Production Uncertainty Research Articles

Изобретая свободу

This work is a brief historical and philosophical essay that allows the author to convey some elements of the mindset formed in the conditions of the transformation of the system – the collapse of one socio-political, civilizational, structure and the formation of a new, intellectual formation in a situation of potentially productive uncertainty.

A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect

The wind energy utilization has attracted worldwide attention. The wind farm is the most important place to convert wind energy into electric energy. The accurate estimation of annual energy production and its uncertainty involves the wind resource assessment and investment risk management for the wind farm. In order to reasonably analyze the uncertainty, a Monte Carlo simulation method is proposed to obtain the annual energy production and its uncertainty for the wind farm. The method proposed in this paper not only includes the uncertainties associated with Weibull distribution parameters, measure-correlation-prediction method, power law exponent, air density and power curve in previous studies, but also incorporates the wind flow model uncertainty and wake effect. Specifically, the method proposed in this paper extrapolates the wind speed from the meteorological mast to each wind turbine in the wind farm based on the uncertainty of the wind flow model. At the same time, the wind speed reduction on the downstream wind turbines due to the wake effect will be considered, which leads to the annual energy production and uncertainty of the whole wind farm. Also, the effectiveness of the proposed method is verified based on the numerical example. The results show that the average annual energy productions of different wind turbines in the wind farm with flat terrain are close, but there are some slight differences among turbines due to the wake effect with the maximum loss of 3.5%. It is also observed that the simulated uncertainty of annual energy production of concerned wind farm is 9.0%, which is larger than 7.48% by square root of the sum of the squares method where correlations among variables are not considered.

Small Field Plots Can Cause Substantial Uncertainty in Gridded Aboveground Biomass Products from Airborne Lidar Data

Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8–33.3 Mg ha−1 for site-specific models (one standard deviation), 11.1–28.2 Mg ha−1 for ecoregion-specific models, and 21.1–22.1 Mg ha−1 for the general model for pixels in the AGB range of 80–100 Mg ha−1. Only 3 of 11 site-specific models had a total uncertainty of <15 Mg ha−1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha−1 using 0.04 ha plots to 10.9 Mg ha−1 using 0.25 ha plots and 10.1 Mg ha−1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales (≥0.25 ha).

Remote estimation of soil organic carbon under different land use types in agroecosystems of Eastern China

Soil organic carbon (SOC) plays a key role in soil function, ecosystem services, and the global carbon cycle. Large SOC stocks accumulate in agroecosystems, but the estimates of SOC distribution and magnitude in agroecosystems still have large uncertainties in global products. Based on multiple high-resolution environmental variables (terrain derived from digital elevation model, climate and organism from Landsat 8 OLI) and 369 observed soil samples from 2019 to 2021, two machine learning methods, random forest (RF) and support vector machine (SVM) models, were used to estimate the content and spatial distribution of SOC in agroecosystems of Eastern China. We proposed a hybrid model in which the optimal predictors of SOC were selected for different land use types (cropland, planted forest and grassland), and it aimed to improve the performance of machine learning. Our results showed that (1) the hybrid models performed better than the global models, and the Random forest-Hybrid (RF-Hybrid) model led to the highest prediction accuracy, with a validation R2 of 0.65 and RMSE of 5.76 g kg−1. (2) The SOC content in the agroecosystems of Eastern China among different land use types ranked in the following order: planted forest (19.92 g kg−1) > cropland (17.51 g kg−1) > grassland (17.30 g kg−1). However, in North China, the SOC content in cropland (14.04 g kg−1) was much lower than that in planted forest (17.21 g kg−1) and grassland (17.07 g kg−1), which was caused by excessive mechanized operation. (3) The agroecosystem in Northeast China presented the highest mean SOC content (26.55 g kg−1) due to low temperature. (4) Our estimations (R2 = 0.53, RMSE = 6.74 g kg−1, 30-m resolution) were more detailed and precise than the existing global SOC maps (Soil Grids: R2 = 0.26, RMSE = 12.44 g kg−1, 250-m resolution; HWSD: R2 = 0.06, RMSE = 41.07 g kg−1, 1000-m resolution). The results may improve the accuracy of agroecosystem carbon mapping and contribute to SOC assessments in agricultural ecosystems.

Generative adversarial network assisted stochastic photovoltaic system planning considering coordinated multi-timescale volt-var optimization in distribution grids

In this paper, a novel photovoltaic (PV) system planning framework is proposed for distribution grids. The main contribution of this work is that the volt-var control (VVC) capability of PV inverters is duly considered during the planning stage to reduce the expected power loss cost and meanwhile counteract uncertain voltage fluctuation and deviation caused by random PV production and load demand. Specifically, the proposed PV system planning framework is formulated as a two-stage stochastic optimization model, where the first stage is to determine the planning decisions of PV systems before the uncertainty realization, and after the uncertainty is observed, the second stage coordinates PV inverters and existing legacy VVC devices (capacitor banks) to minimize the power loss and voltage fluctuation in a multi-timescale manner. To solve the formulated complex optimization problem, the original model is decoupled and solved using an iterative solution method where commercial solvers can be directly used to obtain the optimal solutions. Besides, a data-driven based scenario selection method based on Generative Adversarial Network (GAN) is proposed to capture the uncertainties of PV production and load demand with the full diversity of behaviours. Finally, the proposed framework is tested on IEEE 37-node and 123-node test systems to demonstrate the effectiveness of the proposed method. This paper can provide important insights into investment strategies of renewable resources designed by power grid authorities, which can benefit from the proposed framework to maintain the reliable system operation and reduce the renewable energy curtailment.

A Juggler's manifesto: elevating creativity to stay productive amid uncertainty

PurposeThe Industry 4.0 environment is characterized by fast data, vertically and horizontally interconnected systems, and human–machine interfaces. In the middle stands the manager, whose sustained performance is critical to the organization's success. Business disturbances—such as supply chain disruptions during the pandemic—can quickly test the manager's resiliency. While creativity and flexibility are critical for success in these situations, these skills are often not promoted directly. This paper will discuss strategies for enhancing managers' creativity and resiliency and give suggestions for improving professional development training and post-secondary business education.Design/methodology/approachA synthesis of the literature in business and psychology provides a foundation for creating a conceptual model incorporating strategies to promote managerial creativity and resiliency. While the model focuses on managerial performance under adverse conditions, the tenets of the model also apply during times of relative stability.FindingsFindings based on a synthesis of the literature on creativity in business and psychology provide the foundation for a conceptual model to identify potential elements in training and curriculum design to further managers' creativity and resiliency. This model recommends clear, actionable training and program-level curriculum design suggestions for improved managerial performance.Originality/valueThis paper identifies a conceptual model to enhance managerial creativity leading to increased resiliency through professional development programs and suggestions for educators in post-secondary business education. This model provides tools for managers to deal with adverse and rapidly changing conditions flexibly, promoting employee productivity and satisfaction.

Trends and future projections of Olea flowering in the western Mediterranean: The example of the Alentejo region (Portugal)

Olives are one of the most economically relevant crops in the Mediterranean area but this region is experiencing a strong warming due to climate change. Therefore, it would be of great interest to estimate the possible implications of climate change on olive flowering timing by modelling its thermal requirements, and aerobiological records have proved to be a useful tool for monitoring regional olive phenology. Recently, a new phenological modelling framework has been developed called PhenoFlex. It has shown excellent results in estimating the thermal requirements of fruit trees. However, this framework has not previously been applied to olive trees or tested using aerobiological data, which could present greater spatial distribution than in situ phenological observations. This study has a threefold aim: i) to detect the trends in the main pollen season start date of Olea during the past 25 years in a western Mediterranean region (Alentejo, Portugal); ii) to evaluate the applicability of the PhenoFlex modelling framework to olive aerobiological data, and iii) to use the modelled parameter estimates to evaluate the influence of projected future climate change scenarios on Olea flowering regional onset dates. Our results showed that global warming had not produced any significant trend in the olive flowering onset dates until the present time, and they are not expected to do so in the near future, either. A temperature increase during winter was observed that is delaying the fulfilment of the Olea chilling requirements, but this is compensated for by a reduction in the forcing period due to higher temperatures observed during spring. However, our study points out a significant increase in the interannual variability of flowering onset dates, which could compromise fruit development and adversely affect the yield, raising uncertainty in olive fruit production in the near future.

Geometric uncertainty relations on Wigner–Yanase skew information

We formulate uncertainty relations based on Wigner–Yanase skew information. By using the Kirillov–Kostant–Souriau Kähler structure on the quantum phase space, we present a new geometric uncertainty relation associated to the skew information, which is shown to be tighter than the existing ones. Furthermore, we provide a skew information-based product uncertainty relation, in which the lower bound can also be used to capture the non-commutativity of the observables. We also attempt to generalize the geometric uncertainty inequalities to the case of arbitrary three observables, where the Kähler structure plays a vital role in the proof.

Channel Changes Choice: An Empirical Study About Omnichannel Demand Sensitivity to Fulfillment Lead Time

We examine a large transaction-level data set of an Italian omnichannel furniture retailer to study channel-specific effects of fulfillment lead time on demand. This omnichannel retailer sells the same products and has the same product fulfillment across three channels: showroom, online, and catalog. The showroom channel carries no inventory but allows customers to touch and feel the products. The online channel provides a website for consumers to browse and order the products. The catalog channel sends a product catalog to all the households in Italy, so they can place an order over the phone. We find that the showroom channel makes consumers less sensitive to fulfillment lead time than both online and catalog channels. In particular, a 10% increase in lead time (1.83 days from the sample mean of 18.26 days) causes a 0.29% reduction in the daily sales volume (approximately 29 units from the sample mean of 10,140 units per day) at the showroom, which is less than the reduction of 1.85% and 0.92% in the online and the catalog channels, respectively. This finding contradicts the common practical and theoretical assumption about homogeneous lead time sensitivity across channels. In addition, we find that niche products and experience goods accentuate the difference of lead time sensitivity between showroom and nonphysical channels. Through an online experiment, we show that higher product information precision, such as that provided in a showroom, can alleviate customers’ anxiety around product uncertainty, thus reducing lead time sensitivity and increasing purchase intent. We further build two analytical models to show the implications of our empirical results for showroom network design and inventory rationing decisions. Our study highlights the previously ignored fulfillment time sensitivity aspect of the physical store’s value. This paper was accepted by Vishal Gaur, operations management. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4839 .

Combining monthly wind and inflow uncertainties in the stochastic dual dynamic programming

AbstractFollowing a global trend, intermittent sources, especially wind, have been experiencing accelerated growth in Brazil—in the last decade, wind power grew 13 times and became the second largest source in the electricity mix (12%), just behind hydropower (60%). Currently, although following regulatory guidelines, the representation of wind power in the long-term operation planning model is done in a simplified way, based on the monthly average of the last five years of aggregated generation, thus demanding improvements. The objective of this work is to describe an approach to be used by the Brazilian power industry to represent the uncertainties of monthly wind power production in the SDDP algorithm applied in the long-term operation planning model, keeping the large-scale stochastic problem still computationally viable, when applied to large interconnected systems, especially with hydroelectric predominance. The proposed methodology comprises statistical clustering of wind regimes and definition of equivalent wind farms; evaluation of monthly transfer functions between wind speed and power production; integrated generation of monthly multivariate synthetic scenarios of inflows and winds, considering associated cross-correlations; and representing monthly wind power in the SDDP algorithm. The application to real configurations of the Brazilian interconnected system, including case studies related to the monthly operation program and the calculation of the maximum amount of energy that can be traded in long-term power purchase agreements, points to its effectiveness and the relevance of modeling the wind uncertainties in the long-term operation planning of large hydro-dominated systems.

What reduces product uncertainty in live streaming e-commerce? From a signal consistency perspective

E-commerce live streaming has become a popular emerging trend worldwide. However, as an obstacle to consumer decision-making, product uncertainty is still a concern in e-commerce live streaming contexts. Based on signaling theory and uncertainty literature, we propose that signal consistency can reduce product uncertainty and further facilitate consumers’ purchase intention. Moreover, using a sample of 338 live shoppers, we find that anchor-product fit and live content-product fit negatively affect product quality uncertainty but not product fit uncertainty. Danmaku content-product fit and self-product fit negatively influence product quality uncertainty and product fit uncertainty. Product quality uncertainty positively affects product fit uncertainty, and product quality uncertainty and product fit uncertainty have significant negative effects on purchase intention. Additional analyses suggest that four signal consistency cues and two product uncertainty can be combined in three causal configurations to induce high purchase intention. This study extends relevant theory and literature and guides management decisions for e-commerce live streaming sellers and platforms.

Production lead-time hedging and order allocation in an MTO supply chain

In an make-to-order (MTO) supply chain, as consumers are increasingly sensitive to the delivery time and delivery reliability, quoting a short delivery time while delivering on-time is the major challenge for the retailer. To avoid tardy delivery, the retailer usually requires the manufacturer provide reliable delivery service by hedging against its production uncertainty, which is termed as production lead-time hedging (PLTH). To examine the effect of PLTH strategy on delivery time decision and profits for both retailer and manufacturer, we develop two game models. To our best knowledge, this paper is among the first that study delivery time quotation while considering delivery reliability and production uncertainty control. Furthermore, as multi-sourcing becomes more and more popular, we extend the basic single sourcing model to the multi-sourcing model. The main methodological contribution is the development of a traceable game theoretical model that links operational hedging decisions and order allocation implication, whilst considering delivery unreliability. Our findings show that adopting the PLTH strategy, which enables the retailer to quote a short delivery time in most cases, is the dominant strategy for the retailer. Especially, the multi-sourcing outperforms the single sourcing. The single sourcing Nash game also benefits the manufacturer. However, in the single sourcing Stackelberg game, the manufacturer will join the PLTH strategy only when the consumer's delivery time sensitivity is not sufficiently large. Interesting, when the consumer's delivery time sensitivity becomes sufficiently large or the consumer's on-time delivery sensitivity becomes sufficiently small, the retailer will scarify the on-time delivery reliability.

A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection

Traditional Lamb wave inspection and imaging methods heavily rely on prior knowledge of dispersion curves and baseline recordings, which may not be feasible in the majority of real cases due to production uncertainties and environmental variations. In order to solve this problem, a two-step Lamb wave strategy utilizing adaptive multiple signal classification (MUSIC) and sparse reconstruction of dispersion reconstruction is proposed. The multimodal Lamb waves are initially reconstructed in the f-k domain using random measurements, allowing for the identification and characterization of multimodal Lamb waves. Then, using local polynomial expansion and derivation, the phase and group velocities for each Lamb wave mode could be computed. Thus, the steering vectors of all potential scattering Lamb waves for each grid in the scanning area can be established, thereby allowing for the formulation of the MUSIC algorithm. To increase the precision and adaptability of the MUSIC method, the local wave components resulting from potential scatters are extracted with an adaptive window, which is governed by the group velocities and distances of Lamb wave propagation. As a result, the reconstructed dispersion relations and windowed wave components can be used to highlight the scattering features. For the method investigation, both a simulation and experiment are carried out, and both the dispersion curves and damage locations can be detected. The results demonstrate that damage localization is possible without theoretical dispersion data and baseline recordings while exhibiting a considerable accuracy and resolution.

Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and Dark Target aerosol products under diverse aerosol loading scenarios over South Asia

Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi National Polar-orbiting Partnership (S-NPP) satellite were evaluated over South Asia. The region is reported to be highly challenging to accurate estimation of satellite-based aerosol optical properties due to variations in surface reflectance, complex aerosol system and regional meteorology. Performance of both algorithms were initially evaluated by comparing their ability to retrieve aerosol signal over the complex geographical region under specific air pollution emission scenario. Thereafter, retrieval accuracy was investigated against 10 AERONET sites across South Asia, selected based on their geography and predominance aerosol types, from year 2012–2021. Geo-spatial analysis indicates DB to efficiently retrieve fine aerosol features over bright arid surfaces, and for smoke/dust dominating events whereas DT was better to identify small fire events under dark vegetated surface. Both algorithms however, indicate unsatisfactory retrieval accuracy against AERONET having 56–59% of valid retrievals with high RMSE (0.30–0.33) and bias. Overall, DB slightly underpredicted AOD with −0.02 mean bias (MB) whereas DT overpredicted AOD (MB: 0.13), with seasonality in their retrieval efficiency against AERONET. Time-series analysis indicates stability in retrieving AOD and match-up number for both algorithms. Retrieval bias of DB and DT AOD against AERONET AOD under diverse aerosol loading, aerosol size, scattering/absorbing aerosol, and surface vegetation coverage scenarios revealed DT to be more influenced by these conditions. Error analysis indicates at low AOD (≤0.2), accuracy of both DB and DT were subject to underlying vegetation coverage. At AOD>0.2, DB performed well in retrieving coarse aerosols whereas DT was superior when fine aerosols dominated. Overall, accuracy of both VIIRS algorithms require further refinement to continue MODIS AOD legacy over South Asia.

Climate variability, trends, projections and their impact on different crops: A case study of Gujarat, India

Gujarat, being a coastal state, is likely to be impacted by global warming and climate change not only due to sea level rise and salinity ingress but also due to an increase in the frequency of cyclonic storms and other extreme weather events, causing uncertainty in crop production. An attempt has therefore been made to understand the climate of Gujarat in the past, present, and future based on the works done at the Department of Agricultural Meteorology, Anand. Analysis carried out on climatic trends and climatic extremes using past available data from different stations in Gujarat has been highlighted. Crop simulation models validated with experimental data collected for different crops across Gujarat state were used to understand the response of crops to climatic variability. The climate change impact studies and adaptation strategies carried out under the NPCC project have also been highlighted. And lastly, the work done by the author as an emeritus scientist on climate projections under RCP 4.5 and RCP 8.5 for all the districts of Gujarat and their likely impact on selected crops is presented. The results revealed that in the past, temperatures have shown increasing trends but not reached significant levels except at certain locations at night. Rainfall has also increased, but marginally. Future temperatures have been projected to increase in different parts of Gujarat under RCP4.5 and RCP8.5 with varying magnitudes. Similarly, the rainfall has also been projected to increase, while the sunshine hours are projected to decrease. The ultimate impact would be a drastic reduction in yields in spite of the increase in CO2 level, suggesting that the present-day crop varieties would not be able to sustain crop production levels under a changing climatic scenario.

Can Gross Primary Productivity Products be effectively evaluated in regions with few observation data?

ABSTRACT The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.

Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization

The demand for brown sugar products often falls below the level of production, resulting in unsold goods when market demand surpasses the production capacity. This paper addresses the challenge faced by many brown sugar businesses in estimating production yields. Another issue, apart from production uncertainty, is the presence of a dataset with a significant nominal range. The study focuses on a specific brown sugar producing company in Indonesia. To address the production estimation problem, this research proposes the use of k-NN supervised learning as a forecasting method. However, instead of relying solely on k-NN, the study suggests employing z-score normalization to handle the dataset's large nominal range. The production data used for analysis spans from March 2019 to February 2022, comprising 144 weekly records. The dataset is divided into training and testing data, employing an 8:2 split validation ratio. The proposed method consists of several steps, including data normalization using z-score, processing k-NN based on the Minkowski distance, and concluding with the de-normalization process. The results demonstrate the successful implementation of the proposed method in predicting production levels. The evaluation indicates an average error margin of 3.34%, which is below the 5% threshold. The evaluation of predictive data for k-NN with z-score normalization proves effective in forecasting brown sugar production uncertainty and addressing the challenge of a large nominal range.

OC_3S: An optical classification and spectral scoring system for global waters using UV–visible remote sensing reflectance

Classification of water bodies is an effective means of understanding the optical properties of aquatic environments. However, most existing water classification systems are mainly based on the visible domain, and the ultraviolet (UV) wavelength, which has important impacts on the growth of phytoplankton, has received less focus. In this study, we developed a novel water classification and spectral scoring system (denoted OC_3S) using 26,709 global in situ hyperspectral data. These spectral data range from the UV to visible domain (380 nm to 750 nm), covering waters from clear to turbid. OC_3S classified water types into 30 classes, providing detailed information on the inherent optical properties and apparent optical properties of different water classes and quality assurance of remote sensing reflectance (Rrs) spectra for multiple sensor data. The results show that OC_3S can capture high-quality Rrs data with little loss of data volume. For example, the uncertainty of Rrs (443) can be reduced by ∼1.33 % at the cost of 5 % of observations in oligotrophic oceans. In addition, OC_3S performs more stably in turbid water classes compared to the QA system (Wei, J., Lee, Z., & Shang, S., 2016b. A system to measure the data quality of spectral remote‐sensing reflectance of aquatic environments. Journal of Geophysical Research: Oceans, 8189–8207) because the coefficient of variation of OC_3S scores in turbid waters can be reduced by ∼44 % compared to that of QA scores. Moreover, good consistency was observed when OC_3S was applied to multiple current satellite images due to its superior tolerance to potential errors in atmospheric correction and radiometric calibration. Our OC_3S is capable of reducing the uncertainty of satellite products and could be applied to future sensors with UV bands (e.g., PACE).

Measuring farm productivity under production uncertainty

AbstractThis research introduces a novel empirical application to the assessment of farm productivity growth. While the existing research on productivity change has primarily focussed on ex post output observations, it has been shown that ignoring production uncertainty can lead to unreliable results. Using a state‐contingent framework to represent the stochastic production environment, we extend the recent line of research that merged the state‐contingent approach and efficiency measurement to productivity change using the Malmquist and Luenberger productivity indices. Using a balanced panel of 117 arable crop farms surveyed in 2011 and 2015, we show through the study results that productivity decreased, with technological regress being the major source of productivity change. Differences in productivity change between nonstochastic and stochastic modelling show the relevance to consider the state‐contingent framework when assessing farms' productivity.

Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness

The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency.This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption.