Showing papers on "Solar power published in 2019"

A new prediction model of battery and wind-solar output in hybrid power system

Abstract: In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead–acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.

Solar Energy on Demand: A Review on High Temperature Thermochemical Heat Storage Systems and Materials.

Abstract: Among renewable energies, wind and solar are inherently intermittent and therefore both require efficient energy storage systems to facilitate a round-the-clock electricity production at a global scale. In this context, concentrated solar power (CSP) stands out among other sustainable technologies because it offers the interesting possibility of storing energy collected from the sun as heat by sensible, latent, or thermochemical means. Accordingly, continuous electricity generation in the power block is possible even during off-sun periods, providing CSP plants with a remarkable dispatchability. Sensible heat storage has been already incorporated to commercial CSP plants. However, because of its potentially higher energy storage density, thermochemical heat storage (TCS) systems emerge as an attractive alternative for the design of next-generation power plants, which are expected to operate at higher temperatures. Through these systems, thermal energy is used to drive endothermic chemical reactions, which can subsequently release the stored energy when needed through a reversible exothermic step. This review analyzes the status of this prominent energy storage technology, its major challenges, and future perspectives, covering in detail the numerous strategies proposed for the improvement of materials and thermochemical reactors. Thermodynamic calculations allow selecting high energy density systems, but experimental findings indicate that sufficiently rapid kinetics and long-term stability trough continuous cycles of chemical transformation are also necessary for practical implementation. In addition, selecting easy-to-handle materials with reduced cost and limited toxicity is crucial for large-scale deployment of this technology. In this work, the possible utilization of materials as diverse as metal hydrides, hydroxides, or carbonates for thermochemical storage is discussed. Furthermore, special attention is paid to the development of redox metal oxides, such as Co3O4/CoO, Mn2O3/Mn3O4, and perovskites of different compositions, as an auspicious new class of TCS materials due to the advantage of working with atmospheric air as reactant, avoiding the need of gas storage tanks. Current knowledge about the structural, morphological, and chemical modifications of these solids, either caused during redox transformations or induced wittingly as a way to improve their properties, is revised in detail. In addition, the design of new reactor concepts proposed for the most efficient use of TCS in concentrated solar facilities is also critically considered. Finally, strategies for the harmonic integration of these units in functioning solar power plants as well as the economic aspects are also briefly assessed.

Phase Change Materials (PCM) for Solar Energy Usages and Storage: An Overview

Abstract: Solar energy is a renewable energy source that can be utilized for different applications in today’s world. The effective use of solar energy requires a storage medium that can facilitate the storage of excess energy, and then supply this stored energy when it is needed. An effective method of storing thermal energy from solar is through the use of phase change materials (PCMs). PCMs are isothermal in nature, and thus offer higher density energy storage and the ability to operate in a variable range of temperature conditions. This article provides a comprehensive review of the application of PCMs for solar energy use and storage such as for solar power generation, water heating systems, solar cookers, and solar dryers. This paper will benefit the researcher in conducting further research on solar power generation, water heating system, solar cookers, and solar dryers using PCMs for commercial development.

Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression

Abstract: Renewable energy from wind and solar resources can contribute significantly to the decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless integration with the grid poses significant challenges due to their intermittent generation patterns, which is intensified by the existing uncertainties and fluctuations from the demand side. A resolution is increasing energy storage and standby power generation which results in economic losses. Alternatively, enhancing the predictability of wind and solar energy as well as demand enables replacing such expensive hardware with advanced control and optimization systems. The present research contribution establishes consistent sets of data and develops data-driven models through machine-learning techniques. The aim is to quantify the uncertainties in the electricity grid and examine the predictability of their behaviour. The predictive methods that were selected included conventional artificial neural networks (ANN), support vector regression (SVR) and Gaussian process regression (GPR). For each method, a sensitivity analysis was conducted with the aim of tuning its parameters as optimally as possible. The next step was to train and validate each method with various datasets (wind, solar, demand). Finally, a predictability analysis was performed in order to ascertain how the models would respond when the prediction time horizon increases. All models were found capable of predicting wind and solar power, but only the neural networks were successful for the electricity demand. Considering the dynamics of the electricity grid, it was observed that the prediction process for renewable wind and solar power generation, and electricity demand was fast and accurate enough to effectively replace the alternative electricity storage and standby capacity.

The Calcium-Looping (CaCO3/CaO) process for thermochemical energy storage in Concentrating Solar Power plants

Abstract: Energy storage based on thermochemical systems is gaining momentum as a potential alternative to molten salts in Concentrating Solar Power (CSP) plants. This work is a detailed review about the promising integration of a CaCO3/CaO based system, the so-called Calcium-Looping (CaL) process, in CSP plants with tower technology. The CaL process relies on low cost, widely available and non-toxic natural materials (such as limestone or dolomite), which are necessary conditions for the commercial expansion of any energy storage technology at large scale. A comprehensive analysis of the advantages and challenges to be faced for the process to reach a commercial scale is carried out. The review includes a deep overview of reaction mechanisms and process integration schemes proposed in the recent literature. Enhancing the multicycle CaO conversion is a major challenge of the CaL process. Many lab-scale analyses carried out show that residual effective CaO conversion is highly dependent on the process conditions and the CaO precursors used, reaching values in a wide range (0.07–0.82). The selection of the optimal operating conditions must be based on materials performance, process integration, technology and economics aspects. Global plant efficiencies over 45% (without considering solar-side losses) show the interest of the technology. Furthermore, the technological maturity and potential of the process is assessed. The direction towards which future works should be headed is discussed.

Solar PV Power Potential is Greatest Over Croplands

Abstract: Solar energy has the potential to offset a significant fraction of non-renewable electricity demands globally, yet it may occupy extensive areas when deployed at this level. There is growing concern that large renewable energy installations will displace other land uses. Where should future solar power installations be placed to achieve the highest energy production and best use the limited land resource? The premise of this work is that the solar panel efficiency is a function of the location’s microclimate within which it is immersed. Current studies largely ignore many of the environmental factors that influence Photovoltaic (PV) panel function. A model for solar panel efficiency that incorporates the influence of the panel’s microclimate was derived from first principles and validated with field observations. Results confirm that the PV panel efficiency is influenced by the insolation, air temperature, wind speed and relative humidity. The model was applied globally using bias-corrected reanalysis datasets to map solar panel efficiency and the potential for solar power production given local conditions. Solar power production potential was classified based on local land cover classification, with croplands having the greatest median solar potential of approximately 28 W/m2. The potential for dual-use, agrivoltaic systems may alleviate land competition or other spatial constraints for solar power development, creating a significant opportunity for future energy sustainability. Global energy demand would be offset by solar production if even less than 1% of cropland were converted to an agrivoltaic system.

Exploring Key Weather Factors From Analytical Modeling Toward Improved Solar Power Forecasting

Abstract: Accurate solar power forecasting plays a critical role in ensuring the reliable and economic operation of power grids. Most of existing literature directly uses available weather conditions as input features, which might ignore some key weather factors and the coupling among weather conditions. Therefore, a novel solar power forecasting approach is proposed in this paper by exploring key weather factors from photovoltaic (PV) analytical modeling. The proposed approach is composed of three engines: 1) analytical modeling of PV systems; 2) machine learning methods for mapping weather features with solar power; and 3) a deviation analysis for solar power forecast adjustment. In contrast to the existing research that directly uses available weather conditions, this paper explores the physical knowledge from PV models. Different irradiance components and PV cell temperatures are derived from PV analytical modeling. These weather features are used to reformulate the input of machine learning methods, which helps achieve a better forecasting performance. Moreover, based on the historical forecasting deviations, a compensation term is presented to adjust the solar power forecast. Case studies based on measured datasets from PV systems in Australia demonstrate that the forecasting performance can be highly improved by taking advantage of the key weather features derived from PV models.

Two-dimensional materials as catalysts for solar fuels: hydrogen evolution reaction and CO2 reduction

Abstract: The issues of global warming and fossil fuel shortage have increased the demand for clean and renewable energy. Many researchers are investigating strategies to produce hydrogen and reduce CO2 by using solar power. Two-dimensional (2D) materials, such as graphene, graphene derivatives, and transition metal dichalcogenides (TMDs), have been extensively used owing to their extraordinary electronic and optical properties. In this review, we investigate the recent developments in 2D materials for photocatalytic applications involving the hydrogen evolution reaction and CO2 reduction. The synthesis methods and the photocatalytic properties of TMDs and graphene-based 2D materials are thoroughly discussed. Moreover, a summary of the recently developed 2D nanostructures and devices for solar hydrogen production and CO2 reduction is presented, and it is revealed that the use of 2D catalyst materials has great potential for commercialization in the near future to help overcome the energy crisis.

A Multistage Home Energy Management System With Residential Photovoltaic Penetration

Abstract: Advances in bilateral communication technology foster the improvement and development of home energy management system (HEMS). This paper proposes a new HEMS to optimally schedule home energy resources (HERs) in a high rooftop photovoltaic penetrated environment. The proposed HEMS includes three stages: forecasting , day-ahead scheduling , and actual operation . In the forecasting stage, short-term forecasting is performed to generate day-ahead forecasted photovoltaic solar power and home load profiles; in the day-ahead scheduling stage, a peak-to-average ratio constrained coordinated HER scheduling model is proposed to minimize the one-day home operation cost; in the actual operation stage, a model predictive control based operational strategy is proposed to correct HER operations with the update of real-time information, so as to minimize the deviation of actual and day-ahead scheduled net-power consumption of the house. An adaptive thermal comfort model is applied in the proposed HEMS to provide decision support on the scheduling of the heating, ventilating, and air conditioning system of the house. The proposed approach is then validated based on Australian real datasets.

Operation Optimization of Power to Hydrogen and Heat (P2HH) in ADN Coordinated With the District Heating Network

Abstract: Increasing percentages of distributed generators in active distribution networks (ADNs) have increased the concern on excess generations in the medium and low voltage levels. High capacities of excess power are mainly from intermittent wind, solar power, and the restricted power generations of combined heat and power (CHP) plants, which are determined by the heat demand. Turning excess power to hydrogen has been recognized as a promising method to meet potential hydrogen demand from the traffic sectors. Nevertheless, it suffers from a low power-to-hydrogen efficiency, which is usually below 70% for commercialized electrolysis. By introducing in the heat recovery at the system level, we propose an integrated solution to supply heat to district heating networks (DHNs) while producing hydrogen, i.e., a power-to-hydrogen-and-heat scheme (P2HH). The extra benefit of P2HH resulting from heat supply is the reduction in the output of CHP, which reduces the excess energy in ADNs. This paper establishes a detailed “T-H-H” model to couple the power-to-heat and power-to-hydrogen processes. Then, a simplified P2HH dispatch model is proposed for the electricity-heat-hydrogen dispatch coordinated with ADN and DHN. Finally, the overall benefits of P2HH via improving the system economy and security are demonstrated with a modified IEEE 33-node system.

Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

Abstract: In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN.

A Comprehensive Review of DC–DC Converter Topologies and Modulation Strategies with Recent Advances in Solar Photovoltaic Systems

Abstract: Renewable Energy Sources (RES) showed enormous growth in the last few years. In comparison with the other RES, solar power has become the most feasible source because of its unique properties such as clean, noiseless, eco-friendly nature, etc. During the extraction of electric power, the DC–DC converters were given the prominent interest because of their extensive use in various applications. Photovoltaic (PV) systems generally suffer from less energy conversion efficiency along with improper stability and intermittent properties. Hence, there is a necessity of the Maximum power point tracking (MPPT) algorithm to ensure the maximum power available that can be harnessed from the solar PV. In this paper, the most important features of the DC/DC converters along with the MPPT techniques are reviewed and analyzed. A detailed comprehensive analysis is made on different converter topologies of both non-isolated and isolated DC/DC converters. Then, the modulation strategies, comparative performance evaluation are addressed systematically. At the end, recent advances and future trends are described briefly and considered for the next-generation converter’s design and applications. This review work will provide a useful structure and reference point on the DC/DC converters for researchers and designers working in the field of solar PV applications.

The reality behind solar power’s next star material

Abstract: Companies say they are close to commercializing cheap perovskite films that could disrupt solar power — but are they too optimistic? Companies say they are close to commercializing cheap perovskite films that could disrupt solar power — but are they too optimistic?

Hierarchical Distributed Model Predictive Control of Standalone Wind/Solar/Battery Power System

Abstract: A microgrid is a distributed networked generation system, which can effectively integrate various sources of distributed generation, especially renewable energy sources into the information network. The standalone wind/solar/battery power system is a typical standalone microgrid, in which the wind and solar power generations are the intermittent systems with complex dynamics and multiconstraints. Coordinated optimization between the wind power and solar power generations can effectively meet the load demand, reduce wear and tear of generating units, prolong the lifetime and, thus, guarantee the safety of the power grid. Regarding the large-scale, geographically dispersed standalone wind/solar/battery power generation system, this paper constituted a hierarchical distributed model predictive control (HDMPC). In this HDMPC, the upper layer utilizes an iterative distributed control strategy to realize the coordination of the power dispatch. It thus reaches the economic object, e.g., reducing the torsional shaft torque transmitted to gearbox in wind turbine system. The lower layer utilizes the supervisory predictive control to realize both the economic and tracking property. Under this hierarchical structure, the back-calculation from the lower control layer to the upper layer is utilized to keep the consistency of constraints. Through coordinated optimization among the subsystems, the proposed HDMPC realizes the plug and play of distributed energy. The simulation and the experiment validate the advantages of the proposed method in that it can realize the reliability, high efficiency, flexibility, and interactivity for the microgrid control.