Cost Of Rescheduling Research Articles

Transmission congestion management through sensitivity based rescheduling of generators using improved monarch butterfly optimization

The emergence of competitive power markets in recent decades has paved the way for the enormous increase in the number of transactions between producers and consumers to satisfy the exponentially increasing load demand. These transactions usually occur through direct bilateral, multi-lateral agreements or some common platform. However, this operation puts additional strain on the transmission network, which serves as a conduit between producers and consumers leading to congestion. As a result, satisfying all the committed power transactions under congested transmission lines economically becomes infeasible. Independent System Operator (ISO) is an entity that is responsible for ensuring congestion-free power transactions to preserve the system's security and reliability. The objective of this work is to mitigate the congestion in the network while satisfying the system constraints resulting in more stable operation with reduced line flows and low system losses. The generator sensitivity factor (GSF) is utilized to single out the generating units that need to be rescheduled to alleviate transmission lines congestion. Further, the proposed variants of Monarch Butterfly Optimization (MBO) namely Improved MBO with linear weighting factor (IMBO-LW) and chaos-embedded MBO (CHMBO) are used to reschedule the generators with an aim to minimize the cost of rescheduling and the total change in power after rescheduling. Additionally, standard benchmark functions are used to validate the effectiveness of the proposed MBO variants and the same variants have been scrutinized on modified IEEE 30-bus and New England 39-bus test systems to alleviate the problem of congestion for secure and reliable operation of power system.

Fog Climatology and Stability Index for Plovdiv 1991–2018


 
 
 Fog can cause large economic losses for public services and in particular aviation where the cost of flight delays and rescheduling is estimated to hundreds of millions of euros per year. Despite the continuous improvement of weather prediction fog diagnosis and forecasting remains a challenge. In the US alone, 74% of the fatal car crashes are associated with recorded low visibility but without fog warning in the forecast. Today the operational fog forecasting is mainly done with “in-house” developed tools, which is understandable due to the fog life cycle peculiarity. The aim of this work is to investigate the fog climatology for Plovdiv, Bulgaria, for the period 1991–2018 and to use it for calculation of a threshold value of stability index, which can be implemented as an operational forecast tool. One fog case in January 2013 is selected for numerical weather prediction simulations with the WRF model for the city of Plovdiv. It was found that while the index with its site specific threshold value describes well the fog evolution, the WRF model has large deviations in temperature compared to the observations during daytime.
 
 

A Predictive–Proactive Approach for Slot Management of a Loading Facility With Truck ETA Information

Due to the uncertain and dynamic environment around scheduling systems, timely revisions or reschedules of the master plans are essential for achieving optimal utilization. With the recent development of Industry 4.0 technologies, many researchers perceive the creation of cyber-physical systems as a solution for managing systems under uncertainty. This article focuses on a loading facility under uncertain truck arrivals due to road congestion and proposes utilizing real-time truck location information to improve performance. We do this by developing an integrated system consisting of a predictive model using machine learning (MC) classifiers and a mathematical model for real-time slot rescheduling. The ML classifier is used to predict the presence probabilities of all the incoming trucks at a particular slot based on the historical traffic data and the real-time truck location. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) model is developed to solve a Probabilistic Slot Rescheduling Problem (P-SRP), which uses the estimated truck presence probabilities and minimizes the total expected cost of rescheduling. We implemented this by first testing multiple ML classifiers and selected the ANN classifier for prediction as it outperformed others. Our limited experiments showed that the proposed method reduced the total rescheduling cost by 42%. Furthermore, our sensitivity analysis with different congestion levels, complexity, and rescheduling strategy also showed the practicality of the proposed approach.

Transmission Congestion Management Using Generator Sensitivity Factors for Active and Reactive Power Rescheduling Using Particle Swarm Optimization Algorithm

Independent System Operators have difficulty in fulfilling all contractual power transactions in a competitive energy market due to transmission network congestion. As a result, applications of generator rescheduling become one of the antidotes in alleviating this difficulty in the consequence of ever-increasing numerous power transactions. The goal of this research is to lower the cost of active and reactive power of the generators by reducing the deviation of rescheduled active and reactive power from scheduled values. The inclusion of reactive power rescheduling and voltage stability in this paper is innovative, as compare to other existing methodologies solely examine active power rescheduling. This paper made the following contributions: formulated a multi-objective function for congestion control in an electric transmission network. Furthermore, formulated the generator sensitivity factors to identify overloaded lines and which generators will be involved in congestion management. Developed a particle swarm optimization (PSO) algorithm to solve the multi-objective function of the transmission congestion management system. In addition, the developed PSO method for CM approach was validated on three IEEE standard test system networks (14, 30, and 118). The simulation results prove that reduces active and reactive power, lowering the cost of generator rescheduling, and demonstrating the usefulness of developed PSO method for transmission network congestion. Furthermore, voltage stability and voltage profile improvements demonstrate the performance effectiveness of the PSO algorithm used in this work.

Mitigating congestion by optimal rescheduling of generators applying hybrid PSO\u2013GWO in deregulated environment

Deregulation in power system and increased power demand has introduced congestion in power system. With the depletion of fossil fuels, power sector has turned towards incorporation of the Renewable Energy Systems through private generators. This paper focuses on the non-cost-free method to mitigate congestion by rescheduling the generators for their active power output. The rescheduling is done in such a way that the cost of rescheduling is minimum. This paper presents a new method to merge two tested algorithms Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to generate a new hybrid algorithm, HPSOGWO. The active power output is rescheduled to mitigate congestion with minimum cost of rescheduling. The priority of participating generators in rescheduling is set by generator sensitivity factor and its output is optimized by proposed HPSOGWO. The applied algorithm has reduced the rescheduled power to 16% less as done by GWO. HPSOGWO has moderated the congestion cost to 25% less as compared to GWO. The effectiveness of proposed HPSOGWO algorithm is validated on standard IEEE 30 bus system and results confirms the outperformance of proposed method over GWO and PSO in reducing congestion cost with reduction in power losses to mitigate congestion.

A novel hybrid algorithm for rescheduling-based congestion management scheme in power system

Secure and continuous power flow in the transmission line is one of the critical issues that must be rectified. In fact, rescheduling-based congestion management is considered to be one of the promising solutions for this aspect. Still, the model faces issues on the basis of rescheduling costs. More research works have been addressed so far to solve the problems of congestion management. Optimization algorithms also play a vital role in solving this problem. Under this scenario, this paper introduces a new rescheduling-based congestion management model that incorporates a new algorithm, refractor update-based ROA (RU-ROA) that optimizes the generating power of added generators with the bus system. The proposed RU-ROA algorithm is the hybridization of two algorithms, namely rider optimization algorithm (ROA) and water wave optimization (WWO), that aims to manage the congestion with the reduced cost of rescheduling. Further, the proposed model compares its performance over other conventional models like particle swarm optimization, FireFly, grey wolf optimization, traditional ROA and traditional WWO-based rescheduling strategy with respect to cost analysis and convergence analysis, and proves the efficiency of proposed work over others.

Efficient Event Scheduling of Network Update

Changes in network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update that groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but it avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. This improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to $10 \times $ faster than the flow-level scheduling method for network update events, and its tail ECT is up to $6 \times $ faster. Our P-LMTF method incurs a 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

On optimal dynamic pegging in rescheduling for new order arrival

A rescheduling method for new order arrival is presented based on the concept of dynamic pegging in multi-level production. Upon the arrival of new, unplanned orders, dynamic pegging enables the re-assignment of the Work-In-Progress (WIP) to the existing or newly arrived orders in such a way that the cost of rescheduling is minimized. A simple yet inspiring Mixed Integer Programming (MIP) model is proposed that combines dynamic pegging with rescheduling. The resulting schedules are compared with the ones obtained from two other basic rescheduling approaches. Analysis of the comparisons confirm the superiority of the proposed method in terms of total rescheduling costs.

Study of event-driven and periodic rescheduling on a single machine with unexpected disruptions

This paper studies the rescheduling problem of a single machine facing unexpected disruptions in order to determine which parameters can help reducing the negative impacts of these disruptions on schedule performance. A Genetic Algorithm (GA) is used to generate the initial schedule and the updated ones according to a reactive strategy. The performance of event-driven rescheduling and periodic rescheduling policies are compared in terms of total tardiness and total cost of rescheduling. Other factors that may affect rescheduling such as disruption time, disruption duration and number of disruptions are investigated. The sensitivity of results to both due date tightness and cost factor variation is tested. The results showed that the timing of the occurrence of disruption as related to scheduling horizon has a major effect on determining the best rescheduling policy. Event-driven policy is superior to other policies for short infrequent disruptions. It was found that the periodic policy is more appropriate for long and frequent disruptions.

Tinjauan Hukum Islam terhadap Denda Penjadwalan Ulang Pembiayaan Mikro Murabahah di Bank Syariah Mandiri Cabang Dumai Provinsi Riau

Many Syariah banks in Indonesia are experiencing problematic financing because customers can not pay their debt installments, then the bank takes the settlement step by rescheduling the debt installment. In the process the bank charges a fee to the customer and this incriminates the customer. This research was conducted to analyze Islamic law against the cost charged to the customers in the process of rescheduling micro mur abahah financing conducted by Bank Syariah Mandiri Branch Dumai Riau Province. This research uses data collection method with observation method, interview and library data, then analyzed by using inductive, deductive and comparative method. The results of this study conclude that the cost of micro-financing rescheduling in Bank Syariah Mandiri Branch Dumai Riau Province is not fully in accordance with Fatwa of Shariah and National Shari'ah Council Fatwa that has not been implemented, ie the bank charges the customer in rescheduling in addition to the actual cost required for the rescheduling process. And the authors provide suggestions for improving this by (1) providing the time period so that the customer can afford to pay, (2) the conversion of the financing contract of the mur abahah to qar d financing, and (3) rescheduling by charging real costs at no cost others.

Design and implementation of an android and web-based university timetable customization system

Timetabling is an essential component of scheduling in all sectors of education. University timetabling entails the scheduling of times and venues for lectures and examinations. These activities are logically structured in a time-wise manner and presented on papers and placed on notice boards in order to avoid conflicts of event. However, timetable schedule forgetfulness, misinterpretation, miscopying, cost of rescheduling and slow time to reach for the rescheduled timetables are the challenges posed by the use of paper and notice board-based timetables; hence, the need for automated timetables. In this paper, we discuss the implementation of Android and web-based timetable customization systems for the Faculty of Computer Science and Information Technology, Bayero University, Kano-Nigeria. The web-based system provides an administrative module that facilitates scheduling and rescheduling of lecture, examination and invigilation timetables. In addition, the Android timetable customization system permits the user to view, customize, set reminders and provide rescheduled timetable updates. Keywords : Android, Customization, Timetable and Web based

An efficient particle swarm optimizer for congestion management in deregulated electricity market

This paper proposes a method to manage congestion in deregulated environment using particle swarm optimization technique with improved time-varying acceleration coefficients (PSO-ITVAC). The congestion is alleviated by optimally rescheduling the active power outputs of generators selected based on the magnitude of generator sensitivities to the congested line. The cost of rescheduling is minimized using PSO-ITVAC. The proposed algorithm is tested on IEEE 30-bus system and IEEE 118-bus system. A 33-bus Indian network is also taken as a test network for analyzing the effectiveness of the proposed algorithm. The results obtained using proposed algorithm is compared with those obtained by using particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC).

Penalty Function-Based Two-Level Hybrid Shop Floor Control System

Although several hybrid shop floor control architectures have been proposed in the literature, varying degrees of autonomy of subordinate controllers and their effects on supervisory level performance have not been studied. In this paper, we present a new hybrid control architecture with two levels, where the autonomy of subordinate agents changes adaptively. The penalty function in the architecture represents the degree of negative impact on performance that will result from changing the original schedule. When a disturbance occurs, the disturbance agent invokes rescheduling at the appropriate level depending on the threshold disturbance level. The subordinate agents execute tasks based on the schedule from the supervisory agent in the absence of disturbances; or else revise the original schedule optimally with regard to the supervisory level performance (via penalty function) and the disturbance before executing the tasks. We present math programming formulations, quantitative metrics to indicate the disturbance level and the levels of autonomy. The proposed architecture is illustrated using a job shop problem and is then tested with various other problems. Note to Practitioners-In a hybrid shop floor control system, the objective of global system performance conflicts with the objective of a timely response to local disturbances. For a given disturbance, there is a need to compare the cost (e.g., computation time) associated with supervisory level rescheduling against the potential improvement in system performance that this rescheduling can provide. If the cost of rescheduling is higher, rescheduling at the subordinate level will be appropriate. Otherwise, rescheduling at the supervisory level will be appropriate. In this paper, manufacturing scheduling problems of various complexities are used to discuss the tradeoff between the objectives listed above. We then use these problems to show how to find an appropriate threshold value to trigger subordinate or supervisory level rescheduling. Limited classes of performance metrics are considered. Work is currently being carried out to extend the approach to broader performance metrics. It is believed that our results can be applied to other systems for which hierarchical structures are commonly used. Examples of such systems exist in the transportation, health-care, and defense sectors

An examination of dampening procedures considering rescheduling costs

System nervousness in material requirements planning (MRP) systems has been receiving considerable attention recently. Several strategies are suggested to reduce MRP system nervousness. This paper examines one of these nervousness-reduction methods, dampening procedures. Dampening procedures are non-inventory-oriented, which screen out insignificant rescheduling messages generated by MRP systems so as to reduce schedule disruptions. The cost of rescheduling is incorporated as a component of total cost to evaluate the effectiveness of alternative dampening procedures by a simulation experiment under such operating environments as the presence of production uncertainty, different capacity utilization, and different rescheduling efforts. The dampening procedures tested are the static dampening procedure, automatic rescheduling procedure, and cost-based dampening procedure. The results indicate that the static and cost-based dampening procedures outperform the automatic rescheduling procedure under most operating conditions considered in the study. Furthermore, the relative performance of the static and cost-based procedures depends on the difficulty of rescheduling efforts involved and the lot-sizing rule used.