Assessment of wind power potential at Hawksbay, Karachi Sindh, Pakistan

Khahro, Shahnawaz Farhan; Soomro, Amir Mahmood; Tabbassum, Kavita; Liao, Xiaozhong

doi:10.11591/telkomnika.v11i7.2621

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…The goal of this study was to identify the wind potential sites in KPK; Pakistan using the multi criteria based approach. Results from this study are briefly explained below; Similar results have been discussed for different countries [22][23][24][25][26][27].…”

Section: Resultssupporting

confidence: 57%

Exploring Wind Energy Potential in KPK-Pakistan by Using Multi Criteria Approach

Rafiq¹,

Sapna²

2019

Anadolu University Journal of Science and Technology-a Applied Sciences and Engineering

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Renewable energy resources have great potential to give solution to the long-lasting energy deficiency problems being faced by Pakistan. The aim of this study was to make use of multi criteria analysis for the identification of wind potential sites in KPK, Pakistan by utilizing the monthly 10 years (1983-1993) and 22 years (1983-2005) annual average wind speed data sets for the six districts (Chitral, Peshawar, Dir, DIK, Buner and Mansehra) at 10m and 50m, 100m, 150m, 300m altitudes, Earth Skin Temperature (0 C) and Atmospheric Pressure (kpa) respectively. At 10 m altitude, annual wind speed analysis showed similar pattern among all the districts, each with two maximum peaks one in April and the other between October and November. Multi-criteria Approach involved, creating suitable criteria for selected variables then classification (based on each variable magnitude), scores were then assigned to each class. Based on methodology (MCA) applied in this research, Chitral district appeared as the best location for exploiting in terms of wind power.

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Section: Resultssupporting

confidence: 57%

Exploring Wind Energy Potential in KPK-Pakistan by Using Multi Criteria Approach

Rafiq¹,

Sapna²

2019

Anadolu University Journal of Science and Technology-a Applied Sciences and Engineering

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“…The effectiveness of the WPDF in fitting the data depends on the method used for estimating its scale (c) and shape (k) parameters [11]. Various methods, including the Maximum Likelihood Method (MLM), Empirical Method of Lysen (EML), Method of Moments (MOM), Modified MLM (MMLM), Least Squares Method (LSM), WAsP method, Openwind Method (OWM), Empirical Method of Justus (EMJ), and Standard Deviation Method (SDM), have been employed for estimating both the scale and shape parameters [6,10,11,[13][14][15][16]. MMLM and Openwind methods have been found to outweigh the other methods [11,13].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Wind Power Potential in Mthatha, Eastern Cape Province, South Africa

Shonhiwa,

Makaka,

Mukumba

et al. 2023

Applied Sciences

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South Africa is currently grappling with a national energy crisis and the high infrastructure costs associated with expanding the national grid to remote areas. Simultaneously, the government has made substantial efforts to harness renewable energy technologies, particularly wind energy. The average wind speed in a specific region significantly influences the energy yield from wind turbines. The vast open inland terrains, mountainous regions, and coastal areas in the Northern Cape, Eastern Cape, and Western Cape provinces of South Africa possess the most substantial wind potential. It is imperative to initiate wind energy projects in these provinces to cater to a significant portion of the local electricity demand, especially in remote areas disconnected from the national grid. Wind energy generation is inherently stochastic, subject to variations in both time and space. Consequently, it is essential to gain a comprehensive understanding of the local wind patterns to assess the feasibility of utilizing wind resources. In the Eastern Cape Province, the Mthatha area still lags in household electrification, presenting an opportunity to electrify some households using wind energy. This study aimed to evaluate the wind resource potential for Mthatha area, utilizing data spanning from 2018 to 2023, provided by the South African Weather Services. Two distribution models, the two-parameter Weibull and three-parameter Weibull, were employed to characterize the provided wind data. To determine the parameters associated with each distribution model, two estimation methods, the Maximum Likelihood Method (MLM) and the Method of Moments (MOM), were utilized. The performance of these distribution models was assessed using the Root Mean Square Error (RMSE) statistical indicator. The results showed that Mthatha area predominantly experiences low wind speeds, with an annual average wind speed of 3.30 m/s and an overall wind power density of approximately 48.48 W/m2. The prevailing winds predominantly originate from the south and east–southeast directions. Consequently, Mthatha is recommended for stand-alone applications, with the added suggestion of augmented wind turbines for the area.

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“…Wind speed frequency distribution is the most commonly used statistical tool for predicting wind energy yield at a specific site (Burton et al, 2011;Mathew, 2007). The variable nature of wind speed is better characterized by the Weibull distribution function that has been used for several locations worldwide to assess wind potential for the development of large wind farms (Akpınar et al, 2018;Khahro et al, 2013;NA et al, 2017;Saeidi et al, 2011;Sumair et al, 2020). In Chad, central Africa, a site was selected to develop a wind farm based on wind resource assessment using two-parameter Weibull distribution (Didane et al, 2017).…”

Section: Wind Resource Assessmentmentioning

confidence: 99%

Techno-Economic Analysis and Planning for the Development of Large Scale Offshore Wind Farm in India

Riaz

Khan

2020

Int. J. Renew. Energy Dev.

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Despite India's great potential for offshore wind energy development, no offshore wind farm exists in the country. This study aims to plan a large scale offshore wind farm in the south coastal region of India. Seven potential sites were selected for the wind resource assessment study to choose the most suitable site for offshore wind farm development. An optimally matched wind turbine was also selected for each site using the respective power curves and wind speed characteristics. Weibull shape and scale parameters were estimated using WAsP, openwind, maximum likelihood (MLH), and least square regression (LSR) algorithms. The maximum energy-carrying wind speed and the most frequent wind speed were determined using these algorithmic methods. The correlation coefficient (R2) indicated the efficiency of these methods and showed that all four methods represented wind data at all sites accurately; however, openwind was slightly better than MLH, followed by LSR and WAsP methods. The coastal site, Zone-B with RE power 6.2 M152 wind turbine, was found to be the most suitable site for developing an offshore wind farm. Furthermore, the financial analysis that included preventive maintenance cost and carbon emission analysis was also done. Results show that it is feasible to develop a 430 MW wind farm in the region, zone B, by installing seventy RE power 6.2 M152 offshore wind turbines. The proposed wind farm would provide a unit price of Rs. 6.84 per kWh with a payback period of 5.9 years and, therefore, would be substantially profitable.

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Assessment of wind power potential at Hawksbay, Karachi Sindh, Pakistan

Cited by 11 publications

References 30 publications

Exploring Wind Energy Potential in KPK-Pakistan by Using Multi Criteria Approach

Exploring Wind Energy Potential in KPK-Pakistan by Using Multi Criteria Approach

Investigation of Wind Power Potential in Mthatha, Eastern Cape Province, South Africa

Techno-Economic Analysis and Planning for the Development of Large Scale Offshore Wind Farm in India

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