Assessing the effectiveness of renewable energy sector support mechanisms

2020 ◽  
Vol 26 (6) ◽  
pp. 1392-1413
Author(s):  
S.V. Ratner
Keyword(s):  
Renewable Energy ◽  
Project Management ◽  
Wind Power ◽  
Learning By Doing ◽  
Comprehensive Analysis ◽  
Incentive Mechanisms ◽  
Support Programme ◽  
Support Mechanisms ◽  
Generating Equipment ◽  
Operation Phase

Subject. This article discusses the effectiveness of government programmes to support renewable energy and whether they should continue to be implemented. Objectives. The article aims to conduct a comprehensive analysis of the changes in solar and wind power projects under the State support programme within the period from 2014 to 2019 and assess the effectiveness of the acting incentive mechanisms. Methods. For the study, I used the Learning-by-Doing theory and Project Management principles and methods. Results. The article proposes to consider the local content of the projects implemented as the key effectiveness indicator of the renewable energy support programme in Russia. For solar projects, this figure is currently significantly higher than the planned one, and it corresponds to the planned one for wind projects. In general, therefore, the programme can be considered effective. Conclusions. Further improvements in renewable energy support mechanisms should take into account the need to drastically increase the pace of training in the full cycle of the renewable energy project, including the operation phase of generating equipment and the supply of electricity to the grid.

scholarly journals Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

2021 ◽  
Vol 13 (12) ◽  
pp. 6681
Author(s):  
Simian Pang ◽  
Zixuan Zheng ◽  
Fan Luo ◽  
Xianyong Xiao ◽  
Lanlan Xu
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Wind Power ◽  
Solar Power ◽  
Prediction Method ◽  
Random Fluctuation ◽  
Concentrating Solar Power ◽  
Short Term ◽  
Hybrid Forecasting ◽  
Energy Cluster

Forecasting of large-scale renewable energy clusters composed of wind power generation, photovoltaic and concentrating solar power (CSP) generation encounters complex uncertainties due to spatial scale dispersion and time scale random fluctuation. In response to this, a short-term forecasting method is proposed to improve the hybrid forecasting accuracy of multiple generation types in the same region. It is formed through training the long short-term memory (LSTM) network using spatial panel data. Historical power data and meteorological data for CSP plant, wind farm and photovoltaic (PV) plant are included in the dataset. Based on the data set, the correlation between these three types of power generation is proved by Pearson coefficient, and the feasibility of improving the forecasting ability through the hybrid renewable energy clusters is analyzed. Moreover, cases study indicates that the uncertainty of renewable energy cluster power tends to weaken due to partial controllability of CSP generation. Compared with the traditional prediction method, the hybrid prediction method has better prediction accuracy in the real case of renewable energy cluster in Northwest China.

scholarly journals Analysis of Solar and Wind Power on Access Planning of Multiple Renewable Energy Sources

2021 ◽  
Vol 621 ◽  
pp. 012069
Author(s):  
Jiong Yan ◽  
Zi-xia Sang ◽  
Si-cong Wang ◽  
Zhi Du ◽  
Jia-qi Huang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Energy Sources

scholarly journals Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration

2019 ◽  
Vol 11 (8) ◽  
pp. 2310 ◽  
Author(s):  
Yi Zhou ◽  
Alun Gu
Keyword(s):  
Renewable Energy ◽  
Learning Curve ◽  
Wind Power ◽  
Cost Reduction ◽  
Rapid Development ◽  
Carbon Price ◽  
The United States ◽  
Research Development ◽  
Power Utility ◽  
Total Social Cost

The strategic transition from fossil energy to renewable energy is an irreversible global trend, but the pace of renewable energy deployment and the path of cost reduction are uncertain. In this paper, a two-factor learning-curve model of wind power and photovoltaics (PV) was established based on the latest empirical data from the United States, and the paths of cost reduction and corresponding social impacts were explored through scenario analysis. The results demonstrate that both of the technologies are undergoing a period of rapid development, with the learning-by-searching ratio (LSR) being greatly improved in comparison with the previous literature. Research, development, and demonstration (RD&D) have contributed to investment cost reduction in the past decade, and the cost difference between high and low RD&D spending scenarios is predicted to be 5.5%, 8.9%, and 11.27% for wind power, utility-scale PV, and residential PV, respectively, in 2030. Although higher RD&D requires more capital, it can effectively promote cost reduction, reduce the total social cost of deploying renewable energy, and reduce the abatement carbon price that is needed to promote deployment. RD&D and the institutional support behind it are of great importance in allowing renewables to penetrate the commercial market and contribute to long-term social welfare.

Design of a Magnetic Wall-Climbing Car

2014 ◽  
Vol 526 ◽  
pp. 211-216
Author(s):  
Qiong Ying Lv ◽  
Yu Shi Mei ◽  
Xi Jia Tao
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Magnetic Force ◽  
Mechanical Analysis ◽  
Heavy Duty ◽  
Magnetic Wall ◽  
Force Curves

As the trend of large-scale wind Power, People pay more attention to wind energy, which as a clean, renewable energy. Traditional unarmed climbing and crane lifting has been unable to meet the requirements of the equipment maintenance. Magnetic climb car can automatically crawl along the wall of the steel tower, the maintenance equipment and personnel can be sent to any height of the tower. The quality of the magnetic wall-climbing car is 550kg, which can carry 1.3 tons load. In this paper completed the magnetic wall-climbing car design and modeling, mechanical analysis in static and dynamic, obtained with the air gap and Magnetic Force curves. The application shows that the magnetic wall-climbing car meets the reliable adsorption, heavy-duty operation, simple operation etc..

THE ROLE OF RENEWABLE ENERGY SOURCES IN REGIONAL TOURISM DEVELOPMENT

2009 ◽  
Vol 15 (1) ◽  
pp. 25-36
Author(s):  
Branko Blazevic
Keyword(s):  
Sustainable Development ◽  
Renewable Energy ◽  
Wind Power ◽  
Geothermal Energy ◽  
Renewable Energy Sources ◽  
Biomass Energy ◽  
Energy Sources ◽  
Regional Tourism ◽  
Regional Sustainable Development

In this paper, the author focuses on the fundamental hypothesis that the adoption of a concept of regional sustainable development and the use of renewable energy sources are preconditions to organising an acceptable regional tourism offering based on an eco-philosophy The renewable development of tourism regions is the basic framework for research regarding opportunities for introducing renewable energy sources such as hydro energy, wind power, solar energy, geothermal energy, and biomass energy. The purpose of this paper is to indicate the real opportunities that exist for substituting conventional energy sources with renewable ones and the role of renewables in regional development from economic, environmental and sociological viewpoints. It should also be noted that renewable energy sources have a strong regional importance and can contribute significantly to local employment.

Implementing a Fuzzy Logic Based Algorithm to Predict Solar and Wind Energies in a Hybrid Renewable Energy System

2015 ◽  
Vol 4 (3) ◽  
pp. 10-24 ◽  
Author(s):  
Sanaa Faquir ◽  
Ali Yahyaouy ◽  
Hamid Tairi ◽  
Jalal Sabor
Keyword(s):  
Fuzzy Logic ◽  
Renewable Energy ◽  
Wind Power ◽  
Input Parameter ◽  
Energy System ◽  
System Stability ◽  
Industrial Sectors ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Hybrid Renewable Energy

The use of multi sources systems of energy progressed significantly in different industrial sectors. Between all the existing sources of energy, batteries and renewable sources, such as photovoltaic and wind, contain the highest specified energy. However, solar and wind energies are not available all the time, their performance is affected by unpredictable weather changes and therefore, it is difficult to control as it is not always feasible to obtain an accurate mathematical model of the controlled system. Also, uncertainty of the wind power can affect system stability. This paper presents a computer algorithm based on fuzzy logic control (FLC) to estimate the wind and solar energies in a hybrid renewable energy system from natural factors. The wind power was estimated using the wind speed as an input parameter and the solar power was estimated using the temperature and the lighting as input parameters.

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