考虑风电消纳的区域多微网-配电网能源交易模式
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摘要
随着微网数量不断增加及风电渗透率逐渐提高,微网与配电网频繁的能量交互对配电网的运行产生不利影响。为减少微网与配电网之间非必要的能量交互,促进风电就近消纳,构建了计及风电不确定性,以系统运营主体收益最大化、多微网用电总成本最小化为优化目标的区域多微网-配电网能源交易模式。首先,该模式通过运营主体整合区域内负荷及风电出力情况,划分系统内部峰谷时段,提高风电利用率。其次,为增强应对风电不确定性的能力,交易模式采用负荷转移率刻画峰谷分时(time-of-use,TOU)电价用户响应度,并建立考虑风电不确定性的区域多微网系统峰谷分时电价制定模型。最后,采用与蒙特卡洛方法相结合的带精英策略的非支配排序的遗传算法(elitist nondominated sorting genetic algorithm,NSGA-Ⅱ)对交易模式中的多目标优化问题进行求解。算例验证了所提交易模式有利于降低微网用电成本,提升多微网整体效益,实现区域多微网系统与配电网友好互动。
As the number of microgrids continues to increase,the frequent energy interaction between the microgrid and the distribution network will have an adverse impact on the operation of the distribution network. In addition,the gradual increase in penetration rate of wind power leads to a large amount of wind power entering the distribution network,which also imposes a serious burden on the operation of the distribution network. In order to reduce the unnecessary energy interaction between the microgrid and the distribution network,promote the local consumption of wind power,this paper constructs an energy transaction mode for multi-microgrid and distribution network that considers wind power uncertainty.The optimization goal of this mode is to maximize the revenue of the system operation entity and minimize the total cost of power consumption of the multi-microgrid. This mode integrates the load and wind power output in the area through operation entity,and divides the internal peak-to-valley time of the system to improve the utilization of wind power. In order to enhance the ability to cope with the uncertainty of wind power,the transaction mode uses the load transfer rate to describe the user response of TOU prices and establish a model for the TOU price of regional multi-microgrid systems considering wind power uncertainty. Finally,the multi-objective optimization problem in the model is solved by the NSGA-II algorithm combined with the Monte Carlo method. The example proves that the proposed transaction mode is beneficial to reduce the cost of electricity used in microgrids,improve the overall benefits of the multi-microgrid,and realize the friendly interaction between the multi-microgrid system and the distribution network.
As the number of microgrids continues to increase,the frequent energy interaction between the microgrid and the distribution network will have an adverse impact on the operation of the distribution network. In addition,the gradual increase in penetration rate of wind power leads to a large amount of wind power entering the distribution network,which also imposes a serious burden on the operation of the distribution network. In order to reduce the unnecessary energy interaction between the microgrid and the distribution network,promote the local consumption of wind power,this paper constructs an energy transaction mode for multi-microgrid and distribution network that considers wind power uncertainty.The optimization goal of this mode is to maximize the revenue of the system operation entity and minimize the total cost of power consumption of the multi-microgrid. This mode integrates the load and wind power output in the area through operation entity,and divides the internal peak-to-valley time of the system to improve the utilization of wind power. In order to enhance the ability to cope with the uncertainty of wind power,the transaction mode uses the load transfer rate to describe the user response of TOU prices and establish a model for the TOU price of regional multi-microgrid systems considering wind power uncertainty. Finally,the multi-objective optimization problem in the model is solved by the NSGA-II algorithm combined with the Monte Carlo method. The example proves that the proposed transaction mode is beneficial to reduce the cost of electricity used in microgrids,improve the overall benefits of the multi-microgrid,and realize the friendly interaction between the multi-microgrid system and the distribution network.
引文
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[16]李春燕,陈骁,张鹏,等.计及风电功率预测误差的需求响应多时间尺度优化调度[J].电网技术,2018,42(2):487-494.LI Chunyan,CHEN Xiao,ZHANG Peng,et al.Multi-timescaledemand response dispatch considering wind power forecast error[J].Power System Technology,2018,42(2):487-494.
[17]阮文骏,王蓓蓓,李扬,等.峰谷分时电价下的用户响应行为研究[J].电网技术,2012,36(7):86-93.RUAN Wenjun,WANG Beibei,LI Yang,et al.Customer response behavior in time-of-use price[J].Power System Technology,2012,36(7):86-93.
[18]张涛,章佳莹,王凌云,等.计及用户电价响应的微网和配电网联合调度运行模型[J].电力自动化设备,2018,38(5):177-183.ZHANG Tao,ZHANG Jiaying,WANG Lingyun,et al.Joint dispatch operation model of microgrid and distribution network considering user response to electricity price[J].Electric Power Automation Equipment,2018,38(5):177-183.
[19]程瑜,翟娜娜.基于用户响应的分时电价时段划分[J].电力系统自动化,2012,36(9):42-46,53.CHENG Yu,ZHAI Nana.Electricity price peak and valley periods division based on customer response[J].Automation of Electric Power Systems,2012,36(9):42-46,53.
[2]王守相,吴志佳,袁霜晨,等.区域多微网系统的多目标优化调度方法[J].电力系统及其自动化学报,2017,29(5):14-20.WANG Shouxiang,WU Zhijia,YUAN Shuangchen,et al.Method of multi-objective optimal dispatching for regional multi-microgrid system[J].Proceedings of the CSU-EPSA,2017,29(5):14-20.
[3]杨苹,宋嗣博,张育嘉,等.工业园区多微网特征分析及工程设计[J].电力系统及其自动化学报,2017,29(12):21-27.YANG Ping,SONG Sibo,ZHANG Yujia,et al.Feature analysis and engineering design of multi-microgrid in an industrial park[J].Proceedings of the CSU-EPSA,2017,29(12):21-27.
[4]张释中,裴玮,杨艳红,等.基于柔性直流互联的多微网集成聚合运行优化及分析[J].电工技术学报,2019,34(5):1025-1037.ZHANG Shizhong,PEI Wei,YANG Yanhong,et al.Optimization and analysis of multi-microgrids integration and aggregation operation based on flexible DC interconnection[J].Transactions of China Electrotechnical Society,2019,34(5):1025-1037.
[5]宁阳天,李相俊,麻秀范,等.基于改进粒子群算法的微网动态经济调度算法[J].电力建设,2014,35(6):26-30.NING Yangtian,LI Xiangjun,MA Xiufan,et al.Microgriddynamic economic dispatch based on improved particle swarm optimization[J].Electric Power Construction,2014,35(6):26-30.
[6]狄开丽,李鹏,华浩瑞.计及碳排放成本的交直流混合微网优化运行[J].电力建设,2016,37(7):12-19.DI Kaili,LI Peng,HUA Haorui.Optimaloperation of AC-DChybrid microgrid considering carbon emission cost[J].Electric Power Construction,2016,37(7):12-19.
[7]LI H,ESEYE A T,ZHANG J,et al.Optimal energy management for industrial microgrids with high-penetration renewables[J].Protection and Control of Modern Power Systems,2017,2(1):12.
[8]米师农,张建成,郭伟.一种市场环境下的电网友好型多微网优化调度方法[J].电力建设,2018,39(6):117-124.MI Shinong,ZHANG Jiancheng,GUO Wei.Grid-friendlyoptimal scheduling method for multi-microgrid system in electricity marketing environment[J].Electric Power Construction,2018,39(6):117-124.
[9]王守相,吴志佳,庄剑.考虑微网间功率交互和微源出力协调的冷热电联供型区域多微网优化调度模型[J].中国电机工程学报,2017,37(24):7185-7194.WANG Shouxiang,WU Zhijia,ZHUANG Jian.Optimal dispatching model of CCHP type regional multi-microgrids considering interactivepower exchange among microgrids and output coordination among micro-sources[J].Proceedings of the CSEE,2017,37(24):7185-7194.
[10]FADLULLAH Z M,KATO N.Evolution of Smart Grids[M].Switzerland:Springer,2015:39-51.
[11]刘敦楠,徐尔丰,许小峰.面向园区微网的“源-网-荷-储”一体化运营模式[J].电网技术,2018,42(3):681-689.LIU Dunnan,XU Erfeng,XU Xiaofeng.“Source-network-loadstorage”integrated operation model for microgrid in park[J].Power System Technology,2018,42(3):681-689.
[12]张军,任豪,刘廷章.基于改进NSGA-Ⅱ算法的微电网多目标优化研究[J/OL].计算机应用研究,2019,36(12)1-6[2019-04-26].http://kns.cnki.net/kcms/detail/51.1196.TP.20181011.1313.046.html.ZHANG Jun,REN Hao,LIU Tingzhang.Research on multiobjective optimization of micro-grid based on improved NSGA-IIalgorithm[J/OL].Application Research of Computers,2019,36(12)1-6[2019-04-26].http://kns.cnki.net/kcms/detail/51.1196.TP.20181011.1313.046.html.
[13]刘敦楠,唐天琦,杨建华,等.面向能源互联网的微平衡调度交易设计[J].电力系统自动化,2017,41(10):1-8,36.LIU Dunnan,TANG Tianqi,YANG Jianhua,et al.Autonomous decentralized supplementary damping control based on port energy[J].Automation of Electric Power Systems,2017,41(10):1-8,36.
[14]向月,刘俊勇,魏震波,等.考虑可再生能源出力不确定性的微电网能量优化鲁棒模型[J].中国电机工程学报,2014,34(19):3063-3072.XIANG Yue,LIU Junyong,WEI Zhenbo,et al.Robust model of microgrid energy optimization with uncertain renewable energy sources[J].Proceedings of the CSEE,2014,34(19):3063-3072.
[15]温俊强,曾博,张建华.配电网中分布式风电可调鲁棒优化规划[J].电网技术,2016,40(1):227-233.WEN Junqiang,ZENG Bo,ZHANG Jianhua.Adjustable robustoptimization for distributed wind power planning in distributionnetwork[J].Power System Technology,2016,40(1):227-233.
[16]李春燕,陈骁,张鹏,等.计及风电功率预测误差的需求响应多时间尺度优化调度[J].电网技术,2018,42(2):487-494.LI Chunyan,CHEN Xiao,ZHANG Peng,et al.Multi-timescaledemand response dispatch considering wind power forecast error[J].Power System Technology,2018,42(2):487-494.
[17]阮文骏,王蓓蓓,李扬,等.峰谷分时电价下的用户响应行为研究[J].电网技术,2012,36(7):86-93.RUAN Wenjun,WANG Beibei,LI Yang,et al.Customer response behavior in time-of-use price[J].Power System Technology,2012,36(7):86-93.
[18]张涛,章佳莹,王凌云,等.计及用户电价响应的微网和配电网联合调度运行模型[J].电力自动化设备,2018,38(5):177-183.ZHANG Tao,ZHANG Jiaying,WANG Lingyun,et al.Joint dispatch operation model of microgrid and distribution network considering user response to electricity price[J].Electric Power Automation Equipment,2018,38(5):177-183.
[19]程瑜,翟娜娜.基于用户响应的分时电价时段划分[J].电力系统自动化,2012,36(9):42-46,53.CHENG Yu,ZHAI Nana.Electricity price peak and valley periods division based on customer response[J].Automation of Electric Power Systems,2012,36(9):42-46,53.