应用网络法实现多地区大电力系统的经济调度

不久前提出的网络法是分析和计算电力系统经济调度问题的有效方法。本文进一步开拓了该法的应用范围,用来分析多地区电力系统的经济运行。借助此法,地区系统的电路模型可化简为其Thevenin或Norton等效,使得联合系统的经济效益分析及经济调度的计算大为简化。文中提出了联合系统分层经济调度的算法,并附有实例的计算结果。     

考虑风力发电的电力系统经济调度

随着我国节能减排政策的落实,风力发电装机容量将快速增长。以风力发电为代表的分布式发电系统普遍受到气候因素影响而具有显著的间歇性,而政策规定对风电等必须全部收购,这将对电力系统的安全稳定运行和经济调度方式产生重要的影响。本文结合遗传算法研究在有风力发电装机情况下的电力系统经济调度问题,并对十机组测试系统在有/无风电机组投入情况下进行了经济调度的优化计算,仿真结果表明采用风力发电可以明显降低电力系统整体的一次能源消耗。     

网络规划及其在电力系统经济调度中的应用(一)

本文首先通俗地讲解了网络规划的基本概念和算法，然后介绍它在电力系统经济调度各方面 的应用：考虑线路安全约束的负荷经济分配、水火电协调、梯级水电站调度和多区域联合电 力系统经济调度等。     

遗传算法在有功安全经济调度中的应用

利用遗传算法(简称GA)求解电力系统有功经济调度问题。优化模型中考虑了线路N安全性约 束，对遗传算法中适合度函数、变异概率取值及收敛判据等方面进行了探讨，进一步拓展了 电力系统安全经济调度计算方法的应用前景。最后对IEEE　30节点6机系统进行了数字仿真 ，证明了该方法的简捷和有效。     

电力系统有功功率经济分配的并行算法

电力系统有功功率经济分配是一个重要课题，具有巨大的经济意义。随着经济和社会的发展 ，电力系统的规模越来越大，整个电力系统的经济调度，都由系统的最高级控制中心来计算 和指挥是不现实的也是不合理的，因而必需研究开发电力系统经济调度的并行算法。 本文提出，将整个电力系统分为主系统和N个与主系统有联络线相联的子系统，各子系统进 行低层次的有功功率经济分配计算，主系统进行协调，在主系统与子系统之间进行必要的信 息交换。     

水火联合电力系统中期经济调度系统的研究

根据大型电力系统的特点采用了多阶梯负荷模型。并采用相应的非线性网流法求解水火联合电力系统的中期经济调度问题。在调度系统设计中考虑了实际电网的各种特殊运行情况，并具有能够进行跨月滚动计算等实用功能，所使用的模型和算法可用于统一调度及电力市场。实践证明该系统是实用和有效的。     

网络流规划法在水火联合电力系统经济调度中的应用

本文给出了具有混联水电厂群的水火联合电力系统经济调度问题的数学描述及网络流模型。根据网络流规划的基本思想,结合这一模型的具体特点,文章提出了一种新的实用计算方法。计算结果表明,网络流规划法易于处理复杂约束条件,计算速度快,从而使利用微型计算机进行水火联合电力系统经济调度成为可能。     

基于改进遗传算法的经济负荷分配

研究了遗传算法在水火电力联合系统经济调度中的应用。针对电力系统负荷分配目标的改变，建立在电力市场体制下负荷分配的数学模型；根据负荷分配问题的特点，对初始种群的生成方法进行改进，使得遗传算法在初始种群中的所有个体都是可行解；对过度满足约束条件的个体，采用了一种有效减小冗余的手段。通过数字仿真，证明了该方法的正确性与有效性。     

电力系统智能型调度

介绍了电力系统智能型调度的定义,总结了电力系统中传统型调度、分析型调度、决策支持型调度、智能型调度的发展历史,比较了各个阶段的电力系统调度的特点,显示了智能型调度的优越性.针对目前复杂电力系统快速发展与电力体制改革不断深入的特点与要求,结合我国实际情况论证了实现电力系统智能型调度的必要性和可能性.设计了电力系统智能型调度的集中-分布式框架,指出多代理技术是实现这一框架的解决途径之一,采用了执行Agent、监督Agent、寻优Agent、协调Agent、中央Agent的5层体系结构.对电力系统智能型调度的系统在线监控、系统分析与评估、故障预警与处理、操作的形成与执行等主要功能进行了分析.强调了智能型调度在保持电力系统安全、经济、灵活运行方面的作用与意义.     

用二次规划法解算互联系统经济调度

以分解协调原理和灵敏度分析为基础,将联络线有功功率作为协调变量,构造了互联电力系 统经济调度的 新模型。将原始问题分解成 N 个独立子优化问题和一个简单的协调优化问 题,采用具有松驰技术和参数规 划思想的二次规划算法求解,使计及安全性约束的互联系统 经济调度计算负担小,收敛可靠。以三区域互 联电力系统经济调度为算例,结果表明算法是 可行和有效的。     

基于遗传算法的水电站厂内经济运行新算法

本文提出求解水电站厂内经济运行问题的新方法－遗传算法,它不同于常规优化算法的特点在于,从多个初值点开始,沿多路径搜索实现全局或准全局最优,计算过程不需要存贮状态或决策变量的离散点,减少计算机内存,不必求导计算,编程简单,是一种有效的自适应随机搜索算法。     

基于模糊优化和内点法的水火电系统短期经济调度

针对传统水火电系统短期经济调度模型在经济上的保守性,分析了在市场环境下考虑来水量不确定性的必然性和重要性,总结了目前水火电系统短期经济调度的常用方法.并指出这些方法的不足之处.在此基础上,将模糊集理论应用于来水量不确定性分析,提出了一种水火电系统短期经济调度的新方法.该方法考虑了来水量的不确定因素,用隶属度函数将来水量模糊化,以水火电系统中火电燃料费用最小和水电用水量偏差最小作为优化目标,采用模糊优化方法进行平衡,兼顾了火电和水电2个方面,并采用原对偶内点法求解该多目标模糊最优问题,具有较好的收敛性能.与传统方法相比,提出的方法较好地提高了系统的经济性.在一个测试系统的应用效果表明了该方法的有效性.     

Design of economic dispatch model for Gencos with thermal and wind powered generators

This paper introduces a new generic equation for the economic dispatch model of power systems, which include conventional generators as well as wind powered generators considering the variable nature of wind velocity and power demand. Objective function of the proposed model presents a major novelty in the economic dispatch modeling, namely: dynamic cost coefficient, which is computed using fuzzy logic considering both the variable nature of wind velocity and power demand. This dynamic cost coefficient is designed to benefit both the Wind Power Producers and Genco. This model allows for one hour ahead of revision of conventional and wind generation unit schedule, using the information of ‘Dynamic Feed in Tariff’ of total wind powered generation. This methodology is implemented for a simple test system and Genco system. Genco system comprises of ten thermal units and two wind farms. Genetic Algorithm is used to solve the proposed generic economic dispatch model and find the optimal cost of power generation. In addition, the impact of dynamic cost coefficient on the operation cost of the system is analyzed in the simple test system. Simulation result shows that under critical demand situation, ‘Dynamic Feed in Tariff’ favors reduction of the operating cost of the systems. The result of the simulation done on Genco system shows that this proposed model benefits both Genco and Wind Power Producer.     

Equal embedded algorithm for economic load dispatch problem with transmission losses

This paper presents equal embedded algorithm (EEA) to solve the economic dispatch (ED) problem with quadratic and cubic fuel cost functions and transmission losses. The proposed algorithm involves selection of lambda values, then the expressions of output powers of generators are derived in terms of lambda by interpolation and finally optimal value of lambda is evaluated from the power balance equation by Muller method. The proposed method is implemented and tested by considering 3, 15 and 26 generators to solve the ED problem. Simulation results such as quality of solution, convergence characteristic and computation time of the proposed method are compared with some existing methods like genetic algorithm (GA), particle swarm optimization (PSO) and Lambda iterative method. It is observed from different case studies that the proposed EEA algorithm provides the qualitative solution with less computational time irrespective of the size of the system.     

Chaotic ant swarm optimization to economic dispatch

This paper developed a novel algorithm named chaotic ant swarm optimization (CASO) for solving the economic dispatch (ED) problems of thermal generators in power systems. This algorithm combines with the chaotic and self-organization behavior of ants in the foraging process. It includes both effects of chaotic dynamics and swarm-based search. The algorithm was employed to solve the ED problems of thermal generators. The proposed method was applied to three examples of power systems. Simulation results demonstrated that the method can obtain feasible and effective solutions, and it is a promising alternative approach for solving the ED problems in practical power systems.     

计及可再生能源发电成本的智能电网分布式经济调度

针对包含可再生能源发电单元、常规发电单元与储能单元的智能电网经济调度问题,提出一种计及传输损耗的智能电网最优经济调度模型,该模型实现对传输损耗与可再生能源发电单元运行成本的计量;其次,采用Lagrange乘子法推导此优化问题的协调方程,并通过解析的方式分析发电单元的出力协调关系;然后,提出一种完全分布式算法以有效求解此经济调度问题,该算法不仅能够有效减少计算和通信负担,还能在很大程度上保护发电单元隐私;最终,基于IEEE-30母线系统的3个算例验证所提分布式算法的正确性与有效性。     

A genetic algorithm solution to the optimal short-term hydrothermal scheduling

This paper presents an algorithm for solving the hydrothermal scheduling through the application of genetic algorithm (GA). The hydro subproblem is solved using GA and the thermal subproblem is solved using lambda iteration technique. Hydro and thermal subproblems are solved alternatively. GA based optimal power flow (OPF) including line losses and line flow constraints are applied for the best hydrothermal schedule obtained from GA. A 9-bus system with four thermal plants and three hydro plants and a 66-bus system with 12 thermal plants and 11 hydro plants are taken for investigation. This proposed GA reduces the complexity, computation time and also gives near global optimum solution.     

A Framework for Economic Load Frequency Control Design Using Modified Multi-objective Genetic Algorithm

This article deals with the coordination of security-constrained economic dispatch and load frequency control in an interconnected power system. The realistic and performance optimization inherent of the load frequency control (LFC) and security-constrained economic dispatch are fully considered without simplifying assumptions. For this purpose, modeling security-constrained economic dispatch as a discontinuous control action in the continuous frequency response model of a power system is well addressed. Considering conflict behavior of LFC and security-constrained economic dispatch beside the powerfulness of the multi-objective genetic algorithm (GA) to solve high-dimensional problems with conflicted objective functions makes it attractive for the automatic generation control coordination problem. The employed security-constrained economic dispatch utilizes the advantages of dynamic economic dispatch to achieve more realistic results. The GA is used to compute the decentralized control parameters and centralized generation levels of the on-line units to achieve an acceptable operating point. A significant modification in convergence speed has been performed by using LFC model properties in corporation with the genetic algorithm, so the proposed method gives considerable promise for implementation in multi-area power systems. The efficiency of the proposed algorithm and modification is demonstrated on a three control area power system.     

用遗传算法解算机组组合的研究

用遗传算法解决电力系统机组组合及机组间的负荷分配问题。在机组数目增加时，二进制编码的遗传算法的计算量及存储量会增加很多，并且经典的遗传算法不具有渐近收敛性。针对这些问题，作者采用二进制与浮点数混合的编码方案，并根据这一特点设计了遗传算子；对经典的遗传算法在计算中出现的随机性问题，则采用压缩映射遗传算法使计算过程渐近收敛。计算表明，该算法的具有渐近收敛性，与二进制编码的算法相比，计算所需时间及内存少，而且更易引入问题的相关信息。