A novel Volt-VAR Optimization engine for smart distribution networks utilizing Vehicle to Grid dispatch

• Published in: International journal of electrical power & energy systems Vol. 74; pp. 238 - 251
• Main Authors: Manbachi, Moein, Farhangi, Hassan, Palizban, Ali, Arzanpour, Siamak
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2016
• Subjects: Distribution network; Electric vehicle; Power loss; Smart grid; Vehicle to Grid; Volt-VAR Optimization; Power loss; Volt-VAR Optimization; Electric vehicle; Distribution network; Smart grid; Vehicle to Grid
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2015.07.030

•A novel Volt-VAR Optimization (VVO) engine proposed using Vehicle to Grid dispatch.•Quasi Real-time Advanced Metering Infrastructure data applied as VVO inputs.•Impacts of Electric Vehicles on Smart Grid-based VVO engine fully studied.•How a typical VVO engine could benefit from V2G’s reactive power support demonstrated.•To test the accuracy of proposed VVO, IEEE-123 feeder employed in presence of various EV and load types. In recent years, Smart Grid technologies such as Advanced Metering, Pervasive Control, Automation and Distribution Management have created numerous control and optimization opportunities and challenges for smart distribution networks. Availability of Co-Gen loads and/or Electric Vehicles (EVs) enable these technologies to inject reactive power into the grid by changing their inverter’s operating mode without considerable impact on their active power operation. This feature has created considerable opportunity for distribution network planners to explore if EVs could be used in the distribution network as reliable VAR suppliers. It may be possible for network operators to employ some EVs as VAR suppliers for future distribution grids. This paper proposes an innovative Smart Grid-based Volt-VAR Optimization (VVO) engine, capable of minimizing system power loss cost as well as the operating cost of switched Capacitor Banks, while optimizing the system voltage using an improved Genetic Algorithm (GA) with two levels of mutation and two levels of crossover. The paper studies the impact of EVs with different charging and penetration levels on VVO in different operating scenarios. Furthermore, the paper demonstrates how a typical VVO engine could benefit from V2G’s reactive power support. In order to assess V2G impacts on VVO and test the applicability of the proposed VVO, revised IEEE-123 Node Test Feeder in presence of various load types is used as case study.