兼顾储能系统热管理与电池寿命的可再生能源储能电站经济调度

可再生能源储能电站的长期稳定运行与储能系统的热量管理息息相关,合理的热量管理策略能够保障储能电站的经济性与安全性.为了合理有效地进行储能系统热量管理,本文综合电池寿命成本,以日内实时调度的最高净盈利作为优化目标,提出了考虑热量管理与电池寿命损耗成本的储能电站调度策略.并利用分片McCormick方法将双线性项精细线性化...     

大规模储能电站参与电力系统自动发电控制的协调控制策略优化研究

文章提出了一种双层结构的储能电站参与电网二次调频的优化控制策略。首先,综合对比分析了常规机组与储能电站参与电网二次调频的技术特性,并在调度层面对储能电站与常规机组的功率进行解耦;然后研究了储能电站辅助火电机组调频的协调控制框架,以实现不同调频资源的合理分配;在储能电站主控层面,建立了表征储能实时调频能力的评估模型,并采用改进遗传算法对模型进行求解,以对储能各单元出力进行精细化协调管理;最后,基于仿真模型验证了所提控制策略的有效性。     

基于负荷预测的储能功率分配优化策略

针对目前电力系统调峰、调频储能电站容量有限的情况,文章提出了一种基于负荷预测的储能电站调峰、调频功率分配策略.首先建立了基于遗传算法(Genetic Algorithm,GA)优化BP神经网络的负荷预测模型,对电力系统中的负荷进行精准的预测,为储能电站参与调峰、调频提供计划调度参考;在此基础上,计及储能电站参与调峰、调...     

考虑随机性的微电网日前调度与储能优化模型

针对微电网中可再生能源发电的随机性,以运行成本最小为优化目标,建立了微电网日前调度和储能优化的调度模型。首先,分别对传统发电机组和储能系统建立了确定性模型,并对可再生能源发电机组建立随机模型。然后,考虑到可再生能源发电机组的线性成本函数和传统发电机组的二次成本函数,并结合功率平衡和功率交换限制等约束条件建立了优化模型。通过不同成本函数的可再生能源发电机组和传统发电机进行了仿真。结果表明,该模型可以获得最优调度结果,同时实现储能电池的最优储能。     

考虑冷热电多能流的混合储能系统三阶段能量优化调度策略

针对综合能源系统(Integrated energy system, IES)中可再生能源(Renewable energy, RE)能量耦合的复杂性和能量波动问题,提出了一种改进的混合储能系统(Hybrid energy storage system, HESS)三阶段能量优化调度。分析了IES中各种器件在不同时间尺度下的功率响应特性,表明三阶段能量优化调度方法可以与包括HESS在内的IES很好地耦合。比较分析了HESS在稳定功率波动和延长储能寿命方面优于单一储能系统的优点,提出了三阶段能量优化调度下超级电容的控制方法。根据日前预测数据,一次能源消耗、运营成本、二氧化碳排放被视为日前滚动优化阶段的优化目标。在日内滚动调整阶段,该方法可以减少RE日前预测误差的影响,实现日内能源调度平衡,确保IES设备的安全运行。考虑到IES中可再生能源比例较高的背景,创新性地利用HESS的优势来改善系统的功率响应特性。仿真结果表明,所提方法在提升系统功率响应速度、延长储能电池(Lithium-ion battery,LiB)寿命和减少碳排量上具有显著提升。     

多种调度模式下的光储电站经济性最优储能容量配置分析

光储联合发电系统是促进大容量光伏电站集中并网的解决方案之一。因现阶段储能造价较高,所以合理配置储能容量是提高光储电站经济性的重要前提。该文对光储电站不同调度模式进行分析,从发电企业的角度出发以净收益最优为目标建立储能容量优化模型。模型中,结合电池循环寿命数据,建立电池损耗成本函数,定量计算储能实际运行成本,同时计及售电收益、考核费用,利用粒子群算法求解净收益最优时的储能容量,并通过多种方案对比,仿真验证模型有效性。最后对影响光储电站经济性的敏感因素进行分析,为光储电站在不同调度模式、不同市场环境下的储能容量选择提供参考信息。     

锂电池储能电站一次调频设计优化及验证北大核心CSCD

一次调频是储能电站支撑新型电力系统稳定运行的关键技术。本工作研究了锂离子储能电站一次调频集成设计,包括响应时间和过载能力两方面的分析及优化,主要通过信息采集与传递、策略算法运算周期两方面缩短响应时间,通过储能变流器内部功率器件开关频率控制方案提升其过载能力,并通过20 MW/10 MWh锂电池储能电站实现了上述指标优化验证。结果表明,锂离子储能电站应用于一次调频工况时,变流器响应时间为60~80 ms,变流器过载能力30 s内均能达到150%,提高了储能电站功率性价比。该储能电站在孤网中的一次调频试验中可有效平滑频率波动,减少发电机组的动作频次,可为后续功率型储能电站设计提供思路。     

基于KL散度的储能电站分布鲁棒规划方法

电化学储能的循环寿命受到充放电次数和放电深度的影响,为了更加准确地在新能源电力系统中规划储能电站,提出基于Kullback-Leibler(KL)散度的储能电站分布鲁棒规划方法.根据电化学储能循环寿命的幂函数,建立基于等效全循环次数的储能电站寿命模型,考虑储能电站寿命模型约束和系统运行约束,以储能电站的全寿命周期成本和...     

面向配电网优化的混合储能系统控制策略

文章提出一种面向配电网优化运行的混合储能系统控制策略。首先建立光伏-混合储能系统柔性并网模型,针对配电网"源-网"经济运行和节点电压优化目标,提出光伏-混合储能系统参与配电网优化运行策略,获得混合储能最优目标功率;然后根据荷电状态及其充放电状态,提出混合储能多目标分频和内部能量协调控制策略,合理分配蓄电池和超级电容器的充放电功率。仿真结果表明,该策略不仅可以有效实现配电网经济运行和节点电压越限治理,而且能够充分发挥不同储能介质的技术特性,延长混合储能系统的运行寿命。     

电力市场环境下储能电站和光伏电站合作博弈模型

与传统电力市场主体不同,储能电站既可以参与发电侧,也可以参与用户侧,进而获取利益。文章以发电商、售电公司、用户、储能电站和光伏电站为市场主体,建立电力市场博弈模型;以储能电站在某一时段的运行方式和报价作为博弈策略,计及储能电站必要的运行约束条件,建立储能电站运行模型;基于招标电价制建立光伏电站运行模型。考虑储能电站与光伏电站进行合作博弈,以最大化联盟收益为剩余博弈支付,以联盟内部交换功率和交易电价为协议,基于Shapley值对合作博弈剩余进行分配,通过分析得到模型的Nash均衡。文章通过算例验证了所提出方法的正确性和有效性,所建立的模型适用于储能电站与光伏电站进行合作博弈参与电力市场的情景。     

基于改进粒子群算法的多目标无功优化

提出了一种基于粒子群算法的多目标优化方法,该算法采用Pareto支配关系来更新粒子的个体最优和全局最优值,用存储池保存搜索过程中发现的非支配解;采用聚类算法裁剪非支配解,以保持解的分散性;采用动态惯性权重来平衡粒子的局部和全局搜索能力,并将该算法应用于IEEE14节点系统的多目标无功优化。     

Power system optimization

Long-term gas purchase contracts usually determine delivery and payment for gas on the regular hourly basis, independently of demand side consumption. In order to use fuel gas in an economically viable way, optimization of gas distribution for covering consumption must be introduced. In this paper, a mathematical model of the electric utility system which is used for optimization of gas distribution over electric generators is presented. The utility system comprises installed capacity of 1500 MW of thermal power plants, 400 MW of combined heat and power plants, 330 MW of a nuclear power plant and 1600 MW of hydro power plants. Based on known demand curve the optimization model selects plants according to the prescribed criteria. Firstly it engages run-of-river hydro plants, then the public cogeneration plants, the nuclear plant and thermal power plants. Storage hydro plants are used for covering peak load consumption. In case of shortage of installed capacity, the cross-border purchase is allowed. Usage of dual fuel equipment (gas–oil), which is available in some thermal plants, is also controlled by the optimization procedure. It is shown that by using such a model it is possible to properly plan the amount of fuel gas which will be contracted. The contracted amount can easily be distributed over generators efficiently and without losses (no breaks in delivery). The model helps in optimizing of fuel gas–oil ratio for plants with combined burners and enables planning of power plants overhauls over a year in a viable and efficient way.     

Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique

Shell-and-tube heat exchangers (STHEs) are the most common type of heat exchangers that find widespread use in numerous industrial applications. Cost minimization of these heat exchangers is a key objective for both designer and users. Heat exchanger design involves complex processes, including selection of geometrical parameters and operating parameters. The traditional design approach for shell-and-tube heat exchangers involves rating a large number of different exchanger geometries to identify those that satisfy a given heat duty and a set of geometric and operational constraints. However, this approach is time-consuming and does not assure an optimal solution. Hence the present study explores the use of a non-traditional optimization technique; called particle swarm optimization (PSO), for design optimization of shell-and-tube heat exchangers from economic view point. Minimization of total annual cost is considered as an objective function. Three design variables such as shell internal diameter, outer tube diameter and baffle spacing are considered for optimization. Two tube layouts viz. triangle and square are also considered for optimization. Four different case studies are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO technique are compared with those obtained by using genetic algorithm (GA).     

Thermal placement optimization of multichip modules using a sequential metamodeling-based optimization approach

The study attempts to seek the optimal thermal design of planar multichip module (MCMs) under natural convection through optimal chip placement design. To attain the goal, a sequential metamodeling-based optimization approach is introduced. This approach incorporates a response surface methodology (RSM)-based design of experiment (DOE), three-dimensional (3D) thermal finite element analysis (FEA) and an updating scheme. Essentially, the RSM is used to construct, via quadratic polynomial approximation, the global RS of the chip junction temperature in terms of design variables. For speeding up the DOE and the solution of the optimization, several dynamic experimental design strategies using move limits and different proposed sampling techniques are introduced. The feasibility of the strategies is demonstrated, and their solution accuracy and efficiency are also compared with each other. By the explicit RS-based performance function together with geometry constraints, a constrained thermal optimization subproblem is formed. The optimum of the subspace optimization is sought, which is considered as the nominal starting point of next iteration. The iterative process continues with a new defined design subspace and factorial design plan until convergence is attained. The applicability of the proposed design optimization technique is demonstrated through several design case studies involving various planar MCMs.     

Vortex tube optimization theory

The Ranque–Hilsch vortex tube splits a single high pressure stream of gas into cold and warm streams. Simple models for the vortex tube combined with regenerative precooling are given from which an optimization can be undertaken. Two such optimizations are needed: the first shows that at any given cut or fraction of the cold stream, the best refrigerative load, allowing for the temperature lift, is nearly half the maximum loading that would result in no lift. The second optimization shows that the optimum cut is an equal division of the vortex streams between hot and cold. Bounds are obtainable within this theory for the performance of the system for a given gas and pressure ratio.     

Distributionally robust optimization of home energy management system based on receding horizon optimization

This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.     

基于改进灰狼优化算法的孤岛微网系统容量优化分析

孤岛微网的日前调度安排对岛屿配电网的安全稳定运行意义重大。为了对孤岛微网的日前调度进行安排,更好地优化微网结构,文章针对含有光伏和抽水蓄能电站的孤岛微网进行建模分析,将光伏阵列数量、额定功率和抽水蓄能电站上水库的容积作为决策变量,等年值平均投资成本和负荷失电率为目标函数,使用Matlab对某岛屿的天气和负荷数据进行分析。为了使光电-抽水蓄能协同运行的效益最大化,采用灰狼优化算法求解微网模型。优化结果表明,灰狼优化算法在孤岛微网模型的容量优化问题中可以节约投资成本,实现环境友好型供电。     

Thermodynamic optimization of cross flow plate-fin heat exchanger using a particle swarm optimization algorithm

This study explores the use of particle swarm optimization (PSO) algorithm for thermodynamic optimization of a cross flow plate-fin heat exchanger. Minimization of total number of entropy generation units for specific heat duty requirement under given space restrictions, minimization of total volume, and minimization of total annual cost are considered as objective functions and are treated individually. Based on the applications, heat exchanger length, fin frequency, numbers of fin layers, lance length of fin, fin height and fin thickness or different flow length of the heat exchanger are considered for optimization. Heat duty requirement constraint is included in the procedure. Two application examples are also presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO are validated by comparing with those obtained by using genetic algorithm (GA). Parametric analysis is also carried out to demonstrate the effect of heat exchanger dimensions on the optimum solution. The effect of variation of PSO parameters on convergence and optimum value of the objective has also been presented.     

Model optimization of photovoltaic cells

A simple optimization of a crystalline silicon solar cell is performed using a one-dimensional finite element calculation, and a neural network is subsequently trained to reproduce the data. The quality of this reproduction is discussed. Considering that translation of the original problem to run on a trained neural network permits a reduction of calculation time by three to six orders of magnitude, the scope for continued work on and extension of this method appears appealing.