Prefeasibility Economic and Sensitivity Assessment of Hybrid Renewable Energy System

Sawle, Yashwant; Jain, Siddharth; Babu, Sanjana; Nair, Ashwini Ramachandran; Khan, Baseem

doi:10.1109/access.2021.3058517

Cited by 63 publications

(25 citation statements)

References 26 publications

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“…Data available on request from the authors. Most of data that support the findings of this work are included in the paper or cited in the References 4,5,17,18,30,32‐35,37,38. Some of data are available on request from the corresponding author (Details about different types of solar panel specification available at: https://kenbrooksolar.com/solar-panel/300-watt-solar-panel).…”

Section: Peer Reviewmentioning

confidence: 91%

Optimal design and techno‐economic analysis of a hybrid grid‐independent renewable energy system for a rural community

Pujari

Rudramoorthy

2021

Int Trans Electr Energ Syst

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Hybrid renewable energy systems (HRES) are proving a promising solution for rural electrification in India, where grid power availability is limited. This paper aims to identify the optimal design and pre-feasibility techno-economic analysis of the hybrid grid-independent renewable system for rural electrification. The proposed hybrid system integrates the various renewable energy system configurations to provide continuous power for the electric load demands of Kanakadri palle village, Andhra Pradesh, India. HOMER software is utilized to design the proposed hybrid system, perform optimization, techno-economic analysis, and simulation. Here, six different hybrid system configurations were taken into account to perform a comparative economic analysis. The results obtained shows that one of the hybrid systems, PV/DG/converter/battery system (120 kW-PV modules, 54 kW-DG, 200 number of batteries with 260 Ah ratings, and 50 kW-power converters), is the least economical optimal system based on least net present cost and Levelized cost of energy (COE), highest renewable fraction, and zero unmet electric loads percentage (NPC: $341 280, COE: $0.217, highest renewable fraction: 96.6% and unmet electric loads: 0%). For the site considered, this optimal hybrid system can provide a reliable power supply to the total energy requirements of 332.97 kWh/d and achieve 75.68% carbon emission reduction compared to a diesel generator. Moreover, the comparison of different system configurations was carried out based on economic analysis. This analysis shows that proposed hybrid system can be an appropriate model to provide reliable power at a low generation cost for the village considered in this study and can be recommended for future electrification projects.

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Section: Peer Reviewmentioning

confidence: 91%

Optimal design and techno‐economic analysis of a hybrid grid‐independent renewable energy system for a rural community

Pujari

Rudramoorthy

2021

Int Trans Electr Energ Syst

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“…Many researchers and studies have investigated hybrid renewable energy systems, and from different perspectives, such as energy management systems [13], demand response [14], economic cost [15], carbon dioxide emissions and environmental impact [16], optimizing source size [17,18], communication network [19,20], IoT-enabled smart grid [21][22][23], HRES optimization [24,25], modeling based on international standards [26][27][28][29], optimal location [30], and capacity planning [31]. The author in [13] provided an overview of energy management agent (EMA) framework architectures' ability to manage the energy generation/consumption of DERs/HRES in homes, buildings, and communities.…”

Section: Related Workmentioning

confidence: 99%

“…A case study of a Tunisian petroleum platform has been considered to evaluate a proposed optimization solution. Authors in [18] showed the viability of HRES for isolated urban electrification in India. The HOMER software has been used for sizing the system and perform the technical and financial evaluation.…”

Section: Related Workmentioning

confidence: 99%

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IoT-Based Hybrid Renewable Energy System for Smart Campus

et al. 2021

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There is a growing interest in increasing the penetration rate of renewable energy systems due to the drawbacks associated with the use of fossil fuels. However, the grid integration of renewable energy systems represents many challenging tasks for system operation, stability, reliability, and power quality. Small hybrid renewable energy systems (HRES) are small-scale power systems consisting of energy sources and storage units to manage and optimize energy production and consumption. Appropriate real-time monitoring of HRES plays an essential role in providing accurate information to enable the system operator to evaluate the overall performance and identify any abnormal conditions. This work proposes an internet of things (IoT) based architecture for HRES, consisting of a wind turbine, a photovoltaic system, a battery storage system, and a diesel generator. The proposed architecture is divided into four layers: namely power, data acquisition, communication network, and application layers. Due to various communication technologies and the missing of a standard communication model for HRES, this work, also, defines communication models for HRES based on the IEC 61850 standard. The monitoring parameters are classified into different categories, including electrical, status, and environmental information. The network modeling and simulation of a university campus is considered as a case study, and critical parameters, such as network topology, link capacity, and latency, are investigated and discussed.

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“…An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standard [13,[17][18][19][20].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Chane¹,

Gebru²,

Khan³

2021

J. Infor. Electr. Electron. Eng.

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This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

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Prefeasibility Economic and Sensitivity Assessment of Hybrid Renewable Energy System

Cited by 63 publications

References 26 publications

Optimal design and techno‐economic analysis of a hybrid grid‐independent renewable energy system for a rural community

Optimal design and techno‐economic analysis of a hybrid grid‐independent renewable energy system for a rural community

IoT-Based Hybrid Renewable Energy System for Smart Campus

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

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