GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.

Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research

With the acceleration of urbanization in China,the discharge of domestic sewage and industrial wastewater is increasing,and accidents of sewage spilling out and polluting the environment occur from time to time.Problems such as imperfect facilities and backward control methods are com-mon in the urban drainage network systems in China.Efficient drainage not only strengthens infrastructure such as rain and sewage diversion,pollution source monitoring,transportation,drainage and storage but also urgently needs technical means to monitor and optimize production and operation.Aiming at the optimal control of single-stage pumping stations and the coordinated control between two-stage pumping stations,this paper studies the modelling and optimal control of drainage network systems.Based on the Long Short Term Memory(LSTM)water level prediction model of the sewage pumping stations,and then based on the mechanism analysis of drainage pipe network,the factors that may cause the water level change of pumping station are obtained.Grey correlation analysis is carried out on these influencing factors,and the prediction model is established by taking the factors with a high correlation degree as input.The research results show that compared with the traditional prediction model,the LSTM model not only has higher prediction accuracy but also has better inflection point tracking ability.

Shift scheduling strategy development for parallel hybrid construction vehicles

The shift scheduling system of the transmission has an important effect on the dynamic and economic performance of hybrid vehicles. In this work, shift scheduling strategies are developed for parallel hybrid construction vehicles. The effect of power distribution and direction on shift characteristics of the parallel hybrid vehicle with operating loads is evaluated, which must be considered for optimal shift control. A power distribution factor is defined to accurately describe the power distribution and direction in various parallel hybrid systems. This paper proposes a Levenberg-Marquardt algorithm optimized neural network shift scheduling strategy. The methodology contains two objective functions, it is a dynamic combination of a dynamic shift schedule for optimal vehicle acceleration, and an energy-efficient shift schedule for optimal powertrain efficiency. The study is performed on a test bench under typical operating conditions of a wheel loader. The experimental results show that the proposed strategies offer effective and competitive shift performance.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

A Scheduling Strategy of Light Supplement in Agricultural Greenhouse Based on the Displacement of Light Source Group

LED can effectively promote the growth of crops and improve the yield of crops. In order to make the crops grow evenly in the agricultural greenhouse, the uniformity of illumination is very important. Because of the importance of illumination uniformity to the growth of crops, this paper intends to establish the illuminance model of light source without considering the influence factors such as the difference of each light source group and air scattering. On this basis, the reasonable layout of each light source position in the light source group is considered. Therefore, a light replenishment scheduling strategy based on the displacement of light source group is proposed in this paper Improve the uniformity of illumination in agricultural greenhouse and reduce its operation cost. Experiments show that the strategy is effective.

Three-stage day-ahead scheduling strategy for regional thermostatically controlled load aggregators

Thermostatically controlled loads(TCLs)are regarded as having potential to participate in power grid regulation.This paper proposes a scheduling strategy with three-stage optimization for regional aggregators jointly participating in day-ahead scheduling to support demand response.The first stage is on the profit of aggregators and peak load of the grid.The line loss and voltage deviation of regulation are considered to ensure stable operation of the power grid at the second stage,which guarantees the fairness of the regulation and the comfort of users.A single tempera-ture adjustment strategy is used to control TCLs to maximize the response potential in the third stage.Finally,digital simulation based on the IEEE 33-bus distribution network system proves that the proposed three-stage scheduling strategy can keep the voltage deviation within±5%in different situations.In addition,the Gini coefficient of distribu-tion increases by 20%and the predicted percentage of dissatisfied is 48%lower than those without distribution.

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs create a trusted application(TA)in the TEE to process sensitive information,such as payment or message encryption,which is transparent to the APPs running in the rich execution environments(REEs).In detail,the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs.In this paper,we first present a comprehensive analysis of the performance of open-source TEE encrypted text.We then propose a high energy-efficient task scheduling strategy(ETS-TEE).By leveraging the deep learning algorithm,our policy considers the complexity of TA tasks,which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server.The results show that compared with the default scheduling strategy on the local device,our approach achieves an average of 38.0%energy reduction and 1.6×speedup.This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications,so that the trusted execution environment has both security and high performance.

Optimal scheduling strategy for virtual power plants based on credibility theory

The virtual power plant(VPP)is a new and efficient solution to manage the integration of distributed energy resources(DERs)into the power system.Considering the unpredictable output of stochastic DERs,conventional scheduling strategies always set plenty of reserve aside in order to guarantee the reliability of operation,which is too conservative to gain more benefits.Thus,it is significant to research the scheduling strategies of VPPs,which can coordinate the risks and benefits of VPP operation.This paper presents a fuzzy chance-constrained scheduling model which utilizes fuzzy variables to describe uncertain features of distributed generators(DGs).Based on credibility theory,the concept of the confidence level is introduced to quantify the feasibility of the conditions,which reflects the risk tolerance of VPP operation.By transforming the fuzzy chance constraints into their equivalent forms,traditional optimization algorithms can be used to solve the optimal scheduling problem.An IEEE 6-node system is employed to prove the feasibility of the proposed scheduling model.Case studies demonstrate that the fuzzy chance strategy is superior to conservative scheduling strategies in realizing the right balance between risks and benefits.

Inverse Proportion Technique Based Scheduling Strategy for Energy Storage System Considering Load Demand Differences

The energy storage system(ESS)as a demand-side management(DSM)resource can effectively smooth the load power fluctuation of a power system.However,designing a more reasonable ESS operational strategy will be a prerequisite before incorporating the energy storage device into DSM.As different load levels have different demands for the real-time chargedischarge power of an ESS,this paper proposes a heuristic ESS operation scheduling strategy which can take into account the electrical load demand differences.In this paper,firstly,two demand degree concepts for charging power and discharging power are defined to describe the differentiated ESS demand under the condition of different electrical load levels.Secondly,an inverse proportion technique based ESS scheduling strategy,with the consideration of the load demand difference,is proposed in this paper.Thirdly,some evaluating indices are defined in this paper for describing the influence of the proposed strategy on the smoothing degree of the daily load curve.Finally,several case studies are designed to verify the validity and correctness of the proposed technique,and the results show that the proposed technique can effectively smooth the load curve and improve the ability of peak shaving and valley filling.

Optimizing the Irrigation Scheduling Strategy and the Water Use Efficiency in Steppe and Irrigated Crop Production Ecosystems in the Northwestern China

In an irrigation management problem, decisions are made at various levels for assessment of water availability and requirements, proposing the type of irrigation scheduling, and deriving an actual operational policy for various crop scenarios. In this study, a plan was developed for water management. A general strategy for planning and application of irrigation management was proposed. Since the Penman method was used, the focus was on a synthetic study involving basic project situations, relevant data, water requirement calculation, irrigation scheduling, and discussion on optimizing water use efficiency in the steppe and irrigated crop production ecosystems. Effective use of tabular displays made interpreting and analyzing results easier. Based on the statistical analysis between spring wheat water availability and water requirement, a new type of index called water niche suitability was proposed. The particular type of irrigation scheduling was based on this index together with concrete situation of irrigated areas. The research showed that there are great potentiality of water resources in optimizing Ningxia irrigation management. The irrigation scheduling in this paper was found to be reasonable and demonstrated that results could be used to assist in improving water management decisions in the northwestern China.

GCSS:a global collaborative scheduling strategy for wide-area high-performance computing

Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage resources.However,the geographical distribution of computing and storage resources makes efficient task distribution and data placement more challenging.To achieve a higher system performance,this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing environments.The collaborative scheduling strategy integrates lightweight solution selection,redundant data placement and task stealing mechanisms,optimizing task distribution and data placement to achieve efficient computing in wide-area environments.The experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+,the proposed scheduling strategy reduces the makespan by 23.24%,improves computing and storage resource utilization by 8.28%and 21.73%respectively,and achieves similar global data migration costs.

Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding

In developing countries like South Africa,users experienced more than 1030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid.Residential homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily.This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding.To start with,we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour(KNN)algorithm.Based on an accurate forecast of the future load shedding patterns,we formulate the residents’inconvenience and the loss of power supply probability during load shedding as the objective function.When solving the multi-objective optimisation problem,four different strategies to fight against load shedding are identified,namely(1)optimal home appliance scheduling(HAS)under load shedding;(2)optimal HAS supported by solar panels;(3)optimal HAS supported by batteries,and(4)optimal HAS supported by the solar home system with both solar panels and batteries.Among these strategies,appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels,eliminates the loss of power supply probability and reduces the inconvenience by 92%when tested under the South African load shedding cases in 2023.

Cross-regional emergency scheduling planning for petroleum based on the supernetwork model

Petroleum, the most important energy source in the world, plays an essential role in securing economic development. If a petroleum shortage happens, it will severely disrupt production and life. Cross-regional emergency scheduling can effectively alleviate a petroleum shortage and further enhance the efficiency of the emergency response. Considering the general lack of focus on cross-regional petroleum dispatching management, we propose a three-layer emergency scheduling network for petroleum based on a supernetwork model that can increase the regional emergency correlation by adding a transfer management process. Then, we compare the total demand for petroleum and the emergency costs considered in the petroleum emergency scheduling supernetwork model（the single-region and the cross-region scenarios）.The result shows that the cross-regional emergency scheduling pattern can effectively enhance the efficiency of the emergency preparations and reduce the emergency costs in most cases. However, when the vulnerabilities in the crossregional link grow or the regional linkage decreases, the effect of single-regional scheduling is better. In addition, the advantages of the cross-regional emergency scheduling network will be strengthened with an increase in its maximum emergency capability. Nonetheless, this advantage will disappear when the petroleum demand in the crisis layer reaches the maximum emergency response capacity. Finally, according to the comparative analysis simulation among scenarios,certain strategic policy recommendations are suggested to improve the petroleum emergency scheduling ability in regions.These recommendations include strengthening the cross-regional coordination mechanism, increasing the modes of petroleum transportation and enhancing the carrying capacity of regional emergency routes.

Modeling and Solving Lot-Splitting Scheduling Problem Based on Process

In flexible job-shop batch scheduling problem, the optimal lot-size of different process is not always the same because of different processing time and set-up time. Even for the same process of the same workpiece, the choice of machine also affects the optimal lot-size. In addition, different choices of lot-size between the constrained processes will impact the manufacture efficiency. Considering that each process has its own appropriate lot-size, we put forward the concept of scheduling with lot-splitting based on process and set up the scheduling model of lot-splitting to critical path process as the core. The model could update the set of batch process and machine selection strategy dynamically to determine processing route and arrange proper lot-size for different processes, to achieve the purpose of optimizing the makespan and reducing the processing batches effectively. The experiment results show that, comparing with lot-splitting scheduling scheme based on workpiece, this model optimizes the makespan and improves the utilization efficiency of the machine. It also greatly decreases the machined batches （42%） and reduces the complexity of shop scheduling production management.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.

Teaching mechanism empowered by virtual simulation: Edge computing–driven approach

With the rapid development of network and communication techniques,the teaching forms have become diversified.To enhance the education experience and improve the teaching environment,an increasing number of educational institutions have adopted virtual simulation technology.A typical teaching mechanism is to exploit Virtual Reality(VR)technology,which affords participants an immersive experience.Unquestionably,such a VRbased mode is highly approved.However,the performance of this technology requires further optimization.On one hand,for VR 360video,the current intraframe decision cannot adapt to rapid response demands.On the other hand,the generated data size is considerably large and fast computation may not be realized,depending on the local VR device.Therefore,this study proposes an improved teaching mechanism empowered by edge computing–driven VR,called VE4T,that involves two parts.First,an intraframe decision algorithm for VR 360videos is devised to realize the rapid responses.Second,an edge computing framework is proposed to offload some tasks to an edge server for computation,where a task scheduling strategy is developed to check whether a task needs to be offloaded.Finally,experiments are performed using a practical teaching scenario with some VR devices.The obtained results demonstrate that VE4T is more efficient than existing mechanisms.

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

A PSL Ontology-based Shop Floor Dynamical Scheduler Design

Due to the complex,uncertainty and dynamics in the modern manufacturing environment,a flexible and robust shop floor scheduler is essential to achieve the production goals.A design framework of a shop floor dynamical scheduler is presented in this paper.The workflow and function modules of the scheduler are discussed in detail.A multi-step adaptive scheduling strategy and a process specification language,which is an ontology-based representation of process plan,are utilized in the proposed scheduler.The scheduler acquires the dispatching rule from the knowledge base and uses the build-in on-line simulator to evaluate the obtained rule.These technologies enable the scheduler to improve its fine-tune ability and effectively transfer process information into other heterogeneous information systems in a shop floor.The effectiveness of the suggested structure will be demonstrated via its application in the scheduling system of a manufacturing enterprise.