Farm Power Research Articles

Sustainable value chain approach for livestock-based livelihood strategies for communities of the southeastern coast of Bangladesh

PurposeThe nature of farm animals in the marginalized group of people is varying hurriedly. Livestock is used to add to cash earnings and increase food security, hence helping as a vital component in the household’s source of revenue strategies, particularly at marginal planter’s level. The present study was conducted to assess the numbers of livestock farmers in the study areas, their livelihood options, the value chain of the farmers in different marketing channels and recommendation for the sustainable value chain of the livestock production cycle.Design/methodology/approachThe study precise the baseline condition of marginal livestock farmers for access to value chain activities in terms of inputs, outputs, support services, production, yield, income and enabling environment to enhance livestock farming in the study area. The study was conducted through stratified random sampling of the context using some research tools like in-depth interviews, household surveys, expert opinions and focus group discussions. Structured questionnaires were developed to address issues, such as current livestock farming practices, access to support services, capacity and income.FindingsThe study revealed that this particular context is lagging behind to establish goat value chain activities in the targeted areas. The farmers do not have basic knowledge of goat farming, and the value chain actors are not working properly. The support services are not appropriate to turn the goat farming production to a standard level. Value chain of livestock and livestock products and their goals are essential to develop an idea on learning, investment, market access, sales assurance and quality. Variation in institutional contexts of end markets is linked to different types of coordination and control of enabling environment throughout the chains.Practical implicationsLivestock is an integral component of the complex farming system in Bangladesh as it serves as not only a source of meat protein but also a major source of farm power services as well as employment. Strong private sector alliance along with public–private ventures can bring sustainable agriculture value chain development in these most vulnerable coastal communities in Bangladesh. Strengthening the weak financial structure, reducing power imbalances in the governance structures and low political intervention in community-level organizations, and resolving socio-cultural and environmental concerns are the major concerns on the development of value chains in Bangladesh.Originality/valueGeographical position and climatic condition of Bangladesh have made her coastal areas one of the highly productive areas for livestock production in the world. The study was conducted through qualitative and quantitative analysis, and after finding the authors recommended for sustainable value chain approach for livestock production to a marketing channel for improving the financial condition and self-employment for the communities.

Versatile electric prime mover for agriculture: A solution for small farmers

Amongst various sources of farm power, electrical power through stored electric energy is a potential source to meet the current challenge in agriculture. At ICAR-IARI, an attempt was made to use battery-power for developing a walk behind equipment with 250-800W DC/BLDCC motors for sowing, weeding, cutting and performing operations on a common platform during 2015-20. The study further led to developing a Versatile Electric Prime Mover for Agriculture (VEPMA) using 1.5 kW BLDC motor with the provision of various attachments that can meet the need of small farmers. The VEPMA is assessed to be an economically viable unit with a saving of 5-liter diesel fuel per ha and 5 to 10 times economical to tractor and power tiller operations. Its adoption in one percent cultivable area (1.4 million ha) in India can result in the net saving of approximately 32 million litres of diesel and reduce 85000 tons of CO2 in the atmosphere.

Flow Field Measurement of Laboratory-Scaled Cross-Flow Hydrokinetic Turbines: Part II—The Near-Wake of Twin Turbines in Counter-Rotating Configurations

Cross-flow hydrokinetic turbines have sparked interest among fluid dynamicists for their potential for power enhancement in paired configuration. Following the first part of a laboratory-scaled turbine wake measurement project, this second part presents a monoscopic particle image velocimetry measurement of the near-wake of two cross-flow hydrokinetic turbines in six different counter-rotating configurations. The turbines operated in a small water flume at an average diameter-based Reynolds number of 2×104 with the incoming streamwise velocity of 0.316 m/s. The six configurations included two turbine separation distances, two turbine phase angles differences, and two different relative incoming flow angles. Similar to the observation of the single turbine configurations in part I, a correlation between flow field structures and the corresponding power output was observed. Effects of each parameter of the counter-rotating configurations are further discussed, which suggest guidelines for setting up multiple devices in a power farm. This article is accompanied by all full numeric data sets and videos of the results.

Multi-System Urban Waste-Energy Self-Circulation: Design of Urban Self-Circulation System Based on Emergy Analysis.

The current worldwide state of energy scarcity and low waste utilization has led to a decrease in the supply of ecological services, something that seriously affects the development of cities. In this study, we propose an urban self-circulation design based on multiple systems within the traditional biogas, wetland, rainwater, solar power, and urban farm systems framework to achieve effective improvements in urban waste utilization and the optimization of the urban waste–energy flow cycle. Emergy conversion is used to evaluate system optimization, and the simulation results show that the novel proposed system can effectively improve urban waste utilization with an energy output rate of 3.18 × 10, an environmental load of 4.27 × 10−2, and a sustainability index of 7.45 × 102 in the core system; additionally, it can improve resource utilization of small-scale cities with an energy output rate of 1.85 × 100, an environmental load of 1.20 × 100, and a sustainability index of 1.54 × 100 in the total system. The inter-system energy flow model can significantly optimize urban energy systems based on ecological models with low-emergy resource input, including biogas systems and urban farm systems. This model can reduce the environmental load and effectively compensate for the reduced supply capacity of ecosystem services caused by urbanization, making it suitable for extension to other small-scale built environments that are relatively independent and rich in natural resources.

OFFSHOOTS OF KARNATAKA'S FARM POWER SUBSIDY REGIME

Farm power subsidy figures prominently on the agenda of almost every state government of the country. By including the farm power subsidy regime in their agenda, the states seem to have erroneously convinced themselves that they had contributed to the cause of agriculture and agriculturists. If their conviction is right, by now the problems faced by our agriculturists must have been solved many times over. But the problems have not been solved yet although our agriculture is characterized by small and fragmented landholdings. The reasons are not far to seek. The policy targeted marginal farmers and small farmers but big farmers somehow managed to gain a toehold, thereby reducing the pie earmarked for the marginal and small farmers. The regime rendered it easier for the political and permanent executives to evade responsibilities, adding to the woes of the ESCOMS or electricity supply companies. To make matters worse, government failed to release subventions to the Escoms promptly. Upon inquiry, the researcher was convinced that the farmer-consumers would not oppose a pro rata levy on the power consumed , provided quality power was supplied to them uninterruptedly. This would also minimize the opportunities available to vested interests to pilfer power. It is true that the farm power subsidy regime provides scope to the political and permanent executives to evade responsibilities, adding to the incompetence of Escoms. But there is a way out -- the state government, utility companies and farmer-users should devise a set of politically and financially feasible and socially acceptable alternatives to address the problem. A win-win situation will emerge as a result.

Wind farm yaw control set-point optimization under model parameter uncertainty

Wake steering, the intentional yaw misalignment of certain turbines in an array, has demonstrated potential as a wind farm control approach to increase collective power. Existing algorithms optimize the yaw misalignment angle set-points using steady-state wake models and either deterministic frameworks or optimizers that account for wind direction and yaw misalignment variability and uncertainty. Wake models rely on parameterizations of physical phenomena in the mean flow field, such as the wake spreading rate. The wake model parameters are uncertain and vary in time at a wind farm depending on the atmospheric conditions, including turbulence intensity, stability, shear, veer, and other atmospheric features. In this study, we develop a yaw set-point optimization approach that includes model parameter uncertainty in addition to wind condition variability and uncertainty. To enable computationally efficient online set-point optimization under model parameter uncertainty, a simplified, approximate parameter distribution estimation method is used. The optimization is tested in open-loop control numerical experiments using utility-scale wind farm operational data for which the set-point optimization framework with parametric uncertainty has a statistically significant impact on the wind farm power production for certain wind turbine layouts at low turbulence intensity, but the results are not significant for all layouts considered nor at higher turbulence intensity. The set-point optimizer is also tested for closed-loop wake steering control of a model wind farm in large eddy simulations of a convective atmospheric boundary layer (ABL). The yaw set-point optimization with model parameter uncertainty reduced the sensitivity of the closed-loop wake steering control to increases in the yaw controller update frequency. Increases in wind farm power production were not statistically significant due to the high ambient power variability in the turbulent, convective ABL.

Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis

AbstractWind farm control (WFC) allows coordinated operation of the wind turbines within a wind power plant (WPP). However, its development has traditionally been split into the distinct disciplines of power plant control and farm flow control. As variable renewable energies, in particular wind energy, increase their penetration into the power system, grid code requirements become stricter for WPPs and electricity markets necessarily evolve. In such a context, crossfertilization between the two categories of WFC, targeting an integrated approach where applicable, becomes a priority. An initial overview on how to further align wind farm flow control (WFFC) to grid and electricity markets participation is provided, including an analysis of capabilities and prospects. In addition, greater detail is given about an open‐access dataset serving as market showcases for value demonstration of price‐driven operation of WPPs by means of WFFC.

EKF-based TS fuzzy prediction for eliminating the extremely fast reactive power variations in Manjil wind farm

The inherent time-varying nature of the wind farm power causes undesired voltage flicker in the power network. In order to mitigate the flicker to enhance the performance of the wind power system with very fast dynamics, the static VAr compensator (SVC) is utilized. However, the SVC operates with some delays which negatively affects its performance. This persuades us to predict the reactive power of the wind farm to compensate for the real-world delay. The predicted reactive power is then utilized in the SVC. Therefore, this paper develops a novel fuzzy one-step-ahead prediction approach for the wind farm reactive power. The proposed fuzzy prediction uses a Takagi-Sugeno (TS) fuzzy representation whose unknown parameters are tuned online based on an extended Kalman filter (EKF). The wind farm is modeled as a time-varying current source which its amplitude and phase change every 0.01 s. A large set of the actual data of a wind farm in Manjil, Iran is gathered and directly utilized in the simulation process. Several flicker indices are calculated to evaluate the proposed prediction method. The obtained results show the performance enhancement and flicker mitigation of the suggested power scheme.

Cooperative multiagent optimization method for wind farm power delivery maximization

Reducing wake losses and improving the overall power output of wind farms have become a research focus in attempts to optimize wind farm power generation. A cooperative multiagent optimization method (CMAOM) for wind farm power delivery maximization has been proposed in this paper. In the CMAOM, a wind farm wake distribution calculation model, based on the Jensen wake model, was constructed, and each turbine was then assigned as an agent; the CMAOM was used to reduce wake losses and improve the overall wind farm power output. The agent, multiagent objective function and grid environment were defined in this study using wind turbine characteristics, and the CMAOM, including the neighborhood competition operator, mutation operator, and self-learning operator, were calibrated using wind turbine aerodynamic correlation characteristics. The Danish Horns Rev wind farm was selected as a case study, and the CMAOM and particle swarm optimization (PSO) algorithm were used to conduct analyses there. The results showed that the CMAOM proposed in this paper was more effective than the PSO algorithm and that the wind farm overall power output was increased by 7.51% for a 270° incoming wind direction and an incoming wind speed of 8.5 m/s.

Design and analysis of a wake model for spatially heterogeneous flow

Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The objective of this method is to capture heterogeneous atmospheric effects caused by site-specific terrain features, without explicitly modeling the geometry of the wind farm terrain. The implemented adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the supervisory control and data acquisition (SCADA) recordings and large eddy simulations (LESs). When comparing the performance of the proposed heterogeneous model to homogeneous FLORIS simulations, the results show a 14.6 % decrease for mean absolute error (MAE) in wind farm power output predictions for cases using wind farm SCADA data and a 18.9 % decrease in LES case studies. The results of these studies also indicate that the efficacy of the proposed modeling techniques may vary with differing site-specific operational conditions. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault

Inter-Turn Short Circuit (ITSC) fault in stator winding is a common fault in Doubly-Fed Induction Generator (DFIG)-based Wind Turbines (WTs). Improper measures in the ITSC fault affect the safety of the faulty WT and the power output of the Wind Farm (WF). This paper combines derating WTs and the power optimization of the WF to diminish the fault effect. At the turbine level, switching the derating strategy and the ITSC Fault Ride-Through (FRT) strategy is adopted to ensure that WTs safely operate under fault. At the farm level, the Particle Swarm Optimization (PSO)-based active power dispatch strategy is used to address proper power references in all of the WTs. The simulation results demonstrate the effectiveness of the proposed method. Switching the derating strategy can increase the power limit of the faulty WT, and the ITSC FRT strategy can ensure that the WT operates without excessive faulty current. The PSO-based power optimization can improve the power of the WF to compensate for the power loss caused by the faulty WT. With the proposed method, the competitiveness and the operational capacity of offshore WFs can be upgraded.

Quantile based probabilistic wind turbine power curve model

Wind turbine power curve is an indicator of wind turbine performance and important input of wind farm design or power prediction, therefore can serve the system planning and operation. However, a good power curve model is difficult to obtain because of the uncertain relationship between wind speed and its power output. Existing works focus on a deterministic model or use probabilistic distribution to represent such uncertain relation, which is not easy to be employed by the following decision-makers. This paper presents a novel concept termed as quantile power curve, which generates a series of power curves under any confidence level. Quantile loss based neural network algorithm is proposed to establish the quantile power curve. Index to measure the wind turbine performance and power generation uncertainty is also proposed based on the quantile power curve. Based on the operational data of a Chinese wind farm, the proposed model and index are validated and employed to estimate the wind energy yield when planning a system with wind, solar and electric vehicle charging loads. The results show that quantile power curve provides more comprehensive information about the uncertainty during the power generation process and helps to improve the renewable supply rate to charging loads.

Are wind turbines a mafia windfall? The unintended consequences of green incentives

Over the past decades, national and supranational governments have amply incentivised wind farm power production. In this paper, we reconstruct investments in the wind sector over twenty years of national support schemes in Italy, and we uncover unintended consequences of wind incentives exploited by criminal organization groups. We show that Italian mafia-exposed municipalities register a larger take-up of incentives for wind energy production, measured with the number of wind farms. The differential effect appears when the national energy services authority started withdrawing the excess supply of tradeable Green Certificates. With the end of the national intervention in the market, when the system of incentives became more competitive, installed power capacity sharply decreased. Further, we do not observe any differential in the take-up of mafia-exposed municipalities based on local physical constraints related to the wind potential nor spillover effects in neighboring municipalities. Our analysis highlights the risks of criminal infiltration in the green energy sector, especially when incentive schemes are insufficiently competitive and overly generous.

Load frequency control and tie-line damping via virtual synchronous generator

This paper proposes the participation of the virtual inertia of wind generation in the control of load frequency and damping of oscillations in interconnection lines. The proposed approach considers high penetration of Wind Farm Power Plant (WFPP) connected to hydro generation area. Virtual wind inertia has been widely investigated to control frequency in an interconnected system. As the WFPP is usually connected to the grid through static converters, they can act quickly to provide the Load Frequency Control (LFC) with fast dynamics. Therefore, the main idea is to include an additional fast control action with the objective of aiding the frequency stabilization to damp oscillations in interchange power line. It seeks to reduce the inverse response of hydro power plant and maintain a scheduled wind power. A hydro-wind power system is used to show the effectiveness of the proposed approach.

ANALYSIS OF CATTLE FARMER EXCHANGE RATE (NTP-T) AND THE FACTORS THAT INFLUENCE IT IN SIDOARJO REGENCY

Farmer Exchange Rate (NTP) is a proxy indicator or an indicator of the approach to the welfare level of breeders. The welfare of breeders can illustrate the purchasing power of farmers. The purpose of this study 1) To determine the level of welfare of cattle breeders and 2) To determine the effect of price index factors paid by farmers on the price index received by cattle breeders.This study uses NTP time series data with the base year 2012 = 100 as the basis for calculating the years 2018 - 2019. The location studied was determined by purposive sampling method in 5 districts in Sidoarjo Regency which are centers of beef cattle and dairy cows. The sample was selected by purposive random sampling as many as 75 cattle breeders. The NTP analysis method was carried out descriptively and the analysis of the factors that influenced the NTP was carried out using multiple linear regression. The results showed that the exchange rate of cattle farmers in Sidoarjo regency in 2019 increased by 1.81 percent from 109.41 in 2018 to 111.21 in 2019. This shows that cattle breeders in Sidoarjo district are experiencing a surplus or prosperity. The production input price index which includes the price of seeds, feed, and labor wages that must be paid by cattle breeders is a factor that has a significant effect on the cattle price index received by breeders.

Wind Farm Yield and Lifetime Optimization by Smart Steering of Wakes

Wake steering has an impact on both the wind farm energy yield and turbine loads. We evaluate these effects based on yaw-corrected power and thrust curves and tabulated turbine load simulation results. In a first step, wind direction and wind speed dependent yaw angles were determined by maximization of the wind farm power for an example wind farm. Secondly, the optimizations were repeated for the objective of minimal flapwise blade fatigue loads. The results were then combined such that the power optimized yaw angles dominate the partial load region while the load optimized results were chosen for higher wind speeds. We find that this combination increases the annual energy production in an example wind rose while simultaneously reducing the lifetime damage equivalent loads. The analysis was repeated including wind direction uncertainty into the optimization. This significantly reduced the benefit of wake steering on AEP but caused only a mild decrease of the overall load reduction.

The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines

Abstract

A two-stage model for asynchronously scheduling offshore wind farm maintenance tasks and power productions

This paper studies the model for scheduling both offshore wind farm maintenance tasks and power productions in consideration of their distinct scheduling timescales and the wind power uncertainty. A two-stage adaptive robust optimization model is formulated to derive the optimal schedule, which well handles wind turbine maintenance tasks and maximizes wind farm power productions by anticipating the worst wind power output scenario based on a wind power uncertainty set and a set of metocean conditions. In two decision stages, we schedule maintenance tasks daily and power productions hourly respectively. The proposed model is equivalently transformed to a form which can be efficiently solved by the column-and-constraint generation algorithm. Numerical experiments demonstrate the effectiveness and applicability of the proposed model considering wind farms of different large sizes. Results show that the developed model can maximize the total power production of the wind farm and enhance the robustness of obtained maintenance schedules against the wind power uncertainty.