Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

Dhiman, Harsh S.; Deb, Dipankar; Mureșan, Vlad; Ungureşan, Mihaela-Ligia

doi:10.3390/sym11050675

Cited by 25 publications

(7 citation statements)

References 23 publications

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“…The historical data of the total energy charging demand of HEVs for half-a-year in the test system are used to train the SVR model with adjusting parameters fixed by the MDA [43]. Therefore, the total charging demand of the HEVs over 24 h is predicted using the proposed SVR-MDA based model [44]. To have a fair comparison, the prediction is made over 10 days with the use of some of the most well-known methods such as autoregressive-moving-average (ARMA), artificial neural network (ANN), SVR and SVR-DA (without modifications in MDA) [45].…”

Section: Simulation Resultsmentioning

confidence: 99%

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

et al. 2021

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Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

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Section: Simulation Resultsmentioning

confidence: 99%

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

et al. 2021

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“…Three algorithms including FF, WOA, and PSO algorithms have been selected in this paper for their superiority in solving complex optimization problems. The algorithms are utilized to minimize the size of a hybrid WT/PV/biomass/pump-hydro storage system with a condition of satisfying the load demand with the minimum COE [50][51][52][53][54]. One of the objectives of the present work is to compare between these algorithms and select the best one in terms of accuracy and convergence speed.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems

Alturki

Awwad

2021

Energies

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In this study, a standalone hybrid wind turbine (WT)/photovoltaic (PV)/biomass/pump-hydro-storage energy system was designed and optimized based on technical, economic, and environmental parameters to provide the load demand with an objective function of minimum cost of energy (COE). The constraints of the proposed approach are the loss of power supply probability, and the excess energy fraction. The proposed approach allows the combination of different sources of energy to provide the best configuration of the hybrid system. Therefore, the proposed system was optimized and compared with a WT/PV/biomass/battery storage-based hybrid energy system. This study proposes three different optimization algorithms for sizing and minimizing the COE, including the whale optimization algorithm (WOA), firefly algorithm (FF) and particle swarm optimization (PSO) and the optimization procedure was executed using MATLAB software. The outcomes of these algorithms are contrasted to select the most effective, and the one providing the minimum COE is chosen based on statistical analysis. The results indicate that the proposed hybrid WT/PV/biomass/pump-hydro storage energy system is environmentally and economically practical. Meanwhile, the outcomes demonstrated the technical feasibility of a pump-hydro energy storage system in expanding the penetration of renewable energy sources compared to other existing systems. The COE of the pumped-hydro storage hybrid system was found to be lower (0.215 $/kWh) than that with batteries storage hybrid system (0.254 $/kWh) which was determined using WOA at the same load demand.

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“…• Hybrid Operation Hybrid operation has received significant attention recently in several industries, such as wind farming [8] and automobile manufacturing [9]. Such a device generally uses two or more diverse forms of power sources in various situations, shown as Figure 1.…”

Section: • Hybrid Operationmentioning

confidence: 99%

A Hybrid Mechanism for Helicopters

et al. 2019

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This study successfully provides the empirical practicability of a hybrid mechanism for helicopters. A turbine engine and a set of electricity power systems can operate simultaneously and/or independently as a symmetric structure. The latter power source works as an immediate supplementary device if the former one has malfunction. We look forward to promoting this experimental evidence in the helicopter industry. The ultimate purpose of this manuscript is to decrease the incidents of crashes and save people’s lives.

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Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms

Cited by 25 publications

References 23 publications

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems

A Hybrid Mechanism for Helicopters

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