Blockchain-enabled Power Usage Quotas Allocation Method for Air Conditioning Loads

被引：0

作者：

Jia Q. ^{[1
]}

Chen S. ^{[1
]}

Yan Z. ^{[1
]}

Ping J. ^{[1
]}

机构：

[1] Key Laboratory of Control of Power Transmission and Conversion, Shanghai Jiao Tong University, Ministry of Education, Minhang District, Shanghai

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2020年 / 40卷 / 11期

基金：

国家重点研发计划; 中国国家自然科学基金;

关键词：

Air conditioning load; Blockchain; Demand response; Power usage quotas;

D O I：

10.13334/j.0258-8013.pcsee.190968

中图分类号：

学科分类号：

摘要：

An air conditioning load is an important demand response resource, which can provide demand response capabilities driven by price signals and direct control. Existing price-driven mechanisms are often unable to accurately meet preset load reduction targets, whereas direct control mechanisms are usually unable to respect users' autonomy. A power usage quotas allocation mechanism for air conditioning loads was proposed, including four stages, i.e., submission, reduction, transaction, and settlement. It ensures user's autonomy while being able to achieve expected load reduction targets. In addition, this paper proposed a method to implement the mechanism based on blockchain, which ensures the transparency and security of the mechanism. Based on blockchain technology, the power company only needs to set the load control target rather than directly dispatching air conditioning load. Air conditioner users collectively dispatch their responsive loads on blockchain, reducing the burden of the power company. The simulation results on Ethereum private chain show the effectiveness of this mechanism. © 2020 Chin. Soc. for Elec. Eng.

引用

页码：3393 / 3401

页数：8

共 24 条

[1] Gao Ciwei, Li Qianyu, Li Yang, Bi-level optimal dispatch and control strategy for air-conditioning load based on direct load control, Proceedings of the CSEE, 34, 10, pp. 1546-1555, (2014)
[2] Wang Dongli, Take measures to synthesize harnessing summer air-conditioning peak load, Power DSM, 7, 4, pp. 5-8, (2005)
[3] Chen Sijie, Liu C C., From demand response to transactive energy: state of the art, Journal of Modern Power Systems and Clean Energy, 5, 1, pp. 10-19, (2017)
[4] Tian Shiming, Wang Beibei, Zhang Jing, Key technologies for demand response in smart grid, Proceedings of the CSEE, 34, 22, pp. 3576-3589, (2014)
[5] Jiang Hanyu, The pilot commercial building virtual power plant project in Huangpu District changes the future of power consumption
[6] Approval of Shanghai municipal commission of economic informatization on agreeing to carry out the pilot work of comprehensive demand response programs in Shanghai
[7] Ferreira R S, Barroso L A, Carvalho M M., Demand response models with correlated price data: a robust optimization approach, Applied Energy, 96, pp. 133-149, (2012)
[8] Chen Zhi, Wu Lei, Fu Yong, Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization, IEEE Transactions on Smart Grid, 3, 4, pp. 1822-1831, (2012)
[9] Yoon J H, Baldick R, Novoselac A., Dynamic demand response controller based on real-time retail price for residential buildings, IEEE Transactions on Smart Grid, 5, 1, pp. 121-129, (2014)
[10] Lu Ning, Zhang Yu, Design considerations of a centralized load controller using thermostatically controlled appliances for continuous regulation reserves, IEEE Transactions on Smart Grid, 4, 2, pp. 914-921, (2013)

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