Traditional Scheduling Schemes Research Articles

A two-layer energy management for islanded microgrid based on inverse reinforcement learning and distributed ADMM

The development of a scheduling strategy for an islanded microgrid (IMG) is critical for ensuring the system’s stability and economic efficiency. Traditional scheduling strategies for IMGs predominantly utilize centralized management by the microgrid central controller (MGCC), which introduces a vulnerability to a single point of failure. To address this limitation, this paper presents a two-layer energy management strategy for IMGs based on the improved alternating direction method of multipliers (ADMM) and inverse reinforcement learning (IRL). First, the framework of the proposed strategy, comprising a scheduling layer and a real-time dispatch layer, is outlined. Next, the problem formulation of the scheduling layer is analyzed, and the proposed IRL-based management strategy for the energy storage system (ESS) is presented. Then, a distributed optimization algorithm based on the improved ADMM is proposed for the management of controllable distributed generators (CDGs) in the real-time dispatch layer. Lastly, the case study demonstrates the efficacy of the proposed strategy in diminishing MGCC dependency. The comparative analysis indicates that the proposed strategy outperforms existing scheduling strategies in terms of cost-effectiveness when the forecast error exceeds 3%. Moreover, in contrast to existing scheduling strategies, the proposed strategy mitigates the risk associated with a single point of failure.

A robust real-time energy scheduling strategy of integrated energy system based on multi-step interval prediction of uncertainties

The uncertainties of renewable energies and loads in integrated energy system (IES) result in unstable operation and performance degradation. To consider the impact of the uncertainties, the scheduling strategies of IES with the prediction of uncertainties are required. This paper designs a hybrid solar and gas turbine IES, in which the thermal and electrical storage units are integrated to address the uncertainties. A robust real-time energy scheduling strategy is proposed according to the real-time load and the predicted loads in the future time, in which the errors of multi-step interval prediction of renewable sources and loads are combined. The multi-step prediction method based on gated recurrent unit and time classification is constructed for the interval prediction of uncertain sources and loads by analyzing the probability statistics of prediction errors. In the robust optimization, the penalty factors of time sequences are proposed and considered to decrease the influences of future predicted information on current real-time scheduling, in which the future uncertainties in larger interval with current time has less influence. The optimal results in the hybrid IES demonstrate that the daily operation cost is 13.67 % lower than the method that does not consider the prediction parameters. Compared to the traditional scheduling strategies, the proposed strategy declines the operation cost by 2.93 %. The analysis of the confidence level of predictions ranging from 60 % to 98 % illustrates that the operation cost under the penalty factor of 0.94 is the lowest when the confidence level is from 94 % to 96 %.

Dynamic Merging for Optimal Onboard Resource Utilization: Innovating Mission Queue Constructing Method in Multi-Satellite Spatial Information Networks

The purpose of constructing onboard observation mission queues is to improve the execution efficiency of onboard tasks and reduce energy consumption, representing a significant challenge in achieving efficient global military reconnaissance and target tracking. Existing research often focuses on the aspect of task scheduling, aiming at optimizing the efficiency of single-task execution, while neglecting the complex dependencies that might exist between multiple tasks and payloads. Moreover, traditional task scheduling schemes are no longer suitable for large-scale tasks. To effectively reduce the number of tasks within the network, we introduce a network aggregation graph model based on multiple satellites and tasks, and propose a task aggregation priority dynamic calculation algorithm based on graph computations. Subsequently, we present a dynamic merging-based method for multi-satellite, multi-task aggregation, a novel approach for constructing onboard mission queues that can dynamically optimize the task queue according to real-time task demands and resource status. Simulation experiments demonstrate that, compared to baseline algorithms, our proposed task aggregation method significantly reduces the task size by approximately 25% and effectively increases the utilization rate of onboard resources.

Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multi-energy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues

Multi-time scale operation optimization for a near-zero energy community energy system combined with electricity-heat-hydrogen storage

Hybrid energy storage offers a new way to increase the renewable energy proportion (REP) of near-zero energy community energy system (NZECES). However, the optimization operation method of NZECES combining hybrid energy storage is not yet mature. Therefore, this paper proposes integrating electricity storage, heat storage and hydrogen storage into the NZECES, and conducting multi-time scale (MTS) operation optimization research. Firstly, a NZECES with electricity-heat-hydrogen storage is constructed. Secondly, a MTS optimization operation model of day-ahead scheduling, intra-day rolling, and real-time adjustment is established. Among them, the hourly scheduling scheme targeting the lowest daily operating cost is obtained through the optimization of the day-ahead stage. In the intra-day stage, the scheduling scheme with a time interval of 15 min is obtained by the rolling optimization. The operation scheme of electrical-related equipment with the minimum adjustment cost is obtained in the real-time stage. Finally, a NZEC with different building types is used as the energy supply object. The results show that compared to the traditional day-ahead scheduling scheme, the REP is increased by 3.6 % and the operation cost of the energy system is reduced by 5.3 % by using the MTS operation optimization method.

Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

The microservice-based smart grid service (SGS) organization and the heterogeneous multi-cores-based computing resource supply are the development direction of edge computing in smart grid with high penetration of renewable energy sources and high market-oriented. However, their application also challenges the service schedule for edge computing apparatus (ECA), the physical carrier of edge computing. In the traditional scheduling strategy of SGS, an SGS usually corresponds to an independent application or component, and the heterogeneous multi-core computing environment is also not considered, making it difficult to cope with the above challenges. In this paper, we propose an SGS scheduling strategy for the ECA. Specifically, we first present an SGS scheduling framework of ECA and give the essential element of meeting SGS scheduling. Then, considering the deadline and importance attributes of the SGS, a microservice scheduling prioritizing module is proposed. On this basis, the inset-based method is used to allocate the microservice task to the heterogeneous multi-cores to utilize computing resources and reduce the service response time efficiently. Furthermore, we design the scheduling unit dividing module to balance the delay requirement between the service with early arrival time and the service with high importance in high concurrency scenarios. An emergency mechanism (EM) is also presented for the timely completion of urgent SGSs. Finally, the effectiveness of the proposed service scheduling strategy is verified in a typical SGS scenario in the smart distribution transformer area.

Research on Regional Energy System Planning Considering Flexible Access of Electric Vehicles

The high proportion of renewable energy sources and intermittent loads pose challenges to the flexible operation of regional energy systems. To improve the flexible operation level of regional energy system, this paper considers the uncertainty of energy storage charging and discharging, demand-side management, and V2G of electric vehicles, and establishes a flexible resource supply capacity and cost model. With the goal of minimum flexibility demand and economic optimization, the implementation of flexible supply and demand balance constraints is added. The advantages and disadvantages of the traditional resource scheduling scheme and the unified flexible resource scheduling method are compared from the net load volatility, the maximum operating volatility and the whole time scale flexible supply and demand balance index. The algorithm analysis proves the advantages of the proposed scheduling scheme in improving the flexibility of the regional energy system, which can describe the flexibility of the regional energy system well and can be quantitatively characterized.

An optimal energy-efficient scheduling with processing speed selection and due date constraint in a single-machine environment

To relive the pressure of electricity grid during the peak period, time-of-use (TOU) pricing strategy has been implemented in industries to encourage manufacturers to transfer some processing tasks from peak periods to non-peak periods, and the due date of each job cannot be violated. Under these contexts, this paper addresses a speed-scaling single machine scheduling problem with due date constraint to minimize the total electricity cost (TEC). More precisely, the main innovative works are described as follows: (1) seven critical problem properties (including four theorems and three lemmas) based on different processing time window forms are formally derived; and (2) a property-based genetic algorithm (PGA) with hybrid initialization method is designed according to the characteristics of the studied problem. In the numerical experiments, the Taguchi method of design-of-experiment is employed to seek the optimal combination of four key parameters in PGA. Subsequently, the effectiveness and superiority of the proposed hybrid initialization method and problem properties are separately demonstrated by randomly generated 20 instances. After that, compared with other two traditional scheduling strategies, the proposed energy-efficient strategy can save at least 16% of TEC on average. Next, a relaxation coefficient (CR) is designed to measure the intrinsic link between TEC and the instance parameters (i.e. the due date and normal processing time). Finally, a real case is presented to verify the benefits of the proposed PGA algorithm and variable processing speeds, and the results show that the newly generated scheduling scheme based on the proposed PGA can reduce up to 46.36% of TEC compared with the existing company’s scheduling scheme.

Dynamic Scheduling and Optimization of AGV in Factory Logistics Systems Based on Digital Twin

At present, discrete workshops demand higher transportation efficiency, but the traditional scheduling strategy of the logistics systems can no longer meet the requirements. In a transportation system with multiple automated guided vehicles (multi-AGVs), AGV path conflicts directly affect the efficiency and coordination of the whole system. At the same time, the uncertainty of the number and speed of AGVs will lead to excessive cost. To solve these problems, an AGVs Multi-Objective Dynamic Scheduling (AMODS) method is proposed which is based on the digital twin of the workshop. The digital twin of the workshop is built in the virtual space, and a two-way exchange and real-time control framework based on dynamic data is established. The digital twin system is adopted to exchange data in real time, create a real-time updated dynamic task list, determine the number of AGVs and the speed of AGVs under different working conditions, and effectively improve the efficiency of the logistics system. Compared with the traditional scheduling strategy, this paper is of practical significance for the scheduling of the discrete workshop logistics systems to improve the production efficiency, utilization rate of resources, and dynamic response capability.

Age of Information-Based Scheduling for Wireless D2D Systems With a Deep Learning Approach

Device-to-device (D2D) links scheduling for avoiding excessive interference is critical to the success of wireless D2D communications. Most of the traditional scheduling schemes only consider the maximum throughput or fairness of the system and do not consider the freshness of information. In this paper, we propose a novel D2D links scheduling scheme to optimize an age of information (AoI) and throughput jointly scheduling problem when D2D links transmit packets under the last-come-first-serve policy with packet-replacement (LCFS-PR). It is motivated by the fact that the maximum throughput scheduling may reduce the activation probability of links with poor channel conditions, which results in terrible AoI performance. Specifically, We derive the expression of the overall average AoI and throughput of the network under the spatio-temporal interfering queue dynamics with the mean-field assumption. Moreover, a neural network structure is proposed to learn the mapping from the geographic location to the optimal scheduling parameters under a stationary randomized policy, where the scheduling decision can be made without estimating the channel state information(CSI) after the neural network is well-trained. To overcome the problem that implicit loss functions cannot be back-propagated, we derive a numerical solution of the gradient. Finally, numerical results reveal that the performance of the deep learning approach is close to that of a local optimal algorithm which has a higher computational complexity. The trade-off curve of AoI and throughput is also obtained, where the AoI tends to infinity when throughput is maximized.

Communication‐awareness adaptive resource scheduling strategy for multiple target tracking in a multiple radar system

In this study, a communication-awareness adaptive resource scheduling (CARS) strategy for multiple target tracking in a multiple radar system (MRS) is proposed. The CARS strategy aims to maximise the tracking performance of MRS, whilst minimising the interference from MRS to communication systems (CSs), whose mechanism is to simultaneously control the revisit frame interval of each target and determine the activation of radar nodes, the radar-target assignment and the allocation of transmitted power. Mathematically, the CARS strategy is formulated as an optimization problem, which contains both the continuous variable and the discrete (integer) variable. To tackle the resultant mixed-integer, non-convex, and non-linear problem efficiently, incorporating with the proposed hybrid particle swarm optimization algorithm based on the Kullback–Leibler divergence, a two-stage solution technique is developed to obtain the near-optimal solution. Numerical simulation results are provided to validate the proposed CARS strategy and demonstrate its superiority over the traditional scheduling strategies.

Quantifying the Impact of Time-Sharing on Route Capacity in Cognitive Radio Networks With Full-Duplex Capability

Recent advances in full-duplex (FD) communications along with the introduction of the concept of device-to-device communications have challenged the applicability of traditional routing and scheduling schemes in multi-hop cognitive radio networks (CRNs). In this letter, we derived a close-form expression to quantify the impact of time-sharing of a given channel assignment along a given path on network performance. Based on the derived expression, we mathematically model the channel-assignment problem that minimizes the number of time-shared hops along a given path as a binary-quadratic optimization problem (BQP). Then, we transfer the BQP into a binary linear programming problem (BLP), which is sub-optimally solvable using a polynomial-time sequential fixing method. Simulation results indicate that routing with assignment that minimizes the needed number of time-shared transmissions along a path can provide significant improvement in network throughput.

Discrete optimization model for permutation flow shop under time-of-use electricity tariffs

The time-of-use electricity tariffs (TOU) which is widely used at home and abroad make the timeline for scheduling to have the attribute of different electricity tariffs. In order to solve the difficulty of coordination between electricity price segmentation and product processing time, this paper proposes a discrete optimization model, which uses central moment method modelling to ensure the continuity of job-processing and the uniqueness of processing time points. A discrete optimization model with maximum completion time and total electricity cost is constructed. According to the instance of the processing workshop and the characteristics of GAMS software, a virtual small-scale case of detection is constructed. By setting 11 groups of experiments with different weights, the total electricity cost is reduced by 30% compared with the traditional scheduling scheme after the electricity cost optimization is considered to avoid the peak arrangement of the electricity price. Although the maximum completion time is increased by nearly 30% on the surface, the downtime of staggering the peak is removed, the actual processing time is 42 minutes, and the total completion time is only increased by 5%.

Channel congestion control model based on improved asynchronous back-pressure routing algorithm in wireless distributed networks

Due to the continuous increase of the network bandwidth, the traditional coarse-grained congestion control mechanisms and flow scheduling schemes are difficult to provide satisfying performance. Therefore, a distributed algorithm based on an improved asynchronous back-pressure routing algorithm is proposed for joint channel congestion control, routing and power allocation in this paper. Considering the application scenario of wireless distributed networks with node power constraints and independent buffers for traffic flow, this paper studies joint congestion control, routing and power allocation when channel state information is known. In order to improve the defect of Newton method, an algorithm with second-order convergence performance is designed and matrix decomposition method is used to realize the distributed updating of traffic source rate, link rate and link power in network nodes and links so as to maximize network utility. Compared with the known existing algorithms, our proposed algorithm has faster convergence speed, which improves the network utility and energy utility by optimizing power allocation, and it can control the queue backlog at a very low level.

Computation Offloading and Task Scheduling for DNN-Based Applications in Cloud-Edge Computing

Due to the high demands of deep neural network (DNN) based applications on computational capability, it is hard for them to be directly run on mobile devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks of neural network layers to edges or remote clouds that are equipped with sufficient resources. However, the offloading process might lead to excessive delays and thus seriously affect the user experience. To address this important problem, we first regard the average response time of multi-task parallel scheduling as our optimization goal. Next, the problem of computation offloading and task scheduling for DNN-based applications in cloud-edge computing is formulated with a scheme evaluation algorithm. Finally, the greedy and genetic algorithms based methods are proposed to solve the problem. The extensive experiments are conducted to demonstrate the effectiveness of the proposed methods for scheduling tasks of DNN-based applications in different cloud-edge environments. The results show that the proposed methods can obtain the near-optimal scheduling performance, and generate less average response time than traditional scheduling schemes. Moreover, the genetic algorithm leads to less average response time than the greedy algorithm, but the genetic algorithm needs more running time.

Security and reliability trade‐off analysis of joint user and jammer selection in the face of co‐channel interference

In this study, the authors investigate the security and reliability trade-off (SRT) performance of a multiuser wireless network, where an eavesdropper attempts to overhear the confidential transmissions between a base station and multiple users under the impact of co-channel interference. To enhance the SRT performance of the network, the authors propose two joint user and jammer selection schemes, namely the joint optimal user and random jammer selection (JOU-RJS) scheme and the joint optimal user and optimal jammer selection (JOU-OJS) scheme. For SRT performance comparison purposes, the traditional multiuser scheduling (TMUS) scheme is also considered. The authors derive closed-form outage and intercept probability expressions for the JOU-RJS and JOUOJS schemes. Simulation results show that the SRTs of both JOU-RJS and JOU-OJS schemes are better than that of the TMUS scheme, where JOU-OJS achieves the best SRT performance. Additionally, this study also shows that an expected outage or intercept probability of the TMUS, JOU-RJS, and JOU-OJS schemes can be achieved by giving an appropriate overall rate. Finally, the sum of the outage and intercept probability can be minimised through an optimal power allocation between the selected user and friendly jammer for the proposed JOU-RJS and JOU-OJS.

The Security of Cognitive Multiuser Networks Over Space-and-Time Correlated Channels

In this paper, we investigate the secrecy performance of cognitive multi-input multi-output multiuser (MIMO-MU) wiretap networks over space-and-time correlated fading channels. Specifically, we propose a joint transmit antenna selection (TAS) and multiuser scheduling scheme to improve the security of the considered system. To thoroughly analyze the impact of the joint TAS and multiuser scheduling scheme and the channel correlation on the physical layer security, the new exact and asymptotic closed-form expressions for the secrecy outage probability of cognitive MIMO-MU wiretap networks are derived in two distinct scenarios, i.e., space-and-time correlated fading channels and independent-and-perfect fading channels. From the theoretical and simulation results, several important remarks are concluded as follows. 1) The channel space correlation does not affect the achievable secrecy diversity order in a high main-to-eavesdropper ratio (MER) regime. While it produces different impacts on the secrecy performance with different values of the MER in a low MER regime. 2) The channel time correlation on the main channel can reduce the achievable secrecy diversity order from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N_{\rm A}N_{\rm B}$</tex-math></inline-formula> to 1. 3) The joint TAS and multiuser scheduling scheme can achieve better performance than the traditional random scheduling scheme over space-and-time correlated fading channels.

Adaptive packet scheduling in IoT environment based on Q-learning

In the internet of things (IoT) environment consisting of various devices the traffic condition dynamically changes. Failure to process them in complying with the QoS requirement can significantly degrade the reliability and quality of the system. Therefore, the gateway collecting the data needs to quickly establish a new scheduling policy according to the changing traffic condition. The traditional packet scheduling schemes are not effective for IoT since the data transmission pattern is not identified in advance. Q-learning is a type of reinforcement learning that can establish a dynamic scheduling policy without any prior knowledge on the network condition. In this paper a novel Q-learning scheme is proposed which updates the Q-table and reward table based on the condition of the queues in the gateway. Computer simulation reveals that the proposed scheme significantly increases the number of packets satisfying the delay requirement while decreasing the processing time compared to the existing scheme based on Q-learning with stochastic learning automaton. And the processing time is also minimized by omitting unnecessary computation steps in selecting the action in the iterative Q-learning operations.

Joint Data Sampling and Link Scheduling for Age Minimization in Multihop Cyber-Physical Systems

In cyber-physical system (CPS), sensor-based communication components are widely deployed to monitor real-time physical phenomena and send the information to network to enable control of entities in physical world. The freshness of data is of critical importance in such systems. Age of information (AoI) is a performance metric which measures data freshness at the destination since the last sampling at the data source. In this letter, we consider a multihop CPS with multiple data flows and investigate an age minimization problem by jointly optimizing data sampling at the sources and link scheduling along the path of each data flow. The formulated problem falls in the form of an integer nonlinear program. To solve the problem efficiently, we propose a light-weight scheduling algorithm. Compared with traditional delay-based scheduling scheme, simulation results show that AoI-based scheduling scheme performs better in delivering information in a timely manner. Moreover, we demonstrate that by jointly optimizing link scheduling and sampling time, network-level data freshness can be further improved.

The impact of various carbon reduction policies on green flowshop scheduling

In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia’s carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies.