Solar Power Plant Output Research Articles

Review on Design and Fabrication of Solar Panel Cleaning Mechanism

Today energy demands are increasing sharply therefore as a Renewable source of power, solar energy has an important role in producing the electricity direct from sunlight. The surface of solar panel in solar power plants becomes dirty due to natural factors such as dust and bird droppings. This project aims at increasing the efficiency of solar power plants by solving the problem of accumulation of dust on the surface of solar panel which leads to reduction in plant output and overall plant efficiency. It proposes to develop a Solar Panel Cleaning System which could remove the accumulated dust on its surface on a regular basis and maintain the solar power plant output.[8]

A reference architecture for orbiting solar reflectors to enhance terrestrial solar power plant output

Orbiting reflectors offer the possibility of illuminating large terrestrial solar power plants to enhance their output, particularly at dawn and dusk when their output is low but energy spot prices can be high. While the concept of orbiting solar reflectors has been considered in various forms in the past, there is now a timely overlap of rapidly growing global demand for clean energy services, falling launch costs through reusability and the emergence of in-orbit manufacturing technologies to enable the fabrication of large, ultra-lightweight space structures. This paper provides an end-to-end analysis of a possible minimum initial architecture to deliver such global clean energy services. The analysis will cover orbit selection, attitude control requirements, structural analysis and economical viability, followed by a discussion on regulatory issues, future improvements and further applications.

A robust auto encoder-gated recurrent unit (AE-GRU) based deep learning approach for short term solar power forecasting

The increasing presence of solar power plants shows its potency as one of the key renewable energy resource to fulfill energy needs of the community. This increasing presence can be effectively utilized by using efficient prediction of solar power plant output for stable grid operation. The availability of processors with high computational capability and access to baseline data provide edge to deep learning models for prediction of solar power plants. In this work, sequence to sequence auto-encoder (AE) and Gated Recurrent unit (GRU) based hybrid deep learning approach is developed, which further advances other recent works offered in solar power forecasting. The AE extracts the internal relationship of input solar data, minimizes the reconstruction error and taps the important feature correlation. Furthermore, GRU exploits the time dependencies of the data. For comprehensive evaluation, different deep learning models and their hybrid forms are analyzed for different prediction durations i.e. 24 h, 48 h, and 15 days prediction. The analysis compares models on three major performance indices, i.e., mean square error, mean absolute error, and R2errors with the same hyperparameters for this time series prediction. The outcome ascertains that the proposed AE-GRU based deep learning model performs better than other deep learning models.

Offering Model for Optimization of Virtual Power Plant Operation in Electricity Market

Distributed Energy Sources based on renewable generation have an important place in power systems due to liberalization of electricity markets and increase of environmental problems. The involvement of renewable energy sources (RES) in the energy market poses a serious challenge for RES owners. The main reason for this is the uncertainty about the power output of RES. For example, the power output of wind power plants (WPP) depends on the wind speed and the power output of solar power plants (SPS) depends on the solar radiation and clouds. This brings the risk of not meeting long-term or medium-term electricity delivery contracts. To remove this risk, a single-acting unit in electricity market is formed by combining different types of renewable and non-renewable generation units and storage systems. This unit is defined as Virtual Power Plants (VPP). In this study, an hourly operation scheduling is modeled to get maximum profit for VPP owner participating in day ahead market by saling or purchasing electricity. The VPP in the study consists of wind power plant, conventional power plant and energy storage system. The problem is formulated as a mixed-integer non-linear problem, applied for 24-hours time horizon and tested in GAMS software. The applicability of the method has been demonstrated by showing that the proposed method helps VPP owner about how to provide optimum sale/purchase bids in the day ahead market.

Solar Power Plant Generation Short-Term Forecasting Model

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.

A novel model to estimate the cleaning frequency for dirty solar photovoltaic (PV) modules in desert environment

Accumulated dust on solar photovoltaic (PV) modules can significantly decrease their energy output in desert environment. Therefore, cleaning the deposited dust on the PV module surface is crucial in engineering applications to maintain the high power output of solar power plants, especially in desert areas. Nevertheless, it is difficult to predict the reasonable cleaning frequency for PV modules by traditional methods. In this paper, a novel model to simply estimate the cleaning frequency was developed for dirty PV modules in desert areas based on the dust deposition velocity and the relationship between deposited dust density and PV module power performance. The studied parameters are the installation tilt angles, the dust concentration in the ambient air and the representative average particle diameter. Based on this model, module cleaning frequency for desert regions is approximately 20days when the power output reduction and particle concentration equal to 5% and 100μg/m3, respectively. In addition, the effects of accumulation density, average particle diameter, tilt angles and wind velocity on the cleaning frequency are discussed and analyzed respectively.

Daily forecast of solar thermal energy production for heat storage management

Solar energy offers a renewable source of power but its fluctuating nature raises concerns about the electrical grid balancing. Network regulators have to estimate the upcoming production to match supply with demand; consequently, power plant operators may be asked to provide accurate forecasts. Planning the thermal or electrical output of solar power plants is thus highly required to ensure a stable power chain supply. This paper presents a solution that couples a meteorological model with a solar power plant performance model. The power output is predicted 24 h ahead in the case of a solar Fresnel power plant. The required Direct Normal Irradiance is inferred from the global horizontal irradiance; the thermal production is evaluated from an optical and thermal model. Our approach has been validated on a 1000 m2 Fresnel power plant, paving the way for model-based storage strategy.

Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting – A review

Solar irradiance forecasting can reduce the uncertainty of solar power plant output caused by solar irradiance intermittency. Concentrated solar thermal (CST) plants generate electricity from the direct normal irradiance (DNI) component of solar irradiance. Different forecasting methods have been recommended for a range of forecast horizons relevant to electricity generation. High DNI forecast accuracy is important for achieving accurate forecasts of CST plant output which are shown to increase CST plant profitability. This paper reviews the DNI forecast accuracy of numerical weather prediction models, time series analysis methods, cloud motion vectors, and hybrid methods. The results of the reviewed papers are summarised to identify the best DNI forecast accuracy for particular forecast horizons. The application of DNI forecasts to operate CST plants is also briefly reviewed.This paper found that additional research is required for time series analysis methods to corroborate current results and for satellite-based cloud motion vectors to establish DNI forecast accuracy. It was also concluded that future research should use the same error metrics to report results to facilitate fair comparison of DNI forecast accuracy from different studies. In addition, the creation of a common high quality DNI data set to evaluate all forecasting methods would also help to verify best forecast accuracy. The review of DNI forecasting for CST plants found that using accurate 2-day ahead DNI forecasts can increase revenue and decrease penalty costs. Future research should investigate benefits from using short-term DNI forecasts from the intra-hour forecast horizon up to the 6-h forecast horizon to determine CST plant operation. Another aspect to research is to determine whether the benefit of DNI forecasts for a CST plant is affected by different regulations in different electricity markets.

Solar-heated rotary kiln for thermochemical energy storage

Thermal energy storage (TES) will improve the efficiency and output of solar power plants. TES based on thermochemical cycles is an interesting option as thermochemical cycles can provide high energy storage densities and allow longer heat storage time. The use of multivalent solid oxide reduction–oxidation (REDOX) reactions for thermochemical heat storage is a promising option. Several concepts are feasible for coupling solar energy to the redox reaction. Among those a directly irradiated rotary kiln is one of the most interesting because it is able to provide high mass flow rates and high amounts of active material. A solar-heated rotary kiln was set-up and operated in the solar furnace of DLR for thermal reduction and oxidation of cobalt oxide. The redox material was fed into the reactor batch wise and reduced on-sun at temperatures of about 900°C and re-oxidized off-sun in the same rotary kiln. Both steps were carried out in an air atmosphere. Thirty cycles were performed with one batch showing no evident degradation of the material. The results confirm that the rotary kiln is a feasible reactor set-up for the solar reduction of metal oxides and, hence, for thermochemical energy storage.

Accuracy Verification of Irradiance Forecast using Numerical Weather Prediction Data by Japan Meteorological Agency

In order to integrate a substantial amount of fluctuating solar energy into the power grid, accurate irradiance prediction systems are required to estimate the power output of solar power plants. This study presents a simple statistical approach to predict irradiance on the basis of regional numerical weather prediction data that is forecasted up to three days by the Japan Meteorological Agency. Annual irradiance forecasts of individual sites in the Hokuriku region demonstrate a root mean square error of 0.43-0.45MJ/m2 for the intra-day forecast, increasing to about 0.51MJ/m2 for the two-days-ahead forecast. Furthermore, we calculated error indexes under different conditions to estimate the magnitude of the influence of different assumptions on the results.

The Degradation of Solar Cells under Van Allen Radiation

The electrons and protons of the Van Allen Belt cause radiation damage to silicon solar cells which results in a gradual reduction of the power output of solar power plants on satellites passing through the Belt. The study of the radiation damage caused by monoenergetic electrons and by monoenergetic protons of various energy indicates that N on P solar cells are substantially more radiation resistant than the commonly used P on N cells. From such studies and a knowledge of the composition of the Van Allen Belt, a flux of, for example, 1 Mev electrons can be determined that will cause the same radiation damage per unit time to a bare solar cell as the Van Allen Belt spectrum would produce under a given shielding. Observations of radiation damage on satellites are also conveniently expressed in terms of such an equivalent flux. Measurements of solar cell short circuit current changes and changes in the current gain of specially designed highly radiation sensitive transistors have been performed on the Telstar satellite. The results are in general agreement with the radiation damage expected from the observed particle flux and they indicate that solar power is practical for long-life satellites passing through the Van Allen Belt, particularly if N on P solar cells are used.