Communication-Efficient Distributed Demand Response: A Randomized ADMM Approach

被引:46
作者
Tsai, Shin-Ching [1 ]
Tseng, Yi-Hen [1 ,2 ]
Chang, Tsung-Hui [1 ,3 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Dept Elect & Comp Engn, Taipei 10607, Taiwan
[2] Flytech, Taipei 11494, Taiwan
[3] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China
关键词
Alternating direction method of multipliers (ADMM); demand response (DR); demand-side management; distributed optimization; power balancing; DIRECT LOAD CONTROL; SIDE MANAGEMENT; ENERGY-STORAGE; OPTIMIZATION; CONSENSUS; MODEL;
D O I
10.1109/TSG.2015.2469669
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 distributed demand response (DDR) problem for achieving the real-time power balance in a neighborhood with a large number of load customers and renewable energy sources (RES). While most of the existing DDR schemes require iterative information exchange between the customers and the load aggregator through two-way communications, this paper studies the DDR schemes that rely on neighbor-wise communication between customers only. Such DDR schemes can be realized by low-cost wireless networks. To this end, we propose the use of a randomized alternating direction method of multipliers to develop a fully DDR algorithm. Notably, the proposed DDR algorithm is communication-efficient because it can yield promising power balance performance using a few times of neighbor-wise message exchanges. Moreover, the proposed DDR algorithm does not need synchronization between customers and is robust against random communication errors. For performing online demand response control, we combine the proposed DDR algorithm with the rolling-window-based model-predictive control method, and simple load and RES forecasting methods. By using real solar power data, we demonstrate via simulations that the proposed DDR algorithm improves the real-time power balance substantially and outperforms the existing DDR schemes that use the subgradient method for optimization.
引用
收藏
页码:1085 / 1095
页数:11
相关论文
共 49 条
[1]  
Alizadeh M, 2012, IEEE DECIS CONTR P, P3666, DOI 10.1109/CDC.2012.6426122
[2]   Demand-Side Management in the Smart Grid [J].
Alizadeh, Mahnoosh ;
Li, Xiao ;
Wang, Zhifang ;
Scaglione, Anna ;
Melton, Ronald .
IEEE SIGNAL PROCESSING MAGAZINE, 2012, 29 (05) :55-67
[3]   From Packet to Power Switching: Digital Direct Load Scheduling [J].
Alizadeh, Mahnoosh ;
Scaglione, Anna ;
Thomas, Robert J. .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2012, 30 (06) :1027-1036
[4]  
[Anonymous], POW EN SOC GEN M 201
[5]  
[Anonymous], 2011, CVX MATLAB SOFTWARE
[6]   Demand-Side Management via Distributed Energy Generation and Storage Optimization [J].
Atzeni, Italo ;
Ordonez, Luis G. ;
Scutari, Gesualdo ;
Palomar, Daniel P. ;
Rodriguez Fonollosa, Javier .
IEEE TRANSACTIONS ON SMART GRID, 2013, 4 (02) :866-876
[7]   Efficiency-Fairness Trade-off in Privacy-Preserving Autonomous Demand Side Management [J].
Baharlouei, Zahra ;
Hashemi, Massoud .
IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (02) :799-808
[8]  
BERTSEKAS D. P., 1989, Parallel and distributed computation: numerical methods
[9]  
Bertsekas DP, 2007, DYNAMIC PROGRAMMING, V1
[10]   Distributed optimization and statistical learning via the alternating direction method of multipliers [J].
Boyd S. ;
Parikh N. ;
Chu E. ;
Peleato B. ;
Eckstein J. .
Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122