基于用户侧需求响应的互联微电网研究
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摘要
微电网是利用分布式能源尤其是可再生能源的有效途径之一。风能和太阳能较高的波动性使微电网难以保持发电侧和需求侧的功率平衡,直接导致微电网较高的运行成本和较差的用户满意度。针对供需侧不平衡的问题,提出了结合单微电网电能管理和多微电网协同优化的解决策略。首先,单个微电网通过采取动态电价策略,引导用户从负荷侧改变电能曲线,缓解微电网自身电能短缺状况,微电网运营商根据采用动态电价后电能短缺值从外部购电。其次,通过互联协同使临近的微电网相互补充,根据各个微电网的动态电价进行最优购电。动态电价与微电网互联二者相互关联、互相促进,实现互联微电网系统的效能优化。仿真结果表明,基于单微电网的动态电价管理策略与多微电网的互联优化策略可以有效的缓解供需侧不平衡问题,同时用户参与度越大,优化效果越好,微电网的运营成本在采用优化策略之后得到了节省,提高了系统总体效能。
Micro-grids are one of the most effective ways in which renewable resources are utilized. The fluctuations of wind power and solar power make it hard to maintain the balance between generation and demand,which rises operating cost and bad experiences of users. To solve this problem,we propose a strategy which combines the micro-grid interconnections and dynamic pricing. Firstly,the interconnected micro-grids are spare to each other. Secondly,spot price can make people who are sensitive to the energy price change their power use. And the interconnection and dynamic pricing correlates to each other towards a better result. This strategy will change the micro-grids to a new equilibrium point,and operators of micro-grids will decide which direction to buy or sell to maximize its own benefits based on new equilibriums.Simulation results show that the combination of interconnection and spot pricing can relieve the stress of matching problem and the more positive users are in demand response,the better optimization results will be.
Micro-grids are one of the most effective ways in which renewable resources are utilized. The fluctuations of wind power and solar power make it hard to maintain the balance between generation and demand,which rises operating cost and bad experiences of users. To solve this problem,we propose a strategy which combines the micro-grid interconnections and dynamic pricing. Firstly,the interconnected micro-grids are spare to each other. Secondly,spot price can make people who are sensitive to the energy price change their power use. And the interconnection and dynamic pricing correlates to each other towards a better result. This strategy will change the micro-grids to a new equilibrium point,and operators of micro-grids will decide which direction to buy or sell to maximize its own benefits based on new equilibriums.Simulation results show that the combination of interconnection and spot pricing can relieve the stress of matching problem and the more positive users are in demand response,the better optimization results will be.
引文
[1] A. Abaza,Ahmed M. Azmy. Dynamic pricing for demand-side management within smart grid environment[C]. 2016 IEEE International Conference on Power and Energy(PECon)28-29 Nov. 2016:680-686.
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[9] Wei Yu Chiu,Hongjian Sun,H. Vincent Poor. A Multi-objective Approach to Multi-Microgrid System Design[J]. IEEE Transactions on Smart Grid,2015 Vol. 6,No. 5.
[10]刘振亚.全球能源互联网.北京:中国电力出版社,2015.
[11]L. Gelazanskas,K. A. A. Gamage. Neural Network Based Real-Time Pricing in Demand Side Management for Future Smart Grid[C]. International Conference on Power Electronics Machines and Drives. pp. 1-5,2014.
[12]曹昉,韩放,杨以涵.实时电价的意义与实现[J].电网技术,1995,19(10):56-59.Cao Fang,Han Fang,Yang Yihan. The Significance and Implementation to Spot Price[J]. Power System Technology,1995,19(10):56-59.
[13]L. Gelazanskas,K. A. A. Gamage. Neural Network Based Real-Time Pricing in Demand Side Management for Future Smart Grid[C]. International Conference on Power Electronics Machines and Drives. pp. 1-5 2014.
[14]Dan Chen,Xingying Chen,Jixiang Wang,Kun Yun Yingchen Liao. realtime pricing of distribution network based on node load sensitivity[J].CIRED Workshop,2016:14-15 June 2016.
[15]姚建红,王天娇,梁冬原,等.基于遗传算法的微电网DSM优化模型[J].吉林大学学报(信息科学版),2016,34(3):434-440.Yao Jianhong,Wang Tianjiao,Liang Dongyuan,et al. Optimization of Micro-grid with DSM Based on Genetic Algorithm[J]. Journal of Jilin University(Information Science Edition),2016,34(3):434-440.
[2] Liang Che,Xiaping Zhang,Mohammad Shahidehpour,Ahmed Alabdulwahab,Abdullah Abusorrah. Optimal Interconnection Planning of Community Microgrids with Renewable Energy Sources[J]. IEEE Transactions on Smart Grid:1054-1063.
[3] Yu Wang,Shiwen Mao,R. M. Nelms. On Hierarchical Power Scheduling for the Macrogrid and Cooperative Microgrids[J],IEEE Transactions on Industrial Informatics,11(6),December 2015.
[4] W. Saad,Z. Han,H. V. Poor. Coalitional game theory for cooperative micro-grid distribution networks[C]. IEEE Int. Conf. on Communications Workshops(ICC)pp. 1-5,2011.
[5] C. Wei,Z. Fadlullah,N. Kato,A. Takeuchi. GT-CFS:A Game Theoretic Coalition Formulation Strategy for Reducing Power Loss[J]. IEEE Trans. Parallel and Dist. Syst. vol. 25 no. 9 pp. 2307-2317,2014.
[6] C. Wei,Z. Fadlullah,N. Kato,and I. Stojmenovic. On optimally reducing power loss in micro-grids with power storage devices[J]. IEEE J. Sel. Areas Commun.,vol. 32,no. 7,pp. 1361-1370,Jul. 2014.
[7] J. Ni Q. Ai. Economic power transaction using coalitional game strategy in micro-grids[J]. IET Generation Transmission and Distribution. vol.10 no. 1 pp. 10-18 2016.
[8] H.Dagdougui and R. Sacile. Decentralized control of the power flows in a network of smart microgrids modeled as a team of cooperative agents[J]. IEEE Trans. Control Syst. Technol.,vol. 22,no. 2,pp. 510-519,Mar. 2014.
[9] Wei Yu Chiu,Hongjian Sun,H. Vincent Poor. A Multi-objective Approach to Multi-Microgrid System Design[J]. IEEE Transactions on Smart Grid,2015 Vol. 6,No. 5.
[10]刘振亚.全球能源互联网.北京:中国电力出版社,2015.
[11]L. Gelazanskas,K. A. A. Gamage. Neural Network Based Real-Time Pricing in Demand Side Management for Future Smart Grid[C]. International Conference on Power Electronics Machines and Drives. pp. 1-5,2014.
[12]曹昉,韩放,杨以涵.实时电价的意义与实现[J].电网技术,1995,19(10):56-59.Cao Fang,Han Fang,Yang Yihan. The Significance and Implementation to Spot Price[J]. Power System Technology,1995,19(10):56-59.
[13]L. Gelazanskas,K. A. A. Gamage. Neural Network Based Real-Time Pricing in Demand Side Management for Future Smart Grid[C]. International Conference on Power Electronics Machines and Drives. pp. 1-5 2014.
[14]Dan Chen,Xingying Chen,Jixiang Wang,Kun Yun Yingchen Liao. realtime pricing of distribution network based on node load sensitivity[J].CIRED Workshop,2016:14-15 June 2016.
[15]姚建红,王天娇,梁冬原,等.基于遗传算法的微电网DSM优化模型[J].吉林大学学报(信息科学版),2016,34(3):434-440.Yao Jianhong,Wang Tianjiao,Liang Dongyuan,et al. Optimization of Micro-grid with DSM Based on Genetic Algorithm[J]. Journal of Jilin University(Information Science Edition),2016,34(3):434-440.