Method of Evaluating Social Benefit of Energy-Conservation Based Generation Dispatching in Guangdong Province

Energy-conservation based generation dispatching is the revolutionary change in operation mode which could reduce energy consumption and pollutant emissions, promote power industry restructuring, and achieve sustainable development. Social benefit evaluation of the energy-conservation based generation dispatching under the new situation and environment has come into being as an important theoretical issue. A new scenario analysis based social benefit evluating method, which is implemented by comparing the energy., consumption and pollutant emissions of different scenarios defined by the key indicators, i.e., load factor and coal consumption structure, is proposed in the paper. Then the composition of social benefit is analyzed from the point of the dispatching mode and the coal consumption structure. The method proposed is of clear physical meaning. It is not only practical, but also applicable for the implementation of energy-conservation based generation dispatching in diffferent phases and with different goals.

Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm

In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.

Modeling and analysis of software rejuvenation ondispatcher-based web server farms

Software rejuvenation is a recently proposed practive fault-tolerance approach to counteract software-aging phenomenon. Compared with clusters of a flat architecture, all the nodes share the same functions. The application of software rejuvenation on dispatcher-based web server farms is discussed, which employ rejuvenation both on the dispatcher and the worker pool. Stochastic reward net (SRN)models for time-based and prediction-based rejuvenation policies are constructed respectively and solved by stochastic Petri net package (SPNP). Numerical results show that appropriate rejuvenation strategies on the dispatcher and the worker pool could significantly reduce the expected downtime cost of the whole web server farm.

Multimedia Technology for Monitoring Dispatching and Production Commanding System

This paper describes the multimedia technology applied to the safety monitoring, management dispatching and production commanding system in the coal mine, analyzes and studies the methods of compression hased on model data. In the system a multimedia computer is used to control a subsystem of safety monitoring, a subsystem of attendance management. and a subsystem of industrial TV. The multimedia computer will process the information of graphs. texts, sounds, pictures and images etc. It has the functions of real-time monitoring, emergency rescuing and accident analyzing.

Bi-objective Optimization of Real-time AGC Dispatch in Performance-based Frequency Regulation Market

This paper illustrates a new bi-objective optimization model of real-time automatic generation control dispatch in a performance-based frequency regulation market.It attempts to simultaneously minimize the total power deviation and the regulation mileage payment via optimally distributing the realtime total generation command to different regulation units.To handle this problem,an efficient non-dominated sorting genetic algorithm II is adopted to rapidly obtain a high-quality Pareto front for real-time AGC dispatch.A dynamic ideal point based decision making technique is designed to select the best compromise solution from the obtained Pareto front according to the minimization of the total regulation variation,which can effectively avoid an excessive regulation variation for each unit.Finally,two testing systems are used to verify the performance of the proposed technique.

循证训练理念下签派员技能全生命周期全动模拟机融合训练研究

为了培养高素质签派员队伍,借鉴飞行员技能全生命周期管理(PLM)体系建设相关理论,结合试点开展的“飞行员-签派员”融合训练,梳理其实施的可行性和必要性,探索签派员整个职业生涯岗位胜任力提升的实施路径。基于循证训练(EBT)理念总结“飞行员-签派员”融合训练的保障机制,为优化签派员训练模式提供参考。

卡片导航平衡控制系统性能仿真研究

为了减少装配作业车间内因物料齐套产生的等待浪费,使具有装配约束的关联零件加工进度得到有效协同,设计一类卡片导航平衡控制系统(control of balance by card-based navigation,COBACABANA)。其基于两类卡片循环回路实现任务投放与作业分派的可视化进度协同控制逻辑。本文详细介绍系统的运行机制和系统控制参量,通过构建一般化的装配作业车间仿真模型,探讨在不同装配关联度下各控制参量的性能变化。实验结果表明,COBACABANA系统性能良好,并且选择合适的控制参量就能够有效提升关联零件的进度协同性。

基于改进堆优化算法求解电动汽车并网动态经济调度

针对堆优化算法HBO处理含电动汽车的动态经济调度问题时存在收敛慢和精度低等问题,提出一种改进的堆优化算法RDHBO。首先,在RDHBO中引入了最优成员区域搜索和双种群交互策略。前者引导最优成员移动到更有希望的区域,提升了算法的收敛精度和收敛速度;后者充分利用被淘汰的劣势个体,丰富了种群的多样性,避免了算法陷入局部最优。然后,将RDHBO应用于四种充电场景的10机组电动汽车动态经济调度问题。仿真结果表明,与已有的代表性方法相比,RDHBO在产生低燃料成本和稳定性方面具有很强的竞争力。最后,对RDHBO的两种改进策略进行了消融实验,验证了两种改进策略的有效性。

EBT理念下航空公司“签派资源管理”模块训练场景开发

为提高飞行签派员非技术胜任力,以“签派资源管理”模块训练场景开发为研究对象,发现传统的“签派资源管理”模块存在不贴合运行实际、提升胜任力针对性不足、训练场景更新不及时等问题,依据循证训练(EBT)理念,从训练数据驱动出发,以“成都/双流—北京/首都”临时航线为例,从签派放行、运行控制和飞行监控3个环节开发“签派资源管理”模块训练场景。建议从深入一线调研、融入行业热点和持续优化教案3个方面推进“签派资源管理”训练改革。

CBTA理念下飞行签派员培养课程体系建设及培训效果测评研究

为提高飞行签派员的专业技能,通过设计科学的培训方式解决原有飞行签派员人才培养模式与民航高质量发展契合不足的问题,建立以岗位胜任力量化管理为特征的飞行签派员技能培训体系和培训效果测评体系。研究过程基于能力的训练和评估(CBTA)理念下飞行签派员特色化、精细化的人才培养、课程实施和培训效果评价模式。首先,建立飞行签派员岗位知识地图,覆盖飞行签派员履职应具备的知识、技术和能力,构建具有一定前瞻性的岗位胜任力体系。其次,结合飞行签派员工作应具备的八种胜任力分解出35种飞行运行岗位任务目标,强调对工作绩效衡量和评估。再次,根据不同阶段的培训授课需求,以布鲁姆教学分类法挖掘培训需求,并以培训目标为导向,以学员为中心,绘制出CBTA理念下实施飞行签派员培训课程的流程图。最后,为实现培训贴近生产运行实际,增强培训的效果,采用柯氏四级评估模型梳理各层次评估的内容和方法,从精准确定培训目标等三个方面提出培训建议。

数字孪生技术助力南四湖防洪调度与二级坝工程管理

数字孪生建设是“十四五”期间推进智慧水利建设的重要内容之一,为提高南四湖流域防洪调度水平和二级坝工程运行管理能力,采用南四湖防洪调度、二级坝泄量纠偏及调度仿真等关键技术,建立和完善二级坝感知体系,提升算力能力建设;建设数据底板,加强算据能力建设;研发多种模型,智能展示数据成果;开发具有“四预”功能的防洪调度和水闸综合管理业务应用等系统。系统的建成可全面提高南四湖防洪调度及二级坝工程管理安全运行管理水平,为后续数字孪生沂沭泗建设提供经验样本和技术模板。

储能提升高比例新能源基地低碳-经济-可靠性能的效益分析

储能具备灵活的功率调节与电能时移能力,其与系统多类型调控资源的协调优化有望为新型电力系统低碳、经济、可靠运行提供有力支撑。针对储能提升高比例新能源基地低碳、经济、可靠性能的效益进行研究。首先,建立了高比例新能源基地发电碳排放计量模型,提出高比例新能源基地低碳、经济、可靠性能量化评估指标;其次,建立了储能与高比例新能源基地的联合运行模拟模型,为含储能高比例新能源基地的优化运行提供决策依据;最后,针对蒙西区域电网开展全年时间尺度精细化运行模拟,分析不同储能容量配比下对系统低碳、经济、可靠运行性能的提升效益。算例结果验证了所提运行模拟模型的有效性,并指出随储能容量提升其对系统低碳、低碳、经济及可靠性能提升的边际效益逐步下降,优化制定储能配置容量有利于最大化其投资运行效益。

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements.These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints,such as the valve point effect,power balance and ramprate limits.The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times.In this paper,multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model.Self-learning teaching-learning based optimization(TLBO)is employed to solve the non-convex non-linear dispatch problems.Numerical results onwell-known benchmark functions,as well as test systems with different scales of generation units show the significance of the new scheduling method.

基于自动驾驶车辆调度的停车系统收费策略优化

针对有人驾驶车辆与自动驾驶车辆共存的停车系统,设计基于自动驾驶车辆调度的停车系统收费策略,以提升停车系统运行效率.该策略在停车场无可用停车泊位但存在自动驾驶车辆时向有人驾驶车辆提供自动驾驶车辆调度服务,停车系统向有人驾驶车辆收取一定调度费用后,通过在多个停车场间调度若干自动驾驶车辆,为有人驾驶车辆在其目标停车场创造可用停车泊位.各停车场的调度费用会影响有人驾驶车辆用户的停车选择,进而影响停车系统的运行效率,构建基于智能体的停车仿真模型,采用遗传算法差异化地设置系统内各个停车场的停车调度收费方案.仿真结果显示,采用基于自动驾驶车辆调度的差异化收费策略,可以有效地减少有人驾驶车辆用户的行驶时间、步行时间、总出行时间及行驶里程,增加停车系统营收,降低社会成本,有效地缓解停车矛盾.

多能互补能源基地开发模式及综合效益提升方法

近年来,建设清洁低碳安全高效的能源体系,发展可再生能源替代,构建以新能源为主体的新型电力系统成为我国能源发展的必然趋势。在风光资源富集地区,随着新能源装机不断增加,大型综合能源基地得到快速发展。该文基于主客观赋权法建立多能互补综合能源基地评估体系,对我国“三北”、西南及东部沿海区域发展布局多能互补基地进行评估。为进一步提升多能互补基地经济效益,建立基于长短期记忆神经网络(long short term memory,LSTM)的电价预测模型及多能互补日前优化调度模型,利用粒子群优化算法进行寻优,以实现能源基地综合收益最大化的日前优化调度目标。最后,以甘肃陇东千万kW级多能互补综合能源基地为例,分别开展夏季及冬季典型日的优化调度算例仿真,结果表明,该优化调度方法能够促进基地内新能源消纳的同时最大化能源基地综合收益,为大型综合能源基地的日前优化调度提供技术支撑。

面向配电网削峰填谷的5G基站储能调控方法

针对现有5G基站储能参与配电网调控所面临的集中调控方法计算复杂度高、难以适配配电网负荷波动、无法感知基站储能电池能量与电池损耗等挑战,提出5G基站储能分布式自主调控架构,支撑5G分布式储能资源灵活接入配电网。在此基础上,提出一种基于电池状态感知上置信区间的5G基站储能调控算法,综合考虑基站储能电池容量约束与电池损耗成本,最大化储能调控效益和配电网负荷曲线方差的加权差,实现电池能量与电池损耗感知,辅助配电网削峰填谷。仿真结果表明:相较于现有2种对比算法,所提方法可以使基站储能调控效益与配电网负荷曲线方差的加权差分别提高12.73%和22.33%。研究成果能够很好地满足5G基站储能参与配电网削峰填谷的需求,实现配电网与5G基站储能双向互动。

水风光综合基地多风险量化及长期多目标协调优化调度方法

水风光一体化运行可以发挥多种能源优势,提高综合效益。风光能源大规模并入水电基地,因不确定性增加致使系统能源消纳和电力供应问题更加突出,将面临更高弃水和缺电风险。针对该问题,提出了一种评估各时段蓄水状态的风险量化方法。基于该方法构建了考虑弃水量、发电量、弃水风险和缺电风险的多目标调度模型,基于双层优化,采用动态规划、离散微分动态规划和逐步优化相结合的算法进行高效求解,将分层序列法与线性加权和法嵌入算法中,以协调多目标矛盾。最后,以中国某水风光综合基地为工程背景模拟调度,并将所提模型与传统模型进行对比验证。研究结果表明,所提模型能较好地刻画运行风险,通过调整水电运行方式能合理消纳新能源,并获得更高稳定收益;模型能以较高效率完成求解,并满足实际调度时效性要求;在实际场景与预测场景出现偏差时,能显著降低弃水损失,通过改变水电运行方式以适应新能源日益增长带来的运行风险。

基于灰狼优化的改进粒子群算法求解环境经济调度问题

【目的】为有效地求解电力系统环境经济调度问题,本文以同时优化燃料成本和污染排放量为研究目标,提出了一种基于灰狼优化(GWO)的改进粒子群优化(PSO)算法。【方法】首先对初始粒子群采用折射反向学习产生反向解,以此提高种群的多样性;在算法迭代过程中,结合GWO算法,引导粒子群中的优秀个体进行寻优搜索,提高PSO算法的寻优能力和收敛精度;在算法迭代后期,针对粒子群易陷入局部最优解的缺点,使用Tent混沌映射对最优粒子进行扰动,根据适应度值更新粒子群个体最优值、全局最优值的位置。【结果】将改进算法应用于不同负荷需求的6单元发电机组和40单元发电机组,对比本文算法与PSO算法和GWO算法求解电力系统的收敛曲线。结果表明,本文所提改进算法能更快收敛到最优值,且最后的燃料成本最低。【结论】本文所提改进算法能更好地解决复杂约束优化问题,在寻优精度和稳定性方面表现较好。

基于知识库和场景推理的乌江智能化防洪调度

围绕贵州省内乌江流域梯级水库防洪调度问题,开展了基于知识库和快速场景推理的智能防洪优化调度研究。基于海量的防洪调度方案,对调度经验知识进行组织与重构,按照“单元-网络-图谱”的总体思路,构建流域梯级水库群防洪调度知识库。在此基础上,根据流域防洪方案特征确定用于场景匹配的数值类、要素类及模糊类等特征信息,通过计算防洪调度方案各特征信息的权重,基于快速场景匹配与推理技术,优选与目标案例匹配度最高的典型案例,实现了流域智能化防洪调度。以乌江梯级水库群为实例进行的应用分析表明:典型洪水场景下削峰率达47%,模型求解耗时5.3 s,而采用传统动态规划模型的求解耗时为21.1 s,求解效率大幅提高。所提方法解决了传统调度中实时方案计算的时效性问题,且所得方案蕴含调度决策经验,调度过程对实际洪水的变化适应性更强。

绿证与改进型阶梯式碳交易互动机制下的CHP系统低碳经济调度研究

为了进一步降低热电联产型源荷系统的碳排放量,同时兼顾经济效益,文章改进了普通阶梯式碳交易的交易模型,提出了一种绿证与改进型阶梯式碳交易互动机制下的CHP型源荷系统低碳经济调度策略。构建热电联供型能源系统模型,确定能源测、转换侧、负荷侧模型,同时构建需求响应机制;在数学模型的基础上构建双层调度模型,上层模型是改进型阶梯式碳交易优化模型,利用差分进化算法来实现阶梯式碳交易参数自适应变化;下层源荷系统调度仿真模型,以系统运行成本为目标函数,耦合了碳交易机制和绿证策略,实现直接、间接同时减排,同时利用Cplex求解器进行最优化求解。最后,建立四种典型场景,对比分析得出所提策略能显著降低碳排放量,同时具有较好的经济效益,能够为源荷系统低碳经济调度提供参考。