Agricultural Energy Research Articles

CFD simulation of the stratified atmospheric boundary layer: consistency between Monin-Obukhov similarity theory and the standard k-ε model

Including thermal stratification in CFD simulations of the atmospheric boundary layer (ABL) flow is important for a wide range of applications, from pollutant dispersion over wind energy farm performance to urban thermal microclimate. One of the most important prerequisites for accurate CFD simulations of thermally stratified ABL flow is horizontal homogeneity. Horizontal homogeneity refers to the absence of unintended streamwise gradients in the approach-flow profiles of mean velocity, turbulence quantities and temperature when flowing from the inlet of the domain to the location of interest in the domain, over uniformly rough level terrain. This paper proposes a generic and consistent solution to maintain horizontal homogeneity in CFD simulations of Monin-Obukhov similarity theory (MOST) based stratified ABL flow. A new description is proposed for the coefficient Cε3, which appears in the buoyancy term in the transport equation of the turbulence dissipation rate. This proposed solution is successfully demonstrated by simulations in an empty domain for four stability conditions (1/L = 1/152.4 m−1, 1/1071.7 m−1, 0 m−1 and -1/296.3 m−1), where the standard k-ε turbulence model with the new Cε3 is shown to well maintain the profiles of U, ε and T with only minor deviations for the k profiles. The performance of the turbulence model with the new Cε3 is also illustrated by the flow around a rectangular building under thermal stratification.

Floating wave energy farms: How energy calculations shape economic feasibility?

The aim of this study is to use two different methodologies for calculating the energy produced by a total wave energy farm in the Cantabrian Sea (North of Spain). First method studies the energy produced by the farm taking into account the wave energy converter power matrix and the probability matrix of sea conditions for a particular location. On the other hand, second method is a more simplified methodology in which the wave energy converter power matrix is not taken into account, but it considers the density of the water, gravity, period and height of the wave, as well as the percentage of efficiency, among others. Finally, the energy generated by the farm and the economic parameters that allow calculating its economic feasibility are studied, such as Levelized Cost of Energy, Net Present Value and Internal Rate of Return. Results indicate the importance of using a particular method for calculating the energy produced by a wave energy farm in terms of the variation of its economic feasibility. In this sense, it is shown that despite the fact that the first method is more detailed, the variations between both methods are very small, therefore the simplest model can be used in the future studies, reducing the calculation process in the future.

Evaluation and Long-Term Prediction of Annual Wind Farm Energy Production

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures.

Towards advanced sustainable criteria for choosing the best site for collecting solar energy in cities using multi-criteria GIS

Solar energy has become a prominent and crucial source of clean energy due to the increasing global demand for electricity. There is a prevailing global trend towards the construction of solar farms as a means of energy generation. This study aims to assess multiple criteria encompassing urban, environmental, and social aspects in order to ascertain the optimal site for constructing solar farms. The evaluation primarily focuses on characteristics such as topography, solar radiation, accessibility, land use, and proximity to roads and power stations. In order to acquire the necessary spatial fit model, the data was evaluated utilizing a Geographic Information System (GIS) program, and the criteria were subsequently consolidated into an integrated geographic information system. A comprehensive review of the existing body of literature reveals that environmental factors, specifically solar radiation and aspect, play a crucial role in determining the suitable places for the establishment of solar energy collection projects (Merrouni et al. in Energy Procedia 49:2270-2279, 2013; McKinney in J Student Res Environ Sci Appalac 4:1-14, 2014). Furthermore, other analysis factors such as proximity to built-up regions, closeness to power lines, and proximity to roadways are taken into account (Hott et al. in GIS-based Spatial Analysis For LargeScale Solar Power And Transmission Line Issues: Case Study of Wyoming, U.S. In: Proceedings of the 41st American Solar Energy Society Meeting, 2012; Effat in Int J Adv Remote Sens GIS 2:205-220, 2013). These criteria have an impact on the cost of solar farms. The objective of this study was to assess several aspects influencing the selection of an optimal location for a solar energy farm, taking into consideration the aforementioned criteria. The study was carried out in New Aswan city, utilizing data obtained from the Urban Planning Authority within the Ministry of Housing and Urban Development, as well as the New Aswan City AuthorityI generated cartographic representations and compiled quantitative data, which informed our selection of places that met the established criteria through the utilization of a Geographic Information System (GIS) software. Upon the conclusion of the study, numerous locations that satisfied the established criteria were selected. The present study focuses on regions with high capacity, and the findings are depicted in the form of spatial and objective maps. One of the primary benefits associated with the utilization of this methodology lies in its versatility, as it can be implemented across several domains. Furthermore, a straightforward adjustment of the criteria facilitates its application in the selection of optimal locations for wind farms.

Energy and Delay Aware General Task Dependent Offloading in UAV-Aided Smart Farms

Energy and Delay Aware General Task Dependent Offloading in UAV-Aided Smart Farms

Environmental Planning and Spatial Modeling for Wind Energy Farm Sites (Case Study: Najaf Secondary Region)

Environmental Planning and Spatial Modeling for Wind Energy Farm Sites (Case Study: Najaf Secondary Region)

Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production

Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production

The static voltage stability analysis of photovoltaic energy storage systems based on NPU algorithm

Although the data-driven static voltage stability problems have been widely studied, most of the classical algorithms focus more on improving the accuracy of the system prediction, ignoring the error classification errors generated during the prediction process. Furthermore, current research ignores the utilization of data-driven voltage stability assessment of energy storage systems. Therefore, this paper proposes a static voltage stability assessment method for photovoltaic energy storage systems based on considering the error classification constraint algorithm using Neyman-Pearson umbrella algorithms. Firstly, the Spearman Correlation Coefficient is employed in the feature selection phase. Secondly, an updated voltage stability assessment (VSA) model is proposed. Compared with the existing data-driven prediction of system static voltage stability in the literature, it can realize voltage stability assessment more quickly. Furthermore, on the basis of rapid voltage stability assessment, the umbrella NP classifier can also effectively limit the first-class error and attenuate the effect of error classification by mirroring the control of the number of cycle splits and the type I classification error threshold. Finally, the simulation and experimental results show that the effectiveness and robustness of the scheme proposed in this paper in grid-connected photovoltaic energy farms.

Assessing the sustainability of Blue Economy activities using an ecosystem and life cycle-based approach: Possibilities, challenges and implications for an informed policy making

The global ocean faces increasing exploitation to meet the demand of a rapidly growing globalisation. Human marine activities are leading to local environmental pressures/benefits, for example on marine ecosystems and their services, but also through their value chains on terrestrial ecosystem services, and to global pressures such as global warming. Effective management of marine activities is essential for the conservation of the natural environment. There is a growing need for holistic sustainability assessment tools capable of quantifying environmental impacts at various geographical scales, alongside evidence-informed policies. This study examines the evolving marine policy landscape, identifies key legislation that supports the sustainable growth of the Blue Economy, traces its historical development, and explores the integration of ecosystem services assessment and life cycle assessment as methodologies for assessing environmental sustainability within this legislation. The review shows that current legislation falls short in providing instructions on how to measure sustainability impacts in a consistent way, i.e., which methods/indicators to use. Therefore, this study supports evidence-informed policy-making by proposing a quantitative and comprehensive environmental sustainability impact assessment methodology, integrating both ecosystem and life-cycle based methods, to a Belgian multi-use case study involving offshore wind energy and mussel farming. Considering the impacts that were possible to assess and the limits of the methodology used, the value of the positive impacts of the MUOF was +61.3 M€ y−, while the negative ones were −4.0 M€ y−1, resulting in a net handprint of +57.0 M€ y−1, primarily attributed to the benefits of the local ecosystem service ‘offshore renewable energy’ However, such a solution is not necessarily scalable, due to cumulative impacts. An analysis was conducted to identify areas for enhancing the methodology to more effectively meet policy needs. The study highlights the importance of using scientifically grounded methods to inform policy decisions.

Solar Energy and Midwestern Farms: Utilizing Place-Based Socio-Scientific Issues to Foster Students’ Literacy

Solar Energy and Midwestern Farms: Utilizing Place-Based Socio-Scientific Issues to Foster Students’ Literacy

Hybrid offshore wind–solar energy farms: A novel approach through retrofitting

Due to the inherent variability of the power output of offshore wind farms, their integration into electrical grids poses a challenge to their stability and leads to significant balancing costs. Combining wind and solar power may be expected to mitigate the power output variability. In addition, a number of synergies can be realised—shared infrastructure, shared crews and vessels for operations and maintenance, and last but not least, optimum use of scarce marine space. The objective of this work is to investigate this hybrid approach and, in particular, to determine the optimum capacity of the solar energy subsystem given a certain installed capacity of the wind energy subsystem. Three case studies are considered in Europe and China. Each of them is an existing wind farm that could be retrofitted with floating solar PV. After assessing the local wind and solar energy resources, the optimal size of the floating solar array is calculated with a view to smooth the aggregated power output of each farm. Subsequently, the benefits of this hybrid approach are demonstrated from various perspectives. It is found that a much larger solar array is required for a wind farm with a large installed capacity, but the specific optimal size depends more on the local wind and solar resources. The novelty of this paper lies in its exploration of retrofitting existing offshore wind turbines with floating solar PV systems from the perspective of addressing the variability challenge for the currently operating wind farms. This study could serve as a guideline for project designs aiming to retrofit existing offshore wind farms with solar PV technology, thus reducing balancing costs and facilitating the penetration of offshore renewable energy into national power grids.

Optimization of wind farm energy supply system costs through machine learning

At present, the energy crisis is becoming increasingly severe, and changing the energy structure and developing clean energy have gradually become the consensus of the world. Establishing a clean energy power supply system, using clean energy for power generation, and investing in real power supply are currently unresolved challenges. Based the current high cost of new energy power supply systems, this project proposes an optimization plan based on the wind power supply system, targeting the capacity of lithium battery energy storage equipment, and constructs an optimization model for wind power supply in electric vehicle systems. In addition, in order to use lithium-ion battery devices more efficiently and ensure the evaluation of the efficiency and battery life of lithium-ion battery energy storage devices, we predicted the cyclic aging phenomenon of lithium-ion battery energy storage devices through machine learning technology. This prediction can provide suggestions and references for the use of this lithium battery.

Environmental Life Cycle Assessment of multi-use of marine space: A comparative analysis of offshore wind energy and mussel farming in the Belgian Continental Shelf with terrestrial alternatives

The Belgian Continental Shelf is a highly used part of the North Sea, where many different maritime activities thrive, such as shipping, fishing and energy production. Offshore wind energy in particular has gained importance in the region and concessions zones are allowed to be combined with aquaculture activities. It is unclear what the environmental impact of maritime multi-use is, from a life-cycle perspective, where there is potential to create synergies in the value chains, and how similar the impact compares to currently used alternatives. Therefore, this study performs a Life Cycle Assessment on a combination of a full scale existing wind energy farm and a mussel farm design. When analyzing the net environmental impact results of the multi-use offshore farm at the level of three areas of protection, i.e. human health, ecosystem quality and natural resources, it shows that the mussel farm contributes relatively the most to the net impacts, while the majority of the avoided burdens are attributed to the wind farm. Mainly the supply chain of materials required to manufacture its components followed by the operational activities of the multi-use offshore farm contribute to the environmental footprint. Moreover, taking advantage of joint activities, i.e. combined transport between the wind and mussel farm during operational activities (Scenario 1) and at decommissioning phase (Scenario 2) did not show a significant reduction in the overall net impacts of a multi-use farm. The life cycle assessment results of a multi-use offshore farm are furthermore compared with relevant terrestrial benchmarks in Belgium, i.e. nuclear energy and pork meat production. While the benchmarks have a high burden on the area of protection ecosystem quality due water and land use requirements, the multi-use farm mainly impacts the remaining areas of protection, i.e. human health and natural resources, again as a consequence of the burdens of its supply chain. This study reveals the potential of offshore multi-use farms in terms of environmental sustainability, offering valuable insights to policy-makers and value chain actors, and generally contributes to well-informed decision-making.

Hybrid wind-solar energy potential modeling using ERA5 and solar irradiation data in google Earth Engine

Renewable energies play a significant role in mitigating the climate change impacts. Wind and solar energy are projected to supply larger amount to the total renewable energy capacity of many nations. Long-term wind and solar data availability and analysis is vital for sustainable renewable energy farming. In this research, we analyze wind speed u- and v-components at 10 m height of the ERA5 daily reanalysis aggregates at a mesh of 31 Km during a long period (2010–2020) over the Red Sea and the Gulf of Suez coasts of Egypt. Google Earth Engine (GEE) code is developed and used for the analysis. The prime objective is to derive estimates on the spatio-temporal variability of speed, direction, and the wind energy estimates in the area. Further, the solar energy potential has been evaluated using reference data of the PVOUT – Photovoltaic power potential (kWh/m2/day) data, at nearly 1 km resolution, which is normalized at the evaluated sites, and then used to rank sites with their solar energy potential. Evaluation of estimates is carried out at selected thirteen sites distributed along the coasts to better understand the hybrid wind-solar energy potential. Frequency analysis is carried out to the wind speed spatial estimates to demarcate highest frequency wind events potential for wind farming. Reference in-situ wind speed observations available at daily and monthly time scales for Hurghada weather station is used for investigating the reanalysis data performance. The Hurghada site had the highest average wind speed (6.7 m s−1), frequency of events (75 %), and mean daily (60.4 kWh) and annual (22.02 MWh) wind energy. The windiest time intervals were the September month, 2017 year, and the summer season. The north had wind energy directions of 130°, while the south had 110°. Trend analysis showed declining trends for northern locations and rising yearly wind speed estimates for Berenice (0.005) and Halayeb (0.002). El-Tor location had the highest solar energy output, with a daily mean of 5.52 kWh/m2 and an annual mean of almost 2015 kWh/m2. The Hurghada, Jemsa, G. Zeit, and El-Tor sites achieved the highest rankings in terms of hybrid wind-solar energy potential. The results can be very valuable for optimal location of hybrid solar-wind energy facilities and provide viable and promising solutions for sustainably meeting Egypt's growing clean energy demands.

Energy Audit and Alternative Energy Source for Sustainable Processing of Cassava Products in Oyo State, Nigeria

Recently, the increase in electrical energy tariff in Nigeria coupled with the epileptic electricity supply has led to the rise in production cost and price of cassava products. In this study, an audit of the electrical energy used in three cassava processing industries (Niji farm, ATMANCorp Nigeria, and Psaltry International Limited) in Oyo state, Nigeria was carried out and biofuels as an alternative energy produced from cassava processing waste to enhance the sustainability of cassava processing are presented. The audit investigates the cost of energy consumption in producing different cassava products, ranging from garri, cassava flour, and cassava starch. The computation and analysis of energy use were carried out using the spreadsheet on Microsoft Excel. The results showed that the observed monthly energy consumed with the cost of the energy for Niji farm, ATMAN Corp Nigeria, and Psaltry International Limited were 45002.16 kW/h (NGN1,330,560), 65581.92 kW/h (NGN1,668,535), and 923774.40 kW/h (NGN27,501,120) respectively. The analysis also observed that bioenergy such as bioethanol, biodiesel, biomass, or biogas produced biochemically from cassava processing by-products can be used as a suitable biofuel to run types of machinery in cassava processing. This study proffers a solution to the high cost of energy used in cassava processing industries thereby reducing the production cost resulting in lower prices of cassava products in the market.

The viability of photovoltaics on agricultural land: Can PV solve the food vs fuel debate?

Policies aiding biofuels have supported farm income and rural communities but have also put pressure on food security with questionable benefits related to carbon emissions. Photovoltaics (PV) are poised to become central to the overall energy decarbonization strategy, but because of land requirements they are likely to be developed on farmland, reigniting concerns related to food security. In this work, we study strategies for co-producing food and energy from corn croplands. We find that while traditional PV displaces crops, they can harvest orders of magnitude more energy per unit of land than biofuels. Additionally, systems with elevated PV panels (called PV Aglectric, Agrivoltaics, or Agrophotovoltaics) that allow for crop production underneath them can increase energy production and reduce carbon emissions with minimal impact on crop production. This technology can ease the trade-off between farm income, energy production, crop production, and energy decarbonization. Adoption of PV Aglectric systems may be hindered by high capital costs, but this barrier could be overcome with policy support, especially when crop prices are highly volatile.

A Robust, resilience and risk-aware solar energy farm location by Bi-Level programming approach

A Robust, resilience and risk-aware solar energy farm location by Bi-Level programming approach

Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture

Today, optimization is crucial to solving energy crises, especially in smart homes. However, the optimization-based methods for energy management in smart agriculture available globally need further improvement, which motivates this study. To resolve the problem, an efficient scheduling farm energy management system is required. Therefore, this study proposes a Farm Energy Management System (FEMS) for smart agriculture by adopting a honey-badger optimization algorithm. In the proposed system, a multi-objective optimization problem is formulated to find the best solutions for achieving the set of objectives, such as electricity cost, load minimization and peak-to-average ratio minimization, while considering the farmers' comfort. The proposed system considers commercialized agriculture with the integration of Renewable Energy Resources (RES). Also, the proposed system minimizes both load consumption and electricity costs via the scheduling of farm appliances in response to Real-Time Pricing (RTP) and Time-of-Use (ToU) pricing schemes in the electricity market. Extensive experiments are carried out in MATLAB 2018A to determine the efficacy of the proposed system. The proposed FEMS consists of sixteen farm appliances with their respective power ratings, inclusive of RES. The simulation results showed that a system without FEMS has a high electricity cost of 50.69% as compared to 43.04% for FEMS without RES and 6.27% for FEMS with RES when considering the ToU market price. For RTP market price, a system without FEMS has an electricity cost of 42.30%, as compared to 30.64% for FEMS without RES and 27.24% for FEMS with RES. Besides, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 151.40 kW for FEMS without RES and 18.85 kW for FEMS with RES when considering the ToU market price. Also, for the RTP market price, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 186.40 kW for FEMS without RES and 90.68 kW for FEMS with RES. The significance of the study is to propose a conceptualized FEMS based on the honey badger optimization algorithm. The proposed system provides scheduling of farm appliances that alleviates the burden of the electricity grid and is cost-effective for large and small-scale farmers.

Wave power calculation of a large periodic array of bottom-hinged paddle wave energy converters using Floquet–Bloch theory

In this study we consider a wave energy farm consisting of a large number of bottom-hinged paddles. The paddles are arranged in a doubly-periodic manner, with a finite number of identical rows and each row containing an infinite number of paddles. Each paddle is attached to its own damper and spring, allowing power to be generated through its pitching motion. Unlike previous studies into paddle arrays, we envisage compact arrays consisting of small devices arranged across a large number of rows. The mathematical analysis of this proposed configuration is best suited to methods that exploit the periodicity of the array. It is shown that the velocity potential everywhere within the array can be described by an expansion in terms of suitably-defined Floquet–Bloch eigenmodes and this allows the solution to the scattering problem involving waves incident upon the array to be determined by a simple low-order system of equations. The method used in this study is exact within the setting of linearised water waves and has all of the advantages of classical low-frequency multi-scale homogenisation without the low-frequency restriction. It may also be regarded as a natural extension of the well-known wide-spacing approximation without the large separation restriction. Although the focus of this paper is on the mathematical approach to the solution of the problem, we provide a range of results to illustrate the performance of this proposed wave energy converter concept.

A 3D Numerical Model to Estimate Lightning Types for PyroCb Thundercloud

Pyrocumulonimbus (pyroCb) thunderclouds, produced from extreme bushfires, can initiate frequent cloud-to-ground (CG) lightning strikes containing extended continuing currents. This, in turn, can ignite new spot fires and inflict massive harm on the environment and infrastructures. This study presents a 3D numerical thundercloud model for estimating the lightning of different types and its striking zone for the conceptual tripole thundercloud structure which is theorized to produce the lightning phenomenon in pyroCb storms. More emphasis is given to the lower positive charge layer, and the impacts of strong wind shear are also explored to thoroughly examine various electrical parameters including the longitudinal electric field, electric potential, and surface charge density. The simulation outcomes on pyroCb thunderclouds with a tripole structure confirm the presence of negative longitudinal electric field initiation at the cloud’s lower region. This initiation is accompanied by enhancing the lower positive charge region, resulting in an overall positive electric potential increase. Consequently, negative surface charge density appears underneath the pyroCb thundercloud which has the potential to induce positive (+CG) lightning flashes. With wind shear extension of upper charge layers in pyroCb, the lightning initiation potential becomes negative to reduce the absolute field value and would generate negative (−CG) lightning flashes. A subsequent parametric study is carried out considering a positive correlation between aerosol concentration and charge density to investigate the sensitivity of pyroCb electrification under the influence of high aerosol conditions. The suggested model would establish the basis for identifying the potential area impacted by lightning and could also be expanded to analyze the dangerous conditions that may arise in wind energy farms or power substations in times of severe pyroCb events.