Possibility of Using Fuel Cells for Co-generation of Heat and Power in Venizelio Hospital in Crete, Greece

Mitigation of climate change requires the replacement of traditional energy technologies with novel low carbon energy systems. The possibility of using a fuel cell and a hybrid energy system consisted of a fuel cell and solar-PV panel for energy generation in Venizelio hospital located in Crete, Greece has been investigated. The size, the heat and electricity generated, the H2 required and the capital cost of the fuel cell and the solar-PV system covering the energy requirements in the hospital have been estimated. Existing research has indicated that fuel cells using H2 can cover the heat and electricity needs in various buildings. Our results indicated that a fuel cell at 1 397 KW can produce annually 4 895 MWhel and 4 895 MWhth covering all the electricity and heating needs in Venizelio hospital producing excess heat at 2 451 MWhth. The capital cost of the fuel cell has been calculated at 4 191 000 € while the required H2 at 367.5 tons/year. All the energy requirements of the hospital can be also covered with a hybrid energy system consisted of a fuel cell and a solar-PV system. The size of the fuel cell has been estimated at 697.5 KW and the cost at 2 092 500 €. The electricity generation was at 2 444 KWhel and its heat production at 2 444 KWhth. The size of the solar-PV system has been evaluated at 1 629 KWp and the cost at 1 634 000 €.The annual electricity generation was at 2 451 MWhel. The capital cost of the hybrid energy system at 3 726 500 € is lower than the cost of the fuel cell alone at 4 191 000 €. Our results indicated that the use of novel benign energy systems with zero carbon emissions in Venizelio hospital is technically and economically feasible.

Feasibility Assessment of Hybrid Solar Photovoltaic-Biogas Generator Based Charging Station: A Case of Easy Bike and Auto Rickshaw Scenario in a Developing Nation

The popularity of electric vehicles (EVs) is increasing day by day in the modern world. The charging of EVs from grid-connected charging stations causes a considerable power crisis in the grid. Integrating renewable energy resources (RESs) with conventional energy sources in the power grid is now considered feasible to reduce peak power demand and the inevitable emission effect. Hence, this paper presents an energy solution for EV charging with two RESs, namely, solar photovoltaic (PV) and biogas. HOMER software is utilized to analyze the potency and functionality of solar PV and biogas-based EV charging stations. The proposed system consists of a solar PV system, two biogas engine generators, and a bidirectional converter with battery storage. The variation of different costs, such as net present cost (NPC), initial cost, and cost of energy (COE) for different solar PV systems (3 kW, 4.5 kW, 6 kW, and 9 kW), are analyzed in HOMER software. The 4.5 kW solar PV system is finally selected as the NPC, initial cost, and COE are $93,530, $19,735, and $0.181, respectively, which is efficient. The system’s lifetime is 25 years, where an initial 12 years is required to overcome the system cost, and the remaining 13 years will provide financial benefits. The study also illustrates the effect of solar irradiance, biomass, and the change in the load of the energy management system. The techno–economic analysis shows that the proposed scheme can be an effective energy solution. The emission of greenhouse gases (GHGs), such as CO2, CO, SO2, and NOX, is reduced considerably compared to other existing techniques. The study is expected to be beneficial in renewables-based EV charging systems with techno–economic and environmental feasibility.

Solar Photovoltaic Power Prediction Using Big Data Tools

Solar photovoltaic (PV) installation has been continually growing to be utilized in a grid-connected or stand-alone network. However, since the generation of solar PV power is highly variable because of different factors, its accurate forecasting is critical for a reliable integration to the grid and for supplying the load in a stand-alone network. This paper presents a prediction model for calculating solar PV power based on historical data, such as solar PV data, solar irradiance, and weather data, which are stored, managed, and processed using big data tools. The considered variables in calculating the solar PV power include solar irradiance, efficiency of the PV system, and characteristics of the PV system. The solar PV power profiles for each day of January, which is a summer season, were presented to show the variability of the solar PV power in numerical examples. The simulation results show relatively accurate forecasting with 17.57 kW and 2.80% as the best root mean square error and mean relative error, respectively. Thus, the proposed solar PV power prediction model can help power system engineers in generation planning for a grid-connected or stand-alone solar PV system.