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Zenginis I.,Iquadrat | Vardakas J.S.,Iquadrat | Echave C.,Urban Ecology Agency of Barcelona | Morato M.,Urban Ecology Agency of Barcelona | And 2 more authors.
Applied Energy | Year: 2017

This paper proposes a novel model for the optimal design and power management of a microgrid. The key objective of the proposed model is to indicate the benefits of cooperation in terms of energy cost savings, carbon emission reduction and provision of energy self-sufficiency. The proposed cooperative configuration considers that buildings with different load patterns exchange power through a common DC bus, so that an optimum utilization of the energy generated by local renewables is achieved. The problem of selecting the optimal capacities of photovoltaic arrays, energy storage systems and inverters, as well as of determining the optimal daily power operation plan is formulated as a mixed integer linear programming problem, where the objective function is optimized based on the Nash bargain method. The impact of the daily scheduling of the energy storage systems and electric vehicles, as well as the impact of power exchanges on the equipment sizing and vice versa is also highlighted. We demonstrate the applicability and effectiveness of the proposed cooperative system on a representative Superblock of Barcelona; the results indicate that the proposed approach achieves significant operation cost (15.7%) and carbon emissions (12.9% in average) reduction compared to the case where no power exchange occurs. © 2017 Elsevier Ltd

Moscholios I.D.,University of Peloponnese | Logothetis M.D.,University of Patras | Vardakas J.S.,Iquadrat | Boucouvalas A.C.,University of Peloponnese
Computer Networks | Year: 2015

In this paper, we consider a single link of fixed capacity that accommodates calls of different service-classes with different bandwidth-per-call requirements. The link behaves as a multirate loss system. Calls of each service-class arrive in the link according to a Poisson (random) or a quasi-random process and have an exponentially distributed service time. Poisson or quasi-random arriving calls are generated by an infinite or finite number of traffic sources, respectively. Service-classes are also distinguished according to the behavior of in-service calls, in elastic and adaptive service-classes. Elastic calls can compress their bandwidth by simultaneously increasing their service time. Adaptive calls tolerate bandwidth compression without affecting their service time. All calls compete for the available link bandwidth under the combination of the Threshold (TH) and the Bandwidth Reservation (BR) policies. The TH policy can provide different QoS among service-classes by limiting the number of calls of a service-class up to a predefined threshold, which can be different for each service-class. The BR policy reserves part of the available link bandwidth to benefit calls of high bandwidth requirements. The proposed models, for random or quasi-random traffic, do not have a product form solution for the determination of the steady state probabilities. However, we approximate both models by reversible Markov chains, and prove recursive formulas for the efficient calculation of the call-level performance metrics, such as time and call congestion probabilities as well as link utilization. The accuracy of the proposed formulas is verified through simulation and found to be quite satisfactory. © 2015 Elsevier B.V.

Vardakas J.S.,Iquadrat | Zorba N.,Qatar University | Verikoukis C.V.,Catalonia Technology Center of Telecomunications
Applied Energy | Year: 2015

Smart grid technology is considered as the ultimate solution to challenges that emerge from the increasing power demands, the subsequent increase in pollution, and the outmoded power grid infrastructure. The successful implementation of the smart grid is mainly driven by the utilization of modern communication technologies, which aim at the provision of advanced demand side management mechanisms, such as demand response. In this paper, we present and analyze four power-demand scheduling scenarios that aim to reduce the peak demand in a smart grid infrastructure. The proposed scenarios consider that each consumer is equipped with a certain number of appliances of different power demands and different operational times, while the percentage of consumers that agree to participate in the demand scheduling program is also incorporated in our models. We provide the analysis for the determination of the peak demand in a residential area, based on recursive formulas. The proposed analysis is validated through simulations; the accuracy of the analytical models is found to be quite satisfactory. Moreover, we unveil the consistency and necessity of the proposed scenarios and corresponding analytical models. © 2015 Elsevier Ltd.

Vardakas J.S.,Iquadrat | Zorba N.,Qatar University | Verikoukis C.V.,Catalonia Technology Center of Telecomunications
IEEE Communications Surveys and Tutorials | Year: 2015

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm. © 1998-2012 IEEE.

Zenginis I.,Iquadrat | Vardakas J.S.,Iquadrat | Zorba N.,Qatar University | Verikoukis C.V.,Catalonia Technology Center of Telecomunications
Energy | Year: 2016

Electrification of transportation is considered as one of the most promising ways to mitigate climate change and reduce national security risks from oil and gasoline imports. Fast charging stations that provide high quality of service will facilitate the wide market penetration of electric vehicles. In this paper, the operation of a fast charging station is analyzed by employing a novel queuing model. The proposed analysis considers that the various electric vehicle models are classified by their battery size, and computes the customers' mean waiting time in the queue by taking into account the available charging spots, as well as the stochastic arrival process and the stochastic recharging needs of the various electric vehicle classes. Furthermore, a charging strategy is proposed according to which the drivers are motivated to limit their energy demands. The implementation of the proposed strategy allows the charging station to serve more customers without any increase in the queue waiting time. The high precision of the present analytical model is confirmed through simulations. Therefore, it may be utilized by existing fast charging station operators that need to provide high quality of service, or by future investors that need to design an efficient installation. © 2016 Elsevier Ltd

Vardakas J.S.,Iquadrat | Zorba N.,Qatar University | Verikoukis C.V.,Catalonia Technology Center of Telecomunications
Energy | Year: 2014

In this paper we present and analyze online and offline scheduling models for the determination of the maximum power consumption in a smart grid environment. The proposed load models consider that each consumer's residence is equipped with a certain number of appliances of different power demands and different operational times, while the appliances' feature of alternating between ON and OFF states is also incorporated. Each load model is correlated with a scheduling policy that aims to the reduction of the power consumption through the compression of power demands or the postponement of power requests. Furthermore, we associate each load model with a proper dynamic pricing process in order to provide consumers with incentives to contribute to the overall power consumption reduction. The evaluation of the load models through simulation reveals the consistency and the accuracy of the proposed analysis. © 2014 Elsevier Ltd.

Vardakas J.S.,Iquadrat | Zorba N.,Qatar University | Verikoukis C.V.,Catalonia Technology Center of Telecomunications
Applied Energy | Year: 2016

In this paper we propose novel and more realistic analytical models for the determination of the peak demand under four power demand control scenarios. Each scenario considers a finite number of appliances installed in a residential area, with diverse power demands and different arrival rates of power requests. We develop recursive formulas for the efficient calculation of the peak demand under each scenario, which take into account the finite population of the appliances. Moreover, we associate each scenario with a proper real-time pricing process in order to derive the social welfare. The proposed analysis is validated through simulations. Moreover, the performance evaluation of the proposed formulas reveals that the absence of the assumption of finite number of appliances could lead to serious peak-demand over-estimations. © 2015 Elsevier Ltd.

Vardakas J.S.,Iquadrat | Zenginis I.,Iquadrat
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST | Year: 2015

In this paper we present a survey of recent trends on shortterm electricity-price prediction models. We classify the proposed price prediction methods based on the forecasting horizon into short- mediumand long-term approaches. We provide the key features of the mediumand long- solutions, while we emphasize on short-term prediction models, by providing their classification into statistical, computational intelligent and hybrid methods. We also highlight the key characteristics of the available prediction methods, while the strengths and weaknesses of these solutions are also discussed and analyzed. These important aspects should be considered by researchers that target on the derivation of more efficient and accurate electricity-price prediction models, especially for smart grid applications. © 2015, Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, All rights Reserved.

Shakil M.,University of Jordan | Zorba N.,Iquadrat | Adam C.,MTN Inc | Verikoukis C.,Catalonia Technology Center of Telecomunications
2011 IEEE 16th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2011 | Year: 2011

Handover is the key enabling option to guarantee seamless mobility within any wireless communication system, where the inter-systems handover option has recently acquired large importance, both in the research and commercial fields. The motivation behind the vertical handover can be either based on the user demands to achieve larger data rates or lower service delays, but also it can be operator-based to balance the system load or to increase the system coverage. This paper presents a hybrid technique for vertical handover where both the operator satisfaction and the user satisfaction are concerned whenever a vertical handover is to be accomplished. Results are presented for the system behaviour with and without the proposed handover mechanism. © 2011 IEEE.

Niotaki K.,Catalonia Technology Center of Telecomunications | Georgiadis A.,Catalonia Technology Center of Telecomunications | Collado A.,Catalonia Technology Center of Telecomunications | Vardakas J.S.,Iquadrat
IEEE Transactions on Microwave Theory and Techniques | Year: 2014

In this work, the concept of dual-band resistance compression networks is introduced and applied to the design of rectifier circuits with improved performance. The use of resistance compression networks (RCNs) minimizes the sensitivity of rectifier circuits to variations in the surrounding environment, such as input power level and changes in the rectifier load. The proposed dual-band RCN can be used as the matching network located between the antenna and the rectifying element of a dual-band rectifier for energy harvesting applications. A dual-band ( 915 MHz /2.45 GHz) rectifier based on RCN is designed and characterized showing improved performance in comparison with a conventional dual-band envelope detector by exhibiting improved RF-dc conversion efficiency and reduced sensitivity versus output load and input power variations. © 1963-2012 IEEE.

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