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Feng Li

Alumni

Publications

An Optimal Design of Time-of-Use Tariff to Mitigate Overloads From Residential Electric Vehicle Charging on Distribution Networks
Feng Li
Ilhan Kocar
Antoine Lesage-Landry
High penetration of electric vehicle (EV) charging creates equipment overload risks in power distribution networks, potentially reducing ass… (voir plus)et lifetime. This article develops an optimal time-of-use (TOU) tariff strategy to mitigate overloads by shifting residential EV charging demand, without requiring communication infrastructure or direct load control policies. We formulate a bi-level optimization problem to determine optimal rate levels that restore equipment lifetime while minimizing utility costs. The problem is reformulated as a convex optimization with proven convergence properties, enabling an efficient solution for large-scale networks. The solution guarantees that no new overload occurs after shifting the EV loads. A novel time-series analysis method is used to estimate loading impacts and to evaluate the effectiveness of candidate strategies. The proposed strategy has a simple TOU structure consistent with utilities' rate structure in place, making it suitable for adoption. We validate the approach on the IEEE 8500-node test feeder. Results demonstrate that at 80% EV penetration rate, the strategy reduces transformer overload peaks by 20%, while maintaining equipment's nominal lifetime and achieving 42.2% cost savings for utilities compared to early equipment replacement.
2026-09-30
IEEE Transactions on Industrial Informatics (publié)
doi.org
Practical Solutions to Volt-var Optimization under Uncertainty via Blackbox Optimization
Feng Li
Ilhan Kocar
Antoine Lesage-Landry
In this work, we propose an optimal reactive power dispatch (ORPD) stochastic program for volt-var optimization (VVO) of power distribution … (voir plus)networks. The formulation considers not only control settings of conventional VVO devices, e.g., voltage regulators, capacitor banks, and on-load tap changers, but also optimal settings for volt-var droop curves of distributed energy resources (DERs), compliant with the IEEE 1547-2018 standard. Instead of including the power flow equations in the optimization problem which makes it nonlinear and nonconvex, a power flow solver is utilized and the problem is solved by blackbox optimization (BBO). The feasibility of the derived solution is improved by using unbalanced power flow simulations. The solution is effective under various demand and DER generation scenarios such that device settings are not frequently changed, making it practical for in-field implementations. Through numerical simulations on IEEE test feeders, we illustrate the performance of the solutions of our proposed approach on both in-sample and out-of-sample scenarios. We show that our approach outperforms a benchmark reinforcement learning method, and is also scalable to large-scale distribution networks.
2025-12-31
IEEE Transactions on Power Delivery (publié)
doi.org
Inferring electric vehicle charging patterns from smart meter data for impact studies
Feng Li
Élodie Campeau
Ilhan Kocar
Antoine Lesage-Landry
2024-09-30
Electric power systems research (publié)
doi.org
A Rapid Method for Impact Analysis of Grid-Edge Technologies on Power Distribution Networks
Feng Li
Ilhan Kocar
Antoine Lesage-Landry
This paper presents a novel rapid estimation method (REM) to perform stochastic impact analysis of grid-edge technologies (GETs) to the powe… (voir plus)r distribution networks. The evolution of network states' probability density functions (PDFs) in terms of GET penetration levels are characterized by the Fokker-Planck equation (FPE). The FPE is numerically solved to compute the PDFs of network states, and a calibration process is also proposed such that the accuracy of the REM is maintained for large-scale distribution networks. The approach is illustrated on a large-scale realistic distribution network using a modified version of the IEEE 8500 feeder, where electric vehicles (EVs) or photovoltaic systems (PVs) are installed at various penetration rates. It is demonstrated from quantitative analyses that the results from our proposed approach have negligible errors comparing with those obtained from Monte Carlo simulations.
2024-07-20
2024 IEEE Power & Energy Society General Meeting (PESGM) (publié)
doi.org
Mitigating Equipment Overloads due to Electric Vehicle Charging Using Customer Incentives
Feng Li
Ilhan Kocar
Antoine Lesage-Landry
This paper first presents a time-series impact analysis of charging electric vehicles (EVs) to loading levels of power network equipment con… (voir plus)sidering stochasticity in charging habits of EV owners. A novel incentive-based mitigation strategy is then designed to shift the EV charging from the peak hours when the equipment is overloaded to the off-peak hours and maintain equipment service lifetime. The incentive level and corresponding contributions from customers to alter their EV charging habits are determined by a search algorithm and a constrained optimization problem. The strategy is illustrated on a modified version of the IEEE 8500 feeder with a high EV penetration to mitigate overloads on the substation transformer.
2023-07-15
2023 IEEE Power & Energy Society General Meeting (PESGM) (publié)
doi.org