Farm Capacity Research Articles

Wind farm repowering to mitigate wake effect based on a novel optimization framework

Many old wind farms currently consist of outdated wind turbines with small-capacity and low-efficiency. Wind farm repowering (WFR) is an effective approach to revitalize the old wind farms by replacing the outdated turbines with advanced ones. This paper proposes a novel WFR optimization framework to maximize the annual energy production (AEP) of the repowered wind farms. In this framework, the wake effect is assessed by a three-dimensional Gaussian wake model and the equivalent inflow wind speed is calculated by a rotor discretization method based on a sunflower algorithm. Additionally, the WFR solutions are optimized using a discrete particle swarm optimization algorithm. In this study, a wind farm containing 25 2 MW turbines will be repowered by some 5 MW turbines. Two WFR optimization cases are investigated in this wind farm: maintaining the rated capacity of the wind farm and keeping a fixed number of the new turbines. The results of the first case show that the WFR optimization substantially increases the aerodynamic efficiency and AEP of the wind farm. The highest increment occurs when the wind farm is completely repowered by the 5 MW turbines. The second case provides an optimal number of turbines to replace, considering both the economic factors and aerodynamic efficiency. The fundamental physics of the wake effect is deeply analyzed in each case. This study demonstrates that the proposed framework can provide valuable guidance for the practical WFR.

The Need and Acceptability of Cassava Starch Residue as an Alternative Energy Source in Poultry Feeds: Farmers and Consumers Perspectives in the Ashanti Region of Ghana

This study explores the potential of cassava starch residue (CSR) as a sustainable alternative energy source in poultry feeds in the Ashanti Region of Ghana. With rising maize prices and supply challenges impacting poultry production, CSR offers a promising substitute. Employing a mixed-methods approach, data were collected from 150 respondents including 50 poultry farmers and 100 consumers using semi-structured questionnaires across selected districts. Descriptive statistics, chi-square tests and logistic regression were applied to assess CSR awareness, acceptance, and its perceived impact on profitability and feed efficiency. Results indicate that while overall awareness of CSR is low (22%), farmers with higher education and larger farm capacities show a greater propensity to adopt CSR, primarily due to its potential cost-effectiveness and sustainability benefits. The analysis further highlights that demographic factors such as age and educational background significantly influence perceptions regarding CSR. Notably, chi-square tests showed significant associations between education and CSR awareness (χ2 = 45.66, df= 2, p= 1.21e−10), cost-effectiveness (χ2 = 26.31, df= 6, p= 0.0002), and overall profitability (χ2 = 57.65, df = 6, p= 1.35e−10). Predicted probability models further indicated that farmers aged 26 and above and those with tertiary education had a higher likelihood of adopting CSR, while smaller-scale operations (50–500 birds) were less inclined. The study concludes that targeted educational and policy interventions are crucial to enhance CSR adoption, reduce reliance on maize, and improve the economic viability of poultry production in Ghana.

A Study on the Optimal Configuration of Offshore Substation Transformers

The growing scale of offshore wind farms and increasing transmission distances has driven the demand for optimized offshore substation (OSS) configurations. This study proposes a comprehensive techno-economic framework to minimize the total lifecycle cost (LCC) of an OSS by determining the optimal number of OSSs and transformers considering wind farm capacity and transmission distance. The methodology incorporates three cost models: capital expenditure (CAPEX), operational expenditure (OPEX), and expected energy not supplied (EENS). CAPEX considers transformer costs, topside structural mass effects, and nonlinear installation costs. OPEX accounts for substation maintenance and vessel operating expenses, and EENS is calculated using transformer failure probability models and redundancy configurations. The optimization is performed through scenario-based simulations and a net present value (NPV)-based comparative analysis to determine the cost-effective configurations. The quantitative analysis demonstrates that for small- to medium-scale wind farms (500–1000 MW), configurations using 1–2 substations and 3–4 transformers achieve minimal LCC regardless of the transmission distance. In contrast, large-scale wind farms (≥1500 MW) require additional substations to mitigate transmission losses and disruption risks, particularly over long distances. These results demonstrate that OSS design should holistically balance initial investment costs, operational reliability, and supply security, providing practical insights for cost-effective planning of next-generation offshore wind projects.

Community Urban Farming: Challenges In Maximising Urban Farm Capacity

Community urban farming has significant potential to improve food security, promote environmental sustainability and empower local communities. However, challenges remain when it comes to maximising the capacity of urban farms to make farming practices economically viable and sustainable. In this study, these challenges were explored through in-depth interviews with urban farmers in Malaysia through several case studies. The thematic analysis identifies five key challenges of urban farming namely financial, human capital, environmental, institutional and psychosocial challenges, each with specific details. Identifying these specific details provide greater policy insights. A targeted support system to address these issues can actively promote urban farming initiatives that benefit urban farmers, improve urban farming (UF) practices and increase urban food production.

Wind farm capacity factor forecasting: An Australian case study

Wind farm capacity factor forecasting: An Australian case study

Improving The Sustainability of Silage Adoption in Dairy Cattle Farming: Analysis of Determining Factors and Strategies for Increasing Farm Capacity

One of the challenges of dairy farming is the availability of feed to meet the needs of livestock. This study aims to 1) identify the potential and problems of utilizing local feed, as well as the potential for implementing silage development; 2) analyze factors that influence the adoption of locally sourced feed innovations; 3) develop strategies for sustainable adoption and increasing the capacity of dairy farmers. This study used a participatory approach involving 47 farmers as a two-year action study (2023–2024). Data collection methods include surveys, in-depth interviews, and focus group discussions (FGDs). Data was analyzed using Structural Equation Modeling-Partial Least Square (SEM-PLS) to identify factors that influence the adoption of silage innovations. The results of the study showed that the adoption of silage technology was influenced by the characteristics of farmers, the role of extension workers, and the nature of the innovation. Although the potential for local feed is abundant, its utilization is still low due to limited knowledge of farmers and processing infrastructure. The structural model test showed a significant relationship between silage adoption and increased milk productivity (coefficient 0.508; p = 0.008). This study recommends strengthening the institution of livestock farmers through the People's Livestock School (SPR), continuous training, and integration of crop-livestock systems (SITT) to optimize local resources. The proposed design model includes silage-based feed diversification, utilization of agricultural waste, and partnerships with agribusiness actors. This study provides a reference for sustainable agricultural policies and the development of technological innovations that are adaptive to the characteristics of smallholder farmers.

Offshore Wind Energy Potential

Montenegro, as a signatory to international agreements, is committed to reducing CO₂ emissions and achieving full decarbonization by 2050. To meet these environmental goals, the country must permanently shut down the coal-fired thermal power plant in Pljevlja. This study assesses the potential electricity generation capacity of an offshore wind farm in Montenegro using 15 MW wind turbines at a location identified in prior research. Two offshore wind farm technical capacity criteria are applied: one defined by the World Bank (WB), specifying a capacity of 3 MW/km², and another by the National Renewable Energy Laboratory (NREL) under the U.S. Department of Energy, specifying 5 MW/km². The study also examines two operational scenarios of Montenegro’s electricity system. Results show that a fixed-bottom offshore wind farm in an area of 88,438 km², with sea depths up to 60 meters, could generate 55,71% of the electricity produced by the Pljevlja plant based on WB criteria, or 92,86% based on NREL criteria. For depths over 60 meters, a floating offshore wind farm in 678,16 km² could generate 4,22 times the electricity output of the Pljevlja plant based on WB criteria, or 7,04 times its output based on NREL criteria.

Possibilities of using digital technologies in agriculture in areas with high agrarian fragmentation

The Małopolskie and Podkarpackie provinces in Poland are characterized by many small farms with many small, scattered fields. This farm structure is labeled “agrarian fragmentation”. Using digital technologies in such small farm areas is usually a challenge. However, there are several digital technologies that, with minimal financial investment, can yield results in the form of improved resource management and agricultural production processes, as well as data-driven decision-making. The overall objective of this analysis is to determine the limitations of using digital technologies in farms operating in areas with high agrarian fragmentation. In addition, the aim was also to identify the differences in the potential for implementing individual digital solutions depending on farm size and activity type conducted in the surveyed area. A survey was conducted by the Paper and Pen Personal Interview (PAPI) method, in which 389 farmers took part. Research showed that the technologies most commonly used in the study area include applications recognizing plant diseases and applications supporting decision-making. The use of advanced digital tools related to precision agriculture and the automation of crop production was very rare. Farm size, the age of the farmer managing the farm, and the number of farm activities were significant factors that increased the probability of implementing digital technologies. The main barriers to their implementation were a lack of sufficient knowledge and trust. The implementation of digital technologies in small farms requires actions aimed at increasing farmer knowledge. Meanwhile, designing new digital solutions must take the specific regional conditions into account, such as geographical factors or the limited investment capacity of farms.

Renewable Sources and Energy Storage Optimization to Minimize the Global Costs of Railways

Climate change is one of the biggest global issues for humanity these days, and its effect has become more severe. The transport sector accounts for around 30% of greenhouse gas emissions, which need to be decarbonized urgently. Railway electrification is one of the low-carbon solutions, but it still relies on power grids causing carbon emissions. To further decarbonize electric railways, the renewable energy sources (RESs) and energy storage system (ESS) integration scheme for railway traction power network has been proposed. This paper developed the energy management system to calculate the energy flow and global cost. Moreover, contact wire loss and conversion loss were considered. The optimization problem to find the optimal capacity and location of the PV farm, wind farm and energy storage system to achieve the lowest global daily costs was solved by the Brute Force Algorithm. The traction network of the High Speed 2 Railway in the UK has been taken as a case study. Results revealed that the global cost and carbon emissions are reduced considerably with both ESS and RESs installed. In the scenario of the ESS alone, 1.3% of the global cost is saved by capturing the regenerative energy and reusing it. Furthermore, this figure goes up to 10% and 62% when the PV and wind farms are integrated, respectively. When considering all variables, it is found that installing the wind farm is a more economical option than the PV farm. The study also shows that the optimal locations to install the plants and ESS vary by scenario.

The interannual variations of installed capacity for offshore wind turbines in China: estimations derived solely from remote sensing

ABSTRACT Accurately and thoroughly determining the installed capacity of offshore wind turbines (OWTs) and offshore wind farms (OWFs) is crucial for evaluating offshore wind energy and guiding future development. However, existing statistical data only provide aggregated information on capacity, and detailed attribute data are not publicly available for free. Here, we present a novel pure remote sensing method to estimate the OWT installed capacity, successfully applied to estimate the installed capacity of OWTs in China from 2015 to 2022. This approach first used deep learning to identify turbine shadows from Sentinel-2 images, then estimated the hub height corresponding to the shadows by combining the solar elevation angle, and finally related the height to the capacity through a polynomial model. The results demonstrate that the pure remote sensing method exhibits excellent performance in estimating the installed capacity of OWTs. Comparing the generated single OWT capacity with the officially published results, the root mean square error (RMSE) is 0.27 MW (5.06%). From the end of 2015 to 2022, the total installed capacity of OWTs in China’s mainland increased from 1.06 GW to 30.18 GW, with the highest annual growth rate reaching 149.92%. These remote sensing-based estimates closely match the data documented in the existing reports (R 2 = 0.99, RMSE = 0.62 GW). The average capacity per turbine increased from 4 MW to 4.84 MW, and the maximum capacity of OWFs increased from 632.89 MW in 2015 to 1305.04 MW in 2022 (geographically). By the end of 2022, OWFs with an installed capacity exceeding 100 MW accounted for 90.83% of the total number of OWFs in China’s mainland, indicating a trend toward larger-scale development of OWFs. This study provides a reference for large-scale assessments of OWT installed capacity. Additionally, it can be used for the construction of high-capacity OWFs to design future installations.

Numerical investigation of regenerative wind farms featuring enhanced vertical energy entrainment

Abstract. Numerical simulations of wind farms consisting of innovative wind energy harvesting systems are conducted. The novel wind harvesting system is designed to generate strong lift (vertical force) with lifting devices. It is demonstrated that the trailing vortices generated by these lifting devices can substantially enhance wake recovery rates by altering the vertical entrainment process. Specifically, the wake recovery of the novel systems is based on vertical advection processes instead of turbulent mixing. Additionally, the novel wind energy harvesting systems are hypothesized to be feasible without requiring significant technological advancements, as they could be implemented as multi-rotor systems with lifting devices (MRSLs), where the lifting devices consist of large airfoil structures. Wind farms with these novel wind harvesting systems, namely MRSLs, are termed regenerative wind farms, inspired by the concept that the upstream MRSLs actively entrain energy for the downstream ones. With the concept of regenerative wind farming, much higher wind farm capacity factors are anticipated. Specifically, the simulation results indicate that wind farm efficiencies can be nearly doubled by replacing traditional wind turbines with MRSLs under the tested conditions, and this disruptive advancement can potentially lead to a profound reduction in the cost of future renewable energy.

Optimizing turbine location in upgraded wind farm using grasshopper optimization algorithm

This research explores the use of the grasshopper optimization algorithm (GOA) for optimizing the placement of additional turbines in an established wind farm. The primary objective is to increase the annual energy production (AEP) of the wind farm while minimizing the wake effects caused by both existing and new turbines. The research evaluates three different turbine types (1.5 MW, 2.0 MW, and 2.5 MW) to identify the most appropriate choice for increasing the wind farm's capacity. The GOA’s performance is compared with the commercial software windPRO and validated using WAsP software for energy calculations. Numerical results indicate that the GOA effectively improves wind farm layout, with the 1.5 MW turbines identified as the optimal choice for maximizing AEP and minimizing wake interactions. This study provides practical insights for wind farm operators and contributes to the development of advanced optimization techniques in wind energy.

Барщина в помещичьих имениях Южно-Уральского региона России в первой половине XIX века

The study dwells on the barshchina (corvée) in the landlord estates of the Southern Urals (in the first half of the 19th century, part of the Orenburg Province) in pre-reform Russia. It is shown that during the pre-reform period in the 19th century, the majority of landlord estates of the Southern Urals had corvée as the dominant form of obligations: over 85 % of the landlord peasants in the region performed this type of obligation for their masters. Based on archival materials from the Russian State Archives of Ancient Documents, Russian State Military Historical Archives, Russian State Historical Archives, Department of Written Sources of the State Historical Museum, as well as documents from volumes 2 and 3 of the Proceedings of the Drafting Commissions of 1860, the article analyses the reasons behind the dominant role of corvée in the landlord estates of the Southern Urals (relative abundance of land in the colonized region, tight market of free workers, economic considerations of landlords, economic capacity of peasant farms, etc.). The author determines the dynamics of the intensity of corvée during the first half of the 19th century. To characterize corvée, the working time of corvée peasants as well as the monetary value of their corvée labour were calculated, which made it possible to contrast corvée with the obrok (quitrent) that had been paid by the quitrent peasants in this area. It is noted that, compared to the landlord estates of other Russian regions, per capita corvée in the Southern Urals was generally relatively low due to the specifics of this predominantly agricultural region. It is emphasized that further research into the problem appears promising, since the potential for studying the institution of corvée labour at a regional level is far from being exhausted.

Livestock as a biological pest control: Experimental validation for oak savannas

Abstract Ungulates consume plants and seeds together with the insects that feed on them, which have consequences at the ecosystem level. We carried out an experiment to assess the effects of intraguild predation by livestock on the main acorn pest (Curculio spp. weevils) in Iberian oak Quercus spp. dehesas, a widespread traditional agroecosystem. Acorns are a key food source for free‐range livestock (especially pigs), but, if properly managed, livestock could become a pest control agent. In three dehesa farms, we replicated eight experimental trios, each including one tree from which livestock was excluded, another from which all dropped acorns were removed manually (simulating intensified predation by livestock), and a control one in which livestock was allowed at standard densities. Removal of marked infested acorns by livestock, adult and larval weevil numbers, and acorn production/infestation rates were recorded at all trees. Livestock predated most weevil larvae within the prematurely dropped infested acorns before larvae had time to finish development. Hence, in those trees subjected to intensified predation, the local number of larvae decreased. Consequently, adult weevil abundance in the following year was lower than in trees within the exclosures of livestock. The consequence of the decrease in the number of adults was a reduction in the rates of acorn infestation in control oaks and intensified predation compared to those excluded from livestock (8% and 20%, respectively). C. elephas mobility is low, especially when trees are isolated, and acorn infestation is greatly dependent on the number of weevils emerging beneath the canopy of each oak. Therefore, a locally focused elimination of infested acorns may succeed in reducing the negative impacts of the pest. Synthesis and applications: We suggest intensifying livestock predation on prematurely dropped‐infested acorns by allowing livestock foraging from October 1st onward. Pigs are usually released free range in early November, once weevil larvae have completed their development and escaped predation. The proposed management would increase the availability of healthy acorns, thus increasing the farm capacity and the economic profit by up to 20%, tens of millions of euros of additional profits when translated into the prices of the Iberian pork market.

DHGAR: Multi-Variable-Driven Wind Power Prediction Model Based on Dynamic Heterogeneous Graph Attention Recurrent Network

Accurate and stable wind power prediction is essential for effective wind farm capacity management and grid dispatching. Wind power generation is influenced not only by historical data, but also by turbine conditions and external environmental factors, such as weather. Although deep learning has made significant progress in the field of wind power forecasting, it often fails to account for two key characteristics of the data: dynamic variability and heterogeneity. Specifically, the influence of external variables on wind power changes over time, and due to the diverse nature of the information carried by different variables, simple weighted fusion approaches are insufficient to fully integrate heterogeneous data. To address these challenges, this paper introduces a dynamic heterogeneous graph attention recurrent network (DHGAR), which incorporates dynamic graphs, heterogeneous graph attention mechanisms, and gated recurrent units. Dynamic graphs capture real-time associations between wind power and external variables, while heterogeneous graph attention allows for more effective aggregation of diverse information. These two components are integrated into the gated recurrent units, replacing traditional fully connected layers to better capture temporal dependencies in the wind power time series. Experimental results on three real-world datasets demonstrate the superior performance and practical applicability of the proposed model.

Assessment of Biosecurity in Poultry Farms in Chitwan, Nepal.

The occurrence of poultry disease is one of the major problems for poultry farmers. Proper implementation of biosecurity practices leads to a reduction in entry, occurrence and spread of pathogens on farms, that have negative consequences for animal health, human health and economy. The goal of the study was to assess biosecurity measures implemented by broiler and layer farmers in Chitwan, Nepal. A total of 400 poultry farmers were surveyed using a structured questionnaire. The mean conceptual, structural and operational biosecurity scores obtained by the farms were 4.7±1.2, 11.6±2.7 and 17.1±4.1, respectively. The average biosecurity score recorded was 33.4±6.7. The lowest score obtained by a farm was 7, whereas the highest score obtained was 47. It was found that out of 400 farms, 44.2% (177/400) maintained a satisfactory level of biosecurity, while the remaining 223 (55.8%) exhibited an unsatisfactory level. The chi-square test revealed that the main occupation (χ2=31.832, p<0.001), experience in poultry farming (χ2=13.618, p<0.001), attending poultry farming training (χ2=23.107, p<0.001), biosecurity training (χ2=15.331, p=0.002), farm capacity (χ2=41.794, p<0.001), farm type (χ2=25.002, p<0.001), flooring system (χ2=35.906, p<0.001) and presence of workers in the farm (χ2=44.024, p<0.001) were significantly associated with the biosecurity level in poultry farms. This study reveals that there is much space for improvement in the adoption of biosecurity measures by poultry farms. Future training programs for poultry farmers should focus on providing knowledge on the proper implementation of biosecurity measures as a strategy for disease prevention and control.

Capacity configuration strategy of SOEC- battery based hybrid energy storage system for suppressing fluctuation of wind power

Currently, Chinese wind farms are generally equipped with 10% rated capacity lithium-ion battery energy storage system, which often fails to smooth out wind power fluctuation effectively and fulfill the power grid connection standard. The hybrid energy storage system combining with the solid oxide electrolysis cell (SOEC) and lithium-ion battery system can be adopted to suppress the wind power fluctuation. Firstly, the model of the hybrid energy storage system is built and the transient response characteristics is analyzed in Matlab/Simulink environment. Secondly, the capacity configuration strategy of the hybrid energy storage system is developed with the variational mode decomposition method. Thirdly, the coordinated control algorithm of the hybrid energy storage system is proposed considering the state of charge control for the lithium-ion battery system and the transient response characteristics of SOEC. Finally, the presented methods are verified through simulation based on the actual wind farm power data with the 15 MW rated installed capacity of the wind farm in Shandong Province China. The results indicate that the developed methods can suppress the wind power fluctuation and meet up the power grid connection standard successfully. The range of state of charge for the battery system is from 0.497 to 0.512, which is within the boundary conditions. The electric efficiency of the SOEC system is from 0.817 to 0.972, and the power consumption is from 2.968 to 3.461 kWh/Nm 3 H 2 during the hydrogen production, who are all within the threshold values.

Cross‐Border Cooperation to Mitigate Wake Losses in Offshore Wind Energy: A 2050 Case Study for the North Sea

Offshore wind energy is integral to Europe’s climate and energy goals, with plans to install 500 GW of capacity by 2050. However, wake effects, which involve reductions in wind speed and energy yield caused by upstream turbines, pose a significant efficiency challenge, particularly in dense wind farm clusters in the North Sea. This study examines the implications of large‐scale wakes, focusing on cross‐border effects and mitigation strategies. Through an assessment of the kinetic energy budget of the atmosphere (KEBA), it identifies wake‐induced yield reductions of 30% for the German Bight in the North Sea, with half of these being attributed to the cross‐border accumulation of wakes. The findings demonstrate that redistributing wind farm capacities across borders could reduce wake losses to 18%, thereby enhancing energy yield. This could prevent the equivalent of about 8 GW offshore wind generation capacity being lost due to wakes and reduce the levelised cost of electricity. This study highlights the importance of cross‐border collaboration and sea basin wide planning to optimise wind farm placement, enhance production efficiency, and ensure the economic viability of offshore wind energy.

Wake model selection in offshore wind energy: balancing efficiency and cost in Indian offshore wind farms

Abstract This study addresses the critical need for efficient offshore wind energy utilization in India, focusing on the impact of different wake models on turbine performance and financial viability. By evaluating models such as TurbOPark and Deep Array Wake Loss (DAWL), we examined their effectiveness in predicting wake losses and optimizing turbine layouts in offshore subzones. The findings reveal that higher wind farm capacity densities lead to significant differences in performance across models. The TurbOPark model predicts the highest array losses, resulting in the lowest capacity utilization factors (CUF) and highest levelized cost of energy (LCoE), reflecting its conservative nature. In contrast, the Modified Park and Eddy Viscosity models consistently estimate lower array losses, leading to lower LCoE and reduced financial burdens on the government, particularly when LCoE is fixed. These results underscore the importance of selecting appropriate wake models that balance cost efficiency with accurate performance predictions. The study highlights the need for refining wake models with high-resolution data and complex environmental factors to optimize wind farm design and enhance energy production, especially in emerging markets like India.

Development of wind power plants to increase the fulfilment ratio of renewable energy in Taiwan using system dynamics modelling

ABSTRACT This research aims to improve wind energy development by addressing environmental challenges and market dynamics. To achieve this, a system dynamics model was used to analyze the complex relationships between various factors, including wind energy production, electricity demand, costs, and government policies. The study focuses on increasing the fulfilment ratio and reducing the price of wind energy. By simulating different scenarios, researchers explored the potential impact of expanding wind farm capacity through the addition of onshore and offshore turbines. The research also investigates the role of government incentives, particularly the feed-in tariff (FIT), in stimulating investment in wind energy. The findings suggest that a higher FIT can significantly boost profitability, attract more investment, and accelerate the expansion of wind farm capacity. The optimal FIT level of 5.5 NTD/KWh is projected to lead to a 6.7% annual growth rate and substantial profits by 2040.