Is Nuclear Power Generation a Viable Alternative to the Energy Needs of Pakistan? SWOT-RII Analysis

Pakistan has faced a severe energy crisis for the last two decades. With the considerable power generation expansion, the country still faces power outages with an unsustainable energy mix. Successive energy policies emphasized thermal power deployment which has proved to be a part of the problem. Therefore, the present study has attempted to evaluate and investigate the prospects of nuclear power as a viable alternative in terms of energy security, reliability, and environmental sustainability with the SWOT tool. To further quantify the main drivers and barriers of nuclear energy, a Relative Importance Index (RII) analysis has been done. The results reveal that Pakistan has decades of experience running nuclear power plants satisfactorily. The regulatory framework for nuclear power generation is adequate to expand nuclear power generation. The opportunities are enormous to meet Sustainable Development Goal (SDG), as nuclear is a carbon-free source of energy. The main barriers are global suspicion of nuclear proliferation and less social acceptance.Keywords: SWOT, Delphi, RII Analysis, Nuclear Power GenerationJEL Classifications: P4, Q4DOI: https://doi.org/10.32479/ijeep.11122</p

Aerial identification of flashed over faulty insulator using binary image classification

Flashed over insulator faults are the most significant faults in high voltage line insulators. They are complicated to identify using traditional methods due to their labor-intensive nature. This study proposes a deep learning-based algorithm for detecting flashed over insulator faults in the real time. The algorithm is based on the Resnet 50 architecture, which has been shown to be effective for image classification tasks in the previous studies regarding image analysis. The algorithm is fast, robust and efficient, making it suitable for real-time applications. The algorithm is trained on a dataset of images of flashed over and non-flashed over insulators. This dataset was collected from various transmission lines and National Center of Robotics and Automation, which are located in Pakistan. For validating the effectiveness of the Resnet 50 algorithm, it was compared with the results obtained from the two other widely popular deep learning algorithms, Densenet 121 and VGG 16 (trained and validated on the same dataset). The results showed that the Resnet 50 was able to detect flashed over insulator faults with an accuracy of over 99%. Whereas the Densenet 121 and VGG 16 have achieved an accuracy of less than 51%

A Review of Energy and Power Planning and Policies of Pakistan

Pakistan is facing multiple challenges for harnessing the indigenous energy resources and devise rational energy policies. The country is believed to have abundant energy resources, however, coping substantial electricity supply gap of over 5000 MW. This paper analyses country’s energy and power planning studies conducted since its independence in 1947 and policies announced so far. It is found that water resources management attained more emphasis in early decades of post-independence rather than energy concerns. The first energy and power planning study was conducted in late 1960s and since then various studies were undertaken to supplement five yearly medium term development plans of government. However, it is pertinent to mention that formal energy and power policies were only announced from 1994 onwards owing to growing electricity demand and progressing industrialization. Beside this, the focus of these policies is not only varied but were conceived without undertaking integrated energy planning using energy modeling tools e.g. MARKAL/TIMES; LEAP, ENPEP BALANCE, MESSAGE and EnergyPLAN. It is despite the fact that these tools are successfully applied globally for devising the energy policies and address the complexities of energy system by assisting effective policy formulation. This study recommends that integrated energy planning using energy modeling tools will be helpful to develop sustainable energy policies in Pakistan to eradicate electricity crises

Comparative Investigation of On-Grid and Off-Grid Hybrid Energy System for a Remote Area in District Jamshoro of Sindh, Pakistan

To meet electricity requirements and provide a long-term, sustainable existence, remote areas need to promote renewable projects. Most of the time, wind and solar power sources are selected as renewable energy technologies to help satisfy some of the power requirements. Alternative approaches should be employed, considering the inconsistent characteristics among those resources, to offer efficient and long-lasting responses. Electricity production needs to be conducted with the help of a wide range of energy sources to be productive and efficient. As a result, the current research concentrates on feasible analyses of interconnected hybrid energy systems for such remote residential electricity supply. To help a remote area’s establishment decide whether to adopt renewable electricity technology, this paper evaluates the techno-economic effectiveness of grid-connected and standalone integrated hybrid energy systems. The electricity requirements for the entire selected remote area were determined first. Furthermore, the National Aeronautics and Space Administration, a national renewable energy laboratory, was used to evaluate the possibilities of green energy supplies. A thorough survey was performed to determine which parts were needed to simulate the interconnected hybrid energy systems. Employing the HOMER program, we conducted a simulation, optimizations, and economic research. Considering the net present cost, cost of energy, and compensation time, an economic comparison was made between the evaluated integrated hybrid systems. The assessment reveals that perhaps the grid-connected hybrid energy system is the best option for reliably satisfying remote areas’ energy needs

Short-term forecasting of wind power generation using artificial intelligence

As global warming is increasing due to conventional sources the government and the private sectors introduce policies to minimize it, renewable energy has been developed and deployed because of these strategies. Among the various renewable energy sources, wind energy is the fastest-growing and cleanest energy resource in the world. However, predicting wind power is not easy due to the nonlinearity in wind speed that eventually depends on weather conditions. To reduce these issues improved forecasting models have been used to get the correct results and improve the performance and stability of the power system and thereby its reliability and security.In this work, two models are used to predict the “Output of Wind Turbine” to improve the prediction accuracy of short-term wind power generation. The two models namely the Gated Recurrent Unit (GRU) from the deep learning model and Autoregressive Integrated Moving Average (ARIMA) from Statistical Learning. The data used in this research is collected from the wind power plant, Located in Jhimpir Pakistan. This study compares the accuracy metrics of deep learning models and statistical models to determine which model is the most effective for producing wind power.The results are obtained by using python programming in Jupyter Notebook software and the accuracy metrics of each algorithm are compared with each other as a result Gated recurrent unit (GRU) is the best model among others with the least possible errors and high accuracy. i.e., up to 0.047 root mean square error, 0.89 coefficient of metrics, and 0.03 mean absolute error. Hence, due to its advanced features, then other deep learning, and statistical models the Gated recurrent unit (GRU) Model is suitable for the prediction of wind turbine output power

Wind–PV-Based Hybrid DC Microgrid (DCMG) Development: An Experimental Investigation and Comparative Economic Analysis

The cyclical nature and high investment costs of the wind and photovoltaic renewable energy sources are the two critical issues seeking attention for the use of such systems in backup or isolated applications. This paper aims to present the experimental and economic analysis of a wind&ndash;photovoltaic-based hybrid direct current microgrid (DCMG) system for backup power and off-grid isolated power generation system for emergency purposes. The two distributed generating units comprising photovoltaic panels and wind generator were designed and developed for the experimental study. A lead-acid battery is also added as an energy storage system to enhance the system supply. The electric load of this system comprise of 42 DC light emitting diode (LED) lamps of 12 Watt each and a 25 Watt DC fan. The charge controller provides the control and protection features for the designed system. The complete system design and fabrication of this system have been undertaken at Mehran University of Engineering &amp; Technology (MUET, Jamshoro, Pakistan). The compatibility of the designed system has been analysed by comparing the Levelized Cost of Energy (LCOE) with a conventional gasoline generator system of the same capacity. The capital, running and lifetime costs of DCMG are found to be 1.29, 0.15 and 0.29 times those of the gasoline generator, respectively. Moreover, it is found that per unit cost of gasoline generator is $0.3 (i.e., PKR 31.4) which is almost 3.4 times higher than that of the hybrid DCMG system. The performance and cost evaluation of the designed system indicate its broad potential to be adopted for commercialisation to meet backup power and off-grid power requirements. This study concludes that proposed DCMG system is a not only low cost, but also a pollution-free alternative option compared to the existing traditional small gasoline generator system