Techno-Economic Analysis of a Fuzzy Logic Control Based Hybrid Renewable Energy System to Power a University Campus in Japan

Hinokuma, Tatsuya; Farzaneh, Hooman; Shaqour, Ayas

doi:10.3390/en14071960

Cited by 21 publications

(11 citation statements)

References 31 publications

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“…Having the same consuming characteristics (1 kW power constant consumption), the power plant in Russia would require 35 solar modules against 23 in Australia and a three times bigger hydrogen tank. In India, almost the same number of solar modules (32) as in Russia (35) which makes it possible to reduce the hydrogen tank twofold. However, in terms of hydrogen equipment capacity, there is small difference between India and Australia, but there is large difference in terms of solar modules-32 against 23.…”

Section: Discussionmentioning

confidence: 99%

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The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations

et al. 2021

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The exhaustion of fossil fuels causes decarbonized industries to be powered by renewable energy sources and, owing to their intermittent nature, it is important to devise an efficient energy storage method. To make them more sustainable, a storage system is required. Modern electricity storage systems are based on different types of chemical batteries, electromechanical devices, and hydrogen power plants. However, the parameters of power plant components vary from one geographical location to another. The idea of the present research is to compare the composition of a solar-powered hydrogen processing closed-cycle power plant among the selected geographical locations (Russia, India, and Australia), assuming the same power consumption conditions, but different insolation conditions, and thus the hydrogen equipment capacity accordingly. The number of solar modules in an array is different, thus the required hydrogen tank capacity is also different. The comparison of equipment requires building an uninterrupted power supply for the selected geographical locations, which shows that the capacity of the equipment components would be significantly different. These numbers may serve as the base for further economic calculations of energy cost.

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Section: Discussionmentioning

confidence: 99%

“…However, the situation may change in the decarbonized future, when hydrogen may be considered as one of the primary fuels [32]. Some researchers consider the autonomous locations to be equipped with renewables and hydrogen fuel cell capacities [33].…”

Section: Introductionmentioning

confidence: 99%

The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations

et al. 2021

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“…Later the inference block is where the real data from an expert takes places through membership rules within if-then statements [73]. Finally, the output takes places in a defuzzification process where the linguistic rules of the inference are translated into numerical crisp values [74].…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

et al. 2021

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This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

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“…Renewable energy's contribution to the total electricity generation reached 15.1%, with solar energy, wind power, and hydropower accounting for 10.4%, 4.4%, and 0.1%, respectively [8]. While this sheds hope for the future of the Jordanian energy sector and increases energy resilience [9], an increase in renewable energy in both transmission and distribution levels introduces grid-level operational challenges that must be met to achieve optimized and efficient operation. Renewable energy sources such as solar and wind are non-dispatchable power units characterized by uncertainty, stochasticity, and being intermittent as they are dependent on variable weather conditions, which increase the flexibility needed to achieve supply-demand balance [10] and the need for energy storage systems [11,12].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

2021

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In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

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Techno-Economic Analysis of a Fuzzy Logic Control Based Hybrid Renewable Energy System to Power a University Campus in Japan

Cited by 21 publications

References 31 publications

The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations

The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

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