Examinando por Autor "Genikomsakis, Konstantinos N."

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    Life cycle assessment of a lithium iron phosphate (LFP) electric vehicle battery in second life application scenarios
    (MDPI, 2019-05-01) Ioakimidis, Christos S.; Murillo Marrodán, Alberto ; Bagheri, Ali; Thomas, Dimitrios; Genikomsakis, Konstantinos N.
    This paper presents a life cycle assessment (LCA) study that examines a number of scenarios that complement the primary use phase of electric vehicle (EV) batteries with a secondary application in smart buildings in Spain, as a means of extending their useful life under less demanding conditions, when they no longer meet the requirements for automotive purposes. Specifically, it considers a lithium iron phosphate (LFP) battery to analyze four second life application scenarios by combining the following cases: (i) either reuse of the EV battery or manufacturing of a new battery as energy storage unit in the building; and (ii) either use of the Spanish electricity mix or energy supply by solar photovoltaic (PV) panels. Based on the Eco-indicator 99 and IPCC 2007 GWP 20a methods, the evaluation of the scenario results shows that there is significant environmental benefit from reusing the existing EV battery in the secondary application instead of manufacturing a new battery to be used for the same purpose and time frame. Moreover, the findings of this work exemplify the dependence of the results on the energy source in the smart building application, and thus highlight the importance of PVs on the reduction of the environmental impact.
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    Simulation of wind-battery microgrid based on short-term wind power forecasting
    (MDPI AG, 2017-11-06) Genikomsakis, Konstantinos N.; López, Sergio; Dallas, Panagiotis I. ; Ioakimidis, Christos S.
    The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN) clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.
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    A university e-bike sharing system used as a real-time monitoring emissions tool under a smart city concept
    (AVERE, 2016-12-30) Ioakimidis, Christos S. ; Rycerski, Pawel; Koutra, Sesil; Genikomsakis, Konstantinos N.
    This work intents to describe a new approach that would be able to combine the positive effects from the use of an E-Bike sharing system in a medium-large population urban city of Belgium demonstrated initially in case of the local University Campuses along with the use of the E-Bikes as environmental mobile sensing units.
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    A virtual power plant with the use of the energy box in a smart grid concept
    (European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ), 2013-03) Ioakimidis, Christos S. ; Oliveira, L.; Genikomsakis, Konstantinos N. ; Rycerski, Pawel
    Recent years have witnessed considerable advances in designing and operating of energy grids, while the concepts related to the operation of Smart Grids, Vehicle-to-Grid, responsive demand, among others, are gaining research interest worldwide. From an educational viewpoint, the use of a complementary laboratory approach is expected to contribute to the effective communication of these concepts to the students of engineering courses at university level. To this end, this work presents the development of a virtual lab for educational purposes, simulating the operation of a Virtual Power Plant in a Smart Grid context. The system under study involves renewable energy generation, controllable loads and local electricity storage options from electric vehicles, while allowing the students to experiment with the system parameters and perform real-time monitoring and control of the system operation.
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    Wind power and electricity consumption forecasting on a smart house location
    (European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ), 2013-03) Eliasstam, H.; Genikomsakis, Konstantinos N.; Ioakimidis, Christos S.
    This paper presents the use of an artificial neural network for classification on a residence house that uses wind and electricity consumption predictions to identify patterns at the desired location, in order to obtain a stochastic distribution of the daily wind and electricity profile. This is a step on the further creation of a short-term operation model that allows determining the technical and economic impact of stationary/mobile batteries of electric vehicles in presence of microrenewables along with the electricity consumption. This short-term operation model will be in the day-ahead perfect market operation (unit commitment) where specific changes are made to consider stationary and mobile operation.