Variable Demand Research Articles

Comprehensive Assessment of the Effectiveness of Passenger Transportation Processes using Intelligent Technologies

Introduction This studypresents a comprehensive evaluation of the effectiveness of passenger transportation using intelligent technologies based on economic, technical, and operational efficiency parameters. It reviews existing approaches to efficiency assessment and proposes an optimal set of criteria for assessing the efficiency of the passenger transport process, which will allow for better planning of routes, movement schedules, and choice of transport rolling stock. Objective This study aims to provide a better assessment of the efficiency of passenger transport using intelligent technologies. Aims This article aims to carry out an integral assessment of the efficiency of passenger transport using intelligent technologies based on the criteria of economic and technical-operational efficiency, which will allow better planning of routes and schedules and selection of rolling stock Methods In order to obtain a comprehensive criterion for assessing the efficiency of the transportation process, diverse techniques, including computer and mathematical modeling, algorithm theory, mathematical statistics, observation, and measurement, were employed. Results A general criterion was created in order to evaluate the efficiency of passenger transportation processes based on the overall costs for both the user and the operator. Furthermore, a specific criterion of efficiency for passenger transportation processes has been defined: passenger-kilometers traveled. The applicability of the model is evaluated in a bus corridor in Havana. The results show that due to external factors, namely overcrowding, overall costs decrease at the same rate for both 12-meter and 15-meter buses with different intervals, while the number of passengers transported per kilometer increases at a faster rate for larger buses. Conclusion Choosing a criterion to assess the effectiveness of the passenger transportation process requires considering various aspects of importance that will result in passenger comfort and safety, as well as the effectiveness of the transport process. To solve this problem, it is necessary to do additional research and analyses to determine the most appropriate set of criteria under specific conditions. In particular, consider vehicles of smaller capacity to take into account volumes with variable demand.

Cost–Performance Optimization of a Renewable Energy Resources Based Multimicrogrid System

The growing energy demand, the increase in its price, and the increasing pollution worldwide are considered large problems that need unusual solutions. To overcome these problems, it is necessary to search for innovative solutions using renewable resources and energy management of the energy systems. Energy management means reducing the operating, maintenance, and generation costs of the system and enhancing system performance with methods such as power loss reduction and stability enhancement and alleviating the harmful emissions to the environment. Thus, the energy management of a micro-grid has become one of the most vital aspects of the power or energy system all over the world.The objective of this paper is to optimize a multiobjective problem of a renewable energy resources (RER) based multimicrogid (MMG) system considering the output variation of the photovoltaic (PV) and wind Turbine (WT) as renewable energy resources and variation of the load demand and electricity prices. In this paper three microgrids (MGs) are connected with IEEE 33-bus distribution system each MG consists of a PV and WT. A three objective functions are modeled for this system to optimize the total annual cost, the voltage deviation and the voltage stability index (cost –performance multiobjective function). The optimization problem is solved with particle swarm optimization (PSO) technique for the system with and without RER.A comparison is carried out with two other optimization techniques, mountain gazelle optimization (MGO) and gorilla troop optimization (GTO). The results of the simulation show that the system cost is considerably reduced and the performance optimized.

A viable supplier selection with order allocation by considering robustness, risk-averse and blockchain technology

The research introduces a novel approach called Viable Supplier Selection with Order Allocation considering Robustness and Risk-Aversion (VSSOARR), incorporating Blockchain Technology (BCT). The model utilizes a Robust Stochastic Optimization (RSO) technique with Weighted Value at Risk (WVaR) in the cost function. The objective function of this model combines the expected value and WVaR cost. The BCT platform is recommended to enhance agility in the face of disruptions. The research proposes sustainable requirements such as minimizing CO2 emissions and energy consumption management, ensuring minimum demand satisfaction, and incorporating facility capacity based on specific scenarios to enhance resiliency. The study involves various stakeholders, including suppliers and manufacturers. The main purpose of utilizing BCT is to facilitate swift transactions between components and support risk-averse decision-making. Integrating BCT as an agility instrument resulted in a 16.93% cost reduction for VSSOARR compared to scenarios where BCT was not considered. The research suggests incorporating viability with BCT positively impacts the supply chain’s overall performance. A sensitivity analysis was conducted on the conservatism coefficient, confidence level, agility rate, demand variation, and problem size.

Balancing Possibilist-probabilistic risk assessment for smart energy hubs: Enabling secure peer-to-peer energy sharing with CCUS technology and cyber-security

This paper introduces the Possibilistic-Probabilistic Risk-based Smart Energy Hub (PPR-SEH) scheduling model, addressing cybersecurity challenges in the transition to advanced energy systems characterized by decarbonization, decentralization, and digitalization. The model integrates cutting-edge technologies like Carbon Capture Utilization and Storage (CCUS) and demand response programs. It addresses uncertainties from renewable energy sources, demand variability, fuel supply, and energy price fluctuations using a Z-number-based approach. Covering various energy types—electricity, heat, cooling, gas, and water—the PPR-SEH model offers a comprehensive energy management solution. Employing a mixed-integer linear programming (MILP) framework optimized with the CPLEX solver in GAMS software, it uses an epsilon constraint method and a fuzzy satisfying approach for solution selection. The model also evaluates the economic and environmental impacts of peer-to-peer energy-sharing markets linked with carbon emission trading, enhancing the efficiency and sustainability of energy distribution. The findings reveal that incorporating robust cybersecurity measures and integrating demand response and CCUS significantly influence operational efficiency and sustainability, evidenced by an increase in costs and pollution by approximately 11.43 % and 1.8 %, respectively, compared to deterministic approaches. This study underscores the importance of robust planning and the beneficial impact of a carbon system on load curve management and economic returns in smart energy systems.

OCL-MEC: An online CPU-core prediction based on load balancing framework for offloading resource management in mobile edge computing environment

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

Optimal reconfiguration, renewable DGs, and energy storage units’ integration in distribution systems considering power generation uncertainty using hybrid GWO-SCA algorithms

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.

On States and Traits in Work-Family Research

ABSTRACT Latent state-trait research on work-family conflict confirms that occasion-specific variation in work-related demands is related to corresponding variations in perceptions of work interfering with family (WIF), but WIF also displays moderate (~35%) to substantial (~80%) trait-like stability over time. What is not clear to date is whether this cross-situational stability in WIF is due to stable respondent characteristics (e.g. personality traits) or intrinsic situational stability in the work environment (e.g. work demands). Results from secondary analyses of four diary study data sets indicated that Trait-based (time-invariant) WIF primarily reflects intrinsic stability in work situations (e.g. especially workload) and to a lesser extent Negative (but not Positive) affect. Results support recent theory and add to a growing body of research on stability in work-family conflict across both daily and weekly assessments. Implications for interventions aimed at ameliorating WIF are discussed.

A proposal of analytical formulations to calculate safety lead times under demand variability. A case study

This paper deals with the issue of safety lead time (SLT) calculations in production-inventory systems in presence of both demand and replenishment lead time variability. We provide some formulations of the SLT and numerically show their performance as compared to a benchmark in the literature. Thus the main objective of this paper is to provide analytical formulations to calculate the SLT that contemplate demand variability. To this end, a literature review was done to analyze the approaches and justifications of the different revised research works to identify reference formulations according to the objectives of this work. A supply chain from the automotive sector was used as the study frame and to validate the proposed formulations. This supply chain involved two companies: a car manufacturer and a first-tier supplier. In order to compare the proposed formulations with one another, and with that currently used by the first-tier supplier and is the study object, three parameters were used: safety stock, the number of times stockout occurs and the mean stock. They allowed the final selection of the most suitable SLT formulation for each case study.

Multi-mode monitoring and energy management for photovoltaic-storage systems

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response, photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations and improve installation performance. However, during this procedure other functionalities that energy storage could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that interacts with parameters of the electrical grid at the common connection point and also allows the compensation of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand forecasting model that improves the supervision, automation and analysis of daily energy production. A series of results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively.

DCoMA: A dynamic coordinative merging assistant strategy for on-ramp vehicles with mixed traffic conditions

Merging sections on highways are identified as traffic bottlenecks, leading to congestion and accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology promises an optimized solution to on-ramp merging issues. Existing cooperative merging strategies typically focus on determining the merging sequence of vehicles in specific merging areas, overlooking the influence of the macroscopic traffic flow conditions on the merging process. In this paper, we innovatively introduce the Dynamic Cooperative Merging Assistance (DCoMA) strategy, a traffic management approach designed to enhance merging operations under variable traffic demands. At the macroscopic level, DCoMA employs the fundamental diagram of traffic flow to develop a platoon formation algorithm for mainline vehicles, tailored to the dynamic macroscopic states of traffic. The algorithm adaptively adjusts the size of platoons based on the volumes of both the mainline and the on-ramp. Subsequently, the spatio-temporal dynamics of these platoons function as the ’red phase’ of traffic signals, with the intervals between platoons analogous to the ’green phase’. This information is then converted into a time-series format and transmitted to all vehicles on the on-ramp. At the microscopic level, vehicles on the on-ramp alter their driving strategies in response to this time-series data, ensuring a seamless merging into the mainline flow without causing halting. Through simulations and comparative analysis with three existing strategies (i.e., CoopMA, ALINEA, and X-ALINEAQ), the proposed DCoMA strategy exhibits considerable potential for application across diverse traffic volumes and CAV penetration rates. The results indicate that the proposed approach can significantly improve the efficiency of the mainline and on-ramp, achieving a maximum improvement of 18.32%. More importantly, the DCoMA strategy effectively enlarges merging gaps and, coupled with the speed control capabilities of CAVs, significantly mitigates the risk of accidents and reduces exhaust emissions.

Optimal demand response scheduling and voltage reinforcement in distribution grids incorporating uncertainties of energy resources, placement of energy storages, and aggregated flexible loads

Instead of expanding power plant capacities, which is an extremely expensive investment option, demand response offers an economical solution to the challenges arising from the variability and intermittency of the renewable energy resources and demand variations, particularly during demand peak periods. This paper proposes a multi-objective optimization framework for the optimal power flow problem that integrates a stepwise demand response involving flexible and aggregated loads. The process includes short-term demand forecasting using long short-term memory (LSTM) networks in a smart distribution grid, followed by the optimal allocation of energy storage systems, and load aggregators. By determining the optimal solution point of the multi-objective problem analytically, significant system costs and peak demand can be reduced without compromising system stability. Through numerical studies for a sample study case, a reduction of 22% in system costs, 2% in total voltage variation, and 10% in peak demand is observed for a negligible impact on customers’ convenience.

Optimizing Mixed-Model Synchronous Assembly Lines with Bipartite Sequence-Dependent Setup Times in Advanced Manufacturing

In advanced manufacturing, optimizing mixed-model synchronous assembly lines (MMALs) is crucial for enhancing productivity and adhering to sustainability principles, particularly in terms of energy consumption and energy-efficient sequencing. This paper introduces a novel approach by categorizing sequence-dependent setup times into bipartite categories: workpiece-independent and workpiece-dependent. This strategic division streamlines assembly processes, reduces idle times, and decreases energy consumption through more efficient machine usage. A new mathematical model is proposed to minimize the intervals at which workpieces are launched on an MMAL, aiming to reduce operational downtime that typically leads to excessive energy use. Given the Non-deterministic Polynomial-time hard (NP-hard) nature of this problem, a genetic algorithm (GA) is developed to efficiently find solutions, with performance compared against the traditional branch and bound technique (B&B). This method enhances the responsiveness of MMALs to variable production demands and contributes to energy conservation by optimizing the sequence of operations to align with energy-saving objectives. Computational experiments conducted on small and large-sized problems demonstrate that the proposed GA outperforms the conventional B&B method regarding solution quality, diversity level, and computational time, leading to energy reductions and enhanced cost-effectiveness in manufacturing settings.

Unveiling the evolution and future prospects: A comprehensive review of low-carbon transition in the coal power industry

Transitioning to low-carbon coal power plants is critical in addressing climate change, given the significant contribution of coal-fired power generation to global CO2 emissions. Our understanding of the low-carbon transition in the coal power sector has significantly advanced in recent decades. However, the sector's transition remains contentious due to methodological disparities, inconsistent datasets, and varying assumptions across existing studies, which pose challenges for predicting future research trajectories and actions. To address this issue, we developed a Document-based Information Mining and Statistical Analysis Method, conducting a comprehensive review, bibliometric analysis, and comparative study of 1905 journal articles and 14 databases sourced from international authorities. Our quantitative analysis reveals a notable upswing in publications and international collaborative efforts in coal power transition research, with average growth rates of 8.6%, 9.3%, and 3.7% for authors, countries, and institutions, respectively. These studies depict diverse transition trajectories, projecting global and Chinese coal power capacities ranging from 58-927 GW and 0-395 GW in 2050, driven by six key uncertainties such as carbon budgets and energy demand variations, and advancements in CCUS and renewable technologies. We categorized 44 major coal-dependent countries into three groups based on their engagement with coal power transition research, identified three potential transition patterns aligned with temperature control goals, and outlined four distinct research stages in the evolution of coal power transition. Drawing from these insights, we conclude with valuable perspectives on the expansion of international collaboration, reduction of transition uncertainty, focus for future research, and mitigation of transition risks.

The endogeneity of the natural rate of growth in Latin America

This study estimates the natural rate of growth à la León-Ledesma and Thirlwall for a panel of eleven Latin American countries from 2000 to 2023 using the seemingly unrelated regressions (SUR) estimator. Likewise, in the model, we incorporate a dummy variable in boom periods to determine whether endogeneity is present due to variations in the current rate of growth. Our results show that the behavior of the natural rate has a direct relationship with changes in the current rate; that is, it increases in boom periods and decreases during recessions, confirming the presence of endogeneity. In this sense, it is advisable to continue exploring the nature of the sensitivity of the natural growth rate to the economic situation. By providing recent evidence for Latin American countries with different degrees of economic development, our results allow us to conclude that the hypothesis of full employment on the supply side is not sufficient to explain the economic dynamics, so the inclusion of demand variables is necessary.

The Time‐Varying Price of Financial Intermediation in the Mortgage Market

ABSTRACTWe introduce a new measure of the price charged by financial intermediaries for connecting mortgage borrowers with capital market investors. Based on administrative lender pricing data, we document that the price of intermediation reacts strongly to variation in demand, reflecting capacity constraints of mortgage originators. This positive comovement of price with quantity reduced the pass‐through of quantitative easing. We also find a notable upward trend in this price between 2008 and 2014, likely due to increased legal and regulatory burden in the mortgage market. The trend led to an implicit cost to borrowers of nearly $100 billion over this period.

Testing a novel life cycle assessment based framework for produced water management from offshore oilfield operations

As worldwide reliance on the petroleum industry persists, environmental concerns associated with offshore oilfield operations continue to present a critical challenge for sustainable management. This study develops and tests a novel life cycle assessment (LCA)-based framework to examine strategies for produced water (PW) management from offshore oilfield operations. The framework encompasses experimental designs under a dynamic modelling system coupled with stepwise linear regression, multi-criteria decision analysis (MCDA), and fluctuating scenarios in Enhanced Oil Recovery (EOR) and other internal reuse options including cooling systems, drilling operations, and utility services. Two strategies for treatment train technologies were tested with various PW compositions, sludge management options, and variable EOR demands. When comparing conventional (corrugated plate interceptor, hydrocylone and nutshell filter) with recent treatment/disposal technologies (tubular separation, combined fiber coalescence and mechanical vapor compression), the LCA revealed that the latter increased operational efficiency (14%), reduced cost (6.5%) and the overall environmental impact categories (66%). The scenario that exhibited the lowest emissions incorporated recent treatment technologies and land application for sludge management. This configuration demonstrated considerable environmental advantages, evidenced by significant reductions in key impact indicators including freshwater eutrophication (74%), global warming (64%), marine ecotoxicity (58%), and stratospheric ozone depletion (44%) when compared to the baseline scenario with conventional treatment-disposal methods. The system dynamics model revealed that the internal reuse options exploited 23%–47% of PW inflow, indicating a significant annual surplus of unused PW that presents an opportunity for external applications to enhance sustainability. In closure, we argue that the proposed framework offers a valuable tool for waste management practitioners, facilitating the identification and selection of strategies with minimal environmental impacts for PW management.

Ductility demand estimation of high-rise steel special moment frames due to structure-pile-soil interaction

ABSTRACT This study is concerned with variation of ductility demands in a range of tall buildings comprising 15, 20, 25, and 30-story steel special moment frames resting on pile groups in a flexible soil medium. Nonlinear soil–structure interaction analysis is performed using the direct method by considering an adequate extent of soil around the structures terminating at appropriate boundaries. Eleven consistent ground motions are used for nonlinear dynamic analysis of the systems and the ductility demands are evaluated by tracing the rotation history of the plastic hinges. It is concluded that simultaneous contribution of soil–structure interaction and p-∆ effect results in more than 40% increase of the ductility demand and 20% increase of the story drifts in the worst case that includes the lower stories of the tallest building.

Estimating personal electric vehicle demand and its adoption timeframe: A study on consumer perception in Indian metropolitan cities

India’s transition to electric vehicles has entered its second decade. The government has set a target of having EV sales accounting for 30 % of private cars and 80 % for two-wheelers by 2030. However, despite several efforts of government and industry, the penetration of electric vehicles till-date has not been as per the set targets. This study aims to estimate the end-user demand and adoption timeframe of electric 4-wheelers (e-4 W) and 2-wheelers (e-2 W) in India’s four large metropolitan areas. Binary logit choice models are developed based on a discrete choice experiment carried out by utilizing 2,400 face-to-face interview responses. In addition, ordered logit models are developed to assess the adoption timeframe of the EVs. The study results show a significant geographic variation in demand for e-4Ws and e-2Ws within India. This demand is also driven by vehicle attributes, demographics, infrastructural elements, and user attitudes. Existing vehicle owners are more likely to purchase an EV in the future, and are also likely to drive/ride it more. In addition, consumers who are young and wealthy, and living in homes with dedicated parking spaces are more likely to be early adopters of EVs. These findings would assist policymakers in designing a tailormade and phased EV implementation scheme in India.

Comprehensive review of load balancing in cloud computing system

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

GRAVITY ENERGY STORAGE TECHNOLOGIES: A Review of the Solid Gravity Energy Storage Applications

The use of renewable energy sources is increasing to reduce the carbon footprint. Renewable energy sources provide limited electricity generation and cannot meet the variable energy demand. Large-scale energy storage systems are needed for sustainability. The applicability of energy storage technology depends on many factors such as energy source, site availability, energy density, storage time, storage capacity, system cost, environmental impact, reliability, durability, and system integration capacity. Solid gravity storage technology is seen as a promising new alternative for large-scale energy storage. There are various types of SGES systems classified according to the application method. In this paper, studies on various SGES methods are reviewed. Studies on the applicability factors of SGES systems and their integration into the electricity grid are evaluated. SGES technology has advantages in terms of geographical compatibility, storage volume, security, and energy conversion efficiency. It has been observed that successful results have been obtained in the theoretical studies. However, there is a need for the development of physical simulations and more comprehensive techno-economic analyses.