Modelling of the Ability of a Mixed Renewable Generation Electricity System with Storage to Meet Consumer Demand

In this paper, we explore how effectively renewable generation can be used to meet a country’s electricity demands. We consider a range of different generation mixes and capacities, as well as the use of energy storage. First, we introduce a new open-source model that uses hourly wind speed and solar irradiance data to estimate the output of a renewable electricity generator at a specific location. Then, we construct a case study of the Great Britain (GB) electricity system as an example using historic hourly demand and weather data. Three specific sources of renewable generation are considered: offshore wind, onshore wind, and solar PV. Li-ion batteries are considered as the form of electricity storage. We demonstrate that the ability of a renewables-based electricity system to meet expected demand profiles can be increased by optimising the ratio of onshore wind, offshore wind and solar PV. Additionally, we show how including Li-ion battery storage can reduce overall generation needs, therefore lowering system costs. For the GB system, we explore how the residual load that would need to be met with other forms of flexibility, such as dispatchable generation sources or demand-side response, varies for different ratios of renewable generation and storage.

A New Empirical Approach for Estimating Solar Insolation Using Air Temperature in Tropical and Mountainous Environments

Solar irradiance is an available resource that could support electrification in regions that are low on socio-economic indices. Therefore, it is increasingly important to understand the behavior of solar irradiance. and data on solar irradiance. Some locations, especially those with a low socio-economic population, do not have measured solar irradiance data, and if such information exists, it is not complete. There are different approaches for estimating solar irradiance, from learning models to empirical models. The latter has the advantage of low computational costs, allowing its wide use. Researchers estimate solar energy resources using information from other meteorological variables, such as temperature. However, there is no broad analysis of these techniques in tropical and mountainous environments. Therefore, in order to address this gap, our research analyzes the performance of three well-known empirical temperature-based models—Hargreaves and Samani, Bristol and Campbell, and Okundamiya and Nzeako—and proposes a new one for tropical and mountainous environments. The new empirical technique models daily solar irradiance in some areas better than the other three models. Statistical error comparison allows us to select the best model for each location and determines the data imputation model. Hargreaves and Samani’s model had better results in the Pacific zone with an average RMSE of 936,195 Wh/m2 day, SD of 36,01%, MAE of 748,435 Wh/m2 day, and U95 of 1.836,325 Wh/m2 day. The new proposed model showed better results in the Andean and Amazon zones with an average RMSE of 1.032,99 Wh/m2 day, SD of 34,455 Wh/m2 day, MAE of 825,46 Wh/m2 day, and U95 of 2.025,84 Wh/m2 day. Another result was the linear relationship between the new empirical model constants and the altitude of 2500 MASL (mean above sea level).

Generating Artificial Weather Data Sequences for Solar Distillation Numerical Simulations

According to the natural geographical distribution, developing countries are concentrated in tropical climates, where radiation is abundant. So the use of solar energy is a sustainable solution for developing countries. However, daily or hourly measured solar irradiance data for designing or running simulations for solar systems in these countries is not always available. Therefore, this chapter presents a model to calculate the daily and hourly radiation data from the monthly average daily radiation. First, the chapter describes the application of Aguiar’s model to the calculation of daily radiation from average daily radiation data. Next, the chapter presents an improved Graham model to generate hourly radiation data series from monthly radiation. The above two models were used to generate daily and hourly radiation data series for Ho Chi Minh City and Da Nang, two cities representing two different tropical climates. The generated data series are tested by comparing the statistical parameters with the measured data series. Statistical comparison results show that the generated data series have acceptable statistical accuracy. After that, the generated radiation data series continue to be used to run the simulation program to calculate the solar water distillation system and compare the simulation results with the radiation data. Measuring radiation. The comparison results once again confirm the accuracy of the solar irradiance data generation model in this study. Especially, the model to generate the sequences of hourly solar radiation values proposed in this study is much simpler in comparison to the original model of Graham. In addition, a model to generate hourly ambient tempearure date from monthly average daily ambient temperature is also presented and tested. Then, both generated hourly solar radiation and ambient temperature sequences are used to run a solar dsitillation simulation program to give the outputs as monthly average daily distillate productivities. Finally, the outputs of the simulation program running with the generated solar radiation and ambient temperature data are compared with those running with measured data. The errors of predicted monthly average daily distillate productivities between measured and generated weather data for all cases are acceptably low. Therefore, it can be concluded that the model to generate artificial weather data sequences in this study can be used to run any solar distillation simulation programs with acceptable accuracy.

Science Highlights and Final Updates from 17 Years of Total Solar Irradiance Measurements from the SOlar Radiation and Climate Experiment/Total Irradiance Monitor (SORCE/TIM)

The final version (V.19) of the total solar irradiance data from the SOlar Radiation and Climate Experiment (SORCE) Total Irradiance Monitor has been released. This version includes all calibrations updated to the end of the mission and provides irradiance data from 25 February 2003 through 25 February 2020. These final calibrations are presented along with the resulting final data products. An overview of the on-orbit operations timeline is provided as well as the associated changes in the time-dependent uncertainties. Scientific highlights from the instrument are also presented. These include the establishment of a new, lower TSI value; accuracy improvements to other TSI instruments via a new calibration facility; the lowest on-orbit noise (for high sensitivity to solar variability) of any TSI instrument; the best inherent stability of any on-orbit TSI instrument; a lengthy (17-year) measurement record benefitting from these stable, low-noise measurements; the first reported detection of a solar flare in TSI; and observations of two Venus transits and four Mercury transits.

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&O method.