考虑需求响应的光伏发电系统两阶段随机调度模型
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摘要
西北地区光伏发电比例的不断上升,为电力系统调度带来了巨大的困难和挑战.为此,将需求侧响应作为虚拟备用来克服传统调度备用形式单一的劣势;采用基于不确定性参数模糊化的两阶段随机模型,在充分考虑光伏发电出力的随机性与波动性的基础上充分体现了机侧备用与虚拟备用的经济特性,建立了基于预测误差场景概率的高比例光伏发电系统需求侧联合调度模型;采用细菌群体趋药性算法进行求解,以经典的6机系统和实际西北光伏发电系统作为研究对象进行了仿真分析,从而证明了所提调度模型的有效性以及可行性.
The rising proportion of photovoltaic(PV) power generation in northwest China has brought great difficulties and challenges to power system scheduling. In this paper, the demand side response is used as virtual standby to overcome the disadvantage of traditional scheduling standby. A two-stage stochastic model based on the fuzziness of uncertain parameters is adopted to fully reflect the economic characteristics of both machine side reserve and virtual reserve based on the randomness and volatility of photovoltaic power generation output. The bacterial population chemotaxis algorithm is used to solve the problem; and the simulation analysis is carried out on the classic 6 machine system and the actual northwest PV system, so as to prove the effectiveness and feasibility of the proposed scheduling model.
The rising proportion of photovoltaic(PV) power generation in northwest China has brought great difficulties and challenges to power system scheduling. In this paper, the demand side response is used as virtual standby to overcome the disadvantage of traditional scheduling standby. A two-stage stochastic model based on the fuzziness of uncertain parameters is adopted to fully reflect the economic characteristics of both machine side reserve and virtual reserve based on the randomness and volatility of photovoltaic power generation output. The bacterial population chemotaxis algorithm is used to solve the problem; and the simulation analysis is carried out on the classic 6 machine system and the actual northwest PV system, so as to prove the effectiveness and feasibility of the proposed scheduling model.
引文
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[15] 周玮,孙辉,顾宏,等.计及风险备用约束的含风电场电力系统动态经济调度[J].中国电机工程学报,2012,32(1):47-54. Zhou Wei, Sun Hui, Gu Hong, et al. Dynamic economic dispatch of wind integrated power systems based on risk reserve constraints[J]. Proceedings of the CSEE, 2012, 32(1):47-55.
[16] Bahmanifirouzi B,Farjah E,Niknam T.Multi-objective stochastic dynamic economic emission dispatch enhancement by fuzzy adaptive modified theta particle swarm optimization[J].Journal of Renewable and Sustainable Energy,2012,4(2):023105.
[17] Paterakis N G,Erdinc O,Bakirtzis A G. Load-following reserves procurement considering flexible demand-side resources under high wind power penetration [J].IEEE Transactions on Power Systems,2015,30(3):1337-1350.
[18] 杨楠,王波,刘涤尘.计及大规模风电和柔性负荷的电力系统供需侧联合随机调度方法[J].中国电机工程学报,2013,33(16):63-69. Yang Nan, Wang Bo, Liu Dichen. An integrated supply-demand stochastic optimization method considering large-scale wind power and flexible load [J].Proceedings of the CSEE, 2013, 33(16): 63-69.
[2] 薛禹胜, 罗运虎, 李碧君, 等.关于可中断负荷参与系统备用的评述[J].电力系统自动化, 2007, 31(10):1-6. Xue Yusheng, Luo Yunhu, Li Bijun, et al. A review of interruptible load participating system reserve[J]. Automation of Electric Power Systems, 2007, 31(10):1-6.
[3] 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,2014,38(13):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, 38(13): 115-120.
[4] 王锡凡,肖云鹏,王秀丽.新形势下电力系统供需互动问题研究及分析[J].中国电机工程学报,2014,34(29):5019-5028. Wang Xifan, Xiao Yunpeng, Wang Xiuli. Study and analysis of supply-demand interaction of power systems under new circumstances[J]. Proceedings of the CSEE, 2014, 34(29): 5018-5028.
[5] 江岳文,陈冲,温步瀛.含风电场的电力系统机组组合问题随机模拟粒子群算法[J].电工技术学报,2009,24(6):130-137. Jiang Yuewen, Chen Chong, Wen Buying. Particle swarm research of stochastic simulation for unit commitmen in wind farms integrated power system[J]. Transactions of China Electrotechnical Society, 2009, 24(6): 129-137.
[6] 孙元章,吴俊,李国杰,等.基于风速预测和随机规划的含光伏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.
[7] 张晓辉,闫珂珂,卢志刚,等.基于场景概率的含风电系统多目标低碳经济调度[J].电网技术,2014,38(7):1835-1841. Zhang Xiaohui, Yan Keke, Lu Zhigang, et al. Scenario probability based multi-objective optimized low-carbon economic dispatching for power grid integrated with wind farms[J].Power System Technology, 2014, 38(7): 1835-1841.
[8] Farzad P,Mehdi N,Babak M,et al.A stochastic security approach to energy and spinning reserve scheduling considering demand response program[J].Energy,2011,36:3130-3137.
[9] Li M S,Wu Q H,Ji T Y,et al. Stochastic multi-objective optimization for economic-emission dispatch with uncertain wind power and distributed loads[J].Electric Power Systems Research,2014,116:367-373.
[10] Banos R, Manzano-Agugliaro F, Montoya F G, et al. Optimization methods applied to renewable and sustainable energy: A review[J]. Renewable and Sustainable Energy Reviews, 2011, 15(4): 1753-1766.
[11] 罗运虎,薛禹胜,Ledwich G,等.低电价与高赔偿 2种可中断负荷的协调[J].电力系统自动化,2007,31(11):17-21. Luo Yunhu, Xue Yusheng, Ledwich G, et al. Coordination of low price interruptible load and high compensation interruptible load[J]. Automation of Electric Power Systems, 2007, 31(11): 17-21.
[12] 吴栋梁,王扬,郭创新,等.电力市场环境下考虑风电预测误差的经济调度模型[J].电力系统自动化,2012,36(6):23-28. Wu Dongliang, Wang Yang, Guo Chuangxin, et al. An economic dispatching model considering wind power forecast errors in electricity market environment[J]. Automation of Electric Power Systems, 2012, 36(6): 23-28.
[13] Das S, Biswas A, Dasgupta S, et al. Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications[C]//Foundations of Computational Intelligence Volume 3. Springer, Berlin, Heidelberg, 2009: 23-55.
[14] 周炜,彭昱,孙辉,等.含风电场的电力系统动态经济调度[J].中国电机工程学报,2009,29(25):13-18. Zhou Wei, Peng Yu, Sun Hui, et al. Dynamic economic dispatch in wind power integrated system[J]. Proceedings of the CSEE, 2009, 29(25):13-18.
[15] 周玮,孙辉,顾宏,等.计及风险备用约束的含风电场电力系统动态经济调度[J].中国电机工程学报,2012,32(1):47-54. Zhou Wei, Sun Hui, Gu Hong, et al. Dynamic economic dispatch of wind integrated power systems based on risk reserve constraints[J]. Proceedings of the CSEE, 2012, 32(1):47-55.
[16] Bahmanifirouzi B,Farjah E,Niknam T.Multi-objective stochastic dynamic economic emission dispatch enhancement by fuzzy adaptive modified theta particle swarm optimization[J].Journal of Renewable and Sustainable Energy,2012,4(2):023105.
[17] Paterakis N G,Erdinc O,Bakirtzis A G. Load-following reserves procurement considering flexible demand-side resources under high wind power penetration [J].IEEE Transactions on Power Systems,2015,30(3):1337-1350.
[18] 杨楠,王波,刘涤尘.计及大规模风电和柔性负荷的电力系统供需侧联合随机调度方法[J].中国电机工程学报,2013,33(16):63-69. Yang Nan, Wang Bo, Liu Dichen. An integrated supply-demand stochastic optimization method considering large-scale wind power and flexible load [J].Proceedings of the CSEE, 2013, 33(16): 63-69.