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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

An Optimal Dispatch Strategy for Distributed Microgrids Using PSO

Grid structures are rapidly evolving in view of contemporary energy policies which ensure the addition of more renewable sources to reduce the carbon footprint.Compared to a centralized approach,low voltage grids(decentralized and distributed)are promising approaches to integrating nondispatchable renewable energy sources(RESs).Installing local micro level power generation sources such as fuel cells,microturbines,and energy storage systems are a recent trend which helps in the intermittent effects of RESs and makes microgrids less dependable on the main grid.Due to the increasing variety of distributed generation sources having diverse characteristics,power dispatch scheduling of distributed microgrids is becoming challenging.A dispatch scheduling solution from an operator’s point of view is presented by the authors.The core objective of this study is to minimize the carbon emissions and the cost of each microgrid.Further,it is observed that sales and purchases from the main grid are reduced.Consequently,transmission losses are also decreased.

Edge-cloud collaborative intelligent production scheduling based on digital twin

With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production scheduling problem in large-scale manufacturing environment,digital twin(DT)places high demand on data processing capability of the terminals.It requires both global prediction and real-time response abilities.In order to solve the above problem,a DT-based edge-cloud collaborative intelligent production scheduling(DTECCS)system was proposed,and the scheduling model and method were introduced.DT-based edge-cloud collaboration(ECC)can predict the production capacity of each workshop,reassemble customer orders,optimize the allocation of global manufacturing resources in the cloud,and carry out distributed scheduling on the edge-side to improve scheduling and tasks processing efficiency.In the production process,the DTECCS system adjusts scheduling strategies in real-time,responding to changes in production conditions and order fluctuations.Finally,simulation results show the effectiveness of DTECCS system.

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.