Real-time Scheduling Strategy Research Articles

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 %.

Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method

ABSTRACT With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

Real-time scheduling and routing of shared autonomous vehicles considering platooning in intermittent segregated lanes and priority at intersections in urban corridors

Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban networks, the imperative of devising an efficient real-time scheduling and routing strategy for these vehicles becomes evident if one is to maximize their potential in enhancing travel efficiency. In this study, we address the problem of jointly scheduling and routing SAVs across an urban network with the possibility of platooning the vehicles at intersections to reduce their travel time. We argue that this is especially useful in large urban areas. We introduce a novel vehicle scheduling and routing method that allows a specific number of SAVs to converge at the intersections of urban corridors within designated time intervals, facilitating the formation of SAV platoons. Dedicated lanes and signal priority control are activated to ensure that these platoons go through the corridors efficiently. Based on the above concept, we propose a linear integer programming model to minimize the total travel time of SAVs and the delays experienced by the conventional vehicles due to SAV priority, thereby optimizing the overall performance of the road network. For large instances, we develop a two-stage heuristic algorithm to solve it faster. In the first stage, leveraging an evaluation index that manifests the compatibility of each vehicle-to-request combination, we allocate passenger requests to a fleet of SAVs. In the second stage, a customized genetic algorithm is designed to coordinate the paths of various SAVs, thus achieving the desired vehicle platooning effect. The optimization method is tested on a real-world road network in Shanghai, China. The results display a remarkable reduction of 15.76 % in the total travel time of the SAVs that formed platoons. The overall performance of the road network could be improved with the total travel time increase of conventional vehicles significantly smaller than the reduction observed in SAVs’ total travel time.

Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing

In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer satisfaction. To accurately capture customer satisfaction, a novel comprehensive rating index is introduced, integrating the actual completion cost, time, and processing quality against customer expectations. Furthermore, vehicle constraints are incorporated into the model to accommodate potential delays in transport vehicle availability, thereby enhancing its alignment with real-world manufacturing settings. The proposed mathematical model is solved using an improved three-stage genetic algorithm, which integrates the k-means algorithm and a real-time sequence scheduling strategy to optimise solution quality. Validation against alternative algorithms across various case scales demonstrates the efficacy of the approach in providing practical scheduling solutions for real-case scenarios.

Stochastic optimization of solar-based distributed energy system: An error-based scenario with a day-ahead and real-time dynamic scheduling approach

Distributed energy systems (DES) have garnered global attention as a promising solution for the expansion of renewable energy sources. However, stochastic uncertainty in renewable sources poses significant challenges in the collaboration optimization of system design and operation. In this study, a comparison analysis was conducted to assess the effectiveness of the conventional distribution-based scenario generation (DS) method for stochastic optimization of a distributed energy system in residential buildings. The results revealed that the DS method inaccurately captured extreme scenarios and exhibited limitations in operation optimization, leading to significant performance evaluation bias. To address these challenges, a novel stochastic optimization approach was developed based on error-based scenarios and a day-ahead and real-time dynamic scheduling strategy (ES-DRS). This approach incorporated solar energy prediction errors to more accurately characterize extreme scenarios, while also considering dynamic dual-scale meteorological boundary condition for rolling operation optimization. Furthermore, the buffer storage and coverage periods in ES-DRS were investigated and discussed during dynamic scheduling. Results demonstrated that when the buffer storage and coverage period were set at 36.22 kWh in summer and 24 h, respectively, the DES with ES-DRS achieved optimal multi-objective performance. This resulted in an annual total cost of 3.21 × 104 USD and CO2 emission of 5.82 tons, representing reductions of 26.45% and 61.06%, respectively, compared to the conventional strategy. Overall, this research contributes to advancing uncertainty analysis and scenario-based optimization in DES, highlighting the potential benefits of adopting the ES-DRS approach to maximize overall performance from both economic and environmental perspectives.

A distributed permutation flow-shop considering sustainability criteria and real-time scheduling

Recent advancements in production scheduling have arisen in response to the need for adaptation in dynamic environments. This paper addresses the challenge of real-time scheduling within the context of sustainable production. We redefine the sustainable distributed permutation flow-shop scheduling problem using an online mixed-integer programming model. The proposed model prioritizes minimizing makespan while simultaneously constraining energy consumption, reducing the number of lost working days and increasing job opportunities within permissible limits. Our approach considers machines operating in different modes, ranging from manual to automatic, and employs two real-time scheduling strategies: predictive-reactive and proactive-reactive scheduling. We evaluate two rescheduling policies: continuous and event-driven. To demonstrate the model's applicability, we present a case study in auto workpiece production. We manage model complexity through various reformulations and heuristics, such as Lagrangian relaxation and Benders decomposition for initial optimization as well as four problem-specific heuristics for real-time considerations. For solving large-scale instances, we employ simulated annealing and tabu search metaheuristic algorithms. Our findings underscore the benefits of the predictive-reactive scheduling strategy and the efficiency of the event-driven rescheduling policy. By addressing dynamic scheduling challenges and integrating sustainability criteria, this study contributes valuable insights into real-time scheduling and sustainable production.

Workflow migration in uncertain edge computing environments based on interval many-objective evolutionary algorithm

In edge computing (EC), when the edge server (ES) is processing tasks delivered by the mobile devices (MDs), the MDs move outside the coverage of the ES, where task migration is required to ensure service continuity. Most current research on task migration ignores inter-task dependencies and uncertain computing environments, and it focuses mainly on migration scenarios where MDs have a one-to-one or many-to-one relationship with ESs. Aiming at the problem of workflow migration with multi-MDs and multi-ESs in uncertain environments, this paper proposes an interval many-objective optimized workflow migration in uncertain environments (I-MaOWMUE) model that considers transforming uncertainty factors into interval parameters for processing, along with the migration delay, maximum completion time, energy consumption, and load balancing as an objective function, and at the same time, utilize real-time priority scheduling strategies to achieve the fast response of the tasks. Considering the dependency of tasks and the changing characteristics of ES load in a migration environment, this paper designs a migration-based interval many-objective evolutionary algorithm (MI-MaOEA), which adopts an interval confidence strategy to improve algorithm convergence and formulates an objective-value-dominated hierarchical sorting and dual-migration selection strategy based on the migration delay and the success rate of the migration to improve the diversity of the populations. Simulation results show that MI-MaOEA optimizes 27%, 35%, 14%, and 80% in solving the four objective values of I-MaOWMUE, and enables the solution to have faster converse speed and better distribution.

Improved reinforcement learning-based real-time energy scheduling for prosumer with elastic loads in smart grid

In this study, we investigate the dynamic energy-scheduling problem of a prosumer (producer/consumer) with an energy storage device (EDS) and elastic loads. The goal is to develop efficient real-time scheduling strategies for prosumers, and to minimise their total long-term costs (i.e. cost of energy purchased from the external grid and depreciation cost of the EDS). The challenges are twofold: the uncertainty of the energy output of the prosumer and the time-coupling constraints of the EDS and elastic loads. To address these challenges, we first describe the dynamic energy-scheduling problem as a Markov decision process. Then, an approximate state dual-agent Q-learning algorithm is proposed to solve the optimal dynamic scheduling problem by improving the model-free reinforcement learning(RL) method. Compared with the traditional RL method, the proposed algorithm reduces the system-state dimensions and exhibits improved performance. The proposed algorithm can only be assisted by mutual interactions between the environment with a reward feedback mechanism to dynamically respond to uncertain changes in the environments, without modelling or predicting the system environments. Finally, extensive empirical evaluations using real-world traces are conducted to study the effectiveness of the proposed algorithm. The results show that the proposed algorithm can reduce the total cost of the prosumer by up to 6.3%, 11.7% and 22.4% compared with the traditional RL method, Lyapunov optimisation and greedy algorithm, respectively.

Protection Window Based Security-Aware Scheduling against Schedule-Based Attacks

With widespread use of common-off-the-shelf components and the drive towards connection with external environments, the real-time systems are facing more and more security problems. In particular, the real-time systems are vulnerable to the schedule-based attacks because of their predictable and deterministic nature in operation. In this paper, we present a security-aware real-time scheduling scheme to counteract the schedule-based attacks by preventing the untrusted tasks from executing during the attack effective window (AEW). In order to minimize the AEW untrusted coverage ratio for the system with uncertain AEW size, we introduce the protection window to characterize the system protection capability limit due to the system schedulability constraint. To increase the opportunity of the priority inversion for the security-aware scheduling, we design an online feasibility test method based on the busy interval analysis. In addition, to reduce the run-time overhead of the online feasibility test, we also propose an efficient online feasibility test method based on the priority inversion budget analysis to avoid online iterative calculation through the offline maximum slack analysis. Owing to the protection window and the online feasibility test, our proposed approach can efficiently provide best-effort protection to mitigate the schedule-based attack vulnerability while ensuring system schedulability. Experiments show the significant security capability improvement of our proposed approach over the state-of-the-art coverage oriented scheduling algorithm.

Adaptive real-time reconfiguration gate scheduling scheme using time perceptive stream

An adaptive real-time gate scheduling scheme for time perceptive stream or packet flow is proposed to improve the standards of Ultra Low Latency during data transmission. For highly dynamic network conditions, the conventional configuration scheme is not suitable and therefore an adaptive real-time gate scheduling method is proposed for time perceptive streams. This dynamicity reconfiguration is difficult and the scheduling problem is formulated using Field Programmable Gate Array – Boolean Satisfiability Problem (FPGA-BSP) solver. This proposed scheme highly helps in network dynamicity conditions with good bandwidth utilization and high flexibility. End-to-end latency is required to be on sub-milliseconds order deal with the applications such as Industrial Internet of Things, 5G and 6G mobile, tactile internet and so on. Simulation analysis is carried out to prove the efficiency of the proposed model.

Real-Time Cost Optimization Approach Based on Deep Reinforcement Learning in Software-Defined Security Middle Platform

In today’s business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods.

Hybrid Modeling and Real-time Predictive Scheduling of Wet Flue Gas Desulfurization for Energy Saving and Life Extension

Sulfur dioxide (SO2) is one of the most important air pollutants emitted by power plants and ironworks. To effectively remove SO2 from flue gas, wet flue gas desulfurization (WFGD) technology is widely adopted by most of China’s coal-fired power plants. However, one of the most important issues of WFGD is the relatively high energy consumption, with the main contribution from the power consumed by circulating pumps in the absorber. In this paper, hybrid modeling and real-time predictive scheduling of a wet flue gas desulfurization (WFGD) system for energy saving and life extension are proposed. The hybrid model is based on the mechanism of the SO2 absorption process and modified based on the operation data using a particle swarm optimization (PSO) algorithm. The prediction model can describe the SO2 absorption process well, and the root-mean-square error (RMSE) of the model is 2.20 mg/m3. Effects of parameters such as the inlet SO2 concentration, pH, flue gas flow rate, and combinations of circulating pumps on the outlet SO2 concentration are further explored. The optimal combination of circulating pumps under different conditions is calculated. Finally, a real-time predictive scheduling strategy is proposed and evaluated under different parameter settings. The system shows the best performance when the upper limit, the target of outlet SO2 concentration, and the lower limit are set to 30, 15, and 10 mg/m3, respectively. The energy consumption of the system is 2564.6 kW, which is 10.6% lower than that before optimization.

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

The carbon-capturing process with the aid of CO2 removal technology (CDRT) has been recognised as an alternative and a prominent approach to deep decarbonisation. However, the main hindrance is the enormous energy demand and the economic implication of CDRT if not effectively managed. Hence, a novel deep reinforcement learning agent (DRL), integrated with an automated hyperparameter selection feature, is proposed in this study for the real-time scheduling of a multi-energy system (MES) coupled with CDRT. Post-carbon capture systems (PCCS) and direct-air capture systems (DACS) are considered CDRT. Various possible configurations are evaluated using real-time multi-energy data of a district in Arizona, the United States, and CDRT parameters from manufacturers' catalogues and pilot project documentation. The simulation results validate that an optimised soft-actor critic (SAC) DRL algorithm outperformed the Twin-delayed deep deterministic policy gradient (TD3) algorithm due to its maximum entropy feature. We then trained four (4) SAC DRL agents, equivalent to the number of considered case studies, using optimised hyperparameter values and deployed them in real time for evaluation. The results show that the proposed DRL agent can meet the prosumers' multi-energy demand and schedule the CDRT energy demand economically without specified constraints violation. Also, the proposed DRL agent outperformed rule-based scheduling by 23.65%. However, the configuration with PCCS and solid-sorbent DACS is considered the most suitable configuration with a high CO2 captured-released ratio (CCRR) of 38.54, low CO2 released indicator (CRI) value of 2.53, and a 36.5% reduction in CDR cost due to waste heat utilisation and high absorption capacity of the selected sorbent. However, the adoption of CDRT is not economically viable at the current carbon price. Finally, we showed that CDRT would be attractive at a carbon price of 400-450USD/ton with the provision of tax incentives by the policymakers.

Day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage

• Day-ahead and real-time market bidding and scheduling strategy for wind power participation. • Shared energy storage is used to reduce the real-time market deviation penalty of wind power. • Analyze the influence of deviation penalty coefficient on wind power bidding. • Analyze the impact of energy storage capacity on the income of wind farms and energy storage operator. In order to reduce the impact of wind power output and electricity price uncertainty on the income of wind power participating in the electricity market, this paper proposes a day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage. In the first stage, considering the uncertainty of wind power output and electricity price, aiming at the maximum income of wind farms in the day-ahead market, the optimal bidding power of each wind farm in the day-ahead market is obtained by using quantum genetic algorithm. In the second stage, based on the day-ahead market winning bid volume and actual output, by coordinating each wind farm to use the charging and discharging services of the shared energy storage power station, the net income of multiple wind farms in the day-ahead and real-time market is maximized. Case studies are carried out with three wind farms and one shared energy storage operator in this paper. Compared with the modes that wind farms not configuring energy storage or configuring energy storage independently, it is verified that the shared energy storage can significantly reduce the deviation penalty of wind farms in the real-time market and improve wind farm income.

Optimization design of real-time scheduling scheme for city bus vehicles based on BP neural network

Urban public transportation is inseparably related to people’s travel and life. Prioritizing the development of public transportation is a major national policy proposed by the Chinese government, especially to actively promote the development of intelligent public transportation systems. Developing and building advanced intelligent public transport operation and scheduling management system, designing efficient, flexible and low-cost operation and scheduling mode, improving the management level and service quality of public transport enterprises, and thus improving the road traffic condition of the whole city, is undoubtedly the future development direction of public transport enterprises. This paper combines the problems of bus operation and scheduling in China at the present stage, and proposes the operation and scheduling model and method of the intelligent urban public transportation planning system, with a view to realizing a flexible public transportation operation model suitable for different service situations and providing systematic theoretical support for solving the imbalance between supply and demand of public transportation.

Vehicle Dispatch and Route Optimization Algorithm for Demand-Responsive Transit

Giving priority to the development of public transit is an important way to achieve efficient, convenient, safe, comfortable, economic, reliable, green and low-carbon sustainable development. In view of the highly dispersed and regular passenger flow, demand responsive transit is an important complementary means for traditional public transport to improve passenger satisfaction. However, high operating costs and low load factor will have a bad impact on the operation of public transport and reduce passenger satisfaction. In this work, firstly, by analyzing the demand frequency of historical travel stations, the stations with high demand are extracted by time periods as high probability travel points; On this basis, a dynamic vehicle dispatching optimization model is established, and the static vehicle dispatching is carried out with the goal of minimizing the running mileage of the bus system; Finally, based on the initial static route and the later real-time travel demand, the accurate dynamic planning algorithm is used to optimize the dynamic route with the goal of minimizing the change of the system mileage, so as to achieve timely response to the demand. The results show that the two-phase scheduling optimization model based on the station extraction strategy can provide a reasonable real-time vehicle scheduling and route optimization scheme, improve the utilization rate of vehicles and the passenger load factor, and provide a theoretical basis and application guidance for actual vehicle scheduling.

Real-time optimal scheduling for active distribution networks: A graph reinforcement learning method

To improve the economy and safety of the active distribution network (ADN) operation, this paper proposes a real-time optimal scheduling strategy based on graph reinforcement learning (GRL), achieving online collaborative optimization of controllable equipment. For realizing the perception of inborn graph-structured features of the ADN and the formulation of the real-time scheduling scheme, the active-reactive power coordination process is firstly characterized as a dynamic graph-structured network interaction. A graph attention network (GAT) block is leveraged to extract and aggregate the topology branch correlation and node power information. Then the obtained topology information embedded with node features, as intermediate latent environment states, is fed into the underlying network architecture of deep reinforcement learning (DRL). Moreover, the power regulation problem of controllable equipment is duly formulated as a finite Markov decision process (FMDP). And the complex decision-making is solved by a deep deterministic policy gradient (DDPG) algorithm. Specially, under the electrical fault scenarios with variations in topology structures and feature information, GRL helps the agent efficiently learn and capture new graph-structured knowledge. Case studies are conducted within a modified IEEE 33-bus system. Extensive experimental results verify the superiority and adaptability of our proposed methodology.

Multi-Model Running Latency Optimization in an Edge Computing Paradigm.

Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable multiple model inference tasks to be conducted concurrently on resource-constrained edge devices, allowing us to achieve one goal collaboratively rather than getting high quality in each standalone task. However, the high overall running latency for performing multi-model inferences always negatively affects the real-time applications. To combat latency, the algorithms should be optimized to minimize the latency for multi-model deployment without compromising the safety-critical situation. This work focuses on the real-time task scheduling strategy for multi-model deployment and investigating the model inference using an open neural network exchange (ONNX) runtime engine. Then, an application deployment strategy is proposed based on the container technology and inference tasks are scheduled to different containers based on the scheduling strategies. Experimental results show that the proposed solution is able to significantly reduce the overall running latency in real-time applications.

A Novel Real-Time Deterministic Scheduling Mechanism in Industrial Cyber-Physical Systems for Energy Internet

As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems.