Proposed Scheduling Approach Research Articles

Integrated scheduling of energy supply and demand in microgrids under uncertainty: A robust multi-objective optimization approach

As a Demand Response (DR) based working mode, the integrated scheduling of energy supply and demand provides an effective approach to improve economic and environmental benefits for Microgrids (MGs). However, it is still a challenging issue to cover uncertainties caused by intermittent renewable energy and random loads while optimizing multiple objectives in economy and environment. To tackle this issue, an integrated scheduling approach for MGs is proposed based on robust multi-objective optimization. Firstly, load shifting in a finite time is introduced to express an acceptable DR program for industrial customers. A minimax multi-objective optimization model is formulated to seek the minimum operation costs and emissions under the worst-case realization of uncertainties, which are captured by the robust sets with budgets of uncertainty. Secondly, a strong duality based model transformation method is implemented to cope with the strong coupling and nonlinearity in the proposed formulation. Also, Multi-Objective Cross Entropy (MOCE) algorithm is adopted to solve the reconstructed model for simultaneously optimizing all the objectives. Finally, detailed comparative experiments are conducted in problem level, model level and algorithm level. The simulation results show that the proposed scheduling approach can effectively attenuate the disturbances of uncertainties as well as achieve optimal economic and environmental benefits, compared with single-objective robust optimization scheduling approaches and deterministic multi-objective optimization scheduling approaches. Meanwhile, the validity and effectiveness of the robust multi-objective optimization approach for the MG integrated scheduling problem under uncertainty are confirmed.

Multi-Targeted Downlink Scheduling for Overload-States in LTE Networks: Proportional Fractional Knapsack Algorithm With Gaussian Weights

In Long-Term Evolution (LTE) and beyond systems, radio resource scheduling mechanism plays one of the main roles in system performance maximization. From this perspective, due to the conflicting quality requirements of different traffic types, providing a compromise among all performance targets for heterogeneous traffic is difficult. Moreover, the centralized scheduling mechanism for the ever growing number of users along with the massive variety of services, especially in overload states, is infeasible due to the extensive cost of information acquisition and computations. In this paper, we design resource scheduling policies for supporting the efficient delivery of heterogeneous traffic in overload states of a cell. To this end, we cast the class-based bearer-level resource distribution problem as a Proportional Fractional Knapsack model. The objective of the formulated problem is to meet Quality of Service (QoS) requirements and provide fairness for all standardized service classes. Since the solution of this problem is computationally expensive, due to the uncertainty and limited information on network and user operation, we develop a Gaussian-based analytical model and drive a formula for simplified computation of the weight of service bearers. Then, we propose Proportional Fractional Knapsack algorithm for guaranteeing effective utilization of resources for heterogeneous traffic. Finally, performance evaluation results are provided and demonstrate that the proposed scheduling approach can provide a significant level of fairness, in balance with the QoS and throughput performance targets, comparable with optimal ones.

Simulations and study of a new scheduling approach for distributed data production

Distributed data processing has found its application in many fields of science (High Energy and Nuclear Physics (HENP), astronomy, biology to name only those). We have focused our research on distributed data production, an essential part of computations in HENP. Using our previous experience, we have recently proposed a new scheduling approach for distributed data production which is based on the network flow maximization model. It has a polynomial complexity providing required scalability with respect to the size of computations. Our approach improves the overall data production throughput due to three factors: transfer input files in advance before their processing (allows to decrease I/O latency); balancing of the network traffic (includes splitting the load between several alternative transfer paths); and transfer files sequentially in a coordinated manner (allows to reduce the influence of possible network bottlenecks). In this contribution, we present the results of our new simulations based on the GridSim framework which is one of the commonly used tools in the field of distributed computations. In these simulations we study the behavior of standard scheduling approaches compared to our recently proposed approach in a realistic environment relying on the data from the STAR and ATLAS experiments and considering the influence of the background traffic. The final goal of the research is to integrate the proposed scheduling approach into the real data production framework. In order to achieve this we are constantly moving our simulations towards real use cases, study scalability of the model and the influence of the scheduling parameters on the quality of the solution.

Dynamic Appliances Scheduling in Collaborative MicroGrids System.

In this paper a new approach which is based on a collaborative system of MicroGrids (MG's), is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible Deferrable Loads (DL's), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DL's. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multi-objective optimization (MOO) problems. The first targets the activation schedule of non-flexible DL's and the second deals with the power profiles of flexible DL's. The MOO problems are resolved by using a fast and elitist multi-objective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MG's registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach.

Spatial considerations of an area restriction model for identifying harvest blocks at commercial forest plantations

Abstract In the past few decades, ecological and environmental issues have dominated the forest industry worldwide, but economic aspects have been much less studied in this dynamic period. However, a sustainable and efficient forest biomass supply is critical for socio-economic development in many regions, particularly in rural areas. Nature protection efforts have contributed to reduced harvesting quotas, which have resulted in an imbalance of the environmental functions of the forests and forest management, particularly wood supply. Considering the size and distribution of forest production management units and the forest stands that compose those units, there is a clear need for improved decision-making tools that help forest managers in planning harvest sequences. The optimization of harvest scheduling should consider economic and spatial factors, which may reduce production costs by increasing the logistic efficiency. Moreover, incorporating maximum harvesting opening size constraints into planning can help preserve biodiversity. This article presents a new spatial harvest scheduling model based on the integer programming method; it was developed using real data from a forest production unit located in the northern part of the southeast region of Brazil. The goal of the proposed scheduling approach is to maximize the net present value and concentrate the harvesting locations in each period. In spite of the fact that the object of the study is plantation forest under management different to common conditions in Europe or North America, the model is flexible and can be used in management of forest in Central Europe.

Repetitive Scheduling Method: Requirements, Modeling, and Implementation

AbstractProjects with repeating activities, such as multistory buildings and highways, present unique scheduling challenges. This paper presents the necessary requirements that scheduling systems should address to meet the needs and complexities of repetitive projects in practice. The repetitive scheduling method (RSM) provides a unified scheduling framework that applies to all types of repetitive projects. In RSM, projects are classified as discrete (vertical) or continuous (horizontal or linear) and then as uniform and nonuniform, depending on whether progress is measured using a counting scale or a dominant-attribute scale. RSM modeling elements include three types of activities (line, block, and bar) and 10 types of relationships (links) between activities. RP2, a computer program developed by the authors to validate the proposed scheduling approach, is used to develop the production diagrams and tabular reports of the target schedule for a complex of four-story apartment buildings for a low-income ho...

Representing Job Scheduling for Volunteer Grid Environment using Online Container Stowage

Volunteer grid computing comprises of volunteer resources which are unpredictable in nature and as such the scheduling of jobs among these resources could be very uncer-tain. It is also difficult to ensure the successful completion of submitted jobs on volunteer resources as these resources may opt to withdraw from the grid system anytime or there might be a resource failure, which requires job reassignments. However, a careful consideration of future jobs can make scheduling of jobs more reliable on volunteer resources. There are two possibilities; either to forecast the future jobs or to forecast the resource availability by studying the history events. In this paper an attempt has been made to utilize the future job forecasting in improving the job scheduling experience with volunteer grid resources. A scheduling approach is proposed that uses container stowage to allocate the volunteer grid resources based on the jobs submitted. The proposed scheduling approach optimizes the number of resources actively used. The approach presents online container stowage adaptability for scheduling jobs using volun-teer grid resources. The performance has been evaluated by making comparison to other scheduling algorithms adopted in volunteer grid. The simulation results have shown that the pro-posed approach performs better in terms of average turnaround and waiting time in comparison with existing scheduling algo-rithms. The job load forecast also reduced the number of job reassignments.

Power Allocation Schemes for Multicell Massive MIMO Systems

This paper investigates the sum-rate gains brought by power allocation strategies in multicell massive multipleinput multiple-output systems, assuming time-division duplex transmission. For both uplink and downlink, we derive tractable expressions for the achievable rate with zero-forcing receivers and precoders respectively. To avoid high complexity joint optimization across the network, we propose a scheduling mechanism for power allocation, where in a single time slot, only cells that do not interfere with each other adjust their transmit powers. Based on this, corresponding transmit power allocation strategies are derived, aimed at maximizing the sum rate per-cell. These schemes are shown to bring considerable gains over equal power allocation for practical antenna configurations (e.g., up to a few hundred). However, with fixed number of users (N), these gains diminish as M turns to infinity, and equal power allocation becomes optimal. A different conclusion is drawn for the case where both M and N grow large together, in which case: (i) improved rates are achieved as M grows with fixed M/N ratio, and (ii) the relative gains over the equal power allocation diminish as M/N grows. Moreover, we also provide applicable values of M/N under an acceptable power allocation gain threshold, which can be used as to determine when the proposed power allocation schemes yield appreciable gains, and when they do not. From the network point of view, the proposed scheduling approach can achieve almost the same performance as the joint power allocation after one scheduling round, with much reduced complexity.

An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems

Regenerative braking is an energy recovery mechanism that converts the kinetic energy during braking into electricity, also known as regenerative energy. In general, most of the regenerative energy is transmitted backward along the pantograph and fed back into the overhead contact line. To reduce the trains’ energy consumption, this paper develops a scheduling approach to coordinate the arrivals and departures of all trains located in the same electricity supply interval so that the energy regenerated from braking trains can be more effectively utilized to accelerate trains. Firstly, we formulate an integer programming model with real-world speed profiles to minimize the trains’ energy consumption with dwell time control. Secondly, we design a genetic algorithm and an allocation algorithm to find a good solution. Finally, we present numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results show that the proposed scheduling approach can reduce energy consumption by 6.97% and save about 1,054,388 CNY (or 169,223 USD) each year in comparison with the current timetable. Compared to the cooperative scheduling (CS) approach, the proposed scheduling approach can improve the utilization of regenerative energy by 36.16% and reduce the total energy consumption by 4.28%.

Nano-satellite swarm for SAR applications: design and robust scheduling

In this paper, the design and robust scheduling of a nano-satellite (nanosat) swarm is presented for synthetic aperture radar (SAR) applications. Based on the power budget and bandwidth limit of nanosats, the nanosats’ form factor and swarm size are chosen to ensure that requirements on ground resolution and signal-to-noise ratio are met. Next, an energy-efficient and robust scheduling considering stochastic failures is proposed using scenario optimization with convexification. The effectiveness of our proposed scheduling approach is verified with mathematical rigor as well as extensive simulation results on a realistic SAR application using strip and spot modes.

Performance Analysis of Service Based Scheduler in LTE OFDMA System

Long Term Evolution (LTE) is the wireless broadband technology developed by Third Generation Partnership Project (3GPP) to support higher data rate. Resource scheduling in LTE plays an important role in deciding the overall network performance. In this paper, a new scheduling approach called Service Based Scheduler (SBS) is proposed that is suitable for real time services. In this method, two tag values called maximum tag value (Tmax) and minimum tag value (Tmin) are estimated for the packets based on the type of bearers. Tmax is calculated for Guaranteed Bit Rate (GBR) bearers and Tmin is calculated for non GBR bearers. Then, separate ascending sorted lists are formed for Tmax and Tmin. The packets from the Tmax list are allocated first in spite of users' channel condition. After the allocation of packets from Tmax list, if the current Transmit Time Interval (TTI) has more resources, then packets from Tmin list are allocated strictly based on users' channel conditions. The proposed scheduling approach provides better performance for real time services compared to other schemes like Proportional Fairness (PF), Modified Largest Weighted Delay First (MLWDF), Exponential rule (EXP rule) and Logarithmic rule (LOG rule).

A lookback scheduling framework for long‐term quality of service over multiple cells

AbstractIn current cellular networks, schedulers allocate wireless channel resources to users based on instantaneous channel gains and short‐term moving averages of user rates and queue lengths. By using only such short‐term information, schedulers ignore the users' service history in previous cells and, thus, cannot guarantee long‐term quality of service (QoS) when users traverse multiple cells with varying load and capacity. In this paper, we propose a new long‐term lookback scheduling (LLS) framework, which extends conventional short‐term scheduling with long‐term (QoS) information from previously traversed cells. We demonstrate the application of (LLS) for common channel aware, as well as channel and queue‐aware schedulers. The developed long‐term schedulers also provide a controllable trade‐off between emphasizing the immediate user (QoS) or the long‐term measures. Our simulation results show high gains in long‐term (QoS) without sacrificing short‐term user requirements. Therefore, the proposed scheduling approach improves subscriber satisfaction and increases operational efficiency. Copyright © 2014 John Wiley & Sons, Ltd.

A novel scheduling approach to concurrent multipath transmission of high definition video in overlay networks

Recent advancements in network infrastructures provide increased opportunities to support video delivery over multiple communication paths. However, the high definition (HD) video transmissions still pose crucial challenges due to the high throughput demands and large-size video frames. Motivated by optimizing the delay performance for concurrent multipath transmission of HD video, we propose a novel scheduling approach dubbed FSWG (Frame Splitting based on Weibull distribution and Graph theory) that aims to minimize the end-to-end frame delay while alleviating out-of-order arrivals. First, we analytically construct a delay performance model for HD video streaming in multipath overlay networks based on Weibull distribution and graph theory. Second, we formulate the frame splitting over parallel paths as a constrained optimization problem of minimizing total frame delay and derive its solution based on the water filling algorithm. Third, we design a multipath video transmission system to implement the proposed scheduling approach. The performance evaluation is conducted through extensive simulations in QualNet using H.264 video streaming. Experimental results show that FSWG outperforms the existing schemes in terms of Mean Opinion Score (MOS), Peak Signal-to-Noise Ratio (PSNR), and delay performance metrics.

Cost-based Job grouping and Scheduling Algorithm for Grid computing Environments

The integration of remote and diverse resources and the increasing computational needs of Grand challenges problems combined with faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate over the resources with minimum cost by merging it with conventional job grouping strategy to provide solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making efficient mapping of job to available resources in grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation. General Terms Algorithm, GridSim.

Stochastic multi-objective operational planning of smart distribution systems considering demand response programs

The development of smart grids offers new opportunities to improve the efficiency of operation of Distributed Energy Resources (DERs) by implementing an intelligent Distribution Management System (DMS). The DMS consists of application systems that are used to support the DERs management undertaken by a Distribution System Operator (DSO). In this paper, a conceptual model for a Demand Response Management System (DRMS), conceived as an application system of a DMS, is presented. Moreover, an optimization tool, able to consider the available DERs (conventional or renewable Distributed Generations (DGs) and demand response) is proposed. The optimization tool uses a stochastic multi-objective method in order to schedule DERs and aims at minimizing the total operational costs and emissions while considering the intermittent nature of wind and solar power as well as demand forecast errors. In order to facilitate small and medium loads participation in demand response programs, a Demand Response Provider (DRP) aggregates offers for load reduction. The proposed scheduling approach for DERs is tested on a 69-bus distribution test system over a 24-h period.

Economic-environmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach

In this paper a stochastic multi-objective economical/environmental operational scheduling method is proposed to schedule energy and reserve in a smart distribution system with high penetration of wind generation. The proposed multi-objective framework, based on augmented ε-constraint method, is used to minimize the total operational costs and emissions and to generate Pareto-optimal solutions for the energy and reserve scheduling problem. Moreover, fuzzy decision making process is employed to extract one of the Pareto-optimal solutions as the best compromise non-dominated solution. The wind power and demand forecast errors are considered in this approach and the reserve can be furnished by the main grid as well as distributed generators and responsive loads. The consumers participate in both energy and reserve markets using various demand response programs. In order to facilitate small and medium loads participation in demand response programs, a Demand Response Provider (DRP) aggregates offers for load reduction. In order to solve the proposed optimization model, the Benders decomposition technique is used to convert the large scale mixed integer non-linear problem into mixed-integer linear programming and non-linear programming problems. The effectiveness of the proposed scheduling approach is verified on a 41-bus distribution test system over a 24-h period.

An Energy-Aware Heuristic Scheduling for Data-Intensive Workflows in Virtualized Datacenters

With the development of cloud computing, more and more data-intensive workflows have been deployed on virtualized datacenters. As a result, the energy spent on massive data accessing grows rapidly. In this paper, an energy aware scheduling algorithm is proposed, which introduces a novel heuristic called Minimal Data-Accessing Energy Path for scheduling data-intensive workflows aiming to reduce the energy consumption of intensive data accessing. Extensive experiments based on both synthetical and real workloads are conducted to investigate the effectiveness and performance of the proposed scheduling approach. The experimental results show that the proposed heuristic scheduling can significantly reduce the energy consumption of storing/retrieving intermediate data generated during the execution of data intensive workflow. In addition, it exhibits better robustness than existing algorithms when cloud systems are in presence of I/O intensive workloads.

(m,k)-firm pattern spinning to improve the GTS allocation of periodic messages in IEEE 802.15.4 networks

The IEEE 802.15.4 standard provides a flexible communication support for low-rate wireless personal area networks) applications. When active, the beacon-enabled mode provides a real-time communication to the supported application by adopting a guaranteed time slot (GTS) mechanism. However, this mechanism permits only up to seven real-time communicating devices. One way to deal with this limitation is to share the communication opportunities among the periodic tasks, by skipping some of the task activations in a controlled way. One of the widely accepted periodic task models that allows skips in periodic activations is the (m,k)-firm model. Motivated by this problem, this paper proposes the use of a dynamic GTS scheduling approach based on the (m,k)-firm task model, to deal with the GTS starvation problem. The proposed scheduling approach is based on pre-defined spins of the originally defined (m,k)-firm pattern. The use of an exact schedulability analysis test ensures that for each admitted message stream, at least m messages will be transmitted within each window of k consecutive deadlines. The schedulability analysis may be executed in polynomial time and therefore can be used as an online admission test for GTS requests. The effectiveness of the approach has been assessed both by a set of simulations and an experimental evaluation.

Optimizing Electric Vehicle Charging With Energy Storage in the Electricity Market

The Information and Communication Technologies (ICT) that are currently under development for future smart grid systems can enable load aggregators to have bidirectional communications with both the grid and Electric Vehicles (EVs) to obtain real-time price and load information, and to adjust EV charging schedules in real time. In addition, Energy Storage (ES) can be utilized by the aggregator to mitigate the impact of uncertainty and inaccurate prediction. In this paper, we study a problem of scheduling EV charging with ES from an electricity market perspective with joint consideration for the aggregator energy trading in the day-ahead and real-time markets. We present a Mixed Integer Linear Programming (MILP) model to provide optimal solutions as well as a simple polynomial-time heuristic algorithm based on LP rounding. In addition, we present a communication protocol for interactions among the aggregator, the ES, the power grid, and EVs, and demonstrate how to integrate the proposed scheduling approach in real-time charging operations. Extensive simulation results based on real electricity price and load data have been presented to justify the effectiveness of the proposed approach and to show how several key parameters affect its performance.

Fast MPC-Based Coordination of Wind Power and Battery Energy Storage Systems

In this paper, a model predictive control (MPC)-based coordinated scheduling framework for variable wind generation and battery energy storage systems (BESSs) is presented. On the basis of the short-term forecast of available wind generation and price information, a joint look-ahead optimization is performed by the wind farm and storage system to determine their net power injection to the electric power grid. In conjunction with moderate battery capacity, the excess unpredictable wind generation can be used to charge the battery storage and vice versa. The benefits of the proposed scheduling approach are that (1) the combined profit of wind generation and BESS is increased; (2) the net power injection from the wind farm into the power grid is smoothed out; and (3) the look-ahead optimization updates the price prediction in a moving horizon, which leads to more robust profit for wind farm and BESS against price uncertainties. By formulating the MPC-based coordinated scheduling as a quadratic programming problem, several numerically efficient algorithms to compute the optimal control strategy for wind generation and BESS are proposed. The effectiveness of the proposed algorithm in a modified IEEE 24-bus reliability test system with aggregated plug-in hybrid electric vehicles is demonstrated. It is shown that the proposed algorithm can increase the joint profit of wind farm and BESS while smoothing out the net power injection to the electricity grid. The proposed MPC-based scheduling problem can be solved in approximately 400 ms, which makes the framework implementable in realtime electricity market operations.