Sizing, Optimization, and Financial Analysis of a Green Hydrogen Refueling Station in Remote Regions

Hydrogen (H2) can be a promising energy carrier for decarbonizing the economy and especially the transport sector, which is considered as one of the sectors with high carbon emissions due to the extensive use of fossil fuels. H2 is a nontoxic energy carrier that could replace fossil fuels. Fuel Cell Electric Vehicles (FCEVs) can decrease air pollution and reduce greenhouse gases when H2 is produced from Renewable Energy Sources (RES) and at the same time being accessible through a widespread network of Hydrogen Refueling Stations (HRSs). In this study, both the sizing of the equipment and financial analysis were performed for an HRS supplied with H2 from the excess electrical energy of a 10 MW wind park. The aim was to determine the optimum configuration of an HRS under the investigation of six different scenarios with various numbers of FCEVs and monthly demands, as well as ascertaining the economic viability of each examined scenario. The effect of the number of vehicles that the installation can refuel to balance the initial cost of the investment and the fuel cost in remote regions was investigated. The results showed that a wind-powered HRS could be a viable solution when sized appropriately and H2 can be used as a storage mean for the rejected wind energy. It was concluded that scenarios with low FCEVs penetration have low economic performance since the payback period presented significantly high values.

Economic appraisal of Power-to-Liquid Fischer-Tropsch plants exploiting renewable electricity, green hydrogen, and CO2 from biogas in Europe

The present work analyses the techno-economic potential of Power-to-Liquid routes to synthesize Fischer-Tropsch paraffin waxes for the chemical sector. The Fischer-Tropsch production unit is supplied with hydrogen produced by electrolysis and CO2 from biogas upgrading. In the analysis, 17 preferential locations were identified in Germany and Italy, where a flow of 1 t/h of carbon dioxide was ensured. For each location, the available flow of CO2 and the capacity factors for both wind and solar PV were estimated. A metaheuristic-based approach was used to identify the cost-optimal process design of the proposed system. Accordingly, the sizes of the hydrogen storage, electrolyzer, PV field, and wind park were evaluated. The analysis studied the possibility of having different percentage of electricity coming from the electric grid, going from full-grid to full-RES configurations. Results show that the lowest cost of Fischer-Tropsch wax production is 6.00 €/kg at full-grid operation and 25.1 €/kg for the full-RES solution. Wind availability has a key role in lowering the wax cost.

Optimal design of SSSC and crowbar parameters for performance enhancement of Egyptian Zafrana wind farm

Zafarana Wind Park is considered the largest wind farm in Middle East. By the year 2022, Zafarana wind farm will inject 545 MW to Egypt national electric network (ENEN). This wind farm has been connecting to ENEN in stages (eight stages) from ten years ago. Each stage represents a project of wind farm. Hence, the impact of the emergency condition of ENEN on the performance of the installed stage of Zafarana Wind Park should be studied. Moreover, determining the possible method to reduce the side effects of the emergency condition of ENEN on the installed stage of Zafarana Wind Park becomes an important issue. In this paper, a combination between Static Synchronous Series Compensator (SSSC) and crowbar (Cro-SSSC) is used to enhance the performance of the one installed stage of Zafarana Wind Park. The studied stage of Zafarana Wind Park is Zafarana Z1 wind farm (Z1). Zafarana Z1 wind farm represents that first stage of Zafarana Wind Park that was connected to ENEN. Hence, the paper takes Zafarana Z1 wind farm as the studied case to examine the impact of Cro-SSSC. The crowbar is used to protect the power components of the SSSC from the high current during grid fault condition. Also, in this paper multi-objective lightning attachment procedure optimization algorithm (LAPO) and the sine cosine algorithm (SCA) are used to evaluate and tune the control gains of SSSC and the value of crowbar's resistance. This step is important in order to determine the values of the control's gains of SSSC and the value of crowbar resistance which suit the studied system (one installed stage of Zafarana Wind Park). The sliding mode control (SMC) is used as an example of nonlinear control of SSSC. Also, a hybrid of LAPO and SMC is used to improve the performance of Zafarana Z1 during faults. The results of LAPO, SCA and SMC are compared during three-phase fault and single line to ground fault applied to the studied system.

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts.

Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (anomaly detection model designed at wind park level) algorithms trained only on SCADA data. The normal behavior and the anomalous samples of the wind turbines are identified and several interpretable indicators are proposed based on the predictions of these algorithms, to provide the wind park operators with understandable information with enough time to plan operations ahead and avoid unexpected costs. The stated methodology is validated in a real underproduction wind park composed by 18 wind turbines.