DocumentCode :
6536
Title :
Stochastic DG Placement for Conservation Voltage Reduction Based on Multiple Replications Procedure
Author :
Zhaoyu Wang ; Bokan Chen ; Jianhui Wang ; Begovic, Miroslav M.
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Volume :
30
Issue :
3
fYear :
2015
fDate :
Jun-15
Firstpage :
1039
Lastpage :
1047
Abstract :
Conservation voltage reduction (CVR) and distributed-generation (DG) integration are popular strategies implemented by utilities to improve energy efficiency. This paper investigates the interactions between CVR and DG placement to minimize load consumption in distribution networks, while keeping the lowest voltage level within the predefined range. The optimal placement of DG units is formulated as a stochastic optimization problem considering the uncertainty of DG outputs and load consumptions. A sample average approximation algorithm-based technique is developed to solve the formulated problem effectively. A multiple replications procedure is developed to test the stability of the solution and calculate the confidence interval of the gap between the candidate solution and optimal solution. The proposed method has been applied to the IEEE 37-bus distribution test system with different scenarios. The numerical results indicate that the implementations of CVR and DG, if combined, can achieve significant energy savings.
Keywords :
distributed power generation; distribution networks; energy conservation; stochastic programming; IEEE 37-bus distribution test system; conservation voltage reduction; distributed-generation integration; distribution networks; energy efficiency; load consumption; multiple replications procedure; stochastic DG placement; stochastic optimization problem; Equations; Load modeling; Materials requirements planning; Mathematical model; Reactive power; Stochastic processes; Voltage control; Conservation voltage reduction (CVR); Monte Carlo sampling; distributed generation (DG); multiple replications procedure (MRP); sample average approximation (SAA); stochastic programming (SP);
fLanguage :
English
Journal_Title :
Power Delivery, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8977
Type :
jour
DOI :
10.1109/TPWRD.2014.2331275
Filename :
7072569
Link To Document :
بازگشت