含风电及电动汽车的多目标电力系统调度
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摘要
为应对风电的迅速发展及电动汽车的规模化应用给电力系统调度带来的新挑战,在风电的威布尔随机描述及电动汽车分层调度策略的基础上,以污染排放最低及燃料费用最小为优化目标,构建含风电及电动汽车的电力系统多目标动态环境经济调度模型。该模型综合考虑系统功率平衡、系统网损、旋转备用、车主出行需求、电池特性等约束以及车网能量互动行为。同时,提出改进的基于非支配排序的多目标差分进化求解算法,并针对模型特点以及多重约束条件,提出决策变量行列转换方法和基于动态调整的约束处理策略。最后,以6机组系统并结合4种场景进行研究,通过对新能源接入的动态调度管理,验证所提模型的合理性及算法的正确性。
To cope with the new challenges of power system dispatch bring by the rapid development of wind power and large-scale use of electric vehicles,based on the Weibull stochastic description of wind power and the hierarchical scheduling strategy for electric vehicles,the multi-objective dynamic environmental economic dispatch with wind power and electric vehicles model is proposed to minimize pollution emissions and fuel costs. The model takes into account constraints such as system power balance,system loss,spinning reserve and users travel demand,and battery characteristics etc.,and vehicles network energy interaction behavior. An improved multi-objective differential evolution algorithm based on non-domination sorting is proposed. For the characteristics of the model and multiple constraints,the decision variables row-column conversion method and the constraints adjustment processing strategy based on dynamic adjustment are proposed. Finally,the 6-unit of the system and four scenarios are researched. Through the dynamic scheduling management of new energy access,the rationality of the proposed model and the correctness of the algorithm are verified.
To cope with the new challenges of power system dispatch bring by the rapid development of wind power and large-scale use of electric vehicles,based on the Weibull stochastic description of wind power and the hierarchical scheduling strategy for electric vehicles,the multi-objective dynamic environmental economic dispatch with wind power and electric vehicles model is proposed to minimize pollution emissions and fuel costs. The model takes into account constraints such as system power balance,system loss,spinning reserve and users travel demand,and battery characteristics etc.,and vehicles network energy interaction behavior. An improved multi-objective differential evolution algorithm based on non-domination sorting is proposed. For the characteristics of the model and multiple constraints,the decision variables row-column conversion method and the constraints adjustment processing strategy based on dynamic adjustment are proposed. Finally,the 6-unit of the system and four scenarios are researched. Through the dynamic scheduling management of new energy access,the rationality of the proposed model and the correctness of the algorithm are verified.
引文
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[10]朱永胜,王杰,瞿博阳,等.含电动汽车的电力系统动态环境经济调度[J].电力自动化设备,2016,36(10):16—23.[10]Zhu Yongsheng,Wang Jie,Qu Boyang,et al. Dynamic environmental and economic dispatch of power system with EVs[J]. Electric Power Automation Equipment,2016,36(10):16—23.
[11]Qu Boyang, Qiao Baihao, Zhu Yongsheng, et al.Dynamic power dispatch considering electric vehicles and wind power using decomposition based multiobjective evolutionary algorithm[J]. Energies,2017,10(12):1991.
[12]朱永胜,王杰.采用MOEA/D算法的含风电系统环境经济调度[J].郑州大学学报:工学版,2014,35(4):96—100.[12]Zhu Yongsheng,Wang Jie. Environmental economic dispatch integrating wind power adopting MOEA/D algorithm[J]. Journal of Zhengzhou University:Engineering Science,2014,35(4):96—100.
[13]张程飞,袁越,张新松,等.考虑碳排放配额影响的含风电系统日前调度计划模型[J].电网技术,2014,38(8):2114—2120.[13]Zhang Chengfei,Yuan Yue,Zhang Xinsong,et al. Day-ahead dispatching scheduling for power grid integratedwith wind farm considering influence of carbon emissionquota[J]. Power System Technology,2014,38(8):2114—2120.
[14]常俊晓,游文霞,肖隆恩.含风电的发电资源优化调度与仿真研究[J].计算机仿真,2015,32(4):120—123,128.[14]Chang Junxiao,You Wenxia,Xiao Longen. Optimizeddispatching and simulation for generation resources withwind power connection[J]. Computer Simulation,2015,32(4):120—123,128.
[15]于大洋,黄海丽,雷鸣,等.电动汽车充电与风电协同调度的碳减排效益分析[J].电力系统自动化,2012,36(10):14—18.[15]Yu Dayang,Huang Haili,Lei Ming,et al. Analysis oncarbon emission reduction benefits of electric carcharging and wind power co-scheduling[J]. Powersystem automation,2016,36(10):14—18.
[16]赵俊华,文福拴,薛禹胜,等.计及电动汽车和风电出力不确定性的随机经济调度[J].电力系统自动化,2010,34(20):22—29.[16]Zhao Junhua,Wen Fushuan,Xue Yusheng,et al.Power system stochastic economic dispatch considering uncertain outputs from plug-in electric vehicles andwind generators[J]. Automation of Electric PowerSystems,2010,34(20):22—29.
[17]施泉生,邸超,孙佳佳,等.风电与电动汽车协同并网调度环境及经济模型[J].上海电力学院学报,2017,33(2):113—118.[17]Shi Quansheng, Di Chao, Sun Jiajia, et al. Anenvironmental and economic model of grid dispatchingcoordinated with wind powers and plug-in electricvehicles[J]. Journal of Shanghai University of ElectricPower,2017,33(2):113—118.
[18]Guo Chuangxin,Zhan Junpeng,Wu Q H Dynamiceconomic emission dispatch based on group searchoptimizer with multiple producers[J]. Electric PowerSystems Research,2012,86:8—16.
[19]Zhao Junhua,Wen Fushuan,Dong Zhaoyang,et al.Optimal dispatch of electric vehicles and wind powerusing enhanced particle swarm optimization[J]. IEEETransactions on Industrial Informatics,2012,8(4):889—899.
[20]Zhao Shizheng,Suganthan P N. Two-lbests based multiobjective particle swarm optimizer[J]. EngineeringOptimization,2011,43(1):1—17.
[2]Qiao Baijie,Zhang Xingwu. Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction[J]. Mechanical Systems and Signal Processing,2017,83:93—115.
[3]Granelli G P, Mongtagna M,Pagini G L, et al.Emission constrained dynamic dispatch[J]. Electric Power Systems Research,1992,24(1):55—64.
[4]张晓花,谢俊,朱正伟,等.考虑不确定性的智能电网多目标机组组合研究[J].太阳能学报,2016,37(12):3055—3062.[4]Zhang Xiaohua,Xie Jun,Zhu Zhengwei,et al. Reseach of multi-objective unit combination in smart grid with uncertainty[J]. Acta Energies Solaris Sonica,2016,37(12):3055—3062.
[5]聂永辉,王中杰,李江,等.大规模风电并网电力系统优化潮流[J].太阳能学报,2017,38(11):3180—3187.[5]Nie Yonghui,Wang Zhongjie,Li Jiang,et al. Optimal power flow for larger-scale wind power integration[J].Acta Energies Solaris Sonica,2017,38(11):3180—3187.
[6]Duvall M, Knipping E, Alexander M, et al.Environmental assessment of plug-in hybrid electric vehicles. volume1:nationwide greenhouse gas emissions[R]. Palo Alto, USA:Electric Power Research Institute,2007.
[7]中华人民共和国国务院.《节能与新能源汽车产业发展规划(2012—2020年)》[EB/OL]. http//www.gov.cn/zwgk/2012/07/09/content_2179032.htm2012-07-09.
[8]姚伟锋,赵俊华,文福拴,等.基于双层优化的电动汽车充放电调度策略[J].电力系统自动化,2012,36(11):30—37.[8]Yao Weifeng,Zhao Junhua,Wen Fushuan,et al. A charging and discharging strategy for electric vehicles based on bi-level optimization[J]. Automation of Electric Power Systems,2012,36(11):30—37.
[9]蔡秋娜,文福拴,薛禹胜,等.基于SCUC的可入网混合电动汽车优化调度方法[J].电力系统自动化,2012,36(1):38—46.[9]Cai Qiuna,Wen Fushuan,Xue Yusheng,et al. An SCUC-based optimization approach for power system dispatching with plug-in hybrid electric vehicles[J].Automation of Electric Power Systems,2012,36(1):38—46.
[10]朱永胜,王杰,瞿博阳,等.含电动汽车的电力系统动态环境经济调度[J].电力自动化设备,2016,36(10):16—23.[10]Zhu Yongsheng,Wang Jie,Qu Boyang,et al. Dynamic environmental and economic dispatch of power system with EVs[J]. Electric Power Automation Equipment,2016,36(10):16—23.
[11]Qu Boyang, Qiao Baihao, Zhu Yongsheng, et al.Dynamic power dispatch considering electric vehicles and wind power using decomposition based multiobjective evolutionary algorithm[J]. Energies,2017,10(12):1991.
[12]朱永胜,王杰.采用MOEA/D算法的含风电系统环境经济调度[J].郑州大学学报:工学版,2014,35(4):96—100.[12]Zhu Yongsheng,Wang Jie. Environmental economic dispatch integrating wind power adopting MOEA/D algorithm[J]. Journal of Zhengzhou University:Engineering Science,2014,35(4):96—100.
[13]张程飞,袁越,张新松,等.考虑碳排放配额影响的含风电系统日前调度计划模型[J].电网技术,2014,38(8):2114—2120.[13]Zhang Chengfei,Yuan Yue,Zhang Xinsong,et al. Day-ahead dispatching scheduling for power grid integratedwith wind farm considering influence of carbon emissionquota[J]. Power System Technology,2014,38(8):2114—2120.
[14]常俊晓,游文霞,肖隆恩.含风电的发电资源优化调度与仿真研究[J].计算机仿真,2015,32(4):120—123,128.[14]Chang Junxiao,You Wenxia,Xiao Longen. Optimizeddispatching and simulation for generation resources withwind power connection[J]. Computer Simulation,2015,32(4):120—123,128.
[15]于大洋,黄海丽,雷鸣,等.电动汽车充电与风电协同调度的碳减排效益分析[J].电力系统自动化,2012,36(10):14—18.[15]Yu Dayang,Huang Haili,Lei Ming,et al. Analysis oncarbon emission reduction benefits of electric carcharging and wind power co-scheduling[J]. Powersystem automation,2016,36(10):14—18.
[16]赵俊华,文福拴,薛禹胜,等.计及电动汽车和风电出力不确定性的随机经济调度[J].电力系统自动化,2010,34(20):22—29.[16]Zhao Junhua,Wen Fushuan,Xue Yusheng,et al.Power system stochastic economic dispatch considering uncertain outputs from plug-in electric vehicles andwind generators[J]. Automation of Electric PowerSystems,2010,34(20):22—29.
[17]施泉生,邸超,孙佳佳,等.风电与电动汽车协同并网调度环境及经济模型[J].上海电力学院学报,2017,33(2):113—118.[17]Shi Quansheng, Di Chao, Sun Jiajia, et al. Anenvironmental and economic model of grid dispatchingcoordinated with wind powers and plug-in electricvehicles[J]. Journal of Shanghai University of ElectricPower,2017,33(2):113—118.
[18]Guo Chuangxin,Zhan Junpeng,Wu Q H Dynamiceconomic emission dispatch based on group searchoptimizer with multiple producers[J]. Electric PowerSystems Research,2012,86:8—16.
[19]Zhao Junhua,Wen Fushuan,Dong Zhaoyang,et al.Optimal dispatch of electric vehicles and wind powerusing enhanced particle swarm optimization[J]. IEEETransactions on Industrial Informatics,2012,8(4):889—899.
[20]Zhao Shizheng,Suganthan P N. Two-lbests based multiobjective particle swarm optimizer[J]. EngineeringOptimization,2011,43(1):1—17.