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The purpose of this paper is to explore citizens’ willingness to invest in photovoltaics.

To meet the aim of the research, a questionnaire survey was conducted in the island of Evia in Greece using the method of random stratified sampling. A total of 366 responses were analyzed using both descriptive and inductive statistics methods, such as principal components analysis, K-means cluster analysis, discriminant analysis and binary logistic regression.

The research results indicate that 73per cent of the respondents would invest in renewable energy sources, whereas 55per cent of them would specifically invest in photovoltaics. Regarding their views on photovoltaics, three components were extracted; photovoltaics positive effects, facilitations for investments in photovoltaics and photovoltaics’ performance. Area of residence, annual income and the above-mentioned three components of views on photovoltaics were found to be statistically significant for the dichotomous variable of willingness to invest in photovoltaics. Among the examined variables, photovoltaics performance found to contribute the most in increasing respondents’ willingness to invest in photovoltaics.

The study filled the literature gap concerning citizens’ willingness to invest in photovoltaics in Greece. Furthermore, the research results made feasible to understand the factors that can lead in an investment decision for photovoltaics.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

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.

The purpose of this paper is to find some feasible measures to solve the problems faced in China's renewable energy development and promote the industrial development of China's renewable energy.

The paper summarises the status and studies the problems of China's renewable energy industrial development, and then puts forward some proposals for the industrial development.

The paper finds that most of China's renewable energy technology is still in the transitional period from research and development to industrial production, and that the renewable energy industrial development needs the establishment of a series of technical experiments and demonstration projects to analyze and investigate the resources, the conversion and market development experience, and then form complete sets of equipment design and manufacturing, cultivation and collection of biomass resources and technology development capabilities, and that regulations should be established to provide a solid foundation for the large‐scale development of China's renewable energy. As a result, some development measures are suggested in the paper.

The paper raises the problems faced in China's renewable energy development, and gives some feasible development measures for the industrial development of China's renewable energy.

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.

Energy has a strategic role in the social and economic development of countries. Affordability, accessibility and availability of energy sources are the priorities of the governments in energy supply. Therefore, understanding the robustness of energy supply is an important subject of energy researchers and policymakers. This paper aims to analyze the robustness of the electricity system at the national level.

First, the strengths, weaknesses, opportunities and threats analysis is implemented for a selected case study. Then, the expert panel weighed the parameters’ effect on sustainable power generation, the survey is quantified using fuzzy logic. Finally, cross functional analysis is applied to evaluate the influence/dependence of the parameters.

The results show three determinant parameters which have the most influence on the system: fluctuations in oil prices, governmental acts and sanctions against the country. The most dependent parameters, as objectives variables, are the share of renewables and distributed generation (DG), system reliability, power generation diversity and transmission efficiency.

Using future studies methods in the energy level at the nation level has been done for the first time in the current work.

Spain has one of the greatest photovoltaic potentials in Europe. The boom suffered in the photovoltaic sector during the years 2007 and 2008 led to instability in the electrical system, prompting the legislator to develop a large number of legislative changes trying to control the electric system tariff deficit. These measures profoundly affected plant owners creating a non-transparent secondary market, which are not covered by the current exchange platforms. The purpose of this paper is to analyze the current situation of the photovoltaic market in Spain, try to understand it based on its historical sequence and propose efficiency improvement measures, based on the implementation of best practices and market mechanisms.

This paper studies the legislative evolution in the photovoltaic sector in Spain and its effect on owners and investors. The authors propose an intermediation system that improves the efficiency of the secondary market.

The authors propose an intermediation system that improves the efficiency of the secondary market.

The authors have not found any other paper that proposes the creation of a market for photovoltaic facilities to increase efficiency.

The purpose of this paper is to explore the use of the purchase power of the higher education system to catalyze the economy of scale necessary to ensure market competitiveness for solar photovoltaic electricity.

The approach used here was to first determine the demand necessary to construct “Solar City factories”, factories that possess equipment and processes sized, dedicated and optimized to produce only solar photovoltaic systems. Inexpensive solar cells from these factories could produce solar electricity at rates comparable to conventional fossil‐fuel derived electricity. Then it was determined if sufficient demand could be guaranteed by green purchasing from the international university system.

A focused effort from the university community to purchase on‐sight produced electricity would make it possible to construct truly large‐scale dedicated solar photovoltaic factories rather than follow the piecemeal production increases currently observed in the industry.

Direct economic competitiveness of an energy source having markedly lower environmental, social and health externalities would have a positive‐spiral (virtuous cycle) effect encouraging the transition of the global energy infrastructure away from polluting fossil fuels to green solar energy.

Despite significant commercial progress in the conversion efficiency of sunlight into electricity with solar photovoltaic cells, their widespread adoption is still limited by high costs relative to conventional fossil fuel‐based sources of electricity. The concept outlined and critically reviewed in this paper represents a novel and economical method of transitioning the electric supply system to renewable solar energy.

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.

To foster the grid integration of both electric vehicles (EV) and renewable generators, the purpose of this paper is to investigate the possible synergies between these players so as to jointly improve the production predictability while ensuring a green mobility. It is here achieved by the mean of a grid commitment over the overall power produced by a collaborative system which here gathers a photovoltaic (PV) plant with an EV fleet. The scope of the present contribution is to investigate the conditions to make the most of such an association, mainly regarding to the management strategies and optimal sizing, taking into account forecast errors on PV production.

To evaluate the collaboration added value, several concerns are aggregated into a primary energy criterion: the commitment compliance, the power spillage, the vehicle charging, the user mobility and the battery aging. Variations of these costs are computed over a range of EV fleet size. Moreover, the influence of the charging strategy is specifically investigated throughout the comparison of three managements: a simple rule of thumb, a perfect knowledge deterministic case and a charging strategy computed by stochastic dynamic programming. The latter is based on an original modeling of the production forecast error. This methodology is carried out to assess the collaboration added value for two operators’ points of view: a virtual power plant (VPP) and a balance responsible party (BRP).

From the perspective of a BRP, the added value of PV-EV collaboration for the energy system has been evidenced in any situation even when the charging strategy is very simple. On the other hand, for the case of a VPP operator, the coupling between the optimal sizing and the management strategy is highlighted.

A co-optimization of the sizing and the management of a PV-EV collaborative system is introduced and the influence of the management strategy on the collaboration added value has been investigated. This gave rise to the presentation and implementation of an original modeling tool of the PV production forecast error. Finally, to widen the scope of application, two different business models have been tackled and compared.