[1]BAO Yi,LOU Fengdan,WANG Wanliang.Multiobjective optimization control of intelligent household electricity with demand management[J].CAAI Transactions on Intelligent Systems,2018,13(1):125-130.[doi:10.11992/tis.201705030]
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Multiobjective optimization control of intelligent household electricity with demand management

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 1 Page number: 125-130 Column: 学术论文—智能系统 Public date: 2018-01-24
Title:
Multiobjective optimization control of intelligent household electricity with demand management
Author(s):
BAO Yi1;  LOU Fengdan2;  WANG Wanliang3
1. Hangzhou TianLi Electronic Technology co., LTD, Hangzhou 310051, China;
2. State Network Zhejiang Electric Power Corporation Information Communications Branch, Hangzhou 3100073, China;
3. School of Computer Science and Technology, Zhejiang Univer
Keywords:
demand management; smart grid; multi-objective decision; optimal control; dragonfly algorithm; household electricity; intelligent control; load classification
CLC:
TP18
DOI:
10.11992/tis.201705030
Abstract:
In this paper, we propose a home electricity control system with a smart grid for managing the demand of home appliances and to ease the peak-time grid pressure. We designed an intelligent controller that can obtain user power information and provide users with time-sharing electricity metering, while also being convenient for suppliers to apply the demand management system. To reduce the load power and demand-response delay time, we propose a multi-objective optimization technique. Its convergence rate is rapid and it can satisfy the immediate response requirement. The results for 500 families taking part in the experiment show that the proposed household electricity control system is reasonable, reduces user electricity costs, and reduces the response time delay due to its fast calculation speed, thereby effectively alleviating the peak time of the power grid.
References:
[1] 中国电力企业联合会规划发展部. 2016年电力供需形势分析预测与建议[J]. 中国电力企业管理, 2016(4): 27-29.
China federation of electric power enterprise planning and development. 2016 Analysis and forecast of electricity supply demand and the suggestion[J]. China electric power enterprise management, 2016(4): 27-29.
[2] SIANO P. Demand response and smart grids—a survey[J]. Renewable and sustainable energy reviews, 2014, 30: 461-478.
[3] 薛晨, 黎灿兵, 曹一家, 等. 智能电网中的电网友好技术概述及展望[J]. 电力系统自动化, 2011, 35(15): 102-107.
XUE Chen, LI Canbing, CAO Yijia, et al. An overview and prospects of grid friendly technology in smart grid[J]. Automation of electric power systems, 2011, 35(15): 102-107.
[4] GUNGOR V C, SAHIN D, KOCAK T, et al. Smart grid technologies: communication technologies and standards[J]. IEEE transactions on industrial informatics, 2011, 7(4): 529-539.
[5] GKATZIKIS L, KOUTSOPOULOS I, SALONIDIS T. The role of aggregators in smart grid demand response markets [J]. IEEE journal on selected areas in communications, 2013, 31(7): 1247-1257.
[6] IWAYEMI A, YI Peizhong, DONG Xihua, et al. Knowing when to act: an optimal stopping method for smart grid demand response[J]. IEEE network, 2011, 25(5): 44-49.
[7] 刘壮志, 许柏婷, 牛东晓. 智能电网需求响应与均衡分析发展趋势[J]. 电网技术, 2013, 37(6): 1555-1561.
LIU Zhuangzhi, XU Baiting, NIU Dongxiao. Development tendency of equilibrium analysis and demand response for smart grid[J]. Power system technology, 2013, 37(6): 1555-1561.
[8] 王丹, 范孟华, 贾宏杰. 考虑用户舒适约束的家居温控负荷需求响应和能效电厂建模[J]. 中国电机工程学报, 2014, 34(13): 2071-2077.
WANG Dan, FAN Menghua, JIA Hongjie. User comfort constraint demand response for residential thermostatically-controlled loads and efficient power plant modeling[J]. Proceedings of the CSEE, 2014, 34(13): 2071-2077.
[9] SAELE H, GRANDE O S. Demand response from household customers: experiences from a pilot study in norway[J]. IEEE transactions on smart grid, 2011, 2(1): 102-109.
[10] 陆苏青, 唐楠, 王蓓蓓, 等. 美国需求响应技术和思考(下)[J]. 电力需求侧管理, 2016(1): 60-64.
LU Suqing, TANG Nan, WANG Beibei, et al. Demand response technology and thinking in America[J]. Power demand side management, 2016(1): 60-64.
[11] NORRIS S, PENDAKUR K. Imputing rent in consumption measures, with an application to consumption poverty in Canada, 1997-2009[J]. Canadian journal of economics/revue canadienne d’économique, 2013, 46(4): 1537-1570.
[12] CONSTANTOPOULOS P, SCHWEPPE F C, LARSON R C. Estia: a real-time consumer control scheme for space conditioning usage under spot electricity pricing[J]. Computers and operations research, 1991, 18(8): 751-765.
[13] LEE J, PARK G L, KIM S W, et al. Power consumption scheduling for peak load reduction in smart grid homes[C]//Proceedings of 2011 ACM Symposium on Applied Computing. TaiChung, China, 2011: 584-588.
[14] NASIRAGHDAM H, JADID S. Load model effect assessment on optimal distributed generation (DG) sizing and allocation using improved harmony search algorithm[C]//Proceedings of 2013 Smart Grid Conference. Tehran, Iran, 2013: 210-218.
[15] 张延宇, 曾鹏, 李忠文, 等. 智能电网环境下空调系统多目标优化控制算法[J]. 电网技术, 2014, 38(7): 1819-1826.
ZHANG Yanyu, ZENG Peng, LI Zhongwen, et al. A multi-objective optimal control algorithm for air Conditioning system in smart grid[J]. Power system technology, 2014, 38(7): 1819-1826.
[16] XIONG Gang, CHEN Chen, KISHORE S, et al. Smart (in-home) power scheduling for demand response on the smart grid[C]//Proceedings of 2011 IEEE PES Innovative Smart Grid Technologies. Anaheim, CA, USA, 2011: 1-7.
[17] MOHSENIAN-RAD A H, WONG V W S, JATSKEVICH J, et al. Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid[C]//Proceedings of 2010 Innovative Smart Grid Technologies. Gaithersburg, MD, USA, 2010: 1-6.
[18] BASU B, MAHANTI G K. Thinning of concentric two-ring circular array antenna using fire fly algorithm[J]. Scientia iranica, 2012, 19(6): 1802-1809.
[19] REYNOLDS C W. Flocks, herds and schools: a distributed behavioral model[C]//Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques. New York, NY, USA, 1987: 25-34.
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