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.

Task scheduling for multi-electro-magnetic detection satellite with a combined algorithm

Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver GECODE, which is an open source software. These tests and comparisons yield promising effect.

A Novel Solution Based on Differential Evolution for Short-Term Combined Economic Emission Hydrothermal Scheduling

This paper presents a novel approach based on differential evolution for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: 1) minimizing fuel cost and 2) minimizing emission cost. A penalty factor approach is employed to convert the bi-objective problem into a single objective one. In the proposed approach, heuristic rules are proposed to handle water dynamic balance constraints and heuristic strategies based on priority list are employed to repair active power balance constraints violations. A feasibility-based selection technique is also devised to handle the reservoir storage volumes constraints. The feasibility and effectiveness of the proposed approach are demonstrated and the test results are compared with those of other methods reported in the literature. Numerical experiments show that the proposed method can obtain better-quality solutions with higher precision than any other optimization methods. Hence, the proposed method can well be extended for solving the large-scale hydrothermal sched-uling.

Fast combination of scheduling chains under resource and time constraints

Scheduling chain combination is the core of chain-based scheduling algorithms, the speed of which determines the overall performance of corresponding scheduling algorithm. However, backtracking is used in general combination algorithms to traverse the whole search space which may introduce redundant operations, so performance of the combination algorithm is generally poor. A fast scheduling chain combination algorithm which avoids redundant operations by skipping “incompatible” steps of scheduling chains and using a stack to remember the scheduling state is presented in this paper to overcome the problem. Experimental results showed that it can improve the performance of scheduling algorithms by up to 15 times. By further omitting unnecessary operations, a fast algorithm of minimum combination length prediction is developed, which can improve the speed by up to 10 times.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System ContainingWind-Storage Combined Unit

To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.

Neighborhood Combination Search for Single-Machine Scheduling with Sequence-Dependent Setup Time

In a local search algorithm,one of its most important features is the definition of its neighborhood which is crucial to the algorithm's performance.In this paper,we present an analysis of neighborhood combination search for solv-ing the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness(SMSWT).First,We propose a new neighborhood structure named Block Swap(B1)which can be con-sidered as an extension of the previously widely used Block Move(B2)neighborhood,and a fast incremental evaluation technique to enhance its evaluation efficiency.Second,based on the Block Swap and Block Move neighborhoods,we present two kinds of neighborhood structures:neighborhood union(denoted by B1UB2)and token-ring search(denoted by B1→B2),both of which are combinations of B1 and B2.Third,we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms:the Iterated Local Search Algorithm(ILSnew)and the Hybrid Evolutionary Algorithm(HEA_(new))to investigate the performance of the neighborhood union and token-ring search.Exten-sive experiments show the competitiveness of the token-ring search combination mechanism of the two neighborhoods.Tested on the 120 public benchmark instances,our HEA_(new)has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent metaheuristics.We have also tested the HEA,new algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup time.HEAnew is able to match the optimal or the best known results for all the 64 instances.In particular,the computational time for reaching the best well-known results for five chal-lenging instances is reduced by at least 61.25%.

Antiangiogenic agents combined with chemotherapy in non-small cell lung cancer

As a targeted therapy, antiangiogenic treatment has been increasingly studied for advanced non-small cell lung cancer(NSCLC) and has proven effective for the treatment of advanced NSCLC. Bevacizumab, a monoclonal antibody targeting angiogenesis, is the only antiangiogenic agent approved for use in combination with first-line chemotherapy for non-squamous NSCLC. Small-molecule inhibitors targeting the tyrosine kinase receptor have also shown promise when combined with standard chemotherapeutic agents in patients with advanced NSCLC. However, unlike bevacizumab, not all other antiangiogenic agents show significant benefits when combined with chemotherapy. As for the failures of most other combinations, the combination schedule may be an important reason that has so far been overlooked in clinical trials. This article reviews the combination of angiogenic agents with chemotherapy in the treatment of NSCLC.

基于最优样本和最优属性组合的作业车间调度规则挖掘

作业车间调度问题可使用调度规则解决。为挖掘到高效、准确的调度规则,基于训练样本最优和属性组合最优的核心思想,提出一种基于最优样本与最优属性组合的决策树-遗传算法框架(NDTGA)。该框架在构造训练数据时采用成对比较的方式,在构造属性组合时使用属性原值、差值、对比值等多种组合;在遗传算法的每次寻优过程中,调用决策树挖掘全新的调度规则;最终得到最优训练样本和最优属性组合,进而得到最优的调度规则。通过与经典调度规则和其他机器学习算法的对比实验论证了NDTGA框架挖掘所得调度规则的优越性。

考虑风-光不确定性的风-光-蓄-火联合调度研究

在当前风电和光伏大规模并网的背景下,要确保能源供应的稳定性和高效性,关键任务是确定合适的可再生能源渗透率,并精确配比可再生能源与储能系统的容量。因此,在考虑风、光不确定性的基础上,构建风-光-蓄-火优化调度模型,使用拉丁超立方抽样法模拟模型中风电和光伏出力场景,再基于Kantorovich距离的同步回代消除算法对生成的风、光出力场景进行缩减,并通过改进的粒子群算法对模型进行求解。以白莲河抽水蓄能电站为例,结合当地的实测风速、光照数据,探究有无抽蓄参与以及不同能源容量配比下的调度结果,得到以下结论:①抽蓄电站的加入使得联合系统收益增加了428.06万元,风、光出力的波动性降低了12.24%,可再生能源的弃电率下降了5.62%,污染物排放也得到了减少。②抽蓄与可再生能源装机容量比例在1∶8.33范围内配置较为合理,在1∶5.21时可再生能源弃电率达到最低。③当可再生能源渗透率为71.09%,抽蓄与可再生能源装机容量比例为1∶6.25时,风-光-蓄-火联合运行边际效益最大。

阶梯碳下考虑耦合电转气-碳捕集和热电联产灵活输出的综合能源系统优化调度

“双碳”目标下,为进一步降低综合能源系统(integrated energy system,IES)碳排放,提升可再生能源消纳能力,提出一种IES低碳经济运行优化策略。首先引入阶梯型碳交易机制约束IES的碳排放;然后建立耦合电转气(power to gas,P2G)和碳捕集系统(carbon capture system,CCS)模型,并细化P2G两阶段运行;接着在传统热电联产机组(combined heat and power,CHP)中引入卡琳娜循环与电锅炉联合运行,构造热电灵活输出的CHP模型;最后以系统运维成本、碳交易成本、购能成本和弃风弃光成本之和最小为优化目标,构建IES低碳经济调度模型,并设置不同运行场景对比分析。结果表明:IES碳排放减少38.45%,运行总成本降低10.37%,验证了所建模型的低碳性和经济性。

地铁运营中断下应急接驳公交调度方案研究

【目的】针对传统应急调度模式单一、疏运效率低的特征,研究了多种调度模式组合应急公交调度方法。【方法】考虑应急停车场派车能力、车辆运力、车辆救援时间特性,构建以应急公交车辆疏运成本最小和乘客平均延误最小为目标的多目标组合调度优化模型。根据该模型的特点提出使用快速非支配排序遗传算法(NSGA-Ⅱ)求解,为提高种群的多样性和算法的性能对该算法进行了相应的改进,最终得到Pareto分布优化解集,并采用隶属函数从中选取出最优折衷解。最后,以南昌轨道交通1号线作为算例,对应急公交组合调度方案和单一调度方案分别进行求解。【结果】结果表明:文章提出的组合调度方案相比传统单一调度方案的乘客平均延误时间降低了20.48%,疏运成本降低了16.96%,改进后的NSGA-Ⅱ算法相比较标准的NSGA-Ⅱ算法进行求解时,乘客平均延误时间降低了6.72%,疏运成本降低了3.59%。【结论】通过灵敏度分析发现:在确定滞留乘客疏运需求的情况下,滞留乘客的平均延误时间和车队规模呈负相关,应急公交的疏运成本和车队规模成正相关。

考虑电力-信息-交通网络耦合的配电网韧性提升策略分析

韧性是评估系统在极端灾害下吸收抵御扰动并恢复至正常状态的能力指标。基于电力-信息-交通网络之间紧密耦合的特性,提出考虑多网络耦合特性的多阶段配电网韧性提升策略。首先,在灾前预防阶段提出考虑网络抗毁性与电力信息风险价值的最优线路加固策略,并对应急中心进行优化布点。其次,基于网络损毁导致的拓扑变化对分布式电源与联络开关进行动态联合调控,最小化灾害抵御阶段的负荷损失价值。然后,结合耦合网络间的关联制约属性,提出灾后阶段多网协同恢复策略。最后,建立组合式评估框架对系统韧性进行多时段精细化分析。基于IEEE33节点系统搭建三网耦合系统算例进行仿真,所得结果表明所提策略可有效提高灾害下系统的抵御力并提升恢复效率。

基于动态订单合并的滚动配取路径多目标优化

外卖配取过程中实时订单的不断插入具有强烈的不确定性,需持续进行滚动优化以动态更新配取路径。动态条件下,有效地合并取餐与配送作业(dynamic order combination,DOC)可显著减少冗余路径。本文将动态配取路径规划问题转化为变长开放链滚动优化问题,并构建多目标滚动配取路径规划模型对DOC与节点排序进行集成决策。考虑滚动优化框架下紧前决策对紧后决策的调度影响,模型在兼顾配取效率和客户满意度的同时,考虑了基于look-forward的滚动调度后效性。针对该模型,本文基于NSGA-Ⅲ框架开发了多目标元启发式算法进行求解,并设计了基于插入限制规则的元胞数组解编码和混合PMX&SBX交叉方式以适应模型的复杂可行域结构。通过一系列的仿真实验,本文验证了所提出的模型和算法的有效性与优越性。

A nested partitioning-based solution method for seru scheduling problem with resource allocation

This paper investigates the production scheduling problems of allocating resources and sequencing jobs in the seru production system(SPS).As a new-type manufacturing mode arising from Japanese production practices,seru production can achieve efficiency,flexibility,and responsiveness simultaneously.The production environment in which a set of jobs must be scheduled over a set of serus according to due date and different execution modes is considered,and a combination optimization model is provided.Motivated by the problem complexity and the characteristics of the proposed seru scheduling model,a nested partitioning method(NPM)is designed as the solution approach.Finally,computational studies are conducted,and the practicability of the proposed seru scheduling model is proven.Moreover,the efficiency of the nested partitioning solution method is demonstrated by the computational results obtained from different scenarios,and the good scalability of the proposed approach is proven via comparative analysis.

基于合并分配的云工作流低能耗调度方法

现有云工作流调度方法往往少有在降低执行能耗和缩短完成时间之间取得有效平衡。为此,提出基于合并分配的工作流低能耗调度方法。在考虑工作流结构的基础上将若干相关任务合并为可统一分配至同一服务器的任务串,将各任务串优化调度至尽可能少的服务器,求取各服务器可用时间槽并基于DVFS技术对任务进行松弛。相关实例和仿真实验验证了该调度方法的可行性和有效性,针对经典科学工作流和随机生成工作流两类输入,对比数据表明该方法可以在缩短工作流完成时间的同时有效降低执行能耗。

多市场参与下热电联产虚拟电厂低碳经济协同调度方法

热电联产(combined heat and power,CHP)机组与虚拟电厂(virtual power plant,VPP)结合,可以有效提高能源利用效率,增强电力系统运行的可靠性及稳定性。为保证CHP-VPP灵活、低碳、经济运行,文中提出一种聚合风电、光伏、CHP机组、锅炉、碳捕集设备、燃气轮机、燃料电池、储能及电、热负荷的综合能源VPP,并在参与电-热-旋转备用-碳等多市场下,研究其低碳经济协同调度问题。首先,以各时刻VPP在多市场下整体净收益最大为目标,建立其CHP-VPP两阶段鲁棒优化调度模型;然后,考虑新能源出力、市场价格及负荷的不确定性,利用蒙特卡洛法进行场景削减,从而降低系统风险,增强其鲁棒性;最后,采用列与约束生成算法对模型进行求解,得到最恶劣场景下系统运行的经济性最优调度方案。仿真结果表明:所提综合能源VPP结构合理,可通过动态调整碳捕集设备及储能电池,达到平抑新能源出力波动的效果,从而实现碳排放的大幅降低;所提调度策略可有效保证源-荷-储多侧电、热资源的协同优化运行,提高VPP的灵活性、经济性和低碳性。

电力市场环境下生物质气化耦合燃煤机组与风电场联合自调度优化策略

利用生物质气化耦合燃煤发电技术可以提升火电机组的燃料及运行灵活性,从而促进风电的高水平消纳,同时实现生物质能的有效开发利用。该文首先建立反映生物质气化耦合热电联产机组内部能流过程和外部出力特性的运行模型。然后,从能源供应商视角出发,给出耦合机组与风电场联合参与日前、日内和实时平衡市场的多阶段决策框架,提出反映联合体期望收益和条件风险价值的多目标自调度策略。最后,利用基于字典优化框架的增强ε约束法,给出构建帕累托前沿的多目标优化求解流程。以一台350 MW燃煤热电联产机组和一座600 MW风电场构成的联合体为例进行分析。结果可知,增设生物质气化子系统和联合运行能够使其经济效益提升6%左右,同时有利于降低能源供应商的运营风险。

高校二级排课研究与实践

针对教师和班级的不同要求,分别设计两种排课方法,并提出局部调整的措施,最后进行实例验证。研究工作将为二级学院排课提供有益的借鉴。

考虑设备转换成本的MRI检查预约调度优化

核磁共振成像(MRI)检查是现代医学影像诊断的重要手段之一。MRI设备在扫描不同部位时会产生转换成本,为同时提高设备利用率和患者满意度,建立了马尔可夫决策过程模型,以最小化医院的长期成本。该模型是首个对MRI设备转换成本和患者日间等候成本进行权衡的优化模型。通过策略迭代算法可得到该模型的最优调度。为降低计算复杂度,设计了基于单日规则、开放获取规则和短视规则的多项式时间算法。除此之外,通过忽略容量约束,提出了分解算法,在MRI设备检查能力紧张或充足时,其表现与最优解接近。通过对比上海某大型综合医院的实际排程,验证了上述算法能够在检查能力、患者拒绝比例、患者平均等待天数、日平均检查类型数等指标上取得显著改善。

集成电动汽车的家庭电热综合能源系统负荷调度优化

随着“碳中和”“碳达峰”等政策的提出,新能源和异质能源的调度优化成为减少碳排放的主要措施。而家庭综合能源系统在能源需求侧占比较大,如何构建合理的家庭能源系统以及实现家庭负荷优化的能量管理成为亟待解决的问题。为了实现家庭电热综合能源系统中负荷的灵活调度,充分考虑电动汽车及异质能源间设备的出力影响,提出了一种集成电转热设备热泵和电动汽车的家用燃料电池热电联产(domestic fuel cell-based combined heat and power,DFCCHP)系统综合优化调度方案。首先,根据家庭用电、用热特征将电、热负荷进行细化分类,引入热舒适度评价指标(predicted mean vote,PMV)进行室内温度的控制,建立负荷模型;其次,引入热泵和电动汽车,建立在分时电价和分时气价下以能源购买费用最小为目标的家庭综合能源系统优化调度模型,并使用Cplex求解器对模型求解;最后,通过仿真验证调度模型的合理性、可行性和环保性,以及热泵和电动汽车对系统经济性的影响。结果表明,在不同天气条件下,热泵和电动汽车的引入可有效减少系统的购能成本与碳排放,所得结论为进一步完善家庭综合能源系统拓扑及负荷优化调度提供了一定的理论分析基础。