Costas Elmasides

Energy management in a stand-alone power system for the production of electrical energy with long term hydrogen storage

This paper deals with the importance of an efficient energy (or power) management strategy (EMS) on an existing stand-alone power system that uses renewable energy sources for the production of electrical energy. Due to the intermittent nature of the renewables, part of this energy is used to split the water for the production of hydrogen, which is stored and used later for the production of energy in a PEM fuel cell, in case of high energy demands. The energy management algorithms aim at the reliable and effective control of the energy flow that is basically used to meet the load requirements of the autonomous system. The developed simulated algorithms were compared to each other in order to determine the most efficient strategy as far as hydrogen production and autonomy are of concern. Parametric sensitivity was also a major issue which was studied extensively. All the results and outcomes of such an analysis are considered as the basis for the optimization and control study of similar stand-alone power systems.

Application of Neural Networks Solar Radiation Prediction for Hybrid Renewable Energy Systems

In this paper a Recurrent Neural Network (RNN) for solar radiation prediction is proposed for the enhancement of the Power Management Strategies (PMSs) of Hybrid Renewable Energy Systems (HYRES). The presented RNN can offer both daily and hourly prediction concerning solar irradiation forecasting. As a result, the proposed model can be used to predict the Photovoltaic Systems output of the HYRES and provide valuable feedback for PMSs of the understudy autonomous system. To do so a flexible network based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of SYSTEMS SUNLIGHT S.A. facilities. As a result, the RNN after training with meteorological data of the aforementioned area is applied to the specific HYRES and successfully manages to enhance and optimize its PMS based on the provided solar radiation prediction.

Enhancement of hybrid renewable energy systems control with neural networks applied to weather forecasting: the case of Olvio

In this paper, an intelligent forecasting model, a recurrent neural network (RNN) with nonlinear autoregressive architecture, for daily and hourly solar radiation and wind speed prediction is proposed for the enhancement of the power management strategies (PMSs) of hybrid renewable energy systems (HYRES). The presented model (RNN) is applicable to an autonomous HYRES, where its estimations can be used by a central control unit in order to create in real time the proper PMSs for the efficient subsystems’ utilization and overall process optimization. For this purpose, a flexible network-based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of Systems Sunlight S.A. facilities. The simulation results indicated that RNN is capable of assimilating the given information and delivering some satisfactory future estimation achieving regression coefficient from 0.93 up to 0.99 that can be used to safely calculate the available green energy. Moreover, it has some sufficient for the specific problem computational power, as it can deliver the final results in just a few seconds. As a result, the RNN framework, trained with local meteorological data, successfully manages to enhance and optimize the PMS based on the provided solar radiation and wind speed prediction and make the specific HYRES suitable for use as a stand-alone remote energy plant.

Amorphous silicon-graphene anodes for lithium ion batteries

For the development of the next generation lithium-ion batteries it is primordial to investigate new materials as potential candidates towards the increase of the specific capacity of the anode and the new lighter and efficient cells. In this paper we present our investigation on amorphous silicon (a-Si) deposited by DC-sputtering on top of Single Layer Graphene (SLG) grown by Chemical Vapor Deposition (CVD). Our aim being to improve the mechanical properties of the silicon volume change during charging and discharging cycles, it is found that half cells fabricated with such electrodes can achieve specific capacity values above 2000 mAh/g while avoiding large pulverization phenomena. However, it is also found that the a-Si/SLG interface results in high resistance electrodes and decreases cell performance. We suggest that by improving the a-Si/SLG contact resistance, the performance will further improve.

Automation and Operation Strategies in a Stand-Alone Power System that Uses Solar and Wind Energy in Conjunction with Hydrogen Long-Term Storage

Abstract The description and analysis of a stand-alone power system that exploits solar and wind energy is presented in this study. More specifically, the integrated system consists of a 10kW p photovoltaic (PV) array and three wind generators rated at 1kW p each. A lead-acid accumulator with nominal capacity of 3,000Ah/48V is used to absorb the short energy fluctuations and to regulate the operation and integrity of the system. Furthermore, the long-term needs are fulfilled via a hydrogen based system. Hydrogen is produced by a 4.2kW p PEM electrolyzer and stored in cylinders under pressure for subsequent use in a 4kW p PEM fuel cell. A diesel engine can also be used to provide power in cases of subsystems failure. In the present study the infrastructure of the control system is presented along with the algorithm for the automatic operation which is used to implement the power management strategy. Moreover, experimental results from the operation of each subsystem will be given and a discussion on them will take place. Finally, two operation strategies will be applied through an one-year simulation study, in order to identify their possible implementation in the real system that currently operates under a system operator either remotely or on site.