Adaptive Charging Network Research Portal

ACN Dataset

The Adaptive Charging Network Dataset (ACN-Data) is a dynamic dataset of workplace EV charging sessions. In this paper we describe the dataset, as well as some interesting user behavior it exhibits. To demonstrate the usefulness of the dataset, we present three examples, learning and predicting user behavior using Gaussian mixture models, optimally sizing on-site solar generation for adaptive electric vehicle charging, and using workplace charging to smooth the net demand Duck Curve.

Z. Lee, T. Li, S. H. Low. ACN-Data: Analysis and Applications of an Open EV Charging Dataset, Proc. the Tenth International Conference on Future Energy Systems (e-Energy '19), June 2019
e-Energy, June 2019: Slides

ACN Simulator

The Adaptive Charging Network Simulator (ACN-Sim) is a data-driven, open-source simulator based on our experience building and operating real-world charging systems. This simulator provides researchers who may lack access to real EV charging systems with a realistic environment to evaluate their algorithms and test their assumptions. It also provides a common platform on which algorithms can be evaluated head-to-head, allowing researchers to better understand and articulate how their work fits into the existing literature.
Z. Lee, S. Sharma, D. Johansson, S. H. Low ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research, arXiv:2012.02809 [eess.SY], December 2020

Z. Lee, D. Johansson, S. H. Low. ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research, Proc. of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Beijing, China, October 2019

Infrastructure and Algorithms

ACN Infrastructure and Algorithms

The Adaptive Charging Network (ACN) is a unique framework for large-scale smart EV charging. In these papers we describe the hardware and software architecture of the ACN. We also present an algorithm framework based on convex optimization and model predictive control which can be used to schedule EV charging to achieve various objective such as maximizing energy delivery in constrained infrastructure, reducing costs when subjected to time-varying prices, or following demand response signals.

Z. Lee, G. Lee, T. Lee, C. Jin, R. Lee, Z. Low, D. Chang, C. Ortega, S. H. Low. Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging, arXiv:2012.02636 [eess.SY], December 2020
Z. Lee, D. Chang, C. Jin, G. S. Lee, R. Lee, T. Lee and S. H. Low. Large-Scale Adaptive Electric Vehicle Charging, Proc. of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Aalborg, Denmark, October 2018
G. Lee, T. Lee, Z. Low, S. H. Low and C. Ortega. Adaptive charging network for electric vehicles, Proc. of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, December 2016
LACI, December 2018: Slides

Pricing EV charging service with demand charge

Pricing electric vehicle (EV) charging services is difficult when the electricity tariff includes both time-of-use energy cost and demand charge based on peak power draw. In this paper, we propose a pricing scheme that assigns a session-specific energy price to each charging session at the end of the billing period. The session price precisely captures the costs of energy, demand charge, and infrastructure congestion for which that session is responsible in that month while optimizing the trade-off between inexpensive time-of-use pricing and peak power draw. While our pricing scheme is calculated offline at the end of the billing period, we propose an online scheduling algorithm based on model predictive control to determine charging rates for each EV in real-time. We provide theoretical justification for our proposal and support it with simulations using real data collected from charging facilities at Caltech and JPL. Our simulation results suggest that the online algorithm can approximate the offline optimal reasonably well, e.g., the cost paid by the operator in the online setting is higher than the offline optimal cost by 9.2% and 6.5% at Caltech and JPL respectively. In the case of JPL, congestion rents are enough to cover this increase in costs, while at Caltech, this results in a negligible average loss of $18 per month.

Z. Lee, J. Z. F. Pang, S. H. Low Pricing EV charging service with demand charge. Electric Power Systems Research, 2020

Optimality of Online LP

In its simplest form, optimal charging can be formulated as a linear program (LP) or a quadratic program (QP). An offline LP assumes all future EV arrivals, departures, and energy demands are known and computes the charging profiles of all EVs as an optimal solution of a single LP. An online LP is an iterative algorithm in a model-predictive control fashion, and, in each iteration, computes the charging profiles of existing EVs assuming there will not be any future EV arrival. Offline LP is impractical but serves as a lower bound on the cost of online LP which can be implemented in ACN. Extensive simulations using datasets from Caltech ACN and Google’s charging facilities show that the performance of online LP is extremely close to that of offline LP. We prove that, under appropriate assumptions, when online LP is feasible it indeed attains offline optimal.

L. Guo, K. F. Erliksson and S. H. Low. Optimal online adaptive electric vehicle charging. Proc. of the IEEE Power and Energy Society General Meeting, Chicago, IL, July 2017

Smoothed Least-Laxity First Algorithm

We formulate EV charging as a feasibility problem that meets all EVs’ energy demands before departure under charging rate con- straints and total power constraint. We propose an online algorithm, the smoothed least-laxity-first (sLLF) algorithm, that decides on the current charging rates based on only the information up to the current time. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has significantly higher rate of generating feasible EV charging than several other common EV charging algorithms.

Y. Nakahira, N. Chen, L. Chen and S. H. Low. Smoothed Least-laxity-first Algorithm for EV Charging. Proc. the Eighth International Conference on Future Energy Systems (e-Energy '17), 2017

Optimal Distributed EV Charging

We design a distributed iterative scheduling algorithm for EV charging where, in each iteration, EVs update their charging profiles according to the control signal broadcast by an aggregator, and the aggregator adjusts the control signal to guide their updates. The algorithm converges to optimal charging profiles even when EVs can plug in at different times, update their charging profiles at different times with different frequencies, and may use outdated control signals when they update.

L. Gan, U. Topcu and S. H. Low. Optimal decentralized protocol for electric vehicle charging, IEEE Trans. on Power Systems, 228(2):940–951, May 2013