Agricultural Energy Research Articles

Differences using measured and calculated solar radiation in order to estimate the temperature of the conductor in overhead lines

In the last 20 years, installed wind power capacity in North of Spain has grown from 24 to more than 1,650 MW. In this geographical region, wind energy farms are located in places that are far away from the transmission networks so they have to be integrated into distribution networks. Build new overhead lines dedicated for a distribution energy is not the best way to increase the evacuation energy of the wind farms because the cost is quite strong. So, the aim to solve these issues, is by means of an increase in the capacity of existing lines using the dynamic management of the network. This paper is devoted to show the difference of the conductor temperature between the parameter of solar radiation measured by a pyranometer and the use of the theoretical solar radiation which is explained in CIGRE TB601 [1] and IEEE Std. 738-2012 [2] algorithms.

Site Selection for Wind Energy as an Alternative Source of Energy in Bonny, Nigeria

Power derived via the wind stands out as an appealing form of renewable energy due to its minimal operational, maintenance, and production expenses, coupled with its limited environmental footprint. This investigation focuses on employing geospatial methods to establish a wind farm on Bonny Island, Nigeria. The primary objectives include furnishing data and a spatial wind distribution map for Bonny Island, evaluating the significance of factors crucial for wind farm development in the area, and generating a wind energy suitability map. The study utilizes Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) methodologies to scrutinize five critical parameters influencing location suitability. The findings indicate that Bonny Island possesses potential for wind farm installation, with 3,549.8 hectares, 10,219.6 hectares, and 424.6 hectares categorized as highly suitable, suitable, and unsuitable, respectively. Moreover, wind speed, land use/land cover, distance from the road, distance from the river, and land slope each carry a substantial priority weight of 50%, 25%, 10%, 10%, and 5%, respectively. These weights contribute to the creation of a wind energy suitability map for the study area. This research recommends amongst other things the investment and installation of a wind energy farm in Bonny Island, owing to the comparative advantage over other sources of energy in Nigeria.

Economic viability of floating wave power farms considering the energy generated in the near future

This article aims to analyse the economic viability of floating wave energy farms for the present and the next twenty years. The energy potential of the waves mainly depends on the climate, so the current and near future analysis is crucial to determine the economic viability of wave energy farms in a particular location. Current and near future wave resources were considered to assess the main parameters (Net Present Value (NPV), Internal Rate of Return (IRR) and Levelized Cost of Energy (LCOE)) that allow to know the economic feasibility of wave energy farms. This study takes one step forward in determining the economic evaluation of wave energy farms located in deep waters using their future energy projections. The case of study in this paper is the Atlantic coast of the Iberian Peninsula. Results indicate that the future wave energy reduction principally affects the NPV and LCOE of the wave farm.

Performance Evaluation of Thailand’s 8 MW Wind Farm Feeder Trip, Energy Generation, and Loss Using 5 MWh BESS—A Statistical and Economic Approach

Performance Evaluation of Thailand’s 8 MW Wind Farm Feeder Trip, Energy Generation, and Loss Using 5 MWh BESS—A Statistical and Economic Approach

Techno-economic assessment of offshore wind and hybrid wind–wave farms with energy storage systems

Ocean renewables (such as offshore wind and wave) are abundant and essential energy resources for supporting future emission-free targets. However, their energy intermittency and high cost have hindered commercialization and wide-scale implementations of these ocean energy technologies. This paper focuses on both issues and aims to increase the dispatchability of ocean energy farms by investigating the potential of a hybrid wind and wave energy platform with energy storage systems (ESS). This research introduces an innovative method to assess the necessary ESS capacity for offshore renewable energy farms, ensuring energy dispatchability to the local demand. In addition, the key criteria and the economic feasibility of deploying ESS in offshore energy farms are also discussed. A life-cycle cost model is presented that quantitatively compares different farm configurations, specifically a standalone offshore wind farm and a hybrid configuration incorporating both wind and wave generation systems. The results indicate that the combined wind and wave energy farm significantly reduces the ESS requirement and provides competitive lifecycle costs compared to the stand-alone wind energy farm, though the amount of these benefits vary on the local resource characteristics. The quantitative assessment presented in this study also provides realistic and practical justifications for combined energy farms that complement statistical conclusions derived from variability indexes in previous studies.

Machine learning-based diagnosis in wave power plants for cost reduction using real measured experimental data: Mutriku Wave Power Plant

In comparison to wind farms, the relative scarcity of actual operational data from wave power plants has contributed to a significant research gap in the areas of wave farm forecasting and cost reduction. In this context, this manuscript presents a new Machine Learning-based Power Take-Off (PTO) diagnosis for wave energy generation farms which has the potential to serve as an extensive reference for other wave energy farms and offer substantial benefits to both investors and policymakers involved in the advancement of the emerging wave technologies. The suggested method has been employed at the Mutriku Wave Power Plant (WWP) to facilitate the implementation of predictive maintenance strategies and reduce the Levelized Cost of Energy (LCoE). Hence, the research study considers two main extraction methods, namely, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), used to select the most relevant features for OWC diagnosis. In addition, two classification methods have been considered: Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN). The obtained data show that, although both methods allow to achieve an effective performance with an excellent degree of accuracy, the ANN-based method presents better results with 98% accuracy against 81% for the SVM when using PCA extraction method. Then, the developed classification-based OWC diagnosis has been used for the development of a predictive maintenance strategy at the Mutriku WPP, analyzing its impact on the economic indicators. The results indicate that, using the proposed predictive maintenance strategy, the OpEx may be decreased down to 17%, downtime may be decreased down to 55% and plant availability may be better up to 95%, leading to a 5% LCoE reduction.

Cost-Effective Optimization of an Array of Wave Energy Converters in Front of a Vertical Seawall

The present paper focuses on investigating the cost-effective configuration of an array of wave energy converters (WECs) composed of vertical cylinders situated in front of a vertical seawall in irregular waves. First, the hydrodynamic calculations are performed using a WAMIT commercial code based on linear potential theory, where the influence of the vertical wall is incorporated using the method of image. The viscous damping experienced by the oscillating cylinder is considered through CFD simulations of a free decay test. A variety of parameters, including WEC diameter, number of WECs, and the spacing between them, are considered to determine an economically efficient WEC configuration. The design of the WEC configuration is aided by a cost indicator, defined as the ratio of the total submerged volume of the WEC to overall power capture. The cost-effective configuration of WECs is achieved when WECs are positioned in front of a vertical wall and the distance between them is kept short. It can be explained that the trapped waves formed between adjacent WECs as well as the standing waves in front of a seawall significantly intensify wave fields around WECs and consequently amplify the heave motion of each WEC. A cost-effective design strategy of WEC deployment enhances the wave energy greatly and, consequently, contributes to constructing the wave energy farm.

An advanced geospatial assessment of the Levelised cost of energy (LCOE) for wave farms in Irish and western UK waters

With the climate crisis becoming an ever-growing concern, Europe is striving to reduce its energy related greenhouse gas emissions. On the western edge of this continent, wave energy is a logical future clean energy consideration for Ireland and the U.K. to help realise ambitious targets recently being set in policy. Yet to reach commercialisation, there is still much uncertainty surrounding the financial feasibility of wave farms in the region of interest, and where exactly the most feasible locations are likely to be. The Levelised Cost of Energy (LCOE) is the most commonly used metric in determining the financial feasibility of an energy project, and it is very much site dependant when it comes to wave energy, varying considerably from one location to the next. This study uses the geographic approach to address this and estimate the LCOE of wave energy farms in the region, considering different technology types and the geospatially variable inputs at play. The results reveal areas of high project feasibility in the Atlantic Ocean (off the west coast of Ireland), the Celtic Sea and the Inner Seas off the West Coast of Scotland (ISWCS), with LCOE values below 110€/MWh along the coasts of these areas.

Sizing and scheduling optimisation method for off-grid battery photovoltaic irrigation networks

An algorithm to optimise the number of solar panels and battery size to meet the water demands of an installation has been developed. The algorithm adjusts for seasonal changes in energy use and production in a pressurized irrigation network and production in an off-grid solar panel system. By using this algorithm, we aim to create an efficient and sustainable irrigation system by reducing the infrastructure and reliance on the power grid. This method can enhance irrigation systems in far-off regions, strengthen their endurance, and promote sustainable energy in farming and water administration.

A systematic methodology to assess local economic impacts of ocean renewable energy projects: Application to a tidal energy farm

Ocean renewable energy (ORE) is one of the most important clean sources of energy and a major player towards the EU ambitions of being net zero emission by 2050. However, at present, there are few examples of commercially viable ORE technologies and no large-scale projects currently under implementation. Together with social and environmental analyses, the assessment of economic impacts is one of the key elements to help policy makers build a compelling case to gain local community acceptance and implement ORE projects. This paper presents a systematic methodology to assess local economic impacts of renewable energy projects, including jobs creation and impacts on gross value added and income. By combining the use of Location Quotients – which are indexes informing on local industrial specialisation – with the Input-Output multipliers the method can be used to map the supply-chain potential of a local economy and estimate local impacts compared to global ones. The method has been applied to a tidal project carried out in Orkney, Scotland. The research demonstrates the merit of early economic assessments for understanding the economic benefit of ORE projects, particularly for the local communities in which they are located, and it provides a methodological framework to be tested in other case studies.

Feasibility Study of Offshore Wind Wheel Installation in South China Sea at Malaysia

As the global demand for renewable energy continues to rise, it becomes crucial to investigate the viability of offshore wind power generation. This paper presents a comprehensive feasibility study assessing the viability of the South China Sea wind turbine installation. This study will describe the current situation of wind energy across the world and specifically discusses offshore wind farm technology. The study begins with a comprehensive literature review emphasizing the significance of wind power in mitigating climate change and reducing reliance on fossil fuels. The objectives of this are to study on the availability of offshore wind resources in Malaysia and to analyze the effect of micro-siting towards wind farm energy production and performance. This study indicates the best sites to set up offshore wind farm in Malaysia. According to the data, Mersing has a favorable availability of wind resources, which makes it the best place to put the turbines. Critical review for better understanding about the importance of micro-siting of wind farms social and economic was conducted. The micro-siting technique is helpful since it provides a methodical and well-informed way of choosing a location. It helps decision-makers strike a balance between the project's, the community's, and the environment's demands by considering various elements and tailoring the analysis to the particulars of a certain project. As technology and methodology evolve, the micro-siting approach will become more important in attaining sustainable and efficient development.

Machine learning for monitoring and classification in inverters from solar photovoltaic energy plants

The efficiency of solar energy farms requires detailed analytics and information on each inverter regarding voltage, current, temperature, and power. Monitoring inverters from a solar energy farm was shown to minimize the cost of maintenance, increase production and help optimize the performance of the inverters under various conditions. Machine learning algorithms are techniques to analyze data, classify and predict variables according to historic values and combination of different variables. The 140 kWp photovoltaic plant contains 300 modules of 255 W and 294 modules of 250 W with smart monitoring devices. In total the inverters are of type SMA Tripower of 25 kW and 10 kW. The 590 kWp photovoltaic plant contains 1312 Trina solar 450 W modules. In total the four inverters are SMA Sunny Tripower type of 110–60 CORE 2 with rated power of 440 kW were analyzed and several supervised learning algorithms were applied, and the accuracy was determined. The facility enables networked data and a machine learning algorithm for fault classification and monitoring was developed, energy efficiency was calculated and solutions to increase energy production and monitoring were developed for better reliability of components according to the monitorization and optimization of inverters.

Analyzing the Impact of Rainfall Patterns on Agriculture, Economy and Tourism in India: A Statistical Approach

The current study examines rainfall trends in India, encompassing its effect on various economic aspects and forecasting for 2023-2030. The Mann-Kendall test and Sen’s slope estimator are utilized to analyze annual and seasonal rainfall patterns. Results reveal a pronounced winter decline (2001-2022) alongside significant pre- monsoon, monsoon, and post-monsoon increases. Annual rainfall consistently de- creases, contrasting with rising pre-monsoon, monsoon, and post-monsoon trends. Annual rainfall exhibits the steepest decline (-1.0891 mm/year), while the monsoon season displays the highest increase (3.2538 mm/year). Further, the present study explores relationships between rainfall and economic growth, tourism, and agriculture. A statistically insignificant yet positive correlation is found between annual rainfall and per-capita GDP, indicating other economic drivers. Tourism shows a weak, statistically insignificant link with annual rainfall. In contrast, a robust statistically significant correlation emerges between annual rainfall and food- grain production, highlighting its role in agriculture. Finally, the present research forecasts annual rainfall (2023-2030) using the ARIMA (1,0,0) model, predicting a continued decline. This has profound implications for water resources, agriculture, and the economy, necessitating proactive measures such as water conservation, drought-resistant farming, and alternative energy investments.

Tidal turbine array modelling using goal-oriented mesh adaptation

To examine the accuracy and sensitivity of tidal array performance assessment by numerical techniques applying goal-oriented mesh adaptation. The goal-oriented framework is designed to give rise to adaptive meshes upon which a given diagnostic quantity of interest (QoI) can be accurately captured, whilst maintaining a low overall computational cost. We seek to improve the accuracy of the discontinuous Galerkin method applied to a depth-averaged shallow water model of a tidal energy farm, where turbines are represented using a drag parametrisation and the energy output is specified as the QoI. Two goal-oriented adaptation strategies are considered, which give rise to meshes with isotropic and anisotropic elements. We present both fixed mesh and goal-oriented adaptive mesh simulations for an established test case involving an idealised tidal turbine array positioned in a channel. With both the fixed meshes and the goal-oriented methodologies, we reproduce results from the literature which demonstrate how a staggered array configuration extracts more energy than an aligned array. We also make detailed qualitative and quantitative comparisons between the fixed mesh and adaptive outputs. The proposed goal-oriented mesh adaptation strategies are validated for the purposes of tidal energy resource assessment. Using only a tenth of the number of degrees of freedom as a high-resolution fixed mesh benchmark and lower overall runtime, they are shown to enable energy output differences smaller than 2% for a tidal array test case with aligned rows of turbines and less than 10% for a staggered array configuration.

Scattering of water waves by multiple rows of vertical thin barriers

The reflection and transmission of waves by a periodic array of thin fixed identical vertical barriers extending uniformly through the depth of the fluid is considered. The water wave problem, which also has analogues in acoustics and electromagnetics, involves scattering by a large finite number of equally spaced rows, each consisting of an array of barriers with a linear periodic arrangement extending to infinity. The main purpose of the work is to compare the results of two approximate methods of solution based on homogenisation techniques with exact results allowing us to determine when the results can be expected to be in good agreement. When no approximation is made, a method of solution (Discrete Model) describing the propagation of incident waves through the exact barrier arrangement is derived using Floquet–Bloch eigensolutions for the corresponding infinite periodic array. It is shown how the solution can be reduced to a simple pair of integral equations which results only from matching the region containing barrier arrays to the two exterior domains. Numerical solutions are, nevertheless, relatively complicated and two approximate models are developed which considerably simplify the numerical effort required. In one approximation (Continuum Model I) it is assumed only that the spacing between two adjacent rows of barriers is small and is complementary to the commonly-used wide-spacing approximation. The complication of the geometry is removed by replacing the governing equations and boundary conditions with an effective continuous medium and a simplified pair of integral equations now govern the solutions. In a second approximation (Continuum Model II), a low-frequency homogenisation approach based on a long-wave assumption is invoked and matching now leads to closed-form expressions for the reflection and transmission coefficients. Results concentrate on the comparison between different methods which allows us to assess the validity of each continuum model. This understanding will be important in developing approximate models that describe the operation of wave energy farms consisting of large arrays of oscillating flaps designed to absorb energy.

Life cycle cost analysis of a floating wind farm in the Norwegian Sea

The offshore wind energy industry has witnessed rapid growth in the past decade. Still, there is a lack of commercial floating wind projects due to the relatively high development costs and other factors. To facilitate the holistic evaluation of floating wind farms in Norway, this article investigates the levelized cost of energy of a floating offshore wind farm and its economic feasibility. The Troll field west of Bergen, Norway, is assumed to be the target offshore site, and a farm size of 50 wind turbines with a lifespan of 25 years are considered. Each floating wind turbine has a 15-megawatt turbine mounted on a semi-submersible floater. Based on detailed analysis, the levelized cost of energy of the wind farm is estimated to be approximately 100.7 $/MWh. The capital expenditure is the most prominent cost and constitutes 63.1% of the total cost, and the operational expenditure constitutes the remaining 36.9%. Further, sensitivity analyses show the influence of the lifespan, capacity factor, and project discount rate on the levelized cost of energy. The present study contributes to techno-economic evaluation of floating wind projects at an early phase.

신재생 에너지 최적 활용을 위한 축열조 온도 예측 모델 연구

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

Consistent mapping of marine structures with an autonomous surface vehicle using motion compensation and submap-based filtering

Inspection and maintenance of marine energy farms are becoming increasingly important as more farms are developed and constructed. In this study, structural monitoring system using an autonomous surface vehicle (ASV) has been proposed to enable the inspection of an offshore power plant under harsh ocean disturbances. In particular, the ASV uses guidance and control laws to minimize the deviation from the desired path in waypoint tracking. The ASV was also equipped with multi-modal sensors – sonar and light detection and ranging (LiDAR) – for mapping underwater and surface structures. By utilizing the navigation sensors, the motion of the ASV in ocean disturbances was accurately estimated and incorporated into the mapping process to produce consistent multi-modal maps. Furthermore, dedicated outlier removal methods were proposed to improve the performance over conventional approaches in real offshore data. We considered the sparsity of the sonar and LiDAR data in their respective modality during acquisition and designed novel submap-based filtering to remove the outliers. To validate the proposed system, a field test was conducted on an offshore wave farm structure. It was verified that the ASV can cope with ocean disturbances in its waypoint tracking, and the resulting multi-modal maps are consistent and outlier-free.

Carbon footprint and energy life cycle assessment of wind energy industry in Libya

The recent investigation has demonstrated that wind energy holds great potential as a viable and environmentally friendly energy source in Libya. The study employed a Life Cycle Assessment (LCA) methodology to evaluate various energy, economic, and environmental indicators for potential wind farm installations at multiple suitable locations across Libya. The assessment encompassed estimations of energy requirements and greenhouse gas (GHG) emissions associated with the conversion of wind energy into electricity throughout the entire life cycle of the proposed wind farms. In light of Libya not being a producer of wind energy converters, a novel approach was developed to define the system's boundaries. These boundaries included distinct subsystems, each corresponding to various stages within the life cycle of a wind energy system, encompassing factors such as shipping emissions from the manufacturer's location to Tripoli's marine ports, land transportation to the wind farm sites, energy and emissions associated with installation, operation, maintenance, and eventual disposal of wind turbines.Hourly climate data spanning a 25-year period from 1995 to 2020 were gathered from the SolarGis climate information site. The System Advisor Model (SAM) program was utilized to predict the energy yields of 100 MW capacity wind farms at 12 sites in Libya. Additionally, a novel eco-environmental indicator known as the Life Cycle Levelized Cost of Energy (LCLCOE) was introduced, which factors in all environmental damage costs throughout the entire lifespan of wind energy projects. The study's findings revealed that the Gamesa turbine, with a capital cost of $146,916,400 for a 100 MW capacity wind energy farm, exhibited the most favorable economic and environmental performance. GHG emission factors across all examined cities ranged from 32 to 70 gGHG/kWh, with carbon payback durations spanning from 4.5 to 12.3 months. The estimated energy payback period varied from 13 to 22 months, while the LCLCOE ranged from 4.8 to 8.4 ¢/kWh.

An economic and environmental optimization model for sizing a hybrid renewable energy and battery storage system in off-grid farms

This paper presents a model for optimizing the life cycle economic and environmental impacts of a hybrid renewable energy and battery storage system – as energy supply technologies (EST) for off-grid farms. Micro-hydropower and photovoltaics represent the primary renewable energy (RE) sources, with batteries and a diesel generator considered as possible back-up systems. A modified Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and applied to a case study farm in Southern Spain and provided different EST solutions to optimize a design based on economic (life cycle costing or LCC) and environmental (life cycle assessment or LCA) constraints. The results highlighted that cases with a strong dependence on batteries could achieve minimum costs and lower impacts for climate change and energy resources (non-renewable fuels). In contrast, the optimal system for minimizing the consumption of material resources (metals/minerals) resulted in higher fuel use, which was up to 25% more expensive. The optimal EST solutions used up to 37% of the total energy generated; however, the results were sensitive to energy production-demand distributions. This methodology can help identify the most cost-effective solutions with the lower environmental impacts for decarbonizing the agricultural sector.