Off Peak Hours Research Articles

The role of battery electric vehicles in off-peak hour deliveries: Sustainability assessment of a case study in Stockholm

City logistics faces critical challenges in optimising resource management, utilising time effectively, and reducing costs, particularly in the context of growing environmental concerns. Battery Electric Vehicles (BEVs) and Off-Peak Hour Deliveries (OPHD) have emerged as promising solutions. Despite considerable research efforts, more research is needed to evaluate the impacts of using BEVs during OPHD. To address this need, this paper describes how data from a case study in Stockholm were used to develop a comprehensive overview of the sustainability impacts of OPHD carried out with BEV. A multi-method approach was applied, including an indicator framework, Life Cycle Costing, and a Social Life Cycle Assessment to consider all sustainability dimensions holistically. Our findings reveal that, while BEVs are not yet cost-competitive with Internal Combustion Engine vehicles for daytime operations, OPHD can mitigate these costs by increasing the vehicle utilisation and distances travelled. Furthermore, OPHD with BEV can offer substantial benefits by increasing accessibility and reducing emissions. However, challenges such as increased employee-related transport costs and infrastructure needs must be addressed. The main contributions of this paper are practical, as we add knowledge about the impacts of OPHD using BEV, and methodological, as we used a multi-method approach to holistically assess sustainability impacts. Future research should focus on long-term impacts and refine assessment methods to support the sustainable development of urban logistics. This study highlights the importance of using holistic sustainability assessment to inform and enable stakeholders and decision-makers to leverage the full potential of OPHD when shifting to more sustainable transport.

Guidelines for Activating Ramp Metering Signals in Response to Non-Recurrent Congestion during Off-Peak Hours Using a Statistical Method

Guidelines for Activating Ramp Metering Signals in Response to Non-Recurrent Congestion during Off-Peak Hours Using a Statistical Method

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

A Reinforcement Learning Based Data Caching in Wireless Networks

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity.

An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market.

Automatic Generation Control (AGC) operation in a restructured power system under Availability Based Tariff (ABT) mechanism by considering peak hours and off-peak hours

Objectives: To simulate automatic generation control (AGC) operation in a deregulated power system under availability based tariff (ABT) mechanism to regulate grid frequency during peak and off-peak hours. Method: For the simulation, the two areas, an isolated power system with Area1, which has thermal - hydropower plants with two DISCOs, and Area2 having a thermal – thermal power plant with two DISCOs have been considered. DISCO participation matrix (DPM) has been used for the visualization of bilateral contract by distribution companies (DISCOs) in the restructured environment. An AGC operation of two area power system with 10% load variations during peak hour and off-peak hour has been presented for the 24 hours in graphical form. Findings: The deviations in the grid frequency and Unscheduled Interchange (UI) charges or deviation settlement mechanism (DSM) rates of ABT due to sudden load changes and renewable power found. The frequent AGC operation of generators against load variation to stabilize the grid frequency is increasing wear and tear losses which finally increases maintenance cost and sometimes tripping of generators. Also, in peak hours and off-peak hours the drastic change in UI charges found during AGC operation so, there is a need to regulate grid frequency from the load side using distributed energy sources. Novelty: In proposed work, at the distribution side, Electric vehicle has been considered to regulate grid frequency during peak and off-peak hours. Work has been justified by appropriate data and simulated results. Keywords: AGC; deregulation; peak hours; offpeak hours; ABT

Dynamic Optimal Power Flow Incorporating Gas Turbine Generator Unit with Compressed Natural Gas Systems

In developing countries, gas infrastructures are not well established yet. Consequently, insufficient gas supply for gas turbine generator unit may occur during peak load hours. Dual firing unit can overcome such limitation by switching the fuel to High Speed Diesel Oil. However, it will significantly increase the operation cost since High Speed Diesel fuel is much more expensive than natural gas. Recently, a compressed natural gas system has been widely used to solve inadequacy of gas supply during peak load hours. This system compresses the remaining natural gas supply from gas provider during off-peak load hours and stores it into gas tubes. The compressed gas is then released to top up gas supply to gas turbine during peak load hours. Thus, the unit can fully operate on gas at both peak and off-peak load hours. As a result, the operation cost will be lower. This paper proposes an approach in optimizing system operation cost of large system, which has gas turbine generator unit with compressed natural gas system. Furthermore, load curtailment and implementation of take or pay energy contract is also included. The problem is formulated as dynamic optimal power flow and solved using quadratic programming. The proposed approach is tested using IEEE 30 bus and Jawa Bali 500 kV. The results show the effectiveness of the approach in optimizing daily gas usage as well as satisfying take or pay energy contract.

On-road measurements and modelling of vehicular emissions during traffic interruption and congestion events in an urban traffic corridor

Traffic events such as interruptions and congestions occur on urban roads causing higher vehicular emissions resulting in air-pollution hotspots. This study investigates the performances of two emission models for estimating emissions from passenger cars and auto-rickshaws of different mileages, moving with a traffic fleet during these events. The instantaneous measurements of emissions and speeds were carried out on a test route of 3.8 km by integrating the auto-gas analyser and V-Box for estimating the on-road emission factors (EFs). The measured driving profiles and acceleration/decelerations were used in the IVE emission model and the speed was averaged in the COPERT-IV emission model into 0–10 km/h; 10–25 km/h; 25–35 km/h and >35 km/h. The on-road EFs of CO, HC, CO2 and NOx at peak hours (PHs) and off-peak hours (OPHs) were compared with the modelled EFs. The estimates of COPERT-IV were about 25% less for CO, HC and CO2 and 26–39% more for NOx than IVE during PHs. As compared to the on-road EF, the IVE estimates for CO, HC, CO2 was 5–50% less and 12–50% higher for NOx for auto-rickshaw and passenger car; and the COPERT–IV estimates for CO, HC, CO2 were 30–74% less and 6–86% higher for NOx, showing that instantaneous driving profiles in emission modelling are important. The interruptions during PHs were 80% of the time, a cause of deviations in the models. Thus, the real-world emissions vary with the levels of interruptions and congestions on the roads, and hence emission models must account for these events.

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach.

Association between Off-Peak Hour Birth and Neonatal Morbidity and Mortality among Very Low Birth Weight Infants

To assess the independent association between overnight or "off-peak" hour delivery and 3 neonatal morbidities strongly associated with childhood neurocognitive impairment. Retrospective population based cohort study of all infants with birth weights of 500-1499 g born without severe congenital anomalies in California or Pennsylvania between 2002 and 2009. Off-peak hour delivery was defined as birth between 12:00 a.m. and 6:59 a.m. The study outcomes were death; bronchopulmonary dysplasia, retinopathy of prematurity, and severe (grade 3 or 4) intraventricular hemorrhage among survivors; the composite of each morbidity or mortality; and the composite of death or 1 or more of the evaluated morbidities. Of 47 617 evaluated infants, 9317 (19.6%) were born during off-peak hours. The frequencies of all study outcomes were higher among infants born during off-peak compared with peak hours. After adjusting for maternal, infant, and hospital characteristics, off-peak hour delivery was associated with increased odds of severe intraventricular hemorrhage among survivors (OR 1.39, 95% CI 1.23-1.57) and the composite outcomes of death or severe intraventricular hemorrhage (OR 1.16, 95% CI 1.07-1.25) and death or major morbidity (OR 1.08, 95% CI 1.02-1.15). There was no evidence of subgroup effects based on delivery mode, birth hospital neonatal intensive care level or annual very low birth weight infant delivery volume, or weekday vs weekend off-peak hour delivery for any study outcome. Very low birth weight infants born between midnight and 7:00 a.m. are at increased risk for severe intraventricular hemorrhage and death or major neonatal morbidity.

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

Cost Benefit Analysis of Distribution Network Using Micro Generation

Energy consumption in India is rising rapidly mainly due to growth in economy and population. The only alternative to meet the ever rising demands of the nation is to tap the potential of renewable energy sources and promote Micro generation at the consumer's end. This approach ensures a greener energy with an added benefit of minimized transmission losses. In this paper an attempt has been made to envisage the impact of micro generation at the consumer's premises using renewable sources like solar and wind. In this approach only solar power is considered for computations which are injected during peak and off peak hours in order to reduce the power loss, thus improving the quality of power. This results in reduction of fossil fuel consumption. Power losses of various distribution networks are computed and the results are presented and analyzed. Cost benefit analysis is also carried out on two networks namely IEEE and Andhra Pradesh Eastern Power Distribution Corporation Limited (APEPDCL) to obtain the payback period of the investment.

Politica ambientale e concorrenza imperfetta: i mercati della CO2

Power markets and CO2 cost pass-through: the case of Italy - Francesco Gulli In line with economic theory, carbon ETS determines a rise in marginal cost equal to the carbon opportunity cost regardless of whether carbon allowances are allocated free of charge or not. This paper aims at evaluating to what extent firms in imperfectly competitive markets will into electricity prices the increase in cost. By using the load duration curve approach and the dominant firm with competitive fringe model, we show that the result is ambiguous. The increase in price can be either lower or higher than the marginal CO2 cost, depending on several structural factors: the degree of market concentration, the available capacity (whether there is excess capacity or not), the power plant mix in the market and the power demand level (peak vs. offpeak hours). The empirical analysis of the Italian context (an emblematic case of imperfectly competitive market), which can be split into four sub-markets with different structural features, provides a contribution supporting the model predictions. Market power, therefore, would determine a significant deviation from the full pass-through rule but we can not know the sign of this deviation, a priori, i.e. without before taking carefully into account the structural features of the power market.

Validation of Left-Turn Delay at Two-Way Stop-Controlled Intersections

Control delay for left-turning vehicles at unsignalized intersections was observed in the field and compared with average control delay calculated from the methodologies presented in the 1997 update of the Highway Capacity Manual (HCM). Unsignalized intersections with two-way left-turn lanes on the major street were observed in the peak and offpeak hours, and control delays were recorded for the one-stage and twostage left-turn processes. Next, the methodologies presented in the HCM were used to calculate the control delay for both processes and compared with the observed data. These comparisons were used as the basis for validation of the HCM methodologies regarding left-turn control delay at unsignalized intersections. From the comparisons, the calculated delay closely corresponds with the observed data, with a total approach volume at the intersection of approximately 2,500 vehicles per hour or less. Once the total approach volume increases above this level, the calculated values rapidly increase and the actual observed control delays gradually increase at a much lower rate. As a result, the observed and calculated delays are different when the intersection handles more than 2,500 approach vehicles in an hour. Statistical analyses were performed on the data to determine if the average observed control delay was related to the calculated control delay. Statistically, the observed control delay and the calculated control delay at the 95 percent confidence level show that the two data sets yield similar results for off-peak conditions. However, during the peak hour, when the total approach volumes are higher, the 95 percent confidence interval yields different results. Hence, the HCM procedures produce, on average, greater control delay estimates than the field observations when the total approach volumes are high.

The application of reliability constrained stochastic capacity planning models to the service sector

Emphasis on quality management has recently permeated not only the manufacturing sector, but the service sector as well. Consequently, quality service and consumer satisfaction have become realities for many monopolistic service oriented industries facing competition. In order to effectively implement timely service within these industries (i) capacity plans must be developed which provide adequate staffing during both peakload and offpeak hours, as well as optimal (ii) prices and (iii) reliability of service. This paper builds on the results of Boronico (1992) in illustrating how reliability constrained marginal cost, within which optimal price is embodied, and minimum cost capacity plans may be determined for a service provider facing stochastic demand. Excess demand is not lost, but is deferred: a characteristic that typifies the operation of many delivery systems, such as postal services. Results indicate that marginal costs are convex with respect to reliability of service, while changes in the demand distribution's variability may impact optimal capacity by either increasing or decreasing required capacity.

Cool thermal discharges from ice melting with specified heat fluxes on the boundary

The behavior of cool thermal discharges with direct in situ contact in crystal-melting operations to produce chilled air during off-peak power-consumption hours has been investigated. Equations have been derived for estimating the thickness of the melted ice and the temperature distribution in the liquid region with specified heat fluxes on the boundary. Three examples with different fluxes of cool thermal discharges are discussed. Limitations on the thicknesses of the melted ice layers for cool thermal discharges are delineated.

Design of time-of-use rate periods for a utility

Electric utilities are turning to the time-of-use rate structure as a solution to the pricing dilemma they face. Charging relatively higher prices for peak usage and lower prices for offpeak usage provides the correct pricing signal to the consumer and promotes a more efficient utilization of the utility. Though this rate form has been successfully used in Europe for over 25 years, there has not been much experience with it in this country. An important aspect of time-of-use rate design is the determination of peak and offpeak hours of the day. This paper uses cluster analysis to delineate the appropriate hours for rate design. Utilities build expensive base loads plants to provide large amounts of cheap energy and less capital intensive peaking plants with high operating costs to meet short term emergency requirements leading to higher average total costs as more power is generated. The procedure presented, therefore, groups loads into rating periods such that peak periods contain large loads and offpeak periods contain low loads. The methodology is applied to the determination of time-of-use rating period hours for a large electric utility in the U.S. Since the optimal grouping with four seasons and three time-of-use periods was judged to be not feasible, a “practical best” solution was developed by modifying it appropriately. The utility set the peak hours for winter months (October to April) as 6 a.m. to 12 noon and 4 p.m. to 10 p.m. and summer (May to September) peak hours as 10 a.m. to 10 p.m., with weekends and major holidays being considered offpeak. The time-of-use rate structure has been implemented for commercial and industrial consumers on an experimental basis. A consumer opting for the new structure will be monitored using both structures and billed for the lower amount.