引用本文:郭伟嘉,刘敦楠,王文,韩金山,刘明光,苏舒,张悦,邹孟娇,杨烨.基于智能合约的电动汽车充电服务费自适应调整机制[J].电力自动化设备,2022,42(10):
GUO Weijia,LIU Dunnan,WANG Wen,HAN Jinshan,LIU Mingguang,SU Shu,ZHANG Yue,ZOU Mengjiao,YANG Ye.Adaptive adjustment mechanism of electric vehicle charging service fee based on smart contract[J].Electric Power Automation Equipment,2022,42(10):
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基于智能合约的电动汽车充电服务费自适应调整机制
郭伟嘉1, 刘敦楠1, 王文2, 韩金山1, 刘明光1, 苏舒2, 张悦1, 邹孟娇1, 杨烨2
1.华北电力大学 经济与管理学院,北京 102206;2.国网电动汽车服务有限公司,北京 100032
摘要:
为了解决传统集中式电动汽车充电服务费统一定价难以满足新型电力系统精度与灵活性需求的问题,提出了一种基于智能合约的电动汽车充电服务费自适应调整机制。通过建立考虑购售电双方收益的充电服务费优化模型,从购售电双方收益及负荷引导需求两方面对充电服务费进行优化;同时运用智能合约技术,制定更加精确、更具针对性的单座充电站的充电服务费调整策略。通过算例进行仿真验证,结果表明相较于传统定价模式,所提自适应定价模式下的负荷引导能力和交易双方的收益水平都具备显著优势。
关键词:  电动汽车  充电服务费  定价优化  智能合约  粒子群优化算法
DOI:10.16081/j.epae.202205067
分类号:U469.72
基金项目:国家自然科学基金资助项目(72171082)
Adaptive adjustment mechanism of electric vehicle charging service fee based on smart contract
GUO Weijia1, LIU Dunnan1, WANG Wen2, HAN Jinshan1, LIU Mingguang1, SU Shu2, ZHANG Yue1, ZOU Mengjiao1, YANG Ye2
1.School of Economics and Management, North China Electric Power University, Beijing 102206, China;2.State Grid Electric Vehicle Service Co.,Ltd.,Beijing 100032, China
Abstract:
In order to solve the problem that the traditional centralized unified pricing of electric vehicle charging service fee is difficult to meet the accuracy and flexibility requirements of the new-type power system, an adaptive adjustment mechanism of electric vehicle charging service fee based on smart contract is proposed. By establishing the optimization model of charging service fee considering the benefits of both buyers and sellers, the charging service fee is optimized from two aspects of the benefits of both buyers and sellers and the load guidance demand. At the same time, the smart contract technology is used to formulate a more accurate and targeted charging service fee adjustment strategy for a single charging station. The simulative results show that compared with the traditional pricing mode, the proposed adaptive pricing mode has significant advantages in load guidance ability and benefit level of both parties.
Key words:  electric vehicles  charging service fee  pricing optimization  smart contract  particle swarm optimization algorithm

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