Agricultural Energy Research Articles

Life Cycle Greenhouse Gas Emissions of Brazilian Sugar Cane Ethanol Evaluated with the GREET Model Using Data Submitted to RenovaBio.

Brazil is the second-largest ethanol producer in the world, primarily using sugar cane as feedstock. To foster biofuel production, the Brazilian government implemented a national biofuel policy, known as RenovaBio, in which greenhouse gas (GHG) emission reduction credits are provided to biofuel producers based on the carbon intensities (CI) of the fuels they produce. In this study, we configured the GREET model to evaluate life cycle GHG emissions of Brazilian sugar cane ethanol, using data from 67 individual sugar cane mills submitted to RenovaBio in 2019/2020. The average CI per megajoule of sugar cane ethanol produced in Brazil for use in the U.S. was estimated to be 35.2 g of CO2 equivalent, a 62% reduction from U.S. petroleum gasoline blendstock without considering the impacts of land use change. The three major GHG sources were on-field N2O emissions (24.3%), sugar cane farming energy use (24.2%), and sugar cane ethanol transport (19.3%). With the probability density functions for key input parameters derived from individual mill data, we performed stochastic simulations with the GREET model to estimate the variations in sugar cane ethanol CI and confirmed that despite the larger variations in sugar cane ethanol CI, the fuel provided a robust GHG reduction benefit compared to gasoline blendstock.

Towards blue growth: Multi-use possibilities for the development of emerging sectors in the Brazilian sea

The marine environment has been in the spotlight of economic development due to the growing demand for areas to promote activities associated with the concept of Blue Economy. This is the case of the renewable energy and aquaculture sectors, whose expansion towards offshore is determined by the increase global demand for energy and food, and by exceeding of the carrying capacity of coastal and terrestrial systems. In this context, the multi-use strategy can be an alternative to minimize conflicts between activities and impacts on the surrounding social-ecological environment. This contribution presents a preliminary approach to identify opportunities for individual exploitation and the possibilities of multi-use between wind energy, wave energy and aquaculture in Brazil's Exclusive Economic Zone. Technical, operational, and biological aspects were evaluated, through a Suitability Index validated in previous works, to identify zones with favorable conditions for energy exploitation and farming of six fish species. Additionally, overlaps between conservation areas and multi-use zones were considered to analyze possible spatial conflicts. Zones with multi-use possibilities with different combinations between these sectors were identified: i) wave energy and aquaculture presented the largest areas for multi-use, distributed in the south, southeast and northeast; ii) possibility of combining wind energy and aquaculture was identified in the northeast; and iii) multi-use possibilities in the south for marine energies. Zones with multi-use possibilities were identified in protection and conservation areas, such as the combination of wave exploitation and Greater Amberjack farming, with 63% overlap. Therefore, this case study is a guide for future local studies in the marine region of Brazil, mainly in the selection of sites for analysis. The present contribution represents a starting point for the discussion about multi-use in the country.

An Evolutionary Artificial Neural Network approach for spatio-temporal wave height time series reconstruction

This paper proposes a novel methodology for recovering missing time series data, a crucial task for subsequent Machine Learning (ML) analyses. The methodology is specifically applied to Significant Wave Height (SWH) time series in the field of marine engineering. The proposed approach involves two phases. Firstly, the SWH time series for each buoy is independently reconstructed using three transfer function models: regression-based, correlation-based, and distance-based. The distance-based transfer function exhibits the best overall performance. Secondly, Evolutionary Artificial Neural Networks (EANNs) are utilised for the final recovery of each time series, using as inputs highly correlated buoys that have been intermediately recovered. The EANNs are evolved considering two metrics, the novel squared error relevance area, which balances the importance of extreme and around-mean values, and the well-known mean squared error. The study considers SWH time series data from 15 buoys in two coastal zones in the United States. The results demonstrate that the distance-based transfer function is generally the best transfer function, and that EANNs outperform a range of state-of-the-art ML techniques in 12 out of the 15 buoys, with a number of connections comparable to linear models. Furthermore, the proposed methodology outperforms the two most popular approaches for time series reconstruction, BRITS and SAITS, for all buoys except one. Therefore, the proposed methodology provides a promising approach, which may be applied to time series from other fields, such as wind or solar energy farms in the field of green energy.

Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit

The optimal design of a wind farm which maximizes energy production depends on the spatially variable wind flow field. However, due to the complexity associated with modeling atmospheric winds, especially in the presence of complex terrain, the wind field is inherently uncertain. Uncertainty in the wind field during the design stage reduces the likelihood that the constructed wind farm achieves maximum energy production for a given number of turbines. As such, contemporary wind farms are designed through a two step process. First, experimental data collected from simultaneously constructed meteorological towers are used, along with simplified engineering flow models, to estimate the flow field. Then, the estimated flow field is used to place wind turbines to maximize an objective function, such as wind farm energy production. To increase wind farm energy production and reduce its uncertainty, we propose a farm design methodology that combines both the sensor placement and wind turbine location selection steps into a single sequential decision-making process. Future sensor placements sequentially leverage information gained from previous ones. Rather than placing sensors to attempt to reduce wind field uncertainty, we select sensor locations to maximize wind farm profit. Using profit as the objective function balances the increased revenue, from the increased energy production associated with improved wind field knowledge, against the higher costs from additional sensors. In this proof-of-concept study, we use idealized numerical case studies to demonstrate that sequential wind field sensor placement, compared to concurrent placement, improves the robustness of the resulting wind farm design to imperfect initial knowledge of the wind field. The numerical results suggest a wind farm annual energy output (lifetime profit) increase of approximately 6.55% (16.78%), equaling roughly 0.7 GWh/year (0.35 million USD) per turbine, compared to a typical, two-step wind farm design process which first places sensors concurrently then designs the farm thereafter. Our results also indicate that using a far-sighted sequential method as proposed in this paper outperforms standard metrics, such as entropy, that solely focus on wind field uncertainty minimization. These proof-of-concept findings suggest that future research should be dedicated to improving the wind farm design process through sequential decision-making under uncertainty.

Modelling the impact of trapped lee waves on offshore wind farm power output

Abstract. Mesoscale meteorological phenomena, including atmospheric gravity waves (AGWs) and including trapped lee waves (TLWs), can result from flow over topography or coastal transition in the presence of stable atmospheric stratification, particularly with strong capping inversions. Satellite images show that topographically forced TLWs frequently occur around near-coastal offshore wind farms. Yet current understanding of how they interact with individual turbines and whole farm energy output is limited. This parametric study investigates the potential impact of TLWs on a UK near-coastal offshore wind farm, Westermost Rough (WMR), resulting from westerly–southwesterly flow over topography in the southeast of England. Computational fluid dynamics (CFD) modelling (using Ansys CFX) of TLW situations based on real atmospheric conditions at WMR was used to better understand turbine level and whole wind farm performance in this parametric study based on real inflow conditions. These simulations indicated that TLWs have the potential to significantly alter the wind speeds experienced by and the resultant power output of individual turbines and the whole wind farm. The location of the wind farm in the TLW wave cycle was an important factor in determining the magnitude of TLW impacts, given the expected wavelength of the TLW. Where the TLW trough was coincident with the wind farm, the turbine wind speeds and power outputs were more substantially reduced compared with when the TLW peak was coincident with the location of the wind farm. These reductions were mediated by turbine wind speeds and wake losses being superimposed on the TLW. However, the same initial flow conditions interacting with topography under different atmospheric stability settings produce differing near-wind-farm flow. Factors influencing the flow within the wind farm under the different stability conditions include differing, hill and coastal transition recovery, wind farm blockage effects, and wake recovery. Determining how much of the differences in wind speed and power output in the wind farm resulted from the TLW is an area for future development.

A Techno-Economic Study for Off-Grid Green Hydrogen Production Plants: The Case of Chile

In this study, we present a pre-feasibility analysis that examines the viability of implementing autonomous green hydrogen production plants in two strategic regions of Chile. With abundant renewable energy resources and growing interest in decarbonization in Chile, this study aims to provide a comprehensive financial analysis from the perspective of project initiators. The assessment includes determining the optimal sizing of an alkaline electrolyzer stack, seawater desalination system, and solar and wind renewable energy farms and the focus is on conducting a comprehensive financial analysis from the perspective of project initiators to assess project profitability using key economic indicators such as net present value (NPV). The analyses involve determining appropriate sizing of an alkaline electrolyzer stack, a seawater desalination system, and solar and wind renewable energy farms. Assuming a base case production of 1 kiloton per year of hydrogen, the capital expenditures (CAPEX) and operating expenses (OPEX) are determined. Then, the manufacturing and production costs per kilogram of green hydrogen are calculated, resulting in values of USD 3.53 kg−1 (utilizing wind energy) and USD 5.29 kg−1 (utilizing photovoltaic solar energy). Cash flows are established by adjusting the sale price of hydrogen to achieve a minimum expected return on investment of 4% per year, yielding minimum prices of USD 7.84 kg−1 (with wind energy) and USD 11.10 kg−1 (with photovoltaic solar energy). Additionally, a sensitivity analysis is conducted to assess the impact of variations in investment and operational costs. This research provides valuable insights into the financial feasibility of green hydrogen production in Chile, contributing to understanding renewable energy-based hydrogen projects and their potential economic benefits. These results can provide a reference for future investment decisions and the global development of green hydrogen production plants.

A Study on the System Design of Eco-Friendly Smart Farm Using the Internet of Things and Renewable Energy

The environment changes rapidly due to climate change. This situation has a great influence on the agricultural sector. In particular, prices of agricultural products rise as space for producing agricultural products around the world decreases. To solve this problem, smart farm is recognized as major technologies in the future society. In particular, in the era of the 4th Industrial Revolution, new technologies such as the Internet of Things (IOT) and artificial intelligence will be installed in smart farm. In addition, with the development of the Internet of Things (IOT) technology, temperature, humidity, and concentration of fluids in plant factories can be monitored and controlled with smart-phone through various sensors. This IoT technology is recognized as a very important technology for smart farm. In addition, a sustainable smart farm model by converging with eco-friendly IT technology is also important. Therefore, in this paper, a study was conducted on the system design of eco-friendly smart farm using the Internet of Thing. Such smart farm technology can remotely manage plants through the Internet of Things. The research methods of this study are as follows. First, a literature study was conducted in this paper. Based on this literature study, the researcher conducted a field survey on smart farm. The researcher visited this space in person and conducted in-depth interviews with operators and researchers. The researcher analyzed the results derived through this survey and suggested the design principle for the eco-friendly smart farm system. In addition, this study proposes a method for using the Internet of Things and renewable energy for smart farms in preparation for the era of climate change. The results of this study can be theoretical basic data for changes in the agricultural field in the era of the Fourth Industrial Revolution.

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

AbstractTurbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine and (2) by changing the overall flow resistance in the farm and thus the so‐called farm blockage effect. To better predict these effects with low computational costs, we develop data‐driven emulators of the ‘local’ or ‘internal’ turbine thrust coefficient as a function of turbine layout. We train the model using a multi‐fidelity Gaussian process (GP) regression with a combination of low (engineering wake model) and high‐fidelity (large eddy simulations) simulations of farms with different layouts and wind directions. A large set of low‐fidelity data speeds up the learning process and the high‐fidelity data ensures a high accuracy. The trained multi‐fidelity GP model is shown to give more accurate predictions of compared to a standard (single‐fidelity) GP regression applied only to a limited set of high‐fidelity data. We also use the multi‐fidelity GP model of with the two‐scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimization in the future.

Reconversion of the shipbuilding sector: from production by project to mass production. What is its effect on the LCOE of a wave energy farm?

The objective of this work has been to analyze the variation of different economic parameters (Levelized Cost Of Energy and Internal Rate of Return) for two possible ways of manufacturing: production by project to mass production. For this, 6 possible scenarios have been considered, taking into account the number of equipment manufactured and the economic parameters have been calculated in order to know which alternative provides greater economic viability, thus making the project more profitable.

Optimization of vertical farms energy efficiency via multiperiodic graph-theoretical approach

A systematic method is proposed for enhancing the energy efficiency of vertical farms when integrated into urban infrastructure. The method relies on the optimization of a multiperiod model that determines the system's best operation plan considering variations in its parameters throughout the day. These parameters include factors such as resources availability from the urban infrastructure and fluctuations in electricity prices. The model is formulated and solved via the tools of the P-graph framework, which permits the automatic formulation of the rigorous superstructure and the efficient optimization of the integrated system's operation. The method is illustrated via a case study comprising three scenarios. The most efficient case demonstrates up to 40% electricity cost saving potential for a particular operation day, and up to 31% for the entire year. The method presented constitutes an optimization tool that can use real data on electricity prices and forecasts of weather conditions to reduce operating costs and enhance the energy efficiency and sustainability of vertical farming systems.

Vertical Airborne Wind Energy Farms with High Power Density per Ground Area based on Multi-Aircraft Systems

This paper proposes and simulates vertical airborne wind energy (AWE) farms based on multi-aircraft systems with high power density (PD) per ground area. These farms consist of many independently ground located systems that are flying at the same inclination angle, but with different tether lengths, such that all aircraft fly in a large planar elliptical area that is vertical to the tethers. The individual systems are assigned non-overlapping flight cylinders depending on the wind direction. Detailed calculations that take into account Betz’ limit, assuming a cubically averaged wind power density of 7 m/s, give a potential yearly average PD of 43 MW/km2. A conventional wind farm with typical packing density would yield a PD of 2.4 MW/km2 in the same wind field. More refined simulations using optimal control result in a more modest PD of 6.5 MW/km2 for practically recommended flight trajectories. This PD can already be achieved with small-scale aircraft with a wing span of 5.5 m. The simulations additionally show that the achievable PD is more than an order of magnitude higher than for a single-aircraft AWE system with the same wing span.

ACCESSIBLE SOLAR ENERGY TECHNOLOGY FOR DOMESTIC APPLICATIONS IN THE UK: EDGE SOLAR

AbstractRenewable energy is increasingly used and promoted. In the UK, for example, large scale renewable energy farms have been used to supply electricity with great effect. Given the large number of homes, there is considerable impact to be made by small scale residential renewable energy systems. Despite solar panels being the most common form of residential renewable energy technology, only 4% of buildings in the UK support solar technology of any kind. For direct electricity generation, silicon-based photovoltaic (PV) arrays are the most utilised, and when used in a residential setting, they are typically mounted on the sloped roofs. This is where the problem lies. The technology comes with a high cost, and there is further financial burden of installation and maintenance, making solar energy inaccessible for many UK homeowners. This paper presents a research and design innovation project to make PV technology more accessible in the UK. Edge Solar, the innovative, affordable, new PV system concept for UK homes may become a promising solution to significantly improve the accessibility to the PV technology and renewable energy at the household level in the UK and beyond with further development and commercialisation.

Scrutiny of power grids by penetrating PV energy in wind farms: a case study of the wind corridor of Jhampir, Pakistan

This study examines the problems caused by intermittent renewable energy sources, especially wind farms, and suggests a different solar energy penetration strategy to improve their loading capacity. The study uses real-time data from a wind farm in Jhampir, Pakistan, to analyse and assess various aspects of grid stations connected to wind farms. Electrical Transient Analyzer Program is used to validate the results by linking these with actual grid system. The article focuses on creating a model for a grid connected to a wind farm and the simulation of outcomes following capacity expansion, with the installation of an autotransformer. The original capacity of the wind farm was 750 MW, which was increased to 1,250 MW, i.e., 1.66 times the actual capability. Furthermore, this capacity was further enhanced to 1,540 MW, which becomes 1.23 times the previous capacity by the penetration of a photovoltaic power plant.

The Effect of Agricultural Biogas Plants on the Quality of Farm Energy Supply

Agricultural biogas plants are among the renewable energy sources. While they have many advantages, they are less common than photovoltaic or wind power plants. One of the reasons for the lack of support for the construction of new agricultural biogas plants is the belief that biogas plants will affect the operation of consumers connected in its immediate vicinity through interference introduced into the grid. This article presents the possibilities a biogas plant built on a farm offers. The impact of an on-farm biogas plant on the voltage parameters of a farm specializing in barnless cattle rearing is analyzed in detail. As demonstrated by the authors’ research in one of the agricultural biogas plants (with an electrical capacity of 40 kW), these plants do not introduce significant disturbances to the power quality into the grid. The most significant changes in the parameters of the voltage supplying the farm under study were caused by the operation of the digester mixer installed in the fermenter. Thanks to the research, it was also possible to identify a problem with the effect of the digester mixer on the energy parameters produced in the biogas plant. This problem has so far not been noticed or corrected by biogas plant manufacturers.

Modelling wind speed with a univariate probability distribution depending on two baseline functions

Characterizing the wind speed distribution properly is essential for the satisfactory production of potential energy in wind farms, being the mixture models usually employed in the description of such data. However, some mixture models commonly have the undesirable property of non-identifiability. In this work, we present an alternative distribution which is able to fit the wind speed data decently. The new model, called Normal-Weibull-Weibull, is identifiable and its cumulative distribution function is written as a composition of two baseline functions. We discuss structural properties of the class that generates the proposed model, such as the linear representation of the probability density function, moments and moment generating function. We perform a Monte Carlo simulation study to investigate the behavior of the maximum likelihood estimates of the parameters. Finally, we present applications of the new distribution for modelling wind speed data measured in five different cities of the Northeastern Region of Brazil.

ALFRED reactor and hybrid systems: A test case

Future electricity generation will likely rely on renewable energy sources with consequent power intermittency and grid instability issues that will need to be compensated for. In this work, a model of a Generation IV small modular reactor has been developed in order to study its capabilities as a grid stabilizer. The plant model has been developed in Dymola and exported in the Simulink environment for the control strategy. The hybrid system considered couples a lead-cooled fast reactor to a desalination plant, an energy storage system, renewable energy farms, a variable electrical load, a local grid, and an external interconnected grid. Different desalination plants have been considered. Reverse osmosis has proven to be the most suitable option when coupled with energy production systems. Different configurations of the hybrid system have been considered showing a higher degree of load-following capabilities for the nuclear plant studied.

A Review on Mitigation of Greenhouse Gases by Agronomic Practices towards Sustainable Agriculture

Agriculture is one among the sources of greenhouse gas emission in the World. Agriculture, being a prominent source of economic sectors in developing countries its impact on environmental climate changes both directly and indirectly through emission of greenhouse gases. To achieve reduced GHGs emissions in agriculture sector, there is a need to adopt climate smart activities and improved food and nutritional security to ensure a climate-smart sustainable agriculture. This short article explores the key ways to mitigate green house gases emissions in agriculture and critically highlights the potential for bacterial nitrogen fixation in soybean which is a recent approach. Symbiotic nitrogen fixation shows a great potential for GHGs mitigation while supporting the agriculture simultaneously. Other agronomic practices include tillage, residue management, rice field management, climate smart agriculture, organic farming and bio energy etc. This will help the farmers and other stakeholders to bring an environmentally friendly agriculture towards more ecological farming approach for future sustainability.

Offshore wind farm layouts designer software's

Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

In recent years, a major focus on wind farm wake control is to maximise the production of wind farms. To improve the power generation efficiency of wind farms through wake regulation, this study investigates yaw optimisation for wind farm production maximisation from the perspective of time-varying wakes. To this end, we first deduce a simplified dynamic wake model according to the momentum conservation theory and backward difference method. The accuracy of the proposed model is verified by simulation comparisons. Then, the time lag of wake propagation and its impact on wind farm production maximisation through wake meandering is analysed. On this basis, a yaw optimisation method for increasing wind farm energy capture is presented. This optimisation method uses the proposed dynamic wake model for wind farm prediction. The results indicate that the optimisation period is critical to the effect of the optimisation method on wind farm energy capture.

Resilience of wave energy farms using metocean dependent failure rates and repair operations

Emerging offshore renewable energy technologies are expected to become an important part of the future energy system, and reliability for these new technologies in different metocean scenarios must be guaranteed. This poses a challenge in extreme weather scenarios like storms, in particular for less mature technologies such as wave energy. Not only the offshore survivability must be controlled; the restoration after disruptive events and failures should be addressed and optimized. Offshore operations are costly and cannot be carried out if the weather is too harsh, and the resulting downtime after failures may be financially devastating for projects. In this paper, the resilience of large wave energy systems is studied with respect to wave conditions, metocean dependent failure rates, and weather windows available for offshore repair operations. A metocean- and time-dependent failure rate is derived based on a Weibull distribution, which is a novelty of the paper. The performance of the farm is assessed using the varying failure rates and metocean data at different offshore sites. Critical metocean thresholds for different offshore vessels are considered, and the resilience is quantified using relevant measures such as unavailability and expected energy not supplied. The resilience analysis is coupled to an economic assessment of the wave farm and different repair strategies. Our results show that the commonly used assumption of constant failure rates is seen to overestimate the annual energy production than when a more realistic varying failure rate is used. Two offshore sites are compared, and the availability is found to be higher at the calmer site. Most of the evaluated repair strategies cannot be considered to be economically justified, when compared to the cost of the energy not supplied.