University of Science & Technology
会议名称：
2014 International Conference on Environmental Engineering and Computer Application (ICEECA 2014)
会议时间：
2014-12-25
会议地点：
中国香港
摘要：
The wind power output is affected primarily by external meteorological factors. Under extreme wind condition, individual wind turbine might automatically shutdown once the wind speed is near or exceed
摘要：
A model based on the extreme value theory and copula functions is proposed to assess the probability of potential damages to transmission lines and towers during ice storms. Generalized Pareto distributions of freezing precipitation and wind speed are first derived. Copula functions are then employed to derive the joint probability distribution of ice and wind loads on both transmission lines and towers. Based on the proposed model, probabilities of transmission-line damage and tower collapse can be calculated. The validity and accuracy of the proposed model are verified by comparing the numerical results given by the model with historical data of a real transmission system during two serious ice storms in China. The proposed model will be highly useful for assessing efficacy of power systems emergency controls during ice storms.
关键词：
Extreme learning machine;Genetic algorithm;Power system economic dispatch
摘要：
In this paper a novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed. Traditional genetic algorithm employs genetic operations, such as selection, mutation and crossover to generate the optimal solution. In practice, the child solutions generated by crossover and mutation are largely random and therefore cannot ensure the fast convergence of the algorithm. To tackle the weakness of traditional GA, the ELM is introduced to estimate the nonlinear functional relationships between the parent population and child population generated by genetic operations. The trained downward-climbing and upward-climbing ELMs are then employed to generate candidate solutions, which forms the new population together with the solutions given by genetic operations. The proposed algorithm is applied to the power system economic dispatch problem. As demonstrated in case studies, the modified genetic algorithm is able to locate local minima faster and escape from local minima with a greater probability. The proposed algorithm can therefore ensure the faster convergence and provide more economical dispatch plans. In this paper a novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed. Traditional genetic algorithm employs genetic operations, such as selection, mutation and crossover to generate the optimal solution. In practice, the child solutions generated by crossover and mutation are largely random and therefore cannot ensure the fast convergence of the algorithm. To tackle the weakness of traditional GA, the ELM is introduced to estimate the nonlinear functional relationships between the parent population and child population generated by genetic operations. The trained downward-climbing and upward-climbing ELMs are then employed to generate candidate solutions, which forms the new population together with the solutions given by genetic operations. The proposed algorithm is applied to the power system economic dispatch problem. As demonstrated in case studies, the modified genetic algorithm is able to locate local minima faster and escape from local minima with a greater probability. The proposed algorithm can therefore ensure the faster convergence and provide more economical dispatch plans.
