基于ADMM算法的多主体综合能源系统分布式协同优化研究

针对多园区综合能源系统的分布式调度问题,本文构造了多园区综合能源系统的多主体架构,提出了一种分布式经济调度的基本框架,建立了多决策主体的集中式优化调度模型;利用交替方向乘子法(Alternating Direction Method of Multipliers,ADMM),引入功率协调变量解耦园区间互济功率,将集中式优化调度转换为具有一致性的各园区分散自治子优化问题,构建了基于ADMM分布式调度模型与算法,实现了与集中式优化调度几乎一致的优化结果,同时克服了集中式方法信息隐私安全性低、通信成本高、建模复杂、求解难度大等缺陷。最后,分别以简单和复杂的多园区综合能源系统为算例,验证了ADMM算法的计算性能及其相较于集中式方法的特点和优势。     

A hybrid artificial neural network—differential dynamic programming approach for short-term hydro scheduling

In this paper, a hybrid artificial neural network-differential dynamic programming (ANN-DDP) method for the scheduling of short-term hydro generation is developed. The purpose of short-term hydro scheduling is to find the optimal amounts of generated powers for the hydro units in the system for the next N (N= 24 in this work) hours in the future. In the proposed method, the DDP procedures are performed offline on historical load data. The results are compiled and valuable information is obtained by using ANN algorithms. The DDP algorithm is then performed online according to the obtained information to give the hydro generation schedule for the forecasted load. Two types of ANN algorithm, the supervised learning neural network by Rumelhart et al. and the unsupervised learning neural network by Kohonen, are employed and compared in this paper. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of ten hydro plants. It is concluded from the results that the proposed approach can significantly reduce the execution time of the conventional differential dynamic programming algorithm which is required to reach proper hydro generation schedules.     

一种改进的自然数编码遗传算法在非满载时间窗车辆优化调度问题中的应用

用改进的一种顺序交叉算子代替实数编码遗传算法中的标准顺序交叉算子，应用于时间窗车辆调度优化问题的求解，克服了标准顺序交叉算子在求解该问题时局部寻优能力的不足，一组仿真实验结果表明，改进的顺序交叉算子具有很好的局部寻优能力，改进的算法求解时间窗VSP问题收敛性明显好于标准遗传算法。     

梯级水电站多目标模糊优化调度模型及其求解方法

根据梯级水电站不仅具有电力联系而且具有水力联系的运行特点,提出一种以年发电量和一级水电站耗水量为优化目标的梯级水电站多目标长期优化调度模型.通过定义各目标的隶属度函数,将多目标优化问题模糊化;采用最大模糊满意度法将多目标优化问题转化为单目标非线性规划问题;应用协调粒子群算法(CPSO)求解单目标优化问题.仿真验证了模型的正确性和求解方法的可行性,为梯级水电站优化调度提供了一种新颖有效的途径.     

市场环境下中长期发输电协调检修计划优化

根据电力市场环境中检修计划的特点,建立了协调市场各方利益的发输电一体化检修计划优化的数学模型,该模型综合考虑电力系统安全经济性和电力市场公平性,并考虑机组检修和输电设备检修之间的相互关联关系.针对该优化模型的求解,利用遗传算法(GA)和粒子群优化(PSO)算法相似的优化框架和优化流程,提出一种充分结合GA和PSO算法各自优点的混合智能算法,该算法将群体分成2个子群,分别采用GA和PSO算法进行演化,并充分交换2种算法所获取的优化信息,形成一个紧密耦合的、新型的遗传粒子群优化算法.算例证明,该算法在求解发电、输电设备检修协调优化这样的大规模复杂优化问题时,在全局搜索和局部搜索方面都表现出了良好的均衡性.     

综合能源系统环境下电动汽车分群优化调度

电动汽车(electric vehicles,EV)的大规模接入,给综合能源系统调度带来了机遇和挑战.文章考虑电-热-气混合潮流和电动汽车的调度灵活性,建立了含电动汽车的综合能源系统两层嵌套调度模型,对电动汽车进行分群分层调度,合理制定每辆汽车的充放电策略.调度计划层以调度方案成本最小、能量波动最小和环保性最优为目标函...     

Hydrothermal scheduling of a multireservoir power system withstochastic inflows

A suboptimal method for solving the annual hydrothermal scheduling problem for a multireservoir hydrothermal system is presented. Hydrothermal scheduling is performed in order to find the optimum allocation of hydro energy so that the annual operating cost of a mixed hydrothermal system is minimized. In the present work, the hydrothermal scheduling problem is formulated as a constrained optimization problem which is decomposed into three subproblems; maintenance scheduling of thermal units, dispatch of thermal units, and dispatch of hydroplants. The algorithm takes into consideration the stochastic nature of the water inflows to the reservoirs and the forced outage rates of the thermal units. Results obtained by the application of the algorithm to a simplified version of the Hellenic Hydrothermal Power System, showing the effect of the hydroplants, are reported     

梯级水库群发电优化调度的大系统分解协调模型

针对标准动态规划算法应用于梯级水库群发电优化调度求解时的"维数灾"问题,基于大系统分解协调原理建立了二级(分解、协调)递阶结构的梯级水库群发电优化调度模型。在分解级,应用多线程技术实现了标准动态规划算法的并行化,以对分解后的单库子系统进行优化求解;单库子系统间的耦合关联作为协调级,反复与分解级交换信息,经迭代计算求得模型的最优解。通过应用于李仙江三库梯级系统的实例验证分析,结果表明该方法可在一定程度上提高求解效率,为复杂工程系统优化提供了理论参考。     

Artificial immune system for dynamic economic dispatch

Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. This paper proposes artificial immune system based on the clonal selection principle for solving dynamic economic dispatch problem. This approach implements adaptive cloning, hyper-mutation, aging operator and tournament selection. Numerical results of a ten-unit system with nonsmooth fuel cost function have been presented to validate the performance of the proposed algorithm. The results obtained from the proposed algorithm are compared with those obtained from particle swarm optimization and evolutionary programming. From numerical results, it is found that the proposed artificial immune system based approach is able to provide better solution than particle swarm optimization and evolutionary programming in terms of minimum cost and computation time.     

利用拉格朗日松弛法解决SIS中的负荷经济分配问题

利用拉格朗日松弛法对火力发电厂厂级监控信息系统(SIS)中的负荷经济分配问题进行了求解。该方法以发电费用为目标函数,用拉格朗日乘子将系统的负荷约束松弛,并将得到的拉格朗日函数分解为一个上下两层的优化问题分别进行求解,保证了算法的快速有效性。与混沌优化及遗传算法进行了仿真比较,结果表明所提出的方法收敛速度快,求解精度高,易于掌握,是解决电力系统负荷经济分配问题的有效手段。     

基于干扰管理的矿井机车动态功率调度方法

现有研究很少有直接适用于矿井机车的无线网络通信模型。针对矿井机车不断移动的特性,提出基于干扰管理的矿井机车动态功率调度方法。首先根据传统矿井机车通信过程,建立一种基于SIC的动态功率调度模型。为了使该模型可以在多项式时间内求解,提出SIC集合和最大SIC集合概念,并设计了一个求解所有最大SIC集合的算法,有效的降低求解问题的复杂度。仿真显示,该方法优化效果较之采用普通SIC方法改进率在20%~35%,相比于传统方法改进率在40%~50%。     

海岛独立微电网多时间尺度能量调度方法研究

海岛独立微电网是一个复杂信息物理系统,其能量调度是非线性、多约束、多时间尺度的动态性问题。针对这一问题,研究了改进自适应粒子群算法的多时间尺度优化调度模型,并给出了日前多目标调度模型和日内滚动优化修正模型。日前多目标调度基于改进自适应粒子群算法进行多目标优化,确保微电网系统的经济性和稳定性;日内滚动优化调度采取滚动优化方式修正日前调度,确保日前调度的有效性。仿真算例验证了所提方法的有效性和可行性。     

基于交替乘子法与Shapley分配法的多微网联合经济调度

针对多微网联合经济调度下的多主体利益与系统优化调度矛盾,提出一种考虑动态交流潮流约束的分布式优化与涌现利益再分配的多微网联合经济调度模型。其中,采用交替方向乘子法将全局变量——联络线功率、微电网向主网的购售电功率转化为局部变量,在满足局部优化条件基础上,实现系统整体优化目标;并利用Shapley值法按各微电网主体对系统边际贡献进行涌现收益再分配,解决系统优化与个体利益冲突矛盾。IEEE 33节点系统仿真结果表明:所提模型能有效克服独立式和集中式优化存在的缺陷,明显提高区域系统经济性和本地风电、光伏的消纳利用率,并有降低通信负担和保护微电网主体运行隐私作用,同时有效解决了联盟成员涌现收益分配不公平的问题。     

基于纵横交叉算法的热电联产经济调度

为解决热电联产经济调度优化问题,提出了一种基于纵横交叉算法(Crisscross optimization algorithm,CSO)的新求解方法。CSO采用一种双交叉搜索机制,其中横向交叉引入扩展因子增强全局搜索能力,纵向交叉引入维交叉概念,从而避免维局部最优问题。CSO的全局并行搜索,避免了陷入局部最优,有效提高了收敛速度。以一个包含纯发电机组、热电联产机组、纯发热机组的48机组系统为例,建立了热电联产经济调度问题的模型。仿真结果表明,CSO解决热电联产经济调度问题具有可行性和有效性,为实际调度系统提供了一个较好的方法。     

Generator maintenance scheduling in power systems using metaheuristic-based hybrid approaches

The effective maintenance scheduling of power system generators is very important for the economical and reliable operation of a power system. This represents a tough scheduling problem which continues to present a challenge for efficient optimization solution techniques. This paper presents the application of metaheuristic approaches, such as a genetic algorithm (GA), simulated annealing (SA) and their hybrid for generator maintenance scheduling (GMS) in power systems using an integer representation. This paper mainly focuses on the application of GA/SA and GA/SA/heuristic hybrid approaches. GA/SA hybrid uses the probabilistic acceptance criterion of SA within the GA framework. GA/SA/heuristic hybrid combines heuristic approaches within the GA/SA hybrid to seed the initial population. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the metaheuristic approaches and their hybrid for the test case study are discussed. The results obtained are promising and show that the hybrid approaches are less sensitive to the variations of technique parameters and offer an effective alternative for solving the generator maintenance scheduling problem.     

基于交替方向乘子法的输-配-天然气系统分布式优化调度

随着电力网络与天然气网络耦合程度的加深以及传统配电网向主动配电网的转变,传统输配电气分离下的优化调度无法充分利用各种资源的灵活控制能力,不能获得整体最优解.因此,考虑电气耦合下的输配气协同优化,提出一种输-配-天然气系统优化调度模型.此外,考虑到未来输电系统运营中心、配电系统运营中心与天然气系统运营中心之间的独立性,为了保护各运营中心的信息隐私,提出了一个基于交替方向乘子法的分布式求解框架求解所提模型.为了确保分布式求解算法的收敛性,利用二阶锥松弛方法将气网优化问题转化为凸优化问题.算例结果表明,考虑电气耦合的输配气协同优化能够充分利用主动配电网中的多能耦合资源和储能资源,促进可再生能源消纳,提升系统总体运行经济性.此外,所提分布式求解算法的精确性和收敛性也得到了验证.     

基于解集动态分析含风电接入的多目标机组组合研究

风电的大规模接入给多目标节能减排发电调度带来了新的机遇和挑战。由于风电场出力具有随机性,采用置信区间简化风电场景模拟数量,考虑到多目标模型的复杂性,利用Benders分解技术对模型进行降维,设计一种基于解集动态分析的多目标自适应优化算法对降维后的多目标主问题进行求解,并提出一种提高模型整体求解效率的预处理机制加速收敛。仿真结果表明所提方法能够有效求解含风电的多目标机组组合问题,并验证了所提多目标算法和预处理机制在求解模型中的优势。     

基于免疫遗传算法的超短期发电计划优化

超短期发电计划优化在电力系统调度运行中发挥着越来越重要的作用,但由于其是一个非线性整数约束优化问题,数学模型复杂,很难从理论上找到全局最优解。针对电力系统发电计划优化问题,引入免疫遗传算法,很好地解决了遗传算法局部收敛的问题,实现了群体收敛性和个体多样性间的动态平衡调整,能快速准确求解,及时调整超短期发电计划方案,从而达到安全经济环保调度,优化资源配置的效果。     

An expert system for generator maintenance scheduling usingoperation index

A knowledge-based expert system for generator maintenance scheduling is presented. In the proposed system, generator maintenance scheduling (GMS) is considered as a constrained optimization problem. For different system operation conditions the objective function varies. An operation index is presented to determine an appropriate strategy for a decision-making process. The Taiwan Power Company (TPC) implementation is described. Tests for TPC cases in 1986 and 1987 are presented using the developed prototype GMS expert system. The results can meet TPC GMS requirements, which were still based on manual approaches and other conventional methods. The solution feasibility is discussed     

An improved PSO technique for short-term optimal hydrothermal scheduling

This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE.