Improved multi-objective artificial bee colony algorithm for optimal power flow problem

The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.

Multi-objective optimization for voltage and frequency control of smart grids based on controllable loads

The output uncertainty of high-proportion distributed power generation severely affects the system voltage and frequency.Simultaneously,controllable loads have also annually increased,which markedly improve the capability for nodal-power control.To maintain the system frequency and voltage magnitude around rated values,a new multi-objective optimization model for both voltage and frequency control is proposed.Moreover,a great similarity between the multiobjective optimization and game problems appears.To reduce the strong subjectivity of the traditional methods,the idea and method of the game theory are introduced into the solution.According to the present situational data and analysis of the voltage and frequency sensitivities to nodal-power variations,the design variables involved in the voltage and frequency control are classified into two strategy spaces for players using hierarchical clustering.Finally,the effectiveness and rationality of the proposed control are verified in MATLAB.

Multi-objective Dynamic Optimal Power Flow of Wind Integrated Power Systems Considering Demand Response

This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MDOPF).Within the model,fuel cost,carbon emission and active power losses are taken as objectives,and an integrated dispatch modeof conventional coal-fired generation,WG and DRis utilized.The corresponding solution process to the MDOPF is based on ahybrid of a non-dominated sorting genetic algorithm-II(NSGA-II)and fuwzy satisfaction-maximizing method,where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy.Illustrative cases of different scenarios are performed based on an IEEE 6-units\,30-nodes system,to verify the proposed model and the solution process,as well as the benefits obtained by the DR into power system.

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.

Solving multi-objective optimal power flow problem considering wind-STATCOM using differential evolution

In this paper, a simple strategy based differential evolution was proposed for solving the problem of multi-objective environmental optimal power flow considering a hybrid model （Wind-Shunt-FACTS）. The DE algorithm optimized simultaneously a combined vector control based active power of wind sources and reactive power of multi STATCOM exchanged with the electrical power system to minimize fuel cost and emissions. The proposed strategy was examined and applied to the standard IEEE 30-bus with smooth cost function to solve the problem of security environmental economic dispatch considering multi distributed hybrid model based wind and STATCOM controllers. In addition, the proposed approach was validated on a large practical electrical power system 40 generating units considering valve point effect. Simulation results demonstrate that choosing the installation of multi type of FACTS devices in coordination with many distributed wind sources is a vital research area.

Decomposition-Based Multi-Objective Optimization for Energy-Aware Distributed Hybrid Flow Shop Scheduling with Multiprocessor Tasks

This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It consists of three sub-problems,i.e.,job assignment between factories,job sequence in each factory,and machine allocation for each job.We present a mixed inter linear programming model and propose a Novel MultiObjective Evolutionary Algorithm based on Decomposition(NMOEA/D).We specially design a decoding scheme according to the characteristics of the EADHFSPMT.To initialize a population with certain diversity,four different rules are utilized.Moreover,a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors.To enhance the quality of solutions,two local intensification operators are implemented according to the problem characteristics.In addition,a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence,which can adaptively modify weight vectors according to the distribution of the non-dominated front.Extensive computational experiments are carried out by using a number of benchmark instances,which demonstrate the effectiveness of the above special designs.The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.

Hierarchical Optimal Power Flow Control for Loss Minimization in Hybrid Multi-terminal HVDC Transmission System

A hierarchical control scheme is proposed for optimal power flow control to minimize loss in a hybrid multiterminal HVDC(hybrid-MTDC)transmission system.In this scheme,the lower level is the droop control,which enables fast response to power fluctuation and ensures a stable DC voltage,and the upper level is power flow optimization control,which minimizes the losses during the operation of hybrid-MTDC and solves the contradiction between minimizing losses and preventing commutation failure.A 6-terminal hybrid-MTDC is also designed and simulated in PSCAD according to the potential demand of power transmission and wind farms integration in China to verify the proposed control strategy.First,the steady state analysis is conducted and then compared with simulation results.The analysis shows that the proposed control scheme achieves the desired minimum losses while at the same time satisfying system constraints.The proposed control scheme also guarantees that the hybrid-MTDC not only has a good dynamic response,but also remains stable during communication failure.

A hybrid multi-objective optimization method considering optimization problems in power distribution systems

Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multiple objectives with trade off relationships among each other,and also various constraints in the achievement of these objectives.Therefore,it should be essential to solve multiobjective optimization problems effectively in the applications of these new technologies in power systems.This paper proposes an improved method to realize multiobjective optimization for critical challenges in advanced power systems.To realize that,in an optimal dispersed generation installation problem,that is,one of effective measures for low-carbon power systems,various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed.The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.

Optimal power flow calculation in AC/DC hybrid power system based on adaptive simplified human learning optimization algorithm

This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,respectively.ASHLO algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive strategies.To compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test system.Numerical results indicate that the ASHLO method has good convergent property and robustness.Meanwhile,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power systems.Due to the high complexity with continuous and discrete control variables,modern heuristic optimization algorithms(HOAs)have been widely employed for the solution of OPF.This paper provides an overview of the latest applications of advanced HOAs in OPF problems.The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced,including genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and evolutionary programming(EP),etc.

含混合型潮流控制器的风电并网系统潮流优化

混合型潮流控制器(hybrid power flow controller,HPFC)可以有效解决风电并网系统中存在的支路潮流过载问题,且相较于统一潮流控制器成本更低。针对现有的HPFC潮流优化研究尚未计及支路潮流最大值约束和风电不确定性的问题,提出一种基于场景削减的含HPFC风电并网系统最优潮流模型。首先,建立HPFC的功率注入模型,并推导了注入功率表达式;其次,采用K均值算法削减风电、负荷概率场景,通过CH(+)指标选择最优场景集合;最后,建立兼顾发电机运行成本、系统网络损耗、正常运行及N-1故障下的支路负载率的多目标优化模型,采用多目标粒子群优化(multi-objective particle swarm optimization,MOPSO)算法进行求解,利用模糊满意度函数在Pareto解集中筛选出折衷解。在MATLAB中仿真验证所提方法的有效性,结果表明该方法可以计及风电不确定性,保证电网在不同场景下的安全经济运行。

计及网络重构的旋转潮流控制器规划方法

为了在配电网中合理配置旋转潮流控制器(RPFC),提出一种计及网络内重构的区域间基于RPFC的柔性互联方法,搭建兼顾网络经济性与可靠性的双层模型。规划层对RPFC的选址定容进行求解,运行层制定配电网的重构方案与RPFC运行策略,同时评估故障状态下的网络可靠性。利用RPFC调节区域间互联线路的传输功率,促进新能源消纳,并配合网络重构以充分发挥网络灵活性,降低所需配置RPFC容量。综合考虑网络运行成本、停电损失成本、RPFC投资成本,采用改进蚁狮优化算法与二阶锥规划混合算法对模型进行求解,实现规划-运行双层互动。以改进的IEEE 33节点与IEEE 22节点互联网络为算例进行仿真,验证所提模型的有效性。

Optimization Design of the Grate Cooler Based on the Power Flow Method and Genetic Algorithms

As an important process during the cement production,grate cooler plays significance roles on clinker cooling and waste heat recovery.In this paper,we measured experimentally the heat balance of the grate cooler,which provided initial operating parameters for optimization.Then,the grate cooler was simplified into a series-connected heat exchanger network by power flow method.Constructing the equivalent thermal resistance network provided the global constraints by Kirchhoff’s law.On this basis,with the objectives of the minimum entropy generation numbers caused by heat transfer and viscous dissipation,solving a multi-objective optimization model achieved the Pareto Front by genetic algorithm.Then selecting the scheme of the lowest fan power consumption obtained the optimal operating parameters of the grate cooler.The results showed that the total mass flow of the optimized scheme did not change significantly compared with the original scheme,but the fan power consumption was 25.44%lower,and the heat recovery efficiency was 88.43%,which was improved by 11.35%.Furthermore,the analysis showed that the optimal operating parameters were affected by the local heat load.After optimizing the diameter of clinker particles within the allowable industrial range,the clinker with particle diameter of 0.02 m had the optimal performance.

电气化公路交直流混合供电系统最优潮流分析

针对传统粒子群算法在求解电气化公路供电系统最优潮流问题时易陷入局部最优解等问题,为提高算法的全局搜索能力,采用牛顿拉夫逊法对交直流混合潮流交替迭代求解,得出供电系统潮流分布,提出以最小系统损耗为目标的交直流混合最优潮流模型,设计了一种改进的线性递减惯性权重自适应学习因子粒子群算法(改进LDW-PSO算法).仿真结果显示,与传统粒子群算法和线性递减权重算法相比,改进算法收敛速度更快,计算精度更高,为电气化公路供电系统的最优潮流计算提供了理论支撑和技术参考.

基于混沌免疫混合算法的多目标无功优化

针对目前评价多目标函数解的不足,提出了将多目标函数各个解映射成多维空间中不同的点,利用这些点与理想点之间的欧氏距离来衡量各个解的优劣;同时针对无功优化、混沌优化算法和免疫算法的特点,提出了在采用免疫算法进行无功优化的记忆抗体群中,运用混沌优化方法和免疫算法的交叉和变异等操作对无功优化的连续变量和离散变量进行交替优化求解,并将它们运用于以降低有功损耗,提高电压稳定裕度及减小电压偏移为目标的无功优化中;通过IEEE-30和IEEE-118节点算例系统验证了混合算法及最优解评价方法的正确性和可行性。

基于能源集线器的区域综合能源系统分层优化调度

针对时变电价且运行方法灵活的中小型区域综合能源系统,提出一种基于能源集线器的分层优化模型。研究区域综合能源系统耦合关系和热电比可调机理,建立能源集线器扩展模型。计及包括用能替代的综合需求侧响应,以综合用能成本较低和用能能效较高为目标建立双层优化模型,采用Lagrange乘子法将下层目标KKT条件作为上层优化的可行性度量进行求解。对于具体ICES网络拓扑,考虑能量连接器的动态过程并计算混合潮流,选取电/热/气网运行状态评价指标,对多目标问题进行Pareto决策面搜索,应用模糊综合决策方法在机组分配方案中择优。算例验证了所提方法的有效性。

大型交直流并联输电网网损优化理论及其在南方电网中的实现

交直流并联电网的网损优化问题与传统无功优化问题有很大不同,纯交流电网主要通过合理配置系统无功以降低网损,而交直流并联电网则是通过调整直流功率改变有功潮流分布,从而实现降低全网网损。尤其对于远距离、大容量、交直流并联送电的南方电网,调整交直流潮流分布降低主网网损则具有更加重要的意义。然而实际运行中主要依靠经验调整交直流潮流,亟需一种能够指导交直流输电网损优化的理论方法和适用于大规模系统的网损分析工具。该文系统分析交直流潮流分布影响网损的机理,提出相应网损优化最优潮流模型和最优等网损微增率原则。同时提出适用于大区电网规模的实用计算方法,在保留系统完整拓扑模型基础上实现算法的高准确性、稳定性和快速性。所提出的交直流并联电网网损分析理论和实用方法,已经应用于南方电网实际调度运行工作,并取得了较大的降损效益。

含柔性直流电网的交直流混联系统潮流优化控制

含柔性直流电网的交直流混联系统中换流站功率参考值和下垂系数分别决定了换流站功率分配以及直流电压与直流功率间的斜率控制关系,进而决定了整个交直流混联系统的潮流分布,因此功率参考值和下垂系数的选取至关重要。为此,提出一种含柔性直流电网的交直流混联系统潮流优化控制策略,对功率参考值和下垂系数进行优化,以实现系统网络损耗和直流电压偏差率综合最小的目标。首先,针对含柔性直流电网的交直流混联系统建立了换流站模型、交流系统模型和直流电网模型,并建立了直流电网在主从运行方式或下垂运行方式下的控制方程统一表达式。然后,基于含柔性直流电网的交直流混联系统最优潮流,在线自适应修正换流站功率参考值和下垂系数。最后,对不同运行方式下含五端柔性直流电网的修正IEEE 39和IEEE 118节点交直流混联系统进行计算分析,其结果验证了所述方法的有效性。

基于暂态能量裕度灵敏度计及暂态稳定约束的优化潮流计算

提出了一种基于暂态能量裕度灵敏度的含有暂态稳定约束的优化潮流算法。它用时域仿真法对系统进行暂态稳定分析,提前滤除不失稳故障,并结合混合法计算失稳故障的能量裕度。依据最严重故障的能量裕度与发电机有功出力变化量的近似线性关系及能量裕度对发电机有功出力的灵敏度来调整发电机的有功出力。在计算程序中充分运用变步长搜索策略提高了计算效率,对考虑多个预想故障的含有暂态稳定约束的优化潮流问题具有良好的全局收敛性。最后通过IEEE30节点系统和IEEE57节点系统验证了该方法的有效性。

计及VSC-HVDC的交直流系统最优潮流统一混合算法

进化类算法和内点法交替迭代的混合算法在求解含电压源换流器的高压直流输电(voltage source converter basedhigh voltage direct current,VSC-HVDC)的交直流系统最优潮流(optimal power flow,OPF)问题时由于截断误差的影响和VSC-HVDC控制方式的限制,容易发生振荡,因此提出一种基于差分进化(differential evolution,DE)和原—对偶内点法(primal-dual interior point method,PDIPM)的统一混合迭代算法。算法的主要思想是以DE算法为框架,对离散变量进行优化,在DE算法的每一次迭代过程中,采用PDIPM对每个DE个体进行连续变量的优化和适应度评估。由于采用PDIPM进行DE种群适应度评估,无需设定VSC-HVDC的控制方式,因此提高了算法的全局寻优能力。多个算例结果表明,该混合算法数值稳定性高,寻优能力强,能很好地解决含两端、多端、多馈入VSC-HVDC的交直流系统最优潮流问题。