考虑风电不确定性的风电储能混合系统协调优化计算
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摘要
风电大规模接入使得电网的运行模式和方式发生重大改变,同时风电的随机性和间歇性特点导致电网调峰能力不足,引发大量弃风现象。针对此问题,以风电储能混合系统为例,建立了以弃风量最小为目标的风电储能混合系统协调优化模型,采用蒙特卡罗积分将带有风电不确定项的优化目标函数转化为有效目标函数,消除风电的不确定性因素影响。优化过程中引入低偏差序列的拟蒙特卡罗法改善样本分布的均匀性,更加精确计算有效目标函数,通过鲁棒优化算法搜索协调优化模型的最优解。对比试验表明,协调优化计算方法在风力发电功率不确定情况下能够有效减少弃风,达到消纳风电的目的。
The large-scale access of wind power causes major changes in the operation mode of the grid.At the same time,due to the randomness and intermittent characteristics of wind power,the ability of cyclic operation of the power grid is insufficient,which causes a large amount of wind power curtailment problems.Aiming at the problem of insufficient wind power accommodation and high pressure of cyclic operation of the power grid caused by wind power uncertainty,this paper established a wind energy storage hybrid systems coordination optimization model.The model takes minimization of the amount of wind power curtailment as the objective and uses the Monte Carlo Integral to transform the optimal objective function with wind power uncertainty into an effective objective function to eliminate the influence of wind power uncertainty factors.The Quasi-Monte Carlo method with low deviation sequence is introduced into the optimization process to improve the uniformity of the sample distribution,and the effective objective function is calculated more accurately.The optimal solution of the coordinated optimization model is searched by the robust optimization algorithm.The comparative experiments show that the proposed coordinated optimization calculation method can effectively decrease wind power curtailment in the case of uncertain wind power,achieve the purpose of wind power accommodation.
The large-scale access of wind power causes major changes in the operation mode of the grid.At the same time,due to the randomness and intermittent characteristics of wind power,the ability of cyclic operation of the power grid is insufficient,which causes a large amount of wind power curtailment problems.Aiming at the problem of insufficient wind power accommodation and high pressure of cyclic operation of the power grid caused by wind power uncertainty,this paper established a wind energy storage hybrid systems coordination optimization model.The model takes minimization of the amount of wind power curtailment as the objective and uses the Monte Carlo Integral to transform the optimal objective function with wind power uncertainty into an effective objective function to eliminate the influence of wind power uncertainty factors.The Quasi-Monte Carlo method with low deviation sequence is introduced into the optimization process to improve the uniformity of the sample distribution,and the effective objective function is calculated more accurately.The optimal solution of the coordinated optimization model is searched by the robust optimization algorithm.The comparative experiments show that the proposed coordinated optimization calculation method can effectively decrease wind power curtailment in the case of uncertain wind power,achieve the purpose of wind power accommodation.
引文
[1] 徐硕,鲁杰,庞博,等.联合分布式电源并网应用的储能技术发展现状[J].电器与能效管理技术,2018(11):14-20.
[2] 沈鑫,曹敏.分布式电源并网对于配电网的影响研究[J].电工技术学报,2015,30(S1):346-351.
[3] 韩杏宁,陈雁,文劲宇.风电场储能装置的鲁棒优化配置[J].高电压技术,2015,41(7):2217-2224.
[4] 陈永翀,李爱晶,刘丹丹,等.储能技术在能源互联网系统中应用与发展展望[J].电器与能效管理技术,2015(24):39-44.
[5] 宋艺航,谭忠富,李欢欢,等.促进风电消纳的发电侧、储能及需求侧联合优化模型[J].电网技术,2014,38(3):610-615.
[6] 李建林,靳文涛,惠东,等.大规模储能在可再生能源发电中典型应用及技术走向[J].电器与能效管理技术,2016(14):9-14,61.
[7] 方劲宇,宋子秋,韩晓娟,等.储能协调蓄热式电锅炉主动消纳风电的方法研究[J].电器与能效管理技术,2017(13):16-21.
[8] 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.
[9] 于丹文,杨明,翟鹤峰,等.鲁棒优化在电力系统调度决策中的应用研究综述[J].电力系统自动化,2016,40(7):134-143,148.
[10] 吴雄,王秀丽,李骏,等.风电储能协调系统的联合调度模型及求解[J].中国电机工程学报,2013,33(13):10-17.
[11] LI Z.Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory[C]//IOP Conference Series:Materials Science and Engineering.IOP Publishing,2017,231(1):012037.
[12] TSUTSUI S,GHOSH A,FUJIMOTO Y.A robust solution searching scheme in genetic search[C]//International Conference on Parallel Problem Solving from Nature.Springer,Berlin,Heidelberg,1996:543-552.
[13] 彭博,陈远扬,黄际元,等.考虑储能电池平衡配电网峰谷差经济性的容量配置方法[J].电器与能效管理技术,2018(1):29-32.
[14] 边巧燕,徐开,孙黎滢,等.考虑风电功率概率分布不确定性的输电系统规划[J].电力系统自动化,2015,39(20):60-65,90.
[15] 张慧,韩崇昭,闫小喜.基于拟蒙特卡罗方法的概率假设密度多目标跟踪[J].控制与决策,2012,27(8):1221-1225,1230.
[16] 朱云飞,罗彪,郑金华,等.基于拟蒙特卡罗方法的进化算法搜索鲁棒最优解的性能提高研究[J].模式识别与人工智能,2011,24(2):201-209.
[17] NIEDERREITER H.Quasi-Monte Carlo methods and pseudo-random numbers[J].Bulletin of the American Mathematical Society,1978,84(6):957-1041.
[18] 陈天翼,刘宏勋.基于遗传算法的微电网经济运行优化[J].电器与能效管理技术,2017(21):54-58.
[19] 葛维春,孙鹏,李家珏,等.电网负荷峰值时段不同场景下的风电功率预测可信度鲁棒估计模型[J/OL].高电压技术:1-8[2019-01-19].https://doi.org/10.13336/j.1003-6520.hve.20180822015.
[2] 沈鑫,曹敏.分布式电源并网对于配电网的影响研究[J].电工技术学报,2015,30(S1):346-351.
[3] 韩杏宁,陈雁,文劲宇.风电场储能装置的鲁棒优化配置[J].高电压技术,2015,41(7):2217-2224.
[4] 陈永翀,李爱晶,刘丹丹,等.储能技术在能源互联网系统中应用与发展展望[J].电器与能效管理技术,2015(24):39-44.
[5] 宋艺航,谭忠富,李欢欢,等.促进风电消纳的发电侧、储能及需求侧联合优化模型[J].电网技术,2014,38(3):610-615.
[6] 李建林,靳文涛,惠东,等.大规模储能在可再生能源发电中典型应用及技术走向[J].电器与能效管理技术,2016(14):9-14,61.
[7] 方劲宇,宋子秋,韩晓娟,等.储能协调蓄热式电锅炉主动消纳风电的方法研究[J].电器与能效管理技术,2017(13):16-21.
[8] 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.
[9] 于丹文,杨明,翟鹤峰,等.鲁棒优化在电力系统调度决策中的应用研究综述[J].电力系统自动化,2016,40(7):134-143,148.
[10] 吴雄,王秀丽,李骏,等.风电储能协调系统的联合调度模型及求解[J].中国电机工程学报,2013,33(13):10-17.
[11] LI Z.Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory[C]//IOP Conference Series:Materials Science and Engineering.IOP Publishing,2017,231(1):012037.
[12] TSUTSUI S,GHOSH A,FUJIMOTO Y.A robust solution searching scheme in genetic search[C]//International Conference on Parallel Problem Solving from Nature.Springer,Berlin,Heidelberg,1996:543-552.
[13] 彭博,陈远扬,黄际元,等.考虑储能电池平衡配电网峰谷差经济性的容量配置方法[J].电器与能效管理技术,2018(1):29-32.
[14] 边巧燕,徐开,孙黎滢,等.考虑风电功率概率分布不确定性的输电系统规划[J].电力系统自动化,2015,39(20):60-65,90.
[15] 张慧,韩崇昭,闫小喜.基于拟蒙特卡罗方法的概率假设密度多目标跟踪[J].控制与决策,2012,27(8):1221-1225,1230.
[16] 朱云飞,罗彪,郑金华,等.基于拟蒙特卡罗方法的进化算法搜索鲁棒最优解的性能提高研究[J].模式识别与人工智能,2011,24(2):201-209.
[17] NIEDERREITER H.Quasi-Monte Carlo methods and pseudo-random numbers[J].Bulletin of the American Mathematical Society,1978,84(6):957-1041.
[18] 陈天翼,刘宏勋.基于遗传算法的微电网经济运行优化[J].电器与能效管理技术,2017(21):54-58.
[19] 葛维春,孙鹏,李家珏,等.电网负荷峰值时段不同场景下的风电功率预测可信度鲁棒估计模型[J/OL].高电压技术:1-8[2019-01-19].https://doi.org/10.13336/j.1003-6520.hve.20180822015.