Showing papers in "Journal of Renewable and Sustainable Energy in 2019"
TL;DR: A so-called “ROPES” guideline that describes the desirable characteristics of future solar forecasting studies is proposed, which stands for reproducible, operational, probabilistic and/or physically based, ensemble, and skill.
Abstract: Over the past decade, significant progress in solar forecasting has been made. Nevertheless, there are concerns about duplication, long-term value, and reproducibility; this is referred to as the “solar forecasting bubble.” There is an urgent need to reconcile and improve the current solar forecasting research practice. This discussion paper proposes a so-called “ROPES” guideline that describes the desirable characteristics of future solar forecasting studies. In short, ROPES stands for reproducible, operational, probabilistic and/or physically based, ensemble, and skill. This set of characteristics is intended to facilitate comparison, comprehension, and communication within the solar forecasting field and speed up its development. Compliance with ROPES is evaluated on 79 solar forecasting references published during 2017 January to 2018 July in 6 Elsevier energy journals. Although most current papers fall short of complying with ROPES, evidence suggests that a consensus is forming.
95 citations
TL;DR: In this article, the performance of a model wind farm with five turbine rows under a wide variety of yaw angle distributions was investigated, where electrical servo controllers were used to monitor and control the operating conditions of each model wind turbine.
Abstract: Yaw angle control is known nowadays as a promising and effective technique to mitigate wake effects in wind farms. In this paper, we perform wind tunnel experiments to study the performance of a model wind farm with five turbine rows under a wide variety of yaw angle distributions. Electrical servo controllers are used to monitor and control the operating conditions of each model wind turbine, which consists of a recently developed, highly efficient rotor with a diameter of 15 cm. Each turbine is used as a sensor to detect its own inflow conditions. Using this method ensures us that all the turbines within the wind farm always operate with an optimal rotational velocity, regardless of their yaw angles or inflow conditions. Wind farm power measurements are carried out for more than 200 cases with different yaw angle distributions. Our results show that yaw angle control can increase the overall wind farm efficiency as much as 17% with respect to fully non-yawed conditions. Special emphasis is placed on studying yaw angle distributions with different levels of simplicity and power improvement. Among different yaw angle distributions, the most successful ones are those with a relatively large yaw angle value for the first turbine row, and then, the yaw angle decreases progressively for downwind rows until it eventually becomes zero for the last one. In addition, power measurements show that yaw angle control can improve the wind farm efficiency more noticeably for a larger number of turbine rows although this improvement is expected to reach a plateau after several rows.
82 citations
TL;DR: In this paper, the authors describe and release a comprehensive solar irradiance, imaging, and forecasting dataset, which consists of three years (2014-2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California.
Abstract: We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.
69 citations
TL;DR: A detailed review of battery energy storage technologies pertaining to the latest technologies, benefits, sizing considerations, efficiency, cost, and recycling is presented and the discussion on the recyclability of these batteries is discussed.
Abstract: Dynamics of the world are changing, and people are preferring low cost and reliable power throughout the day. The addition of renewable energy to the existing system is also one of the ways to provide reliable and cheap electricity. The existing bottle neck in transmission lines, continuous contamination of the environment due to heavy reliance on fossil fuels, and the highly fluctuating cost of fossil fuel are few reasons for the widespread use of renewable energy technology. Energy storage technologies are the need of time and range from low capacity mobile storage batteries to high capacity batteries connected to the intermittent renewable energy sources. Selection of different battery types, each having distinguished characteristics in power and energy, depends on the nature of power required and delivered. This paper presents a detailed review of battery energy storage technologies pertaining to the latest technologies, benefits, sizing considerations, efficiency, cost, and recycling. An in-depth analysis in terms of advantages and limitations between the different types of batteries is discussed and compared. In terms of microgrid application, the economic benefits of battery sizing using optimization and probabilistic methods provide a potential solution during the design stage by taking into account various factors affecting the sizing of the battery such as the degradation rate, reliability, and battery placement. This paper ends with the discussion on the recyclability of these batteries and their impact on the environment.
62 citations
TL;DR: In this article, the mean square errors (MSE) evaluated against ground-based measurements and satellite-derived solar irradiance are comparable, which might warrant the use of satellite-based products for regional forecast verification.
Abstract: Satellite-derived irradiance data, as an alternative to ground-based measurements, offer a unique opportunity to verify gridded solar forecasts generated by a numerical weather prediction model. Previously, it has been shown that the mean square errors (MSE) evaluated against ground-based measurements and satellite-derived solar irradiance are comparable, which might warrant the use of satellite-based products for regional forecast verification. In this paper, the 24-h-ahead hourly forecasts issued by the North American Mesoscale forecast system are verified against both ground-based (Surface Radiation Budget Network, or SURFRAD) and satellite-based (National Solar Radiation Data Base, or NSRDB) measurements, at all 7 SURFRAD stations over 2015–2016. Three different MSE decomposition methods are used to characterize—e.g., through association, calibration, refinement, resolution, or likelihood—how well the two types of measurements can gauge the forecasts. However, despite their comparable MSEs, NSRDB is found suboptimal in its ability to verify forecasts as compared to SURFRAD. Nonetheless, if a new forecasting model produces significantly better forecasts than the benchmarking model, satellite-derived data are able to detect such improvements and make conclusions. This article comes with supplementary material (data and code) for reproducibility.Satellite-derived irradiance data, as an alternative to ground-based measurements, offer a unique opportunity to verify gridded solar forecasts generated by a numerical weather prediction model. Previously, it has been shown that the mean square errors (MSE) evaluated against ground-based measurements and satellite-derived solar irradiance are comparable, which might warrant the use of satellite-based products for regional forecast verification. In this paper, the 24-h-ahead hourly forecasts issued by the North American Mesoscale forecast system are verified against both ground-based (Surface Radiation Budget Network, or SURFRAD) and satellite-based (National Solar Radiation Data Base, or NSRDB) measurements, at all 7 SURFRAD stations over 2015–2016. Three different MSE decomposition methods are used to characterize—e.g., through association, calibration, refinement, resolution, or likelihood—how well the two types of measurements can gauge the forecasts. However, despite their comparable MSEs, NSRDB is f...
54 citations
TL;DR: In this article, a residential building with a conventional condition was considered as a sample, and energy efficiency parameters were investigated; using different methods, energy consumption of the building was reduced to zero.
Abstract: The most significant basis for sustainable development, as well as one of the most critical concerns of today's human societies, is energy and how it is consumed. In Iran, about 40% of energy is consumed in residential, commercial, and office buildings. Considering the importance of energy for sustainable development, buildings with zero energy consumption have found a lot of supporters. In the present paper, buildings with zero energy consumption in the north of Iran (Qa'emshahr city) were studied, and the feasibility study for constructing such buildings in this humid mountainous area was done. In this regard, a residential building with a conventional condition was considered as a sample, and energy efficiency parameters were investigated; using different methods, energy consumption of the building was reduced to zero. The research method in this study was based on two principles. At first, using scientific resources, new methods for reducing energy consumption in buildings and how they are managed were researched; in the second step, which is the main part of this paper, using DesignBuilder Software, the energy consumption of the building was determined. In the final step, the power generation capacity and panel area and economic considerations in the payback period were calculated per month. For this purpose, an average of 63 m2 of the solar panel was considered to be the electrical power of the building, which was able to fully produce the building's electricity requirement in 8 months. According to the results, with activities, the amount of electrical energy used for air conditioning in the building has been released by 80% and has been detracted from 34 MW to 7 MW. In the case of return on investment (R0I), to supply the required power generation for the building, it would be possible to receive about 15 000 $per year from the Iranian Ministry of Energy.
53 citations
TL;DR: The most current studies in improving power system resilience through microgrids are reviewed by highlighting their advantages and limitations.
Abstract: An electrical power system is considered as a critical infrastructure (CI), the epicenter of a nation's economy, security, and health. It is interlinked with other CIs such as gas and water supplies and transportation and communication systems. A failure in the power system will immensely affect the functionality of these CIs. Therefore, enhancing power system resilience is crucially needed to ensure continuous operation of these CIs. One of the possible approaches to improve the resilience in a power system is by integrating microgrids in the power system. Microgrids have proven to have self-healing and resilient capabilities in such extreme events which inflict damage out of the conventional scope of failures. Operational flexibility and controllability make microgrids a viable solution for resilience enhancement. This paper reviews the concept of resilience in power systems and the functions of microgrids in enhancement of resilience. The most current studies in improving power system resilience through microgrids are reviewed by highlighting their advantages and limitations.
50 citations
TL;DR: In this article, the authors developed a value chain model of the wind power industry, assessed the competitiveness of the Indian wind power industries, and comprehensively analyzed the factors that have a significant influence on the industry by using the five forces model.
Abstract: India is facing severe energy-related problems, including the deficiency of fossil fuel resources, greenhouse gas emissions, and an increase in power demand and supply gap due to overpopulation and growing industrial needs. In 2018, the average power demand and supply gap was 1617 MW, indicating that there is a deficit of 23 × 109 kWh in the country. In the meantime, a massive increase in electricity prices has made the affordability of electricity very difficult for domestic and industrial users. The development of alternative and renewable energy sources is very crucial to overcome these problems. Wind energy has emerged as a sustainable energy option for India in this respect. At the same time, the wind industry is facing several challenges as well. This paper aims to develop a value chain model of the wind power industry, assess the competitiveness of the Indian wind power industry, and comprehensively analyze the factors that have a significant influence on the industry by using the “Five Forces Model.” We employed a hybrid research methodology. First, we developed a novel value chain model for the wind power industry. Second, we conducted semistructured interviews with industry professionals on different aspects of the wind energy sector. Third, we critically analyzed official statistics and the related literature along with the national policy structure and regulations. As a result, the Five Forces Model was developed. Five main stakeholders of the Indian wind industry, i.e., buyers, suppliers, competitors, substitutes, and potential competitors, were examined to assess their effect on the development of the wind power industry. Research findings reveal the present status, challenges, the rivalry environment, industry's situation in this environment, and the future projections of the Indian wind power industry. Although the Indian government announced several policies with an aim to boost the wind industry, little substantial action has been taken for their meaningful implantation. The major government policies which need improvements are Generation-Based Incentives, Wind Bidding Scheme, and Tariff Policy. Our findings also highlight that there exists a gap between the expected and actual performances of the wind power industry value chain. Essential policy recommendations for the development of the industry have been suggested, including institutional coordination and decision-making, feed-in tariffs, reformations in the grid structure, encouragement of differentiated business models, enhancing research and development activities, developing professional base, and the full range of government support. This study will serve as a guide for government and stakeholders by understanding the dynamic relationship among all the factors influencing the competitiveness of the Indian wind power industry.
47 citations
TL;DR: In this paper, the concept and classification of CAES are reviewed, and the cycle efficiency and effective energy are analyzed in detail to enhance the current understanding of the CAES and the importance of real-gas properties of air is discussed.
Abstract: Due to the high variability of weather-dependent renewable energy resources, electrical energy storage systems have received much attention. In this field, one of the most promising technologies is compressed-air energy storage (CAES). In this article, the concept and classification of CAES are reviewed, and the cycle efficiency and effective energy are analyzed in detail to enhance the current understanding of CAES. Furthermore, the importance of the real-gas properties of air is discussed. Related research on adiabatic CAES and isothermal CAES is also presented.Due to the high variability of weather-dependent renewable energy resources, electrical energy storage systems have received much attention. In this field, one of the most promising technologies is compressed-air energy storage (CAES). In this article, the concept and classification of CAES are reviewed, and the cycle efficiency and effective energy are analyzed in detail to enhance the current understanding of CAES. Furthermore, the importance of the real-gas properties of air is discussed. Related research on adiabatic CAES and isothermal CAES is also presented.
43 citations
TL;DR: Three classes of commonly used references methods, namely, climatology, persistence, and their linear combination, are studied in a day-ahead solar forecasting scenario.
Abstract: Skill scores can be used to compare deterministic (also known as single-valued or point) forecasts made using different models at different locations and time periods. To compute the skill score, a reference forecasting method is needed. Nonetheless, there is no consensus on the choice of reference method. In this paper, three classes of commonly used references methods, namely, climatology, persistence, and their linear combination, are studied in a day-ahead solar forecasting scenario. Day-ahead global solar irradiance forecasts with an hourly resolution are generated using research-grade data from 32 sites around the globe, over a period of 1 year, in an operational manner. To avoid exaggerating the skill scores, it is generally agreed that the most accurate naive forecasting method should be chosen as the standard of reference. In this regard, the optimal convex combination of climatology and persistence is highly recommended to be used as the standard of reference for day-ahead solar forecasting. Skill scores can be used to compare deterministic (also known as single-valued or point) forecasts made using different models at different locations and time periods. To compute the skill score, a reference forecasting method is needed. Nonetheless, there is no consensus on the choice of reference method. In this paper, three classes of commonly used references methods, namely, climatology, persistence, and their linear combination, are studied in a day-ahead solar forecasting scenario. Day-ahead global solar irradiance forecasts with an hourly resolution are generated using research-grade data from 32 sites around the globe, over a period of 1 year, in an operational manner. To avoid exaggerating the skill scores, it is generally agreed that the most accurate naive forecasting method should be chosen as the standard of reference. In this regard, the optimal convex combination of climatology and persisten...
42 citations
TL;DR: In this article, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making them probably the most accurate separation models to date.
Abstract: Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making them probably the most accurate separation models to date. The new models are also tested at 5 European sites with unseen data (i.e., not involved during model parameter fitting); their performance again dominates all benchmarking models. The new separation models leverage half-hourly satellite-derived diffuse fraction. Since satellite data are available globally, the satellite-augmented separation models have universal applicability. However, despite their good performance, empirical separation models suffer from a series of issues. Hence, models driven by atmospheric physics are the “true gems” that one should pursue.Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making th...
TL;DR: In this paper, Wang et al. carried out density functional studies on the adsorption and diffusion of alkali metal ions (Li, Na, and K) on boron-graphdiyne monolayer and bilayers, where multiple adorption sites with strong adsorithmic energies were identified for all the studied alkaline metal ions.
Abstract: Inspired by the recent experimental synthesis of boron-graphdiyne [Wang et al., Angew. Chem. Int. Ed. 57(15), 3968–3973 (2018)], we have carried out systematic density functional studies on the adsorption and diffusion of alkali metal ions (Li, Na, and K) on boron-graphdiyne monolayer and bilayers, where multiple adsorption sites with strong adsorption energies are identified for all the studied alkali metal ions. Bader charge analysis indicates that significant charge transfer occurs upon absorption, leading to ionic bonding with the substrate and exhibiting a high storage capacity of 1294, 1617, and 1617 mAh g−1 for Li, Na, and K, respectively. Moreover, the migration energy barriers are found in the range of 0.36–0.47 eV for Li, 0.28–0.39 eV for Na, and 0.12–0.32 eV for K. These findings suggest that boron-graphdiyne based materials are promising for ion battery applications.
TL;DR: This paper proposed an optimal control technique for power flow control of hybrid renewable energy systems (HRESs) like a combined photovoltaic and wind turbine system with energy storage named WOANN, which predicts the required control gain parameters of the HRES to maintain the power flow, based on the active and reactive power variation in the load side.
Abstract: This paper proposed an optimal control technique for power flow control of hybrid renewable energy systems (HRESs) like a combined photovoltaic and wind turbine system with energy storage. The proposed optimal control technique is the joined execution of both the whale optimization algorithm (WOA) and the artificial neural network (ANN). Here, the ANN learning process has been enhanced by utilizing the WOA optimization process with respect to the minimum error objective function and named as WOANN. The proposed WOANN predicts the required control gain parameters of the HRES to maintain the power flow, based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy source accessibility, storage element state of charge, and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. The proposed technique is implemented in the MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques.
TL;DR: In this article, a nonparametric approach for post-processing, namely, kernel conditional density estimation (KCDE), is proposed to construct a relationship between the bias error (difference between the NWP-based forecast and measurement) and NWP output variables, such as clear-sky index, zenith angle, air temperature, humidity, or surface pressure.
Abstract: Global horizontal irradiance (GHI) forecasts by numerical weather prediction (NWP) often contain model-led bias. There is thus a strong consensus on using post-processing techniques, such as model output statistics (MOS), to correct such errors. As opposed to the conventional parametric methods, this article considers a nonparametric approach for post-processing, namely, kernel conditional density estimation (KCDE). Essentially, KCDE constructs a relationship between the bias error (difference between the NWP-based GHI forecast and measurement) and NWP output variables, such as clear-sky index, zenith angle, air temperature, humidity, or surface pressure. Hence, when a new set of explanatory variables becomes available, the conditional expectation of the bias error can be estimated. Since the ground-based GHI measurements are not available everywhere, the possibility of using satellite-derived GHI data to correct NWP forecasts is also explored. In the case study, two years of GHI forecasts made using the North American Mesoscale forecast system are corrected using both ground-measured and satellite-derived GHI references. As compared to Lorenz's fourth-degree polynomial MOS, additional 10%–16% (using ground-measured GHI) and 5%–13% (using satellite-derived GHI) reductions in the forecast error are observed at 7 test stations across the continental United States.
TL;DR: The Engerer2 separation model as discussed by the authors estimates the diffuse fraction Kd from inputs of global horizontal irradiance, UTC time, latitude, and longitude, and performs best out of the 140 models in global validation studies.
Abstract: The Engerer2 separation model estimates the diffuse fraction Kd from inputs of global horizontal irradiance, UTC time, latitude, and longitude. The model was initially parameterized and validated on 1-min resolution data for Australia and performed best out of the 140 models in global validation studies. This research reparameterizes Engerer2 on a global training dataset and at many common temporal resolutions (1-min, 5-min, 10-min, 15-min, 30-min, 1-h, and 1-day), so that it may be more easily implemented in the future; the need for the user to perform prerequisite calculations of solar angles and clear-sky irradiance has also been removed for ease of use. Comparing the results of the new 1-min parameterization against the original Engerer2 parameterization on a global testing dataset, the root mean squared error (RMSE) improves from 0.168 to 0.138, the relative RMSE from 30.4% to 25.1%, the mean bias error from 8.01% to –0.30%, and the coefficient of determination (R2) from 0.80 to 0.86; hence, there is a significant improvement to the model. Engerer2 was unsuited to 1-day averages; however, it performed remarkably well at all other averaging periods. A climate specific analysis found poor suitability of Engerer2 in polar climates; however, improvement and suitability were found for all other climates and temporal averaging periods. Code for the model are provided as supplementary material in languages R, Python, and Matlab®—selected for their wide-adoption in academia and industry—and they can also be found in the Github repository: Engerer2-separation-model.
TL;DR: The proposed planning minimizes the cost of consumed energy by houses in the third level through optimal utilization and management of all levels and deals with their intermittency nature by means of stochastic programming.
Abstract: This paper presents a multilevel energy management system between homes and the electrical grid. The proposed model includes three levels: the first level is made of the utility grid, and it can send or receive energy from the second level. The second level is formed as a common level that is equipped with a wind turbine, battery energy storage, and a diesel generator. The second level can exchange energy with both the first and third levels. The third level is formed with a set of buildings with different loading patterns, and some of them are also equipped with solar panels. The third level can send or receive energy from the second level. The second level is a common level between two other levels. The proposed planning minimizes the cost of consumed energy by houses in the third level through optimal utilization and management of all levels. The problem optimizes the power between levels 1 and 2, the power between levels 2 and 3, the charging-discharging pattern of the battery in level 2, and the operation pattern of the diesel generator in level 2. The plan optimally utilizes both wind and solar resources (in levels 2 and 3) to minimize the energy cost and deals with their intermittency nature by means of stochastic programming. The plan is also designed to operate under contingency conditions when the utility grid (first level) is out of access. In such a situation, the problem utilizes the available technologies in levels 2 and 3 (i.e., wind, solar, battery, and diesel) to supply the houses in the third level. The diesel generator plays a major role under contingency and emergency conditions to maintain the resiliency of the system.
TL;DR: In this article, the performance of a Hybrid Renewable Energy System (HRES) for the newly proposed grand city NEOM in Saudi Arabia was evaluated and optimized for the minimum net present cost (NPC).
Abstract: This work aims to design and evaluate the performance of a Hybrid Renewable Energy System (HRES) for the newly proposed grand city NEOM in Saudi Arabia. The average value of wind speed and Global Horizontal Irradiance at the proposed location are 4.86 m/s and 6.43 kWh/m2 per day, respectively. The various mixtures and sizes of photovoltaic (PV) arrays, wind turbines, power converters, diesel generators, and batteries are evaluated to find out the optimal system configuration to meet the required peak load of 1353 kW. The recommended HRES is optimized for the minimum net present cost (NPC). The electrical power, economic, and greenhouse gas emission analyses of the optimized HRES architecture are performed. Finally, a detailed sensitivity analysis is carried out to determine the impact of uncertainties in diesel cost and renewable resource variations on various system architectures, NPC, and CO2 emissions. The optimal system consists of two generators 500 kW and 1 MW, one V82 wind turbine (1.65 MW), a 100 kW PV, a 200 kW converter, and 100 batteries. The NPC of the optimal HRES is US$8.13 million, which is US$0.6 million less than the NPC of the diesel-only system. The cost of energy of the proposed HRES is found to be 0.164 US$/kWh as compared to 0.176 US$/kWh from the diesel-only system. Emission analysis shows a 46.5% reduction in CO2 emissions.
TL;DR: In this article, the authors discuss the air gap membrane distillation (AGMD) specifically and its development to date, and the areas for future research in the field of AGMD are suggested.
Abstract: Membrane distillation provides a feasible and optimal solution to potable water issues. The literature contains a number of studies and research studies that aim to understand the behavior of membrane distillation systems and to provide the best possible solutions under different conditions. The purpose of this article is to discuss the air gap membrane distillation (AGMD) specifically and its development to date. The areas for future research in the field of AGMD are suggested. Membranes used in AGMD were discussed, including nanocomposite membranes and graphene membranes. In addition, the long-term performance issues regarding membrane fouling and scaling and the ways to prevent and to reduce them were discussed. Performance parameters that have not been explored sufficiently, such as energy efficiency and performance ratio, are discussed. Evolution of new membrane distillation processes from AGMD, such as the material gap and permeate gap, and conductive gap membrane distillation, is discussed. A generalized theoretical model for heat and mass transfer is presented for air gap membrane distillation systems. Coupling AGMD to form a hybrid combination with renewable energy sources is considered as a good answer to energy specific issues. Hybrid renewable energy systems with AGMD are discussed in detail. Novel designs for coupling AGMD systems with different forms of renewable energies are suggested, which presents an excellent area to be considered for developing advanced hybrid AGMD systems. It is suggested that future research should include economic studies, long-run system performance, operational problems and maintainance requirements, and related issues for better understanding and better acceptance of AGMD systems for industrialization.
TL;DR: A novel blade inspection method based on deep learning and unmanned aerial vehicles is proposed that can reduce the blind area of the WT, the efficiency of subsequent maintenance can be improved, maintenance costs can be reduced, and the economic performance can be increased.
Abstract: As a key component of wind turbines (WTs), the blade conditions are related to the WT normal operation and the WT blade inspection is a significant task. Most studies of WT blade inspection focus attention on acquired sensor signal processing; however, there exist problems of stability, sensor installation, and data storage and processing. Onsite visual surface inspection is still the most common inspection method, but it is inefficient and requires a long downtime. Aimed at solving the above issues, a novel blade inspection method based on deep learning and unmanned aerial vehicles is proposed. Since common defect types are visible, the inspection problem is regarded as an image recognition problem. Three convolutional neural networks are trained by using the constructed dataset for image recognition, and the F1-score is applied to evaluate the models. The VGG-11 model is chosen for the final model due to its best performance. Then, the alternating direction method of multipliers algorithm is employed to compress the model to reduce the requirements on hardware devices. The blind area of the WT can be reduced, the efficiency of subsequent maintenance can be improved, maintenance costs can be reduced, and the economic performance can be increased. Finally, a comparison experiment of different inspection methods is carried out to demonstrate the proposed advantages.
TL;DR: A novel version of the gated recurrent unit (GRU) method is combined with weather forecasts in order to predict solar irradiance and shows that the proposed method is able to outperform other AI methods.
Abstract: In the solar power industry, irradiance forecasts are needed for planning, scheduling, and managing of photovoltaic power plants and grid-combined generating systems. A widely used method is artificial intelligence (AI), in particular, artificial neural networks, which can be trained over both historical values of irradiance and meteorological variables such as temperature, humidity, wind speed, pressure, and precipitation. In this paper, a novel version of the gated recurrent unit (GRU) method is combined with weather forecasts in order to predict solar irradiance. This method is used to forecast irradiance over a horizon of 24 h. Experiments show that the proposed method is able to outperform other AI methods. In particular, GRU using weather forecast data reduces the root mean squared error by 23.3% relative to a backpropagation neural network and 11.9% relative to a recurrent neural network. Compared to long short-term memory, the training time is reduced by 36.6%. Compared to persistence, the improvement in the forecast skill of the GRU is 42.0%. In summary, GRU is a promising technology which can be used effectively in irradiance forecasting.In the solar power industry, irradiance forecasts are needed for planning, scheduling, and managing of photovoltaic power plants and grid-combined generating systems. A widely used method is artificial intelligence (AI), in particular, artificial neural networks, which can be trained over both historical values of irradiance and meteorological variables such as temperature, humidity, wind speed, pressure, and precipitation. In this paper, a novel version of the gated recurrent unit (GRU) method is combined with weather forecasts in order to predict solar irradiance. This method is used to forecast irradiance over a horizon of 24 h. Experiments show that the proposed method is able to outperform other AI methods. In particular, GRU using weather forecast data reduces the root mean squared error by 23.3% relative to a backpropagation neural network and 11.9% relative to a recurrent neural network. Compared to long short-term memory, the training time is reduced by 36.6%. Compared to persistence, the improve...
TL;DR: In this article, the authors report the responses to an online questionnaire sent out to stakeholders to assess the conflict between bat conservation and wind energy production; yet, the majority was confident about solutions and all desired an ecologically sustainable energy transition.
Abstract: Although renewable energy production is widely accepted as clean, it is not necessarily environmental neutral since, for example, wind turbines kill large numbers of airborne animals such as bats. Consequently, stakeholders involved in the planning and operation of wind turbines are often in conflict when trying to reconcile both goals, namely, promoting wind energy production and protecting bats. We report the responses to an online questionnaire sent out to stakeholders to assess this conflict. More than 80% of stakeholders acknowledged the conflict between bat conservation and wind energy production; yet, the majority was confident about solutions and all desired an ecologically sustainable energy transition. All groups, except members of the wind energy sector, disagreed with the statements that wind energy production is of higher priority than biodiversity protection and that global warming is more critical than the biodiversity crisis. All groups agreed that more measures have to be taken to make wind energy production ecologically sustainable and that the society should be included to pay for the implementation of these measures. All stakeholders except for members of the wind energy sector agreed on that revenue losses from wind energy production and delays in the transition process should be acceptable to resolve the green–green dilemma. Among offered choices, most stakeholders suggested engaging in more research, improving the efficiency of energy use and implementing context dependent cut-in speed during wind turbine operation. The suggestion to weaken the legal protection of wildlife species was dismissed by all, underlining the consensus to protect biodiversity.
TL;DR: In this paper, a detailed radiative cooling resource maps for the contiguous United States are presented with the goal of determining the best climates for large-scale deployment of passive radiative heating technologies.
Abstract: Passive cooling devices take advantage of the partially transparent properties of the atmosphere in the longwave spectral band from 8 to 13 μm (the so-called “atmospheric window”) to reject radiation to outer space. Spectrally designed thermophotonic devices have raised substantial attention recently for their potential to provide passive and carbon-free alternatives to air conditioning. However, the level of transparency of the atmospheric window depends on the local content of water vapor in the atmosphere and on the optical depth of clouds in the local sky. Thus, the radiative cooling capacity of solar reflectors not only depends on the optical properties of their surfaces but also on local meteorological conditions. In this work, detailed radiative cooling resource maps for the contiguous United States are presented with the goal of determining the best climates for large-scale deployment of passive radiative cooling technologies. The passive cooling potential is estimated based on ideal optical properties, i.e., zero shortwave absorptance (maximum reflectance) and blackbody longwave emittance. Both annual and season-averaged maps are presented. Daytime and nighttime cooling potential are also computed and compared. The annual average cooling potential over the contiguous United States is 50.5 m−2. The southwestern United States has the highest annual averaged cooling potential, over 70 W m−2, due to its dry and mostly clear sky meteorological conditions. The southeastern United States has the lowest potential, around 30 W m−2, due to frequent humid and/or overcast weather conditions. In the spring and fall months, the Arizona and New Mexico climates provide the highest passive cooling potential, while in the summer months, Nevada and Utah exhibit higher potentials. Passive radiative cooling is primarily effective in the western United States, while it is mostly ineffective in humid and overcast climates elsewhere.Passive cooling devices take advantage of the partially transparent properties of the atmosphere in the longwave spectral band from 8 to 13 μm (the so-called “atmospheric window”) to reject radiation to outer space. Spectrally designed thermophotonic devices have raised substantial attention recently for their potential to provide passive and carbon-free alternatives to air conditioning. However, the level of transparency of the atmospheric window depends on the local content of water vapor in the atmosphere and on the optical depth of clouds in the local sky. Thus, the radiative cooling capacity of solar reflectors not only depends on the optical properties of their surfaces but also on local meteorological conditions. In this work, detailed radiative cooling resource maps for the contiguous United States are presented with the goal of determining the best climates for large-scale deployment of passive rad...
TL;DR: The improved unscented particle filter (IUPF) is presented to track and predict R0 and shows that IUPF has certain advantages, and the SOH estimation error is always less than 3% during the charge-discharge stage.
Abstract: This paper proposes an effective method to estimate the state of health (SOH) of a lithium-ion battery based on the ohm internal resistance R0. Unlike other estimation methods, this work considers the variation of R0 with the state of charge (SOC). The improved unscented particle filter (IUPF) is presented to track and predict R0. That is, an unscented Kalman filter (UKF) is used to generate an importance probability density function in the particle filter, and a method to select the fittest particle in the resampling stage is proposed. Based on the experimental data, a second-order resistance-capacitance equivalent circuit model is set up and the parameters are identified. To verify the accuracy of the proposed method, UKF and IUPF are compared in the prediction of R0 at different SOC points under the same cycle and at the same SOC point of different cycles. The results show that IUPF has certain advantages, and the SOH estimation error is always less than 3% during the charge-discharge stage.
TL;DR: Using a kd-tree to perform AnEn appears to be one of (if not) the fastest approaches to this probabilistic weather forecasting method.
Abstract: Analog ensemble (AnEn) is a popular probabilistic weather forecasting method based on similarity search. In that, forecasters are tasked to search for the top-m nearest neighbors (e.g., in terms of Euclidean distance) to a length-k query, from a set of historical data points in k-dimensional space. This is a straightforward yet time-consuming procedure, and few methods seem to be significantly better than a brute-force computation of all distances. To that end, I recommend using a kd-tree to perform AnEn, which appears to be one of (if not) the fastest approaches.
TL;DR: In this article, a block-matching algorithm was used to identify the bulk motion of clouds relative to the position of the Sun in the sky and select the image pixels for the new features.
Abstract: We introduce a simple and novel technique to extract dynamic features from sky images in order to increase the accuracy of intrahour forecasts for both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) values. The proposed methodology is based on a block-matching algorithm that correctly identifies the bulk motion of clouds relative to the position of the Sun in the sky. Adaptive rectangular- and wedge-shaped Regions Of Interest are used to select the image pixels for the new features. The results show an average increase of 6.8% (6.7%) in forecast skill for GHI (DNI) across all horizons tested as measured against a model with global (nonadaptive) image features. Relative to clear-sky persistence, the new model achieves skills ranging from 20% to 30% (22%–35%) for GHI (DNI), among the highest ever reported for these time horizons. An analysis based on Mutual Information and Pearson correlation coefficients between the image features and the training data reveals overall improvements in all metrics. The proposed adaptive method also improves the predictability of the ramp magnitude and direction.We introduce a simple and novel technique to extract dynamic features from sky images in order to increase the accuracy of intrahour forecasts for both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) values. The proposed methodology is based on a block-matching algorithm that correctly identifies the bulk motion of clouds relative to the position of the Sun in the sky. Adaptive rectangular- and wedge-shaped Regions Of Interest are used to select the image pixels for the new features. The results show an average increase of 6.8% (6.7%) in forecast skill for GHI (DNI) across all horizons tested as measured against a model with global (nonadaptive) image features. Relative to clear-sky persistence, the new model achieves skills ranging from 20% to 30% (22%–35%) for GHI (DNI), among the highest ever reported for these time horizons. An analysis based on Mutual Information and Pearson corre...
TL;DR: In this paper, the role of green hydrogen produced from renewable energy sources to the South African energy sector is presented with the objective of increasing the share of renewable energy in the country's energy mix.
Abstract: The energy sector in South Africa is highly driven by coal, making the country one of the highest greenhouse gas (GHG) emitters in the world As one of the signatories to the UN Framework Convention on Climate Change, the country is determined to reduce its carbon footprint In this paper, the role of green hydrogen produced from renewable energy sources to the South African energy sector is presented with the objective of increasing the share of renewable energy in the country's energy mix First, an overview of the South African energy sector is presented with the aim of pointing out the energy inputs and the usage patterns Thereafter, the potential of renewable energy resources that could support green hydrogen production is highlighted Some of the key findings show that 67% of total primary energy sources in South Africa is coal Industrial and residential sectors account for the bulk of energy usage with 53% and 19%, respectively It is also revealed that industrial and transportation sectors account for 51% and 38%, respectively, of GHG emission with the residential sector contributing only 2% The renewable energy resources in South Africa are encouraging for green hydrogen production Solar has an average 2500 h of sunshine per year with daily total solar irradiation between 4 and 65 kWh/m2 day The annual average wind speed ranges from 56 to 87 m/s with power density between 218 and 693 W/m2 at 10 m anemometer height in the coastal region of the country The biomass potential is estimated as 8391 Teragram per annum with hydropotential of 4851 GW This paper is important as it will serve as a firsthand scientific information for optimal development and investment in green hydrogen production in South Africa
TL;DR: In this article, the status quo of PV power generation for poverty alleviation in rural China is reviewed from the perspective of precision poverty, business modes, and the scale of projects, and a PEST-SWOT strategic analysis model is presented.
Abstract: China is one of the countries with abundant solar energy resources and also has rapid development in the photovoltaic (PV) industry. Since 2014, the Chinese government has begun to implement the PV power generation for poverty alleviation, which not only was in line with the concept of green development but also accelerated the pace of poverty alleviation in rural regions. This paper first reviews the status quo of energy poverty in rural China and elicits the necessity of developing PV power generation in those regions. Then, from the perspective of precision poverty alleviation, the status quo of PV power generation for poverty alleviation is introduced from the types of poverty alleviation, business modes, and the scale of poverty alleviation projects. This paper also introduces the policy status, which mainly involves national and regional policies, and shows that all departments give importance to PV power generation in rural regions. Furthermore, through the PEST-SWOT strategic analysis model, the internalities (strengths and weaknesses) and externalities (opportunities and threats) of PV power generation are analyzed, including policy, economy, society, and technology. According to the analysis of the strategic model, countermeasures are given in terms of policy recommendations, market mechanism, and technical support to solve some existing problems. Finally, this paper puts forward three key conclusions and prospects that the application of power alleviation with PV power generation is promising.
TL;DR: In this paper, the effects of microvortex generators (MVGs) on aerodynamic performance of the NACA 0018 airfoil and an H-type Darrieus wind turbine were investigated.
Abstract: A numerical study was performed to investigate the effects of Microvortex Generators (MVGs) on the aerodynamic performance of the NACA 0018 airfoil and an H-type Darrieus wind turbine. MVGs can delay stall, which may occur for a sustained duration during turbine operation. The flow fields around a single airfoil and the Vertical Axis Wind Turbine (VAWT) rotor are investigated. The purpose of the present work is to determine the best configuration of MVGs. In total, eight different configurations are studied. The results show that MVGs have significantly enhanced the lift of the airfoil near the stall and improve the stall margin. The improved airfoil design with MVGs installed at 20% chord length and 16° to the inlet flow with a rectangle shape has the maximum lift and stall angle. In addition, adding MVGs of the same configuration can significantly improve the power coefficient of the VAWT at a high tip speed ratio, where it typically gives low power production. The flow separation is suppressed in the azimuth angle ranging from 120° to 135°, where the power output increase is observed showing a potential impact for VAWT design.A numerical study was performed to investigate the effects of Microvortex Generators (MVGs) on the aerodynamic performance of the NACA 0018 airfoil and an H-type Darrieus wind turbine. MVGs can delay stall, which may occur for a sustained duration during turbine operation. The flow fields around a single airfoil and the Vertical Axis Wind Turbine (VAWT) rotor are investigated. The purpose of the present work is to determine the best configuration of MVGs. In total, eight different configurations are studied. The results show that MVGs have significantly enhanced the lift of the airfoil near the stall and improve the stall margin. The improved airfoil design with MVGs installed at 20% chord length and 16° to the inlet flow with a rectangle shape has the maximum lift and stall angle. In addition, adding MVGs of the same configuration can significantly improve the power coefficient of the VAWT at a high tip speed ratio, where it typically gives low power production. The flow separation is suppressed in the a...
TL;DR: The results show that—with appropriate modeling and tuning—it is possible to accurately estimate the aggregated PV active power generation of a distribution feeder with minimal or no additional sensor deployment.
Abstract: The increased integration of photovoltaic (PV) systems in distribution grids reduces visibility and situational awareness for utilities because the PV systems' power production is usually not monitored by them. To address this problem, a method called Contextually Supervised Source Separation (CSSS) has been recently adapted for real-time estimation of aggregate PV active power generation from aggregate net active and reactive power measurements at a point in a radially configured distribution grid (e.g., substation). In its original version, PV disaggregation is formulated as an optimization problem that fits linear regression models for the aggregate PV active power generation and true substation active power load. This paper extends the previous work by adding regularization terms in the objective function to capture additional contextual information such as smoothness, by adding new constraints, by introducing new regressors such as ambient temperature, and by investigating the use of time-varying regressors. Furthermore, we perform extensive parametric analysis to study tuning of the objective function weighting factors in a way that maximizes performance and robustness. The proposed PV disaggregation method can be applied to networks with either a single PV system (e.g., megawatt scale) or many distributed ones (e.g., residential scale) connected downstream of the substation. Simulation studies with real field recorded data show that the enhancements of the proposed method reduce disaggregation error by 58% in winter and 35% in summer compared to previous CSSS-based work. When compared against a commonly used transposition model based approach, the reduction in disaggregation error is more pronounced (78% reduction in winter and 45% in summer). Additional simulations indicate that the proposed algorithm is also applicable for PV systems with time-varying power factors. Overall, our results show that—with appropriate modeling and tuning—it is possible to accurately estimate the aggregated PV active power generation of a distribution feeder with minimal or no additional sensor deployment.
TL;DR: A technical architecture proposed by China State Grid Corporation is introduced, based on which the key communication and information technologies of UPIoT are analyzed in detail from the four elements of U PIoT.
Abstract: Ubiquitous Internet of Things refers to the interconnection and interaction of information at any time, any place, anyone, and anything. The ubiquitous power Internet of Things (UPIoT) refers to the application of ubiquitous IoT technology in power systems. Its implementation has the following advantages for power grids: connecting the devices that should have been connected; sharing the data that should have been shared, and the value of data can be used more efficiently. Different from the smart grid, which is designed to build a multienergy integrated network and distributed management intelligent system with intelligent judgment and adaptive adjustment ability, the essence of UPIoT is to realize holistic perception and ubiquitous connection of energy. This paper introduces a technical architecture proposed by China State Grid Corporation, based on which the key communication and information technologies of UPIoT are analyzed in detail from the four elements of UPIoT. The challenges of implementing UPIoT are also introduced.