基于储能调度模式的微电网不平衡功率平抑两阶段优化方法
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摘要
依据2阶段优化理论提出基于储能调度模式的微电网不平衡功率平抑方法,第一阶段以微电网综合平抑成本最小为目标,计及必要约束条件,其中储能电站不平衡功率平抑量通过储能充放电虚拟成本收益模型制定;第二阶段以第一阶段制定的储能电站不平衡功率平抑量为基础,制定储能电站内各个储能的不平衡功率分配方案。通过引入群体反向学习机制和混沌搜索对基本帝国竞争算法进行改进,并采用改进帝国竞争算法对所建立的模型进行求解。最后通过一个算例验证了所提方法能够在有效维持微电网系统功率平衡的前提下,降低不平衡功率平抑成本,具有较好的经济效益。
The paper propose an unbalanced power smoothing method for microgrid based on energy storage scheduling mode according to two-stage optimization theory. In the first stage, the objective is to minimize the overall cost of the unbalanced power smoothing in the microgrid by considering necessary constraints, including the amount of the unbalanced power smoothing in energy storage station determined by the virtual cost and benefit model for the charging and discharging process of the energy storage. In the second stage, the unbalanced power allocations are given for energy storage on the basis of the amount of the unbalanced power smoothing in the first stage. Furthermore, basic imperial competition algorithm is improved by introducing group reverse learning mechanism and chaotic searching, and the proposed model is solved with the improved imperial competition algorithm. Finally, an example is given to verify that the proposed method can effectively maintain the power balance in the microgrid, reduce the cost of the power smoothing, and has good economic benefits.
The paper propose an unbalanced power smoothing method for microgrid based on energy storage scheduling mode according to two-stage optimization theory. In the first stage, the objective is to minimize the overall cost of the unbalanced power smoothing in the microgrid by considering necessary constraints, including the amount of the unbalanced power smoothing in energy storage station determined by the virtual cost and benefit model for the charging and discharging process of the energy storage. In the second stage, the unbalanced power allocations are given for energy storage on the basis of the amount of the unbalanced power smoothing in the first stage. Furthermore, basic imperial competition algorithm is improved by introducing group reverse learning mechanism and chaotic searching, and the proposed model is solved with the improved imperial competition algorithm. Finally, an example is given to verify that the proposed method can effectively maintain the power balance in the microgrid, reduce the cost of the power smoothing, and has good economic benefits.
引文
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[11]鲍雪娜,张建成,徐明,等.基于混合储能的并网光伏电站有功分级控制策略[J].电力系统自动化,2013, 37(1):115-121.BAO Xuena, ZHANG Jiancheng, XU Ming, et al. Active power hierarchical control strategy of interconnected photovoltaic station based on hybrid energy storage[J].Automation of Electric Power Systems, 2013, 37(1):115-121.
[12]张良,严正,冯冬涵,等.采用两阶段优化模型的电动汽车充电站内有序充电策略[J].电网技术,2014, 38(4):967-973.ZHANG Liang, YAN Zheng, FENG Donghan, et al. Twostage optimization model based coordinated charging for EV charging station[J]. Power System Technology, 2014,38(4):967-973.
[13]江知瀚,陈金富.计及不确定性和多投资主体需求指标的分布式电源优化配置方法研究[J].中国电机工程学报,2013, 33(31):34-42.JIANG Zhihan, CHEN Jinfu. Optimal distributed generator allocation method considering uncertainties and requirements of different investment entities[J].Proceedings of the CSEE, 2013, 33(31):34-42.
[14]翟云峰,易国伟,王亦,等.基于改进帝国竞争算法的微电网动态经济调度[J].电力科学与工程,2015, 31(5):34-41.ZHAI Yunfeng, YI Guowei, WANG Yi, et al. Dynamic economic dispatch of micro grid using improved imperialist competitive algorithm[J]. Electric Power Science and Engineering, 2015, 31(5):34-41.
[15]王帅群,敖日格乐,高尚策,等.基于进出口贸易的帝国主义竞争算法[J].计算机科学,2015, 42(5):14-18.WANG Shuaiqun, AORI Gele, Gao Shangce, et al.Imperialist competitive algorithm based on import andexport trade[J]. Computer Science, 2015, 42(5):14-18.
[16]王立平,谢承旺.一种带反向学习机制的自适应烟花爆炸算法[J].计算机科学,2016,43(s2):103-107.WANG Liping, XIE Chenwang. Adaptive fireworks explosion optimization algorithm using opposition-based learning[J]. Computer Science, 2016, 43(s2):103-107.
[17] YOU M, JIANG T. New method for target identification in a foliage environment using selected bispectra and chaos particle swarm optimization based support vector machine[J]. IET Signal Process, 2014, 8(1):76-84.
[18]苏适,周立栋,陆海,等.基于改进混沌粒子群算法的多源独立微电网多目标优化方法[J].电力系统保护与控制,2017, 45(23):34-41.SU Shi, ZHOU Lidong, LU Hai, et al.Multi-objective optimization method of multi-source independent microgrid based on modified CPSO[J].Power System Protection and Control, 2017, 45(23):34-41.
[19]王新普,周想凌,邢杰,等.一种基于改进灰色BP神经网络组合的光伏出力预测方法[J].电力系统保护与控制,2016, 44(18):81-87.WANG Xinpu, ZHOU Xiangling, XING Jie, et al.Prediction method of PV output power based on the combination of improved grey back propagation neural network[J]. Power System Protection and Control, 2016,44(18):81-87.
[20] CUI M J, KE D P, SUN Y Z, et al. Wind power ramp events forecasting using a stochastic scenario generation method[J]. IEEE Transactions on Sustainable Energy,2015, 6(2):422-433.
[21]邢家维,何志恒,金能,等.计及风电不确定性及排放影响的机组组合策略及其效益评估[J].智慧电力,2018,46(7):7-13.XING Jiawei, HE Zhiheng, JIN Neng, et al. Unit combination strategy and benefit evaluation considering influence wind power uncertainty and emission[J].Smart Power, 2018,46(7):7-13.
[22]宋阳,张静页,王磊,佟晓宁.计及预测偏差的光伏消纳多时间尺度调度研究[J].电力工程技术,2018,37(1):58-64.SONG Yang, ZHANG Jingye, WANG Lei, etc. Research on multi-time scheduling of accommodating photovoltaic considering the error of photovoltaic prediction[J]. Electric Power Engineering Technology, 2018,37(1):58-64.
[2]江岳春,邢方方,庞振国,等.基于机会约束规划的微电网运行备用优化配置[J].电力系统保护与控制,2016, 44(14):100-106.JIANG Yuechun, XING Fangfang, PANG Zhenguo, et al.Optimization conf-iguration of operating reserve in microgrid based on chance-constrained programming[J].Power System Protection and Control, 2016, 44(14):100-106.
[3] CAO H Q, XU J, KE DP, et al. Economic dispatch of micro-grid based on improved particle-swarm optimization algorithm[C]. Denver:North American Power Symposium(NAPS), 2016.
[4]丁明,张颖媛,茆美琴,等.包含钠硫电池储能的微电网系统经济运行优化[J].中国电机工程学报,2011, 31(4):7-14.DING Ming, ZHANG Yingyuan, MAO Meiqin, et al.Economic operation optimization for microgrids including Na/S batter storage[J]. Proceedings of the CSEE, 2011, 31(4):7-14.
[5]杨晓东,陈宇,张有兵,等.考虑交互功率与可再生能源功率波动的微电网调度优化模型[J].电力系统保护与控制,2016, 44(23):30-38.YANG Xiaodong, CHEN Yu, ZHANG Youbing, et al.Scheduling optimization model for microgrid considering interactive power and renewable energy output fluctuation[J]. Power System Protection and Control, 2016, 44(23):30-38.
[6]王成山,吴震,李鹏.微电网关键技术研究[J].电工技术学报,2014, 29(2):1-12.WANG Chengshan, WU Zhen, LI Peng. Research on key technologies of microgrid[J]. Transactions of China Electrotechnical Society, 2014, 29(2):1-12.
[7]江渝,黄敏,毛安,等.孤立微电网的多目标能量管理[J].高电压技术,2014, 40(11):3519-3527.JIANG Yu, HUANG Min, MAO An, et al. Multiobjective energy management of isolated microgrid[J].High Voltage Engineering, 2014, 40(11):3519-3527.
[8]魏路平,张小白,卢敏,等.日前电力市场环境下的实时调度[J].电力系统自动化,2007, 31(24):38-41.WEI Luping, ZHANG Xiaobai, LU Min, et al. Study and realization of real-time dispatch in day-ahead power market[J]. Automation of Electric Power Systems, 2007,31(24):38-41.
[9]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.
[10] HU Y Z, LI Y, XU M J, et al. A chance-constrained economic dispatch model in wind-thermal-energy storage system[J]. Energies, 2017, 10(3):326-346.
[11]鲍雪娜,张建成,徐明,等.基于混合储能的并网光伏电站有功分级控制策略[J].电力系统自动化,2013, 37(1):115-121.BAO Xuena, ZHANG Jiancheng, XU Ming, et al. Active power hierarchical control strategy of interconnected photovoltaic station based on hybrid energy storage[J].Automation of Electric Power Systems, 2013, 37(1):115-121.
[12]张良,严正,冯冬涵,等.采用两阶段优化模型的电动汽车充电站内有序充电策略[J].电网技术,2014, 38(4):967-973.ZHANG Liang, YAN Zheng, FENG Donghan, et al. Twostage optimization model based coordinated charging for EV charging station[J]. Power System Technology, 2014,38(4):967-973.
[13]江知瀚,陈金富.计及不确定性和多投资主体需求指标的分布式电源优化配置方法研究[J].中国电机工程学报,2013, 33(31):34-42.JIANG Zhihan, CHEN Jinfu. Optimal distributed generator allocation method considering uncertainties and requirements of different investment entities[J].Proceedings of the CSEE, 2013, 33(31):34-42.
[14]翟云峰,易国伟,王亦,等.基于改进帝国竞争算法的微电网动态经济调度[J].电力科学与工程,2015, 31(5):34-41.ZHAI Yunfeng, YI Guowei, WANG Yi, et al. Dynamic economic dispatch of micro grid using improved imperialist competitive algorithm[J]. Electric Power Science and Engineering, 2015, 31(5):34-41.
[15]王帅群,敖日格乐,高尚策,等.基于进出口贸易的帝国主义竞争算法[J].计算机科学,2015, 42(5):14-18.WANG Shuaiqun, AORI Gele, Gao Shangce, et al.Imperialist competitive algorithm based on import andexport trade[J]. Computer Science, 2015, 42(5):14-18.
[16]王立平,谢承旺.一种带反向学习机制的自适应烟花爆炸算法[J].计算机科学,2016,43(s2):103-107.WANG Liping, XIE Chenwang. Adaptive fireworks explosion optimization algorithm using opposition-based learning[J]. Computer Science, 2016, 43(s2):103-107.
[17] YOU M, JIANG T. New method for target identification in a foliage environment using selected bispectra and chaos particle swarm optimization based support vector machine[J]. IET Signal Process, 2014, 8(1):76-84.
[18]苏适,周立栋,陆海,等.基于改进混沌粒子群算法的多源独立微电网多目标优化方法[J].电力系统保护与控制,2017, 45(23):34-41.SU Shi, ZHOU Lidong, LU Hai, et al.Multi-objective optimization method of multi-source independent microgrid based on modified CPSO[J].Power System Protection and Control, 2017, 45(23):34-41.
[19]王新普,周想凌,邢杰,等.一种基于改进灰色BP神经网络组合的光伏出力预测方法[J].电力系统保护与控制,2016, 44(18):81-87.WANG Xinpu, ZHOU Xiangling, XING Jie, et al.Prediction method of PV output power based on the combination of improved grey back propagation neural network[J]. Power System Protection and Control, 2016,44(18):81-87.
[20] CUI M J, KE D P, SUN Y Z, et al. Wind power ramp events forecasting using a stochastic scenario generation method[J]. IEEE Transactions on Sustainable Energy,2015, 6(2):422-433.
[21]邢家维,何志恒,金能,等.计及风电不确定性及排放影响的机组组合策略及其效益评估[J].智慧电力,2018,46(7):7-13.XING Jiawei, HE Zhiheng, JIN Neng, et al. Unit combination strategy and benefit evaluation considering influence wind power uncertainty and emission[J].Smart Power, 2018,46(7):7-13.
[22]宋阳,张静页,王磊,佟晓宁.计及预测偏差的光伏消纳多时间尺度调度研究[J].电力工程技术,2018,37(1):58-64.SONG Yang, ZHANG Jingye, WANG Lei, etc. Research on multi-time scheduling of accommodating photovoltaic considering the error of photovoltaic prediction[J]. Electric Power Engineering Technology, 2018,37(1):58-64.