基于统计学特征的新能源纳入西北电网备用研究
|
摘要
新能源纳入备用对电网安全运行和新能源充分消纳都具有重要意义,但新能源随机性和难预测等实际问题制约了其作为备用的可靠性。为此本文以西北电网为例,研究提出了基于统计学特征的新能源纳入电网备用方案。首先,分析了新能源纳入备用对电网平衡和新能源消纳的影响,阐述了由此引发的安全性和经济性矛盾;其次,提炼出电网供电保证率、新能源保证出力和保证预测准确率等指标,给出了计算方法并分析了其统计学特征;最后,基于以上指标,提出了"主网采用新能源保证出力以保证供电安全、省网采用新能源保证预测准确率以提高经济性"的新能源纳入电网备用管理方案。实际算例表明:文中方案兼顾供电安全和新能源充分消纳,可以作为大规模新能源接入后电网日前调度方式安排的依据,兼具学术研究和推广应用价值。文中成果已经在国家电网公司西北电力调控分中心得到实际应用,2017年增发新能源电量4700GW·h、降低受阻率3个百分点,有效促进了新能源消纳。
Taking renewable energy into reserve is of great significance to both security of power supply and accommodation ability of renewable energy. However, randomness of renewable energy limits its reliability as reserve. This paper presents a new reserve program of renewable energy coordinated with Northwest China Grid based on statistical characteristics. Firstly, impact of renewable energy reserve on power supply balance and accommodation ability are analyzed, and relationship between security of whole network and benefit of renewable energy is discussed. Secondly, the concepts of guaranteed power supply rate, guaranteed power, guaranteed prediction accuracy are proposed, and computation process and characteristics of above concepts are analyzed. Finally, a program of the renewable energy coordinated with the grid is presented, summed up as "renewable energy is used in major network to ensure power supply security, in provincial network to improve economy". Simulation shows that the program plays a good role in increasing the amount of renewable energy generation and guaranteeing power supply security. It provides a reference for gird scheduling in situation of large scale renewable energy integration. The results have values in both engineering applications and promotion. They are applied in Northwest China Power Grid with the effect that renewable energy generating capacity is increased by 4700 MW·h and its blocked rate is reduced by 3%.
Taking renewable energy into reserve is of great significance to both security of power supply and accommodation ability of renewable energy. However, randomness of renewable energy limits its reliability as reserve. This paper presents a new reserve program of renewable energy coordinated with Northwest China Grid based on statistical characteristics. Firstly, impact of renewable energy reserve on power supply balance and accommodation ability are analyzed, and relationship between security of whole network and benefit of renewable energy is discussed. Secondly, the concepts of guaranteed power supply rate, guaranteed power, guaranteed prediction accuracy are proposed, and computation process and characteristics of above concepts are analyzed. Finally, a program of the renewable energy coordinated with the grid is presented, summed up as "renewable energy is used in major network to ensure power supply security, in provincial network to improve economy". Simulation shows that the program plays a good role in increasing the amount of renewable energy generation and guaranteeing power supply security. It provides a reference for gird scheduling in situation of large scale renewable energy integration. The results have values in both engineering applications and promotion. They are applied in Northwest China Power Grid with the effect that renewable energy generating capacity is increased by 4700 MW·h and its blocked rate is reduced by 3%.
引文
[1]舒印彪,张智刚,郭剑波,等.新能源消纳关键因素分析及解决措施研究型[J].中国电机工程学报,2017,37(1):1-8.Shu Yinbiao,Zhang Zhigang,Guo Jianbo,et al.Study on key factors and solution of renewable energy accommodation[J].Proceedings of the CSEE,2017,37(1):1-8(in Chinese).
[2]Nielsen T S,Madsen H.WPPT-A tool for wind power prediction[C]//EWEA Special Topic Conference.Kassel:IEEE,2000.
[3]Bailey B,Brower M C,Zack J.Short-term wind forecasting[C]//European Wind Energy Conference.Nice,France:IEEE,1999.
[4]崔嘉,杨俊友,杨理践,等.基于改进CFD与小波混合神经网络组合的风电场功率预测方法[J].电网技术,2017,41(1):79-85.Cui Jia,Yang Junyou,Yang Lijian,et al.New method on combined wind power forecasting based on improved CFD and wavelet-HNN model[J].Power System Technology,2017,41(1):79-85(in Chinese).
[5]刘申,褚晓东,张文.基于气象测量场的爬坡时段区域风功率预测[J].电力系统自动化,2015,39(16):29-36.Liu Shen,Chu Xiaodong,Zhang Wen.Regional wind power forecasting during ramp period using meteorological measurement field data[J].Automation of Electric Power Systems,2015,39(16):29-36(in Chinese).
[6]刘燕华,刘冲,李伟花,等.基于出力模式匹配的风电集群点多时间尺度功率预测[J].中国电机工程学报,2014,34(25):4350-4358.Liu Yanhua,Liu Chong,Li Weihua,et al.Multi-time scale power prediction of wind farm cluster based on profile pattern matching[J].Proceedings of the CSEE,2014,34(25):4350-4358(in Chinese).
[7]贾文昭,康重庆,李丹,等.基于日前风功率预测的风电消纳能力评估方法[J].电网技术,2012,36(8):69-75.Jia Wenzhao,Kang Chongqing,Li Dan,et al.Evaluation on capability of wind power accommodation based on its day-ahead forecasting[J].Power System Technology,2012,36(8):69-75(in Chinese).
[8]GB/T 31464-2015,电网运行准则[S].北京:中国标准出版社,2015.
[9]NB/T31055-2014,风电场理论可发电量与弃风电量评估导则[S].北京:中国电力出版社,2016.
[10]国家电力调度控制中心.风电受阻电量计算办法[R].北京:国家电网公司,2012.
[11]姜文玲,冯双磊,孙勇,等.基于机舱风速数据的风电场弃风电量计算方法研究[J].电网技术,2014,38(3):647-652.Jiang Wenling,Feng Shuanglei,Sun Yong,et al.Study on Energy Loss Calculation During Wind Power Curtailment Based on Wind Speed Measured by Turbine Nacelle Anemometers[J].Power System Technology,2014,38(3):647-652.
[12]钱政,裴岩,曹利宵,等.风电功率预测方法综述[J].高电压技术,2016,42(4):1047-1060.Qian Zheng,Pei Yan,Cao Lixiao,et al.Review of Wind Power Forecasting Method[J].High Voltage Engineering,2016,42(4):1047-1060.
[13]Ehsan Taslimi Renani,Mohamad Fathi Mohamad Elias,Nasrudin Abd Rahim.Using data-driven approach for wind power prediction:a comparative study[J].Energy Conversion and Management,2016,118:193-203.
[14]彭小圣,熊磊,文劲宇,等.风电集群短期及超短期功率预测精度改进方法综述[J].中国电机工程学报,2016,36(23):6315-6326.Peng Xiaosheng,Xiong Lei,Wen Jinyu,et al.A summary of the state of the art for short-term and ultra-short-term wind power[J].Proceedings of the CSEE,2016,36(23):6315-6326(in Chinese).
[15]Jiawei Han,Micheline Kamber.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2007.
[2]Nielsen T S,Madsen H.WPPT-A tool for wind power prediction[C]//EWEA Special Topic Conference.Kassel:IEEE,2000.
[3]Bailey B,Brower M C,Zack J.Short-term wind forecasting[C]//European Wind Energy Conference.Nice,France:IEEE,1999.
[4]崔嘉,杨俊友,杨理践,等.基于改进CFD与小波混合神经网络组合的风电场功率预测方法[J].电网技术,2017,41(1):79-85.Cui Jia,Yang Junyou,Yang Lijian,et al.New method on combined wind power forecasting based on improved CFD and wavelet-HNN model[J].Power System Technology,2017,41(1):79-85(in Chinese).
[5]刘申,褚晓东,张文.基于气象测量场的爬坡时段区域风功率预测[J].电力系统自动化,2015,39(16):29-36.Liu Shen,Chu Xiaodong,Zhang Wen.Regional wind power forecasting during ramp period using meteorological measurement field data[J].Automation of Electric Power Systems,2015,39(16):29-36(in Chinese).
[6]刘燕华,刘冲,李伟花,等.基于出力模式匹配的风电集群点多时间尺度功率预测[J].中国电机工程学报,2014,34(25):4350-4358.Liu Yanhua,Liu Chong,Li Weihua,et al.Multi-time scale power prediction of wind farm cluster based on profile pattern matching[J].Proceedings of the CSEE,2014,34(25):4350-4358(in Chinese).
[7]贾文昭,康重庆,李丹,等.基于日前风功率预测的风电消纳能力评估方法[J].电网技术,2012,36(8):69-75.Jia Wenzhao,Kang Chongqing,Li Dan,et al.Evaluation on capability of wind power accommodation based on its day-ahead forecasting[J].Power System Technology,2012,36(8):69-75(in Chinese).
[8]GB/T 31464-2015,电网运行准则[S].北京:中国标准出版社,2015.
[9]NB/T31055-2014,风电场理论可发电量与弃风电量评估导则[S].北京:中国电力出版社,2016.
[10]国家电力调度控制中心.风电受阻电量计算办法[R].北京:国家电网公司,2012.
[11]姜文玲,冯双磊,孙勇,等.基于机舱风速数据的风电场弃风电量计算方法研究[J].电网技术,2014,38(3):647-652.Jiang Wenling,Feng Shuanglei,Sun Yong,et al.Study on Energy Loss Calculation During Wind Power Curtailment Based on Wind Speed Measured by Turbine Nacelle Anemometers[J].Power System Technology,2014,38(3):647-652.
[12]钱政,裴岩,曹利宵,等.风电功率预测方法综述[J].高电压技术,2016,42(4):1047-1060.Qian Zheng,Pei Yan,Cao Lixiao,et al.Review of Wind Power Forecasting Method[J].High Voltage Engineering,2016,42(4):1047-1060.
[13]Ehsan Taslimi Renani,Mohamad Fathi Mohamad Elias,Nasrudin Abd Rahim.Using data-driven approach for wind power prediction:a comparative study[J].Energy Conversion and Management,2016,118:193-203.
[14]彭小圣,熊磊,文劲宇,等.风电集群短期及超短期功率预测精度改进方法综述[J].中国电机工程学报,2016,36(23):6315-6326.Peng Xiaosheng,Xiong Lei,Wen Jinyu,et al.A summary of the state of the art for short-term and ultra-short-term wind power[J].Proceedings of the CSEE,2016,36(23):6315-6326(in Chinese).
[15]Jiawei Han,Micheline Kamber.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2007.