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1
ZHANG, DU, and RAUL VELEZ. "BMS: A KNOWLEDGE-BASED TOOL FOR UNIX PERFORMANCE TUNING." International Journal on Artificial Intelligence Tools 05, no. 03 (1996): 323–45. http://dx.doi.org/10.1142/s0218213096000225.
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This paper presents the design and implementation of a knowledge-based tool for performance tuning of the UNIX operating system. The tool, called BMS, provides an intelligent support and maintenance for identifying performance bottlenecks in UNIX and recommending solutions to the problems. Currently, it handles problems in UNIX resource management, such as memory utilization, disk utilization, CPU scheduling and I/O devices. BMS has been implemented in the EXSYS environment and tested on UNIX V.3. Preliminary results have indicated that such a knowledge-based tool to operating system performance tuning (1) is viable; (2) increases the productivity of system maintenance personnel and reduces the cost of training; and (3) offers a better service to operating system users by providing prompt recommendations to solutions of their system performance problems.
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Zagan, Ionel, and Vasile Gheorghiţă Găitan. "Soft-core processor integration based on different instruction set architectures and field programmable gate array custom datapath implementation." PeerJ Computer Science 9 (April 18, 2023): e1300. http://dx.doi.org/10.7717/peerj-cs.1300.
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One of the fundamental requirements of a real-time system (RTS) is the need to guarantee re-al-time determinism for critical tasks. Task execution rates, operating system (OS) overhead, and task context switching times are just a few of the parameters that can cause jitter and missed deadlines in RTS with soft schedulers. Control systems that are susceptible to jitter can be used in the control of HARD RTS as long as the cumulative value of periodicity deviation and worst-case response time is less than the response time required by that application. This artcle presents field-programmable gate array (FPGA) soft-core processors integration based on different instruction set architectures (ISA), custom central processing unit (CPU) datapath, dedicated hardware thread context, and hardware real-time operating system (RTOS) implementations. Based on existing work problems, one parameter that can negatively influence the performance of an RTS is the additional costs due to the operating system. The scheduling and thread context switching operations can significantly degrade the programming limit for RTS, where the task switching frequency is high. In parallel with the improvement of software scheduling algorithms, their implementation in hardware has been proposed and validated to relieve the processor of scheduling overhead and reduce RTOS-specific overhead.
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Cheng, Zhiping, Xiang Li, Zhongwen Li, Jikai Si, Shuai Xu, and Rui Nie. "Optimal Scheduling Strategy of Building Integrated Photovoltaic Microgrid Considering Virtual Energy Storage." Applied Sciences 10, no. 18 (2020): 6176. http://dx.doi.org/10.3390/app10186176.
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Building integrated photovoltaic (BIPV) is one of the most efficient ways to utilize renewable energy in buildings. However, the stochastic characteristic of PV power generation and load challenges the optimal dispatch of the BIPV. This paper proposes an optimal scheduling strategy of BIPV microgrid considering virtual energy storage (VES), which intends to further improve the operating economy of a BIPV microgrid. Firstly, existing research findings and gaps in this research area were analyzed. Then, the mathematical model of each unit in the system was created, meanwhile, the air conditioner-based VES (ACVES) model and electric water heater-based VES (EWHVES) model are built by exploiting the heat/cold storage capability of building and the electric water heater, respectively. Next, the optimal scheduling model considering the VES is designed. Finally, the optimal scheduling model is solved by CPLEX to obtain the optimal scheduling strategy that reduces the operating cost of the system. Simulation results demonstrate that the proposed strategy can reduce the operating cost of the system. Furthermore, the VES shows the potential to replace the battery energy storage system.
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Chira-Chavala, T., and Christoffel Venter. "Cost and Productivity Impacts of a “Smart” Paratransit System." Transportation Research Record: Journal of the Transportation Research Board 1571, no. 1 (1997): 81–87. http://dx.doi.org/10.3141/1571-11.
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The cost and productivity impacts of an advanced paratransit system are analyzed; the system uses a digital geographical data base and an automated trip scheduling system to automate vehicle and passenger scheduling functions. The results indicate that such a system enables transit agencies to accommodate rapid increases in the paratransit demand in an efficient manner, which could not be accomplished by a manual or semimanual scheduling system. For the case study presented, this new system resulted in 13 percent savings in unit transportation operating cost and a significant increase in the percentage of shared rides. These benefits were achieved without affecting passengers’ travel time or ride comfort.
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Huang, Yuehua, Qing Chen, Zihao Zhang, Xingtao Liu, Jintong Tu, and Lei Zhang. "Optimal Scheduling of Combined Electric and Heating Considering the Control Process of CHP Unit and Electric Boiler." Processes 11, no. 3 (2023): 753. http://dx.doi.org/10.3390/pr11030753.
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In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme.
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Muthana, Shatha Abdulhadi, and Ku Ruhana Ku-Mahamud. "Tri-objective generator maintenance scheduling model based on sequential strategy." PLOS ONE 17, no. 10 (2022): e0276225. http://dx.doi.org/10.1371/journal.pone.0276225.
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A multi-objective modeling approach is required in the context of generator maintenance scheduling (GMS) for power generation systems. Most multi-objective modeling approaches in practice are modeled using a periodic system approach that caters for a fixed maintenance window. This approach is not suitable for different types of generating units and cannot extend the generator lifespan. To address this issue, this study proposes a tri-objective GMS model with three conflicting objectives based on the sequential system approach that accounts for operating hours and start-up times. The GMS model’s objectives are to minimize the total operation cost, maximize system reliability and minimize violation. The main difference between the proposed tri-objective GMS model and other multi-objective GMS models, is that the proposed model uses a sequential strategy based on operating hours and start-up times. In addition, the proposed model has considered the most important criteria in scheduling the generator maintenance, and this reflects the real-life requirements in electrical power systems. A multi-objective graph model is also developed to generate the maintenance units scheduling and used in developing the proposed Pareto ant colony system (PACS) algorithm. A PACS algorithm is proposed to implement the model and obtain solution for GMS. The performance of the proposed model was evaluated using the IEEE RTS 26, 32, and 36-unit systems dataset. The performance metrics used comprise the GMS model objectives. The experimental results showed that the obtained solution from the proposed tri-objective GMS model was a robust solution by considering the different initial operational hours of the units.
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Nagel, Everton, Ricardo Melo Czekster, Thais Webber, and César Augusto Missio Marcon. "A Framework Prototype for Multithreaded Implementation Over Micro-Controllers." Journal of Integrated Circuits and Systems 14, no. 1 (2019): 1–10. http://dx.doi.org/10.29292/jics.v14i1.39.
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Multithreading is pervasive in embedded system applications development. The applications requirements are becoming more rigorous, demanding the execution of concurrent tasks that must also take into account modularity and flexibility. An important part of the operating systems development concerns the implementation of scheduling algorithms. In an embedded system context, it is essential to consider that the scheduling algorithm heavily influences application behavior. Due to restricted and finite hardware resources, it is important to evaluate the use of flexible algorithms to guarantee efficiency. Currently, projects for embedded operating systems do exist for microcontrollers’ devices that implement scheduling algorithms, however, the developer cannot change or add new scheduling policies without implementing kernel tweaks and modifications. The alternatives are not flexible when choosing the scheduling algorithm according to the application needs. This imposes restrictions to many systems, forcing them to run specific static scheduling algorithms because no other options are available. The objective of this work concerns the design and development of a framework that implements a microkernel with a modular scheduler unit, allowing the execution of tailored algorithms according to the application profile. The idea is to provide a flexible platform to conveniently select the most appropriated algorithm. We have employed low capacity hardware to implement multithreading patterns corresponding to sets of concurrent tasks, demonstrating the strengths of adopting our approach. Our results show that the use of modern techniques that combine modularity, multithreading, and scheduling methods for embedded systems yield best executions when compared to its sequential counterparts.
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Zhang, Zhi, Dan Xu, Xuezhen Chan, and Guobin Xu. "Research on Power System Day-Ahead Generation Scheduling Method Considering Combined Operation of Wind Power and Pumped Storage Power Station." Sustainability 15, no. 7 (2023): 6208. http://dx.doi.org/10.3390/su15076208.
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In the proposed wind-storage combined operation technology, the storage side is foreseen to play a significant role in power system day-ahead generation scheduling. Based on the operational characteristics of pumped storage power stations, the day-ahead dispatching method of a power system with wind farms and pumped storage power stations is studied. The dispatching mode that aims at the lowest operating cost is proposed, taking into consideration the coordination relationship between the scheduling benefit of pumped storage power stations and the total peak-shaving economy of the system and the fluctuation of new energy output. First, taking the constraint of reservoir capacity, the output power, and the daily pumping power of the pumped storage power station into account, a day-ahead generation scheduling model is constructed, with the objective of minimizing costs. Then, the imperial competition algorithm is applied to the proposed model. Finally, the algorithm is compared with the standard particle swarm optimization algorithm. The simulation results based on standard 4-unit and 10-unit systems indicate that the proposed method is effective and robust for a power system with wind power and pumped storage power stations.
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Chapalkar*, Sandeep S., and Karibasappa K. "Relative Deadline Analysis in Multitasking RTS using RM & EDF Scheduling." International Journal of Recent Technology and Engineering 10, no. 1 (2021): 246–51. http://dx.doi.org/10.35940/ijrte.a5900.0510121.
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In embedded systems the time required for any process to complete its execution in multitasking environment is an important factor to understand the performance of Real Time System (RTS) and its ability fulfill the deadline requirement of each process under different process load conditions. Even though some non-critical systems provide flexibility over deadlines, the hard real time systems are to be designed to meet the deadline requirement of all processes under peak process load condition. The number of processes available in scheduling queue may vary with time, the dynamic load on processing unit also changes proportionately which in turn affects the relative deadlines of each process. The scheduling policies considered are widely used scheduling policies like Rate Monotonic (RM) and Earliest Deadline First (EDF) to analyze and understand the impact on relative deadline with respect to number of scheduled processes. The real time execution timings of each process is observed on Raspberry Pi 3b+ processing unit operating at standard frequency of 700 MHz in multitasking mode of operation. The results obtained will decisively conclude the suitable scheduling policy for a set of processes under different process load conditions.
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Farah, Alaa, Hamdy Hassan, Alaaeldin M. Abdelshafy, and Abdelfatah M. Mohamed. "Optimal Scheduling of Hybrid Multi-Carrier System Feeding Electrical/Thermal Load Based on Particle Swarm Algorithm." Sustainability 12, no. 11 (2020): 4701. http://dx.doi.org/10.3390/su12114701.
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In this paper, the optimum coordination of an energy hub system, fed with multiple fuel options (natural gas, wood chips biomass, and electricity) to guarantee economically, environmentally friendly, and reliable operation of an energy hub, is presented. The objective is to lessen the total operating expenses and CO2 emissions of the hub system. Additionally, the effect of renewable energy sources as photovoltaics (PVs) and wind turbines (WTs) on energy hub performance is investigated. A comparison of various configurations of the hub system is done. The proper planning of the hub elements is determined by a multi-objective particle swarm optimization (PSO) algorithm to achieve the lowest level of the gross running cost and total system emissions, simultaneously. The outcomes show that the natural gas turbine (NGT) is superior to the biomass generating unit in lowering the gross operating expenses, while using the biomass wood chips plant is most effective in lessening the total CO2 emissions than the NGT plant. Furthermore, the combination of the natural gas turbine, biomass generator, photovoltaics, and wind turbines enhances the operation of the hub infrastructures by lessening both the gross operating cost and overall CO2 emission simultaneously.
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Zhang, Pei, Shu Tao Zhao, Lu Shen, Jing Guo, and Xiao Qian Tian. "Research on Remote Fault Diagnosis of High Voltage Circuit Breakers Based on LabVIEW." Advanced Materials Research 765-767 (September 2013): 2294–98. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2294.
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In order to ensure the reliability of the operational of circuit breaker, we used intelligent monitoring unit to realize the high-speed acquisition of the electrical, mechanical and insulated signal in the operation of the high voltage circuit breaker and actuator. Combined with signal processing and fault analysis theory, we estimate the operating status based on the accurate acquisition of the various operating parameters in breaker actuator. We also developed a system of remote online monitoring of circuit breaker status and fault diagnosis. The system ensures reliable operation of the circuit breaker. It also provides fast and accurate information for operation scheduling and maintenance personnel and accumulates large amounts of raw data for the state overhaul of the circuit breaker.
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Qi, Yong Fu, Dong Mei Zhao, Xing Yu Li, Guo Qing Wei, Jian Chen Hu, and Feng Wu. "Thermal Unit Energy Conservation Load Dispatch Based on Harmony Search Algorithm Combined with Dynamic Unit Commitment." Advanced Materials Research 869-870 (December 2013): 351–55. http://dx.doi.org/10.4028/www.scientific.net/amr.869-870.351.
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The energy conservation generating scheduling is a kind of important work to insist in the power system that thermal power plants dominate. In the paper, a mathematical optimization model was built aiming at reducing the fuel consumption in the thermal units load dispatch. Considering the unit operating constraints, a method for energy saving load dispatch was presented, which combines the dynamic unit commitment strategy with the harmony search algorithm. The method was applied to the scheduling problem in a regional power system. The results show that the method proposed is feasible and plays a significant role in energy conservation in the thermal power distribution.
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Shi, Qingwu, Bin Zou, Lamei Zhang, and Desheng Liu. "Hybrid Parallel FDTD Calculation Method Based on MPI for Electrically Large Objects." Wireless Communications and Mobile Computing 2019 (June 23, 2019): 1–9. http://dx.doi.org/10.1155/2019/7309431.
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At present, the Internet of Things (IoT) has attracted more and more researchers' attention. Electromagnetic scattering calculation usually has the characteristics of large-scale calculation, high space-time complexity, and high precision requirement. For the background and objectives of complex environment, it is difficult for a single computer to achieve large-scale electromagnetic scattering calculation and to obtain corresponding large data. Therefore, we use Finite-Difference Time-Domain (FDTD) combined with Internet of Things, cloud computing, and other technologies to solve the above problems. In this paper, we focus on the FDTD method and use it to simulate electromagnetic scattering of electrically large objects. FDTD method has natural parallelism. A computing network cluster based on MPI is constructed. POSIX (Portable Operating System Interface of UNIX) multithreading technology is conducive to enhancing the computing power of multicore CPU and to realize multiprocessor multithreading hybrid parallel FDTD. For two-dimension CPU and memory resources, the Dominant Resource Fairness (DRF) algorithm is used to achieve load balancing scheduling, which guarantees the computing performance. The experimental results show that the hybrid parallel FDTD algorithm combined with load balancing scheduling can solve the problem of low computational efficiency and improve the success rate of task execution.
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Tiwari, Amit, Adarsh Dhar Dubey, and Devesh Patel. "Comparative Study of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning and Radial Basis Functional Neural Network." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 7, no. 01 (2015): 09–18. http://dx.doi.org/10.18090/samriddhi.v7i1.3307.
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The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling and for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning and operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling and unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flow and to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability and to the reduced occurrences of equipment failures and blackouts. The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.
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Gao, Hong, Kai Liu, Jiangbo Wang, and Fangce Guo. "Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints." Applied Sciences 13, no. 13 (2023): 7661. http://dx.doi.org/10.3390/app13137661.
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Recent advances in vehicle technology offer new opportunities for an electric, automated, modular bus (MB) unit with an adjustable capacity to be applied to transit systems, promising to tackle the resource allocation challenges of traditional buses in coping with uneven travel demand. Drawing on the concept of modular vehicles, this paper introduces a novel scheduling system in which MB units can be combined/separated from fulfilling imbalanced trip demands through capacity adjustments. We develop an optimization model for determining the optimal formation and trip sequence of MB units. In particular, given that the vehicles are electrically powered, battery range limits and charging plans are considered in the system scheduling process. A column-generation-based heuristic algorithm is designed to efficiently solve this model, with constraints related to travel demand and charging station capacity incorporated into the master problem and the trip sequence for modular units with limited energy solved by the subproblem. Taking real data from transit operations for numerical examples, the proposed model performs well in terms of both algorithmic performance and practical applications. The generated optimal MB dispatching scheme can significantly reduce the operating cost from $1534.31 to $1144.26, a decrease of approximately 25% compared to conventional electric buses. The sensitivity analysis on the MB dispatch cost and battery capacity provides some insights for both the scenario configuration and the battery selection for MB system implementation.
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Qian, Ping, Ying Zhen Zhang, and Yu Li. "Design of Voice Control System for Smart Home Based on STM32." Applied Mechanics and Materials 734 (February 2015): 369–74. http://dx.doi.org/10.4028/www.scientific.net/amm.734.369.
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The application of embedded speech recognition technology in the smart home is researched, combining of the Internet of Things, the voice control system for smart home has been designed. The core processor chooses the high-performance Cortex-M4 MCU STM32F407VGT6 produced by STMicroelectronics. The system contains a hardware unit based on LD3320 for speaker-independent speech recognition. RF wireless communication uses ultra-low power chip CC1101 and GSM employ SIM900A. Real-time operating system FreeRTOS is used for multitask scheduling and the operation of household devices. The practical application verifies that this voice control system practicably can identify voice commands quickly and accurately, complete the control actions primely, has a wide application prospect.
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Varganova, Aleksandra V., Vadim R. Khramshin, and Andrey A. Radionov. "Improving Efficiency of Electric Energy System and Grid Operating Modes: Review of Optimization Techniques." Energies 15, no. 19 (2022): 7177. http://dx.doi.org/10.3390/en15197177.
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Continuously growing tariff rates for energy carriers required to generate electrical and thermal energy bring about the need to search for alternatives. Such alternatives are intended for the reduction in the electricity and heat net costs as well as the expenses for the operation and maintenance of system elements and damage from power outages or deteriorated power quality. A way to reduce electricity and heat costs is the introduction of distributed energy resources capable of operating on both conventional (natural gas) and alternative (solar and wind energy, biomass, etc.) fuels. The problem of reducing electricity and, in some cases, heat costs are solved by applying mathematical optimization techniques adapted to a specific element or system of the industry in question. When it comes to power industry facilities, optimization, as a rule, includes reducing active power losses by controlling the system mode or specific power unit parameters; planning generating equipment operating modes; defining the optimal equipment composition; improving the regime and structural reliability of grids; scheduling preventive maintenance of equipment; searching for effective power unit operating modes. Many of the problems listed are solved using direct enumeration techniques; modern technical tools allow quickly solving such local problems with a large number of source data. However, in the case of integrated control over the power system or its individual elements, optimization techniques are used that allow considering a lot of operating limitations and the target function multicriteriality. This paper provides an analytical review of optimization techniques adapted to solving problems of improving the efficiency of the power facility operating modes. The article is made on the basis of the research conducted by the authors in the area of optimization of operating modes for electric energy systems and grids. The authors drew conclusions on the applicability of mathematical optimization methods in the power energy area. While conducting the research, the authors relied on their expertise in the development and introduction of the method to optimize the operation modes of energy supply systems with heterogeneous energy sources.
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Gharsellaoui, Hamza, Mohamed Khalgui, and Samir Ben Ahmed. "New Optimal Solutions for Real-Time Reconfigurable Periodic Asynchronous Operating System Tasks with Minimizations of Response Time." International Journal of System Dynamics Applications 1, no. 4 (2012): 88–131. http://dx.doi.org/10.4018/ijsda.2012100105.
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Scheduling tasks is an essential requirement in most real-time and embedded systems, but leads to unwanted central processing unit (CPU) overheads. The authors present a real-time schedulability algorithm for preemptable, asynchronous and periodic reconfigurable task systems with arbitrary relative deadlines, scheduled on a uniprocessor by an optimal scheduling algorithm based on the earliest deadline first (EDF) principles and on the dynamic reconfiguration. A reconfiguration scenario is assumed to be a dynamic automatic operation allowing addition, removal or update of operating system’s (OS) functional asynchronous tasks. When such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated. The authors propose an intelligent agent-based architecture where a software agent is used to satisfy the user requirements and to respect time constraints. The agent dynamically provides precious technical solutions for users when these constraints are not verified, by removing tasks according to predefined heuristic, or by modifying the worst case execution times (WCETs), periods, and deadlines of tasks in order to meet deadlines and to minimize their response time. They implement the agent to support these services which are applied to a Blackberry Bold 9700 and to a Volvo system and present and discuss the results of experiments.
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Nadhirah Sam’on, Izni, Zuhaila Mat Yasin, and Zuhaina Zakaria. "Ant Lion Optimizer for Solving Unit Commitment Problem in Smart Grid System." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (2017): 129. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp129-136.
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<p>This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources is intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit<em> commitment scheduling can improve the total operating cost significantly. </em></p>
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Mazen Abusharia, Rawan, and Kasim Mousa Al-Aubidy. "Embedded control unit design for energy management in smart homes." Bulletin of Electrical Engineering and Informatics 11, no. 5 (2022): 2537–46. http://dx.doi.org/10.11591/eei.v11i5.4103.
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This paper deals with smart home energy management through load scheduling and optimal use of available energy sources. In this study, three energy sources were considered: the national electricity grid, photovoltaic (PV) energy, and the storage unit. The PV array can provide the maximum power to the load at a given operating point where the output power changes with temperature, radiation and load. Therefore, a real-time controller is proposed to track the maximum power. An energy management algorithm has been proposed in a smart home to achieve the main goal of making the electricity bill as low as possible. The algorithm involves scheduling loads by assigning a priority to each load. The loads are supplied with the required power according to their priorities and the available energy. The obtained results indicate that supplying the PV system with a fuzzy-based MPPT indicates an increase in system efficiency. The results also showed that the use of energy management based on load scheduling led to a significant reduction in the electricity bill.
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Ji, Ying, Jianhui Wang, Jiacan Xu, and Donglin Li. "Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning." Energies 14, no. 8 (2021): 2120. http://dx.doi.org/10.3390/en14082120.
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The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is designed to extract temporal features of uncertainty and generate the optimal scheduling decisions in an end-to-end manner. To optimize the policy with high-dimensional and continuous actions, proximal policy optimization (PPO) is employed to train the neural network-based policy in a data-driven fashion. The proposed method does not require any forecasting information on the uncertainty or a prior knowledge of the physical model of the microgrid. Simulation results using realistic power system data of California Independent System Operator (CAISO) demonstrate the effectiveness of the proposed method.
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Hussin, S. M., M. Y. Hassan, L. Wu, M. P. Abdullah, N. Rosmin, and M. A. Ahmad. "Mixed Integer Linear Programming for Maintenance Scheduling in Power System Planning." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 607. http://dx.doi.org/10.11591/ijeecs.v11.i2.pp607-613.
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This paper discussed the merit of mixed-integer linear programming (MILP)-based approach against Lagrangian relaxation (LR)-based approach in solving generation and transmission maintenance scheduling problem. MILP provides a straightforward solution by formulating coupling constraints equations so that these sub-problems can be solved simultaneously without involving multipliers. In LR-based approach, generation and transmission maintenance scheduling, and security-constrained unit commitment have been solved individually and the integration was realized through a series of multipliers which has caused computational burden to the system. Numerical case studies were evaluated on the 6-bus system. A comparative study is carried out between the MILP and LR approaches. Simulation results indicate that the maintenance schedule derived by the proposed MILP approach outperforms the LR in terms of operational cost savings and gap tolerance. The operating cost could be saved up to 5% and the gap tolerance achieved is 0.01% as compared to 0.14% by LR.
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Abedi, Sajjad, Gholam Hossein Riahy, Seyed Hossein Hosseinian, and Arash Alimardani. "Risk-Constrained Unit Commitment of Power System Incorporating PV and Wind Farms." ISRN Renewable Energy 2011 (December 19, 2011): 1–8. http://dx.doi.org/10.5402/2011/309496.
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Wind and solar (photovoltaic) power generations have rapidly evolved over the recent decades. Efficient and reliable planning of power system with significant penetration of these resources brings challenges due to their fluctuating and uncertain characteristics. In this paper, incorporation of both PV and wind units in the unit commitment of power system is investigated and a risk-constrained solution to this problem is presented. Considering the contribution of PV and wind units, the aim is to determine the start-up/shut-down status as well as the amount of generating power for all thermal units at minimum operating cost during the scheduling horizon, subject to the system and unit operational constraints. Using the probabilistic method of confidence interval, the uncertainties associated with wind and PV generation are modeled by analyzing the error in the forecasted wind speed and solar irradiation data. Differential evolution algorithm is proposed to solve the two-stage mixed-integer nonlinear optimization problem. Numerical results indicate that with indeterminate information about the wind and PV generation, a reliable day-ahead scheduling of other units is achieved by considering the estimated dependable generation of PV and wind units.
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Cong, Yunhua, Feng Zuo, Tongqing Li, Miaozhao Shen, Jin Li, and Lei Zhu. "Optimal Peak Shaving Analysis of Power Demand Response Considering Unit Uncertainty." Journal of Physics: Conference Series 2310, no. 1 (2022): 012087. http://dx.doi.org/10.1088/1742-6596/2310/1/012087.
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Abstract With the increasingly prominent environmental problems and the promotion of the “double carbon” goal, the participation of demand-side response resources in load peak shaving and valley filling is of great significance to the development of the power industry. In this paper, based on the background that demand-side response is being rapidly developed in China, we firstly consider the uncertainty of generating units and the operating constraints of power system and units, and construct the day-ahead output model of generating units by using stochastic production simulation and day-ahead optimal scheduling. Then, based on this model, we consider the impact of demand response on system peak shaving and build a demand response power system optimal scheduling model with unit uncertainty. Finally, the power supply and demand of the IEEE30 node system on two typical days of peak shaving considering demand response are analysed by MATLAB, and the minimum service cost of demand response and the amount of pull-out limit are derived, which clarify the important role of demand response in the process of power system peak shaving.
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Liu, Zifa, and Chengchen Li. "Low-Carbon Economic Optimization of Integrated Energy System Considering Refined Utilization of Hydrogen Energy and Generalized Energy Storage." Energies 16, no. 15 (2023): 5700. http://dx.doi.org/10.3390/en16155700.
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In order to improve the level of new energy consumption in the system and utilize the clean and efficient characteristics of hydrogen energy, an integrated energy system (IES) scheduling model considering refined utilization of hydrogen energy and generalized energy storage is proposed. Firstly, the two-stage hydrogen energy utilization model of power-to-gas (P2G) is finely modeled, and the waste heat of the P2G methanation reaction is innovatively coupled with the Kalina cycle to improve the thermoelectric decoupling capability of the combined heat and power (CHP) unit. Secondly, integrated demand response, electric vehicles, and hydrogen-containing multi-source energy storage equipment are used as generalized energy storage resources to cut peaks and fill valleys. Then, on the basis of considering the ladder-type carbon trading mechanism, the IES conventional operation model is constructed with the minimum operating cost of the system as the objective function. Furthermore, considering the source-load uncertainty of IES operation, a multi-energy complementary optimal scheduling model of hydrogen-containing IES based on conditional value-at-risk was established. Through simulation analysis, it can be seen that the proposed model takes into account both economic and environmental benefits and improves the system’s ability to “peak cutting and valley filling” and measure risk levels.
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Chen, Jingfeng, Ping Yang, Jiajun Peng, Yuqi Huang, Yaosheng Chen, and Zhiji Zeng. "An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System." Energies 11, no. 8 (2018): 2150. http://dx.doi.org/10.3390/en11082150.
Full textAbstract:
A scientific and effective coordinated control strategy is crucial to the safe and economic operation of a microgrid (MG). With the continuous improvement of the renewable energy source (RES) penetration rate in MG, the randomness and intermittency of its output lead to the increasing regulation pressure of the conventional controllable units, the increase of the operating risk of MG and the difficulty in improving the operational economy. To solve the mentioned problems and take advantage of hybrid energy storage system (HESS), this study proposes a multi-time scale coordinated control scheme of “day-ahead optimization (DAO) + intraday rolling (IDR) + quasi-real-time correction (QRTC) + real-time coordinated control (RTCC).” Considering the shortcomings of existing low prediction accuracy of distributed RES and loads, the soft constraints such as unit commitment scheduling errors and load switching scheduling errors are introduced in the intraday rolling model, allowing the correction of day-ahead unit commitment and load switching schedule. In the quasi-real-time coordinated control, an integrated criterion is introduced to decide the adjustment priority of the distributed generations. In the real-time coordinated control, the HESS adopts an improved first order low pass filtering algorithm to adaptively compensate the second-level unbalanced power. Compared with the traditional coordinated control strategy, the proposed improved model has the advantages of good robustness and fast solving speed and provides some guidance for the intelligent solution for stable and economic operation of stand-alone MG with HESS.
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Wang, Xiuyun, Junyu Tian, Rutian Wang, et al. "Multi-Objective Economic Dispatch of Cogeneration Unit with Heat Storage Based on Fuzzy Chance Constraint." Energies 12, no. 1 (2018): 103. http://dx.doi.org/10.3390/en12010103.
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With the increasing expansion of wind power, its impact on economic dispatch of power systems cannot be ignored. Adding a heat storage device to a traditional cogeneration unit can break the thermoelectric coupling constraint of the cogeneration unit and meet the economic and stable operation of a power system. In this paper, an economy-environment coordinated scheduling model with the lowest economic cost and the lowest pollutant emissions is constructed. Economic costs include the cost of conventional thermal power generating units, the operating cost of cogeneration units, and the operating cost of wind power. At the same time, green certificate costs are introduced into the economic costs to improve the absorption of wind power. Pollutant emissions include SO2 and NOx emissions from conventional thermal power units and cogeneration units. The randomness and uncertainty of wind power output are fully considered, and the prediction error of wind power is fuzzy treated according to the fuzzy random theory, and the electric power balance and spinning reserve fuzzy opportunity conditions are established, which are converted into the explicit equivalent. The improved multi-objective particle swarm optimization (MOPSO) was used to solve the model. With this method, the validity of the model is verified by taking a system with 10 machines as an example.
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Do, Insu, and Siyoung Lee. "Optimal Scheduling Model of a Battery Energy Storage System in the Unit Commitment Problem Using Special Ordered Set." Energies 15, no. 9 (2022): 3079. http://dx.doi.org/10.3390/en15093079.
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Nonlinear characteristics of a battery energy storage system (BESS) may cause errors in the stored energy between the operation plan and the actual operation. These errors may hinder the reliability of the power system especially in environments such as microgrids with limited power generation resources and high uncertainty. This study proposes a method to alleviate the occurrence of such errors in the charging/discharging scheduling process of the BESS by piecewise linearizing its nonlinear characteristics. Specifically, the stored energy in a BESS that changes nonlinearly according to the size of the charging/discharging power was modeled using the special ordered set of the type 2 (SOS2) method. The proposed model and the typical BESS-operation models with constant power conditioning system (PCS) input/output power efficiency were applied to the unit commitment (UC) problem in a microgrid environment, and the results were compared with the actual operation results. The proposed model operated similarly to the actual operation compared to the typical model, reducing the error in charging/discharging energy. Consequently, the proposed model was made cost-effective by reducing the cost of error correction and reduced the risk of deviating from operating range of the BESS. This study demonstrates that the proposed method can efficiently solve the operational problems caused by the nonlinear characteristics of BESS.
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SANTOSO, J., G. D. VAN ALBADA, P. M. A. SLOOT, and B. A. A. NAZIEF. "SIMULATION OF HIERARCHICAL RESOURCE MANAGEMENT FOR META-COMPUTING SYSTEMS." International Journal of Foundations of Computer Science 12, no. 05 (2001): 629–43. http://dx.doi.org/10.1142/s0129054101000692.
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Optimal scheduling in meta-computing environments still is an open research question. Various resource management (RM) architectures have been proposed in the literature (e.g. [2][13][12]). In the present paper we explore, through simulation, various multi-level scheduling strategies for compound computing environments comprising several clusters of workstations. We study global and local RM and their interaction. The local RM comprises both the cluster management and operating system schedulers. Each level refines the scheduling decisions of the layer above it, taking into account the latest resource information. Our experiments explore conventional strategies like First Come, First Served (FCFS) and Shortest Job First (SJF) at the global RM level. At all levels, the schedulers strive to maintain a good load balance. The unit of load balancing at the global level is the job consisting of one or more parallel tasks; at the local level it is the task. The results of our simulations indicate that, especially at high system loads, the use of a global RM can result in a significant performance gain.
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Fadil, Salih, Cüneyt Demir, and Burak Urazel. "Solution to Security Constrained Environmental Pumped-Storage Hydraulic Unit Scheduling Problem by Genetic Algorithm." ISRN Power Engineering 2013 (September 22, 2013): 1–12. http://dx.doi.org/10.1155/2013/717625.
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A lossy electric power system area that contains thermal units and a pumped-storage (p-s) hydraulic unit is considered in this paper. The cost function, which is weighted combination of the total fuel cost and the total emission cost of the thermal units, in an operation cycle, is minimized under some possible electric and hydraulic constraints. The dispatch technique that is based on genetic algorithm considers minimum and maximum reservoir storage limits of the p-s unit, upper and lower active and reactive generation limits of the thermal units, upper and lower active pumping/generation power limits of the p-s unit, maximum transmission capacities of the transmission lines, and upper and lower limits of the bus voltage magnitudes in a considered power system. The proposed dispatch technique was tested on an example power system that has 12 buses with five thermal units and a p-s hydraulic unit. The same dispatch problem is solved via an iterative solution method based on modified subgradient algorithm operating on feasible values (F-MSG) and pseudowater price just for comparison purpose. It is seen that the solution technique based on the F-MSG algorithm and pseudowater price gives similar results with the proposed algorithm based on genetic algorithm.
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Chen, Yong, Hai Jin, Han Jiang, Dechao Xu, Ran Zheng, and Haocheng Liu. "Implementation and Optimization of GPU-Based Static State Security Analysis in Power Systems." Mobile Information Systems 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/1897476.
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Static state security analysis (SSSA) is one of the most important computations to check whether a power system is in normal and secure operating state. It is a challenge to satisfy real-time requirements with CPU-based concurrent methods due to the intensive computations. A sensitivity analysis-based method with Graphics processing unit (GPU) is proposed for power systems, which can reduce calculation time by 40% compared to the execution on a 4-core CPU. The proposed method involves load flow analysis and sensitivity analysis. In load flow analysis, a multifrontal method for sparse LU factorization is explored on GPU through dynamic frontal task scheduling between CPU and GPU. The varying matrix operations during sensitivity analysis on GPU are highly optimized in this study. The results of performance evaluations show that the proposed GPU-based SSSA with optimized matrix operations can achieve a significant reduction in computation time.
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Burnell, David, Julia Race, and Phil Evans. "An Overview of the Trunk Scheduling System for the London Ring Main." Water Science and Technology 28, no. 11-12 (1993): 99–109. http://dx.doi.org/10.2166/wst.1993.0650.
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The London Water Ring Main is one of UK's largest water investments. It will eventually carry half of London's water and greatly increase the flexibility of London's trunk distribution network. The Trunk Scheduling System is a core part of decision-support for the enhanced network. It has been written to help Operational Controllers make best use of that extra flexibility. Cost-effective operation makes best use of the cheapest treatment works, lowenergy-loss routes and off-peak tariffs. These need to be combined to meet projected diurnal demands in over 50 zones and leave each of some 20 service reservoirs at a target end volume, whilst taking no more than the declared “reliable output” from each of 10 works. The schedule proposed must be hydraulically viable as well as cheap. Most route constraints arise from pressure limits (so as not to burst mains or leave customers without water). Hydraulic constraints are non-linear and require on-line hydraulic intelligence as the schedule is developed. The key idea is to regard a schedule as a series of “operating regimes”, each of which is applied for a specified duration. Each regime applies within a particular timeslice. It comprises settings for each pump in the network, together with its implications: a unit cost and the effect of those settings on each source and each reservoir under hydraulic equilibrium. Trunk Scheduling provides a series of modules to answer the questions:which regimes (of the billions possible) should make up the schedule?how long should each selected regime be applied for?in which sequence should the chosen regimes be applied?
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Zhang, Tieyan, Zongjun Yao, Jingwei Hu, and Jinfeng Huang. "Multi-Time Scale Rolling Optimization Scheduling of “Nealy-Zero Carbon Park” Based on Stepped Carbon Allowance Trading." International Transactions on Electrical Energy Systems 2022 (September 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/4449515.
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Aiming at the self-government capacity and multi-time scale energy regulation requirements of “Nearly-zero Carbon Park” (NZCP) under the background of “dual carbon goals” and energy Internet, a day-ahead-intraday rolling optimization scheduling method for NZCP based on stepped carbon allowance trading is proposed. First, the energy supply and demand characteristics of liquid-storage Carbon Capture Gas-fired Power Plants (CCGPP) and Power-to-Gas (P2G) equipment are studied, and a combined system model of CCGPP and P2G is established that takes into account the low-carbon emission requirements of NZCP, and waste pyrolysis power generation facilities and manure treatment facilities are introduced to form a Waste Utilization system (WU) to provide energy support for the power grid and gas network. Second, the carbon allowance offset and low-carbon benefit gains of NZCP are considered, a compensation coefficient is introduced to guide the low-carbon behavior of carbon emitters, and a ladder carbon allowance trading model is established. Then, the influence of the source-load prediction error on the optimal scheduling at different time scales is considered, and a two-stage unit output plan is established. Then, the influence of the source-load prediction error on the optimal scheduling at different time scales is considered, and a two-stage unit output adjustment plan is established. The calculation example results verify that the proposed day-ahead-intraday rolling optimization scheduling model for NZCP can effectively reduce system carbon emissions while reducing system operating costs, and the efficient integration of economic and environmental benefits is achieved.
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Amalia Novitrie, Nora, Mey Rohma Dhani, Aulia Nadia Rachmat, and Yessica Novrita Devi. "Study of HAZOPs in the Screening Unit of the Industrial Gas Wastewater Treatment Plant." Fluida 16, no. 1 (2023): 57–62. http://dx.doi.org/10.35313/fluida.v16i1.4499.
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The filtration is the final stage of industrial gas wastewater treatment for clean water generation. Filtration is an operation of separating solid and liquid materials by filter medium. In filtration process the driving force such as pressure difference by gravitation or rotary power cause the feed to flow. The process parameters in the filtration process need to be monitored and controlled because deviation or abnormality of the process parameters will cause operating failures. This condition requires a more in-depth identification so that the risks of operating failures can be minimized. HAZOPs is a method to identify hazard that caused by deviation of parameters using guide words. Steps of the research consisted of early survey, literature study, and filling the HAZOPs worksheet such as determine study node, deviation, parameter, guideword, risk evaluation, and risk control. The results of the study show that there were four potential deviations from three parameters which were flow, pressure, and temperature. Risk values analysed were medium and low. Recommendation of the system are adding alarm, valve, changing filter regularly, and scheduling the routine maintenance.
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Shafi, Uferah, Munam Shah, Abdul Wahid, et al. "A Novel Amended Dynamic Round Robin Scheduling Algorithm for Timeshared Systems." International Arab Journal of Information Technology 17, no. 1 (2019): 90–98. http://dx.doi.org/10.34028/iajit/17/1/11.
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Central Processing Unit (CPU) is the most significant resource and its scheduling is one of the main functions of an operating system. In timeshared systems, Round Robin (RR) is most widely used scheduling algorithm. The efficiency of RR algorithm is influenced by the quantum time, if quantum is small, there will be overheads of more context switches and if quantum time is large, then given algorithm will perform as First Come First Served (FCFS) in which there is more risk of starvation. In this paper, a new CPU scheduling algorithm is proposed named as Amended Dynamic Round Robin (ADRR) based on CPU burst time. The primary goal of ADRR is to improve the conventional RR scheduling algorithm using the active quantum time notion. Quantum time is cyclically adjusted based on CPU burst time. We evaluate and compare the performance of our proposed ADRR algorithm based on certain parameters such as, waiting time, turnaround time etc. and compare the performance of our proposed algorithm. Our numerical analysis and simulation results in MATLAB reveals that ADRR outperforms other well-known algorithms such as conventional Round Robin, Improved Round Robin (IRR), Optimum Multilevel Dynamic Round Robin (OMDRR) and Priority Based Round Robin (PRR)
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Ju, Yuntao, Jiankai Wang, Fuchao Ge, et al. "Unit Commitment Accommodating Large Scale Green Power." Applied Sciences 9, no. 8 (2019): 1611. http://dx.doi.org/10.3390/app9081611.
Full textAbstract:
As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units.
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Hu, Changbin, Lisong Bi, ZhengGuo Piao, ChunXue Wen, and Lijun Hou. "Coordinative Optimization Control of Microgrid Based on Model Predictive Control." International Journal of Ambient Computing and Intelligence 9, no. 3 (2018): 57–75. http://dx.doi.org/10.4018/ijaci.2018070105.
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This article describes how basing on the future behavior of microgrid system, forecasting renewable energy power generation, load and real-time electricity price, a model predictive control (MPC) strategy is proposed in this article to optimize microgrid operations, while meeting the time-varying requirements and operation constraints. Considering the problems of unit commitment, energy storage, economic dispatching, sale-purchase of electricity and load reduction schedule, the authors first model a microgrid system with a large number of constraints and variables to model the power generation technology and physical characteristics. Meanwhile the authors use a mixed logic dynamical framework to guarantee a reasonable behavior for grid interaction and storage and consider the influences of battery life and recession. Then for forecasting uncertainties in the microgrid, a feedback mechanism is introduced in MPC to solve the problem by using a receding horizon control. The objective of minimizing the operation costs is achieved by an MPC strategy for scheduling the behaviors of components in the microgrid. Finally, a comparative analysis has been carried out between the MPC and some traditional control methods. The MPC leads to a significant improvement in operating costs and on the computational burden. The economy and efficiency of the MPC are shown by the simulations.
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AHMADI, REZA. "A NEW APPROACH TO MODELING CONDITION-BASED MAINTENANCE FOR STOCHASTICALLY DETERIORATING SYSTEMS." International Journal of Reliability, Quality and Safety Engineering 21, no. 05 (2014): 1450024. http://dx.doi.org/10.1142/s0218539314500247.
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This paper develops the degradation and maintenance modeling technique for repairable systems subject to deterioration due to aging and damage caused by operating environment factors. This provides a relaxed and generalized approach to modeling condition-based maintenance. The approach can deal with both the maintenance scheduling problem and failures preventing the system functioning further. Controlled by a virtual age process and a general class of process called Piecewise-Stochastic (Damage) process (PSP), the proportional intensity model (PIM) is used to describe the system state. The state of the system is monitored at periodic times and maintenance actions are carried out in response to the system state revealed at inspection times. Assuming a threshold-type policy, the approach aims at minimizing the long-run average cost per unit time subject to maintenance parameters; the inspection interval and preventive replacement threshold. To this end, given some assumptions, expressions for the expected cycle length and expected cost per cycle emerging as solutions of the Fredholm integral equations are obtained. The solution technique has been presented for the case when the effect of operating environment is modeled as a Gamma process.
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Zhang, Yi Hui, Wen Qiang Hu, Shi Ming Liu та Hua Bing Wang. "The Functional Design on Fault Recorder of μC/OS-II-Based Intelligent Substation". Applied Mechanics and Materials 415 (вересень 2013): 209–15. http://dx.doi.org/10.4028/www.scientific.net/amm.415.209.
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Regarding the functional requirements for smart substation in fault recorder, this paper introduced μC / OS-II, an embedded real-time multitasking operating system as the functional design. The program works with the embedded operating system, the state machine theory and new methods for embedded development, whilst tasking full use of the advantages of such functions as task scheduling, message queues, etc., aiming to achieve a fault recorder function of intelligent substation. More than taking care of the difficulty of programming, it could improve the program reliability and portability. The program has been applied in an intelligent substation protection in centralized control unit, which has brought back a good fault recorder performance. On this basis, more functions can be integrated, including network log analysis, state estimation, etc., in order to truly meet the application requirements of the smart substations.
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Piraino, Francesco, Matteo Genovese, and Petronilla Fragiacomo. "Performance analysis of an on-site hydrogen facility for fuel cell trains." E3S Web of Conferences 197 (2020): 05007. http://dx.doi.org/10.1051/e3sconf/202019705007.
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Fuel cell technologies and hydrogen can represent a potential and powerful enabler for replacing traditional diesel vehicles, especially in railways. In this train of thought, the present paper aims to investigate a fuel cell hybrid powertrain for a regional route. The main powertrain components are numerically modeled and the railway operations are simulated. The results achieved, in terms of power demand, efficiency and hydrogen consumption, are discussed and they are useful for properly sizing the refueling system. As a matter of fact, the train will be fueled with compressed hydrogen, produced on-site at a hydrogen central depot, where a hydrogen refueling station is thought to be installed. The hydrogen generation unit is considered to be a PEM unit, operating at 353 K and 20 bar. The produced hydrogen is then compressed by mean of a volumetric compressor and then stored in hydrogen tank type II, at 350 bar. The dispensing scheduling is based on the daily hydrogen demand required by the fuel cell-based train route, according to the railway timetable. The system is indeed investigated from a technical point of view, proving the integration of such systems to represent a clean, sustainable, and flexible option.
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Zhao, Yongjun, Vitali Volovoi, Mark Waters, and Dimitri Mavris. "A Sequential Approach for Gas Turbine Power Plant Preventative Maintenance Scheduling." Journal of Engineering for Gas Turbines and Power 128, no. 4 (2005): 796–805. http://dx.doi.org/10.1115/1.2179470.
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Traditionally, gas turbine power plant preventive maintenance schedules are set with constant intervals based on recommendations from the equipment suppliers. Preventive maintenance is based on fleet-wide experience as a guideline as long as individual unit experience is not available. In reality, the operating conditions for each gas turbine may vary from site to site and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests a gas turbine needs more frequent inspection and maintenance as it ages. A unit-specific sequential preventive maintenance approach is therefore needed for gas turbine power plant preventive maintenance scheduling. Traditionally, the optimization criteria for preventive maintenance scheduling is usually cost based. However, in the deregulated electric power market, a profit-based optimization approach is expected to be more effective than the cost-based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economic factors drive maintenance frequency and expense to more frequent repairs and greater expense as equipment ages is introduced, and a profit-based unit-specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, a conceptual level study is performed using a base load combined cycle power plant with a single gas turbine unit.
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langchao, He, Wu Lizhen, Chen Wei, and Hao Xiaohong. "Research on Bi-level Coordinated Optimal Dispatching Strategy for Microgrid Cluster." E3S Web of Conferences 256 (2021): 01045. http://dx.doi.org/10.1051/e3sconf/202125601045.
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The interconnection of multiple microgrids can form a microgrid cluster (MGC). The economic benefit and operation reliability of the whole system can be improved through the energy mutual-aid and coordination control between microgrids. This paper proposes a bi-level coordinated optimal dispatching strategy for microgrid clusters. The upper layer aims to minimize the operating costs of the microgrid cluster system and the power fluctuation of the tie lines. The exchange power and generation power of each sub-microgrid are coordinated and controlled, which are sent to the lower level model as scheduling instructions. The lower layer takes a single microgrid as the research object. By optimizing the output of power generation unit, the operation cost of single microgrid can be minimized. Iterative solution is adopted between the bi-level optimization models. In order to avoid the premature and local convergence of the algorithm in the optimization process, the improved artificial bee colony algorithm (ABC) is used to solve the objective function. Finally, simulation examples verify the effectiveness and feasibility of the proposed strategy.
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Poyrazoglu and Oh. "Co-Optimization of Transmission Maintenance Scheduling and Production Cost Minimization." Energies 12, no. 15 (2019): 2931. http://dx.doi.org/10.3390/en12152931.
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Regular transmission maintenance is important to keep the infrastructure resilient and reliable. Delays providing on-time maintenance increase the forced outage rate of those assets, causing unexpected changes in the operating conditions and even catastrophic consequences, such as local blackouts. The current process of maintenance schedule is based on the transmission owners’ choice, with the final decision of system operator about the reliability. The requests are examined on a first-come, first-served basis, which means a regular maintenance request may be rejected, delaying the tasks that should be performed. To incorporate optimization knowledge into the transmission maintenance schedule, this study focuses on the co-optimization of maintenance scheduling and the production cost minimization. The mathematical model co-optimizes generation unit commitment and line maintenance scheduling while maintaining N-1 reliability criterion. Three case studies focusing on reliability, renewable energy delivery, and service efficiency are conducted leading up to 4% production cost savings as compared to the business-as-usual approach.
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Shi, Lumin, Man-Wen Tian, As’ad Alizadeh, Ardashir Mohammadzadeh, and Sayyad Nojavan. "Information Gap Decision Theory-Based Risk-Averse Scheduling of a Combined Heat and Power Hybrid Energy System." Sustainability 15, no. 6 (2023): 4825. http://dx.doi.org/10.3390/su15064825.
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This research investigates the optimal management of electric and heat energies in a hybrid energy system (HES). In the studied HES, a pair of photovoltaic and battery storage devices is used to supply the electricity demand, and a boiler system to supply the heat demand directly. In addition, a modified cycle power plant acted as a combined heat and power (CHP) unit to increase the generation capacity and supply reliability. The HES is also able to connect to the electric grid to exchange power according to real-time energy prices. The uncertainty of renewable generation, demand levels, and energy prices challenge the decision-making process. To deal with the uncertainty of these overlapping parameters, a comprehensive information-gap decision theory (IGDT) approach is proposed in this paper that, despite other works, considers the uncertainties in an integrated framework and derives risk-averse and risk seeker strategies in different steps. The problem is modeled as mixed-integer linear programming and solved using the GAMS optimization package. Concerning simulation results, from the viewpoint of a risk-seeking decision maker, the increment of the uncertainty degree by 10.906% results in a reduced operating cost of 8.6%. From the viewpoint of a risk-averse decision maker, the increment of the uncertainty degree by 10.208% results in 8.6% more operating cost.
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Afandi, Arif Nur, Aji P. Wibawa, Syaad Patmantara, Goro Fujita, Slamet Hani, and Yunis Sulistyowati. "Economic Assessment of Power Grid Development Using Artificial Bee Colony Algorithm." Periodica Polytechnica Electrical Engineering and Computer Science 66, no. 1 (2022): 72–79. http://dx.doi.org/10.3311/ppee.18422.
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The electricity system is generally rapidly developing for covering various power demands with requiring a reliable and safe supply where the substructures are expanding further in generation systems, transmission systems, and distribution systems. However, the system must be run economically to access energy at a cost-effective level related to existing energy enterprises and energy consumption in the load which is represented periodically in the total costs of operations for all operating units. As a basis for its determination, the transmission of economic power within the technical limits applicable is taken into consideration. Environmental factors, on the other hand, are also an impediment to technical limitations. As a result, the operation's economic measure is expressed in the process of providing and selling energy to customers. These works use the Artificial Bees Colony algorithm to determine the scheduling of generating units using the basic principle of optimization to describe its relationship as an economic function. The IEEE-30 bus system is used as a basic model for system development. The analysis' findings show that the weighting factor scheme has an impact on the minimum total cost and that the combination of the electricity distribution process and environmental factors has implications for the operational financial condition and electricity production. The power output, in particular, is proportional to the cost of each generating unit.
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Dhas, John T. Mesia, and J. Midhunchakravarthy. "Modern Metrics (MM): Software size estimation using function points for artificial intelligence and data analytics applications and finding the effort modifiers of the functional units using indian software industry." Journal of Discrete Mathematical Sciences and Cryptography 26, no. 3 (2023): 629–40. http://dx.doi.org/10.47974/jdmsc-1734.
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SPM, Software Project Management is an engineering process for scheduling, developing, testing, and maintaining a software system. The size of a software is one of the important attributes in determining all the activities of SPM. Price, time, and effort are the basic factors for developing software in a successful and profitable manner. The fast explosions in technological development of software systems attracted all the fields that existed in the universe. The modern software system uses many programming languages, domains, operating systems, technologies, development cycles, topologies, etc. So, the industry is expecting a new, versatile, and highly updated sizing technique for modern software. Modern Metrics (MM) is a new anticipated size estimation technique for present software systems with updated functional values and metrics using Function Point (FP). This article analyzed the functional unit metric values (effort modifiers) of the MM with real world applications. MM is a novel method for determining the solution to the SPM challenges.
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Melo, Aurelio G., Fabio A. A. Andrade, Ihannah P. Guedes, Guilherme F. Carvalho, Alessandro R. L. Zachi, and Milena F. Pinto. "Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers." Sensors 22, no. 6 (2022): 2173. http://dx.doi.org/10.3390/s22062173.
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Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID’s good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.
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Wu, Yunfei, Jianfeng Liu, and Jian Zhou. "The Strategy of Considering the Participation of Doubly-Fed Pumped-Storage Units in Power Grid Frequency Regulation." Energies 15, no. 6 (2022): 2179. http://dx.doi.org/10.3390/en15062179.
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Facing the challenges of grid stability brought by the large-scale access of new energy to the grid, as one of the important means of frequency regulation of the grid, the doubly-fed pumped-storage units (DFPSU) have great practical significance in the study of the joint frequency regulation strategy with the thermal power unit (TPU). In order to reduce the frequency regulation pressure on the grid caused by the large-scale entry of new energy sources, this paper proposes a joint frequency regulation strategy for the TPU and DFPSU. Based on establishing the optimal scheduling model of the system, taking into account the minimum total operating cost and the optimal time as goals, the chaotic particle swarm optimization (CPSO) is proposed to solve the model. Finally, through MATLAB simulation, the relevant examples are studied and analyzed, and the simulation results verify that the joint frequency regulation strategy in this paper can effectively improve the frequency regulation performance of the unit and the operation economy is good.
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Gao, Hong-Chao, Joon-Ho Choi, Sang-Yun Yun, and Seon-Ju Ahn. "A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method." Energies 13, no. 7 (2020): 1605. http://dx.doi.org/10.3390/en13071605.
Full textAbstract:
As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network.
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Han, Hanxian, Jinman Luo, Shanlong Zhao, and Lina Wang. "Research on the Optimal Scheduling Strategy of Energy Storage Plants for Peak-shaving and Valley-filling." Journal of Physics: Conference Series 2306, no. 1 (2022): 012013. http://dx.doi.org/10.1088/1742-6596/2306/1/012013.
Full textAbstract:
Abstract With the trend of large-scale development of distributed photovoltaics, the problems of frequent start-up and shutdown of units and wind abandonment caused by grid-connected distributed photovoltaics are becoming more and more obvious. In this paper, a method for optimal dispatching of power system was proposed based on the energy storage power station as an independent source. The method takes the minimum net load variance of the power system and the system operating cost as the objective function to optimize the charging and discharging power and dispatching of the energy storage power station, and the effectiveness of the method was verified by experiments. The results show that the energy storage power station can effectively reduce the peak-to-valley difference of the load in the power system. The number of times of air abandonment and switching of charging and discharging and the number of start and stop of the unit is reduced, which effectively prolongs the service life of the unit. When the photovoltaic penetration rate in the power system is greater than or equal to 50%, the peak regulation effect of the energy storage power station is better and has better economic benefits.