Solar Photovoltaic System (SPV) is one of the growing green energy sources having immense penetration in the national grid as well as the off-grid around the globe. Regardless of different solar insolation level at various regions of the world, SPV performance is also affected by several factors: conversion efficiency of PV cell technology, ambient temperature and humidity, soiling and seasonal/weather patterns. The rise in PV cell temperature and soiling is found to be detrimental issues regarding power plant performance and life expectancy leading alterations in the levelised cost of energy (LCoE). In this paper, authors present a short glance about factors affecting the performance of photovoltaic modules and re-discuss their usability in cleaning intervention decision-making models. With some highlights on the essence of cleaning to mitigate the soiling issues in PV power plants, this paper presents the existing cleaning techniques and practices along with their evaluations. The need for an optimal cleaning intervention by using advanced scientific tools rather than by visual inspection is drawing the attention of PV experts. The authors finally suggest a schematic of a decision-making model which involves the use of probable parameters, data processing techniques and machine learning tools. The implementation of data science and machine learning in a solar PV panel cleaning system could be a remarkable advancement in the field of renewable energy.
With the increasing demand for renewable energy, solar photovoltaic technology is being a topic of concern. However, due to the accumulation of dust and dirt over the panel surface, the performance of the photovoltaic system degrades to a noticeable number. To address this issue: a fully automated, cost worthy and efficient system needs to be invented. This paper presents the design and fabrication process of a prototype able to clean the panel surface. The prototype of this system comprises of a cleaning robot and a cloud interface: the cleaning robot is mobile and able to clean the entire solar array back and forth, with its separately driven cleaning rotatory brush; whereas, the cloud interface is a human-machine interface featuring the distant monitoring and control of the robot. Additionally, to notify the performance of distantly placed solar farm, a sensing unit consisting of sensors was added to this system. Furthermore, to add an automatic cleaning feature, a month-long data of totally clean and dusty panel was processed with regression analysis, and the developed regression model was programmed into the sensing unit. The sensing unit added with the regression model is named as an autonomous unit, as it predicts the suitable time for cleaning action. According to the system evaluation done on a demonstration PV module, it was found that the designed system can clean dry dust accumulated over the panel’s surface. Moreover, by attaching the metal rail tracks on a long solar array, the system seems to be implementable on a large scale solar farm.
Nepal is a Himalayan country with its 83% of its geography being composed of hills and mountains. Around 22% of the Nepalese population is not receiving electricity through the national power utility and is forced to identify alternative approaches to electrification. The Micro/Mini-Grid (MG) system is one of the promising approaches in terms of cost, reliability and performance for rural electrification, where electrification through national power utility is not techno-economically feasible. However, various issues must be identified and considered during the implementation of MGs in a rural community. In this paper, numerous technical, social and management issues are identified and discussed relating to the implementation and operation of reliable and stable MGs in the Himalayas. To our knowledge, this is the first scientific work that presents a comprehensive review of Himalayan MGs and their associated elements. This article reviews the available research articles, project documents, and Government reports on MG development, from which a clear roadmap is constructed. From the comprehensive study, it is observed that the existing MGs are not adequately designed for the specific area, considering the local resources and local information. Based on the identified issues, some practical and efficient recommendations have been made, so that future MG projects will address the possible problems during the design and implementation phase.
Hybrid Power System (HPS) is an energy system with combination of different regenerative energy sources like Solar, Wind, Geo-Thermal, Biomass and several others to achieve energy sustainability. This paper investigates the feasibility of grid connected and stand-alone hybrid energy system to meet electric load requirement of a community or organization, by utilizing the available resources. Potentiality of different energy sources like solar, wind, bio-gas, etc. along with currently used energy sources is studied thoroughly by taking a case study of Kathmandu University central campus, located at Dhulikhel, Nepal. Technical and economic analysis of on-grid and off-grid hybrid system is performed to get optimum model that supply continuous energy to the end user. Furthermore, the possibility of net metering with national utility has been analysed. The main objective of this study is to identify the suitable energy mixed model, that provide the sustainable energy supply to the university, and recommend the possible energy generators to be added for fulfilling the continuously increasing load demand. The load profile of several years of the University is taken into consideration for forecasting the power demand. The findings of the research show that system when adopted to hybrid system can meet up to 55% of the load by renewable resources. Maximum renewable fraction is found to be 0.603 and maximum renewable penetration of 812%.
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