With the large-scale integration of renewable energy sources into the grid,distribution networks are increasingly challenged by issues related to renewable energy accommodation and the mainte-nance of power quality st...With the large-scale integration of renewable energy sources into the grid,distribution networks are increasingly challenged by issues related to renewable energy accommodation and the mainte-nance of power quality stability.To address the challenge that existing partitioning methods are inad-equate for the planning and operation needs of active distribution networks under frequently changing power flow conditions,a three-stage dynamic partitioning approach is proposed based on an im-proved sand cat swarm optimization(ISCSO)algorithm.Firstly,a comprehensive dynamic partitio-ning index is developed by integrating both structural and functional metrics,including modularity,voltage regulation capability,and regional renewable energy accommodation capacity.Secondly,to overcome the limitations of the conventional sand cat swarm optimization,namely its weak global ex-ploration ability and tendency to fall into local optima in the later optimization stages,chaotic map-ping is employed to initialize a uniformly distributed population.A nonlinear sensitivity mechanism is introduced to balance global exploration and local exploitation,alongside the design of a particle encoding and position updating scheme tailored for dynamic partitioning.Furthermore,a‘state re-tention-local adjustment-global reconstruction’partitioning structure is developed.To avoid unnec-essary partition changes under minor source-load fluctuations,the concept of overlapping nodes is introduced,enabling fine-tuned adjustments under such conditions.Finally,two experimental sce-narios are designed to validate the proposed method.Simulation results demonstrate strong electrical coupling performance and show that the method enhances voltage regulation and renewable energy integration capabilities across regions.展开更多
综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略...综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略。首先,分析了外部现货市场环境下市场价格不确定性典型场景处理方法,并研究了综合能源系统内部多种源荷可调资源及运行成本结构;其次,建立了在电力市场价格不确定性条件下考虑系统边际成本交易优化模型,并提出沙猫群优化算法进行求解。最后,通过对实际案例的仿真验证。结果表明:该策略不仅可以降低IES的运行成本,还能增强其对市场价格不确定性的适应能力,为综合能源系统在电力现货市场环境下的运行提供了新的思路和方法,有助于实现能源系统参与市场调度的经济性和可靠性双重优化。展开更多
针对农业温室复杂环境中的超宽带(Ultra wide band,UWB)定位精度受非视距(Non line of sight,NLOS)效应和多路径影响的问题,本文提出了一种融合Chan-Taylor与改进沙猫群优化粒子滤波(Chan-Taylor and improved sand cat swarm intellige...针对农业温室复杂环境中的超宽带(Ultra wide band,UWB)定位精度受非视距(Non line of sight,NLOS)效应和多路径影响的问题,本文提出了一种融合Chan-Taylor与改进沙猫群优化粒子滤波(Chan-Taylor and improved sand cat swarm intelligence optimization particle filter,CT+ISCSO-PF)定位算法。首先,利用Chan-Taylor算法实现对目标初始位置的快速估算,为粒子滤波提供准确初值;随后,引入ISCSO(Improved sand cat swarm optimization particle filter)引导粒子向高似然区域移动,通过三角游走策略提升全局搜索能力,结合Levy飞行机制增强局部收敛效率,从而有效抑制粒子退化问题。本文模拟了3种不同噪声水平的环境。仿真结果表明,CT+ISCSO-PF算法在3种环境下,相比于传统的粒子滤波(Particle filter,PF)、Chan-Taylor与粒子滤波(Chan-Taylor and particle filter,CT+PF)、Chan-Taylor与沙猫群优化粒子滤波(Chan-Taylor and sand cat swarm intelligence optimization particle filter,CT+SCSO-PF)、Chan-Taylor与灰狼优化粒子滤波(Chan-Taylor and grey wolf optimizer particle filter,CT+GWO-PF)均表现出明显优势。进一步以农用履带车辆为载体开展温室环境定位试验,结果显示:在LOS场景下,该算法较PF、CT+PF、CT+SCSO-PF和CT+GWO-PF的均方根误差分别降低27.9%、17.8%、7.8%和10.2%;在NLOS场景下,均方根误差降幅分别达21.4%、15.6%、7.6%和5.2%。展开更多
基金Supported by the Technology Project of State Grid Corporation Headquarters(No.5100-202322029A-1-1-ZN)the 2024 Youth Science Foun-dation Project(No.62303006).
文摘With the large-scale integration of renewable energy sources into the grid,distribution networks are increasingly challenged by issues related to renewable energy accommodation and the mainte-nance of power quality stability.To address the challenge that existing partitioning methods are inad-equate for the planning and operation needs of active distribution networks under frequently changing power flow conditions,a three-stage dynamic partitioning approach is proposed based on an im-proved sand cat swarm optimization(ISCSO)algorithm.Firstly,a comprehensive dynamic partitio-ning index is developed by integrating both structural and functional metrics,including modularity,voltage regulation capability,and regional renewable energy accommodation capacity.Secondly,to overcome the limitations of the conventional sand cat swarm optimization,namely its weak global ex-ploration ability and tendency to fall into local optima in the later optimization stages,chaotic map-ping is employed to initialize a uniformly distributed population.A nonlinear sensitivity mechanism is introduced to balance global exploration and local exploitation,alongside the design of a particle encoding and position updating scheme tailored for dynamic partitioning.Furthermore,a‘state re-tention-local adjustment-global reconstruction’partitioning structure is developed.To avoid unnec-essary partition changes under minor source-load fluctuations,the concept of overlapping nodes is introduced,enabling fine-tuned adjustments under such conditions.Finally,two experimental sce-narios are designed to validate the proposed method.Simulation results demonstrate strong electrical coupling performance and show that the method enhances voltage regulation and renewable energy integration capabilities across regions.
文摘综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略。首先,分析了外部现货市场环境下市场价格不确定性典型场景处理方法,并研究了综合能源系统内部多种源荷可调资源及运行成本结构;其次,建立了在电力市场价格不确定性条件下考虑系统边际成本交易优化模型,并提出沙猫群优化算法进行求解。最后,通过对实际案例的仿真验证。结果表明:该策略不仅可以降低IES的运行成本,还能增强其对市场价格不确定性的适应能力,为综合能源系统在电力现货市场环境下的运行提供了新的思路和方法,有助于实现能源系统参与市场调度的经济性和可靠性双重优化。
