Energy Efficiency and Fuel Economy of a Fuel Cell/Renewable Energy Sources Hybrid Power System with the Load-Following Control of the Fueling Regulators

Bizon, Nicu; Thounthong, Phatiphat

doi:10.3390/math8020151

Cited by 25 publications

(10 citation statements)

References 78 publications

(96 reference statements)

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“…Some of the constrains in the implementation of solutions to these problems are the requirement of sophisticated information for controlling the load and the source, thermal loading limit and physical circuit constrains and requirement of thermal management for allowing the uninterrupted power flow [62]. The disadvantages of solar PV are the huge installation and high commission cost and a storage process such as batteries [63]. Further the solar PV comprise charge controller, inverter, battery storage and controllable loads.…”

Section: Research Gapmentioning

confidence: 99%

A Comprehensive Survey on the Impact of Renewable Energy Sources in Power System Operation and Control

Tatikayala¹,

Dixit²

2021

Preprint

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The concern for huge increasing electricity demand, fossil fuel depletion, developed infrastructure reliability, carbon footprint reduction insisted the power utility companies to uptake RES (Renewable Energy Sources). The improved adoption of RES like wind energy and solar energy into the prevailing transmission and distribution networks led to several problems. These problems could be rectified by optimizing the power system parameters like frequency response, inertia, stability, battery usage, efficiency and power loss. This review hence provide a comprehensive analysis on the impact of renewable energy sources like wind and solar energy on power system operation and control in accordance with the major findings of the existing works. This review highlights the difficulties in the installation of solar and wind power with adoptable solutions. The challenges of power systems regarding the encoding of non-linearized function could be rectified by AI (Artificial Intelligence). The paper also insists the importance of artificial intelligence algorithm in the optimization of power system parameters. Artificial intelligence methods is useful for resolving various issues in power systems such as control, scheduling, forecasting etc. Few artificial algorithms such as Atom search optimization, Particle swarm optimization, Salp swarm optimization were investigated in this review for improving the performance of the power system. In spite of optimization analysis, the paper investigate various storage system types for improving the power system in accordance with cost, application and operation characteristics. Proper understanding of these systems is necessary for the future designing and hence through revision of state of art characteristics has been performed in this paper.

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Section: Research Gapmentioning

confidence: 99%

A Comprehensive Survey on the Impact of Renewable Energy Sources in Power System Operation and Control

Tatikayala¹,

Dixit²

2021

Preprint

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“…where Fuel eff P FCnet / FuelFr measures the fuel consumption efficiency, x represents the vector of FC state variables [52,55,[72][73][74][75], and GES variables are the airflow rate (AirFr) and the fuel flow rate (FuelFr) given by (8) and (9) [72,76]: The fueling flow rates for fuel (FuelFr) and air (AirFr) are as follows:…”

Section: The Fuel Economy Strategiesmentioning

confidence: 99%

“…MPP and MEP are usually sought by implementing a tracker that controls the hydrogen and air regulator so that the efficiency or power of the PEMFC is maximized. Different approaches are used to control MPPT/MPET, which can be divided into perturb and observe algorithms [43][44][45], fuzzy logic control [46][47][48], sliding mode control [49][50][51], and extremum seeking control [52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

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Renewable/Fuel Cell Hybrid Power System Operation Using Two Search Controllers of the Optimal Power Needed on the DC Bus

et al. 2020

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In this paper, the optimal and safe operation of a hybrid power system based on a fuel cell system and renewable energy sources is analyzed. The needed DC power resulting from the power flow balance on the DC bus is ensured by the FC system via the air regulator or the fuel regulator controlled by the power-tracking control reference or both regulators using a switched mode of the above-mentioned reference. The optimal operation of a fuel cell system is ensured by a search for the maximum of multicriteria-based optimization functions focused on fuel economy under perturbation, such as variable renewable energy and dynamic load on the DC bus. Two search controllers based on the global extremum seeking scheme are involved in this search via the remaining fueling regulator and the boost DC–DC converter. Thus, the fuel economy strategies based on the control of the air regulator and the fuel regulator, respectively, on the control of both fueling regulators are analyzed in this study. The fuel savings compared to fuel consumed using the static feed-forward control are 6.63%, 4.36% and 13.72%, respectively, under dynamic load but without renewable power. With renewable power, the needed fuel cell power on the DC bus is lower, so the fuel cell system operates more efficiently. These percentages are increased to 7.28%, 4.94% and 14.97%.

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“…The µGs are defined as small local distribution systems that operate with the same philosophy of SGs. They promote the distributed generation (DG) and the integration of RESs such as small hydropower units, wind turbines, and photovoltaic arrays; yet, non-RESs such as fuel cells, gas turbines, and combined cycle units are sometimes considered as well [7,8]. Besides, the µGs can be connected to the electrical grid or operate independently (standalone mode) during external faults or disturbances.…”

Section: Introductionmentioning

confidence: 99%

A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation

et al. 2020

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The electric spring (ES) is a contemporary device that has emerged as a viable alternative for solving problems associated with voltage and power stability in distributed generation-based smart grids (SG). In order to study the integration of ESs into the electrical network, the steady-state simulation models have been developed as an essential tool. Typically, these models require an equivalent electrical circuit of the in-test networks, which implies adding restrictions for its implementation in simulation software. These restrictions generate simplified models, limiting their application to specific scenarios, which, in some cases, do not fully apply to the needs of modern power systems. Therefore, a robust steady-state model for the ES is proposed in this work to adequately represent the power exchange of multiples ESs in radial micro-grids (µGs) and renewable energy sources regardless of their physical location and without the need of additional restrictions. For solving and controlling the model simulation, a modified backward–forward sweep method (MBFSM) is implemented. In contrast, the voltage control determines the operating conditions of the ESs from the steady-state solution and the reference voltages established for each ES. The model and algorithms of the solution and the control are validated with dynamic simulations. For the quasi-stationary case with distributed renewable generation, the results show an improvement higher than 95% when the ESs are installed. On the other hand, the MBFSM reduces the number of iterations by 34% on average compared to the BFSM.

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Energy Efficiency and Fuel Economy of a Fuel Cell/Renewable Energy Sources Hybrid Power System with the Load-Following Control of the Fueling Regulators

Cited by 25 publications

References 78 publications

A Comprehensive Survey on the Impact of Renewable Energy Sources in Power System Operation and Control

A Comprehensive Survey on the Impact of Renewable Energy Sources in Power System Operation and Control

Renewable/Fuel Cell Hybrid Power System Operation Using Two Search Controllers of the Optimal Power Needed on the DC Bus

A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation

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