35 research outputs found
Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guarantees
The operation of the power grid is becoming more stressed, due to the
addition of new large loads represented by Electric Vehicles (EVs) and a more
intermittent supply due to the incorporation of renewable sources. As a
consequence, the coordination and control of projected EV demand in a network
of fast charging stations becomes a critical and challenging problem.
In this paper, we introduce a game theoretic based decentralized control
mechanism to alleviate negative impacts from the EV demand. The proposed
mechanism takes into consideration the non-uniform spatial distribution of EVs
that induces uneven power demand at each charging facility, and aims to: (i)
avoid straining grid resources by offering price incentives so that customers
accept being routed to less busy stations, (ii) maximize total revenue by
serving more customers with the same amount of grid resources, and (iii)
provide charging service to customers with a certain level of
Quality-of-Service (QoS), the latter defined as the long term customer blocking
probability. We examine three scenarios of increased complexity that gradually
approximate real world settings. The obtained results show that the proposed
framework leads to substantial performance improvements in terms of the
aforementioned goals, when compared to current state of affairs.Comment: Accepted for Publication in IEEE Transactions on Smart Gri
On the Evaluation of Plug-in Electric Vehicle Data of a Campus Charging Network
The mass adoption of plug-in electric vehicles (PEVs) requires the deployment
of public charging stations. Such facilities are expected to employ distributed
generation and storage units to reduce the stress on the grid and boost
sustainable transportation. While prior work has made considerable progress in
deriving insights for understanding the adverse impacts of PEV chargings and
how to alleviate them, a critical issue that affects the accuracy is the lack
of real world PEV data. As the dynamics and pertinent design of such charging
stations heavily depend on actual customer demand profile, in this paper we
present and evaluate the data obtained from a node charging network
equipped with Level chargers at a major North American University campus.
The data is recorded for weeks starting from late . The result
indicates that the majority of the customers use charging lots to extend their
driving ranges. Also, the demand profile shows that there is a tremendous
opportunity to employ solar generation to fuel the vehicles as there is a
correlation between the peak customer demand and solar irradiation. Also, we
provided a more detailed data analysis and show how to use this information in
designing future sustainable charging facilities.Comment: Accepted by IEEE Energycon 201
Joint Rate Control and Demand Balancing for Electric Vehicle Charging
Charging stations have become indispensable infrastructure to support the rapid proliferation of electric vehicles (EVs). The operational scheme of charging stations is crucial to satisfy the stability of the power grid and the quality of service (QoS) to EV users. Most existing schemes target either of the two major operations: charging rate control and demand balancing. This partial focus overlooks the coupling relation between the two operations and thus causes the degradation on the grid stability or customer QoS. A thoughtful scheme should manage both operations together. A big challenge to design such a scheme is the aggregated uncertainty caused by their coupling relation. This uncertainty accumulates from three aspects: the renewable generators co-located with charging stations, the power load of other (or non-EV) consumers, and the charging demand arriving in the future. To handle this aggregated uncertainty, we propose a stochastic optimization based operational scheme. The scheme jointly manages charging rate control and demand balancing to satisfy both the grid stability and user QoS. Further, our scheme consists of two algorithms that we design for managing the two operations respectively. An appealing feature of our algorithms is that they have robust performance guarantees in terms of the prediction errors on these three aspects. Simulation results demonstrate the efficacy of the proposed operational scheme and also validate our theoretical results
Competitive Charging Station Pricing for Plug-in Electric Vehicles
This paper considers the problem of charging station pricing and plug-in
electric vehicles (PEVs) station selection. When a PEV needs to be charged, it
selects a charging station by considering the charging prices, waiting times,
and travel distances. Each charging station optimizes its charging price based
on the prediction of the PEVs' charging station selection decisions and the
other station's pricing decision, in order to maximize its profit. To obtain
insights of such a highly coupled system, we consider a one-dimensional system
with two competing charging stations and Poisson arriving PEVs. We propose a
multi-leader-multi-follower Stackelberg game model, in which the charging
stations (leaders) announce their charging prices in Stage I, and the PEVs
(followers) make their charging station selections in Stage II. We show that
there always exists a unique charging station selection equilibrium in Stage
II, and such equilibrium depends on the charging stations' service capacities
and the price difference between them. We then characterize the sufficient
conditions for the existence and uniqueness of the pricing equilibrium in Stage
I. We also develop a low complexity algorithm that efficiently computes the
pricing equilibrium and the subgame perfect equilibrium of the two-stage
Stackelberg game.Comment: 15 pages, 21 figure
Revenue optimization frameworks for multi-class PEV charging stations
The charging power of plug-in electric vehicles (PEVs) decreases significantly when the state of charge (SoC) gets closer to the fully charged state, which leads to a longer charging duration. Each time when the battery is charged at high rates, it incurs a significant degradation cost that shortens the battery life. Furthermore, the differences between demand preferences, battery types, and charging technologies make the operation of the charging stations a complex problem. Even though some of these issues have been addressed in the literature, the charging station modeling with battery models and different customer preferences have been neglected. To that end, this paper proposes two queueing-based optimization frameworks. In the first one, the goal is to maximize the system revenue for single class customers by limiting the requested SoC targets. The PEV cost function is composed of battery degradation cost, the waiting cost in the queue, and the admission fee. Under this framework, the charging station is modeled as a M/G/S/K queue, and the system performance is assessed based on the numerical and simulation results. In the second framework, we describe an optimal revenue model for multi-class PEVs, building upon the approach utilized in the first framework. Two charging strategies are proposed: 1) a dedicated charger model and 2) a shared charger model for the multi-class PEVs. We evaluate and compare these strategies. Results show that the proposed frameworks improve both the station performance and quality of service provided to customers. The results show that the system revenue is more than doubled when compared with the baseline scenario which includes no limitations on the requested SoC
PEV Charging Infrastructure Integration into Smart Grid
Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute to the reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. With the increasing popularity of PEVs, public electric-vehicle charging infrastructure (EVCI) becomes indispensable to meet the PEV user requirements. EVCI can consist of various types of charging technologies, offering multiple charging services for PEV users. Proper integration of the charging infrastructure into smart grid is key to promote widespread adoption of PEVs. Planning and operation of EVCI are technically challenging, since PEVs are characterized by their limited driving range, long charging duration, and high charging power, in addition to the randomness in driving patterns and charging decisions of PEV users. EVCI planning involves both the siting and capacity planning of charging facilities. Charging facility siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. Capacity planning of charging facilities must ensure a satisfactory charging service for PEV users in addition to a reliable operation of the power grid. During the operation of EVCI, price-based coordination mechanisms can be leveraged to dynamically preserve the quality-of-service (QoS) requirements of charging facilities and ensure the profitability of the charging service. This research is to investigate and develop solutions for integrating the EVCI into the smart grid. It consists of three research topics:
First, we investigate PEV charging infrastructure siting. We propose a spatial-temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial-temporal distribution of traffic flows. In the proposed model, we consider transportation network dynamics and congestion, in addition to different characteristics and usage patterns of each charging facility type.
Second, we propose a QoS aware capacity planning of EVCI. The proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors.
Lastly, we investigate the coordination of PEV charging demands. We develop a differentiated pricing mechanism for a multiservice EVCI using deep reinforcement learning (RL). The proposed framework enhances the performance of charging facilities by motivating PEV users to avoid over-usage of particular service classes. Since customer-side information is stochastic, non-stationary, and expensive to collect at scale, the proposed pricing mechanism utilizes the model-free deep RL approach. In the proposed RL approach, deep neural networks are trained to determine a pricing policy while interacting with the dynamically changing environment. The neural networks take the current EVCI state as input and generate pricing signals that coordinate the anticipated PEV charging demand
Mobile Charging as a Service: A Reservation-Based Approach
This paper aims to design an intelligent mobile
charging control mechanism for Electric Vehicles (EVs), by
promoting charging reservations (including service start time,
expected charging time, and charging location, etc.). EV mobile
charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically
mobile plug-in chargers, capable of providing on-site charging
services. With intelligent charging management, readily available
mobile chargers are predictable and could be efficiently scheduled
towards EVs with charging demand, based on updated context
collected from across the charging network. The context can
include critical information relating to charging sessions as well
as charging demand, etc. Further with reservations introduced,
accurate estimations on charging demand for a future moment
are achievable, and correspondingly, optimal mobile chargersselection can be obtained. Therefore, charging demands across
the network can be efficiently and effectively satisfied, with the
support of intelligent system-level decisions. In order to evaluate
critical performance attributes, we further carry out extensive
simulation experiments with practical concerns to verify our
insights observed from the theoretical analysis. Results show
great performance gains by promoting the reservation-based
mobile charger-selection, especially for mobile chargers equipped
with suffice power capacity
Towards efficient battery swapping service operation under battery heterogeneity
The proliferation of electric vehicles (EVs) has posed significant challenges to the existing power grid infrastructure. It thus becomes of vital importance to efficiently manage the Electro-Mobility for large demand from EVs. Due to limited cruising range of EVs, vehicles have to make frequent stops for recharging, while long charging period is one major concern under plug-in charging. We herein leverage battery swapping (BS) technology to provide an alternative charging service, which substantially reduces the charging duration (from hours down to minutes). Concerning in practice that various battery is generally not compatible with each other, we thus introduce battery heterogeneity into the swapping service, concerning the case that different types of EVs co-exist. A battery heterogeneity-based swapping service framework is then proposed. Further with reservations for swapping service enabled, the demand load can be anticipated at BS stations as a guidance to alleviate service congestion. Therefore, potential hotspots can be avoided. Results show the performance gains under the proposed scheme by comparing to other benchmarks, in terms of service waiting time, etc. In particular, the diversity of battery stock across the network can be effectively managed