Showing papers on "Solar power published in 2020"
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Abstract: Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.
446 citations
TL;DR: In this article, the current status of solar panel waste recycling, recycling technology, environmental protection, waste management, recycling policies and the economic aspects of recycling are reviewed and recommendations for future improvements in technology and policy making.
263 citations
09 Oct 2020
TL;DR: A brief review of influential energy forecasting papers can be found in this article, which summarizes research trends, discusses importance of reproducible research and points out six valuable open data sources; makes recommendations about publishing high-quality research papers; and offers an outlook into the future of energy forecasting.
Abstract: Forecasting has been an essential part of the power and energy industry. Researchers and practitioners have contributed thousands of papers on forecasting electricity demand and prices, and renewable generation (e.g., wind and solar power). This article offers a brief review of influential energy forecasting papers; summarizes research trends; discusses importance of reproducible research and points out six valuable open data sources; makes recommendations about publishing high-quality research papers; and offers an outlook into the future of energy forecasting.
223 citations
TL;DR: Techniques for efficient solar collection, thermal storage, and power generation at >700 °C and barriers on the way to the high-temperature CSP are summarized.
213 citations
TL;DR: In this paper, the construction of a solar photovoltaic (PV) power plant within the Malatya Province of Turkey was identified by using Geographical Information Systems (GIS) technology.
160 citations
TL;DR: A taxonomy research of the existing solar power forecasting models based on AI algorithms is provided to help scientists and engineers to theoretically analyze the characteristics of various solar prediction models, thereby helping them to select the most suitable model in any application scenario.
159 citations
TL;DR: Several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction and the effect of prediction time horizon variation for all the algorithms is investigated.
156 citations
TL;DR: In this paper, the authors present a review on thermal energy storage (TES) systems and an update of the latest developments of different technologies of TES that are commercially available or under investigation.
144 citations
TL;DR: A smart irrigation system that helps farmers water their agricultural fields using Global System for Mobile Communication using GSM provides acknowledgement messages about the job's statuses such as humidity level of soil, temperature of surrounding environment, and status of motor regarding main power supply or solar power.
131 citations
TL;DR: The current status of renewable energy in Malaysia is reviewed as well as the initiatives taken before the pandemic to promote solar photovoltaic (PV) technology to meet the energy demands through the low-carbon pathway.
130 citations
TL;DR: The update presented here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil-generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage.
Abstract: The evolving nature of electricity production, transmission, and consumption necessitates an update to the IEEE's Reliability Test System (RTS), which was last modernized in 1996. The update presented here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil-generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage. The update includes assigning the test system a geographic location in the southwestern United States to enable the integration of spatio-temporally consistent wind, solar, and load data with forecasts. Additional updates include common RTS transmission modifications in published literature, definitions for reserve product requirements, and market simulation descriptions to enable benchmarking of multi-period power system scheduling problems. The final section presents example results from a production cost modeling simulation on the updated RTS system data.
11 May 2020
TL;DR: In this article, the authors demonstrate a new and versatile photovoltaic panel cooling strategy that employs a sorption-based atmospheric water harvester as an effective cooling component.
Abstract: More than 600 GW of photovoltaic panels are currently installed worldwide, with the predicted total capacity increasing very rapidly every year. One essential issue in photovoltaic conversion is the massive heat generation of photovoltaic panels under sunlight, which represents 75–96% of the total absorbed solar energy and thus greatly increases the temperature and decreases the energy efficiency and lifetime of photovoltaic panels. In this report we demonstrate a new and versatile photovoltaic panel cooling strategy that employs a sorption-based atmospheric water harvester as an effective cooling component. The atmospheric water harvester photovoltaic cooling system provides an average cooling power of 295 W m–2 and lowers the temperature of a photovoltaic panel by at least 10 °C under 1.0 kW m–2 solar irradiation in laboratory conditions. It delivered a 13–19% increase in electricity generation in a commercial photovoltaic panel in outdoor field tests conducted in the winter and summer in Saudi Arabia. The atmospheric water harvester based photovoltaic panel cooling strategy has little geographical constraint in terms of its application and has the potential to improve the electricity production of existing and future photovoltaic plants, which can be directly translated into less CO2 emission or less land occupation by photovoltaic panels. As solar power is taking centre stage in the global fight against climate change, atmospheric water harvester based cooling represents an important step toward sustainability. Photovoltaic panel conversion generates heat that reduces the energy efficiency and lifetime of the panel. A photovoltaic panel cooling strategy by a sorption-based atmospheric water harvester is shown to improve the productivity of electricity generation with important sustainability advantages.
TL;DR: Zhang et al. as mentioned in this paper analyzed the spatial and temporal distribution of solar energy in China and estimated the solar energy potential from three aspects: geography, technology, and economy, and found that Xinjiang Province was the most optimal site for large-scale photovoltaic station construction.
TL;DR: In this article, the current status, growth, potential, resources, sustainability performance and future prospects of renewable and sustainable energy (RnSE) technologies in the Kingdom of Saudi Arabia according to Saudi Vision 2030 have been reviewed.
TL;DR: An extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting shows the increased application of ANN and indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.
TL;DR: In this article, the authors proposed a model-based approach for comprehensive techno-economic assessments of grid-integrated seasonal storage and explored the conditions (cost, storage duration, and efficiency) that encourage cost competitiveness for seasonal storage technologies.
Abstract: Energy storage at all timescales, including the seasonal scale, plays a pivotal role in enabling increased penetration levels of wind and solar photovoltaic energy sources in power systems. Grid-integrated seasonal energy storage can reshape seasonal fluctuations of variable and uncertain power generation by reducing energy curtailment, replacing peak generation capacity, and providing transmission benefits. Most current literature focuses on technology cost assessments and does not characterize the potential grid benefits of seasonal storage to capture the most cost-effective solutions. We propose a model-based approach for comprehensive techno-economic assessments of grid-integrated seasonal storage. The approach has two major advantages compared to those presented in the literature. First, we do not make assumptions about the operation of the storage device, including annual cycles, asset utilization or depth of discharge. Rather, a model is used to calculate optimal storage operation profiles. Second, the model-based approach accounts for avoided power system costs, which allows us to estimate the cost effectiveness of different types of storage devices. We assess the cost competitiveness of three specific storage technologies including pumped hydro, compressed air, and hydrogen seasonal storage and explore the conditions (cost, storage duration, and efficiency) that encourage cost competitiveness for seasonal storage technologies. This study considers the Western U.S. power system with 24% to 61% of variable renewable power sources on an annual energy basis (up to 83.5% of renewable energy including hydro, geothermal, and biomass power sources). Our results indicate that for the Western U.S. power system, pumped hydro and compressed air energy storage with 1 day of discharge duration are expected to be cost-competitive in the near future. In contrast, hydrogen storage with up to 1 week of discharge duration could be cost-effective in the near future if power and energy capacity capital costs are equal to or less than ∼US$1507 kW−1 and ∼US$1.8 kWh−1 by 2025, respectively. However, based on projected power and energy capacity capital costs for 2050, hydrogen storage with up to 2 weeks of discharge duration is expected to be cost-effective in future power systems. Moreover, storage systems with greater discharge duration could be cost-competitive in the near future if greater renewable penetration levels increase arbitrage or capacity value, significant energy capital cost reductions are achieved, or revenues from additional services and new markets—e.g., reliability and resiliency—are monetized.
TL;DR: A comprehensive review of recent publications and trend of research activities regarding methods of representing uncertain variables and stochastic assessment techniques for power system quality analysis is provided.
Abstract: Many countries have experienced a surge in the level of the penetration of solar PV systems in the last decade. A huge portion of the newly deployed PV systems are connected to low voltage Grid. High Penetration of PVs at this level could potentially disrupt the normal operation of distribution network. A major concern is the impact of these units on power quality indices. Namely, photovoltaic panels could increase the level of voltage and current unbalance, deteriorate harmonic distortion and cause the voltage rise. These concerns may prohibit higher pentation levels of PVs. Thus, proper assessment techniques are vital for network operators for the planning and decision-making process. On the other hand, many characteristics of PV system are inherently uncertain. These uncertainties should be properly modeled in assessment framework. The main effort of research communities is to propose new methodologies that could model the uncertainty of solar power generation and stochastic assessment methods that could accurately estimate the state of the operation of the network with different levels of penetration of solar photovoltaics. This paper provides a comprehensive review of recent publications and trend of research activities regarding methods of representing uncertain variables and stochastic assessment techniques for power system quality analysis.
TL;DR: In this paper, a review of solar and geothermal power systems is presented, highlighting the configurations, mechanisms, and unique features of hybrid solar-geothermal power plants, while also developing a methodology to evaluate their efficacy.
TL;DR: In this article, the authors have maintained a website that tracks the record efficiency and other performance parameters compared to the thermodynamic Shockley-Queisser (SQ) limit for solar cells made from 14 extensively studied semiconductor materials.
Abstract: Since 2016, we have maintained a website(1) that tracks the record efficiency and other performance parameters compared to the thermodynamic Shockley–Queisser (SQ) limit for solar cells made from 14 extensively studied semiconductor materials. In the past four years, solar cells from many of these materials have progressed in efficiency, some very strongly (Figure 1a). This progress is a result of much research effort around the world, recognizing the importance of solar cell efficiency for the future energy supply. Efficiency is a key metric in the development of photovoltaic (PV) systems because the cell cost is only a small fraction of the total cost of a solar power generation system, and hence, increasing efficiency is a near-linear driver for reducing the cost of PV electricity per kilowatt-hour.
TL;DR: It was found that natural intermittent solar-powered mode was more beneficial for microorganisms involved in electron transfer and energy recovery than manual sharp on-off mode, which indicates a promising perspective of microbial biotechnology driven by solar power to boost bioenergy recovery from waste/wastewater.
TL;DR: A convolutional neural network framework for solar prediction based on meteorological data from surrounding sites and different sampling times is established and the chaotic GA/PSO 1 hybrid algorithm is applied to optimize the hyper parameters of the novel framework, which alleviates the imperfect performance caused by improper hyper parameters.
TL;DR: This paper presents an organized and concise review of MPPT techniques implemented for the PV systems in literature along with recent publications on various hardware design methodologies.
Abstract: Renewable energy-based solar photovoltaic (PV) generation is the best alternative for conventional energy sources because of its natural abundance and environment friendly characteristics. Maximum power extraction from the PV system plays a critical role in increasing the efficiency of the solar power generation during partial shading conditions (PSCs). Therefore, a suitable maximum power point tracking (MPPT) technique to track the maximum power point (MPP) is of high need, even under PSCs. This paper presents an organized and concise review of MPPT techniques implemented for the PV systems in literature along with recent publications on various hardware design methodologies. Their classification is done into four categories, i.e. classical, intelligent, optimal, and hybrid depending on the tracking algorithm utilized to track MPP under PSCs. During uniform insolation, classical methods are highly preferred as there is only one peak in the P-V curve. However, under PSCs, the P-V curve exhibits multiple peaks, one global maximum power point (GMPP) and remaining are local maximum power points (LMPP's). Under the PSCs, classical methods fail to operate at GMPP and hence there is a need for more advanced MPPT techniques. Every MPPT technique has its advantages and limits, but a streamlined MPPT is drafted in numerous parameters like sensors required, hardware implementation, cost viability, tracking speed and tracking efficiency. This study provides the advancement in this area since some parameter comparison is made at the end of every classification, which might be a prominent base-rule for picking the most gainful sort of MPPT for further research.
TL;DR: This study proposes a monthly PV power forecasting model that successfully captures the temporal patterns in monthly data and can estimate the potential for power generation at any new site for which weather information and terrain data are available, which will allow planning officials to search for and evaluate suitable locations for PV plants in a wide area.
TL;DR: In this paper, the authors make a critical review of the state-of-the-art approaches to understand and assess the complementarity between grid-connected solar and wind power systems through the analysis of different methodologies and locations.
TL;DR: In this article, more demanding parameter conditions than hitherto considered are used in measurement of the reliability of variable renewable energy resources, and the results show that for 7% weighted average cost of capital, Onsite BLEL can be generated at less than 119, 54, 41 and 33 €/MWhel in 2020, 2030, 2040 and 2050, respectively, across the best sites with a maximum 20,000 TWh annual cumulative generation potential.
TL;DR: In this article, a coordinated operation of hydropower and renewable energy in a provincial power grid is explored to alleviate fluctuation and aid peak shaving, and a day-ahead peak shaving model with the objective of minimizing residual load peak-valley difference is built.
TL;DR: An inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation.
TL;DR: Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for solar power forecasting.
TL;DR: This paper presents a novel fast frequency response and power oscillation damping control by large-scale PV plants controlled as STATCOM, termed PV-STATCOM, to simultaneously enhance frequency regulation and small signal stability of power systems.
Abstract: This paper presents a novel fast frequency response and power oscillation damping control by large-scale PV plants controlled as STATCOM, termed PV-STATCOM, to simultaneously enhance frequency regulation and small signal stability of power systems. Frequency deviations typically occur together with power oscillations in large power systems. The proposed controller comprises: first, power oscillation damping controller based on reactive power modulation and second, fast frequency response controller based on real power modulation, both of which are applied to the plant level controller of PV-STATCOMs. The proposed composite control is shown to successfully reduce frequency deviations, damp power oscillations, and provide voltage regulation both during over-frequency and under-frequency events. The proposed smart inverter control makes effective utilization of the PV inverter capacity and available solar power. For large power flows, the proposed control is shown to be superior than the conventional droop control recommended by North American Electric Reliability Corporation for generating plants. MATLAB/Simulink-based simulations are conducted on two-area power system using generic PV plant dynamic models developed by Western Electricity Coordinating Council, for a wide range of system operating conditions. Such grid support functionality is expected to bring new revenue making opportunities for PV solar farms.
TL;DR: The scenario-based stochastic framework is proposed for optimal scheduling of a CSP plant in the presence of uncertainties to obtain optimal offering curves in order to sell to power market.
Abstract: A central concentrating solar power (CSP) plant is increasing in the power systems as novel technology in the solar energy sources Also, solar thermal storage unit is combined with the CSP plant to increase flexibility and decrease the dependence on the instantaneous solar radiation Furthermore, the CSP plant can be obtained the optimal offering strategies to submit to the electricity market in order to sell the produced power and increase the expected profit In this paper, the uncertainty modeling of solar irradiation and electricity market price is a big challenge for a CSP plant Therefore, the scenario-based stochastic framework is proposed for optimal scheduling of a CSP plant in the presence of uncertainties to obtain optimal offering curves in order to sell to power market Also, the risk related to uncertainties is considered via the downside risk constraints (DRC), which leads to obtain risk-constrained stochastic optimization of a CSP plant The proposed model is formulated as mixed-integer linear programming, which is solved via CPLEX solver under GAMS optimization software Risk-averse strategy is introduced in comparison with risk-neutral strategy to investigate the impacts of DRC implementation, which leads to decrease the expected profit while the expected risk-in-profit reduced