New analytical method for analysing the effectiveness of infrastructure reinforcement in electric power distribution systems

Abstract Technical problems are emerging in electric power distribution systems caused by the recent widespread deployment of new technologies of load, as the plug-in electric vehicles, and generation, as the photovoltaic generators. This fact has increased the volume of studies to evaluate the effectiveness of solutions based on infrastructure reinforcement. In this context, this work proposes an analytical method to update values of voltage magnitude in distribution systems when transformers and/or conductors are replaced. This analytical formulation can be implemented in spreadsheets for single-case analyses or it can be integrated in more-complex approaches, which demand multiple solutions, such as in optimization-based or Monte Carlo-based approaches. For the more-complex approaches the main objective is to speed up the process, as it is not necessary to run several power flows. In this work, the analytical formulation is integrated into a Monte Carlo-based process with two objectives: (a) to supply a wide-scale assessment of reinforcements to increase the electric vehicles hosting capacity in low voltage distribution systems analysing 25,000 real LV systems of a local utility; (b) to supply a systematic and wide validation of the analytical formulation. The results showed that it speeds up the process keeping relatively high accuracy.

[1]  Michael Pertl,et al.  Validation of a robust neural real-time voltage estimator for active distribution grids on field data , 2018 .

[2]  M. Gray,et al.  On the impact of single-phase plug-in electric vehicles charging and rooftop solar photovoltaic on distribution transformer aging , 2017 .

[3]  Jovica V. Milanović,et al.  Voltage Sag Estimation in Sparsely Monitored Power Systems Based on Deep Learning and System Area Mapping , 2018, IEEE Transactions on Power Delivery.

[4]  Matthew J. Reno,et al.  Algorithmic Aspects of a Commercial-Grade Distribution System Load Flow Engine , 2017 .

[5]  Sadjad Galvani,et al.  Optimal power flow considering predictability of power systems , 2019 .

[6]  Walmir Freitas,et al.  A Monte Carlo Simulation Platform for Studying Low Voltage Residential Networks , 2014, IEEE Transactions on Smart Grid.

[7]  M. Z. C. Wanik,et al.  Simplified Approach to Analyze Voltage Rise in LV Systems With PV Installations Using Equivalent Power Systems Diagrams , 2017, IEEE Transactions on Power Delivery.

[8]  F. Shahnia,et al.  Distribution transformer loading in unbalanced three-phase residential networks with random charging of plug-in electric vehicles , 2012, 2012 22nd Australasian Universities Power Engineering Conference (AUPEC).

[9]  Surya Santoso,et al.  Electric Vehicle Charging on Residential Distribution Systems: Impacts and Mitigations , 2015, IEEE Access.

[10]  L.A. Kojovic,et al.  Summary of Distributed Resources Impact on Power Delivery Systems , 2008, IEEE Transactions on Power Delivery.

[11]  Paul D. H. Hines,et al.  Modeling the impact of electric vehicle charging on heat transfer around underground cables , 2013 .

[12]  Luis F. Ochoa,et al.  Control of EV Charging Points for Thermal and Voltage Management of LV Networks , 2016, IEEE Transactions on Power Systems.

[13]  Matthew J. Reno,et al.  Multiphase Distribution Feeder Reduction , 2018, IEEE Transactions on Power Systems.

[14]  John Edisson Cardona,et al.  Decentralized electric vehicles charging coordination using only local voltage magnitude measurements , 2018 .

[15]  Matthew J. Reno,et al.  Fast QSTS for distributed PV impact studies using vector quantization and variable time-steps , 2018, 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[16]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[17]  Nirmal-Kumar C. Nair,et al.  Voltage control in distribution networks with penetration of solar PV: Estimated voltages as a control input , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[18]  José Nuno Fidalgo,et al.  The worth of network upgrade deferral in distribution systems – Truism or myth? , 2016 .

[19]  Rajit Gadh,et al.  Robust pseudo-measurement modeling for three-phase distribution systems state estimation , 2020 .

[20]  Roy Billinton,et al.  Monte Carlo simulation applied to distribution feeder reliability evaluation , 1994 .

[21]  Takeshi Kaneko,et al.  Estimation method for distribution network voltage utilizing smart meter measurements , 2014, 2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[22]  J. C. Cebrian,et al.  Investigation of infrastructural solutions to mitigate the impacts of EV recharging at LV networks , 2017, 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America).

[23]  Matthew J. Reno,et al.  Decision tree ensemble machine learning for rapid QSTS simulations , 2018, 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[24]  Marko Vukobratović,et al.  The evolutionary optimization approach for voltage profile estimation in a radial distribution network with a decreased number of measurements , 2017, 2017 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA).

[25]  Xavier Zambrano,et al.  Estimation of Voltage Sags From a Limited Set of Monitors in Power Systems , 2017, IEEE Transactions on Power Delivery.

[26]  Thomas H. Bradley,et al.  The Efficacy of Electric Vehicle Time-of-Use Rates in Guiding Plug-in Hybrid Electric Vehicle Charging Behavior , 2012, IEEE Transactions on Smart Grid.

[27]  Trung Vu A Stochastic Methodology to Determine Reinforcement Cost of Power Distribution Grid for Integrating Increasing Share of Renewable Energies and Electric Vehicles , 2018, 2018 15th International Conference on the European Energy Market (EEM).

[28]  Luis F. Ochoa,et al.  A statistical analysis of EV charging behavior in the UK , 2015, 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM).