Wind turbine and ultra-capacitor harvested energy increasing in microgrid using wind speed forecasting

Aranizadeh, Ali; Zaboli, Aydin; Gashteroodkhani, Oveis Asgari; Vahidi, B.

doi:10.1016/j.jestch.2019.08.006

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…It is required to change the value of β to buck or boost the WT speed as per varying W S via pitch angle controller for an optimal operation to extract maximum energy from WECS. When W S is lower than the rated speed, then β will increase the pressure on the WT blade surface and enhance the torque, which improves the rotor speed and WT output power; the opposite procedure will be followed when W S is higher than the rated speed 34,35

P_{m} = 0.5 ρ {AW}_{S}^{3} C_{P} (λ, β),

A = π R^{2},

C_{P} = 0.46 (151 / λ_{1} - 0.58 β - 0.002 β^{2.14} - 13.2) (e^{- 18.4 / λ_{1}}),

λ_{1} = [1 / (λ + 0.02 β)] - [0.003 / (β^{3} + 1)] .

…”

Section: General Description Of Systemmentioning

confidence: 99%

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A control strategy incorporating multiple EV charging stations for an islanded micro‐grid energy management and voltage regulation

Gupta,

Suhag

2024

Energy Storage

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An electric vehicle (EV) charging station control strategy (CSCS) is proposed for proper power‐sharing among various components of an islanded microgrid (MG), consisting of the photovoltaic (PV) system, the wind energy conversion system (WECS), variable load (critical and non‐critical), multiple charging stations (CSs) and the dump load (LD). The proposed control strategy is examined in three distinct cases with varying operating conditions owing to solar irradiance, wind speed and load variations. For proper energy management (EM) and voltage regulation (VR), the diverse EVs have varying battery capacities parked in the multiple CSs to either absorb or deliver power through a two‐way power flow between the islanded MG system and the EV‐based CSs, which is reliant on the supply and demand (S&D) energy difference. The simulation is executed via the MATLAB software and two different modes—excess power mode (EPM) and deficient power mode (DPM)—are employed to validate the proposed CSCS's competency. For a realistic assessment of the proposition, the results are also verified by using a hardware‐in‐loop (HIL) real‐time (RT) OPAL‐RT simulator (OP5700). Furthermore, the findings from MATLAB and the OPAL‐RT simulator are compared to establish the robustness of the proposed strategy.

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P_{m} = 0.5 ρ {AW}_{S}^{3} C_{P} (λ, β),

A = π R^{2},

C_{P} = 0.46 (151 / λ_{1} - 0.58 β - 0.002 β^{2.14} - 13.2) (e^{- 18.4 / λ_{1}}),

λ_{1} = [1 / (λ + 0.02 β)] - [0.003 / (β^{3} + 1)] .

…”

Section: General Description Of Systemmentioning

confidence: 99%

“…When W S is lower than the rated speed, then β will increase the pressure on the WT blade surface and enhance the torque, which improves the rotor speed and WT output power; the opposite procedure will be followed when W S is higher than the rated speed. 34,35…”

Section: Wecsmentioning

confidence: 99%

A control strategy incorporating multiple EV charging stations for an islanded micro‐grid energy management and voltage regulation

Gupta,

Suhag

2024

Energy Storage

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“…The harvested wind energy was enhanced by designing wind speed forecasting and modelled by MATLAB to produce more output power. 62 The multi-scale wind MG was developed to meet energy demands and fewer carbon footprints. For operational planning and system sizing, two-stage stochastic programming (2SSP) with Markov decision process algorithms combined.…”

Section: Wind Turbinementioning

confidence: 99%

Microgrids: A review on optimal hybrid technologies, configurations, and applications

Shaikh

Memon

et al. 2021

Int J Energy Res

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This study aims to provide a comprehensive review about the configurations, operation, and integration of multiple energy sources for microgrid (MG) system. The applications of renewable and non-renewable energy sources have been discussed and analysed. Similarly, the several control strategies, protection schemes, and communication networks used for MG have been defined to provide a unique and distinct perspective for an optimal and real-time project. During the review analysis, different used software tools and techniques were observed and applied broadly. However, the most frequently used software and technique are hybrid optimization model for electric renewables (HOMER) Pro and particle swarm optimization algorithm. Additionally, the total global installed capacity for renewable sources is enhanced up to 2588 GW, including 627 GW of PV source, along with the global investment of USD 316.7 Billion on clean energy sources. Furthermore, the least levelized cost of energy for on-grid and off-grid hybrid systems using HOMER Pro was found as $0.072 and $0.145/kWh, respectively. Finally, the work has provided thorough study and future directions about MGs to assist engineers and researchers for their upcoming prospects in the field of renewable energy.

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“…All uncertainties (wind, PV, load demand, water demand) have been considered and obtained. In [26], wind speed forecasting is applied to monitor wind speed. Afterwards, by applying this control method on wind turbine, harvested energy from ultra-capacitor energy storage and wind turbine is increased, and apart from this, the condition of microgrid is improved.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

Dey

Bhattacharyya

Raj

et al. 2020

Journal of Electrical Systems and Inf Technol

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Economic emission dispatch (EED) of a three-unit stand-alone microgrid system supported by a wind farm is percolated in this paper. The adverse effects of stochastic and uncertainty nature of wind energy in raising the generation cost of the microgrid system are studied in this article. Unit commitment (UC) of the generating units is taken into account which helps in reducing the generation cost and provides relaxation time to the generation units. Three cases are contemplated for the study. For the first two cases, the generation cost of the test system was minimized without and with the involvement of wind power, respectively. The third case considered the involvement of wind power along with the UC of the conventional generation units. A novel hybrid of recently developed superior optimization algorithms, viz. grey wolf optimizer (GWO), sine–cosine algorithm (SCA) and crow search algorithm (CSA), is implemented to perform EED, and the results are compared with basic GWO and other hybrid algorithms. Results are then analysed to compare and contrast among these cases and justify the reliable and profitable one. Statistical analysis claims the superiority of the proposed hybrid MGWOSCACSA over other hybrids and GWO.

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Wind turbine and ultra-capacitor harvested energy increasing in microgrid using wind speed forecasting

Cited by 15 publications

References 20 publications

A control strategy incorporating multiple EV charging stations for an islanded micro‐grid energy management and voltage regulation

A control strategy incorporating multiple EV charging stations for an islanded micro‐grid energy management and voltage regulation

Microgrids: A review on optimal hybrid technologies, configurations, and applications

Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

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