A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead (NM) simplex search called FAPSO-NM. In the resulting hybrid algorithm, the NM algorithm is used as a local search algorithm around the global solution found by FAPSO at each iteration. Therefore, the proposed approach improves the performance of the FAPSO algorithm significantly. The algorithm is tested on two typical systems consisting of 13 and 40 thermal units whose incremental fuel cost functions take into account the valve-point loading effects.     

Artificial bee colony optimization for economic dispatch with valve point effect

In recent years, various heuristic optimization methods have been proposed to solve economic dispatch (ED) problem in power systems. This paper presents the well-known power system ED problem solution considering valve-point effect by a new optimization algorithm called artificial bee colony (ABC). The proposed approach has been applied to various test systems with incremental fuel cost function, taking into account the valve-point effects. The results show that the proposed approach is efficient and robust when compared with other optimization algorithms reported in literature.     

Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder-Mead algorithm

In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder-Mead (NM) method (called BF-NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system.     

Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and costs using improved ABC algorithm

The aim of this paper is simultaneous minimization of hydrothermal units to reach the best solution by employing an improved artificial bee colony (ABC) algorithm in a multi-objective function consisting of economic dispatch (ED) considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve considering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetic algorithm (GA), the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.     

Interactive DE for solving combined security environmental economic dispatch considering FACTS technology

This paper presents an efficient interactive differential evolution (IDE) to solve the multi-objective security environmental/economic dispatch (SEED) problem considering multi shunt flexible AC transmission system (FACTS) devices. Two sub problems are proposed.The first one is related to the active power planning to minimize the combined total fuel cost and emissions, while the second is a reactive power planning (RPP) using multi shunt FACTS device based static VAR compensator (SVC) installed at specified buses to make fine corrections to the voltage deviation, voltage phase profiles and reactive power violation. The migration operation inspired from biogeography-based optimization (BBO) algorithm is newly introduced in the proposed approach, thereby effectively exploring and exploiting promising regions in a space search by creating dynamically new efficient partitions. This new mechanism based migration between individuals from different subsystems makes the initial partitions to react more by changing experiences. To validate the robustness of the proposed approach, the proposed algorithm is tested on the Algerian 59-bus electrical network and on a large system, 40 generating units considering valve-point loading effect. Comparison of the results with recent global optimization methods show the superiority of the proposed IDE approach and confirm its potential for solving practical optimal power flow in terms of solution quality and convergence characteristics.     

An improved PSO for dynamic load dispatch of generators with valve-point effects

Dynamic load economic dispatch problem (DLED) is important in power systems operation, which is a complicated nonlinear constrained optimization problem. It has nonsmooth and nonconvex characteristics when generator valve-point effects are taken into account. This paper proposes an improved particle swarm optimization (IPSO) to solve DLED with valve-point effects. In the proposed IPSO method, feasibility-based rules and heuristic strategies with priority list based on probability are devised to handle constraints effectively. In contrast to the penalty function method, the constraint-handling method does not require penalty factors or any extra parameters and can guide the population to the feasible region quickly. Especially, equality constraints of DLED can be satisfied precisely. Furthermore, the effects of two crucial parameters on the performance of the IPSO for DLED are also studied. The feasibility and the effectiveness of the proposed method are demonstrated applying it to some examples and the test results are compared with those of other methods reported in the literature. It is shown that the proposed method is capable of yielding higher-quality solutions.     

A new honey bee mating optimization algorithm for non-smooth economic dispatch

The non-storage characteristics of electricity and the increasing fuel costs worldwide call for the need to operate the systems more economically. Economic dispatch (ED) is one of the most important optimization problems in power systems. ED has the objective of dividing the power demand among the online generators economically while satisfying various constraints. The importance of economic dispatch is to get maximum usable power using minimum resources. To solve the static ED problem, honey bee mating algorithm (HBMO) can be used. The basic disadvantage of the original HBMO algorithm is the fact that it may miss the optimum and provide a near optimum solution in a limited runtime period. In order to avoid this shortcoming, we propose a new method that improves the mating process of HBMO and also, combines the improved HBMO with a Chaotic Local Search (CLS) called Chaotic Improved Honey Bee Mating Optimization (CIHBMO). The proposed algorithm is used to solve ED problems taking into account the nonlinear generator characteristics such as prohibited operation zones, multi-fuel and valve-point loading effects. The CIHBMO algorithm is tested on three test systems and compared with other methods in the literature. Results have shown that the proposed method is efficient and fast for ED problems with non-smooth and non-continuous fuel cost functions. Moreover, the optimal power dispatch obtained by the algorithm is superior to previous reported results.     

Energy production cost minimization in a combined heat and power generation systems using cuckoo optimization algorithm

In this paper, cuckoo optimization algorithm is implemented to solve energy production cost minimization in a combined heat and power (CHP) generation system. This problem is also known as combined heat and power economic dispatch problem, which looks for optimal values of power and heat generation of each CHP unit to minimize the total production cost. Cuckoo optimization algorithm is a new metaheuristic algorithm. It is inspired by the life of a bird family, called cuckoo, that special lifestyle of these birds and their characteristics in egg laying and breeding has been the basic motivation for development of this algorithm. Unlike of the some previous approaches, the effect of valve point is considered in the cost function and clearly formulated in the conventional polynomial cost function as absolute sinusoidal term. The proposed method is applied to three small (with three different test cases), medium, and large test systems in order to evaluate its efficiency and feasibility. The obtained results demonstrated a higher quality solution and superior performance of the proposed cuckoo optimization algorithm method in comparison with many existing methodologies.     

基于NSGWO的火电厂负荷优化分配研究

针对传统多 目标灰狼优化算法易出现局部最优和稳定性差的问题,提出了一种改进的多 目标灰狼算法.引入基于顺序查找策略(ENS)的非支配排序方法提高算法的速率,采用基于参考点的选取策略以均衡算法优化结果分布,并通过模拟二进制交叉进化机制改善算法跳出局部最优解的能力.结合多 目标算法的评价指标,开展基准函数仿真试验,验证了改...     

含DG随机出力的配电网多目标无功优化

针对分布式电源(DG)出力具有间歇性和不确定性的问题,建立了基于两点估计法(2PEM)含DG随机出力的配电网概率潮流计算模型,并将基于Pareto最优前沿解集的多目标进化算法(MOEA)与两点估计概率计算模型相结合,建立了以网损、节点电压偏移量及优化成本为目标函数的多目标无功优化模型。将该优化模型应用于IEEE33标准节点测试系统中的仿真结果表明,该方法具有较好的适应性,能为决策者提供多样性选择,增加了决策的灵活性。     

Parameter Estimation of Fouling Models in Crude Preheat Trains

Several fouling mitigation techniques depend on the capacity of predicting fouling rates. Therefore, the identification of accurate fouling rate models is an important task. Crude fouling rates are usually evaluated through empirical or semiempirical models. In both alternatives, there are parameters that must be determined through laboratory or process data. In this context, the article presents an analysis of the parameter estimation problem involving fouling rate models. A proposed procedure for addressing this problem is described through the development of a computational routine called HEATMODEL. An important aspect of this study is focused on the obstacles associated to the search for the optimal set of parameters of the Ebert and Panchal models and its variants. This optimization problem may present some particularities that complicate the utilization of traditional algorithms. In the article, the performance of a conventional optimization algorithm (Simplex) is compared with a more modern numerical technique (a hybrid genetic algorithm) using real data from a Brazilian refinery. The results indicated that, due to the complexity of the parameter estimation problem, the Simplex method may be trapped in poor local optima, thus indicating the importance of the utilization of global optimization techniques for this problem.     

Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index

A solution to the combined hydro-thermal-wind scheduling problem of multi reservoir cascaded hydro plants is presented employing a novel ant lion optimization (ALO) algorithm. Five objectives, cost, various emissions and power loss, are simultaneously optimized. The optimal schedules of thermal, hydro and wind power (WP) units are determined for continuously varying load subject to a large number of practical operational constraints. The effect of reserve and penalty coefficients and WP uncertainty is also investigated for the multi-objective (MO) problem. The newly proposed ALO algorithm has unique features like random walk, roulette wheel, and boundary shrinking. These operations provide a judicious balance between exploration and exploitation, and create a powerful optimization technique for complex real-world problems.Finding the best compromise solution (BCS) is a tedious task when multiple objectives are involved. A composite ranking index (CRI) is proposed as a performance metrics for MO problems. The CRI helps the decision maker in ranking the large number of Pareto-optimal solutions. The developed model is tested on three standard systems, having a mix of hydro, thermal and wind generators. The performance is found to be superior to published results and comparable with established algorithms like artificial bee colony (ABC) and differential evolution (DE).     

基于改进粒子群算法的火储联合调频优化储能控制策略研究

为了解决火储联合调频协同控制效果与储能系统成本回收的问题,提出一种改进粒子群算法的储能优化控制策略,通过引入自适应权重调整全局搜索方向,并解决储能电荷状态与出力状态的耦合性问题,建立火储联合调频综合性能指标进行储能控制策略研究。案例仿真结果表明：本文所提算法比传统算法在收敛效果方面具有明显优越性,避免了局部最优解问题;当储能系统电荷状态设置在57.5%和42.5%时,提高了机组调频响应效果,延长了储能设备的使用寿命。     

基于改进粒子群算法的风光水互补发电系统短期调峰优化调度

风光水互补发电系统优化调度需要考虑风光电源的间歇性及波动性,同时还要处理梯级水库复杂的水力联系及不同电源之间的电力联系,因而建立风光水互补发电系统短期调峰优化调度模型,并采用粒子群算法进行求解,针对粒子群算法的早熟及后期收敛速度慢等问题,从惯性因子和种群拓扑结构两方面对粒子群算法进行改进,并对福建省电力调控中心管辖的12座常规水电站、木兰溪1座抽水蓄能电站、31座风电场、5座光伏电站组成的风光水多种电源互补系统进行数值分析。结果表明,所建模型能较好地实现对电网负荷的削峰填谷,所提算法显著提高了求解效率和求解质量,是一种解决风光水互补发电系统短期联合优化调峰调度实用性很强的有效算法。     

基于遗传算法的机炉协调系统PID控制器优化

设计有效的机炉协调控制系统对提高热工自动化水平具有重要意义。针对火电机组的机炉控制系统，提出了一种基于遗传算法的多变量PID控制器参数优化方法。利用遗传算法提供的通用框架，在控制系统结构和控制器形式确定的情况下对控制参数进行全局优化。方法具有全局并行优化和面向目标函数的特点，与广义ZN整定方法相比的仿真研究表明，优化后的协调控制系统的动态性能有明显改善。并且客易扩展到其它控制方案下的参数优化，显示了方法的可行性和适用性。     

基于模型预测的多模式供热电厂机组间负荷优化分配调度研究

为解决热电厂机组间负荷分配不合理的问题,提出一种基于模型预测的多模式供热电厂多机组间负荷实时优化分配方法。基于模块化建模原理构建热电厂全厂范围的机理仿真模型,并运用运行数据对模型辨识校准,根据机组特性和电网调峰补贴政策,建立全厂的运行经济性收益评估模型,进而设计基于粒子群算法的负荷实时优化方法,借助性能预测模型预测评估各方案的经济性。以某包含高背压、切缸、抽汽、光轴4种供热模式机组的电厂为例,对不同电、热负荷组合工况下的厂内负荷进行优化分配研究。应用结果表明：该方法可根据热、电负荷的实时指令在线获得经济性优化的厂内机组间负荷分配方案。     

基于煤质预测的多目标优化配煤方法研究

针对火电厂中混煤煤质计算不准确、配煤方案单一且考虑片面等问题,基于粒子群（PSO）优化前馈神经网络算法建立了混煤煤质预测模型;采用非支配排序多目标遗传算法（NSGA III）建立了由最小绝对偏差型和标准差型优化指标组成的多目标优化配煤模型。对某电厂实际燃煤情况中非线性关系的煤质进行分析,并对预测煤质的不同特点和电厂机组运行特点进行分析。结果表明：基于煤质预测的多目标优化配煤方法,对煤质挥发分、灰分和灰熔点的预测精度比线性加权方法提高了4.55%,3.24%和5.60%,筛选出的6组配煤方案,兼顾了经济性、安全性和环保性,更符合配煤特点。     

Design and techno-economical optimization for stand-alone hybrid power systems with multi-objective evolutionary algorithms

The optimal design of the hybrid energy system can significantly improve the economical and technical performance of power supply. However, the problem is formidable because of the uncertain renewable energy supplies, the uncertain load demand, the nonlinear characteristics of some components, and the conflicting techno-economical objectives. In this work, the optimal design of the hybrid energy system has been formulated as a multi-objective optimization problem. We optimize the techno-economical performance of the hybrid energy system and analyse the trade-offs between the multi-objectives using multi-objective genetic algorithms. The proposed method is tested on the widely researched hybrid PV-wind power system design problem. The optimization seeks the compromise system configurations with reference to three incommensurable techno-economical criteria, and uses an hourly time-step simulation procedure to determine the design criteria with the weather resources and the load demand for one reference year. The well-known efficient multi-objective genetic algorithm, called NGAS-II (the fast elitist non-dominated sorting genetic algorithm), is applied on this problem. A hybrid PV-wind power system has been designed with this method and several methods in the literature. The numerical results demonstrate that the proposed method is superior to the other methods. It can handle the optimal design of the hybrid energy system effectively and facilitate the designer with a range of the design solutions and the trade-off information. For this particular application, the hybrid PV-wind power system using more solar panels achieves better technical performance while the one using more wind power is more economical. Copyright © 2006 John Wiley & Sons, Ltd.     

基于改进飞蛾优化算法的水火互联电网负荷频率控制

负荷频率控制是互联电网进行有功调频的重要方法,设计了一种基于改进飞蛾优化算法的水火互联电网负荷频率控制方案,该方案将Lévy飞行策略引入到飞蛾优化算法中,同时在飞蛾的更新公式中引入动态惯性权值和历史最优火焰平均值以改善算法性能,并对互联电网分数阶PID控制器参数进行优化整定,以提高收敛速度及寻优精度。建立三区域水火互联电网LFC系统仿真模型,采用改进飞蛾优化算法搜索获得最优的分数阶PID控制器参数,并在阶跃负荷扰动下进行仿真分析。结果表明,所提方案具有很好的鲁棒性和控制效果。     

A practical algorithm for optimal operation management of distribution network including fuel cell power plants

Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders.