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.
The Adaptive Charging Network Simulator (ACN-Sim)
is a data-driven, open-source simulation environment
designed to accelerate research in the field of smart
electric vehicle (EV) charging. It fills the need in
this community for a widely available, realistic
simulation environment in which researchers can
evaluate algorithms and test assumptions.
ACN-Sim provides a modular, extensible
architecture, which models the complexity of real
charging systems, including battery charging
behavior and unbalanced three-phase
infrastructure. It also integrates with a broader
ecosystem of research tools. These include
ACN-Data, an open dataset of EV charging
sessions, which provides realistic simulation
scenarios, and ACN-Live, a framework for
field-testing charging algorithms. It also
integrates with grid simulators like MATPOWER,
PandaPower and OpenDSS, and OpenAI Gym for training
reinforcement learning agents.
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, 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
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.
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.
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.
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.