Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II

As distributed energy storage equipments, electric vehicles (EVs) have great potential for applications in power systems. Meanwhile, reasonable optimization of the charging time of EVs can reduce the users’ expense. Thus, the schedule of the EV load requires multi-objective optimization. A diversity-maximization non-dominated sorting genetic algorithm (DM-NSGA)-II is developed to perform multi-objective optimization by considering the power load profile, the users’charging cost, and battery degradation. Furthermore, a real-time locally optimal schedule is adopted by utilizing a flexible time scale. The case study illustrates that the proposed DM-NSGA-II can prevent being trapped in a relatively limited region so as to diversify the optimal results and provide trade-off solutions to decision makers. The simulation analysis shows that the variable time scale can continuously involve the present EVs in the real-time optimization rather than rely on the forecasting data. The schedule of the EV load is more practical without the loss of accuracy.

NEW BOUNDS ON TIME AND NUMBER OF PROCESSORS FOR MULTIPROCESSOR OPTIMAL SCHEDULES

Two problems for task schedules in a multiprocessor parallel system are discussed in Ans paper (1) given a partially ordered set of tasks represented by the venices of an acyclic directed graph with their corresponding processing bines, derive the lower bound on the Annimum time(LBMT) needed to process the task graph for a given number of processors. (2) Determine the lower bound on minimum number of processors(LBMP) needed to complete those tasks in minimum bine. It is shown that the proposed LBMT is sharper than previously Known values and the comPUtational aspeCts of these bounds are also discussed.

Microgrid Optimal Scheduling

This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian University (GJU) microgrid system is used for illustration. We present analyses for islanded and grid-connected MG with no storage. The results show a feasible islanded MG with a substantial operational cost reduction. We obtain an average of $1 k daily cost savings when operating an islanded compared to a grid-connected MG with capped grid energy prices. This cost saving is 10 times higher when considering varying grid energy prices during the day. Although the PV power is intermittent during the day, the MG continues to operate with a voltage variation that does not 10%. The results imply that MGs of GJU similar topology can optimally and safely operate with no energy storage requirements but considerable renewable generation capacity.

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.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.

Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

The Effect of Water Saving and Production Increment by Drip Irrigation Schedules

Drip irrigation system can achieve high uniformity. When the system is designed for uniformity coefficient equal or more than 70%, the water application in the field can be expressed as a normal distribution and further simplified to a linear distribution. This paper will describe the irrigation scheduling parameters, percent of deficit, application efficiency and coefficient of variation by simple mathematical model. Using this effective model and the irrigation application, the total yield affected by the total water application for different uniformity of irrigation application can be determined. More over, this paper uses the cost of water, price of yield, uniformity of the drip irrigation system, crop response to water application and environmental concerns of pollution and contamination to determine the optimal irrigation schedule. A case study shows that the optimal irrigation schedule can achieve the effect of water saving and production increment compared with the conventional irrigation schedule in which the whole field is fully irrigated. Key words drip irrigation - linear cumulative frequency curve - optimal irrigation schedule - water saving - production increment CLC number TV 139.1 Foundation item: Supported by the National Natural Science Foundation of China (59379407)Biography: QIU Yuan-feng (1973-), male, Ph. D, research direction: water saving irrigation theory and techniques.

An optimal scheduling algorithm based on task duplication

When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.

An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem

This paper considers the parallel machines scheduling problem where jobs are subject to different release times. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem’s scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.

Optimal Scheduling of Electrical Energy Systems Using a Fluid Dynamic Analogy

The electricity-gas transformation problem and related intrinsic mechanisms are considered.First,existing schemes for the optimization of electricity-gas integrated energy systems are analyzed through consideration of the relevant literature,and an Electricity Hub(EH)for electricity-gas coupling is proposed.Then,the distribution mechanism in the circuit of the considered electricity-gas integrated system is analyzed.Afterward,a mathematical model for the natural gas pipeline is elaborated according to the power relationship,a node power flow calculation method,and security requirements.Next,the coupling relationship between them is implemented,and dedicated simulations are carried out.Through experimental data,it is found that after 79 data iterations,the optimization results of power generation and gas purchase cost in the new system converge to$54,936 in total,which is consistent with the data obtained by an existing centralized optimization scheme.However,the new proposed optimization scheme is found to be more flexible and convenient.

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.

Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop

With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.

Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources into production scheduling has become a research hotspot.For the scheduling problem of the flexible job shop adopting segmented AGV,a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function,and an improved genetic algorithmis designed to solve the problem in this study.The algorithmdesigns a two-layer codingmethod based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling.When initializing the population,three strategies are designed to ensure the diversity of the population.In order to improve the local search ability and the quality of the solution of the genetic algorithm,three neighborhood structures are designed for variable neighborhood search.The superiority of the improved genetic algorithmand the influence of the location and number of transfer stations on scheduling results are verified in two cases.

Layered power scheduling optimization of PV hydrogen production system considering performance attenuation of PEMEL

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer(PEMEL)under the fluctuating input of renewable energy,this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic(PV)electrolysis of water.First,voltage and performance attenuation models of the PEMEL are proposed,and the degradation cost of the electrolyzer under a fluctuating input is considered.Then,the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost.Finally,a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system.In the up-layer optimization,the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi.In low-layer optimization,the power allocation between the PEMEL and battery energy storage system(BESS)is optimized using a non-dominated sorting genetic algorithm(NSGA-Ⅱ)combined with the firefly algorithm(FA).A better optimization result,characterized by lower degradation and total costs,was obtained using the method proposed in this study.The improved algorithm can search for a better population and obtain optimization results in fewer iterations.As a calculation example,data from a PV power station in northwest China were used for optimization,and the effectiveness and rationality of the proposed optimization method were verified.

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.