A Decomposition and Coordination Scheduling Method for Flow-shop Problem Based on TOC

There are many flow shop problems of throughput (denoted by FSPT) with constraints of due date in real production planning and scheduling. In this paper, a decomposition and coordination algorithm is proposed based on the analysis of FSPT and under the support of TOC (theory of constraint). A flow shop is at first decomposed into two subsystems named PULL and PUSH by means of bottleneck. Then the subsystem is decomposed into single machine scheduling problems,so the original NP-HARD problem can be transferred into a serial of single machine optimization problems finally. This method reduces the computational complexity, and has been used in a real project successfully.

A Dynamic Job Shop Scheduling Method Based on Ant Colony Coordination System

Due to the stubborn nature of dynamic job shop scheduling problem,a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment.In ant colony coordination mechanism,the dynamic job shop is composed of several autonomous ants.These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails,by which they can make information available globally,and further more guide ants make optimal decisions.The proposed mechanism is tested by several instances and the results confirm the validity of it.

Coordinate scheduling approach for EDS observation tasks and data transmission jobs

Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.

An optimal strategy for coordinating and dispatching “source-load” in power system based on multiple time scales

Due to the phenomenon of abandoning wind power and photo voltage(PV)power in the“Three Northern Areas”in China,this paper presents an optimal strategy for coordinating and dispatching“source-load”in power system based on multiple time scales.On the basis of the analysis of the uncertainty of wind power and PV power as well as the characteristics of load side resource dispatching,the optimal model of coordinating and dispatching“source-load”in power system based on multiple time scales is established.It can simultaneously and effectively dispatch conventional generators,wind plant,PV power station,pumped-storage power station and load side resources by optimally using three time scales:day-ahead,intra-day and real-time.According to the latest predicted information of wind power,PV power and load,the original generation schedule can be rolled and amended by using the corresponding time scale.The effectiveness of the model can be verified by a real system.The simulation results show that the proposed model can make full use of“source-load”resources to improve the ability to consume wind power and PV power of the grid-connected system.

Coordinated scheduling model for intermodal transit hubs based on GI/M^K/1 queuing system

Coordinated scheduling of multimode plays a pivotal role in the rapid gathering and dissipating of passengers in transport hubs. Based on the survey data, the whole-day reaching time distribution at transfer points of passengers from the dominant mode to the connecting mode was achieved. A GI/M K/1 bulk service queuing system was constituted by putting the passengers' reaching time distribution as the input and the connecting mode as the service institution. Through queuing theory, the relationship between average queuing length under steady-state and headway of the connecting mode was achieved. By putting the minimum total cost of system as optimization objective, the headway as decision variable, a coordinated scheduling model of multimode in intermodal transit hubs was established. At last, a dynamic scheduling strategy was generated to cope with the unexpected changes of the dominant mode. The instance analysis indicates that this model can significantly reduce passengers' queuing time by approximately 17% with no apparently increase in departure frequency, which provides a useful solution for the coordinated scheduling of different transport modes in hubs.

Coordinated Scheduling of Two-Agent Production and Transportation Based on Non-Cooperative Game

A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers.The jobs of two agents compete for the processing position on a machine,and after the pro-cessed,they compete for the transport position on a transport vehicle to be trans-ported to two agents.The two agents have different objective functions.The objective function of the first agent is the sum of the makespan and the total trans-portation time,whereas the objective function of the second agent is the sum of the total completion time and the total transportation time.Given the competition between two agents for machine resources and transportation resources,a non-cooperative game model with agents as game players is established.The job pro-cessing position and transportation position corresponding to the two agents are mapped as strategies,and the corresponding objective function is the utility func-tion.To solve the game model,an approximate Nash equilibrium solution algo-rithm based on an improved genetic algorithm(NE-IGA)is proposed.The genetic operation based on processing sequence and transportation sequence,as well as the fitness function based on Nash equilibrium definition,are designed based on the features of the two-agent production and transportation coordination scheduling problem.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.When compared to heuristic rules such as the Longest Processing Time first(LPT)and the Shortest Processing Time first(SPT),the objective function values of the two agents are reduced by 4.3%and 2.6% on average.

Optimized Operation of Park Integrated Energy System with Source-Load Flexible Response Based on Comprehensive Evaluation Index

To better reduce the carbon emissions of a park-integrated energy system(PIES),optimize the comprehensive operating cost,and smooth the load curve,a source-load flexible response model based on the comprehensive evaluation index is proposed.Firstly,a source-load flexible response model is proposed under the stepped carbon trading mechanism;the organic Rankine cycle is introduced into the source-side to construct a flexible response model with traditional combined heat and power(CHP)unit and electric boiler to realize the flexible response of CHP to load;and the load-side categorizes loads into transferable,interruptible,and substitutable loads according to the load characteristics and establishes a comprehensive demand response model.Secondly,the analytic network process(ANP)considers the linkages between indicators and allows decision-makers to consider the interactions of elements in a complex dynamic system,resulting in more realistic indicator assignment values.Considering the economy,energy efficiency,and environment,the PIES optimization operation model based on the ANP comprehensive evaluation index is constructed to optimize the system operation comprehensively.Finally,the CPLEX solver inMATLABwas employed to solve the problem.The results of the example showthat the source-load flexible response model proposed in this paper reduces the operating cost of the system by 29.90%,improves the comprehensive utilization rate by 15.00%,and reduces the carbon emission by 26.98%,which effectively enhances the system’s economy and low carbon,and the comprehensive evaluation index based on the ANP reaches 0.95,which takes into account the economy,energy efficiency,and the environment,and is more superior than the single evaluation index.

Real-time online rescheduling for multiple agile satellites with emergent tasks

The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.

Coordinated Planning Transmission Tasks in Heterogeneous Space Networks:A Semi-Distributed Approach

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.

面向综合能源协调的虚拟电厂调控平台设计与规划优化

为解决综合能源协调调度难题,文章提出了基于虚拟电厂调控平台设计和综合能源协调调度模型。通过分析虚拟电厂的基本网架结构,设计了虚拟电厂总体架构,分为资源层、平台层和应用层,并设计了网络架构和数据流架构。基于该架构建立了面向综合能源协调的虚拟电厂调度应用模型。针对模型内光伏、风力发电、电解器、燃料电池、氢气储能、电池储能,以可靠性指标作为切入点,提出了全生命周期内成本最小的目标函数。通过算例分析,验证了文章所提出的模型在可靠性和成本方面的优势。

海水淡化厂与区域水-能耦合系统的协同调度

海水淡化厂具有能量密集、运行灵活的特点,是理想的电力需求响应资源。然而,现有关于淡化厂与电网协同运行的研究大多关注电网和淡化厂的一体化调度、无法适应双方自主决策的工程场景,对淡化厂运行约束的刻画也比较粗糙。该文研究反渗透淡化厂与区域水-能耦合系统(waterenergy nexus,WEN)的协同运行方法。首先,考虑淡化厂的生产流程、调节特性和运行约束,分别建立传统及新能源淡化厂的水-能管理模型;其次,在保障两类淡化厂和区域WEN自主决策和信息隐私的条件下,提出以双方调度计划互动为载体的“激励型”和“价格型”两种协同运行机制及调度模型,并提出基于二阶锥松弛和McCormick包络的求解方法。用算例验证所提协同运行机制、调度模型及求解方法的有效性。

基于最优经济运行区域的主动配电网日前日内协同调度方法

传统的日前-日内协同调度通常以与日前时序计划曲线偏差最小作为日内目标函数,当日内新能源出力预测值与日前相差较大时,储能装置(energy storage systems,ESS)等由于其时间耦合约束日内调整范围有限,导致经济性和灵活性较差。对此,提出了基于最优经济运行区域(optimal economic operation region,OEOR)的主动配电网(active distribution networks,ADN)日前-日内协同调度方法。在日前阶段,构建线性化ADN调度模型,基于拉丁超立方采样法生成的大量随机场景下调控设备优化曲线,以全时间段内设备出力上下界内所包含的随机场景优化结果数量最大为目标,考虑储能装置荷电状态的相邻时段约束和微型燃气轮机的爬/滑坡率,构建OEOR生成模型。最后,在日内阶段,调控设备在OEOR内滚动优化调整,当该时段优化值贴近OEOR边界时,考虑相邻时段出力约束,将OEOR扩展为最优经济极限运行区域(E-OEOR)。算例结果表明,所提方法相比于传统方法能够更有效地提升配电网经济性。

风电-光伏发电-抽水蓄能联合优化调度方法

为解决电力市场环境下的风电、光伏发电、抽水蓄能协同互补问题,提出了一种同时参与日前市场和实时平衡市场的风电-光伏发电-抽水蓄能联合优化调度方法。该方法考虑风电和光伏发电出力的不确定性,基于生成对抗网络和峰值密度聚类算法生成风电、光伏发电联合出力典型场景;基于抽水蓄能的灵活调节特性,并考虑风电和光伏发电实时出力引起的平衡成本,构建了风电-光伏发电-抽水蓄能联合参与日前市场和实时平衡市场的两阶段随机联合优化调度模型,最大化风电-光伏发电-抽水蓄能联合发电系统的收益。算例仿真结果表明,该方法具有有效性和合理性。

基于麻雀搜索算法的梯级泵站优化调度

针对梯级泵站系统运行效率普遍偏低、能耗损失较大等问题,建立了梯级泵站优化调度模型,引入了寻优能力强、搜索精度高的麻雀搜索算法(SSA),对算法中安全阈值和侦察者占比两参数进行了比选;据此提出了基于SSA算法的梯级泵站优化调度方法,并将其应用于密云水库调蓄工程中的三级泵站优化调度研究。结果表明,三种不同流量工况下,相较于现状方案,PSO及GA算法所得优化方案其系统运行效率可提升0.03%~0.18%,年运行成本可节省¥9,700~¥69,500。利用SSA算法所获优化方案在两项指标改进方面更为突出,可达到0.98%~1.20%的效率提升及¥369,000~¥443,900的年运行费用的节省,验证了SSA算法在梯级泵站优化调度中的可行性和高效性,可为梯级泵站优化调度提供一种合理可靠的方法。

基于可行域投影理论的新能源电力系统协同运行方法

随着新能源逐步成为电力系统的主体,传统电网体系的深层缺陷也日益显露。跨省区资源优化配置困难和分布式能源利用率低等能源协同体系的不足成为制约新型电力系统建设的瓶颈。为此,该文通过基于外割法的可行域投影等值算法刻画区域系统可行域,并通过并行伞约束辨识法剔除模型冗余约束,进而提出一种基于投影等值理论的新能源电力系统协同运行模式,通过可行域的交互实现各级调度体系间的高效协同,并提出了三种应用场景:配电网/虚拟电厂可行域聚合、省间多通道安全域计算和多时段输配电网协同运行。算例结果表明,所提算法和模式能够有效地降低区域电网规模,从而降低区域电网与调度中心的信息交互量,提升新能源电力系统运行优化效率。

考虑分布式电源的配电网灾中-灾后两阶段协同韧性恢复决策

频繁的极端自然灾害极易引发配电网大规模故障,基于分布式电源的负荷恢复是提高配电网韧性水平的重要手段。然而,现有研究一般将灾中和灾后阶段进行独立决策,忽略了二者决策的连续性和耦合性,延缓了恢复进程。为此,文中提出一种基于灾中-灾后两阶段协同决策的配电网快速恢复方法,充分考虑灾中-灾后过程中的相互作用,综合分布式电源调度、网络拓扑改变、维修人员调度等在内的恢复措施,保障灾中-灾后阶段连续决策的最优性。首先,考虑灾后供能路径对灾中网络拓扑的需求关系,建立灾中阶段以分布式电源为核心的拓扑调整模型与负荷元胞恢复模型,实现部分重要负荷的供电;其次,考虑灾中决策对灾后恢复的影响,建立灾后阶段故障维修人员调度和负荷快速恢复的联合决策模型,实现所有负荷的有序恢复;然后,以灾中-灾后全阶段失负荷量最小为目标,实现灾中-灾后恢复的协同决策;最后,基于IEEE 123节点配电系统验证了所提模型的有效性。

人工智能技术在油气综合能源系统中的应用研究综述

人工智能技术在综合能源系统中的应用主要包括能源管理、油气产能预测、负荷和发电量预测、故障检测和容错控制、储能系统智能调度等方面,其能够有效处理系统的随机性和负荷变化,提高系统的预测和调度能力,优化多能源系统的协调调度,提升整体能源利用效率。从人工智能技术的发展趋势看,基于大语言模型的综合智能体正在成为应用的主流模式,但在其应用过程中,仍然面临以下几个方面的挑战:1)多智能体系统中的协调与控制;2)物联网与边缘计算的应用;3)综合能源系统的优化与人机协作;4)绿色人工智能。

计及灵活性资源的综合能源系统源荷协调优化调度

可再生能源和负荷的波动性、不确定性等给综合能源系统(integrated energy system,IES)的安全灵活运行带来了极大挑战。为提高IES灵活调节能力与可再生能源消纳水平,提出一种计及灵活性资源的IES源荷协调优化调度方法。针对系统内运行灵活性需求,精细刻画各类资源灵活性能力,源侧根据电氢耦合单元运行特性构建热电联产机组(combined heating and power,CHP)和氢燃料电池(hydrogen fuel cell,HFC)联合运行模型,荷侧考虑综合需求响应的灵活性供给能力,建立系统综合灵活性供给模型。根据不同时刻运行灵活性不足问题分成2种调度模式,构建基于IES灵活性约束的优化调度模型,并进行仿真分析。仿真结果表明,所提出的优化调度方法能够有效提高IES灵活调节能力和可再生能源消纳水平。

基于混合变量目标分解模型的热电联产光储热力电力系统运行优化

光伏、储能电池、燃煤热电联产机组组成的综合能源系统,其电力热力系统间孤立运营,为分析其热力电力系统间的信息交互与经济协调调度的关系,依据J.F.Benders混合变量目标分解法对热电联产光储热力电力系统的物理模型按照热力主系统和电力子系统进行分解,通过目标函数和系统边界条件分析子目标之间的信息交互情况,得出光电消纳储存与热电联产协调调度运行的数学模型,并对保护信息加以隔离重定向,以Gurobi求解器迭代计算混合整数规划多维变量问题。基于综合能源系统案例分析了电力热力不同负荷需求下的3种运行工况,发现热力电力运营系统间经济效益协同互补,且信息交互扩大了光电的消纳空间,系统净负荷平均降低了13.63%,交互功率损失每年减少了948.07万元,能源利用效率提高了4.48百分点,可见本模型可以服务于综合能源系统的经济协调调度优化和提高能效。

站点可变型需求响应式公交车辆调度优化研究

站点可变型需求响应式公交是一种既有固定服务站点又允许乘客在可变站点预约的公交运营模式.传统研究聚焦于单个车次的行程规划,而忽视车辆在整个运营周期执行多个车次的接续性.本研究从系统视角建立单个运营周期内车辆发车时刻表编制和车辆调度协同优化模型,以总成本最小化为目标,构建时空网络图,清晰表达站点可变型需求响应式公交的服务逻辑和复杂的时空约束.同时根据问题特征,整合8类邻域算子和改进的最大网络流算法,设计可快速求解现实大规模复杂问题的自适应变邻域搜索算法.以中国广州市黄埔区部分路网为例进行数值实验,结果表明,相比传统两阶段车辆调度模型,本模型车辆使用率提高8.7%以上,总成本下降20%~70%;相比传统算法,在绝大多数订单规模下自适应变邻域搜索算法求解质量更优,在预约订单规模为60时,可节省总成本30%以上.