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。     

基于改进优先顺序法电力市场日调度计划研究

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中越来越重要。针对这一特点提出了在多约束条件下,寻求总的购电费用最低的算法。先采用改进的优先顺序法求解机组组合,再用改进原始对偶法求解网络流规划问题,以达到负荷优化分配。通过实例验证了算法的有效性。     

节能发电调度机组组合算法的研究

根据节能发电调度的要求,设计了顺序投入法与动态规划法相结合的优化方法来确定机组的开机方式,对各时段机组的负荷分配采用等耗微增率法,并将发电机组污染物排放水平引入到机组排序中,顺序投入法与动态规划法的结合有效解决了动态规划法的维数问题,并可弥补优先顺序法可能丢失最优解的缺陷,可较快得到满意解;另外,对机组的功率响应速度约束进行了考虑,通过定义路径,对路径寻优解决了时段耦合问题,实例计算结果表明了该方法的有效性和正确性。     

机组组合问题的扩展Hopfield神经网络算法

从Hopfield神经网络的原理和机组组合问题的特点出发，提出了一种适合解决机组组合问题的扩展Hopfield神经网络算法。该算法结合了Hopfield神经网络和模拟退火算法（SA）的优点，对整数变量取值范围进行了可行的扩展，有效地避免了陷入局部最优。同时，该算法无须进行额外的负荷经济分配，故能迅速，高效地搜索到系统的高质量优化解，对于实际系统的仿真计算结果证明了该算法的有效性，并且，方法本身具有良好的并行性，易于在并行计算机上实现，因此具有实际应用的可能性。     

基于优先顺序法的风电场限出力有功控制策略

随着风电并网容量的增加,风电场应具备有功功率调节能力,能根据电网调度部门指令控制其有功功率输出。结合甘肃酒泉风电基地集群风电有功控制系统的实际需要,以实现最大风能利用、避免风电场频繁起停为目标,基于经典优先顺序法提出了一种应用于变速恒频风电场的限出力有功控制策略。针对风电场限出力控制需要,设计了风电场层有功功率控制框架及限出力控制流程,综合考虑场内风电机组的预测信息、运行状态与控制特性,建立了以风电机组调节性能指标为排序指标的风电场限出力控制序列,进而基于此控制序列,给出了风电场限出力有功分配方法。最后,通过算例验证了所提方法的正确性和有效性。     

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。     

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。     

机组组合问题的优化方法综述

（上接本刊１９９９年第４期第５６页）５拉格朗日松弛法电力系统是一个非常典型的大系统,是大系统优化和控制理论的一个重要应用领域［４２］。大系统的分解协调思想最早见于Ｄａｎｔｚｉｇ和Ｗｏｌｆｅ对于线性规划问题的分解［４３］,而用于机组组合问题的主要是拉格...     

考虑多种约束条件的机组组合新算法

提出了考虑系统降出力备用约束、机组出力变化速率、线路潮流约束和断面传输功率约束的机组组合新算法。算法没有引入任何乘子,计算单调收敛,速度快,并且不需要初始可行解。用ＩＥＥＥ ２４母线系统对算法进行了验证,结果表明,算法对各种约束条件的处理正确,解的质量好。     

Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network

This paper proposes an improved priority list (IPL) and augmented Hopfield Lagrange neural network (ALH) for solving ramp rate constrained unit commitment (RUC) problem. The proposed IPL-ALH minimizes the total production cost subject to the power balance, 15 min spinning reserve response time constraint, generation ramp limit constraints, and minimum up and down time constraints. The IPL is a priority list enhanced by a heuristic search algorithm based on the average production cost of units, and the ALH is a continuous Hopfield network whose energy function is based on augmented Lagrangian relaxation. The IPL is used to solve unit scheduling problem satisfying spinning reserve, minimum up and down time constraints, and the ALH is used to solve ramp rate constrained economic dispatch (RED) problem by minimizing the operation cost subject to the power balance and new generator operating frame limits. For hours with insufficient power due to ramp rate or 15 min spinning reserve response time constraints, repairing strategy based on heuristic search is used to satisfy the constraints. The proposed IPL-ALH is tested on the 26-unit IEEE reliability test system, 38-unit and 45-unit practical systems and compared to combined artificial neural network with heuristics and dynamic programming (ANN-DP), improved adaptive Lagrangian relaxation (ILR), constraint logic programming (CLP), fuzzy optimization (FO), matrix real coded genetic algorithm (MRCGA), absolutely stochastic simulated annealing (ASSA), and hybrid parallel repair genetic algorithm (HPRGA). The test results indicate that the IPL-ALH obtain less total costs and faster computational times than some other methods.     

基于改进的逆序排序法的机组组合优化算法

文章提出了改进的逆序排序法来求解机组组合优化问题.该算法从可用机组全投入运行这一可行解出发,在每次迭代过程中优化一台机组在整个调度周期内的开停状况,以最小化总生产成本或总购电成本,直到连续两次迭代的目标函数值不再减小为止.该方法的显著优点在于计算不会振荡,迭代不会发散,且每次迭代的结果均为可行解.该算法在单机组优化过程中,以机组的最小启停区间而不是单个时段为研究调度对象,缓解了组合爆炸问题,明显地加快了计算速度.     

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.     

A binary-real-coded differential evolution for unit commitment problem

The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem encountered in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which differential evolution (DE) is one of the widely investigated metaheuristics. However, DE is usually applied for solving the integer part of the UCP, along with some other schemes for the real part of the problem. In this paper a binary-real-coded DE is proposed as a complete solution technique of the UCP. Some repairing mechanisms are also incorporated in the DE for speeding up its search process. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed DE is found quite satisfactory in comparison with the previously reported results.     

PSO algorithm‐based scenario reduction method for stochastic unit commitment problem

This paper proposes a particle swarm optimization (PSO ) algorithm‐based scenario reduction method for stochastic unit commitment problems. In this method, the position of each particle is an index set of the preserved scenarios, that is, a possible solution to the optimal scenario reduction problem. The Kantorovich distance between the original scenarios and the preserved scenarios is used to calculate the fitness value of each particle. A repair procedure is carried out to ensure that there are non‐repeating index numbers in the newly generated position of each particle during the iterations. The performance of the PSO ‐based method is tested on two scenario sets of electricity prices in a stochastic profit/price‐based unit commitment (SPBUC ) problem, and is compared with backward reduction and forward selection. Test results show that the PSO ‐based method performs very well with respect to the relative accuracy and running times when reducing large scenario set. Impacts of scenario reduction on the expected profits of the SPBUC problem are also investigated with different numbers of the preserved scenarios of electricity prices, which are obtained by these three different reduction methods from the same original scenario set. Simulation results show that optimal solutions of the SPBUC problem are related not only to the number of the preserved scenarios but also to the scenario reduction methods. The PSO ‐based method can lead to less conservative solutions than the forward selection, while the backward reduction can result in nonconservative solutions.     

基于辅助优化问题的安全约束机组组合约束削减方法

安全约束机组组合(Security-constrained Unit Commitment, SCUC)问题作为制定发电计划的核心环节,在电力系统优化调度等方面具有十分重要的意义。针对考虑故障态约束后SCUC问题规模庞大、难以求解的情况,提出了一种基于辅助优化问题的故障态安全约束削减方法。首先引入与具体故障态安全约束相关的辅助优化问题,从而建立判别相应故障态安全约束是否冗余的充分必要条件。然后探究冗余故障态安全约束辨识过程的具体加速方法,包括松弛辅助优化问题方法,使用可行性判据进行故障态安全约束预分类方法,以及多线程并行计算方法。最后,在IEEE118测试系统上对所提方法的正确性和有效性进行了仿真验证。     

大规模机组组合问题计及网络约束的线性化求解方法

为了提高求解机组组合问题计算效率,给出线性化方法,将目标函数分段线性化,将启机费用作为约束并将其线性化,同时将网络安全约束通过直流潮流模型进行线性化,从而建立较完备的混合整数线性规划的机组组合模型.采用世界上广为流行的CPLEX优化求解器求解,在对偶间隙设定为较合理的情况下,求解速度快.不同测试算例表明,该方法速度快,精度较高,能够求解较大规模的机组组合问题.     

Hydro unit commitment and loading problem for day-ahead operation planning problem

We describe a new model for the hydro unit commitment and loading (HUCL) problem that has been developed to be used as a support tool for day-ahead operation in the Brazilian system. The objective is to determine the optimal unit commitment and generation schedules for cascaded plants with multiple units and a head-dependent hydropower model. In this paper, we propose a new mathematical model for the hydropower function where the mechanical and electrical losses in the turbine-generator are included. We model the HUCL problem as a nonlinear mixed 0–1 programming problem and solve it with a strategy that includes a two-phase approach based on dual decomposition. The computational tool allows the model to effectively schedule hydro units for the problem in the Brazilian regulatory framework. Application of the approach is demonstrated by determining a 24-time step HUCL schedule for four cascaded plants with 4170 MW of installed capacity.     

Projected mixed integer programming formulations for unit commitment problem

The thermal unit commitment (UC) problem is a large-scale mixed integer quadratic programming (MIQP), which is difficult to solve efficiently, especially for large-scale instances. This paper presents a projected reformulation for UC problem. After projecting the power output of unit onto [0,1], a novel MIQP reformulation, denoted as P-MIQP, can be formed. The obtained P-MIQP is tighter than traditional MIQP formulation of UC problem. And the reduced problem of P-MIQP, which is eventually solved by solvers such as CPLEX, is compacter than that of traditional MIQP. In addition, two mixed integer linear programming (MILP) formulations can be obtained from traditional MIQP and our P-MIQP of UC by replacing the quadratic terms in the objective functions with a sequence of piece-wise perspective-cuts. Projected MILP is also tighter and compacter than the traditional MILP due to the same reason of MIQP. The simulation results for realistic instances that range in size from 10 to 200 units over a scheduling period of 24 h show that the projected reformulation yields tight and compact mixed integer programming UC formulations, which are competitive with currently traditional ones.