The search for new fuels to supersede fossil fuels has been intensified these recent decades. Among these fuels, hydrogen has attracted much interest due to its advantages, mainly cleanliness and availability. It can be produced from various raw materials (e.g., water, biomass) using many resources, mainly water electrolysis and natural gas reforming. However, water electrolysis combined with renewable energy sources is the cleanest way to produce hydrogen while reducing greenhouse gases. Besides, hydrogen can be used by fuel cells for producing both electrical and thermal energy. The aim of this work was towards efficient integration of this system into energy efficient buildings. The system is comprised of a photovoltaic system, hydrogen electrolyzer, and proton exchange membrane fuel cell operating as a cogeneration system to provide the building with both electricity and thermal energy. The system’s modeling, simulations, and experimentations were first conducted over a short-run period to assess the system’s performance. Reported results show the models’ accuracy in analyzing the system’s performance. We then used the developed models for long-run testing of the hybrid system. Accordingly, the system’s electrical efficiency was almost 32%. Its overall efficiency reached 64.5% when taking into account both produced electricity and thermal energy.

https://www.mdpi.com/2071-1050/12/12/4832/pdf

Performance Assessment of a Hybrid System with Hydrogen Storage and Fuel Cell for Cogeneration in Buildings

Numerical procedure for accurate simulation of photovoltaic modules performance based on the identification of the single-diode model parameters

Micro-Grid (MG) systems have been extensively studied and deployed to lower the power consumption while reducing the greenhouse gas emissions. Although, the challenge with Renewable Energy Sources (RES) is their uncertain and intermittent nature, things that led the researchers to think about integrating storage devices, especially batteries, into MG systems. The main aim is to store the excess of produced energy for further usage when not enough energy is available. Nevertheless, batteries modeling and characterization is mandatory to identify their parameters and study their performance within MG systems. Moreover, in order to continuously supply electricity to the building, it is required to figure out the optimum size of energy production systems and storage devices. This paper introduces a methodology for MG modeling and performance evaluation. Its main contribution is twofold, (i) battery’s parameters identification, and (ii) modeling and dimensioning method for both standalone and MG systems. An instrumentation platform, composed of recent sensing and actuating equipment, for MG energy management and battery characterization is developed. Simulation and experimental results show the effectiveness of the proposed methodology.

Battery Characterization and Dimensioning Approaches for Micro-Grid Systems

Approach for dimensioning stand-alone photovoltaic systems

The introduction of green hydrogen-based energy storage in association with renewable energy constitutes a promising and sustainable solution to the increase in energy demand while reducing greenhouse gas emissions. However, these hybrid systems face technical, economic, and logistic challenges that require a new transport and distribution architecture. The technical-economic study of these expensive installations requires good modeling and optimal sizing of the system components. This study presents a global model for hydrogen production and storage stations using photovoltaics (PV) and integrating Proton Exchange Membrane Fuel Cell (PEMFC) modules for electric vehicles. The simulations and sizing were based on the implementation of an effective mathematical model capable of accurately simulating the real dynamic behavior of the installation, the electrical and energy yields, the power consumed and produced, and finally the mass of hydrogen stored and/or consumed by the fuel cell. In this model, the hybrid system integrates PV solar panels with a maximum power of 1.2 MW, followed by a 1.0 MW Proton Exchange Membrane (PEM) electrolyzer, a high-pressure hydrogen storage tank, and a PEMFC to convert hydrogen into electricity. The simulation results showed that the energy generated by the PV panels can produce around 200 kg/day of green hydrogen by electrolysis, which makes it possible to power 100 electric cars per day with a range of 250 km for each.

Modeling and Simulation of a Renewable Energy PV/PEM with Green Hydrogen Storage

Renewable energy sources (RES) have been largely deployed as green energy producers in order to reduce power consumption while minimizing greenhouse emissions. However, the intermittent nature of RES (e.g., Solar Energy) must be addressed in order to allow their large scale integration in buildings. Standalone PV systems are generally installed in remote and rural regions since they are not connected to the electrical grid. They use mainly storage devices (e.g., batteries) to store produced/extra solar energy during the day for eventual usage at night. Recently, there have been enormous deployments of grid-connected photovoltaic systems, which use solar panels connected to the electricity grid for producing and feeding extra electricity back to the grid. They are also being able to supply electricity to the load when there is no production. In this paper, we are talking the research question aiming at selecting the suitable dimensions for energy production (PV system). We have, therefore, developed a model for each component of the PV system. A grid-connected PV system is sized, modeled and analyzed according to a dedicated consumption profile. Simulation results show the effectiveness of the proposed dimensioning model.

Modeling and Dimensioning of Grid-Connected Photovoltaic Systems

Electrical grids converge now to a novel concept which named Microgrids (MG). it consists on producing energy with isolated connected to grid to reduce dependency on fuel and main grids due to its fluctuant cost and to decrease harmful emission in the atmosphere. Constituted by renewable sources, energy storage system and controllable sources, a hybrid combination of these DERs is adopted to maintain MG reliability, transparency and efficiency with the deregulate power production according to weather conditions; ( e.g. temperature, solar radiation, wind speed. . . ) of the non-controllable sources and load fluctuation. These perturbations affect frequency as one parameter of quality of energy sensitive to active power balance. That’s why a smart management of controllable sources is highly recommended to minimize this frequency deviation. In this paper, a dynamic model is adopted with PI controller in controllable sources and storage system and presents a novel approach to design PI controller parameters by hybrid Evolutionary Algorithm (EA) GA-TLBO for a robust control of frequency regulation under uncertainty. A simulated isolated MG is tested on scenarios to validate the approach adopted in conceiving the PI parameters to avoid the frequency fluctuation in the different cases of study.

https://www.ijrer.ijrer.org/index.php/ijrer/article/download/9977/pdf

Secondary Control of Islanded Microgrids Using PI-Evolutionary Algorithms Under Uncertainties

Nowadays, the modeling of the photovoltaic model becomes more and more important because it allows the industry to understand more about its electrical circuit, which leads to better reliability. This electrical circuit can have five, seven, or nine parameters depending on the number of diodes. This paper discusses a novel method to extract the seven parameters of a double diode (DD) model based on datasheet parameters. The present approach combines genetic algorithms (GA) and analytical methods: Two parameters (photo-generated current and parallel resistance) are computed analytically, while the other parameters are optimized using the GA. The objective function used contains open circuit, short-circuit, and maximum power equations. The extracted seven parameters generated with this method are compared to those obtained through other methods. To assess the efficacy and reliability of the proposed approach, the P-V and I-V properties are evaluated using datasheet at various temperatures and solar irradiations. The results demonstrate a strong correlation; moreover, the Root Mean Square (RMSE) and the absolute error of current have been computed, and the model’s performance has been confirmed.

Extraction Of Electrical Parameters for Two-Diode Photovoltaic Model Using Combined Analytical and Genetic Algorithm

Load Frequency Control in Autonomous Microgrids using Hybrid Algorithm GWO-CS based Fuzzy logic Sliding Mode Controller

Microgrids (MG) are a new trend on electrical grids. Despite their dependence on intermittent renewable resources, they provide harmful greenhouse gas emissions to our environment and may present a long-term solution to meet future energy needs with sustainable energy resources (e.g. solar example, wind turbine). Initially, the MG adopted is a hybrid system with non-controllable sources as renewable sources, an energy storage system and controllable sources (e.g. diesel generator (DG), fuel cell (FC), micro turbine (MT)) to ensure MG reliability, transparency and efficiency. It’s also recommended to maintain the frequency as part of the quality of electrical energy affected and disturbed by the intermittence of sources and loads that cannot be controlled depending on conditions weather (for example, solar irradiation, wind speed, temperature) and human behavior (load). This article presents a study of the impact of the storage system and the effect of the uncertainties of the MG system and the frequency of noise recovery by applying a secondary control based on a PI control set by combined GATLBO. A simulated isolated MG is tested in scenarios to test the approach adopted to design the PI parameters in order to avoid frequency fluctuation in the different case studies.

Impact of Energy Storage System on Frequency Control of an Islanded Microgrid

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