风力发电与调度在三明电网应用研究

随着分布式发电的大量接入电网,含风力发电的电力系统经济调度问题逐渐凸显出来。由于风电成本的优势可降低发电总成本,所以,将风电成本引入传统的经济调度成本函数。但是,风力具有波动性,风电并入电网会对电力系统的稳定性造成影响,为此,考虑风速的不确定性,提出风速的预测误差,建立目标函数及约束条件,研究风电波动性的电力系统经济调度模型。     

有功经济调度的改进乘子法

本文对水火电混合电力系统的有功经济调度,应用数学变换的方法,提出了改进乘子法有功经济调度的模型和算法。实际计算表明,该法处理约束方便、省机时、省内存。     

基于碳交易的含燃气机组的低碳电源规划

在低碳经济背景下,引入碳交易和发展低碳能源发电成为实现电力系统低碳化发展的重要手段。由于燃气发电具有效率高、碳排放量低、调峰能力强等优点,因此将燃气机组加入到规划方案中,建立基于碳交易的低碳电源规划模型。模型以系统综合成本最小为目标,成本中包含投资运行成本和碳交易成本,同时考虑天然气供应约束、碳交易量约束等约束条件。采用离散细菌群体趋药性算法对模型进行求解,通过模型对比和灵敏性分析得出燃气机组和碳交易机制的加入可优化电源结构,促进低碳机组的投建,实现电力系统低碳减排的结论,验证了模型的有效性。     

基于可再生能源发电优先消纳的电力电量平衡模型研究

传统的电力电量平衡主要以经济调度、机组组合为基础,没有给予可再生能源突出考虑。首先利用聚类分析方法将电力系统内各电源按发电特性划分为电源簇,建立电量消纳优先次序,以充分体现各电源的价值;然后以购电成本最小化、可再生能源发电消纳最大化为目标函数,分别考虑各电源簇的运行约束,建立电力系统中长期电力电量平衡模型,并设计内外层相结合的分层求解策略进行模型求解。其中,模型外层为电源多目标优化配置,采用多目标规划-几何模型（MODM-GMM）算法求解;模型内层为电源发电能力计算,采用逐步优化-增量动态规划（POA-SIDP）算法求解。最后,将模型运用到重庆电网中验证模型可行性和有效性。模型能实现包含水、火、风、光等混合能源系统的购电经济性,并有效利用系统内的可再生能源,促进电网协调、低碳、健康、高质量可持续发展。     

电动汽车和可再生能源经济环保协同并网调度的优化模型

文章在考虑等效负荷均方差最小的基础上,以火电机组的运营成本和污染物排放量最低为目标函数,考虑到风力发电、光伏发电和火电机组的约束条件,建立了一种含电动汽车、风光发电参与下的经济环保调度优化模型。采用动态改变学习因子的粒子群算法,以某地区电力系统为实例,验证了文章所提模型的有效性;并通过4种不同的运行模式的对比分析发现,风力发电、光伏发电和电动汽车协同并网调度,不但能够最大限度地利用光能和消纳风电来平滑负荷的波动,而且有利于削峰填谷和降低火电机组运营成本和污染物排放量。     

水火电力系统多目标优化调度

水火电力系统多目标优化调度模型的确定在整个电网经济、安全运行中起着非常重要的作用.本文首先分析了大型水库的3种水能形式,建立了水电站发电模型,并在此基础上提出了耗水微增率的计算模型.在考虑发电成本时,引进了煤炭价格因素,并建立了新的发电成本函数模型.最后以水电站总发电用水最少和火电站总发电成本最低为两个目标函数建立调度...     

风电场穿透功率风险下基于改进NSGA-2的动态经济调度方法北大核心CSCD

文章以考虑阀点效应后的发电成本和废气排放量最小为目标函数,建立了一种计及风能穿透功率风险的电网综合动态经济调度模型。模型中通过引入系统安全水平约束,确定风能穿透功率范围,并利用改进的NSGA-2算法求解该模型。该算法通过对随机种群的调整和精英保留算子的改进,可以有效解决含等式约束的多目标优化问题,具有较强的鲁棒性和全局搜索能力。算例分析证明了风能穿透功率风险对系统经济调度的影响和改进算法的可行性。     

电力系统日经济调度准确算法

电力系统日经济调度是在已知系统日负荷曲线,已知系统网络结构及发电运行方式安排的情况下,最佳地安排电站出力,从而使得系统全日发电燃料成本最低.这种安排必须满足系统一系列的物理约束条件,例如电源的装机容量约束,系统负荷约束及输电线的容量约束等等.目前国内外广泛应用的日经济调度算法是以B系数法〈1〉—〈3〉计算网损为基础的λ、r轮流迭代(或同时迭代)法〈4〉〈5〉.这种算法具有公式简单、概念清晰、程序实现方便等特     

考虑电网络影响的水火电力系统短期优化调度

为提高发电计划实际执行的可行性,提出考虑电网络影响的水火电力系统短期优化调度方案,即在传统水火电优化调度中引入输电网络潮流约束,构建水火电力系统短期优化调度数学模型。该模型以火电站的总煤耗量最小为优化目标,考虑水火电的发电特性、梯级水电复杂关系、系统运行约束、输电网络约束及传输功率限制。同时为模型求解引入迁徙操作和惯性权重非线性递减策略的改进粒子群算法,并设计了五种约束处理规则以应对复杂的约束条件。最后以典型水火电系统和IEEE-9节点的电网络拓扑为例对所构建的模型和算法进行验证。结果表明,所构建的优化模型和设计的求解方法能满足复杂运行约束的短期调度方案。     

分布式电采暖负荷参与系统经济调度策略

电采暖是具有可时移特性的一种可调度负荷,但其数量多、容量小,集中式的调度方法已难以满足调度时效性,提出一种分布式电采暖负荷参与的电力系统经济调度策略,建立发电侧成本变化量关于发电侧出力变化量的函数关系式和电采暖负荷效益变化量关于用户电采暖负荷变化量的函数关系式。设计基于发电侧成本变化增量和电采暖负荷效益变化增量的多智能体主从一致性算法,求解经济调度问题获到最优经济的分配方案。通过实例仿真验证了调度策略的有效性。     

Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach

Economic load dispatch is the method of determining the most efficient, low-cost and reliable operation of a power system by dispatching the available electricity generation resources to supply the load on the system. Environmental concerns that arise due to the operation of fossil fuel fired electric generators, change the classical problem into multiobjective emission/economic dispatch (MEED) which is formulated as a constrained nonlinear multiobjective mathematical programming (MMP). The proposed MEED formulation includes emission minimization objective, AC load flow constraints and security constraints of the power system which usually are found simultaneously in real-world power systems. The proposed model has a more accurate evaluation of transmission losses obtained from the load flow equations. The MMP approach based on ?-constraint algorithm has been proposed for generating Pareto-optimal solutions of power systems MEED problem. Moreover, fuzzy decision making process is employed to extract one of the Pareto-optimal solutions as the best compromise nondominated solution. The proposed approach is simulated on the IEEE 30-bus six-generator test system and obtained results have been comprehensively compared with some of the most recently published research in the area (from the both aspects of precision and execution tome) which confirms the potential and effectiveness of the proposed approach.     

Reactive-power dispatch by successive quadratic programming

The reactive-power dispatch is formulated as the minimization of real-power losses in the system, utilizing a full set of control variables: generator voltages, switchable shunt susceptances, and transformer taps. The solution of the loss problem is obtained by successively solving quadratic programming problems. First- and second-order loss sensitivity coefficients are derived for the quadratic problem formulation. The derivations are based on the Jacobian method for sensitivity calculations. Sensitivity relations for the dependent constraints are based on the complete reactive-power model of the fast decoupled load flow method. The active-set projection method for quadratic programming is described and utilized as the solution algorithm for the quadratic reactive-power dispatch problems. Tests are conducted on the IEEE 30-bus and Mexican 253-bus systems. The computer results are discussed     

多功用水利系统的最佳调度

本文讨论有发电和灌溉功用的水利系统的最佳调度问题。系统由多年调节水库、可控灌溉水闸和渠网河系组成。水库来水和有关支流来水均假定为马尔柯夫扩散过程,相应地系统状态演变由矢量随机微分方程dx(t)=(A(t)x(t)+B(t)u(t))dt+σdw描述。当以调度期内发电量最多和实际灌溉水量与标准定额灌溉水量偏差最小为目标时,水利系统的最佳调度问题即为线性二次随机最佳控制问题。利用动态规划方法得到了用解析形式表示的系统最佳调度策略。     

Hybrid SOA–SQP algorithm for dynamic economic dispatch with valve-point effects

This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with valve-point effects. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, SOA is used as a base level search, which can give a good direction to the optimal global region and SQP as a local search to fine tune the solution obtained from SOA. Thus SQP guides SOA to find optimal or near optimal solution in the complex search space. Two test systems i.e., 5 unit with losses and 10 unit without losses, have been taken to validate the efficiency of the proposed hybrid method. Simulation results clearly show that the proposed method outperforms the existing method in terms of solution quality.     

水火电系统日负荷经济分配及发电机组启停的实用计算

本文论述了一种计及发电机组启停的水火电系统日负荷经济分配的实用算法,即交替进行系统负荷分配计算与厂内机组组合计算,在得出各电厂出力安排的同时亦可给出厂内机组启停计划。试算结果表明算法可行。     

基于蚁群算法的水电站AGC机组组合与负荷分配优化

针对水电站厂级自动发电控制(AGC)中的经济运行问题,采用蚁群算法求解,所建模型综合考虑了功率平衡、机组出力约束、旋转备用和机组开停机时间约束等多重安全运行限制条件,并根据等微增率原则导出经济负荷分配公式嵌入并行随机搜索模式中,减少负荷分配时的迭代次数。算例中的机组耗量特性曲线根据实际数据采用二次曲线拟合。仿真结果表明,该方法既带有智能算法搜索的随机性又具有传统算法的稳定性,优化速度快且精度较高。     

火电机组间负荷优化分配算法比较与分析

负荷优化分配是电厂节能和运行优化的重要研究内容。在全厂机组组合方式一定的情况下,各台机组间合理的优化分配负荷可以提高整个电厂运行的经济性。为此,文中应用二次规划原理对某电厂4台300MW机组并列运行时的负荷分配进行优化。结果证明,它比线性规划分配负荷的调度方式所用煤耗明显降低。     

Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder-Mead algorithm

In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder-Mead (NM) method (called BF-NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system.     

Unit commitment with voltage constraints

This paper proposes an efficient algorithm for considering reactive power and voltage constraints in unit commitment. Because the problem is formulated as a large mixed-integer programming and has a two-level hierarchical structure, the Benders decomposition method is used. Using decomposition, the master problem solves unit commitment, and the subproblem examines reactive power and voltage constraints in each hour to test the feasibility of the unit commitment solution. In the case of infeasibility, a proper constraint (Benders cut) is introduced and placed in the master problem for unit commitment rescheduling. The iterative process between the master and subproblems provides a minimum cost solution for generation scheduling while satisfying reactive power and voltage constraints. The constrained economic dispatch is used in our unit commitment. The constraints in the subproblem are formulated as linear programming with a block-angular structure. So, the Dantzig–Wolfe decomposition is applied in the subproblem to solve large-scale power system problems. A modified IEEE-30 bus system is presented to demonstrate the efficiency of the method. © 1997 by John Wiley and sons, Ltd.     

Economic dispatch using particle swarm optimization: A review

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper.