基于改进拉格朗日乘子修正方法的逆序排序机组组合

机组组合与经济调度是两个不同范畴的优化决策问题,其优化过程在概念上有本质的区别。用经济调度中的拉格朗日乘子对机组组合中的乘子进行修正有概念含混的误区。文章在阐明机组组合与经济调度中拉格朗日乘子的差异及作用机理的基础上,提出了一种新的逆序排序机组组合中拉格朗日乘子的修正方法,并对机组的搜索范围及机组运行的经济指标作了相应的改进,使原有算法在精度和计算速度上均得到了显著提高。20机、26机及110机测试系统的计算结果表明了文中的改进方法是有效的,进一步增强了机组组合对大规模系统的适应性。     

基于改进布谷鸟算法的火电厂机组组合优化

电力系统机组组合是一个多维,复杂的整数规划问题,利用传统方法较难求解。在通过研究布谷鸟搜索(cuckoo search)算法的基本原理,分析布谷鸟算法的优缺点基础上,结合粒子群算法,提出一种改进的布谷鸟搜索算法。通过在10机组系统中进行验证,结果表明,算法比粒子群算法、标准布谷鸟算法更好。改进的布谷鸟搜索算法同样也在收敛速度等更具有优势。     

电力市场中机组组合的智能优化算法

随着电力市场的兴起，机组优化组合问题的目标函数和约束条件都发生了重大的变化，本文提出了在电力市场机制下机组组合问题的数学模型，并运用智能优化算法－遗传算法求解，该算法不同于常规优于算法的特点在于，能够从最后一代的母体群中产生多个满足约束条件的可行方案，为电钢调度提供了极大的灵活性。而且任何可以用罚因子项表示的约束条件可以考虑到遗传算法，适合大规模及超大规模问题的求解。     

电力市场下基于改进遗传算法的发电公司内部机组组合

针对电力市场条件下火力发电厂机组组合问题，提出了改进的遗传算法。该方法采用二进制编码，并取染色体形式为矩阵形式，无须解码。在形成初始群体时结合单位煤耗优先顺序法，先确定出1个交易区间内的必开和必停机组，从而减小了算法的随机性。在选择过程中，采用排序选择法，加快了算法的收敛，并让变异概率按线性规律变化，充分考虑了算法的全局性。经算例验算表明了该算法在满足安全可靠的多约束下具备可行性。     

基于免疫算法的机组组合优化方法

机组组合是改善传统电力系统运行经济性和电力市场出清的重要手段。基于群体进化的智能优化算法存求解过程中存在计算效率低和易于早熟收敛等缺点。提出机组组合的免疫算法，利用免疫算法保持种群多样性的内在机制和免疫记忆特性改进既有的智能优化方法。新算法扩展了约束处理技术，能更好地对可行解空间搜索，采用一种由后向前、由前及后、双向迂回推进的精简程序改善个体可行解的局部最优性，同时利用优先级顺序法产生能较好反映问题先验知识的初始种群。典型算例证实新算法能获得更优的结果，具有更快的收敛速度，且在系统规模扩大时有大致线性的计算复杂性，是一种新的高效的机组组合智能优化算法。     

用于中期电力市场的启发式机组组合算法

提出了一种用于求解中期电力市场机组组合问题的启发式方法.该算法将局部寻优法与优先顺序法有效结合避免了丢失最优解,同时通过逐次寻优逐步缩小了寻优范围,从而提高了求解速度.该算法在东北区域电力市场的实际应用表明了其实用性和有效性.     

适用于不同电价机制的统一机组组合算法

现有电力市场中存在两种结算电价机制：按机组报价结算(一机一价)和按市场出清价格结算(统一电价)。不同市场之间的结算方式也有所不同,例如,双边交易中采用一机一价结算方式,而实时市场中采用边际电价结算方式。不同结算电价机制下,机组组合的目标函数不同,传统机组组合方法必须根据电价机制的不同进行调整。通过研究发现,两种结算方式下机组组合问题的最优条件具有类似的数学表达形式。基于这一统一的最优条件表达形式,提出了一种新的机组组合算法。与传统拉格朗日松弛法相比,新算法能够有效地求解两种电价机制下的机组组合问题。     

一种电力市场环境下解决机组组合问题的松弛方法

提出一种松弛算法解决电力市场环境下的机组组合问题。主要思想是在满足机组时间约束的基础上，对原问题进行松弛．从而形成一个以松弛子问题序列为依据的切机过程，切机的原则是机组的出力最小；因为松弛子问题的目标函数值逐渐增大逼近最优解，所以通过一个上界算法作为迭代过程终止的判据。本算法经过一个6机系统的检验证明是有效的。     

A multi‐agent approach to unit commitment

This paper proposes a multiagent system to power system unit commitment problems. Multiagent is a new paradigm for developing software applications. Coordinating the behavior of autonomous agents is a key issue in agent‐oriented programming techniques today. Recently, agents are being used in an increasingly wide variety of applications, ranging from comparatively small systems such as E‐mail filters to large, open, complex systems such as air traffic control. Though some agent frameworks have been proposed in the power system field, the number of studies is limited. In this paper, we developed a power system unit commitment application by multiagent architecture. Our multiagent system has the following characteristics: (1) The system consists of a single facilitator agent, two mobile agents, and one or more generator agents which are elements of power system network. (2) The facilitator agent is developed to act as a manager for the process by using the singleton design pattern. The mobile agents migrate to generator‐agents to increase or decrease their power generations. The generator agents have their operational data. (3) Message object is developed to communicate between the agents using KQML‐like object. The proposed approach is applied to a simple model system, and the results show that the multiagent system is an efficient decentralized approach for solving power system unit commitment problems. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 141(2): 41–47, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10057     

求解含风电场机组组合问题的透视割平面邻域搜索算法

提出一种求解含风电场机组组合(Unit Commitment, UC)问题的透视割平面邻域搜索(Perspective Cut Neighborhood Search, PC-NS)算法。基于PC建立了含风电场UC问题的混合整数线性规划模型。以不考虑误差场景UC问题的最优解为中心构造邻域,在此邻域内搜索以获得含风电场UC问题的可行解。以此可行解为中心构造邻域,在此邻域内搜索以获得含风电场UC问题高质量的次优解。最后进行数值仿真,结果表明,所提算法能有效求解含风电场UC问题。和其他方法相比,PC-NS算法获得了更好的次优解,而且用时大大减少。     

应用MAS解决基于利润的机组组合问题研究

电力系统引入放松管制的市场运行机制之后,形成一种基于利润的机组组合问题:①优化目标从费用最小转为利润最大;②各发电公司从自身利益出发,可以不完全满足中心调度的要求。针对以上特点,提出一种基于多Agent系统的解决方法。仿真结果表明,该方法能够适应解决现代电力系统机组组合问题的新需要,能够获得更大的经济效益。     

机组组合问题的罚函数法

机组组合问题是一个大规模的非线性、0、1变量混合整数规划问题 ,是一个难问题。以罚函数方法解决0、1变量整数规划问题是一个新的尝试。文中考虑包括发电机爬坡约束和时间约束等动态约束在内的多种约束条件 ,对机组组合问题的 0、1变量进行松弛 ,并在目标函数中增加一个惩罚函数项 ,将问题变换成一个非线性连续变量的规划问题 ,以SQP法求解。本算法经过一个简单的算例检验 ,说明是行之有效的。     

A method for unit commitment with transmission losses and flow constraint

Researches on the unit commitment with transmission network have been reported recently. However, most of these researches mainly discussed the security constrained unit commitment, while the relationship between unit commitment and transmission losses was not considered. However, from the standpoint of operating reserve for ensuring power supply reliability, a unit commitment considering transmission losses is required. Further, under the deregulation and liberalization of the electric power industry, not only the line's security but also transmission losses are expected to play an important role in calculating the network access charge, and unit commitment taking into account transmission losses is also desired from this viewpoint. In this paper, a unit commitment approach with both transmission losses and line flow constraint is presented. Based on a heuristic iterative optimization method, first, an initial schedule is created by using a successively decommitting unit approach that is proposed in this paper. Then, we determine constraints included in the unit commitment schedule by a heuristic iterative optimization approach, in which an algorithm able to get rid of line overload by DC optimal power flow is developed. Through numerical simulations on two test power systems, the effectiveness of the proposed method is shown. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 142(4): 9–19, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10116     

适合于机组组合问题的扩展优先顺序法

针对传统PL（Priority List）方法采用单一排序指标,即平均满负荷费用AFLC（Average Full-Load Cost）不能全面反映机组优先顺序的不足,提出一种扩展优先顺序法EPL（Extended Priority List）解决机组组合问题。在分析PL方法特点的基础上,定义μ-Load Cost反映机组在不同出力范围内的经济指标,形成不同μ值的机组组合的邻域,而后定义机组的效用系数UUR（Unit Utilization Ratio）优化机组的优先顺序。此外,引入参数控制机组组合邻域的规模并采取策略对机组组合进行调整使其满足所有约束,从而提高计算效率。最后采用26机组、38机组以及45机组24时段等3个系统的测试结果来验证该方法的有效性。     

内点-分支定界法在最优机组投入中的应用

机组投入是现代电力系统编制发电计划的重要优化任务,具有显著的经济效益。从数学上讲,机组投入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。提出运用内点-分支定界法求解最优机组投入问题。该方法将机组投入的离散变量松弛为[0,1]区间上的连续变量,结合有功出力,进行优化。原始-对偶内点法收敛迅速、对初值不敏感,用来求解松弛问题,分支定界法用来处理离散变量。通过对2个算例的计算及与其它算法结果的比较,验证了该算法能得到更好的全局最优解。     

竞争机制下基于改进遗传算法的火电机组启停

电力市场正逐步引入厂网分开竞价上网的竞争机制,而发电厂的发电情况与电网的经济运行有极大的关系。在这种运行模式下,火电机组的优化启停数学模型需要进一步改进。本文从发电厂利润最大化角度出发,建立火电机组启停的数学模型,并提出用优化遗传算法确定火电机组启停的方法。该方法能有效克服一般遗传算法在机组优化组合中的不足,提高了收敛速度,对发电机组优化组合问题具有实用价值。     

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