scholarly journals Solar power generation short-term forecasting model’s implementation experience

2018 ◽  
Vol 208 ◽  
pp. 04005 ◽  
Author(s):  
Elena Kochneva
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Solar Power ◽  
Information Support ◽  
Practical Implementation ◽  
Short Term ◽  
Forecasting System ◽  
Solar Power Plants ◽  
Short Term Forecasting ◽  
Installed Capacity

Recently there is significant increase in the installed capacity of solar power plants in Russia. Thereby there are issues of solar power plants owners information support for participation in wholesale electricity market. The paper describes the experience of short term forecasting system practical implementation. The system is proposed for forecasting the solar power plant generation “a day ahead” as a part of the software for automatic meter reading systems “Energosfera”. The short-term forecasting program modules structure, key parameters and characteristics used during the forecasting process description is presented.

scholarly journals Solar Power Plant Generation Short-Term Forecasting Model

2018 ◽  
Vol 208 ◽  
pp. 04004
Author(s):  
Stanislav Eroshenko ◽  
Elena Kochneva ◽  
Pavel Kruchkov ◽  
Aleksandra Khalyasmaa
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Solar Power ◽  
Weather Conditions ◽  
Solar Power Plant ◽  
Short Term ◽  
Variable Capacity ◽  
Solar Power Plants ◽  
Rapid Pace ◽  
Short Term Forecasting

Recently, renewable generation plays an increasingly important role in the energy balance. Solar energy is developing at a rapid pace, while the solar power plants output depends on weather conditions. Solar power plant output short-term forecasting is an urgent issue. The future electricity generation qualitative forecasts allow electricity producers and network operators to actively manage the variable capacity of solar power plants, and thereby to optimally integrate the solar resources into the country's overall power system. The article presents one of the possible approaches to the solution of the short-term forecasting problem of a solar power plant output.

scholarly journals A neural network model for short-term forecasting of electricity generation by solar power plants

2020 ◽  
pp. 145-158
Author(s):  
Dmitry Tyunkov ◽  
◽  
Alexander Gritsay ◽  
Alina Sapilova ◽  
Alexandr Blokhin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Data ◽  
Predictive Model ◽  
Power Plants ◽  
Solar Power ◽  
Energy Sources ◽  
Short Term ◽  
Solar Power Plants ◽  
Short Term Forecasting

Today, energy consumption in the world is growing and it is becoming urgent to solve the problem of replacing traditional energy sources with alternative ones. The solution to this problem is impossible without a preliminary data analysis and further forecasting of energy production by alternative sources. However, the use of alternative energy sources in the conditions of the wholesale electricity and capacity market currently operating on the territory of the Russian Federation is impossible without the use of short-term predictive “day ahead” models. In this article, the authors perform a brief analysis of the existing methods of short-term forecasting which are used when making forecasts for the generation of electricity by solar power plants. Currently, there are already a fairly large number of predictive models built within each of the selected methods of short-term forecasting, and they all differ in their characteristics. Therefore, in order to identify the most promising method of short-term forecasting for further use and development, the authors used a previously developed classification. In the course of the study, a preliminary processing of initial data obtained from the existing solar power plants using spectral analysis was carried out. Further, to build a predictive model, a correlation analysis of the initial data was carried out, which showed the absence of a linear relationship between the components in the retrospective data. Based on the results of the correlation analysis the authors made a decision to select parameters empirically in order to build a predictive model. As a result of the study, a mathematical model based on an artificial neural network was proposed and a learning sample was generated for it. In addition, the architecture of an artificial neural network was determined, the result of which is a short-term forecast of electric power generation in the "day ahead" mode, and calculations were performed to obtain numerical values of the forecast. From the results of the study, it follows that the developed predictive model in the predicted interval has a mean absolute error of about 13.5 MW. However, at some intervals, the peak discrepancies can reach up to 200 MW. The root mean square error of the model is 27.8 MW.

scholarly journals Applications of a shadow camera system for energy meteorology

2018 ◽  
Vol 15 ◽  
pp. 11-14 ◽  
Author(s):  
Pascal Kuhn ◽  
Stefan Wilbert ◽  
Christoph Prahl ◽  
Dominik Garsche ◽  
David Schüler ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Plants ◽  
Solar Power ◽  
Short Term ◽  
Camera System ◽  
Plant Operations ◽  
Electricity Grids ◽  
Solar Power Plants ◽  
Sky Imager

Abstract. Downward-facing shadow cameras might play a major role in future energy meteorology. Shadow cameras directly image shadows on the ground from an elevated position. They are used to validate other systems (e.g. all-sky imager based nowcasting systems, cloud speed sensors or satellite forecasts) and can potentially provide short term forecasts for solar power plants. Such forecasts are needed for electricity grids with high penetrations of renewable energy and can help to optimize plant operations. In this publication, two key applications of shadow cameras are briefly presented.

scholarly journals Short-term forecast methods of electricity generation by solar power plants and its classification

2019 ◽  
Vol 1260 ◽  
pp. 052033
Author(s):  
D A Tyunkov ◽  
A S Gritsay ◽  
V I Potapov ◽  
R N Khamitov ◽  
A V Blohin ◽  
...  
Keyword(s):  
Electricity Generation ◽  
Power Plants ◽  
Solar Power ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Solar Power Plants

Analiza ekonomske opravdanosti investiranja u solarnu elektranu sa vertikalno postavljenim bifacijalnim fotonaponskim modulima u perspektivnim uslovima slobodnog tržišta

2021 ◽  
Vol 23 (3) ◽  
pp. 37-44
Author(s):  
Đorđe Lazović ◽  
◽  
Kristina Džodić ◽  
Željko Đurišić
Keyword(s):  
Power Plant ◽  
Electricity Market ◽  
Power Plants ◽  
Solar Power ◽  
Renewable Energy Sources ◽  
Economic Feasibility ◽  
Plant Production ◽  
Solar Power Plant ◽  
Solar Power Plants ◽  
East West

After the expiration of governmental incentive measures for renewable energy sources integration, economic feasibility of investing into solar power plants will highly depend on compatibility between production and variable prices. In order to achieve the maximum possible profit of the power plant in liberalized electricity market, it is necessary to consider the possibility of investing in solutions that are not common today, but with the potential of being more profitable in the future. Such a solution is a solar power plant consisting of vertically placed bifacial modules whose active surfaces are oriented in the east-west direction. This configuration of the power plant can achieve higher production in periods of high prices, and thus higher profits from the sale of electricity. On the other hand, such a solution is more expensive than a standard solar power plant with monofacial modules. In this paper, a comparison of return on investment in a bifacial power plant and a monofacial power plant with existing and prospective market conditions is performed. The influence of solar power plant production on the price of electricity was investigated on the example of Germany. Based on this research, a prognostic model of the daily price diagram on the unified European market until 2040 was formed. It served for the analysis of the profitability of investments in the two considered variants of the solar power plant realization.

scholarly journals Trends in the Development of the Solar Energy Generation Market of Ukraine

2021 ◽  
Vol 4 (519) ◽  
pp. 114-119
Author(s):  
V. S. Yakovenko ◽  
◽  
V. V. Harkusha ◽  
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Power Plants ◽  
Solar Power ◽  
Energy Generation ◽  
The State ◽  
Energy Market ◽  
Solar Power Plants ◽  
Generating Equipment ◽  
Installed Capacity

The research is aimed at analyzing and finding the reasons for the specific development of the solar energy market of Ukraine, defining the interaction on the part of the State and its role, furthermore the role of monopolists-generators and small generating equipment in private households. As result of the research, the following issues are considered: specifics of the formation of a share of the segment of solar power plants among renewable energy sources; the largest companies – solar energy generators in Ukraine; dynamics of changes in the «green» tariff and installed capacity of solar power plants over the past decade; development of the direction of small solar power plants installed by households and the largest generating regions of Ukraine by both the number and the installed capacity of the households’ SPP. Analyzing modern analytical reports on renewable energy generation, it is appropriate to note that a system of indicators, which is being created currently to reflect the real state of development of renewable energy, indicates a certain prospect of scientific research in this direction. Modern problems of development of the generating renewable energy market are defined, among which the following are defined: changes in legislation on the «green» tariff, which leads to changes in the investment climate and attractiveness of generation projects, the conflicting mechanisms of tariff auctions; potential threat of shortage of both storage and transport capacities (due to disproportionate development of generation volumes and its infrastructure support) etc. Based on the analysis of existing development trends, conclusions have been drawn about the future state and development of the solar energy generation market. The need to form a balanced national strategy for the development of renewable energy, taking into account the current conditions for reducing the «green» tariff and the cost of generating equipment, as well as taking into account the national interests of the State, the interests of monopolists and households, has been established.

scholarly journals Statistical Analysis Based Survey of Indian Renewable Energy Scenario

2021 ◽  
Vol 16 (2) ◽  
pp. 379-384
Author(s):  
Radhika Swarnkar ◽  
Harikrishnan R
Keyword(s):  
Renewable Energy ◽  
Statistical Analysis ◽  
Power Plants ◽  
Fossil Fuels ◽  
Solar Power ◽  
Renewable Energy Sources ◽  
Green Energy ◽  
Energy Potential ◽  
Solar Power Plants ◽  
Installed Capacity

Renewable energy is a solution for electricity generation for cleaner and green energy. The aim of this paper is to find the energy potential of India in terms of sources, per-capita energy consumption and the main potential consumers. Comparing consumption of fossil fuels and Renewable energy sources (RES) of India in 2019 and 2020 and finally to find whether there is any change in energy generation of two solar power plants in different geographical location of India with the help of independent t-test statistics. In this paper two statistical analysis are proposed. One is the statistical analysis of installed capacity, generation and consumption of fossil fuels and renewable energy in India. Other one is the statistical analysis of two solar power plants located at different geographical locations in India. From the statistical analysis it is found that, installed capacity of coal, RES and hydro is increased in 2020 as compared to 2019. Total demand in January 2020 is 2,77,140.33 MW whereas total installed capacity is 3,71,126 MW, this means that installed capacity is more but are not in running condition. From the statistical analysis of two independent solar power plants it is found that solar power plant-1 generates more energy but with high conversion loss hence poor efficiency.

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