Swarm intelligence based algorithms for reactive power planning with Flexible AC transmission system devices

In the proposed work, authors have applied swarm intelligence based algorithms for the effective Co-ordination of Flexible AC transmission system (FACTS) devices with other existing Var sources present in the network. IEEE 30 and IEEE 57 bus systems are taken as standard test systems. SPSO (Simple Particle Swarm Optimization) and other two swarm based intelligence approaches like APSO (Adaptive Particle Swarm Optimization) and EPSO (Evolutionary Particle Swarm Optimization) are used for the optimal setting of the Var sources and FACTS devices. The result obtained with the proposed approach is compared with the result found by the conventional RPP (Reactive power planning) approach where shunt capacitors, transformer tap setting arrangements and reactive generations of generators are used as planning variables. It is observed that reactive power planning with FACTS devices yields much better result in terms of reducing active power loss and total operating cost of the system even considering the investment costs of FACTS devices.     

PSO based bio inspired algorithms for reactive power planning

Reactive power source planning problem has significant role for secure and economic operation of power system. Reactive power planning problem is nothing but the proper coordination of existing Var sources which lead to loss minimization and cost economic operation of the system. In the proposed approach several bio-inspired algorithms like Particle Swarm Optimization (PSO), Evolutionary Particle Swarm Optimization (EPSO), Adaptive Particle Swarm Optimization (APSO), Hybrid Particle Swarm Optimization (HPSO) and Bacterial Foraging Algorithm (BFA) are used for the reactive power planning problem. Finally a comparison of all these techniques are made on the basis of the results obtained when applied to different standard test system.     

城市电网无功优化规划研究

研究了城市电网无功优化规划的数学模型与算法。以年总支出费用最小为目标,并满足各种运行约束条件;对该非线性规划问题采用改进遗传算法求解。在广东某城市电网中的应用计算表明,所采用的算法能获得理想的优化效果,对工程实际具有较大的应用价格。     

Efficient method for AC transmission network expansion planning

A combinatorial mathematical model in tandem with a metaheuristic technique for solving transmission network expansion planning (TNEP) using an AC model associated with reactive power planning (RPP) is presented in this paper. AC-TNEP is handled through a prior DC model while additional lines as well as VAr-plants are used as reinforcements to cope with real network requirements. The solution of the reinforcement stage can be obtained by assuming all reactive demands are supplied locally to achieve a solution for AC-TNEP and by neglecting the local reactive sources, a reactive power planning (RPP) will be managed to find the minimum required reactive power sources. Binary GA as well as a real genetic algorithm (RGA) are employed as metaheuristic optimization techniques for solving this combinatorial TNEP as well as the RPP problem. High quality results related with lower investment costs through case studies on test systems show the usefulness of the proposal when working directly with the AC model in transmission network expansion planning, instead of relaxed models.     

A convex chance-constrained model for reactive power planning

This paper presents a new approach for long-term reactive power investment planning and operation using a multiperiod mixed-integer stochastic convex model, where load uncertainty is also included. The risk of not meeting the load with a certain level of confidence due to a reactive power deficit is represented by chance constraints. Tap settings of under-load tap-changing transformers are modeled as a set of mixed-integer linear equations. Existing and new discrete and continuous reactive power sources are modeled. These type of problem is challenging and have never been studied before. The proposed model is applied to the CIGRE-32 electric power system.     

电力系统无功规划优化的变尺度混沌优化算法

选择无功设备投资和系统有功网损的综合费用最小作为目标函数,同时考虑满足电压水平来探讨无功规划优化问题。无功优化前,首先用模态分析法确定系统的薄弱节点作为候选补偿点,用电压越限筛选确定最大补偿容量,然后用无功优化模型决定候选补偿点应加装的无功补偿容量。介绍了变尺度混沌优化算法,该算法不断缩小优化变量的搜索空间并不断提高搜索精度,从而有较高的搜索效率。IEEE14节点系统的仿真计算验证了算法的有效性,算法对初值的敏感性也进行了探讨。     

输电网的规划综合成本法及其应用研究

针对电网发展日益受到土地、环境及风险评估等内外部条件约束的现状,提出一种输电网规划与项目决策的新方法.该方法基于全寿命周期管理的理念,以全社会成本为尺度,对输电网扩展方案的技术性和经济性进行综合评判,通过引入土地机会成本和风险评估方法实现了对电网征地和电网风险等关键因素的量化评估.与传统输电网的规划设计方法进行比对分析表明,该方法能够更加准确地计及输电网规划方案的长期性和全面性,为输电网规划设计技术经济比选方法的改进提供了参考依据.     

Quasi-oppositional differential evolution for optimal reactive power dispatch

This paper presents quasi-oppositional differential evolution to solve reactive power dispatch problem of a power system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Quasi-oppositional differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed quasi-oppositional differential evolution (QODE) employs quasi-oppositional based learning (QOBL) for population initialization and also for generation jumping. Reactive power dispatch is an optimization problem that reduces grid congestion with more than one objective. The proposed method is used to find the settings of control variables such as generator terminal voltages, transformer tap settings and reactive power output of shunt VAR compensators in order to achieve minimum active power loss, improved voltage profile and enhanced voltage stability. In this study, QODE has been tested on IEEE 30-bus, 57-bus and 118-bus test systems. Test results of the proposed QODE approach have been compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed QODE based approach is able to provide better solution.     

Two-stage optimization algorithm for short-term reactive power planning based on zonal approach

There are various methods for procurement of reactive power as an ancillary service in deregulated market environment. Reactive power market model based on voltage control zones and two-step optimal power flow algorithm is proposed in this paper. Zones are formed by splitting the power system or a control area of an interconnected power system into autonomous voltage sub-areas with sufficient reactive power reserves using the electrical distance method. Every zone formes a single reactive power market. First step of an optimization algorithm determines zonal reactive power prices based on generator's cost functions for reactive power production. Total cost minimization is the goal of the second optimization step where the final dispatching decision is made according to transmission losses and zonal reactive power prices respecting power system planned conditions and security constraints. Proposed algorithm is tested on IEEE 39 bus test system ensuring the system operator's minimal payment for reactive power and transmission losses, without jeopardizing power system security.     

Loadability enhancement with FACTS devices using gravitational search algorithm

In the present work, GSA (gravitational search algorithm) based optimization algorithm is applied for the optimal allocation of FACTS devices in transmission system. IEEE 30 & IEEE 57 test bus systems are taken as standards. Both active and reactive loading of the power system is considered and the effect of FACTS devices on the power transfer capacity of the individual generator is investigated. The proposed approach of planning of reactive power sources with the FACTS devices is compared with other globally accepted techniques like GA (Genetic Algorithm), Differential Evolution (DE), and PSO (Particle Swarm Optimization). From the results obtained, it is observed that incorporating FACTS devices, loadability of the power system increases considerably and each generator present in the system is being able to dispatch significant amount of active power under different increasing loading conditions where the steam flow rate is maintained corresponding to the base active loading condition. The active power loss & operating cost also reduces by significant margin with FACTS devices at each loading condition and GSA based planning approach of reactive power sources with FACTS devices found to be the best among all the methods discussed in terms of reducing active power loss and total operating cost of the system under all active and reactive loading situations.     

考虑停电损失的配电网网架规划的免疫算法

基于人工免疫算法借鉴生物免疫系统中抗体多样性的保持机制和基于抗体浓度的调节机制,同时又具有一般进化算法的随机全局搜索能力,适合求解多目标问题的特点,综合考虑配电网供电可靠性对用户侧的经济影响,提出了应用免疫算法求解考虑用户侧停电损失的配电网网架规划问题的优化方法。针对配电网辐射性的要求,根据图论知识,采用将新增加负荷节点作为染色体基因座的编码方法,保证了生成解的可行性,避免了辐射性检查,提高了计算速度。最后通过实际算例说明了这种方法的有效性。     

市场条件下输配电网规划新的思路与方法探讨

概括介绍了市场条件下输配电网规划思路的转变及其所面临的不确定性因素,然后具体探讨了市场条件下输配电网规划的新思路与新方法,介绍了不确定条件下输电网的投资决策和负荷不确定性条件下城市配电网的规划.     

A new approach for cost allocation and reactive power pricing in a deregulated environment

Power industry has been facing restructuring problems during the past decade. Appropriate management of reactive power is very essential for supporting power system security. Reactive power has dominant effects on real energy transfer. Furthermore, it can support the secure operation of the system as an ancillary service. However, most researches have been focused on active power as the main good transacted in electricity markets. On the other hand, while reactive power production cost is highly dependent on real power output, it is mainly confined to local consumption. As a result, to avoid market power and to maintain the secure operation of the system, a fair cost allocation method seems to be very essential. Appropriate pricing of reactive power as an ancillary service has been a challenging problem during the past decade. However, most methods proposed so far for reactive power pricing are essentially based on empirical approximations. In this paper, a new method for reactive power cost allocation is proposed. The method is based on calculation of the accurate cost which will be imposed on generators due to supporting reactive power. The proposed method is fair, accurate and realistic and it can be formulated very easily. Furthermore, a new approach based on tracing algorithm is proposed for pricing of reactive power which considers the cost of both active and reactive losses allocated to each generator. Application of the proposed method on IEEE 9-bus standard network confirms its validity and effectiveness.     

基于全寿命周期成本的配电网无功规划研究

在保证电能质量的基础上,评价一个无功优化补偿方案应该以项目寿命周期内的投资净收益为指标,即一个无功补偿方案不仅需要考虑损耗降低所带来的收益,还要考虑由于无功补偿设备寿命周期内建设、运行维护直至报废等成本的增加。综合系统有功网损最小、电压质量最好建立配电网无功优化模型,利用无功二次电阻矩确定无功补偿点,根据改进的遗传算法确定无功补偿的容量,通过引入全寿命周期成本(Life Cycle Cost,LCC)的概念,对于不同的补偿方案进行计及全寿命周期成本的计算,以寿命周期内投资净收益为判断依据选定补偿方案。在Matlab上以IEEE33节点配电系统为例进行仿真计算,结果表明,采用计及全寿命周期成本的无功优化在实际中更具有合理性和有效性。     

基于聚类排挤小生境遗传算法的配电网无功规划研究

针对应用传统排挤小生境遗传算法进行无功规划时,小生境数目设定值的不同会导致寻优结果波动性较大的情况,将聚类分析和排挤小生境遗传算法相结合应用于配电网无功规划。建立了以收益净现值为目标函数的数学模型,该模型更直观地反映了补偿方案的降损节能收益能力;利用聚类排挤小生境遗传算法对配电网进行无功规划,通过调整聚类距离控制收敛到的小生境数目,提高了算法的全局寻优能力和解的稳定性;采用面向对象的Visual 2005C#高级语言开发编制了配电网无功规划计算程序。实例分析表明所提算法收敛速度快,全局寻优能力强, 计算结果稳定高,具有更高的实用性。     

基于遗传算法与蚁群算法的电网规划

针对遗传算法求解到一定范围容易产生大量冗余迭代、求解精度低.蚁群算法初期信息素匮乏、求解速度慢的缺陷,在电网规划算法中,将遗传算法与蚁群算法融合,在网架规划初期采用遗传算法求解出最优解,通过最优解生成蚁群算法的初期信息素,确定吸引强度的初始值,建立强度更新的模型,从而得到满足电网规划的最优方案.最后通过18节点的算例证明,融合算法在收敛性与寻优性上均得到提高.     

引力搜索算法在输电网无功优化中的应用研究

本将GSA应用于求解电力系统的无功优化问题,发现其寻优速度快,数值稳定性好,可以灵活处理离散变量和连续变量算例仿真结果表明,GSA在求解RPO问题时寻优速度和数值稳定性均优于PSO。     

改进教与学方法在电力系统无功优化中的应用研究

以多负荷水平的全年能量损失最小为目标函数,提出一种改进教与学优化方法求解电力系统无功优化问题。教与学方法是一种新颖的无控制参数的群智能算法,包括教阶段和学阶段。为了克服局部收敛,改进教与学方法在此基础上提出一种基于自适应小波变异策略的改进阶段改善算法的性能并在IEEE-30节点系统进行仿真。结果与其他算法进行比较,验证了该算法的优越性。表明该方法是大规模电力系统可推广使用的有效方法。     

分位数拟合的点估计法随机潮流在输电网规划中的应用

为了更全面地描述输电网规划中面临的负荷波动、风电出力这些不确定因素,以点估计法随机潮流为基础、年综合费用最小为目标函数,并计及网络损耗,建立输电网优化规划模型。引入多项式正态变换技术对非正态变量相关性进行处理。采用基于最小二乘的分位数拟合法求取多项式正态变换的系数,有效地避免了积分运算。以总体样本均值和样本方差均值为指标,量化相关性对风电出力的影响。采用改进的粒子群优化算法对修改的IEEE-RTS 24节点系统进行算例分析,结果表明,随着相关性的增强,负荷、风速出现极值的概率增加,电网需要投建更多线路以应对系统中的不确定因素。     

A new optimal unified power flow controller placement and load shedding coordination approach using the Hybrid Imperialist Competitive Algorithm-Pattern Search method for voltage collapse prevention in power system

As power systems become more complex and heavily loaded, voltage collapse has become one of the most destructive events in modern power systems leading to blackouts in electric utilities worldwide. Voltage collapse is mainly caused by operating power systems at lower stability margins due to a surge in electric power demand. This paper presents an optimal unified power flow controller (UPFC) placement and load shedding coordination approach for voltage collapse prevention in N − K (K = 1, 2 and 3) contingency condition using Hybrid Imperialist Competitive Algorithm-Pattern Search (HICA-PS). ICA is the main optimizer of the proposed algorithm while pattern search is applied to further fine tune the results of the ICA. To show the effectiveness of the proposed approach in preventing voltage collapse in complex power systems, we implemented it on the New-England 39 bus power system. Its performance was also compared to that of some classical optimization techniques. Decrease in load shedding amounts, continuity of energy supply and voltage collapse prevention is the main positive features of the proposed approach.