考虑新能源发电联合体接入的多目标优化模型
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摘要
随着新能源发电规模的不断扩大和种类的不断增多,传统电网调度方式已无法满足运行要求。为解决新能源出力稳定性差、波动性大等问题,文中提出将小型风电场、光伏电站和蔗渣发电站等新能源组成发电联合体参与调度,并采用基于满意度和欧氏距离的交互式多目标决策方法对考虑发电联合体接入的多目标优化问题进行建模。基于IEEE30节点系统的仿真结果表明,考虑新能源联合发电体(new energy power generation groups,NG)接入的多目标优化模型能使以氮氧化合物为主要污染物的排污量最小和总成本最低,并能使系统满意程度上升12. 8%。基于IEEE30节点和IEEE118节点的系统仿真结果表明,所提模型能有效地提升计算效率,是可行和适用的。
With the expansion of the scale and the increasing variety of the new power generation,the traditional power grid dispatch mode has been unable to meet the operational requirements. To solve the problems of poor stability and large fluctuation of new energy output,small wind farm,photovoltaic power station, and bagasse power station are combined into a power generation consortium to participate in dispatching,and an interactive multi-objective decision-making method,based on satisfaction and Euclidean distance are used to model the power generation consortium. The simulation results based on the IEEE 30-bus system show that the proposed model can minimize the total cost and the pollutant discharge with the air pollutants that contain mostly nitrogen oxides in the multi-objective optimization of NG access and thus increase the system satisfaction by 12. 8 %. The system based on IEEE 30-bus and IEEE 118-bus is simulated to verify the efficiency of the proposed model. The results reveal that the proposed model can effectively improve thecomputational efficiency,which indicate that the proposed model is feasible and applicable.
With the expansion of the scale and the increasing variety of the new power generation,the traditional power grid dispatch mode has been unable to meet the operational requirements. To solve the problems of poor stability and large fluctuation of new energy output,small wind farm,photovoltaic power station, and bagasse power station are combined into a power generation consortium to participate in dispatching,and an interactive multi-objective decision-making method,based on satisfaction and Euclidean distance are used to model the power generation consortium. The simulation results based on the IEEE 30-bus system show that the proposed model can minimize the total cost and the pollutant discharge with the air pollutants that contain mostly nitrogen oxides in the multi-objective optimization of NG access and thus increase the system satisfaction by 12. 8 %. The system based on IEEE 30-bus and IEEE 118-bus is simulated to verify the efficiency of the proposed model. The results reveal that the proposed model can effectively improve thecomputational efficiency,which indicate that the proposed model is feasible and applicable.
引文
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[13]陈颖,江曦源,于智同,等.区域配电网内分布式电源和负载联盟交易模式设计和分析[J].电力系统自动化,2017,41(14):78-86.
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[15] WANG M Q,GOOI H B. Spinning reserve estimation in micro grids[J]. IEEE Transactions on Power Systems,2011,26(3):1164-1174.
[16]郭凯岳,苏伟,宋存义,等.燃煤电厂粉尘超净排放控制技术研究现状与进展[J].现代化工,2017,37(1):14-17,19.
[2]李滨,梁水莹,祝靖,等.含非粮生物质发电的微网动态经济调度[J].电力系统自动化,2016,40(11):39-46.
[3]张晓辉,闫柯柯,卢志刚,等.基于碳交易的含风电系统低碳经济调度[J].电网技术,2013,37(10):2697-2704.
[4] MEIBOM P,BARTH R,HASCHE B,et al. Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland[J]. IEEE Transactions on Power Systems,2011,26(3):1367-1379.
[5]兰飞,贾雍,陈延明.含可靠性约束的电网接纳风电能力优化研究[J].广西大学学报(自然科学版),2017,42(3):1020-1028.
[6] SINGH L,DHILLON J S. Secure multi-objective real and reactive power allocation of thermal power units[J]. International Journal of Electrical Power&Energy Systems,2008,30(10):594-602.
[7] MANDAL K K,CHAKRABORTY N. Short-term combined economic emission scheduling of hydrothermal power systems with cascaded reservoirs using differential evolution[J]. Energy Conversion and Management,2009,50(1):97-104.
[8]刘文颖,谢昶,文晶,等.交互式输电网检修计划多目标决策方法[J].电网技术,2013,37(2):471-476.
[9]陈碧云,韦杏秋,陈绍南,等.基于多种群遗传算法的电力系统多目标优化[J].电力系统及其自动化学报,2015,27(7):24-29.
[10]龙军,郑斌,李珊,等.电力系统节能环保调度的多目标决策方法[J].电力系统保护与控制,2012,40(18):35-40.
[11] ZHANG H,ZHOU J,ZHANG Y,et al. Short term hydrothermal scheduling using multi-objective differential evolution with three chaotic sequences[J]. In-ternational Journal of Electrical Power&Energy Systems,2013,47(1):85-99.
[12]蔡国伟,孔令国,杨德友,等.大规模风光互补发电系统建模与运行特性研究[J].电网技术,2012,36(1):65-71.
[13]陈颖,江曦源,于智同,等.区域配电网内分布式电源和负载联盟交易模式设计和分析[J].电力系统自动化,2017,41(14):78-86.
[14]刘先正,王兴成,温家良,等.风力发电机组动力学建模与分析[J].电力系统自动化,2015,39(5):15-19,141.
[15] WANG M Q,GOOI H B. Spinning reserve estimation in micro grids[J]. IEEE Transactions on Power Systems,2011,26(3):1164-1174.
[16]郭凯岳,苏伟,宋存义,等.燃煤电厂粉尘超净排放控制技术研究现状与进展[J].现代化工,2017,37(1):14-17,19.