734 research outputs found
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
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
Electric Power Allocation in a Network of Fast Charging Stations
In order to increase the penetration of electric vehicles, a network of fast
charging stations that can provide drivers with a certain level of quality of
service (QoS) is needed. However, given the strain that such a network can
exert on the power grid, and the mobility of loads represented by electric
vehicles, operating it efficiently is a challenging problem. In this paper, we
examine a network of charging stations equipped with an energy storage device
and propose a scheme that allocates power to them from the grid, as well as
routes customers. We examine three scenarios, gradually increasing their
complexity. In the first one, all stations have identical charging capabilities
and energy storage devices, draw constant power from the grid and no routing
decisions of customers are considered. It represents the current state of
affairs and serves as a baseline for evaluating the performance of the proposed
scheme. In the second scenario, power to the stations is allocated in an
optimal manner from the grid and in addition a certain percentage of customers
can be routed to nearby stations. In the final scenario, optimal allocation of
both power from the grid and customers to stations is considered. The three
scenarios are evaluated using real traffic traces corresponding to weekday rush
hour from a large metropolitan area in the US. The results indicate that the
proposed scheme offers substantial improvements of performance compared to the
current mode of operation; namely, more customers can be served with the same
amount of power, thus enabling the station operators to increase their
profitability. Further, the scheme provides guarantees to customers in terms of
the probability of being blocked by the closest charging station. Overall, the
paper addresses key issues related to the efficient operation of a network of
charging stations.Comment: Published in IEEE Journal on Selected Areas in Communications July
201
Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities
Optimization of energy consumption in future intelligent energy networks (or
Smart Grids) will be based on grid-integrated near-real-time communications
between various grid elements in generation, transmission, distribution and
loads. This paper discusses some of the challenges and opportunities of
communications research in the areas of smart grid and smart metering. In
particular, we focus on some of the key communications challenges for realizing
interoperable and future-proof smart grid/metering networks, smart grid
security and privacy, and how some of the existing networking technologies can
be applied to energy management. Finally, we also discuss the coordinated
standardization efforts in Europe to harmonize communications standards and
protocols.Comment: To be published in IEEE Communications Surveys and Tutorial
Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension
The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches
Guidelines for Identifying Business Risks and Opportunities Arising From Ecosystem Change
Outlines the Corporate Ecosystem Services Review, a methodology to help businesses develop strategies for managing operational, regulatory, reputational, market, and financing risks and opportunities arising from their dependence and impact on ecosystems
Electric Vehicle Charging Recommendation and Enabling ICT Technologies: Recent Advances and Future Directions
The introduction of Electric Vehicles (EV) will have a significant impact on the sustainable economic development of urban city. However, compared with traditional gasoline-powered vehicles, EVs currently have limited range, which necessitates regular recharging. Considering the limited charging infrastructure currently available in most countries, infrastructure investments and Renewable Energy Sources (RES) are critical. Thus, service quality provisioning is necessary for realizing EV market.
Unlike numerous previous works which investigate "charging scheduling" (referred to when/whether to charge) for EVs already been parked at home/Charging Stations (CSs), a few works focus on “charging recommendation” (refer to where/which CS to charge) for on-the-move EVs. The latter use case cannot be overlooked as it is the most important feature of EVs, especially for driving experience during journeys. On-the-move EVs will travel towards appropriate CSs for charging based on smart decision on where to charge, so as to experience a shorter waiting time for charging.
The effort towards sustainable engagement of EVs has not attracted enough attention from both industrial and academia communities. Even if there have been many charging service providers available, the utilization of charging infrastructures is still in need of significant enhancement. Such a situation certainly requires the popularity of EVs towards the sustainable, green and economic market. Enabling the sustainability requires a joint contribution from each domain, e.g., how to guarantee accurate information involved in decision making, how to optimally guide EV drivers towards charging place with the least waiting time, how to schedule charging services for EVs being parked within grid capacity.
Achieving this goal is of importance towards a positioning of efficient, scalable and smart ICT framework, makes it feasible to learn the whole picture of grid:
- Necessary information needs to be disseminated between stakeholders CSs and EVs, e.g., expected queuing time at individual CSs. In this context, how accurate CSs condition information plays an important role on the optimality of charging recommendation.
- Also, it is very time-consuming for the centralized Global Controller (GC) to achieve optimization, by seamlessly collecting data from all EVs and CSs, The complexity and computation load of this centralized solution, increases exponentially with the number of EVs.
This paper summaries the recent interdisciplinary research works on EV charging recommendation along with novel ICT frameworks, with an original taxonomy on how Intelligent Transportation Systems (ITS) technologies support the EV charging use case. Future directions are also highlighted to promote the future research
- …