基于随机分形搜索算法的马斯京根模型参数优选

马斯京根模型在河道洪水演算中发挥着重要作用,该模型参数优选对提高洪水演算准确性至关重要。提出利用随机分形搜索算法(SFS)解决非线性马斯京根模型参数优选问题,同时将混沌序列替代SFS更新操作中的随机数。对算例进行洪水演算仿真分析并与多种优化算法比较,结果表明,随机分形搜索算法对非线性马斯京根法模型参数优选问题求解行之有效,且算法实施过程简便、参数解算精度高。     

计及多能设备变工况运行特性的综合能源系统日前优化调度北大核心CSCD

针对传统恒定效率设备模型存在的仿真误差大、适用范围窄等问题,文章将更加精确的设备变工况特性模型应用于系统优化调度中,建立了计及多能设备变工况运行特性的综合能源系统日前优化调度模型。在模型求解过程中,文章应用增量线性化策略,对目标函数和约束条件中的非线性项进行线性化处理,将原始的非线性优化调度模型转化为一个混合整数线性规划,通过调用成熟的商用求解器进行快速求解。在设备变工况运行特性约束的线性化处理过程中,文章提出了一种基于误差控制的分段点选取方法,通过构造偏差函数并控制其极值的上界,可以将最优解的误差水平限制在事先给定的范围内。算例结果表明,文章所提线性化处理方法与分段点选取策略,能够有效求得原始非线性规划问题的近似最优解,并且可将近似最优解的误差水平限制在5%以内。     

一种水库洪水多目标调度的线性综合优化算法

针对水库洪水优化调度的非线性、多目标特性,研究了一种水库洪水多目标调度的线性综合优化算法.理论推导与算例仿真表明,该算法通过有限次的单目标优化计算,可直接获得多目标函数最优解集的前端,从而避免了复杂的多目标进化算法的搜索过程,且其最优解集的目标空间分布曲线清晰,直观地表达了当前洪水状况下优化调度解的分布及各目标间的关系,对水库洪水多目标优化调度的决策具有重要意义.     

基于能量可实现的多源电力系统短期优化调度

针对风电出力的不确定性和波动性在电力系统调度中引起的能量不平衡,在离散时间平均模型的基础上,考虑调度计划的可实现性,引入积分约束,同时将需求侧响应和储能技术纳入,建立以弃风量最小为目标的能量可实现短期联合优化调度模型。并将含积分约束的调度模型转化为线性目标函数和非线性约束的凸规划问题进行求解。实际算例的结果表明,该文提出的优化调度模型在考虑能量可实现的基础上增加电力系统对风电的消纳能力,凸规划的求解方法能快速获得全局最优解。     

计及多智能体调度的冷热电联供型微网并网优化运行研究

基于多智能体技术以实现微网内各个代理之间的通信与协调控制为前提,建立了以发电成本和环境费用最小、并网收益最高为目标的冷热电联供型微网并网优化调度模型。该模型应用多代理混沌粒子群算法获得最优解和各微源最佳出力方案。在算例分析中选取某微网为参考系统,分别设置了3个典型场景,分析了在并网条件下微网加入冷热电联供系统后对优化目标结果的影响。采用多代理混沌粒子群算法,求得3个典型场景下的最优解。通过对比分析,验证了所提方法的可靠性。     

基于多目标优化的配电网负荷转移路径研究

建立了配电网故障后负荷转移路径优化模型,提出了优化的目标函数和约束条件。应用带精英策略的快速非支配排序遗传算法（NSGA-Ⅱ）求解该多目标多约束优化问题,得到的最优解集能更好地反映优化问题的本质,并结合熵权法建立了综合最优解的提取方法。根据提出的算法,编制了应用于配电网负荷转移的程序,用IEEE33节点算例验证了该算法的可行性。     

基于改进粒子群算法的水轮机调速系统建模

针对电力系统稳定计算分析软件中水轮机调速系统模型过于简单、粗略的问题,建立了水轮机调速系统非线性模型,并提出参数实测与改进的粒子群智能优化算法结合的模型辨识方法,获得了调速器模型参数、水轮机-引水道模型参数.现场实测数据的模型仿真结果验证了该模型辨识方法的准确性,该模型优于电力系统稳定计算软件中的模型,更符合实际.     

考虑需求侧管理的光伏微电网多目标优化调度方法

针对考虑需求侧管理(DSM)的光伏微电网优化调度问题开展研究。首先,在对典型光伏微电网结构和运行状态进行简要分析的基础上,以系统总运行费用和储能循环电量最小为目标,综合考虑功率平衡、储能系统荷电状态(SOC)、负荷可转移的时间范围等约束条件,建立在分时电价和DSM机制下的光伏微电网多目标优化模型;其次,针对模型具有多目标、多约束和非线性等特点,提出基于非支配排序遗传算法(NSGA-II)的优化求解策略。最后,在Matlab平台上对不同场景下的具体算例进行仿真分析,获得光伏微电网优化调度问题的多组Pareto最优解集。通过分析不同日照条件下的典型调度方案,验证优化模型的合理性和有效性。     

基于遗传算法的机组再热回热参数优化

再热和回热系统是汽轮机热力系统的基础,对机组经济性有较大的影响.研究包含再热参数优化在内的回热焓升优化分配问题.以几个典型工况下的机组再热压力和抽汽压力为优化参数,构造了适于优化的适应度模型,并在热平衡法的基础上采用遗传算法进行优化.通过算例表明:遗传算法具有很好的收敛性和适应性,能迅速地获得全局最优解,而且便于给出各优化参数(甚至是中间变量)之间的关系,为汽轮机循环的最佳加热分配提供了一种便捷而有效的方法.     

蚁群加速遗传算法在水环境优化问题中的应用

提出了水环境优化问题的一种新方法——蚁群加速遗传算法，给出了实施该算法的详细步骤。并对新方法的收敛性和全局优化性进行了理论和实例分析，在污水处理模型的参数识别问题中，新方法得到了精度较高的全局最优解。新方法具有精度高、速度快和鲁棒性强等特点，是一种既可以较大概率搜索全局最优解，又能进行局部细致搜索的较好的非线性优化方法，可广泛应用于各种水环境优化问题中。     

Optimal scheduling of heat-integrated multipurpose plants under fouling conditions

This work presents a systematic mathematical framework for scheduling the operation of multipurpose plants involving heat-integrated unit operations under fouling considerations. A salient characteristic of this problem is that the performance of each heat-integrated unit, which decreases with time due to fouling, can be restored to its initial state by performing cleaning operations. Based on a uniform time discretization, the overall problem is formulated as a mixed integer non-linear programming (MINLP) model. An iterative procedure is proposed for the solution of the resulting non-convex MINLP model, involving the solution of a series of mixed integer linear programming and non-linear programming subproblems. The optimization algorithm determines simultaneously: (i) the production schedule, (ii) the number of cleaning operations required along with their corresponding timings, (iii) the optimal utility utilization profile and (iv) the heat transfer medium flow rate profiles over time. An example problem is presented to illustrate the applicability of the proposed approach. It is proved that fouling considerations can significantly affect the production schedule as well as heat integration opportunities.     

Application of global optimization strategies to refinery hydrogen network

In this paper, a new method is presented in optimization of hydrogen network. The mixed integer non-linear programming (MINLP) and non-linear programming (NLP) problems have been solved with two methods, simultaneously. The linearization technique for non-linear programming models which proposed by McCormick (1976) and also a new method proposed by Faria and Bagajewicz (2011) have been used to solve these problems. Application of this new method is presented in global optimization of MINLP/NLP, and hydrogen network problem.     

Multi-objective optimized operation of integrated energy system with hydrogen storage

In this paper, an integrated energy system (IES) consisting of wind turbine unit, photovoltaic cell unit, electrolytic hydrogen unit, fuel cell unit, and hydrogen storage unit is proposed, and the construction of multi objectives for day-ahead power dispatching of the IES considering both operation and environment cost is discussed. By adopting piecewise linearization method, the optimization variables are divided into 24 periods, and the day-ahead power dispatching optimization problem is transformed into a 24-h piecewise optimization problem. On the basis, a complete non-linear mixed integer dynamic scheduling optimization model is established. An improved non-dominated sorting genetic algorithm (NSGA-II) is applied to solving the model. In optimization process, an interactive strategy is adopted to solve the coordination between discretization of variables and restriction of switching times of electrolyzer. Optimization results show that, compared with the single objective of minimizing operating costs, the multi-objective optimization scheme can reduce carbon emissions by 3.5% with 2.8% increase of operating cost. Compared with the single objective of minimizing environmental, the multi-objective optimization scheme can reduce operating cost carbon by 5.12% with 2.6% increase of environmental cost.     

基于E-C-K-均值聚类和SOP优化的分布式电源双层规划

在含智能软开关(SOP)的配电网中针对分布式电源(DG)提出一种双层优化配置模型.上层以运营商年收益最大为目标求解DG安装的位置和容量,下层通过对SOP进行约束使网络运行性能最优,考虑到负荷和分布式电源输出的时间序列特征,设计一种新的E-C-K-means聚类算法对四季场景下的风速和辐照度进行聚类,得到典型场景日曲线....     

A novel power system reconfiguration for a distribution system with minimum load balancing index using bacterial foraging optimization algorithm

In this paper, the objective of minimum load balancing index (LBI) for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm (BFOA). The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.     

多级制冷系统超结构优化及其应用研究

在多级制冷系统超结构的基础上，以年操作费用与设备年折旧费用之和的年度总费用最低为目标函数，建立了多机制冷系统优化的混合整数非线性规划。利用该模型对乙烯裂解装置乙烯-丙烯复叠制冷系统进行了优化研究，优化后系统比原制冷系统年度总费用下降15%，经济效益明显。     

用模拟退火算法改进管壳式换热器的优化设计

介绍了一种由两级管壳式换热器组成的换热器系统的优化设计数学模型，数学模型属于典型的有约束非线性规划问题。目前对有约束非线性规划问题还没有通用的求全局最优解的算法。转轴直径搜索可行方向法(DSFD方法)是一种比较有效的求解有约束非线性规划问题的算法。但它只能得到局部最优解。将模拟退火(Simulated Annealing)算法结合DSFD算法，构成了一种DSFD—SA—DSFD算法。在应用模拟退火算法的同时引入了罚函数法．将有约束非线性问题转化为无约束非线性问题。计算结果表明，DSFD—SA—DSFD算法能较快得到换热器优化问题的最优解，克服了单纯用DSFD算法只能得到局部最优解和单纯用SA算法效率不高的缺点。图3表11参8     

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

In this study, the best settings of five heuristics are determined for solving a mixed-integer non-linear multi-objective optimization problem. The algorithms treated in the article are: ant colony optimization, genetic algorithm, particle swarm optimization, differential evolution, and teaching-learning basic algorithm. The optimization problem consists in optimizing the design of a thermoelectric device, based on a model available in literature. Results showed that the inner settings can have different effects on the algorithm performance criteria depending on the algorithm. A formulation based on the weighted sum method is introduced for solving the multiobjective optimization problem with optimal settings. It was found that the five heuristic algorithms have comparable performances. Differential evolution generated the highest number of non-dominated solutions in comparison with the other algorithms.     

计及(火用)分析的综合能源系统多目标优化调度

建立综合能源系统优化调度模型并进行高效求解有利于可再生能源的开发利用,发掘综合能源系统降本增效的潜力。针对含光伏发电的综合能源系统,以系统■效率倒数最小和总运行成本最小为目标,结合电-热-冷综合需求响应模型和运行约束,构建综合能源系统多目标运行模型。针对模型中存在的非凸非线性项进行等价线性转化处理,将问题由多目标混合整数线性分式规划等价转换为多目标混合整数线性规划,进一步采用ε约束法将其转换为一系列单目标混合整数线性规划问题进行高效求解获得帕累托Pareto前沿,并采用TOPSIS法进行决策。算例仿真表明,所建立的含光伏发电的综合能源系统能提升系统运行灵活性,相比于单目标运行,计及■分析的综合能源系统多目标优化调度能够实现系统运行成本和效率的折衷。     

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.