@article{sun_lubkeman_2021, title={Agent-Based Modeling of Feeder-Level Electric Vehicle Diffusion for Distribution Planning}, volume={12}, url={https://doi.org/10.1109/TSG.2020.3013641}, DOI={10.1109/TSG.2020.3013641}, abstractNote={Faced with a rapidly growing electric vehicle (EV) load, distribution planners need a methodology to forecast when and where these loads will likely appear on specific distribution feeders. This paper proposes a new diffusion forecasting approach for residential EV and charging stations using agent-based modeling. Residential EV adoption is treated as a multi-criteria decision making problem modeled via analytic hierarchy process (AHP). The customer adoption model CANE is developed considering customer characteristics including car age, EV attractiveness valued using logistic regression, neighbor influences and customer economics. Distribution feeder topology is combined with property geographic information system (GIS) parcels and household travel survey data to determine geographic and electric locations for EV and charging stations. A Monte Carlo method is utilized to obtain the most likely outcome of the stochastic vehicle decision-making process and perform model calibration. The proposed diffusion forecasting approach is demonstrated using actual distribution feeder data. Using the diffusion results, EV impact on system annual peak, energy, losses, transformer aging and feeder upgrades is evaluated using quasi-static time-series power flow analysis. Case analyses are presented that examine the effect of EV price and charging station placement on EV diffusion and distribution feeder impact.}, number={1}, journal={IEEE Transactions on Smart Grid}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sun, Lisha and Lubkeman, David}, year={2021}, month={Jan}, pages={751–760} }
 @article{sun_lubkeman_2021, title={Agent-Based Modeling of Feeder-Level Electric Vehicle Diffusion for Distribution Planning}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638119}, abstractNote={Faced with a rapidly growing electric vehicle (EV) load, distribution planners need a methodology to forecast when and where these loads will likely appear on specific distribution feeders. This paper proposes a new diffusion forecasting approach for residential EV and charging stations using agent-based modeling. Residential EV adoption is treated as a multi-criteria decision making problem modeled via analytic hierarchy process (AHP). The customer adoption model CANE is developed considering customer characteristics including car age, EV attractiveness valued using logistic regression, neighbor influences and customer economics. Distribution feeder topology is combined with property geographic information system (GIS) parcels and household travel survey data to determine geographic and electric locations for EV and charging stations. A Monte Carlo method is utilized to obtain the most likely outcome of the stochastic vehicle decision-making process and perform model calibration. The proposed diffusion forecasting approach is demonstrated using actual distribution feeder data. Using the diffusion results, EV impact on system annual peak, energy, losses, transformer aging and feeder upgrades is evaluated using quasi-static time-series power flow analysis. Case analyses are presented that examine the effect of EV price and charging station placement on EV diffusion and distribution feeder impact.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Sun, Lisha and Lubkeman, David}, year={2021} }
 @inproceedings{sun_thomas_singh_li_baran_lubkeman_decarolis_queiroz_white_watts_et al._2017, title={Cost-benefit assessment challenges for a smart distribution system: A case study}, volume={2018-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85046337981&partnerID=MN8TOARS}, DOI={10.1109/pesgm.2017.8274167}, abstractNote={The FREEDM system is a technology for a smarter and resilient distribution system that facilitates a higher level of distributed energy resource (DER) integration by offering effective voltage regulation, reactive power compensation and real time monitoring and control. This paper provides a framework for conducting a cost-benefit analysis for such a smart distribution system. The method first identifies the benefits, and then quantifies and monetizes them. OpenDSS time-series based power flow simulation is used to quantify the benefits accurately. The costs associated with the new components of the system are estimated based on prototype units. A cost-benefit analysis is adopted to identify the scenarios where employing such a system by a utility becomes economically attractive.}, booktitle={2017 ieee power & energy society general meeting}, author={Sun, L. S. and Thomas, J. and Singh, S. and Li, D. X. and Baran, M. and Lubkeman, David and DeCarolis, J. and Queiroz, A. R. and White, L. and Watts, S. and et al.}, year={2017}, pages={1–5} }