Resolution of the unit commitment problems by using the hybrid Taguchi-ant colony system algorithm

This work presents a hybrid Taguchi-ant colony system (HTACS) algorithm to solve the unit commitment (UC) problem. The proposed algorithm integrates the Taguchi method and the conventional ant colony system (ACS) algorithm, providing a powerful global exploration capability. The Taguchi method is incorporated into the ACS process before its global pheromone update mechanism. Based on the systematic reasoning ability of the Taguchi method, improved UC solutions are selected quickly to represent potential UC schedules, subsequently, enhancing the ACS algorithm. Therefore, the proposed HTACS algorithm can be highly robust, statistically sound and quickly convergent. Additionally, feasibility of the proposed algorithm is demonstrated on a 10-unit system. Analysis results demonstrate that the proposed algorithm is feasible, robust, and more effective in solving the UC problem than conventional ACS methods.     

Combined heat and power economic dispatch by harmony search algorithm

The optimal utilization of multiple combined heat and power (CHP) systems is a complicated problem that needs powerful methods to solve. This paper presents a harmony search (HS) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The HS algorithm is a recently developed meta-heuristic algorithm, and has been very successful in a wide variety of optimization problems. The method is illustrated using a test case taken from the literature as well as a new one proposed by authors. Numerical results reveal that the proposed algorithm can find better solutions when compared to conventional methods and is an efficient search algorithm for CHPED problem.     

基于蚁群优化算法的机组最优投入

机组最优投入问题（optimal Unit Commitment,UC)是寻求1个周期内各个负荷水平下机组的最优组合方式及开停机计划，使运行费用为最小。该问题是一个高维数、非凸的、离散的、非线性的优化问题，很难找出理论上的最优解，但由于它能带来显著的经济效益，所以受到了国内外很多学者的广泛关注。作者尝试采用一种新型的模拟进化优化算法－－蚁群优化算法（ACO）来求解该问题。首先，利用状态、决策及作者提出的路径概念把UC设计成类似于旅行商（TSP）问题的模式，从而可以方便地利用ACO来求解。其次，由于ACO处理的是无约束优化问题，对于UC这一约束优化问题，提出了不同的方法来处理各种约束。用tabu表限制不满足旋转备用约束和机组最小启／停时间约束的状态；通过附加惩罚项来处理线路N安全性约束。数值算例验证了此算法的可行性和有效性。     

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。     

基于改进离散粒子群算法的电力系统机组组合问题

提出一种新的离散粒子群算法。结合改进的自学习策略优化粒子群算法适用于求解电力系统中的机组组合（unit commitment,UC）问题。算法将UC问题分解为具有整型变量和连续变量的2个优化子问题,采用离散粒子群优化和原对偶内点法相结合的双层嵌套方法对外层机组启、停状态变量和内层机组功率经济分配子问题进行交替迭代优化求...     

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。     

面向节能发电调度的日前机组组合优化方法

节能发电调度是对电网优化调度机制的重大修改。机组组合是电网调度的重要环节,随着节能发电调度的逐步推广,需要结合中国国情研究新形势下机组组合模型与优化方法。提出一种求解电力系统机组组合的新方法,将机组组合问题分解为末状态和状态改变时间优化2个过程。基于节能发电调度通过多贪婪因子完善机组排序指标,利用贪婪算法确定机组组合初始解,进而结合深度优先算法遍历机组组合方案以保证问题优化的深度。10机24时段系统算例表明,该方法可有效处理机组组合各类约束条件及保证节能调度效果。     

Unit commitment by an enhanced simulated annealing algorithm

A new simulated annealing (SA) algorithm combined with a dynamic economic dispatch method has been developed for solving the short-term unit commitment (UC) problem. SA is used for the scheduling of the generating units, while a dynamic economic dispatch method is applied incorporating the ramp rate constraints in the solution of the UC problem. New rules concerning the tuning of the control parameters of the SA algorithm are proposed. Three alternative mechanisms for generating feasible trial solutions in the neighborhood of the current one, contributing to the reduction of the required CPU time, are also presented. The ramp rates are taken into account by performing either a backward or a forward sequence of conventional economic dispatches with modified limits on the generating units. The proposed algorithm is considerably fast and provides feasible near-optimal solutions. Numerical simulations have proved the effectiveness of the proposed algorithm in solving large UC problems within a reasonable execution time.     

基于改进飞蛾扑火算法的单时刻参数可变机组组合优化

机组组合属于高维、离散、非凸的混合整数非线性规划问题,具有NPhard特点。提出结合二进制粒子群算法与混沌飞蛾扑火算法的单时刻参数可变机组组合优化方法,将总时刻机组组合问题依次、逐一分解为单时刻启停状态主问题与单时刻经济分配子问题,对主、子问题分别运用二进制粒子群算法与改进飞蛾扑火算法进行交替迭代求解以提升求解速率。运用参数可变策略与优先次序法概率调整策略对算法参数及候选解进行修正,以提升算法运行效率及候选解质量。测试结果表明,本文所提方法具有良好的运算速率及收敛精度,能有效求解大规模机组组合问题。     

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.     

多阶段输电网络最优规划的并行蚁群算法

多阶段输电网络最优规划是一个复杂的非线性组合优化问题,难以采用传统的数学优化方法求解。蚁群算法是近年来出现的用于解决组合优化问题的一种高效的内启发式搜索技术,但存在着未成熟收敛问题。文中给出了多阶段输电网络最优规划的数学模型及其解的向量形式;详细分析了传统蚁群算法的未成熟收敛现象及其原因;提出一种并行蚁群算法并用于求解多阶段输电网络最优规划问题。并行蚁群算法无需初始可行解,能很好地协调局部搜索与全局搜索,在加快计算速度的同时有效地避免了因参数设置、种群规模等不同而引起的未成熟收敛。对实际算例的计算结果表明,该方法具有很高的计算效率和良好的全局收敛性。     

基于广义Benders分解的启发式机组组合优化

电力系统机组组合问题是一个高维、离散、非线性的工程优化问题。提出了一种基于Benders分解的启发式算法。该算法一方面充分利用研究时段负荷曲线的特征,将问题进行解耦,减小被研究问题的规模。另一方面,利用Benders分解算法在混合整数规划中的有效性,提高了解决问题的效率。算例表明该方法效率高、结果稳定,有较好的实用价值。     

On the design of 1D IIR digital filters with coefficients of finite word length

This paper presents new linear programming techniques for the design of optimal 1D IIR digital filters. the first method is based on a linear minimax criterion and leads to a linear programming approximation problem whose optimal solution is attained by a new fast algorithm. Next, the linear minimax approximation problem is extended and formulated as two linear integer programming problems which permit the design of 1D IIR digital filters with coefficients of finite word length. the first of these methods is formulated as a 0-1 integer linear programming problem and its optimal solution is attained rapidly by a new algorithm. This method appears to be suitable for the design of high-order digital filters. the second method is more general and is formulated as a mixed integer linear programming problem. to solve the design problem efficiently, a new integer tree search algorithm is introduced. the feasibility of the proposed algorithms is illustrated with detailed solutions or numerical examples.     

配电网无功补偿的动态优化算法

配电网无功补偿动态优化的目的是在考虑负荷变化和电容器操作次数约束的条件下,求得各节点电容器的最优动作时间和投入容量。该文将动态优化问题描述为变约束优化问题,并提出一种求解算法。算法先将动态优化问题分解为一系列单节点电容器动态优化子问题,然后通过迭代求解一系列子问题的方式得到整个动态优化问题的最优解。由于给出的单节点电容器动态优化子问题的求解算法可以在变约束条件下搜索到子问题的最优解,而且在迭代求解过程中电容器的投入容量和动作时问都可以得到修正,因此使整个动态优化问题能得到更好的优化结果。算例表明,提出的算法是可行和有效的。     

基于改进多目标引力搜索算法的电力系统环境经济调度

建立了综合考虑系统运行成本和污染物排放成本的电力系统环境经济调度模型,并提出了一种改进多目标引力搜索算法(IGSA)对该模型进行求解。该算法将NSGA-II中的非劣解排序和拥挤距离的思想引入基本引力搜索算法用于处理个体偏序关系。其次针对基本引力搜索算法收敛速度慢的问题,在更新个体位置过程中受粒子群优化算法的启发对引力搜索算法的位置更新公式进行了改进;同时为了引导群体向Pareto最优解集区域靠近并保证算法解集均匀分布,采用精英保留策略;最后采用模糊集理论产生最佳折中解,为决策人员提供调度方案。算例分析验证了所提算法的可行性和有效性,为实现电力系统经济性与环保性的均衡优化提供了一条新的方法。     

Combined cycle unit commitment in a changing electricity market scenario

For Generation Companies (GENCOs) one of the most relevant issue is the commitment of the units, the scheduling of them over a daily (or longer) time frame, with the aim of obtaining the best profit. It strongly depends on the plant operational generation costs, which depend in turn on the choices taken at the design stage; it follows that design technical choices should also aim at determining the best generation cost structure of generating units with respect to the market opportunities. In the paper the unit commitment (UC) problem has been considered, with highlights on changes in the market scenario. The paper analyzes the relevance of some design choices (structure, size, regulation type) on the economics of the operation of gas–steam combined cycle generating units. To solve the UC problem, a recently proposed method for mixed integer nonlinear programming problems, with the use of a derivative free algorithm to solve the continuous subproblems, has been considered. The results for two GENCOs are reported: one managing a single unit and the other managing three units. Numerical examples show the sensitivity of the UC solutions to the market conditions and to the design choices on the regulation type in the evolving scenario of the Italian Electricity Market.     

一种最优匹配注入流与遗传算法结合的电容器优化规划方法

把电容器配置优化问题分解成主-从两个问题,并采用解析和软优化算法结合的混合方法求解。主问题是优化电容器补偿点的个数和补偿地点;从问题是假定补偿点个数和补偿地点都确定的前提下,计算不同负荷水平下的最优补偿容量,并归整到电容器组的可选容量上。基于最优匹配注入流法实现了一个解析算法,用于计算从问题。而采用自适应参数调整的遗传算法来求解主问题。通过对选择压力、变异和杂交算子的自适应调整,改进后的算法可以明显改善遗传漂移现象和提高收敛速度。该算法以最大经济效益为目标,可以计算出各个补偿点的安装容量、单台容量和各负荷水平下的投运容量。最后的算例结果表明该文的算法是有效的。     

A security constrained environmental/economic power dispatch technique using F-MSG algorithm for a power system area including limited energy supply thermal units

A security constrained non-convex environmental/economic power dispatch problem for a lossy electric power system area including limited energy supply thermal units is formulated. An iterative solution method based on modified subgradient algorithm operating on feasible values (F-MSG) and a common pseudo scaling factor for limited energy supply thermal units are used to solve it. In the proposed solution method, the F-MSG algorithm is used to solve the dispatch problem of each subinterval, while the common pseudo scaling factor is employed to adjust the amount of fuel spent by the limited energy supply thermal units during the considered operation period. We assume that limited energy supply thermal units are fueled under take-or-pay (T-O-P) agreement.The proposed dispatch technique is demonstrated on IEEE 30-bus power system with six thermal generating units having non-convex cost rate functions. Two of the generating units are selected as gas-fired limited energy supply thermal units. Pareto optimal solutions for the power system, where the constraint on the amount of fuel consumed by the limited energy supply thermal units is not considered, are calculated first. Later on, the same Pareto optimal solutions for the power system, where the fuel constraint is considered, are recalculated, and the obtained savings in the sum of optimal total fuel cost and total emission cost are presented. The dispatch problem of the first subinterval of the test system was solved previously by means of differential evolution (DE), and a hybrid method based on combination of DE and biogeography based optimization (BBO) for the best cost and the best emission cases in the literature. The results produced by these methods are compared with those of produced by the proposed method in terms of their total cost rate, emission rate and solution time values. It is demonstrated that the proposed method outperforms against the evolutionary methods mentioned in the above in terms of solution time values especially when the exact model of the test system is considered.     

蚁群算法在电力系统机组优化组合中的应用研究

采用模拟进化优化算法———蚁群优化算法来求解机组最优启停问题。引入了状态、决策、路径等概念,把机组最优启停问题设计成蚁群算法模式,通过附加惩罚项来处理各种约束,用tabu表限制不满足约束的状态,使得蚂蚁的搜索总在可行域内进行,对算法的搜索进程起到了有效的引导作用。仿真证明利用蚁群优化算法求解机组最优启停问题是可行的、有效的。     

Machine learning approaches to the unit commitment problem: Current trends,emerging challenges,and new strategies

Traditional power system operation and control decision-making processes, such as the unit commitment (UC) problem, primarily rely on the physical models and numerical calculations. With the growing scale and complexity of modern power grids, it becomes more complicated to accurately formulate the physical power system and more difficult to efficiently solve the corresponding UC problems. As a matter of fact, plenty of historical power system operation records as well as real-time data could provide useful information and insights of the underlying power grid. To this end, machine learning methods could be valuable to help understand the relationship of UC performance to power system parameters, reveal the rationality behind such relationship, and finally address UC problems in a more efficient and accurate way. This article discusses the current practices of using machine learning approaches to solve the mixed-integer linear programming based UC problems. The associated challenges are analyzed, and several promising strategies for adopting machine learning approaches to effectively solve UC problems are discussed in this article. In addition, we will also explore machine learning approaches to promptly solve steady-state nonlinear AC power flow and dynamics differential equations, so that they can be integrated into the UC problems to guarantee AC power flow security and dynamic stability of system operations, as compared to the current DC power flow constrained UC practice. Our studies show that machine learning, as model-free methods, is a valuable alternative or addition to the existing model-based methods. As a result, the effective combination of machine learning based approaches and physical model based methods are expected to derive more efficient UC solutions that can improve the secure and economic operation of power systems.