ahsan kabir

The electricity generation from the large-scale grid-connected solar photovoltaic system expands in Bangladesh in the last decades. The performance of PV modules declines with the external environmental factors, especially power loss due to dust. Being one of the most polluted aircontaining countries, mitigation of dust effect is a challenge for Bangladesh. In this paper, an analysis of dust impact on the performance of PV module in tropical weather countries is compared. Then, a case has been investigated with two small PV systems at Savar, Dhaka. The first PV system was cleaned three times a day, whereas the second one was cleaned after three days. The overall 3% reduction of output power and module efficiency is found for the system, which is cleaned after three days. Furthermore, maximum power is reduced in the afternoon (4.00 PM) in a day. It concludes with the recommendation that; Bangladesh needs to take inclusive dust mitigation strategies for the substantial development in PV systems.

Demand Side Management (DSM) is growing as an efficient load management technique on the distribution side. Due to uncertainty in grid power supply and rising electricity costs in Bangladesh, industries are looking for alternative solutions in demand management. In this paper, two Demand-Side Management (DSM) techniques; Peak load shifting, and Timeshifting - are investigated for the industrial load in Bangladesh. A demand management model has been developed in MATLAB using industrial load data of a year and electricity price. The model is further analyzed for three base cases and for the scenarios after integrating PV into the system. The results are compared in terms of peak load reduction potential and electricity cost savings of the industry. The combination of peak load shifting, and time-shifting shows the best results of evaluated parameters. The paper concludes that assessed DSM techniques can shift a significant peak load for the industry if customers allow changing their operating schedule of flexible load without affecting the business.

PV based hybrid mini grid becomes an alternative choice of solar home system (SHS) for off-grid rural electrification in Bangladesh. Several hybrid mini grid projects are implemented in remote areas and different challenges are indentified. This paper presents a discussion of four key challenges in technical, economic, social and policy area for the implementation of hybrid mini grid projects in Bangaldesh. It concludes with a comaprtive analysis to figure out the key challenges impact in the system.

In this paper, Uniform Manifold Approximation and Projection (UMAP) is used to compress electricity consumption data. The Random Forest (RF) classification algorithm is then used on the compressed data to learn the consumption patterns of two distinctive user base - household consumers and SMEs (small and medium businesses). Compression ratio achieved by UMAP and classification accuracy of our classifier model are compared with conventional methods and various machine learning pipelines proposed in recent studies. The results demonstrate that our proposed technique achieves better compression ratio and classification accuracy compared to the conventional methods.

Imbalance power management is a key operational task for a system operator. Imbalance power is produced from the difference in electrical energy supply and demand in the real operational time which deviates power system stability. In this paper, a balancing market model using demand response is developed to mitigate imbalance power. In context, a case study of balancing market model is investigated using flexible residential load along with nord pool spot market and imbalance data Therefore, the market model is simulated in MATLAB for five weeks. The simulation result is evaluated in order to determine imbalance reduction value, flexibility benefits and demand response value. Finally, the study is concluded with the propositions of future balancing market model contribution in imbalance reduction using demand response.

In smart grids, residential energy management is a vital part of demand-side management. It plays a pivotal role in improving the efficiency and sustainability of the power system. However, challenges such as variability of consumption profiles require machine learning to understand and forecast residential demands. Moreover, machine learning based intelligent load management is required for effective implementation of demand response programs. In this article, applications of machine learning algorithms in residential demand forecasting, load profiling, consumer characterization, and load management are comprehensively discussed. The article also examines the characteristics and availability of relevant databases, and explores research challenges and possibilities.

Ensuring a secure supply of electricity without harming climate is a key challenge for future power system and many renewables-based cutting-edge technologies are introduced to overcome this challenge. This paper aims to study the grid-connected residential PV-battery system at behind-the-meter scenarios in Sweden from a technical and economic perspective. The system is designed with PV arrays, inverters, Lithium-ion or lead-acid batteries. The optimal PV, lithium-ion and lead-acid battery size are determined at two locations (Arlanda and Karlstad) in Sweden based on the highest value of Net Present Value (NPV), Profitability Index (PI), the lowest value of Levelized cost of energy (LCOE) and payback period considering system losses, electricity market price, cost and PV incentives and a comparison is performed between these two locations against some techno-economic performance metrics for two combinations of PV-battery systems. Then, the best-case energy profile, bill savings, battery performance are also investigated. Finally, the system is simulated using System Advisory Model (SAM), a renewable analysis software from NREL, USA, and is found that the grid-connected PV with lithium-ion battery system is feasible and more economical considering available PV incentives in Sweden.

Due to the increasing electricity demand, distributed generation (DG) had been integrated into the power system. Due to the incorporation of DG with the utility grid, the stability of the system subjected to any disturbances is affected severely, including the degradation of power quality. Among other DG, integration of large-scale PV to the grid causes more stability problems due to the lowering of the overall inertia of the system. Hence, an investigation is needed for any PV-connected system to address different stability issues when subjected to any fault. This paper presents a rigorous study on the IEEE 14 bus test system to show the impact of PV integration on system parameters like voltage profile, fault current, rotor angle, frequency and THD value. For a better understanding of the variations of these parameters, short circuit study, transient analysis and harmonic analysis have been carried out on the test system. At the end of this paper, some recommendations have been proposed for a PV integrated system. For modeling and simulation purposes, ETAP software has been used.

Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for fu...

This paper aims to address the economic feasibility of different metering schemes for grid-connected industrial PV systems in Bangladesh. An on-grid PV system is designed for an industrial load and a comparative assessment amongst net energy metering and net billing is carried out under two different scenarios – without any grid limit and with grid limit. The base case simulation was done among each case and further sensitivity analysis was performed. The metering schemes are compared based on system net present value, payback period, annual net savings for different PV installation, net energy delivered to the grid and cost analysis. Sensitivity analysis shows how grid limiting factor affects the overall PV energy scenario. Finally, the most promising tariff alternative is identified for the large-scale implementation of grid-connected industrial PV system in Bangladesh.

Bangladesh has among the lowest per capita energy (240 kg oil equivalents) consumption in the world and is severely dependent on additional environmentally friendly renewable energy resources in the future. Among the possible energy resources that could be explored is the potential geothermal energy in regions of higher geothermal gradients with favorable geo-tectonic setting and ideal petro-physical properties. A preliminary examination of bottom hole temperatures of a large number of onshore wells spread over a vast area in the eastern part of the country, especially in Thakurgaon-Mymensingh-Sunamgonj-Sylhet through in the Bengal fore deep, strongly suggests that several other areas are of great interest for further studies in order to determine their geothermal energy potential. Bangladesh has witnessed a high demand for uninterrupted electricity due to rapid civilization in the last few years. Bangladesh needs now a reliable green energy sources as its power sector beset by many infrastructural problems (inefficient transmission system, very old power stations and cumbersome decision making process). Bangladesh has taken initiative to generate 25000MW electricity within 2021. In this regard, geothermal energy can be a viable and useful alternative and this paper proposes the prospects of its introduction to the power sector of Bangladesh. In this paper, a study is presented that shows the suitable locations in Bangladesh where geothermal power plants can be set up easily. Recently, the Ministry of Power, Energy and Mineral Resources has approved the establishment of the first ever geothermal power plant (200MW) in the country. A total of approximately 1000 MW can be added into the energy grid of Bangladesh through geothermal power systems. The geothermal energy is green, indigenous, locally occurring and continuously available independent of climatic changes. It will help to reduce the huge oil bill that the country is facing now, provided the national planners give adequate attention and support for the development of geothermal energy at a rapid pace to reduce the severe electricity crisis in Bangladesh as other energy resources like peat, hydropower, nuclear, wind, tidal / waves are not significant at present.