Optimal reactive power dispatch using ant colony optimization algorithm

This paper proposed a procedure to solve the optimal reactive power dispatch (ORPD) problem using ant colony optimization (ACO) algorithm. The objective of the ORPD problem is to minimize the transmission power losses under control and dependent variable constraints. Proposed sensitivity parameters of reactive power at generation and switchable sources are derived based on a modified model of fast decoupled power flow. The proposed ACO-based algorithm is applied to the IEEE standard 14-bus, 30-bus systems, and a real power system at West Delta Network as a part of the Unified Egyptian Network. The obtained simulation results are compared with those of conventional linear programming, genetic algorithm, and particle swarm optimization technique. Simulation results show the capability of the proposed ACO-based algorithm for solving the ORPD problem, especially with increasing the system size.     

A new Optimal reactive power planning based on Differential Search Algorithm

This paper proposes a new multi-level methodology based on the optimal reactive power planning. The developed methodology is designed to solve the problem of the non-feasibility solution of the fuel cost minimization problem (for a given operating point) where the classical method such as interior point method (IPM) is applied. The proposed solution to solve this problem is based on the application of the optimal reactive power planning problem considering voltage stability as the initial solution of the fuel cost minimization problem. To improve the latter the load voltage deviation problem is applied to improve the system voltage profile. For à good result improvement, the reactive power planning problem and the load voltage deviation minimization problems are solved using a new optimization method namely the Differential Search Algorithm (DSA). Moreover, the fuel cost minimization problem is solved using IPM. To identify the candidate placements of compensation devices for the optimal reactive power planning problem, a new voltage stability index namely: The Fast Voltage Stability Index (FVSI) is used. The methodology has been tested with the equivalent Algerian power system network, and the simulation results show the effectiveness of the proposed approach to improve the reactive power planning problem and to minimize the system voltage deviation.     

Optimal Thermal Unit Commitment Integrated with Renewable Energy Sources Using Advanced Particle Swarm Optimization

This paper presents a methodology for solving generation planning problem for thermal units integrated with wind and solar energy systems. The renewable energy sources are included in this model due to their low electricity cost and positive effect on environment. The generation planning problem also known by unit commitment problem is solved by a genetic algorithm operated improved binary particle swarm optimization (PSO) algorithm. Unlike trivial PSO, this algorithm runs the refinement process through the solutions within multiple populations. Some genetic algorithm operators such as crossover, elitism, and mutation are stochastically applied within the higher potential solutions to generate new solutions for next population. The PSO includes a new variable for updating velocity in accordance with population best along with conventional particle best and global best. The algorithm performs effectively in various sized thermal power system with equivalent solar and wind energy system and is able to produce high quality (minimized production cost) solutions. The solution model is also beneficial for reconstructed deregulated power system. The simulation results show the effectiveness of this algorithm by comparing the outcome with several established methods. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

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 VAR planning method with multiple power-flow states

The goal of reactive power (VAR) planning is to find the minimum cost installation plan of new reactive power sources so that the system voltage is maintained within an acceptable level. The consideration of multiple contingency states, together with the discrete nature of VAR facilities, creates a large-scale nonlinear mixed-integer programming (MIP) problem. To overcome the discrete nature of VAR facilities, an approximate method for the MIP problem is employed since the method is linear-programming based and thus efficient for large-scale problems. To treat the multiple contingencies, a resource directive decomposition approach is used in the proposed algorithm. If the number of installed VAR sources is fixed, the overall problem can be reduced to independent subproblems. Then subproblems are coordinated to give a VAR installation pattern in which installation cost becomes less than before. The algorithm proposed is tested for a 135-node real-size system and the results show the validity and effectiveness of the algorithm.     

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.     

考虑多无功源的光伏电站两阶段无功电压协调控制策略

在综合考虑各种无功源调节性能基础上,提出了一种综合利用静止无功发生器、光伏逆变器和电容器组的光伏电站无功电压协调控制策略,其包含两个控制阶段:第一阶段为考虑多无功源的协调电压控制,最大化利用站内无功源,在减少电容器组投切次数的同时,快速响应系统无功变化,维持公共连接点电压稳定;第二阶段为动态无功优化控制,利用动态无功(静止无功发生器)与静态无功(电容器组)、快速无功(静止无功发生器)与慢速无功(光伏逆变器)的无功代替,使静止无功发生器保留足够的无功裕度,提高电网应对电压崩溃的能力。最后,将所提出的无功电压控制策略应用到实际光伏电站中,验证了该策略的有效性。     

解决电力市场无功优化问题的序优化级联编码遗传算法

发电机和并联补偿设备是系统中主要的无功源,负责满足负荷的无功需求并维持系统电压水平。在电力市场环境下,对无功的优化调度要考虑各类无功电源的成本,并以此作为建立无功市场机制的基础。建立了一个在电力市场环境下考虑无功成本的无功优化模型,该模型将电容的投切标志作为0-1变量,即描述为一个非线性混合整数规划问题,采用级联编码的遗传算法解决该非线性混合整数规划问题。引入了序优化的思想,以指导遗传算法的个体选择和进化代数,并对计算结果的可靠性进行定量评估。在IEEE 30母线系统上的仿真结果验证了该方法的有效性。根据5种情况的计算结果,分析了考虑无功成本时系统的运行状况以及相应的经济效益。     

A Decomposition Approach to Preventive Planning of Reactive Volt Ampere (VAR) Source Expansion

This paper presents a two-level approach for determining the minimum cost expansion pattern (location and size) of new reactive compensation sources that would ensure steady-state secure operation of large scale power systems not only under a given set of operating conditions for the intact system, but also under contingency conditions. This preventive planning problem is decomposed into a var dispatch (Level 1) problem and a var allocation (Level 2) problem which are related together using the generalized Benders decomposition. The methodology has been implemented in a prototype computer package which can handle power systems with up to 1500 buses. Results of testing the package with practical power systems of different sizes and characteristics are presented.     

Cost analysis of reactive power support

The transfer of real power cannot be realized without reactive power/voltage support. However, compared with real power, the idea of reactive power costing and pricing is still not well defined. In this paper, the economic cost of reactive power is first analyzed. This cost includes the explicit and opportunity cost from various generation sources, such as generators, and the explicit cost from various transmission sources, such as reactive compensators, tap-changing transformers and the cost of transmission losses. Different from previous objectives functions, in this paper a cost-based reactive power dispatch is presented, which minimizes the total cost of reactive power support and determines the resultant reactive profile. This cost-based reactive power dispatch methodology provides information on the costing of reactive power and the information may be further expanded to include the pricing of reactive power     

利用强度Pareto进化算法的多目标无功优化

为更好地解决电力系统多目标无功优化问题,分析了当前多目标无功优化算法存在的缺陷,首次将强度Pareto进化算法(SPEA2)应用于多目标无功优化,为真正意义上的多目标无功优化提供了依据。SPEA2是一种新型的多目标进化算法,参数设置少,收敛速度快,寻优能力强,求得的Pareto最优解分布均匀。IEEE30节点测试系统的算例结果表明所提出的算法在多目标无功优化中具有良好的效果,为各目标之间的权衡分析提供了有效工具,是一种求解多目标无功优化问题的有效方法。     

基于一种新待补偿点定位法的配电网络无功优化

采用一种新的待补偿点确定算法以优化待补偿点的位置和个数.通过减小无效的解空间,结合自适应遗传算法,可进一步提高无功优化的效率和质量.文中以配电网年电能损失、无功补偿设备投资和补偿点固定安装费用之和最小为目标,建立了无功优化的数学模型,并用IEEE 33节点算例验证了该方法的有效性.     

含风电机组的配网无功优化

研究了分布式电源中发展较为成熟的风力发电机组并网后配电网的无功优化问题,提出一种基于场景发生概率的无功优化综合指标,该指标由网损和静态电压稳定裕度两部分构成。基于该指标,提出一种新的无功优化模型。在该模型中,提出风电机组输出功率典型场景的选取策略,无功优化潮流计算中考虑了风电机组的特点,将其作为电压静特性节点处理。在求解方法上,采用基于自适应权重的遗传算法求解。算例表明,提出的模型和算法是可行的,对风电系统的运行具有一定的参考价值。     

A Heuristic Constructive Algorithm for Capacitor Placement on Distribution Systems

This paper presents a heuristic constructive algorithm (HCA) for optimal capacitor placement on distribution systems. This is a nonlinear mixed integer optimization problem. In the proposed approach, the integer variables are represented using sigmoid function, thus interior point optimal power flow formulation can be applied to obtain sensitivity indexes based on Lagrange multipliers, reactive power, and bus voltage. The methodology is applied to two power distribution systems, and the results are compared with those obtained using a genetic algorithm approach.      

电力市场下日无功计划优化模型和算法的研究

针对电力市场下无功辅助服务的特点，提出了计及无功费用的日无功计划数学模型，并应用一种充分结合免疫原理和遗传算法各自优点的混合优化策略来求解该模型。该算法模拟免疫系统中抗体的自我平衡机制，调节遗传算法中个体浓度的抑制和促进；利用免疫记忆功能，实现了优良基因片的重组，提高遗传算法的总体搜索能力。依据抗体结构的可变区和稳定区，该文提出基于有效负荷变化指标的专家知识来对个体的基因进行修补，使遗传算法的优化过程具有指导性。IEEE30节点系统的计算和分析表明了该文所提出模型和算法的正确性和实用性。     

Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads

This paper presents a novel probabilistic algorithm for optimal reactive power provision in hybrid electricity markets. The proposed algorithm is a six-stage multiobjective nonlinear constrained optimization problem which takes into account load forecasting inaccuracies. Considering a set of probable forecasted loads, a three-component expected total market payment function is suggested being minimized as cost function of the first stage. Besides economic issues, expected voltage security margin, deviation from multilateral and pool based energy transactions, deviation from spinning reserve contracts, having adequate local reactive power reserve in each voltage control area of the system and transmission congestion probability are well thought out in stages 2-5 as technical aspects of the market. Finally, in the last stage, using different weighting factors to compromise between all objects, a probabilistic multiobjective function is presented to find the best reactive power market schedule. The proposed algorithm is applied on IEEE 24-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.     

Steady-State Solution for Power Networks Modeled at Bus Section Level

This paper extends the conventional power flow formulation in order to enable the solution of networks modeled at the bus section level. The proposed extension is centered on a methodology to represent zero impedance branches successfully employed in state estimation studies. Accordingly, the active and reactive power flows through switches and circuit breakers are treated as new state variables along with the complex voltage at the network nodes. Information regarding device statuses is included into the power flow problem as new (and linear) equations, producing a solvable non-redundant set of algebraic equations. Applications of the proposed modifications in connection with the power flow solution via Newton-Raphson's method are presented and discussed. The proposed approach provides an efficient tool to directly determine the power flow distribution over selected substations of the network, avoiding unreliable artifices and tedious post-processing procedures required when a conventional power flow formulation is applied. The IEEE 24-bus and IEEE 30-bus test systems are employed to illustrate and evaluate the proposed approach, considering distinct substation layouts.      

计及年电量约束的长期运行计划问题

在电力市场下,长期优化运行计及年电量约束时,不同时段间系统状态不相互独立,这导致常用的随机生产模拟方法无法应用。为此研究了计及可靠性及年电量约束时长期运行计划问题的模型和算法。引入了可靠性约束来计及发电系统可靠性,建立了问题的非线性网络流模型,并将一种新的非线性网络流法与分支定界法相结合,成功地求解了该问题。实践证明,所提出的模型和算法更符合实际系统。     

电力系统无功源最佳配置地点的研究

将先导节点的概念应用于电力系统无功能配置,给出考虑了随机负荷且易于实现的先导节点选择算法,提出了确定无功源最佳配置地点的一整 套方法。     

Economic dispatch of active power by quadrativ programming using a sparse linear complementary algorithm

A new method for economic dispatch of active power is presented. Formulation of the problem as a quadratic program allows the inclusion of a linear network model and operational constraints together with an explicit representation of the cost incurred by transmission losses. The problem is solved by a linear complementary pivoting algorithm which takes full advantage of the sparse form of the objective function and constraints. Computational experience indicates that the method is highly efficient and is capable of solving realistic problems in elapsed times that are compatible with online applications using minicomputer hardware.