改进整数变量辨识方法在机组组合问题中的应用

如何辨识待定整数变量,是机组组合问题中的难点,为此在综合考虑机组不同出力水平对成本的影响、系统时段耦合、系统备用以及网络安全等约束的情况下,提出了待定整数变量辨识方法.首先对各线性化目标函数进行安全约束机组组合松弛计算,根据所得结果按给定规则确定所有在全时段机组状态出现启停的机组集合,有效缩小了机组组合的寻优空间.在不影响最优解的前提下,利用负荷曲线特异性截取技术,加速了待定整数集合识别过程,提高了计算效率.算例结果验证了该方法的有效性     

基于太阳能和燃气的多能互补系统热电负荷分配技术研究

随着新能源大规模并网,多能互补是电网发展的必然趋势,对于拥有多台并列运行机组的热电联产电厂面临着基于厂级的负荷分配问题。本文通过研究光伏电站和燃气-蒸汽联合循环机组的运行特性,确定机组的调度限制范围,并选定典型日负荷曲线对多能互补系统进行调度计算,获得实时负荷分配结果;同时,通过对同一负荷下不同机组运行组合方式的研究,获得该负荷下最优的运行组合方式。仿真计算表明:对于本文的研究对象,两抽凝式组合气耗量最小,一背一抽式次之,3台机组同时运行气耗量较大,因此,在机组运行限度范围内,应优先选用两抽凝式运行组合方式;每日在11:00—22:00时间段采用3台机组同时运行,其余时间段内采用2台抽凝式机组运行,可以有效降低机组启停次数。该结论在实现节能降耗的同时,为热电负荷调度提供数据和理论支持。     

火电机组日前节能发电调度机组组合

机组组合是制定日前节能发电调度计划的重要内容,包括确定机组启停状态和经济负荷分配两方面,对此提出了制定火电机组组合的两种实现方法--分步优化和联合优化.前者基于机组排序表确定机组启停状态后再进行经济负荷分配;后者直接建立以能耗最小为目标、考虑多种约束的日前节能发电调度机组组合模型,采用启发式动态规划结合等微增率的混合算...     

电网火电机组在线节能发电调度方法和策略探讨

针对电网采用火电机组在线供电煤耗监测系统进行节能发电调度的方式,分析了监测系统测量不确定度对负荷调度结果的影响。根据各种容量及参数的机组供电煤耗特性和当前在线供电煤耗监测现状,提出了按照高准确度的性能试验结果优化机组运行组合和依据在线供电煤耗监测结果进行机组间负荷分配的策略。     

考虑机组投运风险的发电和备用协调调度模型

针对机组组合中如何实现机组有功出力与备用容量协调调度的问题,建立了考虑机组投运风险的机组组合模型.讨论了考虑风电接入时模型的处理方法.采用基于条件期望值的求解方法,不仅能够控制系统的失负荷概率,还能在一定程度上限制失负荷事件的后果.通过仿真算例证实了其方法的显著优势,对电力系统机组组合优化调度问题有一定实用价值.     

考虑平抑风电波动的空调冷水机组群控策略

中央空调冷水机组的群控方式使空调系统具有一定的负荷调节能力,可以考虑与波动的风电机组出力相组合从而平抑风电波动性并确定可平抑风电机组的容量。为此,首先根据各台冷水机组的负荷效率特性建立模型,然后确定风电机组波动功率平抑量变化范围的计算方法,并在保证空调制冷/制热效果的前提下建立可平抑风电机组容量与冷水机组负荷分配的最优匹配模型,实现对风电功率接入电网波动性的缓冲。     

基于混沌遗传混合优化算法的短期负荷环境和经济调度

环境和经济短期负荷调度主要由在调度周期内的最优机组组合和负荷分配组成,该文将优先次序法、遗传算法与混沌优化相结合,以应用到电站机组环境／经济运行优化问题中,在混沌遗传算法中采用递阶基因结构,将控制基因用于机组组合全局粗寻优,参数基因用于负荷分配局部优化, 基因修正与罚函数相结合解决约束问题,采用混沌扰动避免遗传算法早熟,运用基于线性搜索的混沌局部优化方法,加快算法的收敛速度和降低计算时间,优化计算结果可以同时得到最优机组组合及负荷最优分配,为实际调度系统提供了一个良好的方法。     

基于优先级排序和内点法的机组优化组合

机组优化组合的目标是确定电力系统煤耗量和网损最小的发电调度方式.优化模型中考虑了机组爬坡率的限制、输电网络断面安全约束,针对寻优效率提出了一种优先级排序和内点法相结合的机组组合优化方法.按能耗指标形成机组优先级排序表,以获得尽可能好的开机方式初始值;用局部寻优法在初始值附近的可行域内寻求最优组合状态;对负荷分配的连续性子问题用内点法求解.通过对IEEE-39节点10机系统进行仿真计算,验证了所提方法收敛速度快、耗时少,对处理机组组合问题具有有效性和适用性.     

基于可中断负荷的机组组合优化调度模型

机组组合是制定日前调度计划的重要内容,主要是确定机组的有功出力和启停状态。在智能电网大力建设背景下,需求响应成为重要的互动资源,本文综合考虑电源侧与负荷侧的特点,比较不同需求响应手段的使用范围,选取可中断负荷并将其作为一种虚拟的发电资源,融入到机组组合中,建立优化调度模型。算例结果表明,所建模型有效降低了系统运行成本,减少效率差的小型机组的频繁启停,同时用户还获得收益,说明考虑可中断负荷的机组组合优化调度模型有利于电力系统的可靠经济运行。     

基于改进二进制粒子群算法的电力系统机组组合

提出了1种改进的BPSO(二进制粒子群)方法求解机组组合问题.首先,利用优先顺序法确定初始的机组组合,根据这个结果,确定优化窗口的范围,在此范围内利用BPSO进行求解.在每次迭代过程中,通过启发式的调整策略使每代中的粒子都满足约束条件.在经济负荷分配问题上,采用经典的拉格朗日乘子法结合二分法进行求解,大大提高了求解效率.最后将所得结果与其他算法所得结果进行比较,证明所提方法有较强的优越性和实用性.     

Unit commitment health analysis for interconnected systems

The concepts previously published for unit commitment and operating reserve assessment in an isolated power system using a system well-being framework are extended in this paper to evaluate unit commitment and spinning reserve requirements in interconnected power systems. The problem of interconnected power systems unit commitment is decomposed into two subproblems. Unit scheduling is first performed in each isolated power system in accordance with the specified operating criterion. This is then followed by interconnected power system evaluation. The analysis is further extended to determine the operating state probabilities for the overall interconnected power system. The paper illustrates how power system well-being is improved by interconnection with other power systems. The concepts contained in the paper are illustrated by application to two radially interconnected IEEE-RTS     

Extending unit commitment health analysis to include transmission considerations

This paper extends the concepts of hierarchical level one (HLI) generating unit commitment health analysis to unit commitment in composite generation and transmission systems (HLII). The problem of unit commitment is decomposed into two subproblems. Generating units are first committed to the system based on HLI operating criteria followed by unit commitment evaluation at HLII. The committed capacity should satisfy the operating criteria at both HLI and HLII. Operating reserve evaluation at HLII recognizes the ability of the transmission system to deliver the generated energy to the major load points. This paper presents a procedure to determine the operating state probabilities using a contingency enumeration technique. For a given load level, the required number of committed units and the associated well-being indices at HLI and HLII are determined. The impact of system lead time on the well-being indices are also presented. The results are compared and discussed by application to the IEEE-RTS.     

A Method for Solving the Fuel Constrained Unit Commitment Problem

The unit commitment problem involves finding the hourly commitment schedule for the thermal units of an electrical system, and their associated generation, over a period of up to a week. For some utilities, contractual or other factors limit the amount of fuel available to certain of the units or plants. This paper describes a new method which solves the unit commitment problem in the presence of fuel constraints. The method uses a Lagrangian decomposition and successive approximation technique for solving the unit commitment problem where the generation, reserve and fuel constraints are adjoined onto the cost function using Lagrange multipliers. All important operating constraints have been incorporated including minimum up and down times, standby operation, ramping limits, time-dependent start-up cost, spinning and supplemental reserve. The method is being applied to a production-grade program suitable for Energy Management Systems applications.     

Thermal unit commitment using binary/real coded artificial bee colony algorithm

This paper presents a binary/real coded artificial bee colony (BRABC) algorithm to solve the thermal unit commitment problem (UCP). A novel binary coded ABC with repair strategies is used to obtain a feasible commitment schedule for each generating unit, satisfying spinning reserve and minimum up/down time constraints. Economic dispatch is carried out using real coded ABC for the feasible commitment obtained in each interval. In addition, non-linearities like valve-point effect, prohibited operating zones and multiple fuel options are included in the fuel cost functions. The effectiveness of the proposed algorithm has been tested on a standard ten-unit system, on IEEE 118-bus test system and IEEE RTS 24 bus system. Results obtained show that the proposed binary ABC is efficient in generating feasible schedules.     

The solution of scheduling of thermal units by mathematical programming techniques

In this paper, the thermal units scheduling problem is considered from both its aspects: the selection of units to be placed in operation, and the load distribution among them. The solution of these problems can be found by considering two separate, but mutually dependent optimization problems, with a general objective of minimizing the system operating costs, subject to demand and operating constraints. Both optimization problems are formulated as mathematical programming problems. The first, usually called the unit commitment problem, is solved by a dynamic programming method. The second, which represents the economic dispatch problem, is solved by both dynamic and linear programming techniques. The solution methods are applied to a real power system, consisting of twelve generating units. The mathematical algorithms for the solution of specified problems are presented and the results of some computer studies on the selected sample system are given.     

A practical hydro, dynamic unit commitment and loading model

We describe the dynamic unit commitment and loading (DUCL) model that has been developed for use in real-time system operations at BC Hydro (BCH) to determine the optimal hydroelectric unit generation schedules for plants with multiple units and complex hydraulic configurations. The problem is formulated and solved with a novel procedure that incorporates three algorithms. First, an expert system is used to eliminate infeasible and undesirable solutions. Second, dynamic programming is used to solve the optimal static unit commitment problem for a given plant loading, feasible unit combinations, and current hydraulic conditions. Third, the DUCL problem is formulated and solved as a large-scale network problem with side constraints. Output from the model includes DUCL schedules, spinning and operating reserve, and trades curves such as that between water usage and the number of unit switches. The innovative use of the procedure allows the model to effectively schedule hydro units for the energy and capacity markets in real-time. Application of the method is demonstrated by determining the 24-time-step DUCL schedule for a 2700 MW plant with ten units of four different unit types     

A generation control system for Hoover Powerplant to optimize plantoperating efficiency

A digital control system which provides unit commitment functions and real-time generation control while optimizing plant operating efficiency was put in service at the Hoover hydroelectric powerplant (USA) on June 1, 1987. The theory of the unit commitment and generation allocation schemes is presented. Results showing the operating efficiency gains during the first year of system operation are given     

计及安全约束的机组最优组合鲁棒优化方法

随着风电出力在电力系统中渗透率的逐步提高,其不确定性给系统安全与经济运行带来了新的问题甚至挑战。在制定电力系统运行方式和调度计划时如何确定计及安全约束的机组最优组合(security constrained unit commitment,SCUC)策略就是一个需要解决的重要课题,也是该文旨在研究的问题。具体地,首先建立基于场景生成的鲁棒优化安全约束的机组最优组合(robust security constrained unit commitment,RSCUC)模型,由此获得的鲁棒机组组合策略满足给定的置信度,对处于置信区间之外的极端场景则采取弃风或切负荷等不得已的措施来维持系统功率平衡,从而在系统运行的经济性和保守性之间合理折衷。之后,采用Benders分解法求解所建模型,将该问题分解为主问题和子问题。其中,主问题为确定性的SCUC问题;子问题则对考虑风电场出力随机变化时的系统状态进行安全性校验,若通过校验则表明所求得的SCUC策略满足鲁棒性约束,否则就生成相应的安全约束即Benders割并反馈给主问题。最后,采用修改的IEEE 39节点系统来说明所提方法的基本特征。     

基于互补机制的水火电力系统机组组合

针对水火电力系统短期发电计划中火电机组的开停机组合与水电调度问题解法复杂性,提出以水电发电量最大、耗水量最小和火电煤耗量最小且具有时序的3个优化子问题的发电调度模型,综合考虑一个调度周期内系统机组的运行状态。基于解耦的水火电力系统优化模型不仅可以确定水电的最佳放水策略,也可以体现水电和火电互补作用,充分提高水火电力系统联合运行的经济性。针对水电系统强非线性特点,采用新型随机全局优化仿电磁学算法进行求解,对火电系统则采用改进微粒群优化算法进行求解。通过一个具有3个体积水电站和5个火电厂的水火电力系统的仿真分析,表明以互补机制为理论基础的优化模型的正确性及算法求解的有效性。     

Uncertainty Management in the Unit Commitment Problem

Uncertainty in power systems operations has been traditionally managed by multistage decision making and operating reserve requirements. A familiar example of multistage decisions is day-ahead unit commitment and real-time economic dispatch. An alternate approach for managing uncertainty is a stochastic formulation, which allows the explicit modeling of the sources of uncertainty. This paper compares stochastic and reserve methods and evaluates the benefits of a combined approach for the efficient management of uncertainty in the unit commitment problem. Numerical studies show that unit commitment solutions obtained for the combined approach are robust and superior with respect to the traditional approach in terms of both economics and reliability metrics.