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In this work, a microstrip antenna array that consists of 16 elements is designed at a frequency of 28 GHz, with a dimension of 35 mm × 33.5 mm and an operational bandwidth of 27.7–28.3 GHz. The idea is putting two antenna arrays next to each other on the same substrate and ground plane, as well as minimizing the overall size. This work presents a mutual coupling reduction for two arrays and studies the performance of the antennas through the suppression of surface wave propagation in a given frequency range. It proposed a new configuration for an artificial magnetic conductor (AMC) created by microstrip technology placed at a closed distance (0.3λ0) between the two antenna arrays. The improvement in the isolation between the two adjacent antenna arrays was studied according to the H-plane with a periodic unit cell of the AMC. The mutual coupling reduces to −40.0 dB at the operation frequency and isolation of 17 dB. The antenna array is also characterized by a good envelope correlation coefficient (ECC). The CST Microwave Studio electromagnetic solver was used to design, improve, and miniaturize the proposed configuration.

Connected electric vehicles (CEVs) can help cities to reduce road congestion and increase road safety. With the technical improvement made to the battery system in terms of capacity and flexibility, CEVs, as mobile power plants can be an important actor for the electricity markets. Especially, they can trade electricity between each other when supply stations are full or temporarily not available. In this article, we propose an advanced decentralized electricity trading framework between CEVs in parking lots based on consortium blockchain, machine learning, and Game theoretic model. We design a distributed smart contract solution based on a stochastic bidding process, which helps CEVs to sell and buy electricity with their maximum profitability. Finally, numerical simulations with MATLAB and Solidity are conducted to prove the effectiveness of our proposed solution. Also, a comparison with another method in terms of CEVs’ profitability improvement and energy trading management is provided.

Smart micro-grid (SMG) is a growing segment of the modern power grid. Besides the benefits brought in terms of improving power reliability, power quality, security, and sustainability, and promoting competitiveness in a new deregulated electricity market, SMG is still in the early commercial stage. This article introduces the SMG con-cept-based forecasting mechanisms. It highlights the role of the power production and profit forecasting methods to drive the SMG deployment with the liberalized electricity market concept. Finally, some open issues in SMG deployment such as standardization and regulations are presented as key drivers for SMG success.

The transportation system needs innovative schemes and applications to facilitate mobility in the cities that is user-friendly, easy, enjoyable and convenient according to citizens' constraints. In this study, the authors propose a decentralised architecture-based game-theoretic model for a community-based transportation system. This scheme, which involves multi-transportation forms, allows the user to be an active prosumer who can travel in the city using public and private forms and also make decisions about the trip cost. The authors propose a decentralised game-theoretic transportation algorithm to manage passenger needs, public bus interests, car ride-sharing and bicycle constraints. The simulations prove the effectiveness of the proposed scheme. The effectiveness of the decentralised game-theoretic transportation model appears more clearly when compared with the multi-mode double dynamic approach in [1], as it gives much better optimisation results.

High electric vehicles (EVs) penetration is expected to increase smart grid solicitation especially with various EV charging demands. As result, the EV charging process at the supply station has to be managed in the way to promote the EV satisfaction level while preserving smart grid stability. In this article, the bidirectional power flow between EV and grid; Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G), is exploited. We make a profit from the unused electric power of EVs and we present an EV load management technique based on EV charging and EV discharging coordination. We propose a peak load management model (PLM) used to schedule EVs for charging or discharging service according to the power demand with the timing and location where each EV need to be served. Also, we propose an Electric Vehicle Supply Equipment (EVSE) selection model to guide EVs to the supply station. We develop a mathematical formalism for handling requests for EV charging/discharging at EVSE based on queuing theory. Those models are evaluated while considering the mobility of vehicles in an urban scenario and time-of-use-pricing (TOUP). Finally, extensive matlab simulations are conducted to validate the proposed approach and demonstrate its effectiveness.

This paper proposes a profit maximization model for a Decentralized Electric Vehicle Supply Equipment (D-EVSE) equipped with a Photovoltaic system. We acknowledge a power connection to the central power grid when necessary. We design a Decentralized Profit Maximization Algorithm (DPMA) to help D-EVSEs take profits from the electricity price variation during the day when selling electricity to EVs or buying it from the grid. Finally, numerical simulations with MATLAB are conducted to prove the effectiveness of our proposed solution.

In this paper, we consider the decentralization of power generation and management problems in electric vehicle supply equipment (D-EVSE). We propose two algorithms to manage the interaction between electric vehicles (EVs) and D-EVSEs while maximizing EV drivers' satisfaction in terms of reducing the waiting time for charging or discharging services and minimizing the stress level for D-EVSEs. The first algorithm is used to schedule charging or discharging service for EVs. The second algorithm is used to manage unscheduled EV needs at D-EVSEs. The simulation results using realistic scenarios are conducted to validate the proposed schemes and demonstrate their efficiency and effectiveness while satisfying the defined constraints. Furthermore, we apply both D-EVSE scheduled and unscheduled schemes to Ontario highways 174, 416 and 417 in the city of Ottawa as a case study.

This paper focuses on transportation models in smart cities. We propose a new dynamic mobility traffic (DMT) scheme which combines public buses and car ride-sharing. The main objective is to improve transportation by maximizing the riders’ satisfaction based on real-time data exchange between the regional manager, the public buses, the car ride-sharing and the riders. OpenStreetMap and OMNET++ were used to implement a realistic scenario for the proposed model in a city like Ottawa. The DMT scheme was compared to a multi-loading system used for a school bus. Simulations showed that rider satisfaction was enhanced when a suitable combination of transportation modes was used. Additionally, compared to the other scheme, this DMT scheme can reduce the stress level of car ride-sharing and public buses during the day to the minimal level.

In this work, we develop a dynamic multi-mode transportation model in which the passenger, for his trip, can use one or a combination of the transportation form such as the car, the bus and the bike. This model is based on the game theory concept based trip cost-optimization. The proposed system is implemented through a realistic scenario in a specific city using the OMNET++, VEINS, SUMO, MATLAB and the OpenStreetMap software tools. The results show that the average trip price and the average trip time are improved when using our proposed model.

In this paper, we take advantage of the communication systems which will be adopted for the new vehicle generations in near future and we propose a new electric vehicle (EV) charge/discharge service scheme for large EV density through vehicular ad-hoc networks communications. First, we present a communication protocol for the EV charging/discharging protocol with the uplink and the downlink EV charge /discharge frame format exchanged between EVs and smart grid. Second, we propose our channel access scheme based on a distributed time slot assignment model (DTSA) to allow the EV charging/discharging service negotiations with smart grid before plug-in phase. This DTSA scheme, which is a contention-free channel access mechanism, can improve the EV charging/discharging service access by increasing the successful rate of channel reservation both on control channel (CCH) and service channel (SCH). We propose also three algorithms to manage the EV charge/discharge service reservation and negotiation, respectively, on CCH and SCH. Finally, extensive simulations with NS-2 and MATLAB are conducted to validate the proposed approach and demonstrate the efficiency and effectiveness of the proposed compared to IEEE802.11p and VeMAC while satisfying the defined constraints.

In this paper, an affordable and suitable mobility system is developed for passenger with trip cost optimization in smart city concept. This system based on trip in the city by using multiple transportation forms of buses and carpooling. A Mobility Traffic Algorithm (MTA) is proposed to manage a passenger’s specific needs based on real-time information of buses and carpooling. The proposed model is implemented with a realistic scenario in the city of Ottawa using the OpenStreetMap program and the OMNET++. The results show that passenger satisfaction is improved when using a combination of multiple transportation forms in the city. Also, the simulation is proved that the stress level of the buses and carpooling during the daytime is reduced to its minimum by using MTA model.

In this paper, we consider the Electric Vehicles (EVs) interaction with a decentralized energy storage system (DESS) located at the public supply station. To manage the EV charging and discharging process, we propose a scheduling algorithm aiming to maximize the EV drivers' satisfaction and minimize the DESSs' stress level. Simulation results using realistic scenarios are conducted to validate the proposed approach and demonstrate its efficiency and effectiveness while satisfying the defined constraints.

International audience

In order to keep Electric Vehicles (EVs) far from causing peaks in power demand during the day, it is important to perform an intelligent management for EVs charging and discharging services by including different metrics in a pricing policy. In this paper, we propose a novel pricing policy for EV charging (G2V) and discharging (V2G) services, in order to reduce the peak load and the energy overflow. Our proposed policy uses cloud computing and smart grid interactions to schedule EV requests. The simulations proved that our proposed pricing policy optimizes the energy load during peak hours and satisfies EVs users and smart grid constraints.

As electric vehicles EVs become more popular, public charging stations for such vehicles will become common. Because the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the charging waiting time. To achieve this goal, we propose two models where vehicles communicate beforehand with the grid to convey information about their charging need and location. In the first model, we develop a mathematical formalism for handling requests for charging vehicles at public charging station based on queuing theory. The second model extends the first one by considering priority queues with two EV classes, high and low, and a cut-off service discipline. Both models are evaluated while considering mobility of vehicles in an urban scenario and time-of-use pricing. Finally, we propose two algorithms for directing vehicles to charging stations in a way to minimize either their waiting time to plug-in or their waiting time to charge completion. Simulation results show the effectiveness of the proposed approaches when considering both real EV and charging station characteristics and constraints. Copyright © 2014 John Wiley & Sons, Ltd.

Cognitive Radio Vehicular Ad hoc Networks (CR-VANETs) utilize CR technology to increase frequency bandwidth for vehicular communications. Cooperative Spectrum Sensing (CSS) exploits spatial and temporal diversity for fast and accurate detection of Primary Users (PUs). This paper provides an overview of CSS techniques for CR-VANETs. We review state of the art CSS techniques proposed in literature highlighting their advantages, implementation challenges, and overhead. We then discuss open research issues and future directions, with emphasis on security threats as well as the control and coordination traffic overhead.

This article studies the performance of decision-based cooperative spectrum sensing using random medium access. Unlike scheduled medium access, random medium access entails a non negligible probability of collision which affects the detection accuracy. Novel closed form expressions are derived for the detection probability, the false alarm probability, and the average reporting time, assuming the absence of a dedicated reporting channel and accounting for decoding errors. Computer simulations are used to verify the accuracy of the derived results.

The ascending increase in the numbers of Plug-in Electric Vehicles (PEVs) in the world brought forward many new challenges to the power grid and utility networks. Some of these challenges are related to finding and managing additional power resources for these PEVs. Green power is one of the alternatives but, we still need to find a cheap way to store this power, PEVs could play a significant role in storing power at a certain time and use it at another time. Never the less, it can play the same role with electric power from grids so it can store power from off peak time to peak time. This role might help the grid to fulfill the growing demands. In this paper, we propose flexible charging and discharging algorithm that effectively addresses and solves the problem of power demand on peak time using the PEV's batteries as a source for backup energy storage. The results show significant enhancement in the power consumption without affecting the performance of electric vehicle.

International audience; As electric vehicles become more popular, public charging stations for such vehicles will become common. Since the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the waiting time for users to have their vehicles charged. In this paper, we present an approach for balancing the load among charging stations in an area while minimizing the charging time of electric vehicles. We propose a model where vehicles communicate beforehand with the grid to convey information about their charging status, and develop a mathematical model of handling requests for charging vehicles at public charging station based on queuing theory. Finally, we propose an algorithm for directing vehicles to charging stations in a way to minimize their waiting time to charge completion. The simulation results show the effectiveness of the proposed approach when considering both real electric vehicle and charging station characteristics and constraints.