Demand Schedules Research Articles

Analyzing global competitiveness of Indonesian palm oil

Indonesia is the largest producer and exporter of palm oil in the world vegetable oil market. However, Indonesia export share had decreasing trend and there is an emerging competition from South America and African palm oil producer. Another issue is a non-competitive strategy from European countries for limiting palm oil import. This study aims to seek the pricing behavior of Indonesian exporter in the world palm oil market. This study shows Indonesian exporter apply Local Currency Price Stabilization Strategy to European countries but more competitive behavior to other importing countries. The LCPS strategy to European countries might be induced by more elastic palm oil demand on that region. The opposite with India and China that might have less elastic palm oil demand schedule. However, Indonesian exporter should have more awareness of future competition in the palm oil market and its pricing strategy, especially to emerging demand from India and China.

Agent-based modeling approach for evaluating underground logistics system benefits and long-term development in megacities

Mobility, pollution, and other barriers against sustainable goods movement are pushing local administrators to seek innovations in urban transportation infrastructure. The urban underground logistics system (ULS) has been recognized as a prospective alternative to realize large-scale automated freight distribution within and around megacities. This paper proposes an integrated approach combing system dynamics and agent-based modeling to evaluate the long-term development and operating status of a city-wide ULS project. The project boundaries regarding underground network expansion, stakeholders’ attributes, and social-environmental benefit metrics were structured as eight highly-interacted agent modules. Critical decision variables of agents in terms of supply-demand equilibrium, investment plan, pricing-to-market and willingness-to-pay were incorporated into three formulized subsystem models. From empirical perspective, the urban territory of Beijing, China, was taken as a case to simulate the development footprints of ULS project under different funding options and market acceptance degrees. Results show that ULS has significant competence with respect to service capacity and profitability, while enabling billions of dollars of external cost-saving annually. Moreover, the comprehensive performance of ULS project regarding economic incomes, benefits, market demand, and construction schedule can reach satisfactory trade-offs through adaptively adjusting the funding policies, incentives and pricing portfolios during project development.

Critical Power Demand Scheduling for Hospitals Using Repurposed EV Batteries

Without a doubt, the healthcare sector is one of the most vulnerable sectors of electricity outages. A microgrid system to be installed in hospitals, if well planned, may offer a continuous and low electricity cost solution for health-care. By constructing an Energy Management System (EMS) specific to the hospitals, this study aims to present the significance of using an energy storage system and an optimum schedule for power utilization to prevent the lethal consequences arising from cut-offs and power quality issues. EMS is a linear (LP) scheduling model including PV panels and re-purposed EV batteries, and its target is to meet the electricity demand of the hospital with the lowest cost for every hour. The proposed scheduling model was run for a 150-bed hospital in Istanbul, Turkey under 5 different scenarios for every hour based on the data of 2016. According to scenario results, it is possible to achieve a 9.4% and 13.4% reduction in electricity bills and the grid electricity usage, respectively.

An iterative algorithm for regret minimization in flexible demand scheduling problems

AbstractA major challenge to develop optimal strategies for allocation of flexible demand toward the smart grid paradigm is the uncertainty associated with the real‐time price and electricity demand. This article presents a regret‐based model and a novel iterative algorithm which solves the minimax regret optimization problem. This algorithms exhibits low computational burden compared with traditional linear programming methods and affords iterative convergence through updates of feasible power schedules, thus enabling a scalable parallel implementation for large device populations. Specifically, our approach seeks to minimize the induced worst‐case regret over all price scenarios and solves the optimal charging strategy for the electrical devices. The convergence of the method and optimality of the computed solution is justified and some numerical simulations are discussed for the case of a single device operating under different types of price realizations and uncertainty bounds.

Traffic Signal Control System Based on Intelligent Transportation System and Reinforcement Learning

Traffic congestion has several causes, including insufficient road capacity, unrestricted demand and improper scheduling of traffic signal phases. A great variety of efforts have been made to properly program such phases. Some of them are based on traditional transportation assumptions, and others are adaptive, allowing the system to learn the control law (signal program) from data obtained from different sources. Reinforcement Learning (RL) is a technique commonly used in previous research. However, properly determining the states and the reward is key to obtain good results and to have a real chance to implement it. This paper proposes and implements a traffic signal control system (TSCS), detailing its development stages: (a) Intelligent Transportation System (ITS) architecture design for the TSCS; (b) design and development of a system prototype, including an RL algorithm to minimize the vehicle queue at intersections, and detection and calculation of such queues by adapting a computer vision algorithm; and (c) design and development of system tests to validate operation of the algorithms and the system prototype. Results include the development of the tests for each module (vehicle queue measurement and RL algorithm) and real-time integration tests. Finally, the article presents a system simulation in the context of a medium-sized city in a developing country, showing that the proposed system allowed reduction of vehicle queues by 29%, of waiting time by 50%, and of lost time by 50%, when compared to fixed phase times in traffic signals.

Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response

Aiming at the problem of insufficient flexibility of the power system caused by large-scale wind power grid integration, a flexible economic dispatch model of the electricity-gas integrated system that considers power-to-gas and demand responses is proposed. First, it elaborates on the scheduling flexibility and demand response model. Secondly, the power system and the natural gas system are regarded as different stakeholders; with the goal of minimizing their respective operating costs, a two-tier distributed coordination optimization model of electricity and gas system is established. In order to achieve the coordination and optimization of the upper and lower systems, slack variables are introduced to describe the infeasible part of the power system scheduling results in the natural gas system and are used for interactive iterative solution of the model. The numerical results of the revised IEEE 30-node power system and 10-node natural gas system illustrate the effectiveness and necessity of the proposed model, as well as the superiority of comprehensively considering power-to-gas and demand response in improving the flexibility and economy of the power system.

Power Energy Supply and Demand Scheduling Method Based on Blockchain Technology

In order to solve the problem of a large difference between the dispatching operation power and the actual required operation power existing in the traditional power energy dispatching, which affects the dispatching efficiency and causes serious energy waste, the design of power energy supply and demand scheduling method based on blockchain technology is carried out. Based on the power energy supply and demand scheduling hierarchy, based on the supply and demand scheduling model of the blockchain technology architecture, the distributed consensus mechanism for the supply and demand scheduling of power energy is established, and a new power supply and demand scheduling method is proposed. Through the experiment, it is proved that compared with the traditional dispatching method, the dispatching power obtained by the new dispatching method can be close to the actual dispatching power on the basis of meeting the normal operation needs of each equipment, realizing the energy saving of electric power, and providing technical support for the construction of green power grid.

A discrete event simulation to evaluate impact of radiology process changes on emergency department computed tomography access.

Hospitals face the challenge of managing demand for limited computed tomography (CT) resources from multiple patient types while ensuring timely access. A discrete event simulation model was created to evaluate CT access time for emergency department (ED) patients at a large academic medical center with six unique CT machines that serve unscheduled emergency, semi-scheduled inpatient, and scheduled outpatient demand. Three operational interventions were tested: adding additional patient transporters, using an alternative creatinine lab, and adding a registered nurse dedicated to monitoring CT patients in the ED. All interventions improved access times. Adding one or two transporters improved ED access times by up to 9.8minutes (Mann-Whitney (MW) CI: [-11.0,-8.7]) and 10.3minutes (MW CI [-11.5, -9.2]). The alternative creatinine and RN interventions provided 3-minute (MW CI: [-4.0, -2.0]) and 8.5-minute (MW CI: [-9.7, -8.3]) improvements. Adding one transporter provided the greatest combination of reduced delay and ability to implement. The projected simulation improvements have been realized in practice.

Intermodal service network design with stochastic demand and short-term schedule modifications

Service network design is a crucial planning task for intermodal operators on the tactical level as it defines the transportation schedule and the routing of commodities through the intermodal network. In many cases, the transportation demand over the planning horizon cannot be known with certainty, resulting in a stochastic service network design problem. Whereas current literature focuses on outsourcing as a recourse action when reacting to fluctuating demand patterns, we introduce a new variant of this problem in which an ad-hoc modification of the planned schedule for the transportation assets is possible. Although this recourse action is commonly applied in practice, a detailed investigation including high-performance solution procedures for this specific service network design problem has been missing until now. We present a two-phase matheuristic and respective computational experiments that demonstrate its superior performance compared to commercial solvers, including the capability of solving large stochastic instances. Moreover, by presenting the results of a real-world case study for a large intermodal rail operator, we illustrate substantial cost reductions compared to an expected value model and a stochastic model without consideration of schedule modifications. Our detailed analysis further reveals a different solution structure when schedule modifications are considered which explains the cost savings achieved.

Two-Objective Mathematical Model Considering Competitive Location, Distribution Scheduling and Priority Demand Points under Uncertainty

In the last two decades, competition between companies in the supply of goods and services has become a great reality for their development. Today, companies and factories need to integrate and flex all production activities from the supply of raw materials to the delivery of the final product to the consumer, in the process of supply and distribution of products. Distribution and support systems are part of the supply chain process, so that the efficient and efficient flow of storage of goods, services and related information is planned, implemented and controlled from the starting point to the point of consumption in order to meet customer needs. The problem of locating distribution centers focuses on how to select distribution centers from a potential set so that the relevant total costs are minimized. This study aims to minimize transportation costs in the supply chain by choosing the best way to deliver the product to the customer and location costs. In this model, a multi-objective function is used, which in the first objective seeks to maximize the profit from sales, and the second objective function seeks to minimize the distribution schedule.

Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization

Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns.

Some unpleasant markup arithmetic: Production function elasticities and their estimation from production data

The ratio estimator of the markup is the ratio of the output elasticity for a flexible input to that input’s cost share in total revenue. We highlight identification and estimation issues pertaining to this ratio estimator, when firm-level output prices are not observed. If the revenue elasticity for a flexible input is used in place of the output elasticity, then profit maximization implies that the ratio estimator is identically equal to one, and thus is uninformative about markups. Concerning estimation of output elasticities: with only revenue data, profit maximization also implies that the output elasticity is not identified non-parametrically from estimation of the revenue production function, if firms have market power. Even with separate output price and quantity data, it is challenging to estimate the output elasticity consistently if there are non-linear productivity dynamics and firms face heterogeneous demand schedules, with unobserved variation in a demand shifter.

Behavioural patterns in aggregated demand response developments for communities targeting renewables

Encouraging consumers to embrace renewable energies and energy-efficient technologies is at stake, and so the energy players such as utilities and policy-makers are opening up a range of new value propositions towards more sustainable communities. For instance, developments of turn-key demand response aggregation and optimisation of distributed loads are rapidly emerging across the globe in a variety of business models focused on maximising the inherent flexibility and diversity of the behind-the-meter assets. However, even though these developments’ added value is understood and of wide interest, measurement of the desired levels of consumer engagement is still on demonstration stages and assessment of technology readiness. In this paper, we analyse the characteristics of the loads, the behaviour of parameters, and in a final extent, the behaviour of each kind of consumer participating in aggregated demand scheduling. We apply both non-automatic and machine learning methods to extract the relevant factors and to recognise the potential consumer behaviour on a series of scenarios that are drawn using both synthetic data and living labs datasets. Our experimentation showcases a number of three patterns in which factors like the community’s demand volume and the consumer’s flexibility dominate and impact the performance of the tested development. The experimentation also makes current limitations arise within the existing electricity consumption datasets and their potential for inference and forecasting demand flexibility analytics.

Predictive Modeling for Rooftop Solar Energy Throughput: A Machine Learning-Based Optimization for Building Energy Demand Scheduling

Abstract The intermittent and fluctuating nature of solar energy is the biggest challenge facing its widespread utilization. Implementing onsite photovoltaic (PV) systems as alternative energy sources has established the need for reliable forecasting procedures to improve scheduling and demand management. This paper presents solar energy forecasting combined with a demand-side prediction algorithm to optimize the utilization of available solar energy resources and manage the demand side accordingly. The algorithm utilizes support vector regression (SVR), a machine learning technique, validated using one-year energy consumption data collected from an office building instrumented as an experimental testbed facility. Power meters and temperature sensors collect the building’s internal climate and energy data, while a solar PV array and a weather station provide the external relevant data. The forecasting method uses the average power output of k-similar days as an added input to the SVR model to enhance its performance. The day-ahead prediction results show that this additional input contributes to higher forecasting efficiency, especially in the hot climate regions, where sunny weather conditions prevail throughout the year. The PV output prediction accuracy for sunny days is above 90%, which offers possibilities for optimized scheduling and leading to smart building energy management. Finally, this paper also proposes a temperature set point optimization algorithm for the building air conditioning system to minimize the difference between the building energy load and the generated solar PV power. Using 24 °C as the upper set point temperature limit reduces the energy demand (consumption) by up to 29% and the associated reduction in CO2 emissions.

647 Adolescent and Young Adult Sleep and Sleep-Related Behaviour Change During the COVID-19 Pandemic

IntroductionSleep disturbance, poor sleep quality, and dissatisfaction with sleep are common among adolescents and young adults (AYAs; e.g., Becker et al., 2018; Hicks et al., 2002; Hysing et al., 2013). Environmental and behavioural factors (e.g., early school start times, evening technology use and social pressures) are barriers to healthy sleep among AYAs that contribute to a “perfect storm” of sleep disturbance during this period (Carskadon, 2011; Crowley et al., 2018). Notwithstanding, few AYAs have access to sleep treatments. The COVID-19 pandemic lockdowns decreased academic and scheduling demands, providing an opportunity to study unconstrained AYA sleep and potentially facilitating better access to sleep interventions (Simpson & Manber, 2020). This study evaluated differences in baseline sleep and sleep-related behaviour change (i.e., how AYAs use an evidence-based app for sleep disturbance) before vs. during the lockdown.MethodsParticipants between the ages of 15 and 24 (M=20.66, SD=2.38) completed a 4-week feasibility study evaluating a free, transdiagnostic sleep self-management app (DOZE) before the lockdown (“Pre-Lockdown”; n=51) or during the lockdown (“Lockdown”; n=29). After 2 weeks of completing baseline sleep diaries, participants could set goals based on feedback and access tips, followed by 2 more weeks of completing sleep diaries.ResultsCompared to Pre-Lockdown, Lockdown demonstrated less variability in their sleep schedules (ps≤.011), less napping (p=.002), but increased time in bed (TIB; p<.001) and total wake time (p=.007). Total sleep time, lingering in bed in the morning, and sleep efficiency did not differ between groups. Relative to Pre-Lockdown, Lockdown showed a greater tendency to set goals to reduce schedule variability (p=.010) and to restrict excessive TIB (p=.005). Rates of goal setting for lingering in bed in the morning, sleepiness, naps, and sleep-interfering substance use did not differ between groups. Rates of accessing tips did not differ between groups.ConclusionEffects of COVID-19 lockdown on AYA sleep included less variability in their schedule and a decreased need for naps, but negative effects on TWT and TIB. As a result, AYAs set different goals during the COVID-19 lockdowns, focusing more on restricting excessive TIB than on schedule variability.Support (if any)Canadian Institutes of Health Research eHealth Innovation Partnership Program (#143551).

Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources

Demand response (DR) programs and reconfiguration of distribution networks are generally adopted in the energy management (EM) problem of microgrid to enhance the technical and economical features of microgrid. Assuming a fixed configuration of distribution network, DR programs usually optimize the generation cost by encouraging the consumers to reduce their energy demands. Whereas reconfiguration of network is done for a pre-defined generation schedule and energy demand. However, separate incorporation of these two operational techniques in the EM problem may lead to a non-optimal solution. In this paper, a joint framework is proposed to integrate a novel incentive-based DR program and reconfiguration method in the EM problem of microgrid on a day-ahead time frame. The objective of the work is to minimize the fuel cost of conventional distributed generation (DG) and the cost of power purchased from the grid, while maximizing the profit for microgrid operator (MGO). The efficacy of the proposed model is tested on a static model of grid-connected 33-bus microgrid which consists of renewable energy (RE) sources and a conventional DG. To account the uncertainties in RE sources, Hong’s (2m+1) point estimation method (PEM) is considered in this work. The result confirms that the incorporation of DR program and reconfiguration method in the EM problem leads to an optimum energy schedule for the microgrid with a minimum lossy network. For the single-day operation of microgrid, it has been found that the power transfer from the grid and power lost in the network is reduced by 10.83% and 34.03% respectively.

Impact of symptom burden on work performance status in Spanish women diagnosed with endometriosis

ObjectiveTo explore work performance status in Spanish women with endometriosis and to identify those endometriosis-related symptoms that potentially contribute to the reduced work performance. DesignCross-sectional study. SettingSpain. PopulationWomen with endometriosis working at enrolment (n = 148). MethodsWork performance status was assessed through the Work Role Functioning Questionnaire (WRFQ). Endometriosis-related symptoms (pelvic pain, catastrophizing thoughts related to pain, chronic fatigue, sleep quality, mood status, gastrointestinal discomfort and social support) were also evaluated through validated scales. Statistical analyses were performed through multivariate linear regression analyses. Main outcome measuresWork performance status. ResultsMedian WRFQ score was 72.0, with the lowest scores found in demands related to worker’s needs to manage the workday from beginning to end and those dynamic and static physical loads required in the conduct of work duties. Severe pelvic pain, depressive mood and poorer sleep quality was inversely related to work performance status (p-values <0.020). Finally, self-perceived social support was borderline associated with better work performance (p = 0.057). Considered together, these four psychosocial factors were responsible to explain the 37.9 % of the limitations on work performance observed in our study sample. ConclusionsSpanish women with endometriosis reported lower WRQF scores (predominantly in work scheduling and physical demands) in comparison with previous studies on Spanish healthy adults. Psychosocial factors, including pelvic pain, sleep quality and depressive mood are related with work performance status. Thus, the effectiveness of physical and psychological rehabilitation programs in work productivity in women with endometriosis should be evaluated in the close future.

Consume, Rent and Own: Redrawing the Market on a Blank Canvas

The aggregate trade in the market in a community must be accounted for per period of time. As such, the market consists of “flows.” Specifically, the flow of quantity traded is determined after interactions between the two flows of demand schedule and supply schedule; the former slopes downward and the latter upward for various reasons. In the period, a particular unit out of a stock quantity, fixed or not, can change hands zero to multiple times. No household would demand more of a “good” at a higher price. The household surely supplies more working hours at a higher wage rate. Then, the so-called “fixed supply” and “backward-bending curves” are counterfactual: they cannot be so in the given accounting period. Such a saying that the demand for liquidity slopes downward because the interest rate is the opportunity cost is also erroneous: demand must be defined in terms of benefit before being compared to its cost, or “price,” yet to be determined in the market. This essay aims to reevaluate, in hopes of correcting notable misconceptions in microeconomics, how demand and supply schedules should be defined in the product, the “production factor” and the asset markets.

«Time preference and investment expenditure»: comment on Hülsmann

Hülsmann (2008) argues that the neglect of time preference changes on the demand side of the time market renders Rothbard’s (1993) analysis incomplete in that it unduly portrays a rise in the volume of investment as a necessary counterpart to a fall in the pure interest rate. Focusing on the determinants of the demand for present goods, this paper shows that though Hülsmann’s strictures are essentially valid, Rothbard has actually explained why the direct impact of time preference changes should display themselves mostly on the supply side. However, the implications have been neglected both by Hülsmann and Rothbard. We explore these implications, demonstrating that the present demand schedule and the volume of investment should be considered as mostly independent from present time preferences and determined instead by past production decisions. We show how this approach allows for a more «dynamic» understanding of the time market and growth processes.&#x0D; Key words: Time Preference, Investment Expenditure, Time Market, Structure of Production, Interest Rate, Growth Types, Austrian Macroeconomics.&#x0D; JEL Classification: B53, E22, E23, E43, O11, O16, O40.&#x0D; Resumen: Hülsmann (2008) argumenta que pasar por alto los cambios en la preferencia temporal del lado de la demanda en el mercado de tiempo hace incompleto el análisis de Rothbard (1993) al representar indebidamente un incremento en el volumen de inversión como contrapartida necesaria a una caída en la tasa de interés pura. Centrándose en los determinantes de la demanda para bienes presentes, este paper muestra que, siendo las críticas de Hülsmann esencialmente válidas, Rothbard realmente explicó por qué el impacto directo de los cambios en la preferencia temporal debería reflejarse fundamentalmente en el lado de la oferta. Sin embargo, las implicaciones derivadas de ello fueron ignoradas por Hülsmann y Rothbard. Las exploraremos, demostrando que la curva de demanda presente y el volumen de inversión deberían considerarse esencialmente independientes de las preferencias temporales presentes, estando determinadas realmente por decisiones pro-ductivas pasadas. Esta aproximación permite una comprensión más «diná-mica» del mercado de tiempo y los procesos de crecimiento.&#x0D; Palabras clave: Preferencia temporal, gasto de inversión, mercado del tiempo, estructura de la producción, tipo de interés, tipos de crecimiento, macroeconomía austriaca.&#x0D; Clasificación JEL: B53, E22, E23, E43, O11, O16, O40.

Design and verification of an effective state‐of‐charge estimator for thermal energy storage

Thermal energy storage (TES) is widely used in district heating and cooling systems (DHCS) to act as a buffer between the supply and demand schedules. The adequate control of charging and discharging modes of TES may improve the overall performance of a DHCS and, to this end, an effective regulation of its state-of-charge (SoC) is required. However, the calculation of SoC depends on the availability and accuracy of temperature measurements. A model-based observer for the calculation of the SoC of water-based TES tanks is presented. A dynamic model of a one-dimensional stratified water tank is adopted to develop the observer. Its effectiveness is assessed through ‘model-in-the-loop’ cosimulations, with the observer and the feedback control system being implemented in MATLAB/Simulink and a high-fidelity water tank component available in Apros being used as the plant model. Simulation results considering three different system configurations demonstrate that the model-based observer accurately estimates the temperature distribution within the tank, leading to an effective SoC computation and control—even in the case of sensor failure or upon limited sensor availability.