基于模型预测控制平抑光伏输出功率波动的储能充放电策略
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摘要
随着光伏发电在电力系统中的渗透率不断上升与储能技术的成熟,利用储能降低光伏输出功率波动对系统造成的影响将是重要的技术手段之一。由于光伏出力的预测精度会随时间尺度的增加而下降,将会增加储能损耗成本;若只考虑当前时刻光伏输出功率波动可能会导致储能对下一时刻的光伏波动难以控制。因此,提出了一种基于模型预测控制平抑光伏输出功率波动的储能充放电策略,建立了综合考虑光伏输出功率波动满足率和储能寿命损耗的目标函数;同时,进一步考虑实际光伏输出功率与预测值之间的偏差,对光储逆变器输出功率进行反馈校正,确保下一时刻光伏输出功率波动计算准确,提高储能动作的正确。最后结合仿真算例验证了该策略的有效性和优越性。
With the increasing penetration of photovoltaic(PV) generation in power system and maturity of energy storage(ES)technologies, the use of ES to reduce the impact of PV fluctuations will be one of the important means in the future. Because the prediction accuracy of PV output will decrease with the increase of time scale, the loss cost of energy storage will be increased. If only considering the current PV fluctuation, it is difficult to control the PV fluctuation in the next moment. Therefore, a strategy of energy storage based on model predictive control is proposed for PV power smoothing. The satisfaction rate of PV fluctuation and the life loss of ES are treated as objective function. Meanwhile, considering the deviation between the actual PV output and the predicted output,feedback correction is introduced as so to improve the computational accuracy of PV fluctuation and to ensure proper operation of ES in the next time. The simulation verifies the effectiveness and superiority of the proposed model.
With the increasing penetration of photovoltaic(PV) generation in power system and maturity of energy storage(ES)technologies, the use of ES to reduce the impact of PV fluctuations will be one of the important means in the future. Because the prediction accuracy of PV output will decrease with the increase of time scale, the loss cost of energy storage will be increased. If only considering the current PV fluctuation, it is difficult to control the PV fluctuation in the next moment. Therefore, a strategy of energy storage based on model predictive control is proposed for PV power smoothing. The satisfaction rate of PV fluctuation and the life loss of ES are treated as objective function. Meanwhile, considering the deviation between the actual PV output and the predicted output,feedback correction is introduced as so to improve the computational accuracy of PV fluctuation and to ensure proper operation of ES in the next time. The simulation verifies the effectiveness and superiority of the proposed model.
引文
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[16]沈枢,张沛超,李中豪,等.平抑长短期风电功率波动的风储协调运行方法[J].电力系统自动化,2015, 39(8):12-18.SHEN Shu, ZHANG Peichao, LI Zhonghao, et al. A coordination operation method of wind power and enenrgy storage hybrid system for smoothing short-term and longterm wind power fluctuations[J]. Automation of Electric Power Systems, 2015, 39(8):12-18.
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[18] GB/T 19964-2012,光伏发电站接人电力系统技术规定[S].
[19]廖强强,邱琳,楼晓东,等.铅炭电池的性能及其在电力储能中的应用[J].电力建设,2014,57(11):117-121.LIAO Qiangqiang, QIU Lin, LOU Xiaodong,et al.Performance of lead-carbon batteries and their applications in electricity storage[J]. Power Construction,2014,57(11):117-121.
[20] XIANG J, DING P, ZHANG H, et al. Beneficial effects of activated carbon additives on the performance of negative lead-acid battery electrode for high-rate partialstate-of-charge operation[J]. Journal of Power Sources,2013, 241:150-158.
[21] XIANG J, HU C, CHEN L, et al. Enhanced performance of Zn(Ⅱ)-doped lead-acid batteries with electrochemical active carbon in negative mass[J]. Journal of Power Sources, 2016, 328:8-14.
[22] WANG J, LIU P, HICKS-GARNER J, et al. Cycle-life model for graphite-LiFePO 4, cells[J]. Journal of Power Sources, 2011, 196:3942-3948.
[23] ZHENG M, MEINRENKEN C J, LACKNER K S. Smart households:Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving[J].Applied Energy, 2015, 147:246-257.
[24]陈志宝,丁杰,周海,等.地基云图结合径向基函数人工神经网络的光伏功率超短期预测模型[J].中国电机工程学报,2015, 35(3):561-567.CHEN Zhibao, DING Jie, ZHOU Hai, et al. A model of very short-term photovoltaic power forecasting based on ground-based cloud images and RBF neural network[J].Proceedings of the CSEE,2015,35(3):561-567.
[2]李升,姜程程,赵之瑜,等.分布式光伏电站接入低压配电网系统暂态电压稳定性研究[J].电力系统保护与控制,2017, 45(8):67-72.LI Sheng, JIANG Chengcheng, ZHAO Zhiyu, et al. Study of transient voltage stability for distributed photovoltaic power plant integration into low voltage distribution network[J]. Power System Protection and Control, 2017,45(8):67-72.
[3]梁君君.含光伏并网发电的储能容量配置方法[J].广东电力,2017, 30(10):52-56.LIANG Junjun. Configuration method for storage energy capacity with grid-connected photovoltaic power generation[J]. Guangdong Electric Power, 2017, 30(10):52-56.
[4]张惠生,赵兴勇,王凯武,等.基于风光互补微电网的复合储能控制策略[J].广东电力,2017, 30(2):48-53.ZHANG Huisheng, ZHAO Xingyong, WANG Kaiwu, et al. Control strategy for hybrid energy storage based on wind/photovoltaic complementary micro-grid system[J].Guangdong Electric Power, 2017, 30(2):48-53.
[5]吴振威,蒋小平,马会萌,等.用于混合储能平抑光伏波动的小波包-模糊控制[J].中国电机工程学报,2014,34(3):317-324.WU Zhenwei, JIANG Xiaoping, MA Huimeng, et al.Wavelet packet-fuzzy control of hybrid energy storage systems for PV power smoothing[J]. Proceedings of the CSEE,2014, 34(3):317-324.
[6] LI Xiangjun, LI Yong, HAN Xiaojuan, et al. Application of fuzzy wavelet transform to smooth wind/PV hybrid power system output with battery energy storage system[J].Energy Procedia, 2011, 12(39):994-1001.
[7]章竹耀,肖欣,郭晓丽,等.基于储能电池的光伏功率波动平抑策略[J].电力建设,2016, 37(8):90-95.ZHANG Zhuyao, XIAO Xin, GUO Xiaoli, et al.Restraining strategy of photovoltaic power fluctuation based on energy storage battery[J]. Electric Power Construction, 2016, 37(8):90-95.
[8] HUND T D, GONZALEZ S, BARRETT K. Grid-tied PV system energy smoothing[C]//Honolulu:Proceedings of the35 th IEEE Photovoltaic Specialists Conference, 2010,2762-2766.
[9] ALAM M J E, MUTTAQI K M, SUTANTO D. A novel approach for ramp-rate control of solar PV using energy storage to mitigate output fluctuations caused by cloud passing[J]. IEEE Transactions on Energy Conversion,2014, 29(2):507-518.
[10]周奇聪,徐耀良,褚世冲,等.一种新型的基于蓄电池储能的风功率平抑策略[J].现代电力,2016, 33(2):59-63.ZHOU Qicong, XU Yaoliang, CHU Shichong, et al. A new strategy for smoothing wind power fluctuation based on battery storage system[J]. Modern Electric Power,2016, 33(2):59-63.
[11]丁明,徐宁舟,毕锐.用于平抑可再生能源功率波动的储能电站建模及评价[J].电力系统自动化,2011, 35(2):66-72.DING Ming, XU Ningzhou, BI Rui. Modeling of BESS for smoothing renewable energy output fluctuations[J].Automation of Electric Power Systems, 2011, 35(2):66-72.
[12]刘皓明,陆丹,杨波,等.可平抑高渗透分布式光伏发电功率波动的储能电站调度策略[J].高电压技术,2015, 41(10):3213-3223.LIU Haoming, LU Dan, YANG Bo, et al. Dispatch strategy of energy storage station to smooth power fluctuations of high penetration photovoltaic generation[J].High Voltage Engineering, 2015, 41(10):3213-3223.
[13]席裕庚.预测控制[M].第2版.北京:国防工业出版社,2013:51-72.
[14] LEE J H. Model predictive control:review of the three decades of development[J]. International Journal of Control,Automation and Systems, 2011, 9(3):415.
[15]刘向杰,孔小兵.电力工业复杂系统模型预测控制——现状与发展[J].中国电机工程学报,2013, 33(5):79-85.LIU XiangJie, KONG Xiaobing. Present situation and prospect of model predictive control application in complex power industrial process[J]. Proceedings of the CSEE, 2013, 33(5):79-85.
[16]沈枢,张沛超,李中豪,等.平抑长短期风电功率波动的风储协调运行方法[J].电力系统自动化,2015, 39(8):12-18.SHEN Shu, ZHANG Peichao, LI Zhonghao, et al. A coordination operation method of wind power and enenrgy storage hybrid system for smoothing short-term and longterm wind power fluctuations[J]. Automation of Electric Power Systems, 2015, 39(8):12-18.
[17]洪海生,江全元,严玉婷.实时平抑风电场功率波动的电池储能系统优化控制方法[J].电力系统自动化,2015, 37(1):103-109.HONG Haisheng, JIANG Quanyuan, YAN Yuting. An optimization control method of battery energy storage system with wind power fluctuations smoothed in real time[J]. Automation of Electric Power Systems, 2015, 37(1):103-109.
[18] GB/T 19964-2012,光伏发电站接人电力系统技术规定[S].
[19]廖强强,邱琳,楼晓东,等.铅炭电池的性能及其在电力储能中的应用[J].电力建设,2014,57(11):117-121.LIAO Qiangqiang, QIU Lin, LOU Xiaodong,et al.Performance of lead-carbon batteries and their applications in electricity storage[J]. Power Construction,2014,57(11):117-121.
[20] XIANG J, DING P, ZHANG H, et al. Beneficial effects of activated carbon additives on the performance of negative lead-acid battery electrode for high-rate partialstate-of-charge operation[J]. Journal of Power Sources,2013, 241:150-158.
[21] XIANG J, HU C, CHEN L, et al. Enhanced performance of Zn(Ⅱ)-doped lead-acid batteries with electrochemical active carbon in negative mass[J]. Journal of Power Sources, 2016, 328:8-14.
[22] WANG J, LIU P, HICKS-GARNER J, et al. Cycle-life model for graphite-LiFePO 4, cells[J]. Journal of Power Sources, 2011, 196:3942-3948.
[23] ZHENG M, MEINRENKEN C J, LACKNER K S. Smart households:Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving[J].Applied Energy, 2015, 147:246-257.
[24]陈志宝,丁杰,周海,等.地基云图结合径向基函数人工神经网络的光伏功率超短期预测模型[J].中国电机工程学报,2015, 35(3):561-567.CHEN Zhibao, DING Jie, ZHOU Hai, et al. A model of very short-term photovoltaic power forecasting based on ground-based cloud images and RBF neural network[J].Proceedings of the CSEE,2015,35(3):561-567.