Integration of fuel cells and supercapacitors in electrical microgrids: Analysis,modelling and experimental validation

This article examines a hybrid storage system comprising fuel cells (FC) and supercapacitors (SC) for an electrical microgrid located in the Renewable Energies Laboratory at the Public University of Navarre. Firstly, the hybrid storage system size was determined based on an energy and frequency analysis of real data for the electrical power generated and consumed in the microgrid over the course of a year in operation. This was followed by the experimental characterisation of the electrical behaviour of the FCs and SCs, in steady-state and dynamic modes of operation. Furthermore, an electrical model was developed for the FCs and another for the SCs, both of which gave satisfactory results in the experimental validations. Finally, a study was made of the storage system, comprising four 1.2 kW proton exchange membrane fuel cells (PEMFC) and three SCs of 83.3 F and 48.6 V each, in a real microgrid operating environment. Specifically, a comparison was made between the storage system solely comprising FCs and the hybrid storage system formed by a combination of FCs and SCs. The hybridisation of the FCs and SCs resulted in a complete, high-capacity energy storage system, to guarantee supply even in those months with low renewable energy resources and, in turn, able to provide the fast dynamic responses regularly required by supply and demand in the microgrid.     

Wind park reliable energy production based on a hydrogen compensation system. Part I: Technical viability

Power production from renewable energy resources is increasing day by day. In the case of Spain, in 2009, it represents the 26.9% of installed power and 20.1% of energy production. Wind energy has the most important contribution of this production. Wind generators are greatly affected by the restrictive operating rules of electricity markets because, as wind is naturally variable, wind generators may have serious difficulties on submitting accurate generation schedules on a day ahead basis, and on complying with scheduled obligations. Weather forecast systems have errors in their predictions depending on wind speed. Thus, if wind energy becomes an important actor in the energy production system, these fluctuations could compromise grid stability. In this study technical and economical viability of a large scale compensation system based on hydrogen is investigated, combining wind energy production with a biomass gasification system. Combination of two systems has synergies that improve final results. In the economical study, it is considered that all hydrogen production that is not used to compensate wind energy could be sold to supply the transportation sector.     

Energy management hypothesis for hybrid power system of H2/WT/PV/GMT via AI techniques

This paper aims to attain an efficient and optimized energy management operation of Hybrid Power System (HPS) by using Artificial Intelligent (AI) controllers. The HPS comprises Wind Turbines (WTs) and Photovoltaic (PV) panels such as primary Renewable Energy Sources (RESs) in addition to both Fuel Cells (FCs) and Gas Micro–Turbines (GMTs) which are used as Backup Sources (BKUSs).To avoid the undesired negative impacts on the HPS functionality because of the RESs intermittency, the Hydrogen Storage System (HSS) is integrated into the system. Two different energy management strategies based on Neural Networks (NN) and Fuzzy Logic Control (FLC) respectively are applied to the HPS for minimizing the energy production cost and keeping the buffer role of HSS. Using MATLAB^?, the proposed two AI introduced solutions are used for reaching adequate energy management operation performance for the overall HPS during 24 h load variation. From the numerical simulations, the superiority of the FLC over the NN control approach is discussed. The proposed HSS can positively act as a buffer solution to avoid drawbacks of RESs during unexpected load peaks and/or discontinuous energy production.     

Analytical model as a tool for the sizing of a hydrogen production system based on renewable energy: The Mexican Caribbean as a case of study

The main advantage of the hybrid system compared with separate array solar photovoltaic and stand-alone wind turbine is the possibility of the surplus energy storage by transforming it to hydrogen that can be use in fuel cells. However the design and sizing of this kind of technologies need to meet the local microclimate in order to reach higher efficacies. A tool based on an analytical model to sizing, analyze and assess the feasibility of the hybrid wind/photovoltaic/H₂ energy conversion systems using real weather data is presented in this work. The model considers an energy balance analysis and electrical variables of the system components; the tool calculates the subsystems efficacy and proposes the improvements to increase the efficiency of the use in surplus energy produced by the hybrid system. To validate the analytical model, simulation based on wind speed and solar radiation measurements from meteorological monitoring station in a Mexican Caribbean City is discussed.     

Economic viability assessment of sustainable hydrogen production,storage, and utilisation technologies integrated into on- and off-grid micro-grids: A performance comparison of different meta-heuristics

In recent years, there has been considerable interest in the development of zero-emissions, sustainable energy systems utilising the potential of hydrogen energy technologies. However, the improper long-term economic assessment of costs and consequences of such hydrogen-based renewable energy systems has hindered the transition to the so-called hydrogen economy in many cases. One of the main reasons for this is the inefficiency of the optimization techniques employed to estimate the whole-life costs of such systems. Owing to the highly nonlinear and non-convex nature of the life-cycle cost optimization problems of sustainable energy systems using hydrogen as an energy carrier, meta-heuristic optimization techniques must be utilised to solve them. To this end, using a specifically developed artificial intelligence-based micro-grid capacity planning method, this paper examines the performances of twenty meta-heuristics in solving the optimal design problems of three conceptualised hydrogen-based micro-grids, as test-case systems. Accordingly, the obtained numeric simulation results using MATLAB indicate that some of the newly introduced meta-heuristics can play a key role in facilitating the successful, cost-effective development and implementation of hydrogen supply chain models. Notably, the moth-flame optimization algorithm is found capable of reducing the life-cycle costs of micro-grids by up to 6.5% as compared to the dragonfly algorithm.     

A techno-economic study for a hydrogen storage system in a microgrid located in baja California,Mexico. Levelized cost of energy for power to gas to power scenarios

In this work a techno economic feasibility study is carried out to implement a Hydrogen based Power to Gas to Power (P2G2P) in a Microgrid, located in a rural area in Baja California, Mexico. The study aims to define the feasibility to store energy throughout seasons with this novel alternative using an electrolyzer to produce green hydrogen from excess renewable energy in winter, to store it during months and re inject it to the grid as electricity by a fuel cell in the high energy demanding season. The Microgrid was modeled in Homer software and simulations of the P2G2P lead to Levelized Cost of Energy data to compare between the P2G2P scenarios and the current diesel-battery based solution to complete the high demand by the community. This study shows that using hydrogen and fuel cells to substitute diesel generators it is possible to reduce CO₂ emissions up to a 27% and that in order for the P2G2P to be cost competitive, the fuel cell should reduce its cost in 50%; confirming that, in the medium to long term, the hydrogen storage system is a coherent alternative towards decarbonization of the distributed energy generation.