计及复杂气象耦合特性的模块化去噪变分自编码器多源–荷联合场景生成
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摘要
气象因素的强随机性与强波动性直接影响新能源出力与用户用电行为。针对基于整体历史数据生成多源-荷联合场景集时难以体现特定气象下的多源-荷概率分布特性的不足,提出一种计及气象因素差异的模块化去噪变分自编码器(modular denoising variational autoencoder,MDVAE)多源-荷联合场景生成模型。首先,分析风速、辐照、负荷等与气象因素相关性,确定源-荷气象耦合特征集;在此基础上,针对历史气象数据集进行聚类,获得具有不同气象特点的聚类结果;之后,以类内所含日期中风速、辐照、负荷历史数据,构建基于数据驱动的MDVAE联合场景生成模型;最后,通过将生成的风速、辐照转化为风-光出力,构建多源-荷场景。实测数据分析表明,新方法生成场景集能体现不同气象条件下差异性,并能有效提高生成场景集与实测数据间概率分布的相似性。
The meteorological randomness and volatility directly affect renewable energy outputs and electricity consumption behavior. To overcome the shortcomings of generating multi source-load scenarios based on total historical data, a model of multi-source/load scenario generation using modular denoising variational autoencoder(MDVAE) was proposed, which takes into account the differences of meteorological factors. Firstly, the correlation between wind speed, radiation, load and meteorological factors were analyzed, and the source-load meteorological coupling features were determined. On this basis, the historical meteorological data was clustered to obtain the clustering results with different meteorological characteristics. Then, the data-driven MDVAE joint scene generation model was constructed with wind speed, irradiation and load history data of various clusters. Finally, multi-source-load scenarios were constructed by converting the generated wind speed and radiation into wind-solar output. The measured data analysis shows that the new method can generate the scenes to reflect the difference under different meteorological conditions, and effectively improve the similarity of probability distribution between the generated scenes and the measured data.
The meteorological randomness and volatility directly affect renewable energy outputs and electricity consumption behavior. To overcome the shortcomings of generating multi source-load scenarios based on total historical data, a model of multi-source/load scenario generation using modular denoising variational autoencoder(MDVAE) was proposed, which takes into account the differences of meteorological factors. Firstly, the correlation between wind speed, radiation, load and meteorological factors were analyzed, and the source-load meteorological coupling features were determined. On this basis, the historical meteorological data was clustered to obtain the clustering results with different meteorological characteristics. Then, the data-driven MDVAE joint scene generation model was constructed with wind speed, irradiation and load history data of various clusters. Finally, multi-source-load scenarios were constructed by converting the generated wind speed and radiation into wind-solar output. The measured data analysis shows that the new method can generate the scenes to reflect the difference under different meteorological conditions, and effectively improve the similarity of probability distribution between the generated scenes and the measured data.
引文
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[17]夏泠风,黎嘉明,赵亮,等.考虑光伏电站时空相关性的光伏出力序列生成方法[J].中国电机工程学报,2017,37(7):1982-1993.Xia Lingfeng,Li Jiaming,Zhao Liang,et al.A PV power time series generating method considering temporal and spatial correlation characteristics[J].Proceedings of the CSEE,2017,37(7):1982-1993(in Chinese).
[18]丁明,解蛟龙,刘新宇,等.面向风电接纳能力评价的风资源/负荷典型场景集生成方法与应用[J].中国电机工程学报,2016,36(15):4064-4071.Ding Ming,Xie Jiaolong,Liu Xinyu,et al.The generation method and application of wind resources/load typical scenario set for evaluation of wind power grid integration[J].Proceedings of the CSEE,2016,36(15):4064-4071(in Chinese).
[19]Al-Otaibi R,Jin Nanlin,Wilcox T,et al.Feature construction and calibration for clustering daily load curves from smart-meter data[J].IEEE Transactions on Industrial Informatics,2016,12(2):645-654.
[20]Kingma D P,Welling M.Auto-encoding variational bayes[C]//Proceedings of the 2nd International Conference on Learning Representations.Banff,Canada:ICLR,2014.
[21]Im D J,Ahn S,Memisevic R,et al.Denoising criterion for variational auto-encoding framework[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.San Francisco,America:AAAI,2017.
[22]Kingma D P,Ba J L.Adam:a method for stochastic optimization[C]//Proceedings of the 3rd ACM SIGKDDInternational Conference on Learning Representations.San Diego,CA,America:ACM,2015.
[23]Borhanazad H,Mekhilef S,Ganapathy V G,et al.Optimization of micro-grid system using MOPSO[J].Renewable Energy,2014,71:295-306.
[24]Akram U,Khalid M,Shafiq S.Optimal sizing of a wind/solar/battery hybrid grid-connected microgrid system[J].IET Renewable Power Generation,2018,12(1):72-80.
[25]Kaur R,Krishnasamy V,Kandasamy N K.Optimal sizing of wind-PV based DC microgrid for telecom power supply in remote areas[J].IET Renewable Power Generation,2018,12(7):859-866.
[26]Chen Yize,Li Pan,Zhang Baosen.Bayesian renewables scenario generation via deep generative networks[C]//Proceedings of the 2018 52nd Annual Conference on Information Sciences and Systems,Princeton,NJ,USA:IEEE,2018.
[2]方华亮,李大虎,彭辉,等.基于“互联网+”的分散式太阳能规划方法[J].中国电机工程学报,2017,37(5):1316-1324.Fang Hualiang,Li Dahu,Peng Hui,et al.Distributed solar energy planning method based on internet plus[J].Proceedings of the CSEE,2017,37(5):1316-1324(in Chinese).
[3]薛禹胜,雷兴,薛峰,等.关于风电不确定性对电力系统影响的评述[J].中国电机工程学报,2014,34(29):5029-5040.Xue Yusheng,Lei Xing,Xue Feng,et al.A review on impacts of wind power uncertainties on power systems[J].Proceedings of the CSEE,2014,34(29):5029-5040(in Chinese).
[4]张宁,周天睿,段长刚,等.大规模风电场接入对电力系统调峰的影响[J].电网技术,2010,34(1):152-158.Zhang Ning,Zhou Tianrui,Duan Changgang,et al.Impact of large-scale wind farm connecting with power grid on peak load regulation demand[J].Power System Technology,2010,34(1):152-158(in Chinese).
[5]王守相,陈海文,李小平,等.风电和光伏随机场景生成的条件变分自动编码器方法[J].电网技术,2018,42(6):1860-1869.Wang Shouxiang,Chen Haiwen,Li Xiaoping,et al.Conditional variational automatic encoder method for stochastic scenario generation of wind power and photovoltaic system[J].Power System Technology,2018,42(6):1860-1869(in Chinese).
[6]董雷,孟天骄,陈乃仕,等.采用马尔可夫链-多场景技术的交直流主动配电网优化调度[J].电力系统自动化,2018,42(5):147-153.Dong Lei,Meng Tianjiao,Chen Naishi,et al.Optimized scheduling of AC/DC hybrid active distribution network using Markov chains-multiple scenarios technique[J].Automation of Electric Power Systems,2018,42(5):147-153(in Chinese).
[7]Baringo L,Conejo A J.Correlated wind-power production and electric load scenarios for investment decisions[J].Applied Energy,2013,101:475-482.
[8]黎静华,孙海顺,文劲宇,等.生成风电功率时间序列场景的双向优化技术[J].中国电机工程学报,2014,34(16):2544-2551.Li Jinghua,Sun Haishun,Wen Jinyu,et al.Atwo-dimensional optimal technology for constructing wind power time series scenarios[J].Proceedings of the CSEE,2014,34(16):2544-2551(in Chinese).
[9]丁苏阳,丁壮状,林湘宁,等.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[J].中国电机工程学报,2018,38(19):5653-5667.Ding Suyang,Ding Zhuangzhuang,Lin Xiangning,et al.The optimum strategy of renewable energy generation configuration schemes in pelagic islands based on gray matching algorithm of probabilistic image[J].Proceedings of the CSEE,2018,38(19):5653-5667(in Chinese).
[10]李湃,管晓宏,吴江.基于大气动力模型的多风电场出力场景生成方法[J].中国电机工程学报,2015,35(18):4581-4590.Li Pai,Guan Xiaohong,Wu Jiang.Wind power scenario generation for multiple wind farms based on atmospheric dynamic model[J].Proceedings of the CSEE,2015,35(18):4581-4590(in Chinese).
[11]王群,董文略,杨莉.基于Wasserstein距离和改进K-medoids聚类的风电/光伏经典场景集生成算法[J].中国电机工程学报,2015,35(11):2654-2661.Wang Qun,Dong Wenlue,Yang Li.A wind power/photovoltaic typical scenario set generation algorithm based on Wasserstein distance metric and revised K-medoids cluster[J].Proceedings of the CSEE,2015,35(11):2654-2661(in Chinese).
[12]Macedo M N Q,Galo J J M,Almeida L A L,et al.Typification of load curves for DSM in Brazil for a smart grid environment[J].International Journal of Electrical Power&Energy Systems,2015,67:216-221.
[13]Chen Yize,Wang Yishen,Kirschen D S,et al.Model-free renewable scenario generation using generative adversarial networks[J].IEEE Transactions on Power Systems,2018,33(3):3265-3275.
[14]王玲玲,王昕,郑益慧,等.计及多个风电机组出力相关性的配电网无功优化[J].电网技术,2017,41(11):3463-3469.Wang Lingling,Wang Xin,Zheng Yihui,et al.Reactive power optimization of distribution network considering output correlation of multiple wind turbines[J].Power System Technology,2017,41(11):3463-3469(in Chinese).
[15]黎静华,韦化,莫东.含风电场最优潮流的Wait-and-See模型与最优渐近场景分析[J].中国电机工程学报,2012,32(22):15-23.Li Jinghua,Wei Hua,Mo Dong.Asymptotically optimal scenario analysis and wait-and-see model for optimal power flow with wind power[J].Proceedings of the CSEE,2012,32(22):15-23(in Chinese).
[16]李湃,管晓宏,吴江,等.基于天气分类的风电场群总体出力特性分析[J].电网技术,2015,39(7):1866-1872.Li Pai,Guan Xiaohong,Wu Jiang,et al.Analyzing characteristics of aggregated wind power generation based on weather regime classification[J].Power System Technology,2015,39(7):1866-1872(in Chinese).
[17]夏泠风,黎嘉明,赵亮,等.考虑光伏电站时空相关性的光伏出力序列生成方法[J].中国电机工程学报,2017,37(7):1982-1993.Xia Lingfeng,Li Jiaming,Zhao Liang,et al.A PV power time series generating method considering temporal and spatial correlation characteristics[J].Proceedings of the CSEE,2017,37(7):1982-1993(in Chinese).
[18]丁明,解蛟龙,刘新宇,等.面向风电接纳能力评价的风资源/负荷典型场景集生成方法与应用[J].中国电机工程学报,2016,36(15):4064-4071.Ding Ming,Xie Jiaolong,Liu Xinyu,et al.The generation method and application of wind resources/load typical scenario set for evaluation of wind power grid integration[J].Proceedings of the CSEE,2016,36(15):4064-4071(in Chinese).
[19]Al-Otaibi R,Jin Nanlin,Wilcox T,et al.Feature construction and calibration for clustering daily load curves from smart-meter data[J].IEEE Transactions on Industrial Informatics,2016,12(2):645-654.
[20]Kingma D P,Welling M.Auto-encoding variational bayes[C]//Proceedings of the 2nd International Conference on Learning Representations.Banff,Canada:ICLR,2014.
[21]Im D J,Ahn S,Memisevic R,et al.Denoising criterion for variational auto-encoding framework[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.San Francisco,America:AAAI,2017.
[22]Kingma D P,Ba J L.Adam:a method for stochastic optimization[C]//Proceedings of the 3rd ACM SIGKDDInternational Conference on Learning Representations.San Diego,CA,America:ACM,2015.
[23]Borhanazad H,Mekhilef S,Ganapathy V G,et al.Optimization of micro-grid system using MOPSO[J].Renewable Energy,2014,71:295-306.
[24]Akram U,Khalid M,Shafiq S.Optimal sizing of a wind/solar/battery hybrid grid-connected microgrid system[J].IET Renewable Power Generation,2018,12(1):72-80.
[25]Kaur R,Krishnasamy V,Kandasamy N K.Optimal sizing of wind-PV based DC microgrid for telecom power supply in remote areas[J].IET Renewable Power Generation,2018,12(7):859-866.
[26]Chen Yize,Li Pan,Zhang Baosen.Bayesian renewables scenario generation via deep generative networks[C]//Proceedings of the 2018 52nd Annual Conference on Information Sciences and Systems,Princeton,NJ,USA:IEEE,2018.