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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.

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.

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.

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).

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.

Energy security is a major concern for India and many rural areas remain un-electrified. Thus, innovations in sustainable technologies to provide energy services are required. Biomass and solar energy in particular are resources that are widely available and underutilised in India. This paper aims to provide an overview of a methodology that was developed for designing and assessing the feasibility of a hybrid solar-biomass power plant in Gujarat.

The methodology described is a combination of engineering and business management studies used to evaluate and design solar thermal collectors for specific applications and locations. For the scenario of a hybrid plant, the methodology involved: the analytical hierarchy process, for solar thermal technology selection; a cost-exergy approach, for design optimisation; quality function deployment, for designing and evaluating a novel collector – termed the elevation linear Fresnel reflector (ELFR); and case study simulations, for analysing alternative hybrid plant configurations.

The paper recommended that for a hybrid plant in Gujarat, a linear Fresnel reflector of 14,000 m2 aperture is integrated with a 3 tonne per hour biomass boiler, generating 815 MWh per annum of electricity for nearby villages and 12,450 tonnes of ice per annum for local fisheries and food industries. However, at the expense of a 0.3 ¢/kWh increase in levelised energy costs, the ELFR can increase savings of biomass (100 t/a) and land (9 ha/a).

The research reviewed in this paper is primarily theoretical and further work will need to be undertaken to specify plant details such as piping layout, pump sizing and structure, and assess plant performance during real operational conditions.

The paper considers the methodology adopted proved to be a powerful tool for integrating technology selection, optimisation, design and evaluation and promotes interdisciplinary methods for improving sustainable engineering design and energy management.

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.

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.

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.

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.

The purpose of this paper is to present the simulation method of power plants and storage system capacity design.

Owing to solar irradiation, wind speed and water flow are highly and randomly changeable, time variation of the signals needs to be taken into consideration as well as some features of the power plants and storage system. A Matlab/Simulink model of the given system – DC microgrid has been developed. The model allows simulation of a few years static simulations of the power balance. Hence, it can be used to size the plants.

An effective method of the power system design has been developed. It allows sizing the plants taking into consideration resources and load profiles, year changes in profiles and future development of the system. The storage system can be optimized to avoid high power unbalance and power cost increasing.

The model describes only static power behaviour of the modelled power system. It does not allow simulating local voltage changes and dynamic properties of the plants and storage.

This technique helps to size the plants and, first of all, storage system taking into consideration several technical and economical issues.

The method gives opportunity to design a storage system's capacity and power and optimize them. The authors have not found similar methods in the literature.

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.

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.

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.

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.

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.

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.

Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives.

In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software.

Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified.

As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.

The purpose of this paper is to enhance the performance of maintenance in a solar power plant by implementing the proactive maintenance (PaM) strategy, measured by the availability and the total maintenance workload.

The prior maintenance strategy was reviewed, and then the strategy was adjusted to focus on PaM. Failure modes and effects analysis (FMEA) was a tool for analyzing the severity and occurrence of the failure modes and effects. Then, the Why‒Why analysis was used for investigating the root causes of failures. The countermeasures were drawn, and the preventive maintenance (PM) plan was revised and carried out. The total maintenance, the PaM and reactive maintenance workload, was obtained, and then the improvements were determined. The values of availability were also obtained.

Previously, the appeared maintenance strategy was not clearly defined. It seemed to have reactive maintenance coupled with PM; it was checked once a year, and corrective actions were made when something wrong was found. Then the management team observed an increase in the reactive maintenance workload, whereas the values of availability were not consistent and tended to drop. After implementing the new maintenance strategy, PaM, the total maintenance workload decreased 14 percent in one year. The average availability of the solar power plant improved from 0.9943 to 0.9969, and the values of availability had better consistency.

The PaM can be applied to solar power plant without limiting the prior maintenance strategy and the complexity of production or machinery. The solar power plant is a quite simple production, and most machines consist of electrical equipment and electrical circuits. The PaM supports to analyze the failure modes, the consequence of the failure events and failure effects, and to decide what should be done. Importantly, PaM can reduce total maintenance workload while the value of availability is higher and consistent.

This paper states how to successfully implement the PaM for the solar power plant. Previously, the plant did not have a clearly defined maintenance strategy; it was checked once a year, and it was corrected when abnormalities were detected. The PaM strategy provides tools and processes for failures and effects analysis. Although there was a more workload of PM, the total maintenance workload decreased, even in the first year.

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.

The purpose of this paper is to shed light to the concept of solar electricity transfer from North Africa to Europe in the frame of Article 9 of the European Renewable Energy Sources (EU-RES) Directive 28/2009/EC, to explain why efforts have not been successful up to now and to provide recommendations on how to proceed.

The authors have compared the “Supergrid” concept that was pursued by some institutions in the past years with the original “TRANS-CSP” concept developed by the German Aerospace Centre in 2006. From this analysis, the authors could identify not only major barriers but also possible ways towards successful implementation.

The authors found that in contrast to the Supergrid approach, the original concept of exporting dispatchable solar power from concentrating solar thermal power stations with thermal energy storage (CSP-TES) via point-to-point high voltage direct current (HVDC) transmission directly to European centres of demand could be a resilient business case for Europe–North Africa cooperation, as it provides added value in both regions.

The analysis has been made in the frame of the BETTER project commissioned by the Executive Agency for Competitiveness & Innovation in the frame of the program Intelligent Energy Europe.

One of the major implications found is that due to the time lost in the past years by following a distracted concept, the option of flexible solar power imports from North Africa to Europe is not any more feasible to become part of the 2020 supply scheme.

To make them a viable option for post-2020 renewable energy systems for electricity development in Europe, a key recommendation of the project is to elaborate a detailed feasibility study about concrete CSP-HVDC links urgently.

The analysis presented here is the first to give concrete recommendations for the implementation of such infrastructure.

Solar as an energy source has a massive potential to reduce dependence on fossil fuels in Gulf Countries (GC). One attractive application of solar energy is solar-powered desalination, which is a viable method to produce fresh water. The most significant factor determining the potential deployment of this application is economics.

In this study, the classical economic analysis model has been modified to assess the penetration of solar technology to power desalination plants at different periods during the project lifetime. Furthermore, the environmental and financial values were combined to assess the incentive of powering desalination plants with solar energy in Saudi Arabia. Three systems of solar technologies accompanied with water desalination based on technical applicability were modeled and economically analyzed to understand the impact of various design and operation parameters.

This study shows that PV-RO is currently more competitive at both market and administrated prices in Saudi Arabia, followed by the MED-CSP system and finally CSP-RO system. CSP-RO system starts to generate positive surplus after 11 years, while the base case shows no positive surplus at all during the entire lifetime. Moreover, the same trend continues to hold with MED-CSP and PV-RO systems. The MED-CSP generates positive surplus after six years and PV-RO after five years only. On average, it takes eight years for a project running based on solar (CAPEX and OPEX) and desalination OPEX to generate positive cash surplus.

This paper discusses the debate about incentives for renewable energy in GC and the impact of coupling water production and solar generation. Given that there is no analytical framework built earlier, this paper provides an alternative methodology for policy analysis to understand the role of economies of scope to incentivize solar generation. In other words, the authors are investigating options to reduce the total cost of solar production as a result of increasing the number of different goods produced.