Optimal Meeting Scheduling in Smart Commercial Building for Energy Cost Reduction

被引:23
作者
Chai, Bo [1 ]
Costa, Alberto [2 ,3 ]
Ahipasaoglu, Selin Damla [4 ]
Yuen, Chau [4 ]
Yang, Zaiyue [5 ]
机构
[1] State Grid Corp China, Global Energy Interconnect Res Inst, Beijing 102209, Peoples R China
[2] Natl Univ Singapore, Singapore, Singapore
[3] Swiss Fed Inst Technol, Future Resilient Syst, Singapore, Singapore
[4] Singapore Univ Technol & Design, Singapore 138682, Singapore
[5] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China
基金
新加坡国家研究基金会;
关键词
Smart grid; demand response management; meeting scheduling; mixed-integer linear programming; score ranking; AIR-CONDITIONING SYSTEM; DEMAND RESPONSE; MANAGEMENT; SIMULATION;
D O I
10.1109/TSG.2016.2625313
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we consider the optimal meeting scheduling problem in a commercial building over a fixed period of time, with the objectives of minimizing the cost of energy consumption by the air-conditioning system and possibly achieving more balanced power distribution. By considering a set of realistic factors, including the eligible time slots of attendees and energy consumption characteristics of meeting rooms, this problem is formulated as a constrained mixed-integer linear program, which then can be solved by an optimization solver, e.g., CPLEX. However, because the computation complexity increases dramatically with the problem size, a fast heuristic algorithm is proposed. The numerical simulations verify that the heuristic algorithm produces a near-optimal result.
引用
收藏
页码:3060 / 3069
页数:10
相关论文
共 31 条
[1]  
[Anonymous], 2013, ILOG CPLEX 12 6 US M
[2]  
ASHRAE-Shaping Tomorrow's Built Environment Today, 30 PERC AEDG FREE DO
[3]   Variable refrigerant flow systems: A review [J].
Aynur, Tolga N. .
ENERGY AND BUILDINGS, 2010, 42 (07) :1106-1112
[4]  
Chai B, 2014, INT CONF SMART GRID, P764, DOI 10.1109/SmartGridComm.2014.7007740
[5]   Iterative learning for optimal residential load scheduling in smart grid [J].
Chai, Bo ;
Yang, Zaiyue ;
Gao, Kunlun ;
Zhao, Ting .
AD HOC NETWORKS, 2016, 41 :99-111
[6]   Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach [J].
Chai, Bo ;
Chen, Jiming ;
Yang, Zaiyue ;
Zhang, Yan .
IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (02) :722-731
[7]   A Distributed Algorithm of Appliance Scheduling for Home Energy Management System [J].
Chavali, Phani ;
Yang, Peng ;
Nehorai, Arye .
IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (01) :282-290
[8]  
Chen F., 2014, J ENERG ENG-ASCE, V141
[9]  
Cheng Z, 2013, INT CONF SMART GRID, P797, DOI 10.1109/SmartGridComm.2013.6688057
[10]   A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches [J].
Deng, Ruilong ;
Yang, Zaiyue ;
Chow, Mo-Yuen ;
Chen, Jiming .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2015, 11 (03) :570-582