Research of Rural Power Network Reactive Power Optimization Based on Improved ACOA

In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.

Dynamic reactive power planning method for CSP-PV hybrid power generation system

Aiming at the faults of some weak nodes in the concentrated solar power-photovoltaic(CSP-PV)hybrid power generation system,it is impossible to restore the transient voltage only relying on the reactive power regulation capability of the system itself.We propose a dynamic reactive power planning method suitable for CSP-PV hybrid power generation system.The method determines the installation node of the dynamic reactive power compensation device and its compensation capacity based on the reactive power adjustment capability of the system itself.The critical fault node is determined by the transient voltage stability recovery index,and the weak node of the system is initially determined.Based on this,the sensitivity index is used to determine the installation node of the dynamic reactive power compensation device.Dynamic reactive power planning optimization model is established with the lowest investment cost of dynamic reactive power compensation device and the improvement of system transient voltage stability.Furthermore,the component of the reactive power compensation node is optimized by particle swarm optimization based on differential evolution(DE-PSO).The simulation results of the example system show that compared with the dynamic position compensation device installation location optimization method,the proposed method can improve the transient voltage stability of the system under the same reactive power compensation cost.

Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode.If the reactive power regulation potentials of new energy and EVs can be tapped,it will greatly reduce the reactive power optimization pressure on the network.Keeping this in mind,our reasearch first adds EVs to the traditional distribution network model with new forms of energy,and then a multi-objective optimization model,with achieving the lowest line loss,voltage deviation,and the highest static voltage stability margin as its objectives,is constructed.Meanwihile,the corresponding model parameters are set under different climate and equipment conditions.Ultimately,the optimization model under specific scenarios is obtained.Furthermore,considering the supply and demand relation-ship of the network,an improved technique for order preference by similarity to an ideal solution decision method is proposed,which aims to judge the adaptability of different algorithms to the optimized model,so as to select a most suitable algorithm for the problem.Finally,a comparison is made between the constructed model and a model without new energy.The results reveal that the constructed model can provide a high quality reactive power regula-tion strategy.

A New Filter Collaborative State Transition Algorithm for Two-Objective Dynamic Reactive Power Optimization

Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.

Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which can deal with the insufficient population diversity and non-global optimal solution issues.The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module.To improve the population diversity,the dynamic neighborhood strategy is first proposed,which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism.The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space,thus obtaining highquality exemplars.Meanwhile,when the global optimal solution cannot update its fitness value,the shuffling mechanism module is triggered to dynamically change the local neighborhood members.The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood.Then,the global learning based particle update approach is proposed,which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage.Finally,the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO.The simulation results show that the proposed DNGL-PSO has excellent adaptability,optimization efficiency and global optimization capability,while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.

含电动汽车和光伏的配电网演化动态博弈调度策略

针对大规模电动汽车的无序充电行为可能导致电网出现的三相不平衡问题,基于演化动态博弈模型,提出一种考虑三相负荷平衡和光伏消纳的电动汽车充电调度策略。首先,基于电动汽车接入时段的主观性以及光伏出力和负荷的不确定性,引入游牧集群和定居集群之间的演化动态博弈方程;然后,考虑用户侧和电网侧双方利益关系,设计以最小化电动汽车充电费用、三相负荷不平衡度和无功需求为目标的调度优化模型;最后,通过对某商业区配电网算例进行仿真求解,对比分析三相有功曲线、三相无功曲线以及荷电状态曲线的变化趋势。算例结果表明,该调度策略可有效降低三相不平衡度,满足无功补偿的需求以及改善用户充电成本。

含新能源和电动汽车充电站并网的主动配电网无功优化模型

针对新能源和电动汽车充电站规模化并网引起的无功优化问题,首先建立以主动配电网运行成本和电压偏差最小,系统稳定性最优的多目标无功优化模型。其次,基于PV曲线上、下半支解的接近程度,提出一种新的静态电压稳定性指标,以改善系统电压稳定性。最后,采用具有交叉反馈机制的混合优化算法对模型进行求解,并提出一种基于Tent混沌映射和自适应变异算子的改进飞蛾扑火算法提高求解效率。仿真结果表明,所提模型在降低系统运行成本,减少电压波动的同时,还能提高系统稳定性,且该混合优化算法能够快速、准确地求解混合整数的非凸、非线性优化模型。

考虑系统稳定边界的同步调相机励磁与升压变参数联合优化

现有提升调相机动态无功特性的参数优化方法侧重于电磁参数的优化,这给生产企业带来较高的工艺要求和较大的成本压力。针对该问题提出考虑系统稳定约束的调相机励磁系统及升压变参数联合优化方法,分析其对电磁参数优化的可替代性。首先,推导了基于Park模型下调相机的无功频域特性,与6阶实用模型下的无功频域特性对比,基于调相机的Park模型可提升调相机动态无功特性的分析精度。然后,提出根据调相机并网系统的稳定边界确定参数的优化区间,采用频域灵敏度方法确定重点参数,并基于人工鱼群算法进行参数优化。最后,通过仿真结果表明,励磁系统与升压变参数的联合优化,可获得与仅优化电磁参数时相近的调相机动态无功性能,验证了电磁参数优化的可替代性,从而降低调相机的制造成本,扩大同步调相机的应用场合和范围。

基于改进灰狼优化算法的含光伏配电网动态无功优化

针对光伏并网对配电网造成的电压波动、线损增加,以及光伏和负荷出力的不确定性等问题,本文构建基于二阶锥规划的线性凸优化模型,通过控制有载调压变压器和电容器组动作,以及光伏逆变器和静止无功发生器无功补偿能力约束,对日前日内双时间尺度无功优化模型进行动态分析,在简化求解过程的同时加大找到全局最优解的可能性。提出一种基于混沌学习初始化、非线性收敛因子、最优粒子柯西扰动结合蜘蛛猴算法位置更新方式的改进灰狼优化算法,防止算法陷入局部最优并增强其全局搜索能力。最后,运用改进的灰狼优化算法对含光伏的IEEE 33节点系统进行建模仿真,结果表明该算法具有寻优效率高、收敛速度快的优点,验证了算法的可行性和高效性。

考虑电力-交通耦合网动态协调的EV集群灵活性挖掘与优化调度

电动汽车(EV)作为兼具交通与能量双重属性的跨域主体,发挥其时空灵活性将助力电力-交通耦合网的协调运行。为此,文中考虑电力-交通耦合网综合效益,提出了EV集群调控策略。首先,基于弧阻抗函数构建了动态交通网络加载模型。其次,考虑EV用户的各特征参数存在的差异性与耦合性,构建了单体EV灵活运行域模型,并采用基于zonotope线性近似的闵可夫斯基和算法对其聚合,得到EV集群的时变灵活运行域。在此基础上,提出了动态交通流最优分配下EV集群灵活性调控的双层模型,迭代求解得到上下两层耦合变量构成的瞬时单位流量出行成本,从而引导EV的出行与充放电行为。最后,通过与最短路径引导策略进行比较,验证了所提EV集群调控策略的有效性。

源网荷储协同优化的主动配电网日前调度

在“双碳”目标下新能源发展迅速,但其出力的不确定性会影响电网安全稳定运行,主动配电网具备对各类分布式能源组合控制的能力,是解决目前存在的新能源并网问题的有效手段,同时需求响应作为控制负荷侧资源的有效手段,与主动配电网相结合能够有效降低源荷双侧不确定性对电网的影响。构建“源网荷储”协同优化的主动配电网日前优化调度模型,以主动配电网收益最大为目标,综合考虑电动汽车参与需求响应、电网安全、储能电池配置以及各组成单元的成本及约束条件。仿真结果表明:所构建的主动配电网优化调度模型合理,能够降低有功网损,减小尖峰负荷,降低负荷峰谷差,提高配电网收益。

基于谱聚类的主动配电网多时间尺度无功优化策略

高比例分布式光伏接入配电网后,传统优化方案无法有效平抑电压波动,分布式光伏逆变器的无功调控能力难以充分利用。为此,提出一种基于谱聚类的主动配电网多时间尺度无功优化策略,该方法分为日前优化和日内实时优化两个阶段。首先,对离散设备的时间耦合性进行解耦,以配电网网损、平均电压偏差、电压波动严重程度为目标函数,建立基于社交网络搜索算法的日前无功优化模型,确定离散设备静态最优档位序列;其次,通过谱聚类的方法进行耦合,确定离散设备动态最优档位序列,结合改进的分布式光伏逆变器就地控制策略,建立日内实时优化模型,从而抑制日前预测数据偏差导致的电压波动;最后,基于改进后的IEEE33节点系统进行仿真实验。仿真结果表明,所提策略可以有效降低运算难度、提高求解效率,验证了该策略的有效性和优越性。

融合路网-电网信息的电动汽车充放电行为引导与调控策略

在智慧城市理念快速发展的背景下,电动汽车(EV)无引导的出行与无序的充放电行为影响着电力-交通耦合网络的安全高效运行。针对此问题,提出一种融合路网-电网信息的EV充放电行为引导与调控两阶段优化策略。首先,分析电力-交通耦合网络的交互影响因素和方式,提出EV充放电出行路径引导和调控架构;其次,提出融合路网-电网信息的EV充放电出行决策电价动态更新策略,并建立计及用户时间-经济成本的EV充放电出行路径决策优化模型;然后,兼顾多方主体利益,建立EV充放电调控模型;最后,仿真结果表明,所提策略能够综合考虑路网和电网实时运行状态,充分挖掘EV在路径引导与充放电调控过程中的可调潜力,实现系统协调运行和多方共赢。

考虑柔性资源灵活支撑的电网有功/无功优化调度

电气化交通是交通电力耦合的重要场景,大规模电动汽车接入对于电力系统的有功/无功调度具有重要影响。提出了一种考虑电动汽车有序充电无功支撑的配电网有功/无功优化方法。首先,基于电动汽车充电特性,建立电动汽车有序充电功率控制模型,实现电动汽车并网接入的有序充电控制;其次,构建结合用户侧和电网侧因素的电动汽车有序充电无功支撑双层优化模型,实现电动汽车与电网间的有功/无功功率交互;最后,通过仿真验证所提方法的合理性和可靠性。通过控制电动汽车有序充电的功率因素,能够大幅降低用户侧充电支出,同时还能有效改善电网负荷波动和降低网络损耗,为解决电动汽车大规模接入电网问题提供新思路。

源荷波动时含固态变压器的有源配电网动态无功优化方法

针对分布式电源并网引起的双向潮流导致网损增大以及分布式电源、负荷的波动导致节点电压波动等问题,文章基于固态变压器(Solid State Transformer,SST)两侧电力电子变换器的脉冲宽度调制技术,提出了一种控制潮流的方法。该方法首先建立了含SST的有源配电网动态无功优化模型;然后以多时刻的有功网损和电压波动为优化目标,采用改进多目标粒子群算法对SST的一、二次侧的电力电子变换器的调制角和调制系数等多个控制变量进行求解;最后建立仿真模型并与基于有载调压变压器的有源配电网动态无功优化方法进行比较。结果证明了所提方法在降低配电网网损和维持节点电压稳定方面的优越性。

基于非线性仿射的风电场电压实时计算和优化方法

该文提出一种基于非线性仿射的风电场电压实时计算和优化方法。首先,以网损和并网电压波动综合最小为目标,建立风电场多时段动态无功优化模型,利用半不变量法分析风电功率不确定性对机端电压的影响,采用改进内点法实现模型的高效求解;其次,提出一种非线性仿射方法,快速准确地获得计及风电功率波动后的系统电压状态,并通过计算风机无功调节量来实现系统电压实时优化;再次,设计考虑风电功率对机端电压和并网电压综合影响的事件触发算法,降低风电功率波动平缓时风机的无功控制频率;最后,在我国某实际风电场的仿真试验表明,非线性仿射法能够快速获得系统实时电压状态,电压实时优化方法能够有效地防止机端电压越限并减小并网电压波动。

台风灾害下考虑修复不确定性和V2G的弹性城市电网动态供电恢复方法

快速可行的灾后恢复是弹性电力系统的重要一环。提出一种弹性导向的考虑修复不确定性和V2G(vehicle to grid,V2G)的城市电力系统动态供电恢复方法,灾后短期内应用V2G站中的有限电动汽车资源为城市电网提供电能支撑,同时派遣抢修队对故障线路进行抢修作业,实现系统快速恢复。对V2G和电动汽车灾前、灾后行为进行建模,通过灾前自由调度统计各V2G站附近的电动汽车数量,按照响应意愿比例得到灾后阶段各V2G站反向输电功率;对包含行驶行为和修复行为的动态修复行为进行建模,采用滚动时域优化方法决策修复的先后顺序,得到抢修队最优动态修复方案。最后,设计多组算例以验证所提方法的可行性,结果表明,该方法可有效支撑灾后城市电力系统快速恢复,提高系统弹性。

考虑氢混燃机动态混氢特性的电-气互联系统优化调度

电–气互联系统是综合能源系统的重要组成部分,燃气机组是其中关键的能源耦合元件,而随着能源系统低碳化转型,燃气机组掺氢燃烧发电的技术应运而生。传统氢混燃机相关研究大多只考虑固定掺氢比运行的情况,并未考虑机组动态调整掺氢比运行的影响。传统电–气互联系统优化调度相关研究主要面向运行经济性,而对计及氢混燃机的能量与备用统一出清鲜有研究。为此,该文提出了一种考虑氢混燃机动态混氢比的电–气互联系统优化调度方法,基于动态混氢特性建立氢混燃机精细化模型,并通过电解水制氢技术为氢混燃机提供氢气来源,配置储氢罐和储气罐作为联合体为系统提供备用;采用二项式松弛方法对氢混燃机耗气流量方程进行线性转化,将原有非凸非线性模型转化为混合整数线性规划模型求解;最后通过仿真验证所提方法的有效性,分析不同场景下系统运行状态,实现了“风电–电解水制氢–氢混燃机”模式下风电消纳潜力的深度挖掘。

基于数据驱动的低感知度配电网动态无功优化

由于配电网网络通信基础设施较差,且节点监控覆盖不完全,因此存在无法实时采集数据的节点,导致无法进行传统无功优化。为此,提出了一种数据驱动的低感知度配电网动态无功优化方法。通过K-means算法聚类节点历史负荷,对非实时观测节点依据特征分类;选择最优超参数基于时间卷积网络进行量测数据补全;最终通过改进后的社交网络搜索算法实现动态无功优化,并仿真验证了方法的有效性。

动态无功补偿装置电压调节增益自动优化研究

针对动态无功补偿装置电压调节器的参数仅能在一定的短路容量范围内保持快速稳定的响应,当应用于短路容量变化范围较大的新能源汇集系统时,响应可能会变得非常慢或出现振荡。提出一种动态无功补偿装置电压调节增益自动优化方法,对应用于实际工程时电压波形畸变、电流波形畸变、其他自动电压补偿装置对短路容量计算结果的影响进行了研究并提出解决方案。在RTDS中对提出的增益自动优化方法进行了仿真研究,结果表明所设计的电压调节增益自动优化方法可以根据系统需求自动调整和优化增益。