混沌粒子群算法及其在发电资源调度中的应用

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于混沌粒子群算法的调度方案。设计了水火电力系统资源调度问题的数学模型,给出了混沌粒子群调度算法的框架,通过引入最优粒子的混沌搜索机制、优势粒子和劣势粒子的权重自适应调节机制,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,本算法方案能有效解决水火发电资源调度问题,具有较好的应用价值。     

差分进化粒子群算法求解发电资源调度问题

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于差分进化粒子群的调度算法。设计了水火电力系统资源调度问题的数学模型,给出了差分进化粒子群优化算法的框架,通过PSO种群和DE种群之间的信息交流机制以寻求全局最优位置,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,该算法能有效解决水火发电资源调度问题,具有较好的应用价值。     

Couple-based particle swarm optimization for short-term hydrothermal scheduling

A novel couple-based particle swarm optimization (CPSO) is presented in this paper, and applied to solve the short-term hydrothermal scheduling (STHS) problem. In CPSO, three improvements are proposed compared to the canonical particle swarm optimization, aimed at overcoming the premature convergence problem. Dynamic particle couples, a unique sub-group structure in maintaining population diversity, is adopted as the population topology, in which every two particles compose a particle couple randomly in each iteration. Based on this topology, an intersectional learning strategy using the partner learning information of last iteration is employed in every particle couple, which can automatically reveal useful history information and reduce the overly rapid evolution speed. Meanwhile, the coefficients of each particle in a particle couple are set as distinct so that the particle movement patterns can be described and controlled more precisely. In order to demonstrate the effectiveness of our proposed CPSO, the algorithm is firstly tested with four multimodal benchmark functions, and then applied to solve an engineering multimodal problem known as STHS, in which two typical test systems with four different cases are tested, and the results are compared with those of other evolutionary methods published in the literature.     

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.     

Large scale economic dispatch of power systems using oppositional invasive weed optimization

This paper presents an evolutionary hybrid algorithm of invasive weed optimization (IWO) merged with oppositional based learning to solve the large scale economic load dispatch (ELD) problems. The oppositional invasive weed optimization (OIWO) is based on the colonizing behavior of weed plants and empowered by quasi opposite numbers. The proposed OIWO methodology has been developed to minimize the total generation cost by satisfying several constraints such as generation limits, load demand, valve point loading effect, multi-fuel options and transmission losses. The proposed algorithm is tested and validated using five different test systems. The most important merit of the proposed methodology is high accuracy and good convergence characteristics and robustness to solve ELD problems. The simulation results of the proposed OIWO algorithm show its applicability and superiority when compared with the results of other tested algorithms such as oppositional real coded chemical reaction, shuffled differential evolution, biogeography based optimization, improved coordinated aggregation based PSO, quantum-inspired particle swarm optimization, hybrid quantum mechanics inspired particle swarm optimization, modified shuffled frog leaping algorithm with genetic algorithm, simulated annealing based optimization and estimation of distribution and differential evolution algorithm.     

基于混合混沌磷虾算法的短期水火电调度研究

Short-Term Hydrothermal Scheduling (STHS) is a nonlinear, multi-constrained and time-varying optimization problem. When the valve point effect is considered, the problem becomes non- convex and more complicated. In order to improve the search ability of the Krill Herd Algorithm (KHA) in the STHS problem, the hybrid chaotic map is introduced to improve the global convergence speed of KHA. In order to avoid premature convergence of the algorithm, by recording the number of times that the fuel cost values of the best individual in each generation remain unchanged and making the decision that a positional mutation in the non-positionally dominant individual within its feasible domain, a hybrid chaotic krill herd algorithm (HCKHA) is proposed. HCKHA and KHA, CKHA were applied to the standard STHS test systems such as "four hydro and three thermal plants" and "four hydro and ten thermal plants", independently. The simulation results show that HCKHA has better optimization ability, fuel cost values and transmission loss values than KHA, CKHA and the optimization methods in other related literatures.     

A novel Coral Reefs Optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids

In this paper we propose a Coral Reefs Optimization algorithm with substrate layers (CRO-SL) to tackle the battery scheduling optimization problem in micro-grids (MGs). Specifically, we consider a MG that includes renewable generation and different loads, defined by their power profiles, and is equipped with an energy storage device (battery) to address its scheduling (charge/discharge duration and occurrence) in a real scenario of variable electricity prices. The CRO-SL is a recently proposed meta-heuristic which promotes co-evolution of different exploration models within a unique population. We fully describe the proposed CRO-SL algorithm, including its initialization and the different operators implemented in the algorithm. Experiments in a real MG scenario are carried out. To show the good battery scheduling performance of the proposed CRO-SL, we have compared the results with what we called a deterministic procedure. The deterministic charge/discharge approach is defined as a fixed way of using the energy storage device that only depends on the pattern of the loads and generation profiles considered. Hourly values of both generation and consumption profiles have been considered, and the good performance of the proposed CRO-SL is shown for four different weeks of the year (one per season), where the effect of the battery scheduling optimization obtains savings up 10 % of the total electricity cost in the MG, when compared with the deterministic procedure.     

Hybrid probabilistic-harmony search algorithm methodology in generation scheduling problem

This paper attempts to propose a fair solution in generation scheduling problem in the presence of inherent uncertainties in short-term power system operation. The proposed methodology incorporates probabilistic methodology in the uncertainties representation section, while harmony search algorithm is adopted as a fast and reliable soft computing algorithm to solve the proposed nonlinear, non-convex, large-scaled and combinatorial problem. As an indispensable step towards a more economical power system operation, the optimal generation scheduling strategy in the presence of mixed hydro-thermal generation mix, deemed to be the most techno-economically efficient scheme, comes to the play and is profoundly taken under concentration in this study. This paper devises a comprehensive hybrid optimisation approach by which all the crucial aspects of great influence in the generation scheduling process can be accounted for. Two-point estimation method is also adopted probabilistically approaching the involved uncertain criteria. In the light of the proposed methodology being implemented on an adopted test system, the anticipated efficiency of the proposed method is well verified.     

A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch

Management and scheduling of reactive power resources is one of the important and prominent problems in power system operation and control. It deals with stable and secure operation of power systems from voltage stability and voltage profile improvement point of views. To this end, a novel Fuzzy Adaptive Heterogeneous Comprehensive-Learning Particle Swarm Optimization (FAHCLPSO) algorithm with enhanced exploration and exploitation processes is proposed to solve the Optimal Reactive Power Dispatch (ORPD) problem. Two different objective functions including active power transmission losses and voltage deviation, which play important roles in power system operation and control, are considered in this paper. In order to authenticate the accuracy and performance of the proposed FAHCLPSO, it applied on three different standard test systems including IEEE 30-bus, IEEE 118-bus and IEEE 354-bus test systems with six, fifty-four and one-hundred-sixty-two generation units, respectively. Finally, outcomes of the proposed algorithm are compared with the results of the original PSO and those in other literatures. The comparison proves the supremacy of the proposed algorithm in solving the complex optimization problem.     

Optimal maintenance scheduling of generators using multiple swarms-MDPSO framework

In this paper, a challenging power system problem of effectively scheduling generating units for maintenance is presented and solved. The problem of generator maintenance scheduling (GMS) is solved in order to generate optimal preventive maintenance schedules of generators that guarantee improved economic benefits and reliable operation of a power system, subject to satisfying system load demand, allowable maintenance window, and crew and resource constraints. A multiple swarm concept is introduced for the modified discrete particle swarm optimization (MDPSO) algorithm to form a robust algorithm for solving the GMS problem. This algorithm is referred to by the authors as multiple swarms-modified particle swarm optimization (MS-MDPSO). The performance and effectiveness of the MS-MDPSO algorithm in solving the GMS problem is illustrated and compared with the MDPSO algorithm on two power systems, the 21-unit test system and 49-unit Nigerian hydrothermal power system. The GMS of the two power systems are considered and the results presented shows great potential for utility application in their area control centers for effective energy management, short and long term generation scheduling, system planning and operation.     

Design and analysis of SSSC-based supplementary damping controller

Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this paper. The design problem of the proposed controller is formulated as an optimization problem, and real coded genetic algorithm (RCGA) is employed to search for the optimal controller parameters. Both local and remote signals with associated time delays are considered in the present study and a comparison has been made between the two signals. The performances of the proposed controllers are evaluated under different disturbances for both single-machine infinite-bus power system and multi-machine power system. Simulation results are presented and compared with a recently published modern heuristic optimization technique under various disturbances to show the effectiveness and robustness of the proposed approach.     

绿色能源互补智能电厂云控制系统研究

针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题, 基于云控制系统理论, 以智能电厂为研究对象, 本文提出了智能电厂云控制系统(Intelligent power plant cloud control system, IPPCCS)解决方案. 基于智能电厂云控制系统, 针对绿色能源发电波动性强、抗扰能力差的问题, 利用机器学习算法对采集到的风电、光伏输出功率进行短时预测, 获知未来风、光机组功率输出情况. 在云端使用经济模型预测控制(Economic model predictive control, EMPC)算法, 通过实时滚动优化得到水轮机组的功率预测调度策略, 保证绿色能源互补发电的鲁棒性, 充分消纳风、光两种能源, 减少水轮机组启停和穿越振动区次数, 在为用户清洁、稳定供电的同时降低了机组寿命损耗. 最后, 一个区域云数据中心的供电算例表明了本文方法的有效性.     

水火电力系统短期优化调度的一种改进粒子群算法

针对水火联调问题,建立满足电量平衡、水量平衡、机组特性及综合利用要求的短期优化调度模型,提出了一种改进粒子群算法(MPSO).MPSO针对粒子群算法易早熟收敛的弊端,引入了变异操作,使粒子以一定的概率向其他粒子个体最好解学习;针对粒子群算法在进化后期多样性受损易陷入局部最优的缺陷,引入了迁徙操作,在种群聚集程度不能容忍时重新生成解空间内均匀分布的粒子.对某典型水火电力系统优化问题的求解结果表明,MPSO比其他方法更有效.     

Hybrid real coded genetic algorithm solution to economic dispatch problem

This paper presents a new, two-phase hybrid real coded genetic algorithm (GA) based technique to solve economic dispatch (ED) problem with multiple fuel options. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local optimization by direct search and systematic reduction in size of the search region method is next employed to do the fine tuning. Constraint satisfaction technique has been employed to improve the solution quality and reduce the computational expenses. In order to validate the effectiveness of the proposed hybrid real coded genetic algorithm, the result of 10-generation unit ED problem with multiple fuel options is considered. The result shows that the proposed hybrid algorithm not only improves the solution accuracy and reliability but also makes the algorithm more efficient in terms of number of function evaluations and computation time. The simulation study clearly demonstrates that the proposed hybrid real coded genetic algorithm is practical and valid for real-time applications.     

基于深度强化学习的模糊作业车间调度问题

针对具有模糊加工时间和模糊交货期的作业车间调度问题,以最小化最大完工时间为目标,以近端策略优化(PPO)算法为基本优化框架,提出一种LSTM-PPO(proximal policy optimization with Long short-term memory)算法进行求解.首先,设计一种新的状态特征对调度问题进行建模,并且依据建模后的状态特征直接对工件工序进行选取,更加贴近实际环境下的调度决策过程;其次,将长短期记忆(LSTM)网络应用于PPO算法的行动者-评论者框架中,以解决传统模型在问题规模发生变化时难以扩展的问题,使智能体能够在工件、工序、机器数目发生变化时,仍然能够获得最终的调度解.在所选取的模糊作业车间调度的问题集上,通过实验验证了该算法能够取得更好的性能.     

Quadratic approximation based differential evolution with valuable trade off approach for bi-objective short-term hydrothermal scheduling

Short-term combined economic emission hydrothermal scheduling (CEES) is a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. In this paper, quadratic approximation based differential evolution with valuable trade off approach (QADEVT) has been developed to solve the bi-objective hydrothermal scheduling problem. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. In this paper, heuristic rules are proposed to handle the water dynamic balance constraints and heuristic strategies based on priority list are employed to handle active power balance constraints. A feasibility-based selection technique is also introduced to satisfy the reservoir storage volumes constraints. To demonstrate the superiority of the proposed approach, simulation results have been compared with those obtained by differential evolution (DE) and particle swarm optimization (PSO) with same heuristic strategies and the earlier reported methods available in literature. The simulation results reveal that the proposed approach is capable of efficiently providing superior solutions.     

An improved self-adaptive PSO technique for short-term hydrothermal scheduling

This paper presents an improved self-adaptive particle swarm optimization algorithm (ISAPSO) to solve hydrothermal scheduling (HS) problem. To overcome the premature convergence of particle swarm optimization (PSO), the evolution direction of each particle is redirected dynamically by adjusting the two sensitive parameters of PSO in the evolution process. Moreover, a new strategy is proposed to handle the various constraints of HS problem in this paper. The results solved by this proposed strategy can strictly satisfy the constraints of HS problem. Finally, the feasibility and effectiveness of proposed ISAPSO algorithm is validated by a test system containing four hydro plants and an equivalent thermal plant. The results demonstrate that the proposed ISAPSO can get a better solution in both robustness and accuracy while compared with the other methods reported in this literature.     

基于GA-PSO的天基预警系统资源调度方法

为解决天基预警系统中的卫星资源调度问题,从预警任务特点出发,在对预警任务进行分解的基础上,建立了资源调度模型．结合传统遗传算法（GA）和粒子群算法（PSO）的优点,采用一种混合遗传粒子群（GA-PSO）算法来求解资源调度问题．该算法在解决粒子编解码问题的前提下,将遗传算法的遗传算子应用于粒子群算法,改善了粒子群算法的寻优能力．实验结果表明,提出的算法能有效解决多目标探测时天基预警系统的资源调度问题,调度结果优于传统粒子群算法和遗传算法．     

实值化学反应优化算法

化学反应优化（CRO）是近年来提出的一个基于种群的元启发式算法,而实数编码化学反应优化（RCCRO）是CRO的一个变体,针对该算法存在的不足提出了一个新的实值化学反应优化（RVCRO）算法求解全局数值优化问题。该算法改进了传统CRO的框架和搜索模式,将控制参数从8个减少到5个;采用一组常用的基准函数来测试其性能,比较结果表明,该算法在最终误差值和收敛速度方面,性能有较大提升。     

射频供能传感网面向融合检测的部署调度方法

当射频供能传感网应用于目标检测时,对节点的部署位置和充电/感知调度表进行合理规划可有效提高系统检测质量.基于融合检测模型,首先归纳了使得系统检测质量最大化的节点部署和调度联合优化问题,证明了该问题是NP完全问题.然后分析了融合半径对检测率的影响,提出了基于贪婪算法的节点部署调度联合优化算法.通过小规模网络、大规模网络及基于真实数据集的仿真,将该算法分别与全局最优解、分阶段优化贪婪算法进行了性能比较.实现结果表明：所提出的联合优化贪婪算法获得的系统检测质量在各组仿真中均优于分阶段贪婪算法,并且在小规模网络中接近于全局最优解.