多智能体技术在机组组合运行与管理分析中的应用

电力系统机组组合问题是一个典型的大规模混合整数的非线性组合优化问题,很难得到理论上的最优解.该文提出了求解机组组合问题的多智能体体系结构,并且提出了自治与分级管理相结合的3层MAS系统体系结构,最终通过协调Agent、任务Agent、发电Agent的协商、谈判,获得了机组组合问题的一个较为满意的解.     

一种用于AGC机组调配的混沌多Agent双重粒子群算法

AGC机组调配问题是一个含连续和离散变量的混合非线性优化问题,提出了一种基于混沌多Agent的双重粒子群算法。该算法以混沌和粒子群优化算法以及多Agent技术为基础,利用混沌映射提高初始种群的质量,引入临界算子增强Agent的多样性。在算法迭代中,每一个Agent通过与其随机配置的邻居竞争、合作,并且吸收了粒子群优化算法的进化机制,可以更稳定、快速地收敛到全局最优解。通过算例仿真结果表明,所提出的算法具有质量高的解、稳定性好的收敛特征和快的寻优速度。     

基于多Agent遗传算法的水电厂机组优化组合研究与实现

基于多Agent系统理论,构建了一个多Agent机组优化组合系统。对于问题优化模型,提出了一种新颖的多Agent遗传算法,解决了大规模遗传算法的效率问题。对于机组的非线性耗水量特性,提出了一种由Agent动态地管理与维护的神经网络。基于与FIPA兼容的多Agent中间件JADE平台,给出了一个详细的具体实施系统。仿真结果验证了所提出的优化模型与实施方案的合理性和可操作性。     

基于多Agent遗传算法的水电厂机组优化组合研究与实现

基于多Agent系统理论,构建了一个多Agent机组优化组合系统.对于问题优化模型,提出了一种新颖的多Agent遗传算法,解决了大规模遗传算法的效率问题.对于机组的非线性耗水量特性,提出了一种由Agent动态地管理与维护的神经网络.基于与FIPA兼容的多Agent中间件JADE平台,给出了一个详细的具体实施系统.仿真结果验证了所提出的优化模型与实施方案的合理性和可操作性.     

求解机组组合问题的改进离散粒子群算法

电力系统机组组合问题是一个高维数、离散、非线性的大规模复杂工程优化问题.文中提出了一种基于改进离散粒子群优化算法求解机组组合问题的新方法.首先采用新的策略生成粒子,以保证所有生成的粒子均为满足基本约束条件的可行解,使整个算法只在可行解区域进行优化搜索;然后引入优化窗口的概念和启发式的规则以缩短计算时间和提高优化精度.仿真结果表明所提出的算法具有解的质量高、收敛速度快的特点,充分证明了它能很好地解决机组组合问题.     

基于MAS和矩阵算法的组合开关保护测控系统的研究

介绍了组合开关、Agent及多Agent技术的概念,构建了一种新型的MAS体系结构,提出了基于多Agent技术的组合开关保护测控方案,将矩阵算法引入到该系统的辨识与重构中,并详细介绍了各Agent的功能与实现。通过实验室中的调压器、继电保护测试仪和微电脑移相器等设备模拟现场,完成控制功能试验、保护功能试验、无故障运行试验。从模拟试验结果可知,文中提出的方案在保护测控方面可行可靠,并较大提高了系统软件通用性,真正实现了组合开关的任意组合。     

基于MAS和矩阵算法的组合开关保护测控系统的研究

介绍了组合开关、Agent及多Agent技术的概念,构建了一种新型的MAS体系结构,提出了基于多Agent技术的组合开关保护测控方案,将矩阵算法引入到该系统的辨识与重构中,并详细介绍了各Agent的功能与实现.通过实验室中的调压器、继电保护测试仪和微电脑移相器等设备模拟现场,完成控制功能试验、保护功能试验、无故障运行试验.从模拟试验结果可知,文中提出的方案在保护测控方面可行可靠,并较大提高了系统软件通用性,真正实现了组合开关的任意组合.     

基于粒子群修正策略的机组组合解耦算法

机组组合问题是电力系统优化运行的一个难点,理论上难以得到最优解。提出了一种基于粒子群修正策略的解耦算法。首先采用集结投影次梯度的拉格朗日松弛算法得到机组组合的对偶解;然后依据对偶信息中的备用乘子及对偶组合状态建立粒子群优化空间;而后利用无约束的标准粒子群优化算法实现拉格朗日乘子的局部更新,通过粒子的调整和粒子间信息的传递改变机组启停,进而修正拉格朗日对偶解,最终得到机组组合问题的近似最优解。6个系统的仿真计算验证了该方法的求解速度及计算精度。     

用于机组组合优化的蚁群粒子群混合算法

提出了一种用于求解机组组合优化问题的蚁群粒子群混合优化算法。通过将机组组合解编码为机组操作序列,降低了蚁群算法搜索的难度,使其空间复杂度由指数型降为线性型,使采用蚁群算法求解更大规模的机组组合问题成为可能。采用协同粒子群算法求解多时段负荷的经济分配问题时,用一个粒子群处理一个时段的优化问题,通过共享粒子群间的惩罚项解决了机组爬升率的约束问题。10机和20机系统的仿真实验和分析结果验证了该方法正确性、有效性和优越性。     

发电机组旋转备用宽度的概念、性质及应用(二):在具有正负旋转备用约束机组组合中的应用（英文）

该文是两篇系列论文中的第2篇,论述在第1篇中提出的机组旋转备用宽度概念在具有正、负旋转备用约束机组组合问题求解中的应用。正、负旋转备用约束是系统接入大规模出力高不确定性电源后,机组组合问题中常见的重要系统约束,该文提出此类问题可行解的解析充分必要条件。该条件由与机组启停状态有关的5个线性不等式组成,不等式中包含了机组旋转备用宽度在内的若干参数。这组充分必要条件可作为有效不等式使用,数值测试发现,将该组不等式加入原始机组组合模型后,求解机组组合问题的商业优化软件包的计算效率可明显提高,同时发现该可行性条件提供了一种判定机组组合解鲁棒性的高效途径,可直接用于基于多场景的机组组合随机规划模型中。     

考虑输电网络影响的火电机组优化组合的一种实用算法

提出一种考虑输电网络损耗及线路过负荷的火电机组优化组合的实用算法。用动态规划法建立一个初始解,运用启发式手法对初始解进行修正,使之逐个满足各约束条件,得到运行可能解,并通过更新发电机起动优先顺序使此过程反复进行直至得到(准)最佳解。在求解过程中引入最优潮流计算,使考虑输电网络损耗及线路过负荷等网络因素对发电机组优化组合的影响成为可能,并提出一种调节发电机出力和改变发电机组合相结合的消除线路过负荷的方法。在IEEE-118母线(36机)系统上对所提出的算法进行了各种条件下的仿真计算,考察了网络损耗及线路过负荷对发电机组优化组合的影响,验证了所提算法对解决考虑输电网络因素影响的发电机组优化组合问题的有效性。     

大型水火电力系统短期最优机组启停调度

给出了描述大型水火电力系统短期机组启停调度的数学模型,应用大系统分散控制理论将这一复杂问题分解为3个简单的子问题,分别用一维动态规划和网络规划法术解,最后,通过拉格朗日乘子协调控制得出最优可行解。该算法也可同时考虑火电的最优启停机计划和水电的优化调度。     

Emerging solution of large-scale unit commitment problem by Stochastic Priority List

This paper presents a new approach for unit commitment problem using Stochastic Priority List method. In this method, rapidly some initial unit commitment schedules are generated by Priority List method and priority based stochastic window system. Excess units are added with system dependent probability distribution to avoid overlooking a desired solution during repeated search. Constraints are not considered in this stage. Then schedules are modified gradually using the problem specific heuristics to fulfill constraints. To reduce calculations, heuristics are applied only to the solutions, which can be expected to improve. Besides, sign vector is introduced to reduce economic load dispatch (ELD) overhead recalculations. This process is repeated for optimal solution. The proposed method is tested using the reported problem data set. Simulation results for the systems up to 100-unit are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from Genetic Algorithm and others.     

Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms

Hybrid models for solving unit commitment problem have been proposed in this paper. To incorporate the changes due to the addition of new constraints automatically, an expert system (ES) has been proposed. The ES combines both schedules of units to be committed based on any classical or traditional algorithms and the knowledge of experienced power system operators. A solution database, i.e. information contained in the previous schedule is used to facilitate the current solution process. The proposed ES receives the input, i.e. the unit commitment solutions from a fuzzy-neural network. The unit commitment solutions from the artificial neural network cannot offer good performance if the load patterns are dissimilar to those of the trained data. Hence, the load demands, i.e. the input to the fuzzy-neural network is considered as fuzzy variables. To take into account the uncertainty in load demands, a fuzzy decision making approach has also been developed to solve the unit commitment problem and to train the artificial neural network. Due to the mathematical complexity of traditional techniques for solving unit commitment problem and also to facilitate comparison genetic algorithm, a non-traditional optimization technique has also been proposed. To demonstrate the effectiveness of the models proposed, extensive studies have been performed for different power systems consisting of 10, 26 and 34 generating units. The generation cost obtained and the computational time required by the proposed model has been compared with the existing traditional techniques such as dynamic programming (DP), ES, fuzzy system (FS) and genetic algorithms (GA).     

一种考虑网络损耗及线路过负荷的火力发电机起停计划方法

以面向实际应用为目的，提出了一种考虑输电网络损耗及线路过负荷约束的火力发电机起停计划方法。首先用动态规划法建立一个不含约束条件的初始解，然后运用启发式方法对初始解进行修正使之逐个满足各约束条件得到运行可能解，并通过修改各发电机的起动优先顺序使此过程反复进行直至得到（准）最佳解。在此过程中引入最优潮流计算考虑输电网络损耗及线路过负荷等网络因素对发电机起停计划的影响成为可能，并提出一种调节发电机出力和改变发电机起停计划相结合的消除线路过负荷的方法。在一个8机44母线的测试系统上对提案方法进行了各种条件下的试算，验证了所提出的方法对解决考虑网络因素影响的发电机起停计划问题有效性。计算结果还表明：不仅线路过负荷起停计划问题的有效性。计算结果还表明：不公线路过负荷约束，网络损耗也对发电机起停 计划有较大影响。     

A study of a unit commitment problem with transmission constraints

Unit commitment problem is an optimization problem to determine the start‐up and shut‐down schedule of thermal units while satisfying various constraints, for example, generation‐demand balance, unit minimum up/down time, system reserve, and so on. Since this problem involves a large number of 0–1 type variables that represent up/down status of the unit and continuous variables expressing generation output, it is a difficult combinatorial optimization problem to solve. The study at present concerns the method for requiring the suboptimum solution efficiently. Unit commitment method widely used solves the problem without consideration of voltage, reactive power, and transmission constraints. In this paper, we will propose a solution of unit commitment with voltage and transmission constraints, based on the unit decommitment procedure (UDP) method, heuristic method, and optimal power flow (OPF). In this method, initial unit status will be determined from random numbers and the feasibility will be checked for minimum start‐up/shut‐down time and demand‐generation balance. If the solution is infeasible, the initial solution will be regenerated until a feasible solution can be found. Next, OPF is applied for each time period with the temporary unit status. Then, the units that have less contribution to the cost are detected and will be shut down based on the unit decommitment rules. This process will be repeated until suboptimal solution is obtained. The proposed method has been applied to the IEEE 118‐bus test system with 36 generating units with successful result. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(3): 36–45, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10187     

A multi agent system for monitoring industrial gas turbine start-upsequences

This paper introduces a novel technique for the condition monitoring of gas turbine start up sequences. The vast amount of data and the complex processes behind online fault detection indicate the need for an automated solution. A multi agent system that views the problem as the interaction of simple independent software entities, for effective use of the available data, is presented. The overall solution is derived from the combination of partial solutions provided by the components of the multi-agent system. As a consequence, data interpretation is achieved by converting the data into appropriate information and combining individual agents' information, resulting in an automatic fault diagnosis for the engineers. This multi-agent system can employ various intelligent system techniques and has been implemented using the ZEUS Agent building Toolkit     

电力系统无功优化的多智能体粒子群优化算法

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,提出了一种全新的优化算法一多智能体粒子群优化算法来求解此类优化问题。该算法结合multi-agent系统和粒子群优化技术,构造了一个格子环境,所有Agent都固定在格子环境中。每一个Agent相当于粒子群优化算法中的一个粒子,它们通过与其邻居的竞争、合作和自学习操作,并且吸收了粒子群优化算法的进化机理,能够更快地、更精确地收敛到全局最优解。在IEEE30节点系统上进行校验,并与其它方法比较,结果表明,提出的算法具有质量高的解、收敛特性好、运行速度快的突出优点。     

基于MAQPSO 的电力系统无功优化研究

文中提出一种多智能体量子粒子群优化算法(Multi Agent Quantum Particle Swam Optimization,MAQPSO)求解电力系统无功优化问题,改善了传统量子粒子群算法后期收敛速度慢、易陷入局部最优解等缺点。该算法结合了量子粒子群算法和多智能体进化思想,每一个Agent相当于量子粒子群优化算法中的一个粒子,通过Agent的邻域竞争、自学习等操作,使得算法能够更迅速、更精确地收敛到全局最优解。通过对IEEE14、30、57和118节点系统的优化仿真,结果表明该算法有收敛精度高、寻优速度快等优点。