会议名称：
IEEE International Conference on Smart Grid Communications (SmartGridComm)
会议时间：
NOV 06-09, 2016
会议地点：
Sydney, AUSTRALIA
会议主办单位：
[Huang, Jingjie;Meng, Ke] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia.^[Lai, Mingyong;Zhou, Renjun] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Hunan, Peoples R China.^[Zheng, Yu;Ma, Xiyuan] China Southern Power Grid Co, Elect Power Res Inst, Guangzhou, Guangdong, Peoples R China.
会议论文集名称：
International Conference on Smart Grid Communications
关键词：
run-off algorithm;extension position;river width;altitude;carbon equivalent conversion coefficients;distributed combined cool and heat and power
摘要：
A new optimization algorithm, named run-off algorithm, is proposed in this paper to solve the nonlinear, nonconvex and often discontinuous optimization problems. The algorithm a kind of modern heuristic algorithm and has advantages of low randomness and high accuracy. Comparing with standard optimization algorithms such as gradient descent, its main characteristic is no need of functions and variables to be continuous and differentiable, so it is suitable for solving discrete and multidimensional problem. In order to get more convincing results, it should compare with other heuristic algorithms. It has similar shortage with current heuristic algorithms but has better performance. Therefore, it is applied to solving optimal dispatching problem of distributed combined cool, heat and power (DCCHP) system. The carbon equivalent conversion coefficient is proposed for actualizing the conversion between different pollution emissions, and the environmental cost model is further simplified by equivalent transformation between the SO 2 , NOx and CO 2 emissions. The simulation result shows that compared with genetic algorithm and standard particle swarm algorithm, run-off algorithm can get optimization solution better, faster and more stable than others. Meanwhile, optimization result proves the feasibility and superiority of this algorithm in solving such problems.
作者机构：
[周任军] Hunan Province Key Laboratory of Smart Grids Operation and Control (Changsha University of Science and Technology), Changsha, 410004, China;[陆佳政; 陈跃辉; 刘乐平; 周胜瑜] Electric Power of Hunan Province of State Grid, Changsha, 410007, China;[陈瑞先] State Grid Yantai Power Supply Company, Yantai, 264000, China
作者机构：
[陈彦秀; 范龙; 周任军] Smart Grids Operation and Control Key Laboratory of Hunan Province (Changsha University of Science and Technology), Changsha, 410114, China;[李献梅] Yiyang Power Supply Company, State Grid Hunan Electric Power Company, Yiyang, 413100, China
通讯机构：
[Fan, L.] S;Smart Grids Operation and Control Key Laboratory of Hunan Province (Changsha University of Science and Technology)China
作者机构：
[陈瑞先; 闵雄帮; 章杰; 周任军; 刘志勇; 童小娇; 李献梅] Hunan Province Key Laboratory of Smart Grids Operation and Control (Changsha University of Science and Technology), Changsha, Hunan Province, China
关键词：
多步整合模型;风险限制调度;线路阻塞;条件风险;随机变量;日前调度
摘要：
针对国际学界提出的电网智能运行中风险限制调度的框架,新建考虑线路阻塞的风险限制调度多步整合模型,包括日前三步整合调度模型、时前两步整合调度模型和紧急调度模型。日前三步整合调度是在日前调度中计入预估的时前、紧急调度的随机信息;时前两步整合调度计入了预估的紧急调度随机信息。随机信息主要考虑风电出力,并作为模型中的随机变量。通过定义线路阻塞条件风险(conditional value at risk,CVa R)值,将线路安全约束转化为线路阻塞风险限制约束,实现线路阻塞的风险限制调度。仿真结果表明:线路阻塞CVa R值的限值与置信水平两个值取值合理时,多步整合调度与传统的日前、时前、紧急三步调度相比运行成本较低;当前值限定在一定水平时,后值越大,多步整合调度的成本越高;后值一定时,前值越大,多步整合调度的成本越低。线路阻塞风险限制的多步整合调度模型是解决电力系统智能运行中风险限制调度的有效途径。
作者机构：
[Zhou, Peng; Yi, Guowei; Tong, Xiaojiao; Zhou, Renjun] College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan Province, China
作者机构：
[周鹏; 易国伟; 童小娇; 周任军] College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan Province, China
作者机构：
[周胜瑜; 周任军; 李红英; 康信文] Hunan Province Key Laboratory of Smart Grids Operation and Control, Changsha University of Science and Technology, Changsha 410004, China
通讯机构：
[Zhou, S.-Y.] H;Hunan Province Key Laboratory of Smart Grids Operation and Control, Changsha University of Science and Technology, Changsha 410004, China
关键词：
混合语言信息群决策方法;城市电力负荷密度预测;BP神经网络;三大类指标;指标综合评分值
摘要：
传统城市空间负荷密度预测法在实际预测过程中其结果的可信度依赖于大量有效的样本数据，而在实际中收集到较齐全的可行样本数据存在很大的难度。为此提出了一种将混合语言信息群决策方法和 BP 神经网络相结合的城市电力负荷密度预测法。该方法采用基于混合语言信息的群决策方法，通过各决策者的评价，计算城市各小区相应的经济、人口、地理环境的综合评分值，并利用 BP 神经网络，训练各指标综合评分值与相应的小区负荷密度，利用训练后的网络结构和待定小区的各指标综合评分结果，预测城市该小区的负荷密度。通过对城市若干小区的负荷密度及各指标综合评分值做比较分析，预测了部分小区的负荷密度值。结果表明预测计算过程摆脱了需要大量收集特定指标定量数据的问题，并且预测结果具有较高的可信度。
作者机构：
[周任军; 刘志勇; 李献梅; 陈瑞先; 闵雄帮; 尹权] Smart Grids Operation and Control Key Laboratory of Hunan Province (Changsha University of Science and Technology), Changsha;Hunan Province;410114, China;[周任军; 刘志勇; 李献梅; 陈瑞先; 闵雄帮; 尹权] Hunan Province;[周任军; 刘志勇; 李献梅; 陈瑞先; 闵雄帮; 尹权] 410114, China
通讯机构：
Smart Grids Operation and Control Key Laboratory of Hunan Province (Changsha University of Science and Technology), Changsha, Hunan Province, China
