计及风电功率预测误差的需求响应多时间尺度优化调度
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摘要
风电功率预测存在误差,增加了电力系统运行调度的困难,影响系统风电消纳,降低系统运行的经济性。为了提高系统风电消纳能力,考虑风电功率预测误差,结合需求响应特点,从日前、日内和实时构建了电力系统需求响应多时间尺度优化调度模型。分析居民用户需求对电价的响应,构造日前价格型需求响应的初调度策略,并在日内根据风电功率预测偏差进行电价调整后的价格型需求响应再调度。分析工业用户和商业用户的调度补偿策略,以电网运行费用最低为目标,考虑平衡风电功率实时预测误差,建立激励型需求响应的实时调度优化模型,以提高系统风电消纳能力,降低系统弃风量。算例分析表明:所提出的优化调度策略能够较好地平衡风电功率预测误差,增加系统风电消纳量,节约系统运行成本,保证用户用电的自主性,最小化需求响应调度对其的影响。
Wind power forecast error increases difficulty in power system scheduling, affects wind power accommodation, increases wind power curtailment and decreases system operation economy. A multi-time-scale demand response dispatch model was proposed considering wind power forecast error and characteristics of different kinds of demand responses. Firstly, a day-ahead price-based demand response dispatch strategy was built by analyzing residential users' response to price. Then in the day, the price-based demand response was dispatched again according to price deviation based on wind power forecast error. Secondly, a real-time incentive-based demand response dispatch model was established by analyzing compensate strategies for industrial and commercial users. This model took the minimal operation cost of the grid as objective function to decrease influence of the wind power forecast error on system dispatch and wind power accommodation. Lastly, case study showed that the multi-time-scale optimal dispatch could increase wind power accommodations, decrease system operating costs, ensure autonomy of consumers and reduce impact of demand response on them.
Wind power forecast error increases difficulty in power system scheduling, affects wind power accommodation, increases wind power curtailment and decreases system operation economy. A multi-time-scale demand response dispatch model was proposed considering wind power forecast error and characteristics of different kinds of demand responses. Firstly, a day-ahead price-based demand response dispatch strategy was built by analyzing residential users' response to price. Then in the day, the price-based demand response was dispatched again according to price deviation based on wind power forecast error. Secondly, a real-time incentive-based demand response dispatch model was established by analyzing compensate strategies for industrial and commercial users. This model took the minimal operation cost of the grid as objective function to decrease influence of the wind power forecast error on system dispatch and wind power accommodation. Lastly, case study showed that the multi-time-scale optimal dispatch could increase wind power accommodations, decrease system operating costs, ensure autonomy of consumers and reduce impact of demand response on them.
引文
[1]牛东晓,李建锋,魏林君,等.跨区电网中风电消纳影响因素分析及综合评估方法研究[J].电网技术,2016,40(4):1087-1093.Niu Dongxiao,Li Jianfeng,Wei Linjun,et al.Study on technical factors analysis and overall evaluation method regarding wind power integration in trans-provincial power grid[J].Power System Technology,2016,40(4):1087-1093(in Chinese).
[2]于东,孙欣,高丙团,等.考虑风电不确定出力的风电并网协调优化模型[J].电工技术学报,2016,31(9):34-41.Yu Dong,Sun Xin,Gao Bingtuan,et al.Coordinated optimization model for wind power integration considering wind power uncertainty output[J].Transactions of China Electrotechnical Society,2016,31(9):34-41(in Chinese).
[3]翟俊义,任建文,周明,等.含风电电力系统的多时间尺度模糊机会约束动态经济调度模型[J].电网技术,2016,40(4):1094-1099.Zhai Junyi,Ren Jianwen,Zhou Ming,et al.Multi-time scale fuzzy chance constrained dynamic economic dispatch model for power system with wind power[J].Power System Technology,2016,40(4):1094-1099(in Chinese).
[4]徐乾耀,康重庆,江长明,等.多时空尺度风电消纳体系初探[J].电力系统保护与控制,2013,41(1):28-32.Xu Qianyao,Kang Chongqing,Jiang Changming,et al.Preliminary analysis on wind power accommodation system from multiple temporal and spatial scale perspective[J].Power System Protection and Control,2013,41(1):28-32(in Chinese).
[5]张宁宇,高山,赵欣.一种考虑风电随机性的机组组合模型及其算法[J].电工技术学报,2013,28(5):22-29.Zhang Ningyu,Gao Shan,Zhao Xin.An unit commitment model and algorithm with randomness of wind power[J].Transactions of China Electrotechnical Society,2013,28(5):22-29(in Chinese).
[6]葛晓琳,张粒子.考虑调峰约束的风水火随机机组组合问题[J].电工技术学报,2014,29(10):222-230.Ge Xiaolin,Zhang Lizi.Wind-hydro-thermal stochastic unit commitment problem considering the peak regulation constraints[J].Transactions of China Electrotechnical Society,2014,29(10):222-230(in Chinese).
[7]卢鹏铭,温步瀛,江岳文.基于多时间尺度协调机组组合的含风电系统旋转备用优化研究[J].电力系统保护与控制,2015,43(5):94-100.Lu Pengming,Wen Buying,Jiang Yuewen.Study on optimization of spinning reserve in wind power integrated power system based on multiple timescale and unit commitment coordination[J].Power System Protection and Control,2015,43(5):94-100(in Chinese).
[8]杨佳俊,雷宇,龙淼,等.考虑风电和负荷不确定性的机组组合研究[J].电力系统保护与控制,2014,42(3):63-70.Yang Jiajun,Lei Yu,Long Miao,et al.Study of unit commitment considering the uncertainty of wind farm and load[J].Power System Protection and Control,2014,42(3):63-70(in Chinese).
[9]艾小猛,韩杏宁,文劲宇,等.考虑风电爬坡事件的鲁棒机组组合[J].电工技术学报,2015,30(24):188-195.Ai Xiaomeng,Han Xingning,Wen Jinyu,et al.Robust unit commitment considering wind power ramp events[J].Transactions of China Electrotechnical Society,2015,30(24):188-195(in Chinese).
[10]孙元章,吴俊,李国杰,等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29(4):41-47.Sun Yuanzhang,Wu Jun,Li Guojie,et al.Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming[J].Proceedings of the CSEE,2009,29(4):41-47(in Chinese).
[11]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,13(38):115-120.Bie Zhaohong,Hu Guowei,Xie Haipeng,et al.Optimal dispatch for wind power integrated systems considering demand response[J].Automation of Electric Power Systems,2014,13(38):115-120(in Chinese).
[12]刘文颖,文晶,谢昶,等.考虑风电消纳的电力系统源荷协调多目标优化方法[J].中国电机工程学报,2015,35(5):1079-1088.Liu Wenying,Wen Jing,Xie Chang,et al.Multi-objective optimal method considering wind power accommodation based on source-load coordination[J].Proceedings of the CSEE,2015,35(5):1079-1088(in Chinese).
[13]文晶,刘文颖,谢昶,等.计及风电消纳效益的电力系统源荷协调二层优化模型[J].电工技术学报,2015,30(8):247-256.Wen Jing,Liu Wenying,Xie Chang,et al.Source-load coordination optimal model considering wind power consumptive benefits based on bi-level programming[J].Transactions of China Electrotechnical Society,2015,30(8):247-256(in Chinese).
[14]王健,鲁宗相,乔颖,等.高载能负荷提高风电就地消纳的需求响应模式研究[J].电网技术,2017,41(7):2115-2123.Wang Jian,Lu Zongxiang,Qiao Ying,et al.Research on demand response mechanism of wind power local accommodation utilizing energy-intensive loads[J].Power System Technology,2017,41(7):2115-2123(in Chinese).
[15]鞠立伟,于超,谭忠富.计及需求响应的风电储能两阶段调度优化模型及求解算法[J].电网技术,2015,39(5):1287-1293.Ju Liwei,Yu Chao,Tan Zhongfu.A two-stage scheduling optimization model and corresponding solving algorithm for power grid containing wind farm and energy storage system considering demand response[J].Power System Technology,2015,39(5):1287-1293(in Chinese).
[16]Shan J,Botterud A,Ryan S M.Impact of demand response on thermal generation investment with high wind penetration[J].IEEETransactions on Smart Grid,2013,4(4):2374-2383.
[17]Wang Z Q,Leng Y.Review of smart grid demand response with clean energy access[C]//Advanced Research in Material Science and Mechanical Engineering,Pts 1 and 2,Applied Mechanics and Materials,446-447.Stafa-Zurich:Trans Tech Publications Ltd,2014:847-852.
[18]Nojavan S,Aalami Ha.Stochastic energy procurement of large electricity consumer considering photovoltaic,wind-turbine,microturbines,energy storage system in the presence of demand response program[J].Energy Conversion and Management,2015,10(103):1008-1018.
[19]李丹,刘俊勇,刘友波,等.风电接入后考虑抽蓄-需求响应的多场景联合安全经济调度模型[J].电力自动化设备,2015,35(2):28-34,41.Li Dan,Liu Junyong,Liu Youbo,et al.Joint secure&economic dispatch considering wind power pumped storage and demand response[J].Electric Power Automation Equipment,2015,35(2):28-34,41(in Chinese).
[20]王蓓蓓,刘小聪,李扬.面向大容量风电接入考虑用户侧互动的系统日前调度和运行模拟研究[J].中国电机工程学报,2013,33(22):35-44.Wang Beibei,Liu Xiaocong,Li Yang.Day-ahead generation scheduling and operation simulation considering demand response in large-capacity wind power integrated systems[J].Proceedings of the CSEE,2013,33(22):35-44(in Chinese).
[21]刘小聪,王蓓蓓,李扬,等.计及需求侧资源的大规模风电消纳随机机组组合模型[J].中国电机工程学报,2015,35(14):3714-3723.Liu Xiaocong,Wang Beibei,Li Yang,et al.Stochastic unit commitment model for high wind power integration considering demand side resources[J].Proceedings of the CSEE,2015,35(14):3714-3723(in Chinese).
[22]江岳文,温步瀛.结合风电功率超短期预测值偏差的实时市场调度[J].电力自动化设备,2015,35(3):12-17.Jiang Yuewen,Wen Buying.Real-time market dispatch based on ultra-short-term forecast error of wind power[J].Electric Power Automation Equipment,2015,35(3):12-17(in Chinese).
[23]范高锋,王伟胜,刘纯.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123.Fan Gaofeng,Wang Weisheng,Liu Chun.Wind power forecast based on artificial neural network[J].Proceedings of the CSEE,2013,37(4):967-973(in Chinese).
[24]张钦,王锡凡,王建学,等.电力市场下需求响应研究综述[J].电力系统自动化,2008,32(3):97-106.Zhang Qin,Wang Xifan,Wang Jianxue,et al.Survey of demand response research in deregulated electricity markets[J].Automation of Electric Power Systems,2008,32(3):97-106(in Chinese).
[25]戴文威.风电并网对电力市场需求侧价格响应的影响[D].长沙:湖南大学,2013.
[26]胡田田.经济学基础与应用[M].第2版.上海:复旦大学出版社,2014.
[2]于东,孙欣,高丙团,等.考虑风电不确定出力的风电并网协调优化模型[J].电工技术学报,2016,31(9):34-41.Yu Dong,Sun Xin,Gao Bingtuan,et al.Coordinated optimization model for wind power integration considering wind power uncertainty output[J].Transactions of China Electrotechnical Society,2016,31(9):34-41(in Chinese).
[3]翟俊义,任建文,周明,等.含风电电力系统的多时间尺度模糊机会约束动态经济调度模型[J].电网技术,2016,40(4):1094-1099.Zhai Junyi,Ren Jianwen,Zhou Ming,et al.Multi-time scale fuzzy chance constrained dynamic economic dispatch model for power system with wind power[J].Power System Technology,2016,40(4):1094-1099(in Chinese).
[4]徐乾耀,康重庆,江长明,等.多时空尺度风电消纳体系初探[J].电力系统保护与控制,2013,41(1):28-32.Xu Qianyao,Kang Chongqing,Jiang Changming,et al.Preliminary analysis on wind power accommodation system from multiple temporal and spatial scale perspective[J].Power System Protection and Control,2013,41(1):28-32(in Chinese).
[5]张宁宇,高山,赵欣.一种考虑风电随机性的机组组合模型及其算法[J].电工技术学报,2013,28(5):22-29.Zhang Ningyu,Gao Shan,Zhao Xin.An unit commitment model and algorithm with randomness of wind power[J].Transactions of China Electrotechnical Society,2013,28(5):22-29(in Chinese).
[6]葛晓琳,张粒子.考虑调峰约束的风水火随机机组组合问题[J].电工技术学报,2014,29(10):222-230.Ge Xiaolin,Zhang Lizi.Wind-hydro-thermal stochastic unit commitment problem considering the peak regulation constraints[J].Transactions of China Electrotechnical Society,2014,29(10):222-230(in Chinese).
[7]卢鹏铭,温步瀛,江岳文.基于多时间尺度协调机组组合的含风电系统旋转备用优化研究[J].电力系统保护与控制,2015,43(5):94-100.Lu Pengming,Wen Buying,Jiang Yuewen.Study on optimization of spinning reserve in wind power integrated power system based on multiple timescale and unit commitment coordination[J].Power System Protection and Control,2015,43(5):94-100(in Chinese).
[8]杨佳俊,雷宇,龙淼,等.考虑风电和负荷不确定性的机组组合研究[J].电力系统保护与控制,2014,42(3):63-70.Yang Jiajun,Lei Yu,Long Miao,et al.Study of unit commitment considering the uncertainty of wind farm and load[J].Power System Protection and Control,2014,42(3):63-70(in Chinese).
[9]艾小猛,韩杏宁,文劲宇,等.考虑风电爬坡事件的鲁棒机组组合[J].电工技术学报,2015,30(24):188-195.Ai Xiaomeng,Han Xingning,Wen Jinyu,et al.Robust unit commitment considering wind power ramp events[J].Transactions of China Electrotechnical Society,2015,30(24):188-195(in Chinese).
[10]孙元章,吴俊,李国杰,等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29(4):41-47.Sun Yuanzhang,Wu Jun,Li Guojie,et al.Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming[J].Proceedings of the CSEE,2009,29(4):41-47(in Chinese).
[11]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,13(38):115-120.Bie Zhaohong,Hu Guowei,Xie Haipeng,et al.Optimal dispatch for wind power integrated systems considering demand response[J].Automation of Electric Power Systems,2014,13(38):115-120(in Chinese).
[12]刘文颖,文晶,谢昶,等.考虑风电消纳的电力系统源荷协调多目标优化方法[J].中国电机工程学报,2015,35(5):1079-1088.Liu Wenying,Wen Jing,Xie Chang,et al.Multi-objective optimal method considering wind power accommodation based on source-load coordination[J].Proceedings of the CSEE,2015,35(5):1079-1088(in Chinese).
[13]文晶,刘文颖,谢昶,等.计及风电消纳效益的电力系统源荷协调二层优化模型[J].电工技术学报,2015,30(8):247-256.Wen Jing,Liu Wenying,Xie Chang,et al.Source-load coordination optimal model considering wind power consumptive benefits based on bi-level programming[J].Transactions of China Electrotechnical Society,2015,30(8):247-256(in Chinese).
[14]王健,鲁宗相,乔颖,等.高载能负荷提高风电就地消纳的需求响应模式研究[J].电网技术,2017,41(7):2115-2123.Wang Jian,Lu Zongxiang,Qiao Ying,et al.Research on demand response mechanism of wind power local accommodation utilizing energy-intensive loads[J].Power System Technology,2017,41(7):2115-2123(in Chinese).
[15]鞠立伟,于超,谭忠富.计及需求响应的风电储能两阶段调度优化模型及求解算法[J].电网技术,2015,39(5):1287-1293.Ju Liwei,Yu Chao,Tan Zhongfu.A two-stage scheduling optimization model and corresponding solving algorithm for power grid containing wind farm and energy storage system considering demand response[J].Power System Technology,2015,39(5):1287-1293(in Chinese).
[16]Shan J,Botterud A,Ryan S M.Impact of demand response on thermal generation investment with high wind penetration[J].IEEETransactions on Smart Grid,2013,4(4):2374-2383.
[17]Wang Z Q,Leng Y.Review of smart grid demand response with clean energy access[C]//Advanced Research in Material Science and Mechanical Engineering,Pts 1 and 2,Applied Mechanics and Materials,446-447.Stafa-Zurich:Trans Tech Publications Ltd,2014:847-852.
[18]Nojavan S,Aalami Ha.Stochastic energy procurement of large electricity consumer considering photovoltaic,wind-turbine,microturbines,energy storage system in the presence of demand response program[J].Energy Conversion and Management,2015,10(103):1008-1018.
[19]李丹,刘俊勇,刘友波,等.风电接入后考虑抽蓄-需求响应的多场景联合安全经济调度模型[J].电力自动化设备,2015,35(2):28-34,41.Li Dan,Liu Junyong,Liu Youbo,et al.Joint secure&economic dispatch considering wind power pumped storage and demand response[J].Electric Power Automation Equipment,2015,35(2):28-34,41(in Chinese).
[20]王蓓蓓,刘小聪,李扬.面向大容量风电接入考虑用户侧互动的系统日前调度和运行模拟研究[J].中国电机工程学报,2013,33(22):35-44.Wang Beibei,Liu Xiaocong,Li Yang.Day-ahead generation scheduling and operation simulation considering demand response in large-capacity wind power integrated systems[J].Proceedings of the CSEE,2013,33(22):35-44(in Chinese).
[21]刘小聪,王蓓蓓,李扬,等.计及需求侧资源的大规模风电消纳随机机组组合模型[J].中国电机工程学报,2015,35(14):3714-3723.Liu Xiaocong,Wang Beibei,Li Yang,et al.Stochastic unit commitment model for high wind power integration considering demand side resources[J].Proceedings of the CSEE,2015,35(14):3714-3723(in Chinese).
[22]江岳文,温步瀛.结合风电功率超短期预测值偏差的实时市场调度[J].电力自动化设备,2015,35(3):12-17.Jiang Yuewen,Wen Buying.Real-time market dispatch based on ultra-short-term forecast error of wind power[J].Electric Power Automation Equipment,2015,35(3):12-17(in Chinese).
[23]范高锋,王伟胜,刘纯.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123.Fan Gaofeng,Wang Weisheng,Liu Chun.Wind power forecast based on artificial neural network[J].Proceedings of the CSEE,2013,37(4):967-973(in Chinese).
[24]张钦,王锡凡,王建学,等.电力市场下需求响应研究综述[J].电力系统自动化,2008,32(3):97-106.Zhang Qin,Wang Xifan,Wang Jianxue,et al.Survey of demand response research in deregulated electricity markets[J].Automation of Electric Power Systems,2008,32(3):97-106(in Chinese).
[25]戴文威.风电并网对电力市场需求侧价格响应的影响[D].长沙:湖南大学,2013.
[26]胡田田.经济学基础与应用[M].第2版.上海:复旦大学出版社,2014.