Showing 14 results for Scheduling
M. H. Yaghmae, , G. Khojasteh Toussi,
Volume 5, Issue 2 (10-2008)
Abstract
Quality of Service (QoS) refers to a set of rules or techniques that help the network administrators use the available network resources optimally to manage the effects of congestion and to treat the applications according to their needs. The differentiated services architecture (DiffServ) allows providing quality of service to users. The major DiffServ premise is that individual flows with similar QoS requirements can be aggregated in larger traffic sets and identified as classes. Relative service differentiation is a simple and easily deployed approach compared to the absolute differentiation service. In this paper, we use fuzzy logic systems to design a novel algorithm for queue management and scheduling of classified packets in the differentiated service IP networks. The proposed model consists of two parts. The first part of the proposed model is used for absolute differentiated services and tries to optimize QoS parameters and to share sources between different requests fairly. The second part of the proposed fuzzy system is dedicated to relative
Diffserv model. As one of the famous existing algorithms for both buffer management and scheduling in the relative differentiated service is Jobs algorithm, in the second parts of the proposed system, we modify the traditional Jobs algorithm and proposed a fuzzy based modification of existing Jobs algorithm. The proposed algorithm uses different fuzzy logic controllers to differentiate the delay of traffic classes. Simulation results confirm that the proposed fuzzy system, can provide better delay differentiated than the traditional algorithms.
M. Rajabi Mashhadi, M. H. Javidi, M. S. Ghazizadeh,
Volume 6, Issue 2 (10-2009)
Abstract
Load frequency control includes the operating system and functions that provide real time balance of load and generation. On the other hand, depending on technical capabilities and operational constraints, generating units may be classified for participating in frequency control. Simultaneous scheduling of generation together with primary reserve, in market based power system, has been investigated during the past decade. However, in most approaches, real technical capabilities of modern generating units (such as the capability of selecting governor operation modes and selecting the ramp rate) together with operational constraints of generating units have not been considered. In this paper we propose a new approach for simultaneous scheduling of generation and primary reserve considering real technical capabilities and operational constraints of the units. The optimal scheduling is obtained using a heuristic iterative method supported by genetic algorithm. The simulations confirm that our formulation results in a more appropriate solution as compared with previous method.
M. Pourakbari-Kasmaei, M. Rashidi-Nejad, S. Piltan,
Volume 7, Issue 2 (4-2010)
Abstract
This paper proposes a novel technique for solving generation scheduling and ramp rate constrained unit commitment. A
modified objective function associated with a new start-up cost term is introduced in this paper. The proposed method is
used to solve generating scheduling problem satisfying SRR, minimum up and down time as well as ramp rate
constraints. Two case studies are conducted to implement and show the effectiveness of the proposed method. One is a
conventional 10-unit system and its multiples while the other is a 26-unit system with 24-h scheduling horizon. A
comparison between the results of the proposed technique with those of some methods demonstrates a significant
improvement.
M. Rouholamini, M. Rashidinejad,
Volume 9, Issue 2 (10-2012)
Abstract
The simultaneous scheduling of energy and primary reserve is one of most important recent researches. However, in these researches, operation constraints and transmission limitations have been neglected. So it is possible that the power flow results be infeasible since some real constraints are not considered. In this paper congestion constraint in transmission lines, technical capabilities and real constraints of modern generating units are considered in modeling of optimization problem. Congestion constraint is applied using DC power flow. Optimization problem is solved using a novel method based on binary genetic algorithm. Finally, the proposed method has been implemented on IEEE-39 bus case system. The obtained results verify the efficiency of proposed method. In this survey, it is assumed that the market structure is pay as bid.
M. Rajabi Mashhadi, M. Ghazizadeh, M. H. Javidi,
Volume 9, Issue 2 (10-2012)
Abstract
Frequency control, as an ancillary service, is usually provided by generation reserves. In modern generating units the ramp rate limit can be selected; e.g. their ramp rate can be selected to be either normal or fast. In this paper, the impact of selecting the fast ramp rate of generating units on electricity price and its volatility is investigated in simultaneous scheduling of energy and primary reserve. Simulation results show that using this capability of generating units, results in reduction of both the electricity price and its volatility. While, in minimum load condition, this capability may not necessarily be effective, it will be very essential for peak load condition. The effectiveness of this capability for medium load conditions depends on the load value and the units committed.
M. Mollahassani-Pour, M. Rashidinejad, A. Abdollahi, S. Nikbakhsh,
Volume 12, Issue 3 (1-2016)
Abstract
Preventive maintenance scheduling of generating units is addressed as a long-term scheduling in power system studies aiming to increase the reliability incorporating cost reduction. It consists of knowing which generating units should be shut down for regular safety inspection. In this paper, a new formulation of preventive maintenance scheduling associated with cost reduction index (CRI) is presented. Mainly, the purpose of the maintenance problem is minimizing the operation as well as maintenance costs over a specified time horizon. CRI is introduced in such a way to reduce the operation cost along the scheduling time while determining the most proper maintenance scheme. The proposed framework is structured as a mixed integer linear programming (MILP) and solved using CPLEX solver. The suggested model is applied to a standard IEEE reliability test system (RTS) and the promising results show the effectiveness of the proffered model.
M. Aminian, S. Jadid,
Volume 13, Issue 3 (10-2016)
Abstract
In this paper, has been proposed the collaboration between smart buildings as a solution for improving the energy efficiency and increasing the contribution of the consumers in energy consumption management of residential buildings integrated with micro-grid. In the proposed model, the scheduling of power consumption in smart building and the optimal operation of distributed energy resources through molding of the exchange energy among smart buildings is presented using mixed-integer linear programming (MILP). The smart buildings are equipped with building energy management system (BEMS), distributed generation, and energy storage system. Being considered as a small micro-grid, each building is able to feed parts of its own electricity demand and all thermal demand. The proposed model is applied to the two smart buildings consisting of 30 units and 90 units. The simulation results indicate that the collaboration among smart buildings will lead to the more power trade options, the reduction of energy consumption costs, the reduced received power from the grid, and the balance between supply and demand.
Ashkan Talebi, Dr Alireza Hatami,
Volume 19, Issue 1 (4-2022)
Abstract
HVAC systems, as the largest commercial and household power consumers, play an important role in demand response programs. In this research, using weather forecast data and using electricity price forecast, optimal scheduling for HVAC systems set points is performed in such a way that the user’s electricity consumption cost is minimized. Electricity price forecasting is achieved using an ARIMA model. A Markov’s chain has been employed for temperature forecasting. As these forecasts are not deterministic, the risk must be considered. To accomplish this, several scenarios were considered and CVaR was utilized to handle the risk aspect. In this research, the above-mentioned data was employed, and GWO algorithm was invoked to schedule the optimal set points of the HVAC systems and symmetrical dead band width. The results show that this planning method can have an important effect on the participation of the HVAC systems in demand response programs. Also, it has been shown that the dead-band width management plays an important role in energy consumption management. Different users with different attitudes toward the risk were analyzed; the results show that risk-averse users are more involved in demand response programs; and hence, they bear lower energy costs.
Dr Mehdi Nikzad, Dr Abouzar Samimi,
Volume 19, Issue 1 (4-2022)
Abstract
In this paper, a two-stage stochastic programming model based on a multi-objective optimization has been proposed for optimal operation of smart micro-grid (MG) aiming at minimizing operational costs and environmental emissions in presence of renewable resources and demand response. In the presented model, the forecasting error of the renewable resources productions is modeled by probability density functions and demand response has been implemented to cover the uncertainty of the renewable resources. Here, it is assumed that the MG operator decides on two stages for optimum management of its network; first stage is the operation in the base state and the second one is pertaining to the domains of different scenarios for generation of renewable resources. The base state of the micro-grid refers to the situation in which the active power productions of renewables are equal to the predicted values. To solve the problem, Multi-Objective Particle Swarm Optimization method has been used and TOPSIS technique has been applied to extract the output from the Pareto Frontier. The proposed approach is applied to a typical MG and the numerical results show the efficiency of demand side management in reducing costs and environmental emissions as well as covering the uncertainty resulting from renewables.
Hamed Ziaei, Prof. Masoud Rashidinejad, Dr. Amir Abdollahi, Ebrahim Pirmoradi,
Volume 19, Issue 1 (4-2022)
Abstract
Traditional systems are faced with problems such as energy loss, growing in energy demand, reliability and security, so they are turning into smart grids (SGs) to solve these problems. SGs provide a bi-directional energy connection between service providers and consumer. On the other hand, SGs have various devices for monitoring, analysis and controlling in different parts of the network. Therefore, the SGs needs a communication infrastructure between these different devices. As a result, this connectivity will be achieved by a new infrastructure such as the internet of things (IoT). IoT helps SGs systems to perform various network functions throughout the generation, transmission, distribution, and consumption of energy by using IoT equipment (such as sensors, actuator, and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, the day-ahead generation scheduling is optimized in the deregulated power market with IoT infrastructure where the goal is to maximize the profit of generation companies (GENCOs). Moreover, this paper has used the demand response (DR) programs to better explore the challenges ahead in the future of SGs with IoT infrastructure. The results help to assess the quality and efficiency of such a system.
Salman Sanaei, Dr. Mahmud-Reza Haghifam, Dr. Amir Safdarian,
Volume 19, Issue 4 (9-2022)
Abstract
After a permanent fault occurs if it is not possible to supply the load in the network, the optimal load restoration scheme allows the system to restoration the load with the lowest exit cost, the lowest load interruption, and in the shortest possible time. This article introduces a new design called Smart Load Shedding, abbreviated SLS. In the proposed SLS scheme, the types of devices in smart homes are divided into four categories: adjustable, interruptible, shiftable, and uncontrollable loads. In this design, a new two-layer algorithm is proposed to solve the service restoration problem. In the first layer, a heuristic method based on graph theory for optimal system configuration is presented. In the second layer, for optimal load restoration, tools such as rescheduling of distributed generation sources, load shedding, load curtailment, and sensitive load curtailment are used. This layer is a nonlinear mixed-integer nonlinear programming problem. To achieve the optimal global solution and less solution time, the model changes from nonlinear programming of non-convex mixed integer nonlinear programming to mixed-integer linear programming. A modified RBTS distribution system is used as a test system to demonstrate the effectiveness of the proposed method.
Elham Sadeghi, Dr. Mostafa Gholami,
Volume 20, Issue 2 (6-2023)
Abstract
Today, with the increasing consumption of electricity and environmental problems caused by fossil fuels, the tendency to use renewable sources is increased. Also, due to the restructuring of power networks from the traditional structure to the market-based structure, different energy markets and ancillary services is designed. On the other hand, due to the uncertainty of the renewable sources generation, energy storage units are considered as a participant in energy and ancillary service markets alongside renewable sources or as an independent unit. Due to the high speed of battery energy storages, using these resources in frequency ancillary service markets such as the automatic frequency restoration reserve market is an appropriate choice. One of the issues in scheduling these units is how to participate in energy or ancillary services markets simultaneously, to maximize the net profit. In this paper, an optimal strategy for the simultaneous participation of the battery energy storage unit in the energy and the automatic frequency restoration reserve markets is proposed. Simulation results show the good performance of the proposed strategy for the simultaneous participation in the energy and the automatic frequency restoration reserve markets to maximize the battery energy storages profit.
Ensieh Tohidi, Dr Akram Beigi,
Volume 21, Issue 1 (3-2024)
Abstract
Air travel has significantly grown as a fast and safe means of transportation. Therefore, creating a smooth air traffic and proper flight scheduling for safe landings with minimal time changes is necessary to avoid wasting time and money. In most studies, the aircraft landing scheduling problem has been considered a static issue. However, this challenge has a dynamic nature in real-world problems. One of the optimizing approaches in this scope is swarm intelligence optimization algorithms, which are simple and highly capable in solving optimization problems. Among these algorithms, Spider-Monkey optimization algorithm is more efficient than traditional algorithms by using few parameters, maintaining search history, controlling searches, and grouping members of the population if needed. In this study, an active scheduling method for aircraft landing scheduling using Spider-Monkey optimization algorithm and genetic algorithm has been presented. The proposed method is examined by some datasets of single and multi-runways (single and multi-objective aircraft landing). The achieved results show an improvement in flight schedules and reduced costs.
Hassan Golzari-Kolur, Dr. Seyed Mohammad-Taghi Bathaee, Dr. Turaj Amraee,
Volume 21, Issue 4 (12-2024)
Abstract
With the increasing penetration of renewable energy sources (RES) in power systems, small-signal stability (SSS) is challenged due to the decreasing inertia. This paper proposes a sequential generation rescheduling model considering a generic dynamic model for RES, formulated as an optimal power flow (OPF) with SSS constraints (SSS-OPF) to improve the system damping ratio. The dynamic modeling of wind turbine generators, photovoltaic sources, and energy storage systems is possible using this general dynamic model. The sensitivity of eigenvalues with respect to active and reactive powers of generating units has been used to describe the SSS constraints. By employing the semi-definite programming (SDP) relaxation technique, the SSS-OPF model is converted to a convex optimization model. This leads to enhanced convergence reliability and improved computational efficiency in solving the model, rendering the proposed method suitable and applicable for large-scale power systems. This optimization model can be solved using the SDPT3 solver in MATLAB software. Case studies on the IEEE 9-bus and IEEE 39-bus systems are conducted to validate the proposed algorithm. The proposed generation re-scheduling method increases the damping ratio of the power system with a high penetration of RES.
