偏差电量考核机制下含DG的售电公司多目标优化调度模型

doi:10.19595/j.cnki.1000-6753.tces.L80620

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1273 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Energy deviation penalty is a key factor that affecting the profit of electricity retailers, electricity retailers with DG can balance the energy deviation by schedulable resources, but this may cause an impact on the stability of the power grid operation. How to reduce the energy deviation as much as possible and increase the profits of the electricity retailers while improving the operation stability of system are demanding prompt solve. Taking the maximum operation profits of electricity retailers and the minimum voltage excursion as optimization objectives, a multi-objective optimization scheduling model for electricity retailers is established. In order to obtain a more reasonable strategy of day-ahead purchase and output pareto optimal solution set, an iterative solution of the model can be achieved by NSGA-II under the convergence criterion of minimum energy deviation. Finally, the simulation test is carried out on the modified IEEE 33-node distribution system under various scenarios. The results shows that the method proposed in this paper can reduce the energy deviation penalty expenditure of electricity retailers effectively, and realize the maximization of operation profits of electricity retailers while improving the operation stability of system.

Key words： Energy deviation electricitly retailers distributed generation multi-objective optimization NSGA-II

Received: 05 July 2018 Published: 13 August 2019

PACS:

TM73

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhang Tao
	Wang Cheng
	Wang Lingyun
	Wang Yalei
	Lei Shuiping

Cite this article:

Zhang Tao,Wang Cheng,Wang Lingyun等. Multi-Objective Optimal Dsipatching Model of Electricity Retailers with Distributed Generator under Energy Deviation Penalty[J]. Transactions of China Electrotechnical Society, 2019, 34(15): 3265-3274.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L80620 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I15/3265

[1] 石帮松,张靖,李博文,等.多类型售电公司共存下竞价售电的市场均衡研究[J].电力系统保护与控制,2018,46(5): 62-67.
ShiBangsong, Zhang Jing, Li Bowen, et al. Market equilibrium study on multiple types of electric power retailers bidding[J].Power System Protection and Control, 2018,46(5):62-67.
[2] 曾博, 杨煦, 张建华. 考虑可再生能源跨区域消纳的主动配电网多目标优化调度[J]. 电工技术学报, 2016, 31(22):148-158.
Zeng Bo, YangXu, ZhangJianhua. Multi-objective optimization for active distribution network scheduling considering renewable energy harvesting across regions[J]. Transactions of China Electrotechnical Society, 2016, 31(22):148-158.
[3] 丁坚勇, 张银芽, 杨东俊, 等. 基于频率偏差的跨区电网交易偏差电量责任判定及定价方法[J]. 电力系统自动化, 2017, 41(16):105-110.
DingJianyong, ZhangYinya, YangDongjun, et al. Responsibility determination and pricing method of deviation electric quantity based on frequency deviation of cross-regional grids trading[J]. Automation of Electric Power Systems, 2017, 41(16): 105-110.
[4] 赵博石, 严宇, 刘永辉, 等. 基于区域发电成本核准的跨区跨省电力交易偏差电量定价方法[J]. 电网技术, 2016, 40(11): 3334-3341.
Zhao Boshi, Yan Yu, LiuYonghui, et al. Pricing method of deviation electric quantity based on regional generation cost of cross-regional and cross-province electricity transaction[J]. Power System Technology, 2016, 40(11): 3334-3341.
[5] 郭曼兰, 陈皓勇, 张聪, 等. 偏差电量考核机制下售电公司的最优经营策略[J]. 电力系统自动化, 2017, 41(20): 17-25.
GuoManlan, Chen Haoyong, Zhang Cong, et al. Optimal marketing strategy of retailers under energy deviation penalty[J]. Automation of Electric Power Systems, 2017, 41(20): 17-25.
[6] 舒隽, 关睿, 韩冰. 工业大用户分时电价优化方法[J]. 电工技术学报, 2018, 33(7): 1552-1559.
Shu Jun, Guan Rui, Han Bing.Method of optimal time-of-use price for large industrial customers[J]. Transactions of China Electrotechnical Society, 2018, 33(7): 1552-1559.
[7] 邹鹏, 李春晖, 郭静, 等. 考虑可中断负荷的配售电公司最优购售电策略[J]. 南方电网技术, 2017, 11(2): 71-77.
ZouPeng, Li Chunhui, Guo Jing, et al. Optimal marketing strategy of distribution and retail companies considering interruptible load[J]. Southern Power System Technology, 2017, 11(2): 71-77.
[8] 任艺, 周明, 李庚银. 考虑用户需求响应的售电公司购售电决策双层模型[J]. 电力系统自动化, 2017, 41(14): 30-36.
Ren Yi, Zhou Ming, Li Gengyin.Bi-level model of electricity procurement and sale strategies for electricity retailers considering users’ demand response[J]. Automation of Electric Power Systems, 2017, 41(14): 30-36.
[9] 罗琴, 宋依群. 售电市场环境下计及可中断负荷的营销策略[J]. 电力系统自动化, 2015, 39(17): 134-139.
Luo Qin, Song Yiqun.Marketing strategy in competitive retail market considering interruptible load[J]. Automation of Electric Power Systems, 2015, 39(17): 134-139.
[10] 杨萌, 艾欣, 唐亮, 等. 计及风险规避的售电公司平衡市场优化交易策略研究[J]. 电网技术, 2016, 40(11): 3300-3308.
Yang Meng, Ai Xin, Tang Liang, et al.Optimal trading strategy in balancing market for electricity retailer considering risk aversion[J]. Power System Technology, 2016, 40(11): 3300-3308.
[11] 国务院. 国发[2002]5号,关于印发电力体制改革方案的通知[Z]. 2002.
[12] 白杨, 李昂, 夏清.新形势下电力市场营销模式与新型电价体系[J].电力系统保护与控制, 2016, 44(5): 10-16.
Bai Yang, Li Ang, Xia Qing.Electricity business marketing modes in the new environment and new electricity pricing systems[J].Power System Protection and Control, 2016, 44(5): 10-16.
[13] 宋艺航, 谭忠富, 于超, 等.需求侧峰谷分时电价对供电公司购售电风险影响分析模型[J].电工技术学报, 2010, 25(11): 183-190.
Song Yihang, Tan Zhongfu, Yu Chao, et al.Analysis model on the impact of demand-side TOU electricity price on purchasing and selling risk for power supply company[J].Transactions of China Electrotechnical Society, 2010, 25(11): 183-190.
[14] Wan Can, Xu Zhao, Pinson P, et al.Probabilistic forecasting of wind power generation using extreme learning machine[J]. IEEE Transactions on Power Systems, 2014, 29(29): 1033-1044.
[15] 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.
[16] 尤毅, 刘东, 钟清, 等. 主动配电网优化调度策略研究[J]. 电力系统自动化, 2014, 38(9): 177-183.
You Yi, Liu Dong, Zhong Qing, et al.Research on optimal dispatch strategy of active distribution network[J]. Automation of Electric Power Systems, 2014, 38(9): 177-183.
[17] 王豹, 徐箭, 孙元章, 等. 基于通用分布的含风电电力系统随机动态经济调度[J]. 电力系统自动化, 2016, 40(6): 17-24.
WangBao, XuJian, SunYuanzhang, et al. Stochastic dynamic economic dispatch of power systems considering wind power based on versatile probability distribution[J]. Automation of Electric Power Systems, 2016, 40(6): 17-24.
[18] 王先齐, 吕智林, 汤泽琦.基于分时电价机制的并网型微网多目标动态优化调度[J].电力系统保护与控制, 2017, 45(4): 9-18.
WangXianqi, LüZhilin, TangZeqi.Multi-objective dynamic optimal scheduling of grid-connected microgrid based on time-of-use price mechanism[J].Power System Protection and Control, 2017, 45(4): 9-18.
[19] 郭红霞, 白浩, 刘磊, 等.统一电能交易市场下的虚拟电厂优化调度模型[J].电工技术学报, 2015, 30(23):136-145.
GuoHongxia, BaiHao, Liu Lei, et al.Optimal scheduling model of virtual power plant in a unified electricity trading market[J].Transactions of China Electrotechnical Society, 2015, 30(23): 136-145.
[20] 肖浩, 裴玮, 孔力. 含大规模电动汽车接入的主动配电网多目标优化调度方法[J]. 电工技术学报, 2017, 32(2): 179-189.
Xiao Hao, Pei Wei, Kong Li.Multi-objective optimization scheduling method for active distribution network with large scale electric vehicles[J]. Transactions of China Electrotechnical Society, 2017, 32(2): 179-189.