电力市场中电力供应商竞标策略的选择

对于电力市场中一个独立的电力供应商而言,除了解决传统意义上的机组组合或水火电联合经济调度问题,更重要的是如何选择竞标策略以使利益最大化。文中提出利用序优化的方法解决日竞标电力市场中电力供应商的竞标策略选择问题。首先建立电力市场仿真模型并构造一个尽量小而好的竞标策略子集S,并保证S在概率意义上含有“足够好”的解,然后通过对S中每一个元素的精确计算得到其中最好的竞标策略,并以此作为最终的竞标策略。基于一个拥有10个火电机组的独立电力供应商和美国加州电力市场历史价格数据的仿真结果表明,序优化在解决电力市场综合资源的调度与竞标问题时不失为一种有效的方法。     

电力市场中电力供就商竞标策略的选择

对于电力市场中一个独立的电力供应商而言,除了解决传统意义上的机组组合或水火电联全经济调度问题,更重要的是如何选择竞标策略以使利益最大化。文中提出利用序优化的方法解决日竞标电力市场中电力供应商的竞标策略选择问题。首先建立电力市场仿真模型并构造了一个昼小而好的竞标策略子集Ｓ,并保证Ｓ在概率意义上含有”足够好“的解,然后通过对Ｓ中每一个元素的精确计算得到其中最好的竞标策略,并以此作为最终的竞标策略。基于     

小型电力供应商的竞标策略研究

对一类其竞标策略对电力市场实时价格的影响可以忽略不计的小型电力供应商（pricetaker）,就如何选择竞标策略的问题进行了研究。在3个相关定理的基础上,利用蒙特卡罗方法对由Scenario方法和经济调度方法分别给出的竞标策略进行评估,并由此给出一种竞标策略选择的系统方法。该方法不仅可以解决小型电力供应商的竞标策略选择,也可用于一般电力供应商的竞标策略选择。     

节能调度与经济调度的异同分析

节能调度是指在保障电力可靠供应的前提下,按照节能、经济的原则进行电力设度;经济调度是指在考虑全系统经济性的前提下,既考虑机组的最佳组合并经济地分配各发电厂的有功负荷,又考虑电力网线损坏,使得在相同的产出条件下力求能源消耗最少。重点分析了节能调度与经济调度的异同。最后指出,在经济调度机制比较成熟,节能降耗目标实现到一定程度的时候再引入市场定价,这样将进一步促进资源的优化配置。     

基于多智能体的微电网中分布式发电的市场博弈竞标发电

为了解决微电网中分布式发电经济调度问题,在电力市场交易环境下研究基于分层多智能体系统的微电网市场竞标调度策略,进而实现利益分配最大化。针对电力交易特性,构建两层优化模型,并将其转化为平衡问题平衡约束模型,然后基于博弈论,以最大市场统一出清价原则构建辅助目标函数,并通过纳什均衡来求取唯一纳什均衡解,即为电力市场交易策略,从而解决了分布式电源经济调度最优化问题。两种博弈模式的经济调度仿真研究结果证明了微电网市场竞标调度策略的可行性和经济性。     

电力市场下考虑煤质影响的火电机组多目标优化调度

为了解决火电厂发电的经济效益与环境保护之间的矛盾,研究了电厂入炉煤质对煤耗和排放的影响,建立了电力市场下火电机组短期多目标协调优化调度模型,并采用重构归一化欧氏距离评价函数的方法处理多目标问题。采用5个火电机组的系统算例进行仿真计算,结果表明:多目标优化调度能够协调经济与环境之间的矛盾;入炉煤质影响电厂效益和污染排放;重构归一化欧氏距离评价函数能够获得较满意的多目标优化结果;电力市场下的优化调度能获得更高的经济效益。     

AGC机组参与电力市场辅助服务的探讨

电力系统频率调整是保证电力系统安全稳定运行的重要任务，也是电力市场中辅助服务的重要部分之一。AGC机组是调频服务的主要提供者，为解决AGC机组参与电力市场辅助服务面临的诸多问题，制定合理的调频市场规则，形成有序竞争的调频市场并保障电力市场环境下系统频率调整的正常进行，作者提出了以下相关问题并进行了探讨：（1）分析AGC机组在电力市场中所面临的调度和交易问题；（2）在分析AGC机组的运行成本的基础上，探讨了对AGC机组的服务进行合理经济补偿的办法以及对AGC机组所提供的辅助服务的质和量进行评估等问题；（3）分别提出在无辅助服务市场的机制下，公平、合理地调度AGC机组的原则和在有辅助服务市场机制下的几种AGC机组竞争模式；（4）为调频服务市场的建立和实施，提出了一些可行性建议。     

基于粗糙集数据挖掘技术的发电机竞标能力评估

提出通过粗糙集数据挖掘算法对历史竞标样本进行分析的新方法。对发电机竞标能力评估的概念和所需解决的问题以及影响竞标能力的因素进行了探讨。介绍了算法的基本概念、算法以及规则的确定。一个考虑市场需求水平、机组报价水平、机组容量等多因素的算例说明了该算法的实现过程并得到机组的负荷率与这些因素一些潜在的规则性知识,从而得到不同特征机组在市场中的竞标能力。这些知识对支持发电厂商决策和电力中介机构点电力交易中心(PowereXchange PX)以及独立系统运营中心(IndependentSystemOper ator     

电力超载负荷平衡调度模型研究与仿真

在科技不断发展的现今,对电力机组超载负荷情况进行调度,已经成为一个重点研究课题。在电力机组超载负荷调度的研究中,使用传统的PID控制调度算法进行控制较为烦琐,在负载平衡过程中会产生大时滞、大惯性和参数时变等问题。针对上述问题,提出了一种基于模糊反馈的电力机组超载负荷调度器设计模型。结合模糊和反馈控制方案,对电力机组超载负荷调度进行有效补偿,同时对电力机组超载负荷因为外界因素所受到的影响进行模糊PID反馈控制,并对控制通道延后性大于干扰通道延后性情况进行预估补偿,最终获取模糊反馈调度模型。实验证明,基于模糊反馈的电力机组超载负荷调度系统鲁棒性强、稳定性好,能够满足电力机组负载平衡的实际需求。     

电力配电网络节能调度模型的仿真

为了应对越来越严峻的能源和环境形势,我国颁布并实施了电力配电网络节能调度方法,但该方法只提供了基本调度原则,缺少符合政策的节能调度模型。为了解决上述问题,在分析现有节能调度模型不足的基础上,提出一种综合考虑能耗和排放的电力配电网络节能调度模型,给出了综合考虑能耗和排放的电力配电网络节能调度目标函数,综合分析了SO_2排放量与脱硫效率,针对提出的目标函数,介绍了其机组排序约束、常规机组特性约束、机组正负旋转备用约束,机组的最大最小出力约束、机组爬坡约束及线路潮流约束,在此基础上,采用粒子群算法对目标函数进行求解。在标准粒子群算法的基础上引入惯性权重因子,以优化粒子群算法的收敛性,从而实现电力配电网络节能调度模型的建立。仿真实验结果表明,所提模型具有很高的节能性能,且收敛速度快。     

具有相同机组水火电调度问题的新算法

对Lagrangian松弛法求解水火电调度问题时由机同机级引起解震荡现象进行了研究。通过一个例子分析了震荡产生的根本原因。对此，在松弛函数中引入了惩罚项并采用了伪次梯度法来修正乘子。新算法在求解低层子问题时并不同时求解，使震荡现象在很大程度上得以克服，同时可大幅度地降低偶解对约束的违反程度。通过简单的例子和对一个包含两组机同机组的短期发电调度问题的计算表明，对偶解的约束违反程度明显地降低，解震荡明显地减弱且最后可行解的质量有显著的改善。     

Optimization-based scheduling of hydrothermal power systems withpumped-storage units

This paper presents an optimization-based method for scheduling hydrothermal systems based on the Lagrangian relaxation technique. After system-wide constraints are relaxed by Lagrange multipliers, the problem is converted into the scheduling of individual units. This paper concentrates on the solution methodology for pumped-storage units. There are, many constraints limiting the operation of a pumped-storage unit, such as pond level dynamics and constraints, and discontinuous generation and pumping regions. The most challenging issue in solving pumped-storage subproblems within the Lagrangian relaxation framework is the integrated consideration of these constraints. The basic idea of the method is to relax the pond level dynamics and constraints by using another set of multipliers. The subproblem is then converted into the optimization of generation or pumping; levels for each operating state at individual hours, and the optimization of operating states across hours. The optimal generation or pumping level for a particular operating state at each hour can be obtained by optimizing a single variable function without discretizing pond levels. Dynamic programming is then used to optimize operating states across hours with only a few number of states and transitions. A subgradient algorithm is used to update the pond level Lagrangian multipliers. This method provides an efficient way to solve a class of subproblems involving continuous dynamics and constraints, discontinuous operating regions, and discrete operating states     

Coordination of Midterm Outage Scheduling With Short-Term Security-Constrained Unit Commitment

The proposed model solves the coordinated generation and transmission maintenance scheduling with security-constrained unit commitment (SCUC) over the scheduling horizon of weeks to months. The model applies the Lagrangian relaxation technique to decompose the optimization problem into subproblems for generation maintenance scheduling, transmission maintenance scheduling, and short-term SCUC. The decomposition and cooperation strategy is applied to the first two subproblems for the scheduling of generation and transmission maintenance. The SCUC solution is based on the mixed integer programming (MIP) technique. The optimal hourly results for maintenance scheduling, generation unit commitment, and transmission flows are obtained using a chronological load curve. Effective strategies are applied for accelerating the convergence of the hourly solution. The numerical examples demonstrate the effectiveness of the proposed model.     

Scheduling of hydrothermal power systems

The authors present a method for scheduling hydrothermal power systems based on the Lagrangian relaxation technique. By using Lagrange multipliers to relax system-wide demand and reserve requirements, the problem is decomposed and converted into a two-level optimization problem. Given the sets of Lagrange multipliers, a hydro unit subproblem is solved by a merit order allocation method, and a thermal unit subproblem is solved by using dynamic programming without discretizing generation levels. A subgradient algorithm is used to update the Lagrange multipliers. Numerical results based on Northeast Utilities data show that this algorithm is efficient, and near-optimal solutions are obtained. Compared with previous work where thermal units were scheduled by using the Lagrangian relaxation technique and hydro units by heuristics, the new coordinated hydro and thermal scheduling generates lower total costs and requires less computation time     

发电经济调度可行解判据及其求解方法

发电经济调度问题是一个经典混合整数规划问题,然而用拉格朗日松弛法得到的对偶解对原问题通常是不可行的,要获得可行解必须先得到一种可行的机组组合.本文分析了现有可行化条件中存在的问题,提出了一个易于检验的可行化条件,并证明它是充分必要的.随后,介绍了获得可行解的方法.     

Optimal generation scheduling with ramping costs

In this paper, a decomposition method is proposed which relates the unit ramping process to the cost of fatigue effect in the generation scheduling of thermal systems. The objective of this optimization problem is to minimize the system operation cost, which includes the fuel cost for generating the required electrical energy and starting up decommitted units, as well as the rotor depreciation during ramping processes, such as starting up, shutting down, loading, and unloading. According to the unit fatigue index curves provided by generator manufacturers, fixed unit ramping-rate limits, which have been used by previous studies, do not reflect the physical changes of generator rotors during the ramping processes due to the fatigue effect. By introducing ramping costs, the unit on/offstates can be determined more economically by the proposed method. The Lagrangian relaxation method is proposed for unit commitment and economic dispatch, in which the original problem is decomposed into several subproblems corresponding to the optimization process of individual units. The network model is employed to represent the dynamic process of searching for the optimal commitment and generation schedules of a unit over the entire study time span. The experimental results for a practical system demonstrate the effectiveness of the proposed approach in optimizing the power system generation schedule     

A decentralized scheduling method for flowshop problems with resource constraints

In this paper, we propose a decentralized scheduling method for flowshop scheduling problems with resource constraints using the Lagrangian decomposition and coordination approach. When a flowshop scheduling problem with resource constraints is decomposed into machine‐level subproblems, the decomposed problem becomes very difficult to solve so as to obtain the optimal solution, even when the production sequence of operations is given. In this study, the decomposed subproblems are solved by a simulated annealing algorithm combined with dynamic programming. By decomposing the problem into single machine subproblems, the changeover cost can easily be incorporated in the objective function. In order to reduce the computation time, a heuristic algorithm for calculating the starting times of operations is also proposed. The performance of the proposed method is compared with that of the simulated annealing method by which the schedule of the entire machine is successively improved. Numerical results have shown that the proposed method can generate better solutions than the conventional method. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 149(1): 44–51, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10364     

Sequential Lagrangian-MILP approaches for Unit Commitment problems

The short-term Unit Commitment (UC) problem in hydro-thermal power generation is a fundamental problem in short-term electrical generation scheduling. Historically, Lagrangian techniques have been used to tackle this large-scale, difficult Mixed-Integer NonLinear Program (MINLP); this requires being able to efficiently solve the Lagrangian subproblems, which has only recently become possible (efficiently enough) for units subject to significant ramp constraints. In the last years, alternative approaches have been devised where the nonlinearities in the problem are approximated by means of piecewise-linear functions, so that UC can be approximated by a Mixed-Integer Linear Program (MILP); in particular, using a recently developed class of valid inequalities for the problem, called “Perspective Cuts”, significant improvements have been obtained in the efficiency and effectiveness of the solution algorithms. These two different approaches have complementary strengths; Lagrangian ones provide very good lower bounds quickly, but they require sophisticated heuristics—which may need to be changed every time that the mathematical model changes—for producing actual feasible solutions. MILP approaches have been shown to be able to provide very good feasible solutions quickly, but their lower bound is significantly worse. We present a sequential approach which combines the two methods, trying to exploit each one’s strengths; we show, by means of extensive computational experiments on realistic instances, that the sequential approach may exhibit significantly better efficiency than either of the two basic ones, depending on the degree of accuracy requested to the feasible solutions.     

大型水火电力系统短期最优机组启停调度

给出了描述大型水火电力系统短期机组启停调度的数学模型,应用大系统分散控制理论将这一复杂问题分解为3个简单的子问题,分别用一维动态规划和网络规划法术解,最后,通过拉格朗日乘子协调控制得出最优可行解。该算法也可同时考虑火电的最优启停机计划和水电的优化调度。     

Implementation of a Lagrangian relaxation based unit commitmentproblem

An effort is made to provide an understanding of the practical aspects of the Lagrangian relaxation methodology for solving the thermal unit commitment problem. Unit commitment is a complex, mixed integer, nonlinear programming problem complicated by a small set of side constraints. Until recently, unit commitment for realistic size system has been solved using heuristic approaches. The Lagrangian relaxation offers a new approach for solving such problems. Essentially, the method involves decomposition of the problem into a sequence of master problems and easy subproblems, whose solutions converge to an ϵ-optimal solution to the original problem. The authors concentrate on the implementation aspects of the Lagrangian relaxation method applied to realistic and practical unit commitment problems