Operation Schedule Research Articles

Techno-Economic Assessment of PV Power Systems to Power a Drinking Water Treatment Plant for an On-Grid Small Rural Community

This paper shows the technical–economic assessment of two power systems based on renewable electricity to cover the energy consumption requirements of a drinking water treatment plant in the town of Pile, Ecuador, with a planning horizon of 15 years. A stand-alone and a grid-connected solar PV system were proposed to power this plant, which was designed considering the maximum daily potable water supply condition. This plant operated under two scenarios: (1) 12 h during daylight hours and (2) 24 h. Both schedules were proposed to assess the impact of PV power systems on plant operation. We modeled and optimized a total of four scenarios, where each scenario consisted of one of the proposed PV power systems and the plant with one of its operating schedules. Homer Pro software was used to size and find an optimal solution in each scenario, considering the net present cost (NPC) as the main criterion for optimization. The results showed that the change in the plant operation schedule significantly influenced the parameters of each scenario, such as component sizing, electricity production, initial capital, NPC, and electricity purchase/sale capacity from the plant as a grid power service user to the electric utility company.

A Hybrid Heuristic Algorithm for Energy Management in Electricity Market with Demand Response and Distributed Generators

Optimal energy management trends are indispensable in improving the power grid’s reliability. However, power usage scheduling for energy management (EM) poses several challenges on a practical and technical level. This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal EM and overcome the major challenge in demand response (DR) implementation. This work aims to solve the power usage scheduling problem for EM to optimize utility bill, peak energy demand, and pollution emission while considering the varying pricing signal, distributed generators (DGs), household load, energy storage batteries, users, and EUC constraints. The ECS is based on a stochastic algorithm (genetic wind-driven optimization (GWDO) algorithm) because generation, DGs, demand, and energy price are stochastic and uncertain. The ECS based on the GWDO algorithm determines the optimal operation schedule of household appliances and batteries charge/discharge for a day time horizon. The developed model is analyzed by conducting simulations for two cases: home is not equipped with DGs, and home is equipped DGs in terms of utility bill, peak energy demand, and pollution emission. The simulation results validated the proposed model’s applicability to EM problems.

Predicting Diverse Behaviors of Occupants When Turning Air Conditioners on/off in Residential Buildings: An Extreme Gradient Boosting Approach

Occupant behavior (OB) has a significant impact on household air-conditioner (AC) energy use. In recent years, bottom-up simulation coupled with stochastic OB modeling has been intensively developed for estimating residential AC consumption. However, a comprehensive analysis of the diverse behavioral preference patterns of occupants regarding AC use is hampered by the limited availability of large-scale residential energy demand data. Therefore, this study aimed to develop a prediction model for the residential household’s AC usage considering various OB-related diversity patterns based on monitoring data of appliance-level electricity use in a residential community of 586 households in Osaka, Japan. First, individual operation schedules and thermal preferences were identified and quantitatively extracted as the two main factors for the diverse behaviors across the whole community. Then, a clustering analysis classified the target households, finding four typical patterns for schedule preferences and three typical patterns for thermal preferences. These results were used, with time and meteorological data in the summer seasons of 2013 and 2014, as inputs for the proposed prediction model using Extreme Gradient Boosting (XGBoost). The optimized XGBoost model showed a satisfactory prediction performance for the on/off state in the testing dataset, with an F1 score of 0.80 and an Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.845.

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory’s (NREL’s) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

Recovery of electromagnetic coils insulation under varying conditions in ADITYA-U tokamak

Insulation resistance (IR) of electromagnetic (EM) coils show a tremendous variation with the changing weather condition. This effect has a major impact on the operation of the ADITYA-U tokamak operation schedule. The failure of EM coil insulation can lead to terrible failure of the coil system and subsequently, the component and respective power system. Series of circumstances are driven to check their impact on the relative humidity (RH) and simultaneously their effect on the IR of the EM coils has been observed. Different techniques are employed to control the humidity and temperature of the experiment hall to maintain the IR of the different EM coils within the experimental limits. In this paper, different factors have been reviewed which affect and change the IR values of the EM coils. Experience gained during years of operation during insulation strength degradation and its recovery is presented in this work.

Optimal mining cut definition and short-term open pit production scheduling under geological uncertainty

Short-term planners must define an operational schedule based on blasthole data, which might be incomplete or imperfect. As a result, there is uncertainty in the true grade of each Selective Mining Unit. This can impact the definition of mining cuts and dig-limits, and the fulfillment of long-term targets. In this work, we propose a stochastic mixed integer program to define an operational mining cut configuration and a short-term schedule under grade uncertainty. The objective of the model is to maximize revenue and minimize deviations from production targets. We test the model using real data from a copper deposit and use grade simulations for quantifying the uncertainty in blasthole data. The results show that the stochastic model achieves higher profit margins and lower deviations from production targets compared to a deterministic variant based on a single estimated scenario. At the same time, the numerical experiments show the potential impact of blasthole uncertainty in profit and compliance of short-term schedules. Therefore, the use of this stochastic formulation can help planners find optimal mining cuts and improve target fulfillment and profitability of short-term operational plans in the presence of grade uncertainty. Finally, we propose several extensions to include new sources of uncertainty.

THE IMPORTANCE OF VALUES IN THE ALGERIAN EDUCATIONAL SYSTEM

Values have been included at three levels in the recent reforms of the Algerian education system (2003): the philosophy that is the basis of this reform and its main objectives, the targeted values, as well as the values education strategies. Determining the precise place of values in these reforms, allows a better understanding of the phenomenon of values education in educational institutions, as well as its practices. It can also be a tool for further evaluation and reconstruction of the reform. The targeted values are identity and universal. Values education curricula combined the hidden curriculum with the direct approach. Nevertheless, a set of gaps has been identified, the most important of which is the lack of a clear and progressive value system in the early school years, and the lack of a specific value plan defined by operational schedules. Also, the lack of training of trainers on how to carry out values education and train them on its stages and strategies.

Tahapan dan Bentuk Partisipasi Petani Dalam Pemberdayaan Oleh Komunitas Metode Hayati Indonesia (MHI) di Desa Andongsari Kecamatan Ambulu Kabupaten Jember

The current agricultural development approach emphasizes the community empowerment approach. Empowerment of farmers by the MHI community in Andongsari Village, Ambulu District, Jember Regency is not easy because all of farmers are not active in every stage of empowerment activities. The method of determining the research area is carried out by a purposive method with several considerations. The research method used is qualitative research with a case study approach. Determination of informants is done by purposive sampling method. Data collection techniques through interviews, observation and documentation. The data analysis used is an interactive model of Miles and Huberman's data analysis. The validity of the data used is source triangulation. The results showed that: 1. The stages of farmer participation in the development of Biological Control Agents in Andongsari Village are (a) participation in decision making by understanding the program, being involved in operational planning and scheduling, and determining implementers and coordinators. (b) participation in the implementation phase through resource contribution and coordination. (c) participation in profit sharing, including material benefits, social benefits and personal benefits. (d) participation in the evaluation; 2. The forms of participation provided by farmers in APH development activities are (a) participation in the form of ideas (b) participation in the form of labor (c) participation in the form of property (d) participation in the form of skills and (e) participation in the form of social.

A Novel Approach for Optimizing Building Energy Models Using Machine Learning Algorithms

The current practice with building energy simulation software tools requires the manual entry of a large list of detailed inputs pertaining to the building characteristics, geographical region, schedule of operation, end users, occupancy, control aspects, and more. While these software tools allow the evaluation of the energy consumption of a building with various combinations of building parameters, with the manual information entry and considering the large number of parameters related to building design and operation, global optimization is extremely challenging. In the present paper, a novel approach is developed for the global optimization of building energy models (BEMs) using Python EnergyPlus. A Python-based script is developed to automate the data entry into the building energy modeling tool (EnergyPlus) and numerous possible designs that cover the desired ranges of multiple variables are simulated. The resulting datasets are then used to establish a surrogate BEM using an artificial neural network (ANN) which is optimized through two different approaches, including Bayesian optimization and a genetic algorithm. To demonstrate the proposed approach, a case study is performed for a building on the campus of the Florida Institute of Technology, located in Melbourne, FL, USA. Eight parameters are selected and 200 variations of them are supplied to EnergyPlus, and the produced results from the simulations are used to train an ANN-based surrogate model. The surrogate model achieved a maximum of 90% R2 through hyperparameter tuning. The two optimization approaches, including the genetic algorithm and the Bayesian method, were applied to the surrogate model, and the optimal designs achieved annual energy consumptions of 11.3 MWh and 12.7 MWh, respectively. It was shown that the approach presented bridges between the physics-based building energy models and the strong optimization tools available in Python, which can allow the achievement of global optimization in a computationally efficient fashion.

Forecast-driven stochastic optimization scheduling of an energy management system for an isolated hydrogen microgrid

Balancing supply and demand constitutes the most important and challenging task in an isolated microgrid. Accordingly, it is essential to develop an optimization scheduling strategy for an energy management system of an isolated microgrid operation. In this study, a novel forecast-driven stochastic scheduling strategy was devised for the optimal operation of an isolated hydrogen microgrid. First, the change in wind power and load over 24 h was forecast using a bidirectional and long short-term memory convolutional neural network modeled end-to-end. To the best of the authors’ knowledge, this is the first application of end-to-end modeling for wind-power forecasting. Based on the forecast results, the stochastic optimization scheduling of the energy management system was resolved through deep reinforcement learning to minimize the microgrid lifecycle cost. Deep reinforcement learning combines the advantages of deep learning and reinforcement learning and uses statistical models to effectively solve sequence decisions of features of high-dimensional spaces. In addition, stochastic scenarios were generated using Monte Carlo simulations to analyze the uncertainties in wind and load. Furthermore, the energy capacity degradation of the energy storage system was considered. Finally, the effectiveness of the proposed approach was validated based on comparisons of different benchmark models and the latest models. The proposed scheduling strategy can realize high operational efficiency and reliable energy management system scheduling and is expected to serve as a reference for future research in this area.

Strategy towards model-based design and testing of the ITER Plasma Control System

The plasma control system (PCS) forms the backbone of safe and high performance plasma operation in all tokamaks. The ITER tokamak will be significantly larger than its predecessors and will become a nuclear environment at a later stage of its operation. Therefore, new control algorithms for the ITER PCS have to follow a systematic, rigorous and formal design and testing procedure. Then, controllers have to be formally commissioned preceding their ultimate deployment on the PCS for real-time operation. Considering the significant cost of operating the ITER machine and its tight operational schedule, limited time is available for controller commissioning. Therefore, the design team for the ITER PCS is implementing a model-based design and testing approach to exploit models not only for the design and tuning of controllers, but also the verification and validation (V&V) tests. To this end, controllers are first designed in the Plasma Control System Simulation Platform (PCSSP) in Simulink. The PCSSP has been deployed on a continuous integration (CI) server on which V&V and functional performance tests are automatically run and analysed to scrutinize the design. Once controllers have passed the design phase in PCSSP, automatic code generation or manual coding allows implementation on the real-time framework (RTF) forming the real PCS. Components on the RTF are continuously validated against their PCSSP counterparts to make sure the implementation is bug-free. The increasing complexity of the interplay between system requirements, modules, controllers, and tests results is managed in a structured Plasma Control System Database (PCSDB). This PCSDB ultimately allows the monitoring of the PCS design and implementation progress. In this work, we present the stages ITER controllers have to pass and the new tools that are being implemented to facilitate and structure this model-based design and testing workflow.

The Optimization of Working Time for a Consecutively Connected Production Line

Most factory production processes are completed by machines and workers on production lines. The operation schedule is arranged to reduce the cost of the enterprises to obtain the maximum economic profit for sustainable running. Previous studies usually investigated the working time while only considering the workers’ conditions. This study proposed a method to optimize the operation schedule by jointly considering the workers’ fatigue states and the operation states of machines. This method was proposed based on a system structure called the multistate consecutively connected system (MCCS), which has been widely applied in many areas, such as electronic communications. This structure is also an analogy of the production line. The corresponding model is constructed based on the universal generating function (UGF) since it is a powerful tool in modeling a consecutively connected system. The proposed model can be used to evaluate the different productivities of different types of workers in different states and to realize the screening of the whole scheme through simulation. According to the proposed method, we obtained the optimal operation schedule, including the working time, rest time and allocation strategy for a production line system. Some examples are provided to illustrate the proposed method.

The journey of demand responsive transportation: Towards sustainable services

The concept of Demand Responsive Transport (DRT) describes a technology-enabled shared mobility service that has a flexible operating schedule and/or provides virtual stops and/or flexible routes. While these on-demand services are not new, the developments in communication and tracking technology (vehicle positioning systems) have revived these services in the past decade. The benefits of adopting demand responsive transport services for intra-community and FLM travel needs are widely accepted, but there is still a cautious approach towards their implementation due to the failure of many promising demand responsive transport schemes in the past. This article 1) creates an overview of the various on-demand services introduced across the world, to understand the factors that may have contributed to the failure of these services in the past 2) identifies the progress made towards sustainable demand responsive transport ventures through analysis of global case studies 3) provides an overview of the flexibility of vehicle and deployment technologies in the demand responsive transport sphere. A bibliometric analysis, where the top keywords were further categorised using VOSviewer’s default clustering algorithm, highlighted the importance of sustainability in demand responsive transport ventures. By the progress made towards sustainable demand responsive transport ventures, it can be concluded that environmentally sustainable demand responsive transport ventures can be achieved through the adoption of electric and autonomous vehicles for demand responsive transport services, by reducing mileage of the vehicle and/or adjusting the length of route. The study concludes by reviewing existing research gaps regarding performance expectation, and recommending policy and practice implications, based on the case study of the Bus-on-Demand in Dubai, UAE.

Technical and technological solutions regarding the development of pillars during final mining of deep open pits

When mining operations achieve the total depth of open pits within their boundaries, a significant share of balance ore reserves remains in pillars under transport communications as well as under reloading points. Liquidation of the available transport communications should follow by the development of the new ones. Hence, mining stages of a steep-grade field and periodicity of a pillar initiation within both working open pit flank and temporarily nonworking one have been studied. Нannivskyi open pit has been taken as an example to substantiate the expediency to extract balance reserves occurring under crushing and reloading point within ±0 m level. The substantiation of technical and technological solutions concerning the development of a protective pillar involved cuts of geological ore formation which supported the idea of availability of the preserved reserves and topicality of the selected problem. Operation schedules to mine the pillars have been developed for general conditions; the required equipment set has been analyzed. New technological solutions have been obtained using current excavating and transportation machinery. Engineering solutions as for possibility to open the pillars and liquidate them in terms of the limited technical and technological potential have been substantiated. The technological schemes of pillar mining have been systematized depending upon the technological processes, taking place in the open pit, and the applied equipment set. Scientific significance of the paper is the developed systematization of facilities in terms of operation schedules of pillar mining. Practical significance is the developed operation schedules to mine and liquidate the pillars. Technical and technological expediency to use skip hoist while reactivating temporarily nonworking open-pit walls for rock mass transportation from the pillars up to the surface has been substantiated. It has been proved that during final mining operations in an open pit, complicated by difficulties in electricity delivering to the preservation pillar, it is expedient to apply loaders in one operation schedule. Use of loaders and skip hoist to mine the preservation pillar is the optimum strategy. It has been substantiated that the proposed procedure is more advantageous to compare with the conveyor transport.

Optimal Design of Integrated Semi-Flexible Transit Services in Low-Demand Conditions

Semi-flexible transit (SFT) is commonly discussed as a cost-effective alternative to serving public transportation users in low-demand conditions. We hypothesize that joint optimization of service headway and slack time per trip for route deviation is essential for designing a schedule for the operation of an integrated SFT that can meet both fixed-route and paratransit demand. An integrated SFT has the potential to lower the cost of transportation for regular transit users (both operators and riders) while redirecting potential paratransit riders to less expensive transit modes; thus, reducing demand for overwhelmed paratransit services operating with limited resources. The optimization problem has three competing objectives: minimizing operator costs, minimizing user costs, and maximizing service benefits. Two state-of-the-art multi-objective evolutionary algorithms NSGA-II and SMPSO are compared to obtain the most representative deterministic Pareto optimal solution set. This study has three major contributions. First, quantile regression is used to suggest multiple slack time values for a given headway that transit planners can consider when generating a static schedule for SFT operation. Second, relationships derived to analyze cost trade-offs suggest that headway governs operator cost and is negatively correlated, user cost is positively and equally influenced by both variables, and slack time governs service benefit and is positively correlated. Third, sensitivity analysis for an integrated SFT operation reveals that low-capacity minivans and standard vans offer higher vehicle occupancy and cost efficiency, mostly economical for low to medium demand (5-20 pass/hr), low permissible deviation from the fixed route is desirable during peak hours to avoid delays for passengers on-board, and extreme weather conditions dramatically and negatively influence costs. Policy recommendations for integrated SFT implementation include a recommendation for fare structure design addressing service equity through surcharges/discounts, vehicle technology and service booking technology advancements for cost reduction, and fleet mix design through estimation of passenger loading profile. The application of the study methodology is demonstrated for a low-demand bus route in Regina, Canada.

Evaluation of variable speed drives to improve energy efficiency and reduce gas emissions: Case study

Variable speed drives are the most promising technique for reducing electric motors' energy consumption. This paper discusses energy savings by installing variable speed drives to control rotating equipment motors such as pumps, compressors, blowers, and fans in oil processing facilities. In addition to energy savings, variable speed drives will improve overall equipment efficiency, increase reliability, and reduce greenhouse gas (GHG) emissions. An energy audit was performed on a case study to investigate energy consumption for all electric motors. Technical and operational constraints for installing and operating variable speed drives were discussed. Installation requires adjustments in operation schedules and parameters to allow reducing energy consumption. The case study has illustrated how to calculate energy savings for pipelines, air coolers, air blowers for furnaces, pumps with variable flow rates, and reciprocating compressing systems. Variable speed drives were technically and economically accepted in air blowers, fans, and pumps. Energy consumption and GHG emission were reduced by 67%. The payback period for the whole project was less than one year. Meanwhile, it was not valid for reciprocating compressors as the payback period was 6.2 years.

An artificial intelligence approach for managing water demand in water supply systems

Water demand management is essential for water utilities, which have the critical task of supplying drinking water from water sources to end-users through the distribution network. Therefore, the water utilities have to make decisions for the current and future functioning of the water distribution system. In this context, the artificial intelligence approach with data-driven methods can be used to develop powerful tools to improve overall water management. In fact, data-driven methods can model water demands for plenty of tasks and applications such as demand forecasting or anomaly detection. In this work, we propose and discuss a practical application of an artificial neural network to model the urban water demand of a water supply system. The flexibility of the proposed method allows the prediction of water demand on different horizons. Moreover, this developed model can effectively support water utilities on different operational schedules and decision tasks.

RESEARCH OF A COMBINED HEAT SUPPLY SYSTEM WITH ALTERNATIVE ENERGY SOURCES

The configuration of an energy-efficient combined heat supply system of the educational building of the Odessа Polytechnic National University with alternative energy sources is proposed. An experimental database of wind speed, which is tied to the dislocation of the combined heat supply system, was obtained. The averaged data on the wind speed of the Odesa weather site during the heating period have been adjusted. The conditions for using the energy potential of the wind to create a combined system are considered. A calculation model was obtained for the integral repeatability of the wind speed according to the Weibull probability distribution for the heating period. On the basis of the developed mathematical model of the dynamics of thermal processes of the elements of the combined system and the model of the energy potential of the wind, the modes of operation of the heat supply system when the temperature of the outside air changes are investigated. With the application of a mathematical model of the heating system, the optimal daily schedule of the intermittent operation of the heating system in terms of energy efficiency is given. The optimal schedules of the electrical and thermal load of the elements of the combined heat supply system in the intermittent heating mode have been worked out. Daily schedules of charge-discharge modes of the water heat accumulator and the electric accumulator battery have been developed, which ensure optimal loading of the system elements. The evaluation of the effectiveness of the substitution coefficient at a change in the outside air temperature from 5 to –15 °С was carried out. The energy balances of the combined system were studied in order to substantiate the rational parameters of its elements and the system as a whole. The possibility of creating a highly efficient autonomous heating system based on modern energy technologies using alternative energy sources and taking into account their local potential has been confirmed.

An ordered precedence constrained flow shop scheduling problem with machine specific preventive maintenance

In reality, the machines may interrupt because of the nature of deterioration of the machines. Thus, it is inevitable to perform maintenance alongside production planning. The preventive maintenance is a schedule of strategic operations that are performed prior to the failure occurring, to retain the system operating at the preferred level of consistency. Thus, preventive maintenance plays a significant role in flow shop scheduling models. With its practical significance, this study addresses a practical three-machine n jobs flow shop-scheduling problem (FSSP) in which machine specific preventive maintenance, where each machine is given with a maintenance schedule is considered. In addition, a practical ordered precedence constraint in which some set of jobs has to process in the specified order irrespective of their processing times is also considered. The problem’s goal is to establish the optimal job sequence and preventive maintenance such that the overall cost of tardiness and preventive maintenance is as minimum as possible. An efficient heuristic approach is designed to tackle the present model, resulting in total cost savings. A comparative analysis is not conducted due to absence of studies on the current problem in the literature. However, Computational experiments are carried out on some test instances and results are reported. The reported results may be useful for future studies.

Justification of the parameters and efficiency indicators of the technological process TO-1 of MAN TGL trucks

Purpose. Justification of the parameters and efficiency indicators of the MAN TGL truck MAINTENANCE-1 technological process to achieve its specified productivity at the expense of three types of resources, namely: changes in the number of workers, equipment and posts. Methods. The study is based on the modeling of maintenance technological processes using the basic principles of graph theory and schedules, a heuristic algorithm for the distribution of the entire set of maintenance operations of a certain type, among workers at the maintenance point posts, taking into account the amount of equipment, with the aim of obtaining a schedule of operations that minimized cycle duration. Results. The results of the simulation are the dependence of the duration of the technological process of maintenance of MAN TGL vehicles on the number of involved workers, equipment and posts, as well as the dependence between the number of different resources involved and the efficiency indicators of their use. Conclusions. The analysis of the research results showed that the increase in the number of workers slightly shortens the duration of technological processes of maintenance of MAN TGL vehicles, and also increases the utilization rates of workers' time funds and equipment. Keywords: MAN TGL vehicle, maintenance, technological process, modeling, parameters and performance indicators.