Dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants

This paper proposes dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed‐integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), multi‐population DEEPSO (MP‐DEEPSO), and brain storm optimization have been applied so far. When optimal operational planning of numbers of energy plants is calculated simultaneously in a data center, a challenge is to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for the challenge is speeding up by parallel and distributed computing. It utilizes numbers of processes and countermeasures for various faults of the distributed processes should be considered. Moreover, successive calculation at every control interval is required for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality is required even if some of the calculation results cannot be returned from distributed processes. It is verified that total energy cost by the proposed dependable multi‐population improved brain storm optimization with differential evolution strategy based method is lower than those by the compared methods, and higher quality of solutions can be kept even with high fault probabilities.     

计及差异化需求响应的微电网源荷储协调优化调度

为应对微电网中源荷匹配性较差及弃风弃光的问题,计及需求响应对微电网源荷储协调优化调度进行研究。为了更准确地体现实际需求响应的特点,根据电量价格响应弹性的非线性特点和不同类型负荷响应弹性的差异性,提出基于指数变化的差异化需求响应机制;建立以系统运行成本最低为目标的微电网源荷储协调优化调度模型;通过引入多核并行运行环境和双策略微分进化变异机制构造并行双策略微分进化算法,该算法兼顾寻优深度和寻优速度,实现了对模型的高效求解。算例结果表明,所提方法能够有效改善源荷两侧的匹配度以实现削峰填谷,并能提升系统风光消纳量以及节约运行成本。     

电力系统暂态稳定紧急控制并行序贯优化算法

电力系统暂态稳定紧急控制可建模成一个大规模的动态优化问题,为此,提出一种基于并行灵敏度分析的双层优化算法。在仿真层,采用有限元正交配置离散微分代数方程,并结合并行计算技术求解状态量及灵敏度;在优化层,采用预测校正内点法求解较小规模的非线性规划问题。算例结果表明,所提方法具有较高的计算效率。     

采用辅助问题原理的多分区并行无功优化算法

针对大规模电网集中式串行无功优化计算所面临的计算问题和瓶颈问题,基于分布式并行计算思想建立一种多分区并行无功优化模型,并采用辅助问题原理综合考虑D–变量提出一种附加函数,有效地将全网的优化问题完全分解为多个子区独立的优化问题,同时解决了分区计算所引起的多平衡点问题。该方法实现了完全的分布式优化,解决了数据集中上传的瓶颈问题,有效降低了优化问题的求解规模,大大缩短了总运算时间,提高了计算与控制的实时性和灵活性。仿真结果表明：该方法有效可行,计算速度快,收敛性好。     

Multi-Core CPU Parallel Power Flow Computation in AC/DC System Considering DC Control

In order to integrated the control mode of the High-Voltage Direct Current (HVDC) into the power flow solution method and make it applicable to large-scale AC/DC hybrid system, a multi-core CPU parallel power flow computation method was proposed. First, the mathematical model of HVDC system and the frequently-used control methods of converter are introduced, and then models that have not been considered by conventional power flow programs, such as the adjustment of converter transformer tap and reactive power control management of the converting station, are built. Next, the loop-iteration algorithm is proposed to simulate the conversion of the HVDC control modes, and the AC/DC sequential solution is adopted to solve the AC/DC hybrid power flow. To solve the low speed problem of power flow solution for large-scale systems containing multi-circuit two-terminal DC, this paper handles each circuit separately so that the parallel computation capability of multi-core CPU can be used to improve the computation speed. Finally, the accuracy of the algorithms is validated in a small-scale hybrid system with 5-node and 2-circuit DC, and the excellent convergence and computation efficiency of the algorithms are proved by a large-scale hybrid system with 9241-node and 60-circuit DC as well.     

并行自适应粒子群算法在电力系统无功优化中的应用

针对传统粒子群优化算法"早熟"与后期收敛速度慢的缺点,提出了一种基于并行自适应粒子群优化算法的电力系统无功优化方法。该方法首先将初始种群随机划分成N个子群,然后分别在各子群中以所提方法寻优,从而实现了算法的并行计算。为避免各子群陷入局部最优解,采用二值交叉算子使各子群间的信息共享并更新相关粒子位置,保证了算法的全局搜索能力并维持了种群的多样性。同时,各子群寻优过程中,根据利己、利他及自主3个方向对当前搜索方向自适应更新,提高了算法的收敛速度。将所提出算法在IEEE 30节点系统上进行了仿真验证,结果证明了并行自适应粒子群算法用于无功优化的可行性和有效性。     

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.     

A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy.     

基于混合优化微分进化算法的电力系统无功优化

阐述了一种基于混合优化微分进化算法的无功优化方法。混合优化微分进化算法是一种直接随机搜索方法,由在当前种群中随机采样的个体之间的基因差异来驱动,混合优化微分进化算法的主要思想是采用不同的策略产生变异算子,并在进化过程中采取父代和子代合群处理,来提高进化速度。将该无功优化方法在IEEE 30节点系统上进行了校验,并与基于其它算法的无功优化方法进行比较,仿真结果表明该算法具有收敛速度快、鲁棒性好、计算精度高的优点。     

基于SA-PSO的电力系统无功优化

粒子群优化算法是一种简便易行,收敛快速的演化计算方法。但该算法也存在收敛精度不高,易陷入局部极值的缺点。针对这些缺点,对原算法加以改进,引入了自适应的惯性系数和模拟退火算法的思想,提出了一种新的模拟退火粒子群优化(simulated annealing particle swarm optimization,SA-PSO)算法,并将其应用于电力系统无功优化。对IEEE14节点系统进行了仿真计算,并与PSO算法作了比较,结果表明SA-PSO算法全局收敛性能及收敛精度均较PSO算法有了较大提高。     

电力系统最优潮流的分布式并行算法

在构造电力系统分解协调模型的基础上，采用辅助问题原理进行分布式并行最优潮流运算，并用通用增广拉格朗日法加快收敛速度，对分布式并行优化算法做了详细推导。仿真结果表明，该算法可显著加快大系统的优化速度，提高控制的实时性和可靠性，符合电力市场发展的需求。     

A simulated rebounding algorithm applied to the multi-stage security-constrained transmission expansion planning in power systems

This research discusses the multi-stage security-constrained transmission network expansion planning. In modern power systems, the problem is formulated as a large-scale, mixed-integer, non-linear programming problem, which for a real power systems is very difficult to solve. Although remarkable advances have been made in optimization techniques, finding an optimal solution to a problem of this nature can still be extremely challenging. In this paper, a new constructive heuristic approach, based on a local controlled random search (simulated rebounding algorithm) is proposed to choose the decision variables. The model can produce better solutions than other references techniques such as particle swarm optimization, evolutionary particle swarm optimization, genetic algorithms, and simulated annealing algorithm, among other evolutionary methods. The methodology is applied to assess the capabilities of the proposed approach in the Ecuadorian and Chilean Power Systems as an example of application. Simulation results show that the proposed approach is accurate and very efficient, and it has the potential to be applied to real power system planning problems. The algorithm has been presented and applied to the multi-stage security-constrained transmission expansion planning.     

基于REI等值的多区域无功优化并行计算(英文)

将REI等值技术应用于求解多区域电力系统的无功优化并行计算问题,对系统各个分区的外部网络进行REI等值化简,并对相角传递、等值网络初值计算、外层协调计算与REI等值网络修正等关键问题提出了具体的解决办法。并以此为基础建立了适合多区域无功优化的并行计算模式,通过采用Matlab并行计算平台实现无功优化并行计算,以IEEE 39节点和某695节点实际系统作为算例,通过与集中优化方法和基于Ward等值的多区域无功优化并行算法进行比较,对所提方法的有效性和优缺点进行了详细分析。     

基于混合粒子群优化算法的PSS参数优化

将一种新的进化算法—粒子群优化算法(PSO)应用到电力系统稳定器(PSS)参数优化当中,文中使用引入交叉操作的混合粒子群优化算法(HPSO),可以获得更好的全局搜索能力和收敛速度。先以低频振荡范围内(0.1~2Hz)PSS产生的附加阻尼转矩ΔTe与Δω尽可能同相位为目标优化PSS超前-滞后环节参数;再以小扰动时发电机功率和角速度振荡最小为目标整定PSS放大倍数。优化结果表明,HPSO算法可以有效地解决PSS参数优化问题。     

A solution for unit commitment using Lagrangian relaxation combined with evolutionary programming

This paper proposes an approach which combines Lagrangian relaxation principle and evolutionary programming for short-term thermal unit commitment. Unit commitment is a complex combinatorial optimization problem which is difficult to be solved for large-scale power systems. Up to now, the Lagrangian relaxation is considered the best to deal with large-scale unit commitment although it cannot guarantee the optimal solution. In this paper, an evolutionary programming algorithm is used to improve a solution obtained by the Lagrangian relaxation method: Lagrangian relaxation gives the starting point for a evolutionary programming procedure. The proposed algorithm takes the advantages of both methods and therefore it can search a better solution within short computation time. Numerical simulations have been carried out on two test systems of 30 and 90 thermal units power systems over a 24-hour periods.     

基于CIM的配电网重构的并行遗传计算

针对基于遗传算法的配网重构计算搜索时间过长、过早收敛的问题,引入发布式并行计算方法,从函数级主从式并行计算模型和群体级对等式计算模型两个方面组织局域网内的多台机器进行网络重构的并行计算,在配电网的建模中采用基于公用信息模型(C IM)的配网模型以及基于此模型的广度优先遍历方法有效地提高配网拓扑分析的效率,同时扩展了配网重构算法的开放性,算例结果表明,两种并行计算模型取得了较好的优化结果,显著地提高了计算速度。     

基于混合差异进化优化算法的电力系统无功优化

无功优化是电力系统实现电压和无功功率最优控制和调度的基础，阐述了一种基于混合差异进化算法的新无功优化方法。混合差异进化算法是一种直接随机搜索方法，由在当前种群中随机采样的个体之间的基因差异来驱动，且为缩短计算时间、避免陷入局部最优，在算法中嵌入了加速操作和种群迁移操作。将该无功优化方法在IEEE 30节点系统上进行了校验，并与基于其他算法的无功优化方法进行比较，仿真结果表明该算法具有收敛速度快、鲁棒性好、计算精度高的优点。     

无功优化协同进化计算的控制变量分区方法研究

在无功优化协同进化计算中,将控制变量合理地分区分组是算法正常运行的前提,也是获得良好并行性能的关键。参考无功优化控制变量分区问题与分级电压控制中电网分区问题之间的关系,提出将控制变量分区问题转换为降阶电网分区问题,并构造降阶电网分区优化模型。在此基础上,引入种子分区编码方法,提出一种能自动确定分区数目的方法。该方法使用向上归并法进行初步分区,降低了分区规模,并采用种子分区编码法将分区数目等信息编入染色体,解决了分区数目难以确定的问题。系统计算表明,新分区方法能自动确定分区数目,快速地对系统控制变量进行合理地划分。将该方法应用到协同进化计算中,能提高协同进化计算的并行性,保证算法寻优效率。     

An improved PSO technique for short-term optimal hydrothermal scheduling

This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE.     

多阶段输电网络最优规划的并行蚁群算法

多阶段输电网络最优规划是一个复杂的非线性组合优化问题,难以采用传统的数学优化方法求解。蚁群算法是近年来出现的用于解决组合优化问题的一种高效的内启发式搜索技术,但存在着未成熟收敛问题。文中给出了多阶段输电网络最优规划的数学模型及其解的向量形式;详细分析了传统蚁群算法的未成熟收敛现象及其原因;提出一种并行蚁群算法并用于求解多阶段输电网络最优规划问题。并行蚁群算法无需初始可行解,能很好地协调局部搜索与全局搜索,在加快计算速度的同时有效地避免了因参数设置、种群规模等不同而引起的未成熟收敛。对实际算例的计算结果表明,该方法具有很高的计算效率和良好的全局收敛性。