A pooled-neighbor swarm intelligence approach to optimal reactive power dispatch

This paper presents a pooled-neighbor swarm intelligence approach (PNSIA) to optimal reactive power dispatch and voltage control of power systems. The proposed approach uses more particles’ information to control the mutation operation. The proposed PNSIA algorithm is also extended to handle mixed variables, such as transformer taps and reactive power source in- stallation, using a simple scheme. PNSIA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system. Simulation results showed that the proposed approach is superior to current methods for finding the optimal solution, in terms of both solution quality and algorithm robustness.

Learning the optimal power flow:Environment design matters

To solve the optimal power flow(OPF)problem,reinforcement learning(RL)emerges as a promising new approach.However,the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment.In this work,we collect and implement diverse environment design decisions from the literature regarding training data,observation space,episode definition,and reward function choice.In an experimental analysis,we show the significant impact of these environment design options on RL-OPF training performance.Further,we derive some first recommendations regarding the choice of these design decisions.The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

Optimal reactive power dispatch of wind power plant cluster considering static voltage stability for low-carbon power system

The implementation of developing the wind power is an important way to achieve the low-carbon power system.However,the voltage stability issues caused by the random fluctuations of active power output and the irrational regulations of reactive power compensation equipment have become the prominent problems of the regions where large-scale wind power integrated.In view of these problems,this paper proposed an optimal reactive power dispatch(ORPD)strategy of wind power plants cluster(WPPC)considering static voltage stability for lowcarbon power system.The control model of the ORPD strategy was built according to the wind power prediction,the present operation information and the historical operation information.By utilizing the automatic voltage control capability of wind power plants and central substations,the ORPD strategy can achieve differentiated management between the discrete devices and the dynamic devices of the WPPC.Simulation results of an actual WPPC in North China show that the ORPD strategy can improve the voltage control performance of the pilot nodes and coordinate the operation between discrete devices and the dynamic devices,thus maintaining the static voltage stability as well.

Distributionally Robust Optimal Reactive Power Dispatch with Wasserstein Distance in Active Distribution Network

The uncertainties from renewable energy sources(RESs)will not only introduce significant influences to active power dispatch,but also bring great challenges to the analysis of optimal reactive power dispatch(ORPD).To address the influence of high penetration of RES integrated into active distribution networks,a distributionally robust chance constraint(DRCC)-based ORPD model considering discrete reactive power compensators is proposed in this paper.The proposed ORPD model combines a second-order cone programming(SOCP)-based model at the nominal operation mode and a linear power flow(LPF)model to reflect the system response under certainties.Then,a distributionally robust optimization(WDRO)method with Wasserstein distance is utilized to solve the proposed DRCC-based ORPD model.The WDRO method is data-driven due to the reason that the ambiguity set is constructed by the available historical data without any assumption on the specific probability distribution of the uncertainties.And the more data is available,the smaller the ambiguity would be.Numerical results on IEEE 30-bus and 123-bus systems and comparisons with the other three-benchmark approaches demonstrate the accuracy and effectiveness of the proposed model and method.

Optimal reactive power dispatch with wind power integrated using group search optimizer with intraspecific competition and le´vy walk

This paper presents the mean–variance(MV)model to solve power system reactive power dispatch problems with wind power integrated.The MV model considers the profit and risk simultaneously under the uncertain wind power(speed)environment.To describe this uncertain environment,the Latin hypercube sampling with Cholesky decomposition simulation method is used to sample uncertain wind speeds.An improved optimization algorithm,group search optimizer with intraspecific competition and le´vy walk,is then used to optimize the MV model by introducing the risk tolerance parameter.The simulation is conducted based on the IEEE 30-bus power system,and the results demonstrate the effectiveness and validity of the proposed model and the optimization algorithm.

Multi-timescale Affinely Adjustable Robust Reactive Power Dispatch of Distribution Networks Integrated with High Penetration of PV

Photovoltaic(PV)power generation has highly penetrated in distribution networks,providing clean and sustainable energy.However,its uncertain and intermittent power outputs significantly impair network operation,leading to unexpected power loss and voltage fluctuation.To address the uncertainties,this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch(MTAAR-RPD)method to reduce the network power losses as well as alleviate voltage deviations and fluctuations.The MTAAR-RPD aims to coordinate on-load tap changers(OLTCs),capacitor banks(CBs),and PV inverters through a three-stage structure which covers multiple timescales of“hour-minute-second”.The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min.The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time.The three stages are coordinately optimized by an affinely adjustable robust optimization method.A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively.The proposed method is verified through theoretical analysis and numerical simulations.

基于人工蜂群算法的配电网无功优化调度研究

为了改善现有无功功率调度时算法执行效率,解决模型易陷入局部极值问题,提出一种基于改进人工蜂群算法模型。在分析等式和不等式约束的情况下,建立最小化实际功率损耗、电压偏差和电压稳定增强的无功功率调度目标函数。提出一种基于邻域半径选择的改进人工蜂群算法,设计一种新的邻域选择机制和改进的搜索策略。实验阶段,以IEEE39总线电力系统为例,对所提算法有效性和执行效率进行验证。与线性规划(LP)、粒子群优化(PSO)算法和基础人工蜂群(ABC)算法相比,改进ABC算法比其他算法执行效率更优,且解的质量更高。该模型为配电网电力调度及稳定性运行的发展提供了一定借鉴作用。

计及工况预测误差的主动配电网日前无功优化调度策略

为解决工况预测误差较大时,日前无功优化调度方案优化效果不佳的问题,提出了计及工况预测误差的主动配电网日前无功优化调度策略。首先,使用轻量级梯度提升机算法建立日前工况功率预测模型;其次,考虑大规模高比例分布式电源接入主动配电网,以调度时段内所有时间断面的多目标加权累加和为目标函数建立日前无功优化调度模型;最后,设计了一种变寻优粒子空间的改进引力搜索算法对日前无功优化调度模型进行求解,该算法根据历史工况预测误差评价指标调整寻优粒子空间各维度的上下限矩阵,从而抑制了当无功区域内工况预测误差较大时可控设备调度异常的缺陷。最后采用拓展的IEEE 33节点系统算例进行有效性验证。

基于区间-概率潮流分析的配电网调压手段适应性评估方法

随着可再生能源的大规模接入,配电网面临着频繁的电压越限问题,亟待对以光伏逆变器为代表的多种新型调压手段能否满足配电网日常调压需求进行研究。为对配电系统调压手段适应性做出客观评价,提出基于区间-概率潮流分析的配电网调压手段适应性评估方法,实现调压效果与调压成本的综合分析。首先,基于区间潮流与概率潮流结果,构建配电网调压效果评价指标;其次,以最优无功方案对应成本作为成本基准值,构建配电网调压成本评价指标;在此基础上,基于层次分析法对调压效果指标和成本指标进行加权,构建配电网调压适应性综合评价指标体系;最后,基于所构建的评价指标,完成配电网调压手段适应性分析,并通过多场景对比评价验证了所提方法的合理性。

含分布式发电的智能配电网无功功率优化调度

分布式发电在配电网中的应用程度不断提高,配电网中的电压分布影响较大。由于电压是一种局部现象,来自分布式能源的功率注入仅影响附近的总线电压,因此,通过将电网划分为多个电压相互依赖的控制区域,可以有效地解决电压调节问题。本文提出了通过优化无功功率分配分布式发电的电压控制区域,以调节电网电压。利用基于电压对无功功率敏感度的分层聚类算法识别区域,采用元启发式算法优化确定各区域分布式发电机的无功功率调度设定值。采用改进的IEEE 37节点中压测试网络和分布式生成,通过联合仿真平台对所提出的电压控制方案进行了评估。结果表明,基于区域的无功功率分配对改善电网电压分布是有效的。

含高渗透可再生能源的动态网络重构与无功电压调整协同优化

具有出力不确定性的可再生能源高渗透并入,导致传统单一的主动管理策略难以实现配网最优运行。在此背景下,提出一种动态网络重构与无功电压调整协同优化方法。首先,采用基于形态和幅值的双尺度相似性度量谱聚类算法对负荷进行聚类,进一步利用反映负荷曲线波动特征的自适应聚合算法对时段做出合理划分。其次,计及风光不确定性,构建考虑配电网动态重构与储能和微型燃气轮机出力调控协同的机会约束双层优化模型,模型上层以配电网综合运行成本最小为目标,下层以系统电压总偏差最小为目标,实现对配电网运行经济性和系统电压质量的综合优化。然后,针对该模型提出了基于十进制编码的改进鲸鱼优化算法和内点法相结合的混合策略对模型进行求解。最后,在改进的IEEE33节点系统中,对所提的运行优化模型进行分析和验证。仿真结果表明:所提双层优化模型能够有效提高电压质量和降低配电网运行费用,所提时段划分方法能够得到符合曲线变化趋势的分段方案,改进后的算法大大提高了求解效率。

基于改进非支配排序遗传算法的电力系统无功–潮流多目标协同优化调度

提出了一种改进非支配排序遗传(Non-dominated sorting genetic algorithms,NSGA)-Ⅱ算法,其种群局部搜索和外部种群的设置有效提高了算法的收敛性和解集的多样性。将该算法应用于电力系统无功–潮流多目标协同优化调度问题的求解。采用IEEE-14和IEEE-30母线系统进行算例分析。算例仿真的结果表明,应用所提方法能够兼顾电力系统运行的无功优化目标和潮流优化目标,实现无功最优和潮流最优的折中;同时,改进NSGA-Ⅱ算法在收敛性和解集多样性上都优于传统NSGA-Ⅱ算法。

基于改进人工鱼群算法的主动配电网日前两阶段优化调度

分布式电源(distributed generation, DG)渗透率的不断提高使得传统火电机组正逐步被清洁能源所取代。为降低新能源接入对系统经济运行和稳定运行带来的影响,提出一种主动配电网(active distribution network, ADN)日前优化调度的两阶段模型:第1阶段根据分时电价、DG燃料成本、可中断负荷切除代价进行经济调度,进而确定DG的机组组合、从大电网的购电量及可中断负荷削减量;第2阶段对系统进行无功优化,在第1阶段经济调度结果的基础上,以DG和无功补偿装置的无功补偿量为调节措施,建立综合考虑系统有功损耗和电压质量的无功优化模型,对于所提优化模型提出一种基于变量分离思想的反哺人工鱼群算法(cross feedback artificial fish swarm algorithm, CFAFSA)进行求解,该算法在实现了实型变量和整型变量解耦的同时,降低了变量的维度,提高了算法的收敛速度和全局寻优能力。以IEEE 33节点主动配电系统为算例,验证了模型的有效性及算法的快速性。

不确定条件下风力发电系统的无功优化调度

经典的最优无功功率调度(ORPD)通常被公式化为一个确定性优化问题,这个优化问题关系到系统的安全性和经济性。然而,电力系统本质上是随机的。其中,包括多种技术不确定性,如网络结构、需求和风力发电机等间歇性来源。本文研究了一个包含上述不确定因素和电力系统安全约束的安全约束最优无功调度(SC-OPRD)问题。针对引入的问题,提出了一个技术经济目标函数。该目标函数包括将降低电网电压稳定风险作为技术和运营成本作为经济目标。运营成本是指电力系统损耗和发电机提供的无功功率的成本。NSGA-II优化算法用于寻找非支配解帕累托前沿(PF)。蒙特卡罗模拟(MCS)用于近似不确定参数的潜在分布。IEEE 30总线测试系统用于验证所提出的SC-OPRD。结果分析表明,所提出的方法是解决考虑不确定性的SC-OPRD问题。

电力系统无功功率优化调度研究

随着电力系统规模的不断扩大和结构的日趋复杂,系统电压稳定性与运行成本问题日益凸显。利用无功功率优化调度方案,可以实现对无功功率的合理分配和管理,对于确保系统稳定性和运行高效性意义重大。提出了一种无功功率优化调度方法,通过构建电力系统无功优化数学模型,给出了一种改进的整数编码遗传算法(Improved Integer-coded Genetic Algorithm,IIGA)用于模型求解。同时,从负荷特性精细化考量、设备精准调度的角度出发,设计了3项无功优化调度措施。算例结果表明,所提方法可以有效减小电压偏差,为电力系统的安全运行提供有力保障。

无功电压技术支持下电力调度自动化系统的设计与实现

基于数据挖掘的电力调度自动化系统通过预测电力负荷实现电力的调度,由于缺乏对局部电压的有效控制,导致调度效率较低。因此,提出无功电压技术支持下的电力调度自动化系统。首先,在硬件方面,采用激励线圈等设计出电力调度模块,并结合线性规划方法对局部最优电压进行控制,设计调节电路实现调度信号的接收与发送。其次,在软件方面,采用无功电压技术,结合线性规划方法实现对最优电压的控制。最后,对电力调度自动化系统进行调度效率的检验,经实验分析证明,利用电力调度自动化系统对电力进行调度时,调度效率较高,具备较为优秀的调度性能。

长时间尺度下计及光伏不确定性的配电网无功优化调度

针对高渗透率分布式光伏接入配电网所引起的电压控制问题,分析了无功补偿型和电压控制型两种光伏发电系统的无功电压调控特点,在考虑负荷和光伏出力不确定性的基础上,以系统总运行成本最小为目标,提出了长时间尺度下考虑电压越限风险的配电网无功优化调度模型。针对电压控制型光伏无功电压调控特点,提出了含PV类型节点的配电网概率潮流计算方法。最后,通过时间解耦并采用灾变遗传算法求解得到含高渗透率光伏的配电网长时间尺度下的无功优化调度方案。仿真算例表明:与现有光伏无功控制方法相比,所提方法能够有效降低光伏并网引起的电压越限风险,其中基于电压控制型光伏的配电网无功调度方案在降损和抑制电压波动方面的控制效果更明显。

用于无功优化控制分区的两层搜索方法

电网分区可用于状态估计、分区控制和并行计算等领域。针对电力系统的无功优化控制分区,文章给出了一种两层搜索的方法,上层由遗传算法负责搜索各分区的第一个节点;下层根据节点间的电气距离和连通关系搜索其它节点,最终将电网划分为内部节点联系紧密的若干个子区域。由于遗传算法只搜索各分区的第一个节点,因此搜索空间大大缩小;下层搜索可自动保证子区域的连通性。分区时考虑各区域规模的平衡和无功控制设备的均衡分布。对多个IEEE系统的分区测试证明了该方法的有效性。

实时无功优化调度中的邻域搜索改进遗传算法

为了避免遗传算法的早熟收敛及改善其收敛性能,提出了电力系统最优无功调度和电压控制的一种改进遗传算法。算法中采用了分组整数编码技术和锦标赛选择机制;结合十进制整数编码的特点,提出了邻近变异的操作算子,避免了二进制编码中的海明悬崖(Hammingcliffs);在遗传迭代结束后再引入邻域搜索技术,以极短的时间完成搜索。将此算法应用在海南电网的实时无功优化调度中,结果表明该算法能迅速获得全局优化解,具有很好的全局收敛性能,能满足电力系统无功优化实时调度的要求。

基于电压控制分区的发电机无功备用优化方法

发电机无功备用对电力系统电压稳定和电压控制具有重要意义。提出了基于电压控制分区的区域发电机无功备用定义,该定义综合衡量了区域发电机无功备用对静态电压稳定和准稳态电压控制的价值以及无功备用的可供给性。分析了控制变量的特性和选取方式,建立了用于优化区域无功备用和区域电压水平的二次规划模型,提出了逐次分区优化求解算法。通过3节点系统算例验证了无功备用定义和控制变量选取方式的合理性。通过IEEE 118系统算例验证了优化方法的正确性和可行性,优化方案显著提高了区域无功备用量和电压稳定裕度,改善了电压水平。区域无功备用优化方法将高维的无功备用优化原问题简化为少量的低维区域优化子问题,具有较高的容错性和计算效率,可用于较大规模系统的发电机无功备用评估和优化。