Reliable Supply Research Articles

Achieving tractable and reliable agriculture supply chain operations through Industry 4.0 tools to support Lean Six Sigma application

Achieving tractable and reliable agriculture supply chain operations through Industry 4.0 tools to support Lean Six Sigma application

A double-layer optimization strategy for distribution networks considering 5G base station clusters

The reliability of the power supply for 5G base stations (BSs) is increasing. A large amount of BS backup energy storage (BES) remains underutilized. This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning model for BS-joining grid market ancillary services is proposed. The upper-layer model addresses optimal tidal flow problems in DNs to minimize integrated operating costs, while the lower-layer model focuses on BES economic optimization. The double-layer model changes into a single-layer linear model using the Karush–Kuhn–Tucker (KKT) condition and the Big M method. Simulation validation using the IEEE-33 node DN proves that this approach can reduce DN operating costs, regulate voltage fluctuations, and guarantee economical and safe DN operation.

The Role of Labor Force, Physical Capital, and Energy Consumption in Shaping Agricultural and Industrial Output in Pakistan

The labor force, physical capital, and energy sources are key economic drivers that enhance the output levels of developing economies. Despite their significance, the impact of these factors on agricultural and industrial output in Pakistan remains underexplored. This study aims to address this gap by examining the effects of the labor force, physical capital, and energy consumption—including electricity, oil, and gas—on agricultural and industrial output. Additionally, we explore the combined effects of electricity and oil consumption on these sectors. Based on unit root test results, which indicate that the variables as either I(0) or I(1), the Autoregressive Distributed Lag (ARDL) technique is selected. This method is particularly effective for handling mixed integration orders and provides robust estimates in small sample sizes, allowing for a thorough examination of both short- and long-run dynamics. Using annual time-series data from Pakistan for the period 1990–2022, the empirical results reveal that higher levels of the labor force, physical capital, electricity consumption, oil consumption, and gas consumption significantly increase agricultural and industrial output in both the short and long run. Furthermore, the findings reveal that the combined effect of electricity and oil consumption has a stronger impact on agricultural and industrial output compared to their individual effects. These results are further validated using alternative econometric techniques such as DOLS and FMOLS. To enhance Pakistan’s agricultural and industrial performance, policies should prioritize investment in human capital and physical capital development, ensure reliable and efficient energy supplies, and promote integrated energy management strategies. These steps are crucial for fostering sustainable economic growth and productivity in both sectors.

Firm resilience, stressors, and entrepreneurial well-being: insights from women entrepreneurs in Ghana

PurposeThis study draws on the conservation of resources logic to theorize the role of firm resilience in explaining variations in entrepreneurial well-being under varying conditions of supply chain disruption and dependency ratio.Design/methodology/approachThe study uses ex-post survey data from 373 women entrepreneurs in diverse agricultural supply chains in Ghana, a sub-Saharan African country. Moderated regression analysis is employed to test the research hypotheses.FindingsThe results indicate that firm resilience has both positive and negative relationships with economic and subjective well-being, depending on the level of supply chain disruption and dependency ratio women entrepreneurs face. Notably, the findings suggest that firm resilience contributes more to economic and subjective well-being of women entrepreneurs when dependency ratio is low and supply chain disruption is high.Originality/valueThe study integrates firm resilience research and entrepreneurial well-being literature to provide new insights into theorizing and analyzing the benefit of firm resilience for women entrepreneurs’ well-being.

Protecting ancient water harvesting technologies in India: strategies for climate adaptation and sustainable development with global lessons

IntroductionAncient water harvesting systems, such as those from the Indus Valley Civilization (~3500 BCE), have been vital for irrigation and climate resilience, especially in arid regions. One such prominent system in South Asia, called tank irrigation, initially thrived through community management but declined post-independence due to colonial policies and neglect in Sri Lanka and India. This study evaluates current policy frameworks and rehabilitation programs to enhance the resilience of these systems in India, develop strategies for their protection and adaptation to climate change, and integrate global lessons for sustainable development.MethodsA systematic meta-analysis of grey literature was conducted to aggregate data on policy constraints. Policy analysis involved detailed investigations of relevant documents, regulations, and comparative analyses of frameworks at regional and national levels. Pilot projects on tank rehabilitation were assessed through reported case studies and field surveys to gauge impact. Thematic analysis was used to explore the global potential of these systems in climate resilience and overall environmental sustainability.ResultsThe analysis showed that pilot projects for tank rehabilitation had limited success in achieving sustainability under current climate conditions. Tank irrigation systems are crucial for adapting to extreme weather, including floods, droughts, and heat waves, replenishing groundwater, reducing soil erosion, and ensuring reliable water supplies. Traditional water harvesting technologies support 17 Sustainable Development Goals (SDGs), including clean water access, hunger reduction, gender equality, and climate action. Integrating AI and machine learning in water management benefits disaster response, while eco-tourism aids system maintenance and cultural awareness.DiscussionThe study underscores the need for policy reforms to enhance tank rehabilitation and institutional arrangements. It calls for increased beneficiary participation and constitutional recognition of current practices. Strategic, national-scale assessments and resilience targets are recommended to improve the effectiveness of such water harvesting systems in mitigating natural hazards and enhancing environmental services.

Electric vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization

The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage leading to a need for more coordinated and grid aware EVs charging and discharging strategies. However, coordinating power flow from multiple EVs into the grid requires sophisticated algorithms and load-balancing strategies as the complexity increases with more control variables and EVs, necessitating large optimization and decision search spaces. In this paper, we propose an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid, by utilizing EVs to store surplus energy and discharge it during periods of energy deficit. The optimization problem is addressed using Harris Hawks Optimization (HHO) considering criteria related to energy grid balancing, time usage preference, and the location of EV drivers. The EVs schedules, associated with the position of individuals from the population, are adjusted through exploration and exploitation operations, and their technical and operational feasibility is ensured, while the rabbit individual is updated with a non-dominated EV schedule selected per iteration using a roulette wheel algorithm. The solution is evaluated within the framework of an e-mobility service in Terni city. The results indicate that coordinated charging and discharging of EVs not only meet balancing service requirements but also align with user preferences with minimal deviations. The assessment of the determined solutions’ quality and efficacy shows promising outcomes, with convergence after 100 iterations reflected in a generational distance of 0.35 and a Pareto front error of 1.01, while the distribution of solutions exhibits strong hypervolume thus covering a significant portion of the objective space.

Optihybrid: a modified firebug swarm optimization algorithm for optimal sizing of hybrid renewable power system

AbstractIn areas where conventional energy sources are unavailable, alternative energy technologies play a crucial role in generating electricity. These technologies offer various benefits, such as reliable energy supply, environmental sustainability, and employment opportunities in rural regions. This study focuses on the development of a novel optimization algorithm called the modified firebug swarm algorithm (mFSO). Its objective is to determine the optimal size of an integrated renewable power system for supplying electricity to a specific remote site in Dehiba town, located in the eastern province of Tataouine, Tunisia. The proposed configuration for the standalone hybrid system involves PV/biomass/battery, and three objective functions are considered: minimizing the total energy cost (COE), reducing the loss of power supply probability (LPSP), and managing excess energy (EXC). The effectiveness of the modified algorithm is evaluated using various tests, including the Wilcoxon test, boxplot analysis, and the ten benchmark functions of the CEC2020 benchmark. Comparative analysis between the mFSO and widely used algorithms like the original Firebug Swarm Optimization (FSO), Slime Mold Algorithm (SMA), and Seagull Optimization Algorithm (SOA) demonstrates that the proposed mFSO technique is efficient and effective in solving the design problem, surpassing other optimization algorithms.

Impact of Climate Change on Agricultural Production and Food Security

This comprehensive analysis provides a detailed examination of the impacts of climate change on the agricultural sector, offering sustainable solutions with the objective of ensuring global food security. The detrimental impact of climate change on agricultural production, including global warming, significant alterations in precipitation patterns, and extreme weather events, has been meticulously assessed, with careful consideration given to their potential consequences for future food supply and security. In this context, a number of critical strategies and policies have been proposed, including the strengthening of water management policies, the advancement of sustainable agricultural practices, the development of climate-resilient seeds, and the enhancement of agricultural technologies. Moreover, the imperative for international collaboration and shared accountability has been forcefully emphasised, underscoring the necessity of maintaining this pivotal issue at the forefront of the global agenda. It is evident that a comprehensive approach is essential for combating climate change and ensuring sustainable agricultural production. Sustainable solutions and international cooperation are vital for guaranteeing a reliable future food supply and food security. In this context, the implementation of the proposed strategies and policies represents a crucial step towards aligning the agricultural sector with future needs and effectively addressing climate change.

Operational assessment of solar-wind-biomass-hydro-electrolyser hybrid microgrid for load variations using model predictive deterministic algorithm and droop controllers

This study presents the operation and assessment of a solar-wind-biomass-hydro-electrolyser hybrid microgrid for different types of load variations by using a proprietary derivative-free algorithm (Model Predictive Deterministic Algorithm) and voltage IQ droop controller. The proposed hybrid microgrid integrates various renewable energy sources and an electrolyser to generate hydrogen and produce electricity. The objective is to optimize the microgrid’s performance and provide reliable power supply to meet varying load demands. The proprietary derivative-free algorithm is used to determine the optimal dispatch of power from each energy source to meet the load demand while minimizing the overall cost of energy. Additionally, a voltage IQ droop and voltage Q droop controller are employed to regulate the voltage, frequency and active power of the microgrid and maintain its stability during load variations. The proposed hybrid microgrid is assessed under different load scenarios for both grid tied and isolated modes. The results demonstrate that the proposed hybrid microgrid with the derivative-free algorithm and voltage droop controllers can effectively operate and provide reliable power supply under different load variations while maintaining the power system stability of the microgrid. Further, the mutual performances of the controller are compared to find the best controller for the specific microgrid. The findings of this study can contribute to the development of efficient and reliable hybrid microgrid systems for sustainable energy production and distribution.

Modelling long-term operational dynamics of grid-connected hydro- photovoltaic hybrid systems

The development of variable renewable energy sources (VRESs), such as wind and solar photovoltaic (PV), has become a strategic choice for ensuring the security of electricity supplies and the decarbonization of power systems. The hybridization of hydropower with VRESs is an important measure for guaranteeing the smooth and cost-effective integration of VRESs into the power system. However, the highly variable weather- and climate-driven spatial and temporal fluctuations in VRES performance have significant potential to affect the optimal trajectory of hydropower operations. Therefore, it is essential to understand how hydropower responds to VRESs over long-term horizons, thereby allowing informed operation decisions to be made for hybrid generation systems (HGSs). In this study, a long- and short-term nested operation model is developed for hydropower operations considering PV integration. Specifically, the long-term model determines the day-by-day hydropower production over many years, and the short-term model simulates the day-ahead generation scheduling and real-time operation processes of the HGS. Performance metrics are established for evaluating the complementary operation in comparison with separate operation of hydropower and PV plants. China's two representative hydro-PV HGSs (Zhongyu and Longyangxia) are selected as case studies. The results indicate that the hybrid generation of hydropower and PV could, on average, increase total electricity generation by 11.33 %, reduce the load loss rate by 9.31 %, and decrease the PV curtailment rate by 36.60 %. However, hydro-PV hybridization may lead to an increase of 9.22 % in the water spillage rate and a decrease of 0.094 kWh/m3 in the water–electricity conversion coefficient. Therefore, hybrid generation of hydropower and PV could further enhance energy yield and supply reliability. Also, the importance of improving hydropower flexibility is highlighted.

Competing buyers’ investment and ordering strategies under supply disruption

Supply chain disruptions can be costly and disruptive, and reducing supplier uncertainty through investments can be an effective strategy for mitigating these risks. In this study, we explore ordering and investment strategies in a downstream competitive environment, considering supply uncertainty. We consider two buyers ordering from a single supplier and competing in a Cournot market. Buyers must balance investment benefits against costs when making investment decisions. We find that without downstream competition, a relatively low investment proportion can yield a “win-win” outcome. Under downstream competition, an increased number of investors does not necessarily lead to higher supplier reliability. When both buyers invest at a high ratio, the significant decline in order quantities may results in lower supplier reliability. When there is only one investing buyer in the market, even though the competitor may benefit from spillover reliability enhancement — free-riding, the buyer may also be willing to provide an investment subsidy to improve the supplier’s reliability. When both buyers invest at a moderate ratio, downstream competition may benefit all supply chain members. Moreover, increasing wholesale price differentiation and endogenous investment ratios can effectively reduce free-riding behavior of the non-investing buyer. These findings have practical implications for supply chain managers aiming to reduce supply risks and allocate investment resources efficiently.

Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar panels and advanced energy storage systems ensure reliable electricity supply. Smart inverters and grid-tied systems enhance energy management. Government incentives and decreasing installation costs have increased solar power accessibility. The proposed methodology, utilizing machine learning techniques, achieved an R-squared value of 0.95 and a Mean Squared Error of 0.02 in forecasting solar panel power output, demonstrating high accuracy in predicting energy production under varying environmental conditions. By improving operational efficiency and anticipating power output, this approach not only reduces carbon footprints but also promotes energy independence, contributing to the global transition towards sustainability.

Multifunctional applications of seaweed extract-infused hydroxyethyl cellulose-polyvinylpyrrolidone aerogels: Antibacterial, and antibiofilm proficiency for water decontamination

Establishing a reliable and secure water supply is still a significant challenge in many areas that need more infrastructure. Eliminating harmful bacteria from water systems is a critical obstacle to managing the spread of waterborne illnesses and protecting public health. Thus, this work focuses on enhancing the efficiency of using marine waste extract, namely seaweed, by its integrating its extract into aerogels based on Hydroxyethyl cellulose (HEC) and polyvinylpolypyrrolidone (PVP). Four formulations were created with increasing concentrations of Padina extract (PE): PE-0, PE-1, PE-2, and PE-3 loaded HEC-PVP aerogels. PE-3 loaded HEC-PVP aerogel showed remarkable efficacy in completely deactivating several types of bacteria, including Escherichia coli, Salmonella enterica, Enterococcus faecalis, and Bacillus subtilis. This antibacterial impact was seen within a short time frame of 75 min after treatment, making it the most significant outcome. Significantly, it had the greatest level of inhibition against E. coli (IZD: 24 mm) and showed potent inhibitory effects against S. enterica, E. faecalis, and B. subtilis, with IZD values of 18, 15, and 14 mm, respectively. These results indicate that the aerogel's ability to prohibite the harmfull microorganisms may be due to its surface qualities, which help release antimicrobial substances from the PE contained in the aerogel.

Advanced smart HVAC system utilizing borehole thermal energy storage: Detailed analysis of a Uppsala case study focused on the deep green cooling innovation

This article presents and thoroughly examines an innovative, practical, cost-effective, and energy-efficient smart heating, ventilation, and air conditioning (HVAC) system. The fundamental component of this concept is a state-of-the-art method called Deep Green Cooling technology, which uses deep drilling to utilize the ground's heating and cooling potential directly without the need for machinery or heat pumps. This method satisfies demands with the least energy use, environmental impact, and operational costs. In order to effectively oversee and regulate energy production, storage, and utilization, the system consists of an intelligent control unit with many smart controllers and valves. Renewable energy deployment is made easier, and the intelligent automation unit is more compatible with the help of a high-temperature cooling resource with a high supply temperature of 16 °C. The technical, environmental, and financial aspects of the suggested smart office building system in the southern region of Uppsala, Sweden, are evaluated using TRNSYS software. According to the results, boreholes provide more than 28.5 % of the building's energy requirements by utilizing the ground's ability to generate affordable, dependable seasonal thermal energy. The district heating network satisfies the remaining demand, amounting to 787.2 MWh, highlighting the benefits of combining conventional and renewable energy sources for increased supply security and dependability. The borehole thermal energy storage system meets the building's entire cooling need, underscoring the importance of high-temperature cooling systems. The most expensive part of the system is the borehole thermal energy storage, which accounts for over half of the total investment. The system has an appropriate payback period of ten years, proving its long-term profitability and cost-effectiveness, thanks to removing the machinery and heat pump. With 3138 MWh of ground-source heating and cooling, the system saves 17,962 USD by reducing CO2 emissions by about 143.7 t, sufficient to grow 16.3 ha of trees throughout the payback period.

Optimizing compressed air energy storage with organic Rankine cycle and ejector refrigeration for sustainable power and cooling provision

In the pursuit of sustainable energy systems, integrating storage technologies is crucial. Compressed air energy storage (CAES) emerges as a significant option for ensuring reliable power supply during peak hours. This study focuses on a configuration combining a CAES unit with two integrated organic Rankine cycle and ejector refrigeration units (ORCERC). The primary objective is to ensure adequate power supply during peak hours in the discharge period, while also providing cooling capacity to enhance system flexibility and efficiency. The proposed system is evaluated from thermodynamic, exergoeconomic, and exergoenvironmental perspectives to determine optimal operating conditions for the CAES unit. A fluid selection process identified a suitable zeotropic mixture, R141b/Hexane, as the working fluid for both ORCERCs. The results demonstrate a net power production of 20,749.91 kW and a cooling load of 1448.23 kW, with cost and exergoenvironmental impact rates of $1193.59/h and 143.42 Pt/h, respectively. Additionally, the configuration achieves an exergy round-trip efficiency of 65.85 % and a payback period of 2.88 years. Increasing the HTES temperature from 1150 to 1250 K improved both the round-trip efficiency (RTE) and exergetic round-trip efficiency (ERTE) from 52.22 % to 53.39 % and 62.98 %–63.71 %, respectively, while reducing the product cost and exergoenvironmental impact rate from $1128 to $1117/h and 134.35 to 127.93 Pt/h, respectively. Three triple-objective optimization scenarios were considered, yielding diverse outcomes, each contributing unique insights into the system's performance and potential improvements.

A two-stage optimal operation strategy for community integrated energy system considering dynamic and static energy characteristics

The community integrated energy system (CIES) can provide users with reliable and economical energy supply through centralized utilization of various energy equipment. However, due to the significant difference in inertia between different energy sources, the response speed of equipment using different energy sources varies greatly, making it difficult to cooperate well and limiting their ability to cope with source-load uncertainty. Based on this consideration, a two-stage optimal operation strategy for CIES is proposed to utilize dynamic and static energy separately. First, the dynamic and static energy are defined and divided, and their different functions are analyzed. Based on this, a two-stage optimal operation architecture is proposed. Then, a day-ahead interval optimal operation model considering dynamic and static energy matching is established. In which, the uncertainty of source and load is characterized by nonlinear interval numbers, and the interval possibility degree is used for deterministic transformation of nonlinear interval numbers. Thirdly, a dynamic and static energy replacement mechanism based on model predictive control is given, and an intra-day rolling optimal operation model considering dynamic and static energy replacement is established. Finally, a CIES is used as an example to demonstrate the rationality and effectiveness of the proposed strategy. Simulation results show that the proposed strategy can effectively utilize different types of energy equipment and significantly reduce the adverse effects of source-load uncertainty.

Assessing the fiscal implications of changes in critical minerals’ demand in the low-carbon energy transition

This study introduces the Fiscal Revenue Risk Index (FRRI), a novel metric designed to assess the impact of market fluctuations in critical minerals due to the materialization of climate-related transition risks on countries highly dependent on these resources. Focusing on minerals essential for renewable energy technologies, such as lithium, cobalt, nickel, and rare earth elements, our analysis evaluates how shifts in global demand might influence government revenues, providing crucial insights for policymakers. The FRRI methodology involves several key steps, including assessing fiscal revenue dependency, estimating price volatility, computing minerals’ production changes for future transition scenarios, and integrating these factors into a weighted risk model. This approach allows the computation of aggregate risk scores across various minerals and scenarios, reflecting a country’s exposure to fiscal risks. The study findings indicate that the demand for critical minerals could grow up to sevenfold over the coming decades, significantly impacting fiscal revenues in emerging markets and developing economies (EMDEs). While these countries stand to benefit from increased exports, they also face challenges related to supply reliability and price volatility. Overall, the FRRI is essential for identifying economic risks and opportunities related to the low-carbon transition in EMDEs.

Development of a Maintenance System for Low Voltage Electricity Distribution Networks

This research focuses on important efforts to ensure the availability and reliability of electricity supply through the design and implementation of effective systems for monitoring and maintaining low voltage electricity distribution networks. The methods used in this research include analysis of maintenance needs, development of software for network monitoring, and integration of sensor technology for early detection of disturbances. The developed system aims to increase maintenance efficiency by providing real-time information about network conditions, enabling preventive action before disruptions occur. In addition, this system is also designed to reduce downtime and maintenance costs, as well as increase the reliability of electricity supply for customers. The research results show that implementing advanced sensor technology and monitoring software can significantly improve response to disruptions and minimize the impact on customer service. Thus, this system not only increases operational efficiency but also provides long-term benefits in maintaining the stability and reliability of electricity supply.

Revolution in Renewables: Integration of Green Hydrogen for a Sustainable Future

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies.

Smooth control strategy for emergency switching of multi-port flexible interconnected distribution system modes

IntroductionWith the rise of distributed energy resources, the interconnection of distribution networks and Flexible Multi-State Switch (FMSS) has become a key technology in the construction of new distribution networks. FMSS plays a significant role in enhancing the reliability and flexibility of the system.MethodsThis paper investigates the impact of mode-switching in FMSSs on voltage shocks, current shocks, and power fluctuations in the event of a feeder fault in a multi-port flexible interconnected distribution system. Firstly, an improved state-tracking control method is proposed to effectively mitigate these impacts. Secondly, for feeder faults connected to the fixed DC bus voltage (Udc-Q) control port, a reselection method for the Udc-Q control port at the main station is introduced, aiming to select the optimal port to maintain the stability of the DC bus voltage.ResultsSimulation experiments have validated the effectiveness of the proposed control method in reducing voltage and current shocks as well as power fluctuations. Additionally, the proposed control strategy has demonstrated an enhancement in the safe and stable operation of the system under emergency fault conditions.DiscussionThe control strategy presented in this paper is capable of addressing the challenges posed by feeder faults and ensuring the stability of the DC bus voltage and reliable power supply to feeder loads, thereby enhancing the overall performance and reliability of the flexible interconnected distribution system. The research findings have been verified on the MATLAB/Simulink simulation platform.