Budget Uncertainty Research Articles

Optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering uncertainty of carbon-source-load

In the context of green grid, it is crucial to enhance the low-carbon transformation of integrated energy systems. To improve the degree of low-carbon in these systems, this paper proposes an optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering the uncertainty of carbon-source-load. Firstly, this paper provides an analysis of uncertainties of carbon-source-load and uses budget uncertainty sets to model the three uncertain parameters. Secondly, in the integrated energy system, the by-product O2 of hydrogen production by electrocution and the oxygen storage device are used to provide oxygen-enriched combustion conditions for the gas turbine to reduce the CO2 generated in the combustion process. At the same time, the carbon capture device is used to collect CO2, and the oxygen enriched combustion-carbon capture integrated energy system is formed. Finally, an improved multi-populations quantum-behaved particle swarm optimization algorithm is proposed to solve the proposed model. This method considers the uncertainty of carbon emission intensity, which makes the optimal allocation of integrated energy system more inclined to low-carbon rather than economic. In addition, in terms of model structure, this method integrates oxygen enriched combustion-carbon capture into the integrated energy system to form a low-carbon model, which directly improves the low-carbon performance of the system. The simulation results show that the carbon emission of the proposed method is reduced by 38.8 % compared with other methods.

Increase in gross primary production of boreal forests balanced out by increase in ecosystem respiration

Changes in the net carbon sink of boreal forests constitute a major source of uncertainty in the future global carbon budget and, hence, climate change projections. The annual net ecosystem exchange of carbon dioxide (CO2) controlling the terrestrial carbon stock results from the small difference between respiratory CO2 release and the photosynthetic CO2 uptake by vegetation. The boreal forest, and the boreal biome in general, is regarded as a persistent and even increasing net carbon sink. However, decreases in photosynthetic CO2 uptake and/or concurrent increases in respiratory CO2 release under a changing climate may turn boreal forests from a net sink to a net source of CO2. Here, we assessed the interannual variability of the boreal forest net CO2 sink-source strength and its two component fluxes from 1981 to 2018. Our remote sensing approach - trained by net CO2 flux observations at eddy covariance sites across the circumpolar boreal forests - employs satellite-derived retrievals of snowmelt timing, landscape freeze-thaw status, and yearly maximum estimates of the normalized difference vegetation index as a proxy for peak vegetation productivity. Our results suggest that for the period 2000–2018, the mean annual evergreen boreal forest CO2 photosynthetic uptake (gross primary productivity) was 2.8±0.2 Pg C y−1 (1.6±0.1 Pg C y−1 for Eurasia and 1.2±0.1 Pg C y−1 for North America). In contrast to earlier studies results obtained here do not indicate a clear increasing trend in the circumpolar evergreen boreal forest CO2 sink. The increase in photosynthetic CO2 uptake is compensated by increasing respiratory releases with both component fluxes showing considerable interannual variabilities.

Collaborative Service Network Design for Multiple Logistics Carriers Considering Demand Uncertainty

Collaborative designing of service networks using multiple logistics carriers can bring advantages in both economic and environmental terms, and these carriers have symmetry in their service areas. To enable such a collaborative service network and the corresponding benefits, this study proposes a problem of collaborative service network design (CSND) considering demand uncertainty. This problem is formulated as a two-stage robust optimization model using a budget uncertainty set to handle the uncertain demand. A column-and-constraint generation algorithm is developed to accurately solve the robust model. Numerical experiments show that the proposed algorithm outperforms the Benders decomposition algorithm in terms of solving efficiency and quality. Through comparative experiments, this research validates the advantages of collaborative designing and the robustness of model solutions. In addition, three allocation mechanisms are tested to investigate the importance of allocation in CSND.

Computing the worst-case due dates violations with budget uncertainty

We study the problem of maximizing the violation of due dates when considering either the total violation, or the number of jobs that are tardy. We consider classical completion times and a variant useful in heuristics. The four problems arise when solving (exactly or heuristically) robust scheduling problems with release and due dates/deadlines and processing time uncertainty, and also routing problems with (soft) time windows and travel time uncertainty. We provide polynomial dynamic programming algorithms for the four problems.

Robust combinatorial optimization problems under budgeted interdiction uncertainty

AbstractIn robust combinatorial optimization, we would like to find a solution that performs well under all realizations of an uncertainty set of possible parameter values. How we model this uncertainty set has a decisive influence on the complexity of the corresponding robust problem. For this reason, budgeted uncertainty sets are often studied, as they enable us to decompose the robust problem into easier subproblems. We propose a variant of discrete budgeted uncertainty for cardinality-based constraints or objectives, where a weight vector is applied to the budget constraint. We show that while the adversarial problem can be solved in linear time, the robust problem becomes NP-hard and not approximable. We discuss different possibilities to model the robust problem and show experimentally that despite the hardness result, some models scale relatively well in the problem size.

Extratropical storms induce carbon outgassing over the Southern Ocean

The strength and variability of the Southern Ocean carbon sink is a significant source of uncertainty in the global carbon budget. One barrier to reconciling observations and models is understanding how synoptic weather patterns modulate air-sea carbon exchange. Here, we identify and track storms using atmospheric sea level pressure fields from reanalysis data to assess the role that storms play in driving air-sea CO2 exchange. We examine the main drivers of CO2 fluxes under storm forcing and quantify their contribution to Southern Ocean annual air-sea CO2 fluxes. Our analysis relies on a forced ocean-ice simulation from the Community Earth System Model, as well as CO2 fluxes estimated from Biogeochemical Argo floats. We find that extratropical storms in the Southern Hemisphere induce CO2 outgassing, driven by CO2 disequilibrium. However, this effect is an order of magnitude larger in observations compared to the model and caused by different reasons. Despite large uncertainties in CO2 fluxes and storm statistics, observations suggest a pivotal role of storms in driving Southern Ocean air-sea CO2 outgassing that remains to be well represented in climate models, and needs to be further investigated in observations.

Mars Mission’s Monetary Roller Coaster Hits New Lows

In February, the NASA research center laid off more than 500 people, citing congressional budget uncertainties over the controversial Mars Sample Return mission. What is its path forward?

Multistage scenario-based planning for locating of pharmaceutic distribution centers for emergency patients by considering practical constraints under budget uncertainty conditions (case study: Kerman city in Iran)

In the realm of healthcare, medicine and the pharmaceutical supply chain exert notable influence. Recognized as a strategic commodity, any disruption in its supply chain can precipitate significant crises. The dispersed nature of pharmacies and the critical need for prompt medication delivery to patients underscore the paramount importance of designing an optimal pharmaceutical supply chain. This necessity is particularly pronounced when addressing the needs of diabetic and epilepsy patients, who, being more susceptible and in urgent need of medication, intensify the significance of a robust supply chain. Given the critical role of medicine and the pharmaceutical supply chain in healthcare, the repercussions of supply chain disruptions are far-reaching. A well-designed pharmaceutic supply chain is indispensable, especially in ensuring timely access to medications for vulnerable patient groups. To address this imperative, this study proposes a multi-stage scenario-based mathematical model aimed at enhancing the responsiveness of demand regions amidst uncertain budgets and feasible scenarios for meeting the insulin and phenobarbital requirements of patients. By incorporating uncertainties in both demand and procurement costs, the practicability of applying this model in real-world contexts has been strengthened. A case study focusing on Kerman City in Iran is conducted to evaluate the efficacy of the proposed model.

Mengoptimalkan Efisiensi Anggaran Birokrasi : Tantangan dan Solusi

The bureaucratic budget is an important aspect in managing a country's government. Efficiency in bureaucratic budget management is very important in ensuring that public resources are used as well as possible. This research uses qualitative, descriptive methods which tend to use analysis that focuses on in-depth observation. The findings of this research are that there are challenges in optimizing bureaucratic budget efficiency, including high operational costs, lack of accountability and transparency, budget uncertainty, and ineffective policies. . Thus, to overcome these challenges, there are solutions that can be implemented such as the use of technology, increasing transparency, long-term budget planning, program and policy evaluation, bureaucratic reform, and collaboration with the private sector and NGOs.

Robust optimisation for vertiport location problem considering travel mode choice behaviour in urban air mobility systems

With the rapid development of novel vehicle technologies, air taxis are becoming a new transport servicing model to achieve better urban air mobility (UAM) systems. The UAM system can mitigate severe traffic congestion problems on the ground in many metropolitan areas effectively. This research aims to design a robust UAM network to serve broader mobility needs in the future. In this study, we consider the two main competition transportation modes of air taxis (luxury ground taxis and ordinary ground taxis) and establish a travel mode choice behaviour model based on the multinomial logit choice approach to determine the aggregate air taxi demand flow. Furthermore, we account for the varying temporal preferences of different UAM passengers by incorporating different time values for heterogeneous passengers, such as leisure and business passengers, into the travel mode choice behaviour model. Additionally, to address uncertainties in user demand, we introduce a robust optimisation model for vertiport location under a budget uncertainty set. This model is then reformulated as a mixed-integer linear programming model to address computational challenges. Ultimately, we conduct a series of numerical experiments to showcase the effectiveness of our proposed mathematical model. The outcomes of this research offer valuable managerial insights and implications, aiding governments and UAM operators in scientifically designing UAM networks and making strategic decisions regarding infrastructure investments.

How Does the Pinatubo Eruption Influence Our Understanding of Long‐Term Changes in Ocean Biogeochemistry?

AbstractPinatubo erupted during the first decadal survey of ocean biogeochemistry, embedding its climate fingerprint into foundational ocean biogeochemical observations and complicating the interpretation of long‐term biogeochemical change. Here, we quantify the influence of the Pinatubo climate perturbation on externally forced decadal and multi‐decadal changes in key ocean biogeochemical quantities using a large ensemble simulation of the Community Earth System Model designed to isolate the effects of Pinatubo, which cleanly captures the ocean biogeochemical response to the eruption. We find increased uptake of apparent oxygen utilization and preindustrial carbon over 1993–2003. Nearly 100% of the forced response in these quantities are attributable to Pinatubo. The eruption caused enhanced ventilation of the North Atlantic, as evidenced by deep ocean chlorofluorocarbon changes that appear 10–15 years after the eruption. Our results help contextualize observed change and contribute to improved constraints on uncertainty in the global carbon budget and ocean deoxygenation.

Robust data envelopment analysis with variable budgeted uncertainty

Including uncertainty in data envelopment analysis (DEA) is vital to achieving stable and reliable performance evaluations for the field of operational research as business environments are becoming increasingly volatile and unpredictable. Robust DEA models with budgeted uncertainty have been drawing particular attention in the DEA literature for modelling uncertainties, aiming to obtain robust efficiency scores in a way that guarantees the feasibility of solutions. A concern with such robust DEA models – which has been largely ignored in the literature – is that incorporating high uncertainty levels might result in too conservative efficiency measures, possibly reducing the decision support value of such information. To address this concern, this paper tackles uncertainties by employing variable budgeted uncertainty, which is a generalisation of the budgeted uncertainty. We introduce a novel robust DEA model with variable budgeted uncertainty that is less conservative than extant robust DEA models. Furthermore, we suggest a solution for specifying the probabilistic bounds for constraint violations of the uncertain parameters in robust DEA models. A comparison of the introduced robust DEA model with existing robust DEA models based on a numerical example shows an average reduction in the price of robustness by approximately 20%. Finally, the usefulness and applicability of the suggested model are demonstrated by using a large-scale data set in the context of grocery retailing.

The Impact of Climate Forcing Biases and the Nitrogen Cycle on Land Carbon Balance Projections

AbstractEarth System Models (ESMs) project that the terrestrial carbon sink will continue to grow as atmospheric CO2 increases, but this projection is uncertain due to biases in the simulated climate and how ESMs represent ecosystem processes. In particular, the strength of the CO2‐fertilization effect, which is modulated by nutrient cycles, varies substantially across models. This study evaluates land carbon balance uncertainties for the Canadian Earth System Model (CanESM) by conducting simulations where the latest version of CanESM's land surface component is driven offline with raw and bias‐adjusted CanESM5 climate forcing data. To quantify the impact of nutrient limitation, we complete simulations where the nitrogen cycle is enabled or disabled. Results show that bias adjustment improves model performance across most ecosystem variables, primarily due to reduced biases in precipitation. Turning the nitrogen cycle on increases the global land carbon sink during the historical period (1995–2014) due to enhanced nitrogen deposition, placing it within the Global Carbon Budget uncertainty range. During the future period (2080–2099), the simulated land carbon sink increases in response to bias adjustment and decreases in response to the dynamic carbon‐nitrogen interaction, leading to a net decrease when both factors are acting together. The dominating impact of the nitrogen cycle demonstrates the importance of representing nutrient limitation in ESMs. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement.

Nitrate losses from forest during snowmelt: An underestimated source in mid-high latitude watershed

The forest nitrate cycle is a crucial part of the watershed nitrate load but has received limited attention compared to that of agricultural and residential land. Here, we analyzed the status and sources of riverine nitrate fluxes and identified the characteristics and contribution of forest nitrate loss to the riverine system in a mid-high latitude forested watershed using monthly field sampling and a modified Soil and Water Assessment Tool (SWAT) with enhanced forest nutrient cycle representation. The results indicate that nitrate losses in the headwater stream and downstream exhibit different seasonal characteristics. The nitrate losses in the headwater stream show a bimodal pattern due to lower temperatures and snowmelt runoff. Redundancy analysis (RDA) revealed that, unlike nitrogen (N) fertilizer-induced nitrate loss in the rainy season, forest loss has a positive effect on headwater stream nitrate concentration during the snowmelt season. The modified SWAT was then utilized to simulate nitrate losses in forest lands. The forest nitrate export per unit area of the headwater stream (1.58 ± 1.78 kg/ha/yr) was observed to be higher than that of the downstream (0.67 ± 0.74 kg/ha/yr) due to high snowmelt and mineralization of active organic N. At watershed scale, forest lands contributed 8.18 ± 3.94 % of the total nitrate losses to the water system in the headwater watersheds during the snowmelt season, representing the highest level within the entire basin. A comparison with forest streams in similar low-temperature conditions worldwide revealed that increasing nitrate loss occurred after extreme cold weather or soil freezing events, with an average increment of 6.32 kg/ha/yr. Therefore, forest nitrate losses should be better characterized and included in future watershed N budgets in low-temperature regions, which might help to reduce the N budget uncertainty and improve watershed management.

Prioritizing Imperiled Native Aquatic Species for Conservation Propagation

Abstract Native aquatic species are in decline, and hatcheries can play an important role in stemming these losses until larger ecological issues are addressed. However, as more federal and state agencies face budget uncertainty and the number of imperiled species increases, it is necessary to develop a tool to prioritize species for conservation propagation. Our objective was to create prioritized lists of aquatic species that may benefit from conservation propagation for five states in the United States. Biologists developed an influence diagram and provided information for multiple attributes affecting prevalence of species. The influence diagram and information for each species was used in a Bayesian belief network to generate a score to prioritize propagation of a species and the feasibility of propagation. When all taxa were ranked together within a state, mussels, amphibians, and a crustacean were included among fishes in the top species that may benefit from propagation. We recognize that propagation is one tool for conservation of imperiled species and that additional factors will need to be addressed to ensure species persistence. Nevertheless, we contend our quantitative approach provides a useful framework for prioritizing conservation propagation.

Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space.

Carbon dioxide and methane emissions are the two primary anthropogenic climate-forcing agents and an important source of uncertainty in the global carbon budget. Uncertainties are further magnified when emissions occur at fine spatial scales (<1 km), making attribution challenging. We present the first observations from NASA's Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer showing quantification and attribution of fine-scale methane (0.3 to 73 tonnes CH4 hour-1) and carbon dioxide sources (1571 to 3511 tonnes CO2 hour-1) spanning the oil and gas, waste, and energy sectors. For selected countries observed during the first 30 days of EMIT operations, methane emissions varied at a regional scale, with the largest total emissions observed for Turkmenistan (731 ± 148 tonnes CH4 hour-1). These results highlight the contributions of current and planned point source imagers in closing global carbon budgets.

On the complexity of robust multi-stage problems with discrete recourse

We study the computational complexity of multi-stage robust optimization problems. Such problems are formulated with alternating min/max quantifiers and therefore naturally fall into a higher stage of the polynomial hierarchy. Despite this, almost no hardness results with respect to the polynomial hierarchy are known.In this work, we examine the hardness of robust two-stage adjustable and robust recoverable optimization with budgeted uncertainty sets. Our main technical contribution is the introduction of a technique tailored to prove Σ3p-hardness of such problems. We highlight a difference between continuous and discrete budgeted uncertainty: In the discrete case, indeed a wide range of problems becomes complete for the third stage of the polynomial hierarchy; in particular, this applies to the TSP, independent set, and vertex cover problems. However, in the continuous case this does not happen and problems remain in the first stage of the hierarchy. Finally, if we allow the uncertainty to not only affect the objective, but also multiple constraints, then this distinction disappears and even in the continuous case we encounter hardness for the third stage of the hierarchy. This shows that even robust problems which are already NP-complete can still exhibit a significant difference in computational complexity between column-wise and row-wise uncertainty.

Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties

Grid scale battery integration plays an important role in renewable energy integration and the formation of smart grid. To mitigate the problems of insufficient frequency response and peak regulation capacities faced by modern power grids with high wind energy uptake, a day-ahead optimization dispatch strategy considering operational risks is proposed in this study. In the day-ahead dispatch model, generation units and a large-scale battery energy storage station (LS-BESS) are coordinated to participate in multi-type frequency control ancillary services (FCASs). For optimal performance, scheduling in different timescales and the complementarity between power and energy types of requirements are coordinated, with various service uncertainties considered. Then the conditional value-at-risk theory is utilized to achieve the optimal mix of multiple resources for day-ahead regulation reserves, to realize the minimum operation cost. To tackle the uncertainty of wind power over a large timescale, a robust optimization (RO) approach based on the budget uncertainty set is employed, which considers robustness and economy. This can help avoid over-conservation of the standard RO and enhance the applicability of the decisions made. Simulation studies and comparison analysis of multiple schemes verify the effectiveness of the proposed optimal day-ahead dispatch strategy, which also demonstrate that a LS-BESS participating in multiple FCASs for day-ahead dispatch can help realize secure, reliable, and economic power grid.

Electric bus fleet scheduling under travel time and energy consumption uncertainty

The public transportation system is experiencing a substantial shift due to the rapid expansion of electromobility infrastructure and operations. This transformation is anticipated to contribute to decarbonizing and promoting environmental sustainability significantly. Among the most pressing planning issues in this area is the optimization of operational and strategic costs associated with electric fleets, which has recently garnered the attention of researchers. This paper investigates the scheduling and procurement problem of electric fleets under travel time and energy consumption uncertainty. A novel mixed-integer linear programming model is proposed, which determines the number of buses required to cover all trips, yields the schedule of the trips, and creates bus charging plans. The robust optimization paradigm is employed to address uncertainty, and a new budget uncertainty set is introduced to control the robustness of the solution. The efficiency of the model is evaluated through an extensive Monte Carlo simulation. Additionally, a case study is conducted on the off-campus college transport network at Binghamton University to demonstrate the real-world applicability of the model. The numerical results have shown that ignoring uncertainty can lead to schedules where up to 48% of the trips are affected, which are either delayed or missed. The proposed approach can also be applied to other transportation networks with similar characteristics and uncertainties.

Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

AbstractWetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data‐driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH4 y−1) and overlaps with top‐down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra‐tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).