遗传算法在电力系统日有功优化调度中的应用

随着电力市场的不断深入，系统的有功优化调度在电力系统运行中占有越来越重要的地位。针对这一特点，本文提出了在多约束条件下，寻求全网经济效益最优的算法，先用遗传算法求解机组组合，再用等微增率法求解负荷的最优分配，在求解的过程中，采用不同听方法来处理各种约束条件。通过模拟系统的实例验算表明了所提出的算法十分有效。     

基于矩阵实数编码遗传算法求解大规模机组组合问题

该文提出了一种采用矩阵实数编码遗传算法(MRCGA)进行机组组合优化的新方法：采用矩阵实数编码方式对整体发电计划进行编码后，可直接运用遗传操作求解机组组合问题，避免将其分解成机组启停安排和经济负荷分配的两层优化问题进行求解；采用多窗口变异技术，增强了算法的搜索能力。此方法提出了一种新的个体调整方法，可以处理各项约束条件，保证了结果的可行性。文中通过2个算例及与其它算法的对比分析，验证了所提出的方法在大规模机组组合问题求解时具有很强的适应性和全局搜索能力。     

基于竞价的日发电计划混合智能优化算法

针对电力市场下基于竞价的日发电计划的特点，提出一种充分结合遗传算法和排队算法各自优点的混合智能算法。该算法利用遗传算法在求解离散组合优化问题上的强收敛性和鲁棒性进行机组优化组合，利用排队算法的简洁性和快速性进行经济功率分配。同时，利用基于专家知识的免疫遗传算法来提高机组优化组合的计算速度。通过对某实际电力市场基于竞价的日发电计划的计算和分析，验证了该算法的正确性和实用性。     

基于电力系统日发电计划的混合智能messy遗传算法

机组组合是电力系统日发电计划中主要的优化任务,在满足各种约束条件下求得全局最优解是一个比较困难的问题.传统遗传算法的二进制编码和随机遗传操作不适合于求解大规模机组组合问题.针对电力系统日发电计划的特点,提出了一种混合智能messy遗传算法(HIMGA),该算法实现简单,大大减小了求解问题的规模,保证了群体的多样性,提高了算法的搜索效率,改善了算法的收敛性.仿真计算结果表明了该算法的有效性和实用性.     

社会演化算法在机组组合中的应用

机组组合是电力系统优化运行的一个重要方面，从数学角度讲，机组组合问题是一个多约束的NP难组合优化问题，很难得到理论上的最优解，该文将一种新的方法—社会演化算法用于解决该问题。该算法用认知主体取代了传统遗传算法的基于编码的可行解生成方式；用基于“范式学习与更新”的进化寻优机制取代了传统遗传算法中基于模仿基因的遗传和变异的进化寻优机制，使其计算效率及收敛稳定性均优于传统遗传算法。最后通过算例验证了该算法的优越性。该算法不仅为解决机组组合问题带来了新的思路和方法，在求解其它带有复杂约束条件的组合优化问题方面也有非常重要的启发意义。     

基于粒子群优化算法的AGC机组调配研究

电力市场下AGC机组的调配问题是辅助服务领域中的一个重要研究内容.提出了一种基于粒子群优化算法的AGC机组调配方案.该方法基于AGC机组调配的数学模型,考虑了机组调节容量,调节速率等约束条件.介绍了算法的基本原理,并分析了参数的不同取值对算法收敛性的影响.实际系统的算例表明,利用粒子群优化算法,不仅可以克服整数规划法可能得不到最优解的缺点,而且与遗传算法比较具有收敛性好,收敛速度快的优点,从而为AGC机组的调配问题提供了一种新的有效算法.     

电力市场中一种基于遗传算法的最优发电计划的实现

针对电力市场环境下的负荷分配的特点,在遗传算法的基础上,提出了最优发电计划问题的数学模型和求解方法,模型计及了机组启停。算例表明,这种数学模型和算法可以解决任意形状报价曲线的最优发电计划问题,而且计算速度较快,具有较高的实用价值。     

实时交易模式下的日前电力市场出清

针对日前电力市场出清的制订过程是一个比较复杂的优化问题,提出了一种基于改进遗传算法的市场清算算法,在对水电机组进行合理处理后,通过二进制编码对机组开停机约束条件进行适当处理,使整个交易算法过程简化,最后用算例验证了该方法的可行性。     

电力市场下AGC机组调配的遗传算法

对电力市场下自动发电控制(AGC)机组的调配问题进行研究发现，现有利用整数规划的方法是基于机组AGC调节容量不变这一条件，因此所得到的结果可能不是最优解。提出了计及机组AGC调节容量变化的数学模型，对利用遗传算法求解这一非线性混合整数规划问题进行了探索。实际系统的算例表明，利用遗传算法可以克服现有利用整数规划方法所存在的问题。文中还对遗传算法参数的选取与调整进行了讨论。     

电力市场中一种基于遗传算法的最优发电计划的实现

针对电力市场环境下的负荷分配的特点,在遗传算法的基础上,提出了最优发电计划问题的数学模型和求解方法,模型计及了机组启停.算例表明,这种数学模型和算法可以解决任意形状报价曲线的最优发电计划问题,而且计算速度较快,具有较高的实用价值.     

改进遗传算法及其在机组优化组合中的应用

提出了一种新的基于浮点数和二进制数统一编码的改进遗传算法。该算法有效利用了浮点数编码FGA(Float—coaded Genetic Algorithm)收敛迅速、不易陷入局部最优解、具有较高收敛精度的优点,同时结合二进制编码BGA(Binary—coaded Genetic Algorithm)模拟机组启停状态的优点,解决了机组优化组合的O-1混合整数非线性规划问题。该算法已成功地运用到16台机组的优化组合中,并取得很好的效果。     

机组优化组合的协同变异遗传算法

本文提出了一种解决电力系统机组优化组合问题的协同变异遗传算法。这种算法沿用了遗传算法的基本思想，但在遗传操作算法的基本思想，但在遗传操作过程中实现基因的协同变异，以提高遗传算子的搜索效率，仿真实验表明这种算法具有很高的搜索效率和很好的适应性。     

Evaluation of costs for procuring reserve capacity in a deregulated power system using multi‐agent model

In this paper, we assume 2 models for securing reserve capacity. One is “Commitment‐based Security Model” and the other is “Reserve Market‐based Security Model.” In Commitment‐based security model, ISO commits procurement of reserve energy to a particular generation company. Meanwhile, in Reserve market‐based security model, ISO procures reserve energy through reserve market. The main object of this research is to investigate which model will be preferable for the viewpoint of consumer's cost. To compare these models, two things are considered in this paper. One is bidding behavior of agents which bids to energy market and reserve market. To consider this, Q‐Learning of multi‐agent model is used. Also, the Unit Commitment (UC) is considered to calculate generation cost. This is to calculate the cost for securing reserve power more precisely. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(1): 18– 25, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20705     

Unit Commitment by improved pre-prepared power demand table and Muller method

This paper proposes a new methodology for solving Unit Commitment (UC) problem. In the proposed approach, Improved Pre-prepared Power Demand (IPPD) table solves the UC problem and then the Muller method solves the Economic Dispatch (ED) sub-problem. The proposed method has been tested on 3-, 10-, 38- and 40-100 units. Comparison of the simulation results of the proposed method with the results of previous published methods shows that the proposed method provides better solution with less computational time.     

实际报价结算下的灵敏度矩阵消除阻塞的算法

为了保证系统运行的安全性提出了一种基于灵敏度矩阵的阻塞消除算法,在按机组实际报价结算时,针对出现阻塞的支路,形成相应的灵敏度系数矩阵,以调整费用最小为目标函数,整体解决过载支路的阻塞。它以过载支路的过载量和相应的发电机组的灵敏度系数和调整量的乘积形成等式约束,并和目标函数构成拉格朗日函数,采用拉格朗日求极值的方法,通过推导得出各发电机的调整量。整个计算过程只需求解一个线性方程组,计算较为简单快捷。通过对IEEE9和IEEE30节点系统两个算例的计算,验证了所提算法的正确性和有效性。     

动态比较法确定机组的最优组合

本文提出了一种确定机组最优组合的新方法──动态比较法。该方法根据优先次序确定递增机组，进而确定机组最优递增负荷，再根据负荷曲线确定机组的最优开、停机时刻。其计算方法非常简单，计算速度快。用Ｃ语言编制了程序，对四种算例进行了计算，结果表明该方法具有较好的实用性。     

Unit Commitment solution using an optimized genetic system

This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process and therefore greater economy. The proposed methodology resulted in a better performance with faster operation by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start-up and shut-down ramps, have also been considered. A number of important parameters (standard and new parameters) of the UC problem have been identified. The proposed method is implemented and tested using a C# program. The tests are carried out using two systems including 10 and 20 units during a scheduling period of 24 h. The results are finally compared with those obtained from genetic schemes in other similar investigations through which the effectiveness of the proposed scheme is affirmed.     

电力市场环境下对华北电网调度自动化系统的思考

探讨了调度自动化系统与电力市场支持系统之间的相互关系。就华北电网调度自动化系统中存在的一些可能影响电力市场支持系统正常运行的技术及管理问题,进行了讨论和说明。同时为今后华北电网有限公司电力市场支持系统的建设以及网调自动化系统(如EMS,电能量计量系统)中存在的问题提出了建议。     

Fast solutions for UC problems by a new metaheuristic approach

Due to its combinatorial nature, the Unit Commitment problem has for long been an important research challenge, with several optimization techniques, from exact to heuristic methods, having been proposed to deal with it.

In line with one current trend of research, metaheuristic approaches have been studied and some interesting results have already been achieved and published. However, a successful utilization of these methodologies in practice, when embedded in Energy Management Systems, is still constrained by the reluctance of industrial partners in using techniques whose performance highly depends on a correct parameter tuning. Therefore, the application of metaheuristics to the Unit Commitment problem does still justify further research.

In this paper we propose a new search strategy, for Local Search based metaheuristics, that tries to overcome this issue. The approach has been tested in a set of instances, leading to very good results in terms of solution cost, when compared either to the classical Lagrangian Relaxation or to other metaheuristics. It also drastically reduced the computation times. Furthermore, the approach proved to be robust, always leading to good results independently of the metaheuristic parameters used.     

A solution to unit commitment problem using fire works algorithm

This paper presents a new approach using swarm intelligence algorithm called Fireworks Algorithm applied to determine Unit Commitment and generation cost (UC) by considering prohibited operating zones. Inspired by the swarm behaviour of fireworks, an algorithm based on the explosion (search) process and the mechanisms of keeping the diversity of sparks has been developed to minimize the total generation cost over a given scheduled time period and to give the most cost-effective combination of generating units to meet forecasted load and reserve requirements, while adhering to generator and transmission constraints. The primary focus is to achieve better optimization while incorporating a large and often complicated set of constraints like generation limits, meeting the load demand, spinning reserves, minimum up/down time and including more realistic constraints, such as considering the restricted/prohibited operating zones of a generator. The generating units have certain ranges where operation is restricted based upon physical limitations of machine components or instability, e.g., due to steam valve or vibration in shaft bearings. Therefore, prohibited operating zones as a prominent constraint must be considered. In this paper the incorporating of complicated constraints of an optimization problem into the objective function is not considered by neglecting the penalty term. Numerical simulations have been carried out on 10 – unit 24 – hour system.