区域环境系统规划的灰色方法

本文以厦门市饮用水源流域农业非点源污染控制规划为实例,探讨了流域环境系统规划的灰色方法问题。主要报告了如何建立灰色规划模型,如何求取规划模型的灰色解,如何将灰色规划解应用于流域规划等。文中介绍的求解灰色规划解的方法是一种近似方法,但十分有效,可以应用于求解类似的规划问题。研究表明,对流域环境系统进行灰色规划更加合理和可靠,用灰色系统理论研究区域环境系统的规划问题具有美好的前景。     

放松管制环境下的配网侧分布式发电协调规划模型

基于电力投资市场放松管制的背景,从系统角度出发分析了配网公司与投资商共同参与下的分布式发电协调规划问题,提出一个两阶段动态层次优化框架,该框架从投资侧构建了投资收益最大的分布式发电投资决策模型,从配网侧构建了考虑购电成本、电量削减、投资成本的多目标协调规划模型,并运用混合整数线性规划和非线性规划进行优化、求解。最后通过算例分析验证了在满足相关技术约束的条件下,该协调规划模型能有效优化分布式发电布局及容量投资,同时能增加分布式发电量、降低系统网损、提高电压质量。     

运筹学在水电能源系统规划中的应用

本文应用运筹学的优化技术——线性规划、非线性规划、整数规划和动态规划,建立了五个数学模型,以解决梯级水电站水库死水位的优化选择,水电站群装机容量的优化分配等在水电能源系统规划阶段的课题。通过工程实例验算,证明这些模型是可行的,成果也是可信的。     

机组经济运行的负荷优化分配方法研究

并列运行的火电机组负荷优化分配是火电厂乃至电力系统优化运行的重要部分。在分析燃耗特性的基础上，建立了负荷优化分配的等微增率模型、线性规划模型和二次规划模型．并对某电力系统十台并列运行的单元机组进行求解计算，得到三种方法不同的负荷分配优化值和系统燃耗量。结果表明：二次规划分配方法节能效果最明显，线性规划分配方法的节能效果介于二次规划分配方法和等微增率分配方法之间。     

利用MATLAB工具箱进行机组负荷优化分配

提出一种利用MATLAB曲线拟合工具箱和优化工具箱进行机组负荷优化分配的方法，并利用该方法对机组负荷分配中的线性规划和二次规划算法进行比较，通过仿真计算得到的图形和数据对比，直观地得出二次规划比线性规划具有更优的分配效果。利用该方法可以简洁，快速地使传统算法应用到实践中去，且节能效果好。     

水库群优化调度理论的研究—SEPOA方法

本文研究确定来水条件下库群优化调度问题,建立了数学模型并提出了状态(水位)极值逐次优化解法(SEPOA),证明了四个定理,这些定理表明,采用SEPOA方法从某一可行调度线开始,计算将收敛稳定于唯一的最优调度线.在水电站的优化调度及库群优化补偿计算中,几乎所有的数学规划方法,如线性规划、非线性规划以及各种动态规划方法都曾采用.这些研究克服了维数障碍,也带来新的问题,如目标函数极值的性质问题,局部极值与整体极值的关系问题,而这些对于实际问题是十分重要的.此外,各种方法的有效程度,极大的决定于实际问题的性质特点和具体条件,而过去这一点往往没有引起足够的重视.水库问题的优化算法,应更多的建立在问题本身所固有的特征和特性上.从这一点出发,我们着重于水库本身的特点,并力求在较为一般的条件下,研究一种算法,并讨论计算的收敛,最优解的存在及唯一性.     

考虑外部条件的某堆石坝料场规划方法及应用

针对传统的料场规划方法未将综合效益最大作为规划目标的不足,考虑料场规划中存在但难以量化的外部性因素,采用层次分析法确定各外部因素的优先度,并以料场调配总费用最低为优化目标,建立线性规划模型,利用Matlab求出在考虑外部因素时料场规划的最优解。实例应用结果表明,该方法可实现综合效益最大,更符合工程施工要求。     

多水库优化调度的研究

本文针对红水河上四个梯级水电站建立了确定型,四库优化调度的数子模型,并用动态规划求得初始解的基础上应用逐次优化方法求解该问题。本文中对逐次优化法的收敛进行了证明,编制了计算软件,并给出计算结果。     

河流水质灰色规划

基于河流水质灰色模型，可以对河流水质进行灰色规划．灰色规划考虑了实际河 不质系统中客观存在的不确定因素和偶然因素影响，概述了河流水质系统的灰色规划模型的构造，河流水质系统灰色规划模型的求解及其实际应用。     

水库群系统分析的一个数学规划模型

本文为树枝状水库群的系统分析和优化运行建立了以水量守恒方程为约束条件,以发电效益为目标函数的数学规划模型;讨论了模型在水资源系统规划与电力生产系统运行中的应用;为数值解的方便,还给出了模型的等价的动态规划形式和非线性网络规划形式。     

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。     

A model for optimization of process integration investments under uncertainty

The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency.     

水电站水库系统死库容优选的非线性网络流模型

本文针对水能系统参数设计中的水库群死库容选择问题建立了非线性网络流模型,提出了逐次线性化与原单纯形图算法联合求解方法。通过求解模型,获得统一调节期系统保证出力与年平均电能不同组合下的多种死库容方案及相应的系统最优运行策略。实例研究表明,文中提出的模型与算法具有计算速度快和占用内存量小的优点。     

虚拟电厂内部资源调度算法的对比分析及应用

分布式发电资源的广泛应用对各类分布式发电机组的协调并网运行及其与传统发电机组的联合调度提出了新的要求。结合微型热电联产机组等分布式发电机组的特性,引入通用虚拟电厂技术,分析虚拟电厂内部发电资源优化调度算法,基于传统调度方法提出三种改进的调度算法,并通过实例分析对比了三种算法的寻优结果及运行时间。结果表明,基于混合整数线性规划算法与启发式算法的启发式线性规划改进算法有利于虚拟电厂业主获得更好的经济与环保收益以及更快的运算时间。     

楼宇型分布式能源系统设备容量和运行策略优化研究

设备容量优化和运行策略优化是分布式能源系统设计,运行的关键问题。为实现分布式能源系统的经济效益,能效水平和环境效益最大化,针对楼宇型分布式能源系统建立了相对普适化的物理模型和数学模型,以粒子群优化算法和线性规划相结合,采用两阶段优化方法计算系统的最优容量配置,并给出运行策略。以某写字楼的分布式能源系统为例,得到最优的系统设备容量和全年逐时运行策略,并采用遍历法验证计算结果的准确性。优化的分布式能源系统与传统供能系统相比,费用年值降低7.79%,年总能耗降低24.18%,污染物排放量减少了62.77 %。     

Two-stage robust energy management of a hybrid charging station integrated with the photovoltaic system

The optimal management of charging stations has become a critical issue in recent years. In this paper, the energy management of a hybrid charging station composed of an electrolyzer, fuel cell and hydrogen storage is analyzed that is integrated with a photovoltaic system. As well, the station is connected to the local power market to increase flexibility and it is assumed that the manager of the charging station is an intelligent decision-maker who tries to minimize the cost of vehicle. Due to the existence of uncertainties, generation of photovoltaic, market price and load demand are considered as uncertain parameters and two-stage stochastic programming is applied to model them. To achieve optimal management, a robust optimization approach is proposed for the uncertainty of day-ahead market price where the decision-maker adjusts the conservatism level. The presented method is linear risk-constrained programming that the results for risk-neutral and risk-averse strategies are compared. To validate the accuracy and robustness of the approach, interval-based stochastic programming is also implemented. According to the robust optimization, day-ahead market price uncertainty increases the total expected cost by about 8.9%. In return, the risk of scheduling is reduced significantly with the risk-averse strategy.     

Optimal charging of electric drive vehicles in a market environment

With a potential to facilitate the integration of renewable energy into the electricity system, electric drive vehicles may offer a considerable flexibility by allowing for charging and discharging when desired. This paper takes the perspective of an aggregator that manages the electricity market participation of a vehicle fleet and presents a framework for optimizing charging and discharging of the electric drive vehicles, given the driving patterns of the fleet and the variations in market prices of electricity. When the aggregator is a price-taker the optimization can be stated in terms of linear programming whereas a quadratic programming formulation is required when he/she has market power. A Danish case study illustrates the construction of representative driving patterns through clustering of survey data from Western Denmark and the prediction of electricity price variations through regression on prices from the Nordic market. The results show that electric vehicles provide flexibility almost exclusively through charging. Moreover, the vehicles provide flexibility within the day but only limited flexibility from day to day when driving patterns are fixed.     

A comparison study of battery size optimization and an energy management strategy for FCHEVs based on dynamic programming and convex programming

Aiming to address the hydrogen economy and system efficiency of a fuel cell hybrid electric vehicle, this paper proposes comparison research of battery size optimization and an energy management strategy. One approach is based on a bi-loop dynamic programming strategy, which selects the optimal one by initializing the battery parameters in the outer loop and performs energy distribution in the inner loop. The other approach is a framework based on convex programming, which can simultaneously design energy management strategies and optimize battery size. In the dynamic programming algorithm, the influence of the different discrete steps of state variables on the results is analysed, and a discrete step that can guarantee the accuracy of the algorithm and reduce computational time is selected. The results based on the above two algorithms and considering the transient response limitations of the fuel cell are analysed as well. Finally, two driving cycles are chosen to verify and compare the performance of the proposed methodology. Simulation results show that the dynamic programming-based energy management strategy and battery size provide more accurate results, and the transient response of the fuel cell has little effect on the optimization results of the battery size and energy management strategies.     

考虑实时SOC与动态循环效率的电池损耗评估及储能定容策略

笔者提出一种高精度电池寿命损耗评估模型,该模型能充分挖掘厂家提供数据中的有效信息,从而建立起动态循环效率、放电深度（DOD）与实时荷电状态（SOC）应力等因素之间的映射关系。同时,针对采用更精细化的储能模型所引入的非线性项,提出一种基于滚动McCormick松弛方法的优化求解算法,该算法可将原本包含混合整数非线性规划问题转化为混合整数线性规划问题,从而实现优化问题的数值化求解。最后,通过算例证明所提出的精细化电池寿命模型与滚动McCormick求解算法的合理性和优越性。     

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.