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Case study
Publication date: 22 December 2016

Sidharth Sinha

In the wake of the December 2015 Paris COP21 (Conference of Parties), and India's announced renewable energy commitments, Reliance Power is reviewing its renewable energy…

Abstract

In the wake of the December 2015 Paris COP21 (Conference of Parties), and India's announced renewable energy commitments, Reliance Power is reviewing its renewable energy investments to arrive at a long term strategy for the role of renewable energy in its power generation portfolio and the financing of renewable projects. The case reviews the Indian government's policies to promote renewable energy; the evolution of the renewable energy sector; and Reliance Power's financing of renewable energy investments. The case requires identification of alternative long term strategies and their financing implications. This case serves as an introduction to renewable energy from the perspective of Reliance Power, a large private power generator of the country. These projects also provide a learning opportunity for Reliance Power to deal with fast evolving renewable technologies.

Details

Indian Institute of Management Ahmedabad, vol. no.
Type: Case Study
ISSN: 2633-3260
Published by: Indian Institute of Management Ahmedabad

Keywords

Article
Publication date: 14 January 2020

Nedjma Abdelhafidi, Nour El Islam Bachari, Zohra Abdelhafidi, Ali Cheknane, Abdelmotaleb Mokhnache and Loranzo Castro

Integrated solar combined cycle (ISCC) using parabolic trough collector (PTC) technology is a new power plant that has been installed in few countries to benefit from the…

Abstract

Purpose

Integrated solar combined cycle (ISCC) using parabolic trough collector (PTC) technology is a new power plant that has been installed in few countries to benefit from the use of hybrid solar-gas systems. The purpose of this paper is to investigate the challenges in modeling the thermal output of the hybrid solar-gas power plant and to analyze the factors that influence them.

Design/methodology/approach

To validate the proposal, a study was conducted on a test stand in situ and based on the statistical analysis of meteorological data of the year 2017. Such data have been brought from Abener hybrid solar-gas central of Hassi R’mel and used as an input of our model.

Findings

The proposal made by the authors has been simulated using MATLAB environment. The simulation results show that the net solar electricity reaches 18 per cent in June, 15 per cent in March and September, while it cannot exceed 8 per cent in December. Moreover, it shows that the power plant responses sensibly to solar energy, where the electricity output increases accordingly to the solar radiation increase. This increase in efficiency results in better economic utilization of the solar PTC equipment in such kind of hybrid solar-gas power plant.

Practical implications

The obtained results would be expected to provide the possibility for designing other power plants in Algeria when such conditions are met (high DNI, low wind speed, water and natural-gas availability).

Originality/value

This paper presents a new model able to predict the thermal solar energy and the net solar-electricity efficiency of such kind solar hybrid power plant.

Article
Publication date: 1 October 2019

Eralp Sener, Irem Turk, Isil Yazar and Tahir Hikmet Karakoç

The aviation industry has started environment friendly and also conventional energy independent alternative energy dependent designs to reduce negative impacts on the…

Abstract

Purpose

The aviation industry has started environment friendly and also conventional energy independent alternative energy dependent designs to reduce negative impacts on the nature and to maintain its future activities in a clear, renewable and sustainable way. One possible solution proposed is solar energy. Solar-powered aerial vehicles are seen as key solutions to reduce global warming effects. This study aims to simulate a mathematical model of a solar powered DC motor of an UAV on MATLAB/Simulink environment.

Design/methodology/approach

Maximum power point tracking (MPPT) is a critical term in photovoltaic (PV) array systems to provide the maximum power output to the related systems under certain conditions. In this paper, one of the popular MPPT techniques, “Incremental Conductance”, is simulated with solar-powered DC motor for an UAV design on MATLAB/Simulink.

Findings

The cascade structure (PV cell, MPPT, buck converter and DC motor models) is simulated and tested under various irradiance values, and results are compared to the DC motor technical data. As a result of that, mathematical model simulation results are overlapped with motor technical reference values in spite of irradiance changes.

Practical implications

It is suggested to be used in real time applications for future developments.

Originality/value

Different from other solar-powered DC motor literature works, a solar-powered DC motor mathematical model of an UAV is designed and simulated on MATLAB/Simulink environment. To adjust the maximum power output at the solar cell, incremental conductance MPPT technique is preferred and a buck converter structure is connected between MPPT and DC motor mathematical model. It is suggested to be used in solar-powered UAV designs for future developments.

Details

Aircraft Engineering and Aerospace Technology, vol. 92 no. 2
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 4 July 2016

Xiongfeng Zhu, Zheng Guo, Zhongxi Hou, Xianzhong Gao and Juntao Zhang

The purpose of this study is to present a methodology for parameters’ sensitivity analysis of solar-powered airplanes.

Abstract

Purpose

The purpose of this study is to present a methodology for parameters’ sensitivity analysis of solar-powered airplanes.

Design/methodology/approach

The study focuses on a preliminary design and parameters’ relations of a heavier-than-air, solar-powered, high-altitude long-endurance unmanned aerial vehicle. The methodology is founded on the balance of energy production and requirement. An analytic expression with four generalized parameters is derived to determine the airplane flying on the specific altitude. The four generalized parameters’ sensitivities on altitude are then analyzed. Finally, to demonstrate the methodology, a case study is given on the parameters’ sensitivity analysis of a prototype solar-powered airplane.

Findings

When using the presented methodology, the nighttime duration and the energy density of batteries are more sensitive on flight altitude of the prototype airplane.

Practical implications

It is not easy to design a solar-powered airplane to realize high-attitude and long-endurance flight. For the current state-of-art, it is a way to figure out the most critical parameters which need prior consideration and immediate development.

Originality/value

This paper provides an analytical methodology for analyzing the parameters’ sensitivities of solar-powered airplanes, which can benefit the preliminary design of a solar-powered airplane.

Details

Aircraft Engineering and Aerospace Technology: An International Journal, vol. 88 no. 4
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 1 December 2005

İres İskender

To analyze the operating performance of a fuzzy logic control (FLC) based solar energy conversion modular system controlled by a digital signal processor (DSP) microcontroller.

2050

Abstract

Purpose

To analyze the operating performance of a fuzzy logic control (FLC) based solar energy conversion modular system controlled by a digital signal processor (DSP) microcontroller.

Design/methodology/approach

A range of published works relevant to the solar energy conversion modular systems are evaluated and their limitations are indicated in the first section of the paper. The circuit diagram of the panel‐boost converter system is described in the second section. In the third section, a neural network model is suggested for the photovoltaic panel and the model is created in the MATLAB/SIMULINK and then combined with other blocks existing in the system. The design of the FLC method is described in section 4. The simulation and experimental results corresponding to the control of the duty‐cycle of the converter to set the operating point of the solar panel at the maximum power point (MPP) are given in sections 5 and 6, respectively. Section 7, summarizes the results and conclusions of the study.

Findings

The paper suggests a simple dc‐dc boost converter controlled by FLC method. The proposed converter model can be used to obtain maximum power from a photovoltaic panel.

Research limitations/implications

In preparing this paper, the resources books existing in the library of our university and the resources relative to the solar energy conversion and FLC published in English language and reachable through the internet were researched.

Practical implications

The paper suggests a neural network model for a solar panel, which can be used in the simulation of the solar energy panel‐boost converter system. The solar energy panel‐boost converter system proposed in this study can be used by the researchers who are working in the solar energy conversion area.

Originality/value

The suggestion of a neural network model for a solar panel and creation of this model in the MATLAB/SIMULINK environment provides researchers to simulate and to analyze the performance of the solar energy panel‐boost converter system using the MATLAB/SIMULINK simulation program. In addition, since the control approach proposed in this paper does not require the information on temperature and solar irradiance that affect the maximum output power, can effectively find the MPP of the solar panel.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 24 no. 4
Type: Research Article
ISSN: 0332-1649

Keywords

Open Access
Article
Publication date: 13 August 2020

Mariam AlKandari and Imtiaz Ahmad

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on…

2539

Abstract

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 23 March 2012

Robert Bogue

The purpose of this paper is to review the technology and applications of solarpowered sensors.

Abstract

Purpose

The purpose of this paper is to review the technology and applications of solarpowered sensors.

Design/methodology/approach

Following a short introduction, this paper first considers photovoltaic technology and then describes a selection of solarpowered sensors and their applications.

Findings

It is shown that solarpowered sensors may be used as nodes in wireless sensor networks and also as stand‐alone devices. They offer a number of key operational and economic benefits and find applications in such diverse fields as structural and environmental monitoring, traffic management, weather forecasting, agriculture, process control, gas detection, satellite remote sensing and healthcare.

Originality/value

The paper illustrates the important role that solarpowered sensors and systems play in a wide range of applications and industries.

Details

Sensor Review, vol. 32 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 23 September 2019

Omprakash Ramalingam Rethnam, Sivakumar Palaniappan and Velmurugan Ashokkumar

The purpose of this paper is to focus on life cycle cost analysis (LCCA) of 1 MW roof-top Solar Photovoltaic (PV) panels installed in warm and humid climatic region in…

Abstract

Purpose

The purpose of this paper is to focus on life cycle cost analysis (LCCA) of 1 MW roof-top Solar Photovoltaic (PV) panels installed in warm and humid climatic region in Southern India. The effect of actual power generated from solar PV panels on financial indicators is evaluated.

Design/methodology/approach

LCCA is done using the actual power generated from solar PV panels for one year. The net present value (NPV), internal rate of return (IRR), simple payback period (SPP) and discounted payback period (DPP) are determined for a base case scenario. The effect of service life and the differences between the ideal power expected and the actual power generated is evaluated.

Findings

A base case scenario is evaluated using the actual power generation data, 25-year service life and 6 percent discount rate. The NPV, IRR, SPP and DPP are found to be INR 13m, 8 percent, 10.9 years and 18.8 years respectively. It is found that the actual power generated is about one-third less than the ideal power estimated by consultants prior to project bidding. The payback period increases by 70–120 percent when the actual power generated from solar PV panels is considered.

Originality/value

The return on investment calculated based on ideal power generation data without considering the operation and maintenance related aspects may lead to incorrect financial assessment. Hence, strategies toward solar power generation should also focus on the actual system performance during operation.

Details

Built Environment Project and Asset Management, vol. 10 no. 1
Type: Research Article
ISSN: 2044-124X

Keywords

Article
Publication date: 28 July 2020

Kada Bouchouicha, Nadjem Bailek, Abdelhak Razagui, Mohamed EL-Shimy, Mebrouk Bellaoui and Nour El Islam Bachari

This study aims to estimate the electric power production of the 20 MWp solar photovoltaic (PV) plant installed in the Adrar region, South of Algeria using minimal…

Abstract

Purpose

This study aims to estimate the electric power production of the 20 MWp solar photovoltaic (PV) plant installed in the Adrar region, South of Algeria using minimal knowledge about weather conditions.

Design/methodology/approach

In this study, simulation models based on linear and nonlinear approaches were used to estimate accurate energy production from minimum radiometric and meteorological data. Simulations have been carried out by using multiple linear regression (MLR) and artificial neural network (ANN) models with three basic types of neuron connection architectures, namely, feed-forward neural network, cascade-forward neural network (CNN) and Elman neural network. The performance is measured based on evaluation indexes, namely, mean absolute percentage error, normalized mean absolute error and normalized root mean square error.

Findings

A comparison of the proposed ANN models has been made with MLR models. The performance analysis indicates that all the ANN-based models are superior in prediction accuracy and stability, and among these models, the most accurate results are obtained with the use of CNN-based models.

Practical implications

The considered model will be adopted in solar PV forecasting areas as part of the operational forecasting chain based on numerical weather prediction. It can be an effective and powerful forecasting approach for solar power generation for large-scale PV plants.

Social implications

The operational forecasting system can be used to generate an effective schedule for national grid electricity system operators to ensure the sustainability as well as favourable trading performance in the electricity, such as adjusting the scheduling plan, ensuring power quality, reducing depletion of fossil fuel resources and consequently decreasing the environmental pollution.

Originality/value

The proposed method uses the instantaneous radiometric and meteorological data in 15-min time interval recorded over the two years of operation, which made the result exploits a fact that the energy production estimation of PV power generation station is comparatively more accurate.

Expert briefing
Publication date: 28 January 2019

Solar power has taken centre stage in the new-found Middle Eastern drive to diversify energy sources, meeting rising power demand, sustaining oil and gas exports and…

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