基于概率图像灰度比对算法的远洋海岛可再生能源机组配置方案优选策略研究
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摘要
由于远洋海岛无法获得大陆主电网的支持,且其常规机组的燃料补给线超长,传统发电配置方法并不能直接适用于对远洋海岛可再生能源发电机组进行配置和方案优选。对此类远洋海岛而言,储量丰富的可再生能源应作为建设绿色海岛能源供应系统的主要依托。该文基于全清洁化远洋海岛能量供给系统的拓扑特性与基本框架,并计及远洋海岛自然环境和功能定位,分别建立契合海岛可再生能源出力及负荷短时间尺度波动规律的概率模型。在此基础上提出的基于归一化积相关算法(normalized product correlation,NPROD)能有效支撑长时间尺度基础负荷、并能实现海岛能量供给系统出力/电量—负荷最佳匹配的可再生能源机组配置方案优选策略。最后,通过算例仿真验证所提配置方案优选策略的合理性和优越性。
As the pelagic islands cannot rely on the support from the main power grid, together with the extreme long fuel replenishment line, the traditional energy generation configuration methods cannot be directly applied for configuring the renewable energy generation units of pelagic islands. Instead, the abundant renewable energy should be the major support of pelagic island power supply system. To solve this problem, the appropriate probabilistic models of renewable energy generation and island electrical loads were established in this paper. On the basis of above, a normalized product correlation(NPROD) based scheme optimally-selecting strategy was proposed, which can effectively support the base load and realize the perfect matching between the generation and the load demand of pelagic island energy supply system. Finally, the rationality and superiority of the proposed strategy was verified through extensive case studies.
As the pelagic islands cannot rely on the support from the main power grid, together with the extreme long fuel replenishment line, the traditional energy generation configuration methods cannot be directly applied for configuring the renewable energy generation units of pelagic islands. Instead, the abundant renewable energy should be the major support of pelagic island power supply system. To solve this problem, the appropriate probabilistic models of renewable energy generation and island electrical loads were established in this paper. On the basis of above, a normalized product correlation(NPROD) based scheme optimally-selecting strategy was proposed, which can effectively support the base load and realize the perfect matching between the generation and the load demand of pelagic island energy supply system. Finally, the rationality and superiority of the proposed strategy was verified through extensive case studies.
引文
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[2]Giatrakos G P,Tsoutsos T D,Mouchtaropoulos P G,et al.Sustainable energy planning based on a stand-alone hybrid renewableenergy/hydrogen power system:application in Karpathos island,Greece[J].Renewable Energy,2009,34(12):2562-2570.
[3]Weis T M,Ilinca A.Assessing the potential for a wind power incentive for remote villages in Canada[J].Energy Policy,2010,38(10):5504-5511.
[4]Iqbal M T.A feasibility study of a zero energy home in Newfoundland[J].Renewable Energy,2004,29(2):277-289.
[5]龚伟俊,李为相,张广明.基于威布尔分布的风速概率分布参数估计方法[J].可再生能源,2011,29(6):20-23.Gong Weijun,Li Weixiang,Zhang Guangming.The estimation algorithm on the probabilistic distribution parameters of wind speed based on Weibull distribution[J].Renewable Energy Resources,2011,29(6):20-23(in Chinese).
[6]樊磊.分布式光伏电源并网出力概率分布模型的研究[D].北京:华北电力大学,2012.Fan Lei.Research on probabilistic distribution model of on-grid distributed photovoltaic power[D].Beijing:North China Electric Power University,2012(in Chinese).
[7]王敏.分布式电源的概率建模及其对电力系统的影响[D].合肥:合肥工业大学,2010.Wang Min.Research on the probabilistic modeling of distributed generation and its influences on power systems[D].Hefei:Hefei University of Technology,2010(in Chinese).
[8]张俊,陈国宝,陈作志,等.南沙南部陆架海域渔业资源声学评估[J].南方水产科学,2015,11(5):1-10.Zhang Jun,Chen Guobao,Chen Zuozhi,et al.Acoustic estimation of fishery resources in southern continental shelf of Nansha area[J].South China Fisheries Science,2015,11(5):1-10(in Chinese).
[9]刘柏良,黄学良,李军.计及可时移负荷的海岛微网电源优化配置[J].中国电机工程学报,2014,34(25):4250-4258.Liu Bailiang,Huang Xueliang,Li Jun.Optimal sizing of distributed generation in a typical island microgrid with time-shifting load[J].Proceedings of the CSEE,2014,34(25):4250-4258.
[10]吕懿.海南电网年负荷曲线变化趋势[J].中国科技信息,2013(24):43-44.LüYi.The annual load changing trend of Hainan Grid[J].China Science and Technology Information,2013(24):43-44(in Chinese).
[11]陈伟,乐丽琴,崔凯,等.典型用户负荷特性及用电特点分析[J].能源技术经济,2011,23(9):44-49.Chen Wei,Yue Liqin,Cui Kai,et al.Analysis on power load and consumption characteristics of representative consumers[J].Energy Technology and Economics,2011,23(9):44-49(in Chinese).
[12]冯晓蒲.基于实际负荷曲线的电力用户分类技术研究[D].保定:华北电力大学(保定),2011.Feng Xiaopu.Research on classification of electricity customers by real load curves[D].Baoding:North China Electric Power University,2011(in Chinese).
[13]尹玉芬.地区电力系统负荷特性分析与需求侧管理研究[D].广州:华南理工大学,2010.Yin Yufen.Analysis of load characteristics and studies on demand-side management in regional power systems[D].Guangzhou:South China University of Technology,2010(in Chinese).
[14]李欣然,姜学皎,钱军,等.基于用户日负荷曲线的用电行业分类与综合方法[J].电力系统自动化,2010,34(10):56-61.Li Xinran,Jiang Xuejiao,Qian Jun,et al.A classifying and synthesizing method of power consumer industry based on the daily load profile[J].Automation of Electric Power Systems,2010,34(10):56-61(in Chinese).
[15]李金颖,牛东晓.非线性季节型电力负荷灰色组合预测研究[J].电网技术,2003,27(5):26-28,50.Li Jinying,Niu Dongxiao.Study on gray combined forecasting of nonlinear seasonal power load[J].Power System Technology,2003,27(5):26-28,50(in Chinese).
[16]韩晓娟,王丽娜,高僮,等.基于成本和效益分析的并网光储微网系统电源规划[J].电工技术学报,2016,31(14):31-39,66.Han Xiaojuan,Wang Lina,Gao Tong,et al.Generation planning of grid-connected micro-grid system with PVand batteries storage system based on cost and benefit analysis[J].Transactions of China Electrotechnical Society,2016,31(14):31-39,66(in Chinese).
[17]张晓辉,闫鹏达,葛顺平,等.可再生能源激励制度下多场景灵活电源规划[J].电工电能新技术,2016,35(3):26-33.Zhang Xiaohui,Yan Pengda,Ge Shunping,et al.Multiscenario flexible generation expansion planning under incentive system of renewable energy[J].Advanced Technology of Electrical Engineering and Energy,2016,35(3):26-33(in Chinese).
[18]马艺玮,杨苹,郭红霞,等.风-光-沼可再生能源分布式发电系统电源规划[J].电网技术,2012,36(9):9-14.Ma Yiwei,Yang Ping,Guo Hongxia,et al.Power source planning of wind-PV-biogas renewable energy distributed generation system[J].Power System Technology,2012,36(9):9-14(in Chinese).
[19]刘思夷,赵波,汪湘晋,等.基于Benders分解的独立型微电网鲁棒优化容量配置模型[J].电力系统自动化,2017,41(21):119-126,146.Liu Siyi,Zhao Bo,Wang Xiangjin,et al.Capacity configuration model for robust optimization of stand-alone microgrid based on benders decomposition[J].Automation of Electric Power Systems,2017,41(21):119-126,146(in Chinese).
[20]饶俊飞.基于灰度的图像匹配方法研究[D].武汉:武汉理工大学,2005.Rao Junfei.Research on intensity-based image matching method[D].Wuhan:Wuhan University of Technology,2005(in Chinese).
[21]刘思雨,王雪梅,杨小冈,等.前视红外末制导Nprod目标识别算法适应性研究[J].科学技术与工程,2013,13(15):4375-4378,4383.Liu Siyu,Wang Xuemei,Yang Xiaogang,et al.Research on applicability of Nprod target recognition algorithm for FLIR terminal guidance[J].Science Technology and Engineering,2013,13(15):4375-4378,4383(in Chinese).
[22]邓汉华,杨靓,黄士坦.基于归一化互相关系数的图像匹配的连续消算法[J].微电子学与计算机,2006,23(12):227-230.Deng Hanhua,Yang Liang,Huang Shitan.The successive elimination algorithm in the image matching based on normalized cross-correlation coefficient[J].Microelectronics&Computer,2006,23(12):227-230(in Chinese).