Supply Scenarios Research Articles

Carbon-neutral power system enabled e-kerosene production in Brazil in 2050

Rich in renewable resources, extensive acreage, and bioenergy expertise, Brazil, however, has no established strategies for sustainable aviation fuels, particularly e-kerosene. We extend the lens from the often-studied economic feasibility of individual e-kerosene supply chains to a system-wide perspective. Employing energy system analyses, we examine the integration of e-kerosene production into Brazil’s national energy supplies. We introduce PyPSA-Brazil, an open-source energy system optimisation model grounded in public data. This model integrates e-kerosene production and offers granular spatial resolution, enabling federal-level informed decisions on infrastructure locations and enhancing transparency in Brazilian energy supply scenarios. Our findings indicate that incorporating e-kerosene production can bolster system efficiency as Brazil targets a carbon-neutral electricity supply by 2050. The share of e-kerosene in meeting kerosene demand fluctuates between 2.7 and 51.1%, with production costs varying from 113.3 to 227.3 €/MWh. These costs are influenced by factors such as biokerosene costs, carbon pricing, and export aspirations. Our findings are relevant for Brazilian policymakers championing aviation sustainability and offer a framework for other countries envisioning carbon-neutral e-kerosene production and export.

Water allocation scenarios for public supply and irrigated agriculture in the São Francisco River integration project

To mitigate the problems derived from the water shortage in the semi-arid northeastern region, the São Francisco River Integration Project (PISF) is being developed, transferring waters from the São Francisco River to other watersheds in the states of Pernambuco, Ceará, Rio Grande do Norte, and Paraíba. The sustainable management of these flows will be vital to guarantee their uses. This research aimed to evaluate scenarios of water allocation for public supply and irrigated agriculture, in order to assist in the management of water resources and the maintenance of these uses in the upper course of the Paraíba River (PB), East Axis of the PISF. The evaluation of multiple uses was carried out through the analysis of the permits issued for the Poções and Epitácio Pessoa reservoirs in 2019. Water allocation scenarios were undertaken considering the presence and absence of the PISF waters, based on the application of the AcquaNet model. The most significant water demands identified were, in Poções reservoir, public supply (1), and in Epitácio Pessoa reservoir, public supply (1) and irrigation (583). The water allocation scenarios showed deficits in meeting public supply and for irrigation in the simulations with and without PISF waters; however, in the scenarios involving PISF or when it provided a greater flow, these deficits were reduced. The results of this research provide information that contributes to decision-making for sustainable water management and to ensure the multiple uses of water, contributing to the improvement of the quality of life of the beneficiary population.

Hyperstable Eutectic Core-Spun Fiber Enabled Wearable Energy Harvesting and Personal Thermal Management Fabric.

Electronic textiles have gradually evolved into one of the most important mainstays of flexible electronics owing to their good wearability. However, textile multifunctionality is generally achieved by stacking functional modules, which is not conducive to wearability. Integrating these modules into a single fiber provides a better solution. In this work, a core-spun functional fiber (CSF) constructed from hyper-environmentally stable Zn-based eutectogel as the core layer and polytetrafluoroethylene as the sheath is designed. The CSF achieves a synergistic output effect of piezoelectricity-enhanced triboelectricity, as well as reliable hydrophobicity, and high mid-infrared emissivity and visible light reflectivity. A monolayer functionalized integrated textile is woven from the CSF to enable effective energy (mechanical and droplet energy) harvesting and personal thermal management functions. Furthermore, scenarios for the energy supply, motion detection, and outdoor use of electronic fabrics for electronics applications are demonstrated, opening new avenues for the functional integration of electronic textiles.

Feasible supply of steel and cement within a carbon budget is likely to fall short of expected global demand

The current decarbonization strategy for the steel and cement industries is inherently dependent on the build-out of infrastructure, including for CO2 transport and storage, renewable electricity, and green hydrogen. However, the deployment of this infrastructure entails considerable uncertainty. Here we explore the global feasible supply of steel and cement within Paris-compliant carbon budgets, explicitly considering uncertainties in the deployment of infrastructure. Our scenario analysis reveals that despite substantial growth in recycling- and hydrogen-based production, the feasible steel supply will only meet 58–65% (interquartile range) of the expected baseline demand in 2050. Cement supply is even more uncertain due to limited mitigation options, meeting only 22–56% (interquartile range) of the expected baseline demand in 2050. These findings pose a two-fold challenge for decarbonizing the steel and cement industries: on the one hand, governments need to expand essential infrastructure rapidly; on the other hand, industries need to prepare for the risk of deployment failures, rather than solely waiting for large-scale infrastructure to emerge. Our feasible supply scenarios provide compelling evidence of the urgency of demand-side actions and establish benchmarks for the required level of resource efficiency.

Biogas potential of biowaste: A case study in the state of Rio de Janeiro, Brazil

Anaerobic digestion has been widely applied for waste treatment, renewable energy generation and biofertilizer production. The biogas potential in Brazil is sizable, but the state of Rio de Janeiro is largely dependent on fossil fuels, and there is a lack of biogas potential assessments in the state. Thus, this study evaluated biomethane, electricity and biofertilizer potentials in the region. Three different scenarios of biomass supply were considered for four major biowaste streams: sewage sludge; cattle manure; sugarcane processing waste; and food waste. Biomethane generation from the assessed sources could reach 0.6–1.3 billion Nm3 year−1, corresponding to 1,768–3,961 GWh year−1 of electricity and 1.6–3.3 million Mg year−1 of biofertilizer. Cattle manure was responsible for 73–84% of the projected biomethane production, presenting an opportunity to reduce the significant emissions from livestock farming. The estimated biofertilizer production could meet the demands of the state, and the produced electricity could offset up to 10% of the demand. The gas grid could facilitate the distribution of upgraded biomethane, and 10–22% of the natural gas demand could be met. The findings of this work highlight the high potential for biogas generation in Rio de Janeiro, which is up to seven times larger than the current production.

Agronomic Analysis of the Replacement of Conventional Agricultural Water Supply by Desalinated Seawater as an Adaptive Strategy to Water Scarcity in South-Eastern Spain

Climate change is affecting water resources in south-eastern Spain, and this mainly affects irrigated agriculture. In this context, seawater desalination is an adaptive strategy that has provided increasing water allotments to agriculture for the last decade, to replace decreasing conventional resources. Farmers are concerned about the agronomic effects of this substitution and its economic consequences. This study focuses on the potential agronomic impacts of the progressive replacement of the irrigation water from the Tagus–Segura transfer (TST) with desalinated seawater (DSW) on the main crops of south-eastern Spain. To that end, five main agronomic concerns were selected and analyzed under three water supply scenarios using increasing rates of DSW (0, 50, and 100%). The results indicated that, in addition to other economic or environmental considerations, sufficiently relevant agronomic aspects exist that need to be considered when replacing the TST supply with DSW. This study evidences the risks of phytotoxicity and soil alkalinization, due to the increase in boron concentration and the imbalance between monovalent and divalent cations in the DSW, respectively, and also a slight increase in the cost of fertilizers. In addition, the irrigation water salinity effect on production and total irrigation requirements was negligible, as both water sources present sufficiently low salinity. The detrimental effects were mitigated under a partial replacement scenario, so the blended use of DSW with conventional resources seems the most recommendable option for its agricultural management, rather than irrigating with DSW alone.

Electric Vehicle Charging Station Power Supply Optimization with V2X Capabilities Based on Mixed-Integer Linear Programming

The European Union is committed to both lowering greenhouse gas emissions and promoting the adoption of electric vehicles (EVs) on its roads. To achieve these goals, it is imperative to speed up the development of the charging infrastructure as well as to ensure the effective integration of the charging infrastructure into distribution networks. Given that EV charging costs significantly contribute to the total cost of owning an EV, it is important to hedge against rising electricity prices and ensure affordable charging for the end users. Connecting solar power plants and battery storage to the electric vehicle charging stations (EVCSs) serves as a measure of hedging against potential future electricity price increases but also as an option that can contribute to reducing impact on the distribution network loading. In addition to this, connecting EVCS through grid connections of existing consumers (office/residential buildings, shopping malls, etc.) can reduce grid connection costs for EVCS but also contribute to electricity cost reduction for both EVCS and existing end consumers. Additionally, by integrating advanced charging strategies like the vehicle-to-everything (V2X) approach, the overall charging costs can be reduced even further. This paper focuses on optimizing the power supply and operation of EVCS by considering strategic investments in grid connection, photovoltaic plants, and battery energy storage. The research explores the potential savings derived from reduced energy/charging costs, along with the reduction in peak power expenses for different power supply options. In addition to this, the research explores the effect of different EV charging strategies as well as EVCS grid connection on optimal investments and total system costs. The combined investment and energy management problem is focused on determining the optimal EVCS power supply and operation while minimizing total investment and operation expenditures over the project lifetime. The underlying optimization problems for different supply scenarios are cast as mixed-integer linear programming problems that can be solved efficiently. The results show the influence of different grid connection options and EV charging strategies on the joint operation and costs of EVCS and existing buildings.

Capacity acquisition and PPE distribution planning during the COVID-19 pandemic

As demand for personal protective equipment (PPE) surged during the COVID-19 pandemic and healthcare facilities faced supply shortages, the availability and distribution of PPE, being critical in fighting the pandemic, required pooling resources from multiple sectors. We explore such a resource pooling problem and propose to build up capacity by acquiring distribution and storage capacity from private carriers in return for compensation. We present a capacity acquisition and PPE distribution planning model under sufficient supply to optimally acquire storage and distribution capacity from third parties to fulfill demand. We then consider insufficient supply and propose a priority-based model based on pandemic severity. Finally, we model uncertain demand through two-stage stochastic programming with recourse and solve it using Benders decomposition and sample average approximation. We build a test case for the province of Ontario, Canada and provide detailed analysis under varying supply and demand scenarios.

Medium and long-term hydrogen production technology routes and hydrogen energy supply scenarios in Guangdong Province

Hydrogen is a type of clean secondary energy, and the hydrogen energy supply is vital to regional energy development. Guangdong is at the forefront of hydrogen energy deployment in China, and aims to achieve peak carbon by 2030. However, hydrogen production research is challenging due to complex technical routes, technical maturity levels, life-cycle carbon emissions, and costs. Multi-time-scale research is necessary to explore optimal production paths and their contributions to CO2 emission reduction. This study discussed key technologies and the technical maturity of five hydrogen production routes: production from industrial byproducts and coal, wind, hydro-, and photovoltaic power. Policy scenarios were designed to investigate the potential of these routes and technology deployment intensity under medium- and long-term scenarios. This paper proposes a roadmap for hydrogen development and provides a reference for hydrogen production planning in Guangdong. This methodology can be applied in other regions for hydrogen energy development research.

Innovative system for BWRO desalination powered by PV and pumped hydro storage – Economic and GHG emissions analysis

Brackish water reverse osmosis (BWRO) desalination driven by photovoltaic (PV) system as a primary energy source and pumped hydro storage (PHS) as an intermediate storage offers an energy-efficient and competitive solution to overcome freshwater scarcity. This innovative system for drinking water production from brackish groundwater was developed and technically analyzed in a prior work. In this paper, the entire system with two power supply scenarios: scenario 1 (PV, PHS and battery storage) and scenario 2 (PV, PHS and grid) is economically assessed and its greenhouse gas (GHG) emissions are analyzed. Thereafter, this system is compared with the state-of-the-art and BWRO systems powered by a conventional energy source (grid or diesel generator) to discuss and evaluate the future role of this innovative system in the BWRO desalination market. For off-grid operation, the innovative system with scenario 1 is the most economical system for drinking water production from brackish groundwater compared to the other examined systems as long as the diesel fuel price is equal to or more expensive than 0.58 US$/L. Moreover, the minimum specific GHG emissions produced per unit of drinking water production in scenario 1 is 46.8 % less than in the current system (PV and battery storage).

Creación de un parque urbano Nuevo índice ASD con un coeficiente de distribución espacial (Estudio de caso: región 1 de Teherán)

The literature study demonstrates how GIS uses techniques like network analysis and fuzzy logic to carry out analyses in accordance with spatial balance. The aforementioned analytical techniques are technically difficult and costly since they need a large database. This study aims to measure geographical disparity, which indicates the lack of access to park services, identify circumstances where population demand is the main driver of demand, and identify general causes for the lack of service. The results of the GIS study suggest that the warning range is displayed in the parks with new­ ASD index of -25> new ASD> 170, while the range is displayed in the parks with a new ASD index of 2000> new ASD>74250. It also demonstrates how adverse the supply and demand scenario is for parks with tiny areas and close proximity to one another. This is because smaller parks have less ability to draw visitors from far away.

WEAP Model Based Evaluation of Future Scenarios and Strategies for Sustainable Water Management in the Chennai Basin, India

Abstract The gap between water supply and demand is increasing in several urban clusters of the world. This study uses the water evaluation and planning model to assess the water supply and demand dynamics in one of the large metropolitan regions of the Chennai hydrological basin. The primary water supply sources, including reservoirs, groundwater, inter-basin transfer, and desalination plants, were integrated into the model to simulate the current and future water demand and supply scenario. Three rainfall scenarios (excess, normal, and deficit) were utilized to assess their impacts on water supply. The study highlights the increase in unmet demand for normal and deficit rainfall scenarios. In response, various mitigation options were explored, including increasing groundwater recharge, reservoir capacity enhancement, water treatment plant expansion, additional storage, and utilization of water stored in rock quarries. The findings provide valuable insights for policymakers and stakeholders to develop sustainable water management strategies in the Chennai Basin.

Techno-economic analysis of developing miscanthus biorefinery for the production of platform chemicals and renewable resins

Biorefining to produce chemical materials is a viable path to a renewable and sustainable future. The energy crop Miscanthus is grown with high yield and low cost. In this study, platform chemicals (acetic acid, glycolaldehyde, and acetol) and novolac resins were produced using Miscanthus at a baseline plant capacity of 15 t/h. The profitability of different biochar applications (soil amendment, heat supply, and activated carbon) was investigated by mass balance and energy consumption analysis and economic assessment. The heat supply scenario had the best profitability with an internal rate of return (IRR), payback period, levelized cost, and minimum selling price of 24.5%, 4.32 years, CNY 8,480/t, and CNY 10,840/t. Sensitivity analysis showed that IRR was most sensitive to resin price, plant capacity, and capital costs. The minimum plant capacity with an IRR over 25% was 25 t/h. Monte-Carlo simulation results showed a low investment risk for soil amendment and heat supply scenarios.

Variable renewable energy penetration impact on productivity: A case study of poultry farming.

Like all current industrial systems, agriculture overwhelmingly relies on energy supply from controllable sources, mainly fossil fuels and grid electricity. Power supply from these sources can be adapted to perfectly match the timing of power requirements of demand systems. The energy transition largely consists in substituting renewable power-which is intermittent by nature-to controllable sources, leading to disconnection between instantaneous power production and demand. Energy storage is a potential solution for balancing production and demand and safeguarding the operating conditions of the demand system. In this paper we quantify the effects of renewable power supply (solar and wind) on the operation of a standard poultry farm. We model the balance of power generation and demand considering the growth conditions of poultry and local weather data including temperatures, wind speed and solar radiation. We assess scenarios of renewable power supply in function of the size of the power plant, the wind-to-solar power generation mix and energy storage, and assess the impact of power supply patterns on the operating intensity (productivity) of the demand system. We show that, with a limited storage capacity, it is possible to achieve non-negligible shares of renewable power penetration without major loss in farm productivity. However, a full transition to renewable power would require the combination of i)-large energy storage compared to the annual demand, ii)- significant oversizing of the power production plant, and iii)-the exclusion of power generation combinations (wind/solar) that deviate from the timing of demand. Storage and power plant oversizing is all the more critical as production and demand are uncorrelated over the year. The ratio of useful to unused energy storage by the end of the year varies with the energy mix and operating intensity (productivity) of the farm. We discuss the implications of different energy configurations on the performance of the demand system.

Sustainability assessment of energy supply scenarios: case study of Mali

Mali is endowed with significant untapped renewable energy potentials paradoxically the country is identified as an energy-poor nation characterized by very high dependency on imports of petroleum products and heavy reliance on biomass (wood-fuel and charcoal). Access to electricity remains very low, with significant disparities across urban and rural remote areas. The gap between the electricity demand and supply keeps increasing yearly, and power shortages get frequent and longer, especially during dry periods from March to June. The energy demand increase, due to of population grow and rapid urbanization (causing more use of fossil fuels resources in the energy mix) bears the unsustainability of the country’s current energy supply. The challenge for the country is then to meet this growing energy demand with a sustainable energy supply system. In the present work, Analytical Hierarchy Process technique is applied to perform Multicriteria Decision Making analysis to identify and assess the most sustainable long-term energy supply options in Mali considering technical, environmental, social, and economic dimensions. The current situation and five alternatives of energy supply based on the country’s current and future energy supply and climate change policies are proposed for assessment. Results show that the highest priority indicators by stakeholders’ survey are under economical dimension followed by the technical ones. The best scenario considers deploying renewable energy to up to 42% of the energy mix as the sustainable option for energy supply. Adopting such scenario requires measures as a strong political will to subsidize renewable energy equipment in order to make them affordable and also policies that encourage the use of renewable energy (such as lower taxes and duties). The suggested framework gives decision-makers, authorities, practitioners, and researches an effective tool for the country future energy planning.

Managing Healthcare Product Demand Effectively in The Post-Covid-19 Environment: Navigating Demand Variability and Forecasting Complexities

Managing the demand for healthcare products is a complex problem requiring in-depth knowledge of epidemiological trends, supply chain dynamics, and regulatory considerations. The intricate management of healthcare product demand in the post-COVID-19 era is thoroughly explored in this study. It is divided into nine sections exploring various facets of this critical field, emphasizing the persistent demand variations and difficulty in forecasting in the healthcare industry. It underscores the necessity for efficient demand management techniques in the changing healthcare environment. The significance of historical demand trends and provides examples of how studying historical data is essential for comprehending and forecasting the demand for healthcare products. Identifying seasonal fluctuations, forecasting disease outbreaks, and improving inventory management are emphasized. Investigating the difficulties in predicting the need for healthcare products, deals with problems caused by epidemiological ambiguity, supply chain breakdowns, patient demographics, regulatory adjustments, and technological advancements. The importance of data availability and quality in accurate demand forecasting is emphasized in this section. It encompasses scenario planning, resource allocation, technology adoption, supplier diversity, flexible manufacturing, collaborative and information sharing, data-driven decision-making, demand forecasting models, and strategic stockpiling. "Data Analytics and Predictive Modeling in Demand Forecasting," emphasize the function of these techniques in comprehending and forecasting demand trends. All covered are time series analysis, machine learning algorithms, epidemic predictive modeling, supply chain integration, scenario planning, optimization, risk assessment, in-the-moment monitoring, and feedback loops. "Supply Chain Resilience and Demand Response" explains why it's crucial to create robust supply chains and successfully adapt to demand changes. Supplier diversity, strategic stockpiling, agile production, real-time visibility, teamwork, data-driven decision-making, scenario planning, and regulatory flexibility are all covered

A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal.

Can hydrogen storage in metal hydrides be economically competitive with compressed and liquid hydrogen storage? A techno-economical perspective for the maritime sector

The aim of this work is to evaluate if metal hydride hydrogen storage tanks are a competitive alternative for onboard hydrogen storage in the maritime sector, when compared to compressed gas and liquid hydrogen storage. This is done by modelling different hydrogen supply and onboard storage scenarios and evaluating their levelized cost of hydrogen variables. The levelized cost of hydrogen for each case is calculated considering the main components that are required for the refueling infrastructure and adding up the costs of hydrogen production, compression, transport, onshore storage, dispensing, and the cost of the onboard tanks when known. The results show that the simpler refueling needs of metal hydride-based onboard tanks result in a significant cost reduction of the hydrogen handling equipment. This provides a substantial leeway for the investment costs of metal hydride-based storage, which, depending on the scenario, can be between 3400 - 7300 EUR/kgH2 while remaining competitive with compressed hydrogen storage.

Forecasting of Domestic Electricity Consumption in Assam, India

Though the power supply scenario in Assam is not comparable with other developed states of India, yet it is improving gradually. With increasing household electrification rate, increasing income and technological development, the share of Domestic electricity consumption in the state is experiencing a rapid growth in comparison to the other sectors like Industry, Commerce, Agriculture etc. and therefore the forecasting of domestic electricity consumption become very important to meet the power needs of the consumer. The aim of this study is to suggest the best forecasting model for domestic electricity consumption in Assam. For this purpose, three models viz. Multiple Linear Regression considering the effects of population and per capita income; ARIMA and Growth curves viz Linear, Quadratic, Cubic, Exponential, Logarithmic and Inverse are used considering the time period 1980-2018. When growth curves are compared in terms of diagnostic criteria- Adjusted R2, RMSE, AME and MAPE, then it is found that exponential model fits the data better than the other growth models. After that, comparisons are made among the models Multiple Linear Regression, ARIMA and Exponential by considering their average relative error and found the efficiency of ARIMA the highest followed by Exponential and Multiple Linear Regression models. Therefore, forecasting of domestic electricity consumption in Assam has been done with ARIMA model for the next 10 years.

Machine learning modeling of water footprint in crop production distinguishing water supply and irrigation method scenarios

Crop water footprint (WF) calculation via physical process-based crop or hydrological models has basic requirements in terms of cost, time, and knowledge threshold. It is unsuitable for laypersons such as policymakers or cross-disciplinary researchers. Here, machine learning (ML) is an effective solution owing to low cost, high speed, and low processing difficulty. However, the feasibility and applicability of ML modeling for crop WFs at multiple spatial scales remain unverified. Here, we quantitatively evaluated the applicability, optimal model parameters and importance of the feature variables of four ML methods, including random forest (RF), light gradient boosting machine (LightGBM), generalized additive model (GAM), and artificial neural network (ANN), for constructing WF models for crop production at site and provincial scales. The models were tested through the case study for major grain crops (wheat, maize, soybean, and rice) in China over 2000–2019, distinguishing between irrigated and rainfed crops and between different irrigation methods. The findings show that the WF simulation accuracy for the irrigated scenario was higher than that for the rainfed scenario. The type of feasible ML model varied with crop types, variable combinations, water supply scenarios, and spatial scales. At the site scale, the RF model was generally suitable for simulating WF of maize, soybean, and rice, with R2 values ranging from 0.7 to 0.78 under different irrigation scenarios; the LightGBM was more suitable for simulating wheat with R2 values ranging from 0.76 to 0.82. The LightGBM was deemed to be the best model at the provincial scale, with some regions presenting a 50% lower root mean square error in the crop WF simulation than in RF. Variables related to spatio-temporal class feature were found to be the most important in both the RF and LightGBM simulations. This study provides a global methodological reference for different countries or regions to establish methods for rapid WF assessments in crop production with cross-disciplinary researchers or macro policymakers as the target users.