Periods Of High Demand Research Articles

Determining Appropriate Number of Labors in Yellow Noodle SMEs Using K-Means Clustering Method: Meeting Demand While Minimizing Company Costs

Yellow noodle SMEs face the challenge of aligning their workforce with the fluctuating customer demand. Addressing this issue involves scaling up production staffing during peak yellow noodle demand. This study aimed to determine when and how much production labor should be added. Workforce expansion occurred through contractual arrangements during demand surges. This study used the K-Means clustering method to determine demand clusters. Each cluster underwent detailed analysis to calculate the precise number of required production workers. The study used the noodle sales in 2021–2022. Results showed that yellow noodle SMEs had eight permanent workers. Demand clusters were categorized into three classes. High-demand periods were observed in May and December, while moderate demand occurred in January, February, March, April, June, July, and August. September, October, and November constituted the low-demand cluster. Based on these findings, the study recommended adding one worker during high-demand and moderate-demand periods to maintain operational efficiency. Remarkably, no workforce additions were advised during the low-demand phase to optimize resource allocation and cost-effectiveness. In summary, this research addresses labor management challenges for yellow noodle SMEs. Leveraging data-driven approaches and cluster analysis offers SMEs a strategic framework to enhance workforce planning thereby improving operational efficiency and cost-effectiveness. Keywords: yellow noodle SMEs, workforce demand clustering, operational efficiency

Electrical Conductivity of Binary, Ternary, Quaternary and Quinary Molten Salt Mixtures Based on NaCl-CaCl2

As intermittent energy sources like solar energy and wind power emerge, the need for energy storage becomes important, energy availability needs to be ensured also when the Sun is not shining, and the wind is not blowing. Energy storage can also be used for peak shaving purposes during periods of high demand. Energy storage solutions need to be inexpensive and reliable. Novel all-liquid batteries are considered one option for stationary energy storage and the Na-Zn battery is currently being investigated. During charging Na metal is formed on the negative electrode from a NaCl containing electrolyte and ZnCl2 is formed from a Zn pool on the positive electrode. The electrical conductivity of the molten salt is an important factor in the ohmic loss through the electrolyte. The composition of the electrolyte decides the electrical conductivity, and this conductivity also changes during the charge/discharge cycles of the battery as the electrolyte composition changes accordingly. Electrical conductivity has been measured on different compositions of NaCl-CaCl2, NaCl-CaCl2-LiCl, NaCl-CaCl2-BaCl2, NaCl-CaCl2-ZnCl2, NaCl-CaCl2-BaCl2-SrCl2, NaCl-CaCl2-BaCl2-ZnCl2 and NaCl-CaCl2-BaCl2-SrCl2-ZnCl2 molten salts in an in-house built apparatus. The smaller ions (Li and Na) give higher electrical conductivity, while the larger ions (Ba, Sr, and Zn) reduce the electrical conductivity.

DESENVOLVIMENTO DE PROTÓTIPO UTILIZANDO MICROCONTROLADORES PARA CONTROLE DE EFICIÊNIA ENERGÉTICA EM RESIDÊNCIAS UNIFAMILIARES

The search for solutions that aim to improve the way we use electrical energy in our daily lives is of great importance when it comes to sustainability. Energy efficiency plays a fundamental role in the development of sustainable technologies. In a period of high demand for electricity and concerns about the environment, energy efficiency has become increasingly important, both in industrial and domestic applications. In this scenario, microcontrollers play a fundamental role in optimizing and controlling electrical energy. Among its various applications is monitoring and managing energy consumption in real time, making intelligent decisions to minimize waste. These systems, when integrated with sensors and other devices, allow the development of solutions that not only save energy, but also make environments smarter and more sustainable. Observing this context, this study aims to develop a device capable of providing accurate information about the electrical energy consumption of equipment. The developed prototype aims to provide detailed information about the energy consumption of monitored equipment, contributing to better energy management. The developed energy meter prototype demonstrated the potential to contribute significantly to energy efficiency, by providing accurate and real-time data, thus resulting in a more intelligent use of the energy consumed, as, with knowledge of real-time data, it is possible to monitor and work on energy optimization.

Electric load prediction using SARIMAX

ABSTRACT Electricity demand variation is one of the prevalent phenomena in Nepal. This variation can cause excess electricity during low-demand periods and a shortage during high-demand periods. An early prediction of electricity demand allows for levelling such fluctuations by introducing dynamic tariffs based on varying demands. This study aims to use Python’s time series models to make predictions about electrical demands and propose a dynamic tariff system. The dataset of Panama City’s hourly electrical load is brought into use. Two models, namely the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Factors (SARIMAX) and the BATS model, are deemed to be the two best time series models. The selection of optimal seasonal and non-seasonal orders in SARIMAX is made by comparing Bayes Information Criterion (BIC). For the SARIMAX model, a daily dataset has been taken by summing the hourly data for each day, whereas for the BATS model, hourly data for the year 2018 has been used. The SARIMAX model resulted in the best fit with the evaluation metrics values: Root Mean Square Error (RMSE) of 975,336.88, Mean Average error (MAE) of 739.09, and R2 score of 0.75. However, SARIMAX’s limitation is that the use of daily data does not allow dynamic tariff prediction, which is possible with the BATS model.

Numerical study of a hybrid system of solar panels, a diesel generator, and a wind turbine using a battery system

This paper gives an overall quantitative analysis of a hybrid renewable energy system (HRES) incorporating solar photovoltaic system, wind system, diesel generator and battery storage system. The system is aimed at coordination the management of the energy resources for providing the off-grid or remote facilities. The best intervention is thus to increase the consumption of renewable energy sources to minimize the reliance on diesel generators. The findings show that the hybrid system is capable of supplying different levels of power, which is pumped from the solar PV panels at maximum of 30.38 kW for the photovoltaic system at maximum solar intensity, and the wind power system to a maximum of 3213 kW of power generation under standard wind potential. The diesel generator serves a backup power source, which ranges from 91.24 kW and 864 at a rate of 250 kW during high activity and 5 kW during high-demand periods. Battery storage integration has a crucial part in matching the short-term supply-demand imbalance problem. The study suggests that the hybrid system reduces diesel use, emissions, and improves energy sustainability making the system a worthy proposition for remote areas. The findings therefore affirm the use of hybrid systems in off grid electrification since they operate economically and are eco-friendly.

Integrated risk management and maintenance planning in Oil and Gas Supply Chain operations under market uncertainty

The Oil and Gas Supply Chain (OGSC) is a multifaceted network comprising diverse activities and echelons. Instability or interruptions can cause economic fluctuations, impacting industries, markets, and consumers. Maintenance activities, which pause production but extend facilities' life, are recommended during non-peak demand periods to avoid production losses and meet customer demand. To mitigate these effects, decisions on operations planning, maintenance scheduling, and maintenance team assignments should be optimized in a risk management framework. The proposed model adopts a mixed-integer linear programming (MILP) framework and is solved via a sequential approach that incorporates the relax-and-fix (RF) heuristic in order to find a solution that is close to optimal. Subsequently, the solution serves as an initial solution for the CPLEX solver, which employs a branch-and-cut algorithm to attain the exact optimal solution. The practicality of this model has been showcased through its application to the supply chain in Saudi Arabia. The model efficiently schedules maintenance activities evenly and consistently across the OGSC plants over the planning period to reduce lost sales by keeping plants operational during high-demand periods. Furthermore, a sensitivity analysis was conducted to investigate the influence of the decision-maker's risk attitude on the outcomes that were obtained.

Patterns and characteristics of visits to psychiatric emergency departments: a three-year data study in China.

The composition and characteristics of emergency patients in the Affiliated Brain Hospital, Guangzhou Medical University during 2020-2022 were retrospectively analyzed to provide data support for the optimization of the process of psychiatric emergency and the elastic allocation of emergency medical staff. This study collected data from patients who sought medical attention at the emergency department of the Affiliated Brain Hospital, Guangzhou Medical University between January 1, 2020, and December 31, 2022. The fundamental information of these patients was statistically analyzed using descriptive analytic methods. In addition, a comprehensive statistical analysis was performed on the data of patient visits, which included precise triage time points, months, and seasons, in order to evaluate the temporal distribution of patient visits. The patient population had an average age of 36.4 years and was slightly more female (54.08%). The mean age of the male and female patients was 36.4 ± 18.91 and 36.4 ± 16.80 years, respectively. There was no statistically significant age difference between the male and female patients (p > 0.05). The top five diseases were mental disorder (6,483 cases), bipolar disorder (3,017 cases), depressive episode (2522 cases), schizophrenia (1778 cases) and anxiety state (1097 cases), accounting for 35.63%, 16.58%, 13.86%, 9.77% and 6.03% of the total, respectively. Additionally, a notable record of psychiatric drug intoxication was noted. Significant comorbidity with physical disorders, such as hypertension (9.36%), hypokalemia (3.41%), diabetes (2.83%), and cerebral infarction (2.79%), was also seen. The results of seasonal and monthly analysis indicated that emergency attendance patterns fluctuated, peaking in the spring and fall. The patterns of daily visits also revealed two peak times. The first peak occurs from 8:00 to 10:00, and the second peak occurs from 14:00 to 16:00. This study emphasizes the increasing occurrence of mental problems in psychiatric crises, particularly among younger populations, underscoring the necessity for comprehensive care methods. Specialized treatment methods and collaborative networks are required to address the substantial prevalence of psychiatric medication poisoning. Efficient allocation of resources and heightened security protocols are vital in emergency departments, particularly during periods of high demand and in handling instances of patient hostility.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

Impact of Iron Intake and Reserves on Cognitive Function in Young University Students.

Iron is a key nutrient for cognitive function. During periods of high academic demand, brain and cognitive activity increase, potentially affecting iron intake and reserves. The present study aimed to investigate the impact of iron levels on cognitive function in a university sample, considering the influence of gender. A cross-sectional study was conducted with 132 university students (18-29 years) from the University of Castilla-La Mancha (Spain). A dietary record was formed through a questionnaire to analyze iron consumption, and blood and anthropometric parameters were measured. The Wechsler Adult Intelligence Scale-IV was used to determine the Intelligence Quotient (IQ), as well as the Verbal Comprehension Index (VCI), Working Memory Index (WMI), Processing Speed Index (PSI), and Perceptual Reasoning Index (PRI), to assess cognitive abilities. Among women, the prevalence of iron deficiency (ID) and iron deficiency anemia (IDA) was 21% and 4.2%, respectively. No ID or IDA was found in men. The impact of iron intake on IQ and cognitive abilities was mainly associated with the female population, where a positive association between iron intake, serum ferritin, and total IQ was revealed. In conclusion, low iron intake is related to poorer intellectual ability, suggesting that an iron-rich diet is necessary to maintain the academic level of university students.

Dynamic pickup and delivery problem for autonomous delivery robots in an airport terminal

Autonomous delivery robot (ADR) operation for short-range delivery purposes is becoming an increasingly popular mode of service. Assigning orders to robots is critical in ensuring high quality in these automated delivery operations. This paper considers a dynamic pickup and delivery problem using autonomous robots (DPDP-AR), where a fleet of ADRs picks up desired items from stores and delivers them to customers. The arrival time of orders is uncertain, with a hard delivery deadline for each order. This study considered the battery consumption of ADRs, which has been less extensively covered in previous research, and devised a battery recharging strategy for the operation. To handle this stochasticity and dynamism in the problem, we developed a reassignment algorithm that reschedules previously assigned orders at the arrival of a new order. Additionally, as periods of high demand can be estimated, peak time management of the battery is proposed to enhance ADR utilization at peak periods. Computational experiments were performed on real-world ADR food delivery service instances in an airport terminal. Test instances on various demand scenarios demonstrated improvement in service quality when devised policies were used compared to current practice. We substantiated that the proposed algorithms found efficient solutions within short computation times, validating their applicability to real-world operations.

Fundamental Studies of Discharge Mechanism and Capacity Limits of Lead Acid Battery Electrodes

Long-duration energy storage provides key benefits such as consistent power supply during periods of high demand for grid reliability and flexibility. For environmental benefits, it allows for more renewable energy integration and helps reduce reliance on fossil fuels to lower greenhouse gas emission. Lead batteries have the economic potential to continue serving as an important energy storage technology for a decarbonized sustainable energy future given they are earth-abundant, inexpensive, and 99% recyclable. Advancing the design of lead batteries with higher material utilization, fast recharge rates, and long-cycle life requires a fundamental understanding of the electrochemical and chemical processes taking place at the atomic and molecular scales. In this work, we will discuss how the use of well-defined electrode interfaces allows us to gain insights into the fundamental limits of discharge capacity and recharge rates. We were able to unveil the relationships between discharge rates and PbSO4 particle size/layer thickness that ultimately governs the maximum discharge capacity accessible as a function of discharge rate on both the negative and positive lead electrodes. This data helped us develop a mathematical model that captures the key processes concerning lead ion and (bi)sulfate ion gradients coupled to nucleation and growth that explain, from first principles, the origin of the well-known empirical Peukert law. In this context, exploring how variables such as acid concentration, temperature, and the presence of lignosulfonate additives in the electrolyte further influence discharge capacity allows us to understand how thermodynamic, kinetic, and even mass transport play a huge role in governing the accessible capacity from the electrodes. This study paves the way for a deeper understanding of the variables that are most important in improving the discharge capacity of lead-acid batteries, and thus batteries that can be utilized at their full potential.

Performance Analysis of Multiple Energy-Storage Devices Used in Electric Vehicles

Considering environmental concerns, electric vehicles (EVs) are gaining popularity over conventional internal combustion (IC) engine-based vehicles. Hybrid energy-storage systems (HESSs), comprising a combination of batteries and supercapacitors (SCs), are increasingly utilized in EVs. Such HESS-equipped EVs typically outperform standard electric vehicles. However, the effective management of power sources to meet varying power demands remains a major challenge in the hybrid electric vehicles. This study presents the development of a MATLAB Simulink model for a hybrid energy-storage system aimed at alleviating the load on batteries during periods of high power demand. Two parallel combinations are investigated: one integrating the battery with a supercapacitor and the other with a photovoltaic (PV) system. These configurations address challenges encountered in EVs, such as power fluctuations and battery longevity issues. Although batteries are commonly used in conjunction with solar PV systems for energy storage, they incur higher operating costs due to the necessity of converters. The findings suggest that the proposed supercapacitor–battery configuration reduces battery peak power consumption by up to 39%. Consequently, the supercapacitor–battery HESS emerges as a superior option, possibly prolonging battery cycle life by mitigating stress induced by fluctuating power exchanges during the charging and discharging phases.

Gran Canaria energy system: Integration of the chira-soria pumped hydroelectric power plant and analysis of weekly daily demand patterns for the year 2023

Due to its insular condition, Gran Canaria operates an isolated energy system that requires high self-sufficiency in energy generation and that covers its energy need, 3,327,872.76 MWh/year. The future integration of the Chira-Soria Pumped Hydroelectric Power Plant, scheduled for 2030, is expected to radically transform the energy dynamics of Gran Canaria's electricity system, facing different challenges and opportunities. Challenges include the need for greater flexibility due to growing renewable energy sources, going from 381,000 MW of renewables today to an increase of more than 750,000 MW, environmental commitments, and the operation must address potential operational constraints related to downstream hydrological effects. On the other hand, the opportunities lie in the ability of the reversible hydropower plant to provide balancing services during off-peak hours, improving the integration of intermittent energy sources such as wind and solar power, in addition, the Pumped Hydroelectric (PHES) technology is recognized as mature and efficient for large-scale energy storage, contributing significantly to the integration of renewable energy sources. Implementing innovative approaches such as integrating Big Data into construction projects can also improve efficiency and decision-making in the project delivery process. This facility will facilitate energy storage through the pumping of water at high levels, allowing it to be subsequently turbined in periods of high demand, which will be essential to improve the management and efficiency of the island's energy system. This energy demand will be studied, which follows certain patterns according to the day of the week and, continuing with the line of research established in previous works, a detailed analysis of the existing system, simulation and algorithmic optimization of the integration of the Chira-Soria Pumped Hydroelectric Power Plant into the energy system of Gran Canaria in the year 2023 will be carried out, providing the expected results of such optimal integration by differentiating the demand patterns of each day of the week, previously establishing these annual representative days.

Assessing optimal dispatch and pool market (symmetric and asymmetric) results for different periods

This document presents a solution for an assignment related to the Energy Markets and Regulation curricular unit of PDEEC. The assignment consists of assessing optimal dispatch and pool market (symmetric and asymmetric) results for different periods. To check the technical feasibility of implementing the dispatch recommended by the pool market, a DC power flow is analyzed, by accounting for a network with six busbars. Results show that in some periods of higher demand, there could be an overload in some transmission lines of the considered network for certain results of market dispatch.

Comprehensive Performance Analysis of Hybrid Solar Water Heating Systems: Synergizing Photovoltaic and Thermal Technologies

Abstract: Solar water heaters are an efficient and sustainable technology for meeting the growing demand for hot water in residential and commercial applications. This abstract presents a comprehensive overview of the performance evaluation of solar water heater systems through an extensive experimental study. The study aims to assess the effectiveness and efficiency of different solar water heater configurations under varying operating conditions. The experimental investigation involved the measurement and analysis of key performance parameters such as solar collector efficiency, heat transfer efficiency, thermal storage capacity, and overall system efficiency. A range of factors including solar radiation intensity, ambient temperature, water flow rate, and collector tilt angle were systematically varied to examine their impact on system performance. The results of the experimental study demonstrated the significant potential of solar water heaters in providing cost-effective and environmentally friendly hot water solutions. The solar collector efficiency was found to be highly influenced by the solar radiation intensity, with higher levels of solar irradiation leading to increased thermal energy generation. The heat transfer efficiency was optimized by adjusting the water flow rate and collector tilt angle, ensuring efficient transfer of heat from the collector to the water. Furthermore, the thermal storage capacity of the system played a crucial role in maintaining a consistent supply of hot water during periods of low solar radiation or high demand. Overall system efficiency was found to be strongly dependent on the integration of efficient heat exchangers and insulation materials. Based on the experimental findings, recommendations for system design and optimization were formulated, highlighting the importance of proper sizing, selection of suitable materials, and appropriate control strategies. The results also identified areas for future research, such as the integration of advanced heat transfer enhancement techniques and the utilization of hybrid solar water heater systems

Occurrence of Fusarium Crown Rot Caused by Fusarium culmorum on Winter Wheat in Xinjiang Uygur Autonomous Region, China.

Wheat (Triticum aestivum L.) is the predominant grain crop and plays a pivotal role in grain production in Xinjiang Uygur Autonomous Region (XUAR), China. Its cultivated area constitutes approximately half of the total sown area of grain crops in XUAR, with 1.14 million hectares in 2021. Fusarium crown rot (FCR) of wheat, caused by Fusarium culmorum (W.G. Smith) Sacc., is one of the most devastating soil-borne diseases known to seriously reduce grain yield (Ma et al. 2024; Saad et al. 2023). In 2016, FCR of wheat, caused by F. culmorum, was firstly identified in Henan Province, China (Li et al. 2016). In June 2023, during the investigation of FCR of wheat in Aksu Prefecture, XUAR, FCR on winter wheat (cv. Xindong 20) was found (82.761349°E, 41.612202°N). The grain-filling period for winter wheat in early June coincided with a period of high temperatures and water demand in Aksu Prefecture. Approximately 8% of the Xindong 20 wheat plants exhibited symptoms of white heads and browning at the stem base, with the disease present in 82% of the wheat fields surveyed. To identify the pathogens, 20 samples of diseased stem basal tissue, each 0.5 cm in length, were collected and sterilized with 75% alcohol for 30s and 5% NaOCl solution for 2 min, followed by three rinses with sterile water. These samples were then plated onto potato dextrose agar (PDA) medium at 25°C for 5 days. A total of 17 isolates with consistent morphological characteristics were obtained using single-spore technique, with an isolation rate of 85%. The isolated strains exhibited rapid growth on PDA, producing fluffy, pale-yellow hyphae, and accumulating a pale-yellow to dark red pigment on the bottom of the medium. On carnation leaf agar (CLA), these strains formed orange colonies due to the aggregation of a large number of macroconidia. The macroconidia were short and thick, with three to four septa and rounded apical cell, averaging 31.94 to 40.96 × 5.62 to 6.71 μm (Magnification of ×400). Microconidia were not observed. These morphological characters were consistent with those of F. culmorum (Leslie and Summerell. 2006). Two isolates (D-9 and D-11) were selected for molecular identification. The EF-1α gene fragment was amplified using primers EF1/EF2 (5'-ATGGGTAAGGARGACAAGAC-3'/5'-GGARGTACCAGTSATCATG-3') as previously described by O'Donnell et al. (1998). The two 665 bp PCR products were sequenced and submitted to GenBank (GenBank Accession No: PP763247 and PP763248) with 99. 7% identity to the published F. culmorum sequences (e.g., OP985478, OP985477, MG195126, KX702638). The molecular identification was further confirmed by F. culmorum species-specific PCR primers FcOIF/FcOIR (Nicholson et al. 1998). The expected PCR products of 553 bp were produced only in F. culmorum. Strains D-9 and D-11 were used to conduct the pathogenicity experiment on 7-day-old winter wheat (cv. Xindong 20) using drip in the lower stem inoculation method with a 10-μl of 106 macroconidia ml-1 suspension, and the control 7-day-old winter wheat were treated with sterile water (Xu et al. 2017). The experiments were replicated five times in a greenhouse at temperatures ranging from 20℃ to 25℃. After 4 weeks, all inoculated wheat seedlings showed stem base browning or even death. No symptoms were observed on the control plants. The fungus was reisolated from all inoculated wheat plants by the method described above and identified by morphological and PCR amplification using F. culmorum species-specific primers FcOIF/FcOIR. No F. culmorum was isolated from the control wheat plants, fulfilling Koch's postulates. To the best of our knowledge, this is the first report of F.culmorum causing FCR on winter wheat in XUAR, China. Considering wheat is the predominant grain crop and plays a pivotal role in grain production in China, necessary measures should be taken to prevent the spread of F. culmorum to other regions.

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Optimizing Grid Performance in Trinidad and Tobago: The Role of Vehicle-to-Grid (V2G) Technology

This study investigates the impact of integrating 10,000 battery electric vehicles (BEVs) into the electrical grid of Trinidad and Tobago through three charging scenarios: non-incentivized charging, charging at work, and a Vehicle-to-Grid (V2G) program. The results reveal that non-incentivized charging exacerbates peak demand and grid strain, while workplace charging provides only modest peak demand mitigation. In contrast, the V2G scenario significantly reduces peak load impacts and enhances grid stability by leveraging BEVs as dynamic energy storage units that contribute to grid services during high-demand periods. The study proposes a V2G tariff scheme that includes compensation for battery degradation, aiming to incentivize participation and offset potential costs. Economic analysis shows that while V2G involves higher per-MWh costs than conventional storage technologies, it avoids the need for substantial capital investment in static energy infrastructure, presenting a cost-effective solution for energy management in island nations. The findings highlight the potential of V2G technology to facilitate sustainable energy transitions, emphasizing its role in enhancing grid resilience, optimizing renewable energy usage, and reducing carbon emissions. This research underscores the transformative potential of V2G systems as critical enablers of sustainable energy strategies in regions facing similar challenges to Trinidad and Tobago.

Subclinical hypocalcemia in primiparous lacaune ewes

Peripartum in sheep is a critical period of high nutritional demand, as the animal's body undergoes several physiological changes due to the high energy expenditure for the development of one or more fetuses and for maintenance, and metabolic disturbances can occur. The aim of this study was to assess the influence of subclinical hypocalcemia in the pre-partum period on the metabolism of primiparous Lacaune ewes in the peripartum period. Thirteen primiparous Lacaune ewes with an average body weight of 73.54 ± 10.70 kg, an average body score of 3.12 ± 0.42 and an average age of 0.85 ± 0.55 pairs of teeth were used. Blood samples and body condition score evaluations were taken from the ewes on days -14, -9 and -4 days before they were due to give birth and on days 1, 3, 7 and 14 days after giving birth. The ewes' serum was analyzed for calcium (Ca), magnesium (Mg), non-esterified fatty acids (NEFA), beta hydroxy butyrate (BHB), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), paraoxonase 1 (PON1), albumin (ALB), total plasma proteins (PPT) and urea. The animals were categorized according to their average prepartum Ca levels into the normocalcemic group (NC ≥ 9.41) and subclinical hypocalcemia (HC < 9.41 mg/dl). The data was analyzed using SAS software. The number of lambs at birth did not influence serum calcium levels in primiparous ewes (P= 0.4687). There was an interaction between group and collection on calcium (P=0.0166) and magnesium (P=0.0428) levels. The highest level of magnesium was observed in the normocalcemic group at collection -14, while the lowest level of calcium was observed in the hypocalcemic group at collection -14. There was a collection effect on body condition score (BCS), NEFA, BHB, AST and GGT (P<0.05). The lowest BCS (P = 0.0300) and the highest levels of NEFA (P = 0.0143) and BHBA (P = 0.0316) were observed on day 14 after calving. Higher levels of AST and GGT were observed in the postpartum period (P<0.05). The number of lambs at birth does not influence calcemia, and negative energy balance is more pronounced after birth in primiparous Lacaune ewes.

Decentralised control strategy for effective utilisation of distributed community energy storage in PV-rich LV distribution network

Integrating a high-capacity residential-scale photovoltaic (PV) system into a low-voltage (LV) network results in voltage rise. Conversely, voltage drops occur during periods of high demand. Furthermore, PV generation characteristics vary with seasonality and daily weather conditions, complicating voltage rise and drop management. On the other hand, community energy storage (CES) emerges as a solution for future community energy management. This study presents an application of distributed CES to effectively manage voltage rise and drop, addressing a prominent challenge accompanying the widespread adoption of PV. However, upon introducing the CES and its accompanying energy capacity, the control strategy must effectively incorporate the utilisation of the CES’s available capacity year-round. In brief, CES control should adapt to the characteristics of PV by charging the CES during high PV generation. It should also release active power to meet high demand by discharging the CES. This will enable both voltage management and effective CES capacity utilisation in response to PV variations due to seasonality and daily weather conditions. The goal mentioned above can be effectively accomplished by forecasting PV output and using the data to adapt to PV characteristics in a centralised manner. However, due to the challenges associated with centralised control, the present paper proposes a decentralised control algorithm that determines charging and discharging power rates for distributed CES, aligning with the PV and load characteristics in an LV distribution network. The proposed control framework consists of two stages: the first stage regularly updates power references based on clear-sky PV profiles, active power measurements at the LV transformer, and CES active power and energy level. The second stage is activated to mitigate voltage transient caused by fast-moving clouds and small increments in solar irradiance, load between consecutive updates of the first stage. To illustrate and validate the concept, a 13-bus LV distribution network is used, and the proposed control algorithm is implemented on a co-simulation platform that combines MATLAB with a real-time digital simulator.