Determining the optimal setting of voltage regulators for day-ahead management of distribution smart systems

Environmental pollution and greenhouse gas emissions, as well as oil depletion are prime motivators for the development and adoption of renewable power sources. However, the traditional operating philosophy of power systems and the random nature of these sources represent an important barrier for their massive deployment. Fluctuations of wind and solar power generation integrated as distributed sources can induce important variations on the voltage profile, leading to values out of the range typically suggested by power quality standards. To deal with this problem, in this paper the optimal setting of voltage regulators (VRs) at each hour has been optimally determined by implementing a genetic algorithm with integer codification, so that it can be effectively integrated with the load flow methodologies currently available in the literature without requiring any linearization process. Results obtained from the analysis of a case study reveal the behavior of the optimal VR settings and reactive power compensation on a daily basis, which are highly correlated with the daily load profile.

[1]  J. O. Petinrin,et al.  Impact of renewable generation on voltage control in distribution systems , 2016 .

[2]  Martin Braun,et al.  Local voltage control strategies for PV storage systems in distribution grids , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[3]  Ahmad Zahedi,et al.  Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation , 2016 .

[4]  Rodolfo Dufo-López,et al.  Design and control strategies of PV-Diesel systems using genetic algorithms , 2005 .

[5]  Gerard Ledwich,et al.  Coordinated Control of Grid-Connected Photovoltaic Reactive Power and Battery Energy Storage Systems to Improve the Voltage Profile of a Residential Distribution Feeder , 2014, IEEE Transactions on Industrial Informatics.

[6]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[7]  Peiyuan Chen,et al.  On Coordinated Control of OLTC and Reactive Power Compensation for Voltage Regulation in Distribution Systems With Wind Power , 2016, IEEE Transactions on Power Systems.

[8]  J. Teng A direct approach for distribution system load flow solutions , 2003 .

[9]  D. Das,et al.  Simple and efficient method for load flow solution of radial distribution networks , 1995 .

[10]  W.H. Kersting Distribution Feeder Voltage Regulation Control , 2009, IEEE Transactions on Industry Applications.

[11]  Christoforos N. Hadjicostis,et al.  A Two-Stage Distributed Architecture for Voltage Control in Power Distribution Systems , 2013, IEEE Transactions on Power Systems.

[12]  I.N. da Silva,et al.  Real-Time Voltage Regulation in Power Distribution System Using Fuzzy Control , 2010, IEEE Transactions on Power Delivery.

[13]  Tomonobu Senjyu,et al.  Optimal Voltage Control Using Inverters Interfaced With PV Systems Considering Forecast Error in a Distribution System , 2014, IEEE Transactions on Sustainable Energy.

[14]  Taskin Koçak,et al.  Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.

[15]  V. Calderaro,et al.  Optimal Decentralized Voltage Control for Distribution Systems With Inverter-Based Distributed Generators , 2014, IEEE Transactions on Power Systems.

[16]  Ehab F. El-Saadany,et al.  A Novel Cooperative Protocol for Distributed Voltage Control in Active Distribution Systems , 2013, IEEE Transactions on Power Systems.

[17]  Jen-Hao Teng,et al.  Modelling distributed generations in three-phase distribution load flow , 2008 .

[18]  K. T. Tan,et al.  Coordinated Control of Distributed Energy-Storage Systems for Voltage Regulation in Distribution Networks , 2016, IEEE Transactions on Power Delivery.

[19]  K. M. Muttaqi,et al.  Online Voltage Control in Distribution Systems With Multiple Voltage Regulating Devices , 2014, IEEE Transactions on Sustainable Energy.

[20]  İnci Okumuş,et al.  Current status of wind energy forecasting and a hybrid method for hourly predictions , 2016 .