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.

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.

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.

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.

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.

A Projection and Decomposition Approach for Multi-agent Coordinated Scheduling in Power Systems

Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty,a generic projection and decomposition(P&D)approach is proposed in this letter.The canonical min-max-min two-stage robust optimization(TSRO)model with coupling constraints is equivalent to a concise robust optimization(RO)model in the version of mixed-integer linear programming(MILP)via feasible region projection.The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm,which ensures the fast convergence to a high-quality solution in the distributed optimization.Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

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.

基于多智能体与改进目标级联法的输配协同优化调度

输配协同优化调度问题呈现多时间尺度耦合、非凸非线性特征,其本质上是非确定性多项式难问题。随着系统可调节资源的增加,控制变量呈指数增长,“维数灾”现象愈加明显,仅基于物理模型的求解策略难以兼顾计算精度与求解效率。为此,提出一种基于改进多智能体双延迟深度确定性策略梯度(IMATD3)和Peaceman-Rachford(PR)目标级联(ATC)法的输配协同多时间尺度优化调度方法。根据PR分裂方法改进ATC法中主-子系统的信息迭代策略,提出基于PR-ATC的输配协同日前调度方法;通过多智能体与输配网络的离线交互提取连续调度策略的时序特征,并基于物理模型并行计算各系统调度问题,形成基于深度强化学习的离线训练和基于物理模型的在线应用联合调度决策方法。以T6-D7-D7和IEEE 118-D9-D33-D69输配网络为例,验证了所提方法的有效性。

基于时间规律聚合电动汽车的有源配电网源荷储协同调控

针对可再生能源间歇性、随机性对有源配电网调控带来的挑战,通过对电动汽车(EV)充电时间规律分析,提出了一种基于时间规律聚合电动汽车的有源配电网协同调控方法。首先提出基于充电时间规律的EV聚合分组方法,再据此建立有源配电网源荷储协同优化调控模型,最后将调控指令精准分解下达,实现新能源消纳、负荷波动平抑及网损降低。基于改进IEEE 33节点系统的仿真验证了所提策略的有效性,其能显著改善系统运行指标,有效降低EV充电计划偏差值,同时提升模型的求解速度,更具有现实指导意义。

Coordinated Scheduling of Energy Resources for Distributed DHCs in an Integrated Energy Grid

As more and more distributed renewable energy resources connected to electric power grids,the conventional power system evolves into an integrated energy grid(IEG)in order to satisfy various types of energy demands.This paper proposes a model of the coordinated scheduling of energy resources(CSoERs)for distributed district heating and cooling systems(DHCs)in an IEG.The model takes into consideration both the dispatchable grid-connected generators and distributed renewable energy resources,such as wind energy,solar energy and natural gas.The objective is to minimize the operation costs in the IEG in order to satisfy not only the electrical loads but also the heating loads and cooling loads.Furthermore,an energy storage system for heating loads and cooling loads is also developed in the DHCs to improve the operation reliability in the distributed DHCs.Detailed simulation studies are carried out to verify the effectiveness of the CSoERs under two different operation scenarios:grid-connected scenario and stand-alone scenario.Simulation results demonstrate that the performance of the CSoERs contributes to significant energy saving and reliability operation in both grid-connected scenario and standalone scenario in the IEG.

Coordinated scheduling model of power system with active distribution networks based on multi-agent system

With the large-scale development of distributed generations(DGs) and the connection into the main grid of active distribution networks(ADNs), traditional centralized dispatch of power system has encountered enormous challenge. In a bilateral electricity market, introducing ADN resources in the day-ahead generation schedule will not only enrich the dispatch patterns to the power system,but also reflect the initiative of ADNs. This paper proposes a coordinated scheduling model of power system with a plurality of ADNs based on multi-agent system where ADN agents are brought in the day-ahead market clearing.The process of market clearing and the dispatch of DGs in ADNs are independent with each other but linked together through the market clearing price(MCP) and bid volume.The optimal operating point of the whole system is achieved through multiple information exchange. In comparison with the dispatch without interaction between ADNs and the market operator(MO), the coordinated scheduling model is applied in a system with four ADNs to verify that the proposed method can improve the overall interests of ADNs. Finally, the effects of storage device and tie-line power limit are analyzed.

Cooperative Game Theory Based Coordinated Scheduling of Two-Machine Flow-Shop and Transportation

A cooperative game theoretical approach is taken to production and transportation coordinated scheduling problems of two-machine flow-shop(TFS-PTCS problems)with an interstage transporter.The authors assume that there is an initial scheduling order for processing jobs on the machines.The cooperative sequencing game models associated with TFS-PTCS problems are established with jobs as players and the maximal cost savings of a coalition as its value.The properties of cooperative games under two different types of admissible rearrangements are analysed.For TFS-PTCS problems with identical processing time,it is proved that,the corresponding games areσ_(0)-component additive and convex under one admissible rearrangement.The Shapley value gives a core allocation,and is provided in a computable form.Under the other admissible rearrangement,the games neither need to beσ_(0)-component additive nor convex,and an allocation rule of modified Shapley value is designed.The properties of the cooperative games are analysed by a counterexample for general problems.

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。

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。

含抽水蓄能电站的高比例新能源发电系统多时间尺度调度模型

针对高比例新能源接入电网的消纳问题,提出一种含抽水蓄能电站的高比例新能源发电系统多时间尺度协调调度模型.依据风电、光伏、负荷功率预测精度随时间尺度缩短逐级提高的特点,以系统总运行成本最小为目标构建日前24 h、日内1 h、实时15 min三阶段协调调度模型.通过多时间尺度的协调配合,逐级修正发电计划,实现抽蓄、风电、光伏、火电出力良好跟踪负荷.基于CPLEX商业优化软件,利用混合整数线性规划法对含6台抽蓄机组的风光火抽蓄互补发电系统展开仿真分析.结果表明:抽水蓄能机组可发挥其负荷响应时间短、功率调节速度快的优势,减轻火电机组调节负担,有效缓解火电机组调节压力.通过多时间尺度的协调配合,可减少弃风弃光,提高新能源消纳水平.

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。