Solar Power Output Research Articles

Probabilistic Steady State Voltage Stability Assessment Method for Correlated Wind Energy and Solar Photovoltaic Integrated Power Systems

Herein, a novel method for the assessment of steady state probabilistic voltage stability margins (PVSMs) in power systems considering generation and load demand uncertainty is investigated. The steady state VSM in an uncertain environment is jointly analyzed through continuous power flow (CPF) and the three point estimation method (3PEM). The statistical distribution of the VSM is obtained using Cornish–Fisher (CF) expansion series. Results are compared with the benchmark Monte Carlo simulation method. Probabilistic models of grid‐connected wind and solar photovoltaic energy sources are utilized in this study. For identification of weak/strong buses and contingency ranking of transmission lines, a steady state voltage stability index, the L‐index, is utilized. The validation of the proposed method is analyzed through IEEE 24 bus and 118 bus test systems. For a realistic study of the system, correlations among wind speeds, solar power outputs, and load demands are considered with various static load models, i.e., constant impedance (CZ), constant current (CI), constant power (CP), and ZIP. Moreover, the concept of relative electrical distance (RED) is applied for selecting the conventional generators for power balancing. The results reveal that factors such as load characteristics and correlations have significant effects on PVSM.

A simple and low cost measurement technology for solar PV modules

The appropriate measurement of solar power output plays a critical role in the performance analysis of any solar power plant or a photovoltaic array. The performance under standard test conditions (STC), as mentioned by the manufacturers, is seldom attained as measurements of a PV module/array are done in outdoor terms. To evaluate the efficiency of performance, a photovoltaic module/array accurately, an on-line characterization of such photovoltaic (PV) set-ups at non-standard test conditions (NSTC) is always necessary. In the backdrop of the absence of reliable and competitively priced PV module characterization systems, a smart, digital and portable test set-up seem to be highly significant, and such a model has been developed and analyzed in this work. This system uses supercapacitors as the load to the PV module under test. At present, such characterizations are carried out using imported devices, which cost around INR 200-300 thousand. The system so developed will cost around INR 30 thousand and is aimed at import substitutions of such measurement devices. This is very important with regard to field installations testing and will also be of extreme help to researchers in the laboratories, who require all cannot use extensive testing and costly imported instruments. Proper attention has been paid in this work to determine the suitability of the method concerning I-V plotting time and accuracy. Thus it becomes imperative that the quality of the new PV metrology be verified experimentally and duly validated to instill confidence among the users. A detailed investigation regarding the quality of measurements has been carried out, taking into consideration the effect of a wide range of climatic variations. It has been found that these values are consistently in good agreement with the results obtained at the standard Electronics Regional Test Laboratory (ERTL Govt. Of India) test set-up. Statistical analysis of the PV measurements is ensured by regression analysis (RA) of the respective electrical parameters and the standard deviation (SD) of fill factor (FF) values. Experimental evaluation of quality parameters like Fill-Factor (FF) has yielded satisfactory ranges of 70% to 79% for FF. Elaborate regression analysis (RA) of principal PV parameters has yielded consistently high values exceeding 99%. At this time, when India is planning to install large-scale PV plants, there is a significant need to measure the electrical parameters of PV modules of different technologies and, after that, choose the appropriate one for optimum performance in a specific region.

Short term forecasting of solar radiation and power output of 89.6kWp solar PV power plant

Abstract Short term forecasting of solar radiation is useful for power plant operations, grid balancing, real-time unit dispatching, automatic generation control and trading. Solar forecasting is an essential tool in solar PV power plant to improve quality of energy delivery to the grid and to reduce weather dependent ancillary costs. In this paper short term forecasting of solar radiation and power output of 89.6 kWp solar PV power plant has been conducted. A new model has been proposed to conduct short term solar forecasting for different days of the year at Amity University Haryana (AUH) campus (28.4595°N, 77.0266°E) located in the northern region of India whereas auto-regressive integrated moving average (ARIMA) model is applied to forecast power output from the solar PV power plant. Root mean square error (RMSE) and Forecast Score (FS) has been used to for accessing the quality of forecasting models. The proposed model for prediction of solar radiation on tilted surface is simple and has very high accuracy. The model has ability to incorporate uncertainty due to environmental conditions. The proposed model is compared with Smart Persistence model and ARIMA model and it has been observed that it has better RMSE and Forecast Score than both Smart Persistence model and ARIMA model for both 15 min and 30 min time horizon. ARIMA model provides a reliable forecast for both solar radiation and solar PV power output. It is flexible enough to accept more information and its performance improves with the increase in number of data points.

Efficient solar‐powered water pump with single‐input dual‐output DC–DC converter employing four‐phase SRM drive

The present work deals with the design of an efficient and inexpensive autonomous photovoltaic array-based water pumping system utilising a new configuration of a single-input balanced dual supply buck–boost DC–DC converter. A four-phase switched reluctance motor (SRM) drive is selected to pump the water effectively and to make the system reliable. The utilised DC–DC converter can provide two balanced outputs using a single solar power source. It offers continuous output supply with minimum ripple to the SRM pump. The new control logic is also discussed in this work to provide equal voltages for two capacitors of the split converter in both starting and steady-state conditions. An improved perturb and observer-based maximum power point tracking control is also utilised to enhance the solar output power at varying irradiance. Moreover, owing to the pulse-width modulation switching of a split converter, a reduction is achieved in the size of split DC-link capacitors. The MATLAB/Simulink-based simulation is done to validate the real-time implementation of a developed system. These obtained responses are again validated through test results obtained via the developed prototype.

On‐ and off‐design thermodynamic analysis of a hybrid polar solar thermal tower power plant

Concentrated solar power (CSP) is one challenging renewable technology for the production of electricity. Within this concept central receiver solar plants combined with gas turbines are being investigated because of their promising efficiencies and reduced water consumption. Hybrid plants incorporate a combustion chamber in such a way that in periods of low solar irradiance power output can be kept approximately constant and so, electricity production is predictable. An integrated, non-complex solar thermodynamic model of a hybrid gas turbine solar plant is developed employing a reduced number of parameters with a clear physical meaning. The solar subsystem is modeled in detail, taking into account the main heliostats field losses factors as cosine effect, blocking and shadowing, or attenuation. An heliostat field with polar symmetry together with a cavity receiver are considered. The model is implemented in our own software, developed in Mathematica language, considering as reference SOLUGAS solar field (Seville, Spain). Heliostats field configuration is determined for the design point and its associated efficiency is computed. First, an on-design analysis is performed for two different working fluids (dry air and carbon dioxide), for recuperative and non-recuperative modes. A pre-optimization process is carried out regarding the pressure ratio of the gas turbine for different configurations. Some significant efficiency and power rises can be obtained when pressure ratio is adapted for each specific configuration and working fluid. Maximum achievable plant overall efficiency is 0.302 for both fluids in the recuperative mode, taking a pressure ratio of 7 for dry air and 16 for carbon dioxide. In non-recuperative configurations maximum overall efficiency is obtained for dry air, about 0.246. Moreover, a dynamic study is performed for four representative days of each season. Then, efficiencies and solar share are plotted against time. In addition, fuel consumption and greenhouse emissions are computed for all seasons.

MPPT based on Artificial Intelligence system for Photovoltaic System using Virtual Instrumentation

Renewable Energy is a future of the globe. Especially Solar power will make can changes in the power system. Many algorithms is used to achieve maximum solar power output. This paper applies an Artificial intelligence system to control the Maximum Power in Photovoltaic system with the maximum response time. Many MPPT Algorithm is available. In this Perturbation and observation algorithm is used the achieve the Maximum power in Photovoltaic system by Fuzzy logic system. This system has simulated by Virtual instrumentation tool.

Studi Numerik Dengan Model Empat Parameter Untuk Memprediksi Daya Luaran Dari Panel Surya

Solar panel is one of the most promising renewable energies technology that can be an alternative of the fossil fuel energy. Furthermore, an accurate model to simulate the solar panel efficiency and power output is required. In this study, four parameters model is used to simulate the power output of the solar panel. Four parameters are extracted from single diode equation by using Matlab software then validate with manufacturer datasheet and one-day experiment condition. The error value from datasheet and experiment are 0.03% and 3.54%, respectively. Findings show that four parameters model is accurate. In the future, this result can be taken as a valuable reference for researcher and industrial to modelling energy output of the solar panel.

Economic and climate impacts of reducing China's renewable electricity curtailment: A comparison between CGE models with alternative nesting structures of electricity

To mitigate climate change impacts and achieve low-carbon transformation, China has accelerated the development of renewable energy, which is severely challenged by the curtailment of renewable electricity. This study uses a dynamic multi-sectoral CGE model with alternative nesting structures and substitution elasticities for electricity with different power sources to capture the economic and environmental feasibility of reducing renewable electricity curtailment across all economic sectors in China. The reduction of renewable electricity curtailment is simulated during 2021–2030 from the curtailment rates of 2015–2017. We found that the reduction of renewable electricity curtailment would lead to a significant expansion in the output of renewable electricity and a moderate decrease in non-renewable electricity production. Among the renewable electricity, wind power has the most significant output gain (over 9%), with solar power and hydropower outputs rising by over 5% and 1.5%, respectively. However, without the cost-neutrality assumption, the impacts of reducing electricity curtailment would be largely over-estimated with CGE models simulated by improved technology. The disparity between results from the models with alternative nesting constant elasticity of substitution (CES) functions for electricity sectors is highly dependent on the difference between their substitution elasticities. Accompanying the changes in electricity generation, the reduction of renewable electricity curtailment would bring multiple green co-benefits like significantly reducing CO2 and air pollutants emitted from electricity sectors, and improvements in real GDP and employment.

Current and future potential of solar and wind energy over Africa using the RegCM4 CORDEX-CORE ensemble

Renewable energy is key for the development of African countries, and knowing the best location for the implementation of solar and wind energy projects is important within this context. The purpose of this study is to assess the impact of climate change on solar and wind energy potential over Africa under low end (RCP2.6) and high end (RCP8.5) emission scenarios using a set of new high resolution (25 km) simulations with the Regional Climate Model version 4 (RegCM4) produced as part of the CORDEX-CORE initiative. The projections focus on two periods: (i) the near future (2021–2040) and ii) the mid-century future (2041–2060). The performance of the RegCM4 ensemble mean (Rmean) in simulating relevant present climate variables (1995–2014) is first evaluated with respect to the ERA5 reanalysis and satellite-based data. The Rmean reproduces reasonably well the observed spatial patterns of solar irradiance, air temperature, total cloud cover, wind speed at 100 m above the ground level, photovoltaic power potential (PVP), concentrated solar power output (CSPOUT) and wind power density (WPD) over Africa, though some biases are still evident, especially for cloud-related variables. For the future climate, the sign of the changes is consistent in both scenarios but with more intense magnitude in the middle of the century RCP8.5 scenario. Considering the energy variables, the Rmean projects a general decrease in PVP, which is more pronounced in the mid-century future and under RCP8.5 (up to 2%). Similarly, a general increase in CSPOUT (up to 2%) is projected over the continent under both the RCP2.6 and RCP8.5 scenarios. The projection in WPD shows a similar change (predominant increase) in the near and mid-century future slices under both RCPs with a maximum increase of 20%. The present study suggests that the RCP2.6 emission scenario, in general, favours the implementation of renewable energy in Africa compared to the RCP8.5.

Full-day dynamic characteristics analysis of a solar aided coal-fired power plant in fuel saving mode

Solar aided (coal-fired) power generation (SAPG) technology has been rapidly developed over the last few years for its fewer coal consumption rate than the original coal-fired power plant and the lower initial investment than the solar alone thermal power generation. However, due to the frequent variation of meteorological conditions, the operation behaviors of the SAPG plant are ever-changing. In this paper, according to the transient models of SAPG plant, the continuous operation behaviors of a 330 MW SAPG system in a full day are studied. The coupling and decoupling times of the solar field are optimized, and variations of oil and feed water parameters in the solar field during the coupling and decoupling processes are discussed. Besides, some dynamic thermal performances of the SAPG system are studied under the control strategy of maintaining the solar field outlet heat transfer fluid (HTF) temperature constant when the direct normal irradiance (DNI) varies. The research results indicate that during a whole day, the main operating parameters of the SAPG system can operate within the safety ranges, and the system power generation almost keeps 330 MW constant, both the solar output power and the carbon dioxide emission reductions are 207.7 MWh and 186.7t/day, respectively.

Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method

An optimal operation system is a potential solution to increase the energy efficiency of a power network equipped with stochastic Renewable Energy Sources (RES). In this article, an Optimal Power Flow (OPF) problem has been formulated as a single and multi-objective problems for a conventional power generation and renewable sources connected to a power network. The objective functions reflect the minimization of fuel cost, gas emission, power loss, voltage deviation and improving the system stability. Considering the volatile renewable generation behaviour and uncertainty in the power prediction of wind and solar power output as a nonlinear optimization problem, this paper uses a Weibull and lognormal probability distribution functions to estimate the power output of renewable generation. Then, a new Golden Ratio Optimization Method (GROM) algorithm has been developed to solve the OPF problem for a power network incorporating with stochastic RES. The proposed GROM algorithm aims to improve the reliability, environmental and energy performance of the power network system (IEEE 30-bus system). Three different scenarios, using different RES locations, are presented and the results of the proposed GROM algorithm is compared to six heuristic search methods from the literature. The comparisons indicate that the GROM algorithm successfully reduce fuel costs, gas emission and improve the voltage stability and outperforms each of the presented six heuristic search methods.

An Efficient Algorithm for Power Prediction in PV Generation System

Aiming at the existing photovoltaic power generation prediction methods, the modeling is complicated, the prediction accuracy is low, and it is difficult to meet the actual needs. Based on the improvement of the traditional wavelet neural network, a dual-mode cuckoo search wavelet neural network algorithm combined prediction method is proposed, which takes into account the extraction of chaotic features of surface solar radiation and photovoltaic output power. The proposed algorithm first reconstructs the chaotic phase space of the hidden information of each influencing factor in the data history of PV generation and according to the correlation analysis, the solar radiation is utilized as additional input. Next, the proposed algorithm overcomes the limitations of the cuckoo search algorithm such as the sensitivity to the initial value and searchability and convergence speed by dual-mode cuckoo search wavelet neural network algorithm. Lastly, a prediction model of the proposed algorithm is proposed and the prediction analysis is performed under different weather conditions. Simulation results show that the proposed algorithm shows better performance than the existing algorithms under different weather conditions. Under various weather conditions, the mean values of TIC, EMAE and ENRMSE error indicators of the proposed forecasting algorithm were reduced by 43.70%, 45.75%, and 45.41%, respectively. Compared with the Chaos-WNN prediction method, the prediction performance has been further improved under various weather conditions and the mean values of TIC, EMAE and ENRMSE error indicators have been reduced by 25.55%, 27.26%, and 36.83%, respectively.

Advances of Solar PV System Output Improvement through Cooling Technologies in Malaysia

Solar photovoltaic (PV) panel is the most widely used renewable energy technology in Malaysia due to constant irradiation throughout the year as the country is located near the equator. According to the current state of the Malaysian weather, the ambient temperature is around 25°C and can go up to 30°C. The electrical efficiency of a PV panel is sensitive towards temperature where it is known that the power and efficiency decrease at the rate of ±0.5%/°C and ±0.05%/°C respectively as the ambient temperature increases [1]. This paper discusses the cooling of solar PV technologies, the methodologies of the cooling system and its effectiveness. Generally, the effectiveness of the studies conducted were derived from the solar PV panel temperature reduction and solar PV electricity power output increment. This paper elaborates the trend of methodologies for cooling of solar PV in Malaysian climate. The methodologies that were used by the researchers vary according to the specific objectives which can be categorized as active cooling and/or passive cooling. The gaps of the studies were also identified alongside with the possible solutions to mitigate the problems.

Performance of ACO-MPPT and Constant Voltage Method for Street Lighting Charging System

Electricity consumption continues to increase every year. Alternative energy source such as solar panel have been easily developed and do not cause pollution. By utilizing the sun's rays on its surface, solar panels can produce electricity depending on the level of surface temperature and the conditions of the intensity of solar radiation. Most street lighting electrical systems with conventional solar panels have minimal output power due to not having the Maximum Power Point Tracking (MPPT) function. In addition, the absence of a charging function results in an uncontrolled voltage magnitude affecting the parameters of electrical energy (accumulators), especially when the accumulator's State of Charge (SOC) approaches the full charging limit. Therefore, a system of street lighting is used which is capable of carrying out MPPT functions and charging functions with an electrical system that uses a CUK type DC-DC Converter. Ant Colony Optimization (ACO) algorithm as MPPT control, and the Constant Voltage (CV) method as charging control with measurements, SOC uses the coulomb counting method. The performance has proven to optimize a 200 Watt-peak solar panel power output up to 18.92 Watts, maintain a voltage value with a maximum error difference of 2% only, which can read the battery SOC accurately.

The effect of flow rate on small solar chimney performance

ABSTRACT The relationships between temperature rise, flow rate, and power output of turbine are presented for a small solar chimney unit. An experimental solar chimney system was constructed at Engineering Faculty of Trakya University in Turkey. Solar radiation, temperature rise, chimney inlet velocity, air flow rate, and power output were monitored. Solar chimney power output can be controlled by air flow management and maximum power point can be achieved for different operating conditions. Results showed that it is possible to obtain up to 50% more power output by using an air flow controller in solar chimney systems. Typical temperature rises for 400–600 W/m2 solar radiation is found to be in the range of 12–15°C. Power is changed in the range of 30–60 W according to different flow rates.

Stabilizing solar farm power output by micro gas turbine integration: Emission performance

Worldwide growth of Photovoltaics (PV) technology has been close to exponential rate in the past decades. However, the increase of power generation by PV imposes new challenges on the existing electrical grid due to the unpredictability of PV power output that depends mainly on solar irradiance. One of the solutions to stabilize solar farm power output fluctuation by hybridizing Micro Gas Turbines (MGTs) that is controlled to meet the total power production target. Thus, the objective of this paper is to investigate the effect of different operation strategies on the environmental performance on MGT integrated solar farm. Different operation strategies for MGT integrated solar farm have been implemented to constantly produce a total power output of 800 kW throughout the year. The three strategies then compared by analysing emission produced by MGTs. A simulation was conducted in Simulink environment using real ambient temperature and global sun irradiance data. It was found that Operation Strategy 3 reduced carbon dioxide (CO2) by 13.8%, nitrogen oxides (NOx) emission by 3.2%, and carbon monoxide (CO) by 97.9%. This shows that the proposed strategy could stabilize the total power output of the solar farm at minimum emission level.

Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Numerous literatures can be found on the topic of PV fault detection through the implementation of artificial intelligence. The novel part of this research is the successful development, deployment and validation of a fault detection PV system using radial basis function (RBF), requiring only two parameters as the input to the ANN (solar irradiance and output power). The results obtained through the testing of the developed ANN on a PV installation of 2.2 kW capacity, provided an accuracy of 97.9%. To endorse the accuracy of the newly developed algorithm, the ANN was tested on another PV system, installed at a remote location. The total capacity of the new system was significantly higher, 4.16 kW. A vital part of the test was to see how the proposed ANN would perform with ‘scaled-up’ input data, during normal operation as well as partial shading scenarios. The validation process provided an overall fault detection accuracy of above 97%. The decrease in accuracy was due to the varying nature of the two systems in terms of total capacity, number of samples and type of faults.

Net solar generation potential from urban rooftops in Los Angeles

Rooftops provide accessible locations for solar energy installations. While rooftop solar arrays can offset in-building electricity needs, they may also stress electric grid operations. Here we present an analysis of net electricity generation potential from distributed rooftop solar in Los Angeles. We integrate spatial and temporal data for property-level electricity demands, rooftop solar generation potential, and grid capacity constraints to estimate the potential for solar to meet on-site demands and supply net exports to the electric grid. In the study area with 1.2 million parcels, rooftop solar could meet 7200 Gigawatt Hours (GWh) of on-site building demands (~29% of demand). Overall potential net generation is negative, meaning buildings use more electricity than they can produce. Yet, cumulative net export potential from solar to grid circuits is 16,400 GWh. Current policies that regulate solar array interconnection to the grid result in unutilized solar power output of 1700 MW. Lower-income and at-risk communities in LA have greater potential for exporting net solar generation to the grid. This potential should be recognized through investments and policy innovations. The method demonstrates the need for considering time-dependent calculations of net solar potential and offers a template for distributed renewable energy planning in cities.

Increasing the output power of solar panel by using cooling system

Solar panel is a device to convert light energy from the sun into electrical energy, most of solar panel is made from semiconductor such as silicon. Solar panel is still having many disadvantages such as expensive prices, low efficiency, the power output that depended on light intensity and temperature, etc. During the day, solar panel temperature could increase and affect the power output. It is found that temperature would reduce solar panel voltage output that also reduce the power output. In this research, 100WP Monocrystalline solar panel power output will be enhanced by using cooling system to reduce the temperature of the solar panel. Cooling system of the solar panel is applied by flowing the water on the surface of the solar panel using 12V DC water pump. The experiment starts from 11:00 AM to 01:00 PM. The result of this experiment is the power output of the solar panel increased up to 5.79 Watt with the average about 2.96 Watt and the temperature of the solar panel decreased by 20.99°C. The temperature of the solar panel without cooling is about 52.83°C and with the cooling system is about 31.85°C.

Probabilistic solar irradiance transposition models

Transposition models convert the solar irradiance received on a horizontal surface to in-plane irradiance. All transposition models to date, unfortunately, only produce deterministic (as oppose to probabilistic) estimates. In modern energy meteorology, having the entire predictive distribution is more desirable than relying only on deterministic estimates. To that end, this paper outlines two strategies for creating probabilistic transposition models (PTMs), that can quantify the various types of uncertainty involved in the modeling process. The first strategy seeks the analytic expressions of measurement, model, and parameter uncertainty, and the final predictive variance is the sum of these three types of uncertainty. On the other hand, the second strategy directly models the overall uncertainty as a whole, and uses ensemble model output statistics to estimate the predictive distribution through optimizing a loss function. Both strategies generate estimates of tilted irradiance with Gaussian predictive distributions. As compared to their deterministic counterparts, PTMs clearly offer more insights on uncertainty quantification, during solar energy system design, simulation, performance evaluation, and power output forecasting.