Title :
Voltage control in a smart distribution network using demand response
Author :
Petinrin, J.O. ; Shaaban, Mohamed
Author_Institution :
Centre of Electr. Energy Syst. (CEES), Univ. Teknol. Malaysia, Johor Bahru, Malaysia
Abstract :
Increasing demand on the conventional grid coupled with the unwillingness to add new transmission facilities, constitute a potential threat that can ultimately sprawl to jeopardize the grid´s reliability. Demand response (DR) is a potent smart grid technology that can take care of that perceived threat, instead of constructing more power plants to meet the increasing demand. DR provides electricity consumers with opportunities to manage their electricity usage for the purpose of reducing their electricity bills and alleviating the power peak-average-ratio. A Genetic Algorithm (GA) based-optimization approach is developed in this paper to consider the optimum scheduling of energy utilization for consumers, participating in the DR program, to reduce voltage deviations and feeder losses. The IEEE 123 test feeder is considered as the test system. Effectiveness of the proposed method is validated through a time sequence analysis over a 24-hourly simulation period. The corresponding voltage profile is analyzed under different operating conditions, with a high penetration level of wind energy. Test results show that the DR tool causes reduction in system losses and enhances system capability to maintain voltages within the permissible limits.
Keywords :
demand side management; distribution networks; genetic algorithms; power generation scheduling; smart power grids; testing; voltage control; DR; GA based-optimization approach; IEEE 123 test feeder; demand response; electricity bill reduction; electricity usage management; energy utilization scheduling; feeder loss; genetic algorithm; grid reliability; power peak-average-ratio; smart distribution network; smart grid technology; time sequence analysis; voltage control; voltage deviation reduction; voltage profile; wind energy; Electricity; Genetic algorithms; Load management; Optimization; Pricing; Smart grids; Voltage control; Demand response; genetic algorithm; renewable generation; smart grid; voltage control;
Conference_Titel :
Power and Energy (PECon), 2014 IEEE International Conference on
Print_ISBN :
978-1-4799-7296-8
DOI :
10.1109/PECON.2014.7062464