UltraFlex and MegaFlex - Advancements in Highly Scalable Solar Power

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

Ensuring long term investment for large scale solar power stations: Hedging instruments for green power

95/04897 Diffuse solar radiation correlations: Applications to Turkey and Australia

The residual load is defined by energy demand minus energy production from renewable sources (wind and solar power). High values of residual load correspond to a high energy demand that cannot be covered by renewable energies and must be compensated with fossil energies. On the opposite, low/negative values of residual load correspond to a surplus of renewable energies with a too low demand. In this case, energy must be exported, or wind turbines and solar panels curtailed.We compute the residual load using a demand model and a solar and wind power model. For the solar power model, we use the CAMS solar energy dataset interpolated to the position of all single solar panels installed in Germany and model the solar power output with the PVlib Python package. The wind power model is using ERA5 windspeed and air density interpolated to turbine location and turbine hub height to be later fed in the power curve of all single turbines in Germany. The demand model is a linear regression of 2m-temperature from ERA5, industry production index, day-of-week, public holidays and installed solar capacity. The residual load is then given by: ResLoad = Demand - (Wind + Solar)We look at both tails of the distribution of the residual load. High values of residual load are mostly observed in winter when temperatures and renewable energy production are low (mostly during blocked regimes). These events correspond to high energy prices. Negative values of residual load are mostly observed on Sundays, public holidays and in summer, where demand is low and temperatures and solar and wind power production are high. They usually correspond to negative energy prices. Both events described above are critical for the energy industry. Skillful forecasts of these events in the sub-seasonal range help to reduce stress to the energy market. The results presented herein lay the foundation for a more comprehensive, statistical analysis of the predictability characteristics of residual load extremes.

<p>This research accounts for the outcome of a machine learning algorithm project to predict short term solar and wind power in Ontario. A Long Short Term Memory (LSTM) model was developed to monitor short term power output predictions from 4 hours ahead to 6 hours ahead. This study demonstrates a unique approach of utilizing nearby publicly available meteorological data to predict renewable energy power output that could potentially be used for wind and solar farm scheduling to prevent curtailment of clean energy. The wind power and the solar power was predicted 6 and 4 hours ahead with a coefficient of correlation (R2) of 0.817 and 0.738 respectively. It has demonstrated the usage of a LSTM network as a reliable tool for the prediction of renewable energy that can be implemented to power output on systems that require accurate prediction of wind and solar power within the 4 to 6 hours. </p>

This paper describes categories of solar technologies and identijies those that are economic. It compares the private costs of power from solar, wind, nuclear, coal, oil, and gas generators. In the southern United States, the private costs of building and generating electricity from new solar and wind power plants are less than the private cost of electricity from a new nuclear power plant. Solar power is more valuable than nuclear power since all solar power is available during peak and mid‐peak periods. Half of the power from nuclear generators is off‐peak power and therefore is less valuable. Reliability is important in determining the value of wind and nuclear power. Damage from air pollution, when factored into the cost of power from fossil fuels, alters the cost comparison in favor of solar and wind power. Some policies are more effective at encouraging alternative energy technologies that pollute less and improve national security.

In solar PV fields, solar photovoltaic panels are typically arranged in parallel rows one after the other. This arrangement introduces variations in the distribution of solar irradiance over the entire field, compared to measurements made at meteorological weather stations and data obtained from solar radiation databases. This is due to the difference in the view factors between the rows of the solar PV field and a single reference surface, as well as the presence of shade on rear rows and in the space separating the rows. These phenomena combined will reduce the energy yield of a solar PV field. Accurate estimation of solar radiation on solar fields requires knowledge of the sky, ground, and rear surface of the preceding row view factors, and an estimation of the time and space occupied by the row’s shadow. Prior literature has addressed this issue using two-dimensional (2D) techniques such as Crossed-Strings Method, which this study proved to be inaccurate particularly in the case of Rooftop solar PV fields. This study uses a novel three-dimensional (3D) analytical and numerical analysis to determine the view factors associated with solar fields using hourly solar irradiance data acquired from Solar-GIS for the period 2007-2020, including global, beam, and sky diffuse irradiance components on horizontal plane. The study uses both isotropic and anisotropic transposition analyses to determine solar irradiance incident on the solar field with varying tilt angles of solar panels and distance separating the rows.

Predictive control technique for solar photovoltaic power forecasting

Solar photovoltaic (PV) power generation has strong intermittency and volatility due to its high dependence on solar radiation and other meteorological factors. Therefore, the negative impact of grid-connected PV on power systems has become one of the constraints in the development of large scale PV systems. Accurate forecasting of solar power generation and flexible planning and operational measures are of great significance to ensure safe, stable, and economical operation of a system with high penetration of solar generation at transmission and distribution levels. In this paper, studies on the following aspects are reviewed: (1) this paper comprehensively expounds the research on forecasting techniques of PV power generation output. (2) In view of the new challenge brought by the integration of high proportion solar generation to the frequency stability of power grid, this paper analyzes the mechanisms of influence between them and introduces the current technical route of PV power generation participating in system frequency regulation. (3) This section reviews the feasible measures that facilitate the inter-regional and wide-area consumption of intermittent solar power generation. At the end of this paper, combined with the actual demand of the development of power grid and PV power generation, the problems that need further attention in the future are prospected.

This unique project explored one possible solution for photovoltaic (PV) generation to become a reliable and dispatchable energy resource similar to conventional baseload fossil fuel electricity generation. Solar power generation is a renewable resource that varies naturally from day-to-day as well as seasonally. To use solar power as a baseload generation asset requires a flexible storage system that can recover power on both a daily and seasonal basis. Converting solar power to natural gas (i.e., first to hydrogen and then to methane) and having access to utility-scale storage in the natural gas network has the potential to make solar power generation a baseload asset. This will: increase the net energy yield from solar resources by increasing the effective capacity factor and maximize the energy produced during the assets’ lifetimes; improve the economics of energy storage for solar project developers, thereby accelerating the deployment of new solar generation; reduce the impact of increasing solar penetration on congestion of the electrical transmission system by shifting power delivery in time using the gas pipeline network for long-duration storage; reduce the impact of increasing solar generation on electrical distribution system control; and offer alternative revenue sources for solar power generation beyond electricity including: hydrogen (H2) production for stationary fuel cells and fuel cell electric vehicles, conversion of carbon dioxide (CO2) to methane (CH4) as a direct drop-in replacement for fossil natural gas use, and transmission as high volumetrically-dense CH4 for use in transportation, heating, power generation, chemical production or conversion back to H2 at the point of use. Additional work was added to this CRADA in modification 4 having a primary objective to characterize the performance of the electrolyzer, Southern California Gas (SoCalGas) bioreactor and balance of plant to demonstrate production of renewable natural gas (RNG) from renewable H2 and CO2 using single-cell, self-replicating organisms. The additional scope of work allowed researchers to ramp up gas flowrates and pressure of the system over the designed range to grow the cells and begin to show the load-following capabilities of this anaerobic gas fermentation process. These activities enabled the research team to predict system performance at much greater scales; namely 10’s of mega-watts (MWs) of electrolyzer nameplate capacity.

Technological advancements have improved solar energy generation and reduced the cost of installing photovoltaic (PV) systems. However, challenges such as low energy-conversion efficiency and the unpredictability of electricity generation due to shading or climate conditions persist. Despite decreasing costs, access to solar energy generation technologies remains limited. This paper proposes a multi-criteria decision support system (MCDSS) for selecting the most suitable PV set (comprising PV modules, inverters, and batteries) for microgrid installations. The MCDSS employs two multi-criteria decision-making methods (MCDM) for analysis and decision-making: AHP and TOPSIS. The system was tested in two case studies: Barreiras, with a global efficiency of 14.4% and an internal rate of return (IRR) of 56.0%, and Curitiba, with a worldwide efficiency of 14.8% and an IRR of 52.0%. The research provided a framework for assessing and selecting PV sets based on efficiency, cost, and return on investment. Methodologically, it integrates multiple MCDM techniques, demonstrating their applicability in renewable energy. Managerially, it offers a practical tool for decision-makers in the energy sector to enhance the feasibility and attractiveness of microgeneration projects. This research highlights the potential of MCDSS to improve the efficiency and accessibility of solar energy generation.

While human population has been multiplied by four in the last hundred years, the world energy consumption was multiplied by ten. The common method of using fossil fuels to provide energy and electricity has dangerously disturbed nature's and climate's balance. It has become urgent and crucial to find sustainable and eco-friendly alternatives to preserve a livable environment with unpolluted air and water. Renewable energy is the unique eco-friendly opportunity known today. The main challenge of using renewable energy is to ensure the constant balance of electricity demand and generation on the electrical grid. This paper investigates whether the solar electricity generation is correlated with the urban electricity consumption in hot climates. The solar generation and total consumption have been compared for three cities in Florida. The hourly solar generation has been found to be highly correlated with the consumption that occurs 6 h later, while the monthly solar generation is correlated with the monthly energy consumption. Producing 30% of the electricity using solar energy has been found to compensate partly for the monthly variation in the urban electricity demand. In addition, if 30% of the world electricity is produced using solar, global CO2 emissions would be reduced by 11.7% (14.6% for India). Thus, generating 30% solar electricity represents a valuable asset for urban areas situated in hot climates, reducing the need for electrical operating reserve, providing local supply with minimal transmission losses, but above all reducing the need for fossil fuel electricity and reducing global CO2 emission.

SUMMARY Advancing in the learning curve of solar thermal power plants (STPP) requires detailed analysis for reducing exergy losses in the energy conversion chain. This requirement should be applied to any configuration proposed for the solar field and the power block. The aim of this work is to perform this type of analysis for two ways of structuring the power plant. The first plant structure consists of a subdivision of the solar collector field into specialized sectors with specific goals conveying different requirements in temperature. The second plant structure is based on a dual thermal energy storage system with a defined hierarchy in the storage temperature. The subdivision of the solar field into different sectors reduces the exergy losses in the heating process of the working fluid. Moreover, the average temperature of the heat transfer fluid in the solar field decreases when it is compared to the conventional solar field, reducing this way the exergy losses in the collectors. The dual thermal energy storage system is devised for keeping the exergy input to the power block at its nominal level for long periods of time, including post-sunset hours. One of the storage systems gathers a fluid heated up to temperatures above the nominal value and the second one is the classical one. The combination of both allows the manager of the plant to keep the nominal operation of the plant for longer periods than in the case of classical system. Numerical simulations performed with validated models are the basis of the exergy analyses. The configurations are compared to a reference STPP in order to evaluate their worth. Furthermore, the behaviour of the configurations is analysed to study the irreversibility of the included devices. Special attention is paid to the storage systems, as they are a key issue in both plant structures. Copyright © 2013 John Wiley & Sons, Ltd.

The design of a solar field is one of the crucial aspects when a solar tower system is realized. In general, shading and blocking effects, which are the main causes of solar power losses, are minimized displacing the heliostats each other quite distant, with typical land coverage less than 20%, and thus, strongly limiting the construction of these plants to low value lands. A new method is proposed here to improve the collected energy for solar tower systems with high land coverage (greater than 30%), based on the chance for each heliostat to rotate about the normal passing through the center of its surface. Then, shading and blocking are minimized by optimization of the relative orientations. To this aim, a small solar field composed of 150 rectangular flat heliostats has been considered, and its performances with and without the proposed optimization have been computed and compared for a wide variety of cases. In particular, a systematic analysis is presented to study the effect of the shape of the heliostats on the solar field performance: in a series of simulations, maintaining constant the area of each heliostat, the ratio between its two sides has been varied in a range between 1 (squared heliostats) and 3 (very stretched heliostats), and optimized and nonoptimized systems have been compared. Also, the total energy collected by the solar field has been calculated for optimized and nonoptimized heliostats' orientations, considering towers of different heights. Finally, the real PS10 solar plant has been considered, demonstrating that also for an optimized, very low coverage plant (about 14%), heliostats rotation can still improve the energy collection efficiency by a non-negligible amount.

In the last few decades, Renewable energy has received remarkable attention. This is partially because fossil fuels are depleting and the necessity for energy is rising because of the increasing population of the world. Among renewable sources of energy, solar energy is one such type that is becoming increasingly popular because it is inexhaustible, clean, and reliable. Solar radiation is obtained as heat and light that can be converted by solar thermal power generation into valuable thermal energy or electricity generation. With regard to efficiency and financial viability, the availability of trusted data on solar radiation is crucial to forecast the performance of solar power plants at different places in the country. This paper describes a general integrated structure for assessing land suitability for ideal positioning of photovoltaic solar energy panels based on a variety of geographic information system (GIS), remote sensing methods. The Location Viability Assessment, i.e. the areas appropriate for harvesting Solar Energy, is measured based on Global, Diffuse, Direct Solar Radiation Data that estimates the potential of the Solar Power Generation sites. Two interrelated steps were implemented in this study. First, Remote Sensing was used, including LANDSAT 8 satellite data, to extract the study area’s land use/land cover map. Second, the Digital Elevation Model (DEM) was used to model the research zone map of aspect angles and slope maps. These results indicate the potential sites for Solar Power Generation.