分布式可再生能源发电并网的无功优化研究

提出了基于杂交粒子群优化算法的分布式可再生能源并网的无功优化算法,从网损和静态电压稳定裕度两个角度出发,构建了含分布式发电系统的配电网无功优化的数学模型.在美国PG&E 69节点配电系统上进行效验.结果表明,该算法收敛性好、精度高;分布式电源并网后能有效降低系统的有功网损,提高电压稳定性,对分布式电源并网运行具有一定的...     

A hierarchical optimization model for energy data flow in smart grid power systems

Environmental concerns and high prices of fossil fuels increase the feasibility of using renewable energy sources in smart grid. Smart grid technologies are currently being developed to provide efficient and clean power systems. Communication in smart grid allows different components to collaborate and exchange information. Traditionally, the utility company uses a central management unit to schedule energy generation, distribution, and consumption. Using centralized management in a very large scale smart grid forms a single point of failure and leads to serious scalability issues in terms of information delivery and processing. In this paper, a three-level hierarchical optimization approach is proposed to solve scalability, computational overhead, and minimize daily electricity cost through maximizing the used percentage of renewable energy. At level one, a single home or a group of homes are combined to form an optimized power entity (OPE) that satisfies its load demand from its own renewable energy sources (RESs). At level two, a group of OPEs satisfies energy requirements of all OPEs within the group. At level three, excess in renewable energy from different groups along with the energy from the grid is used to fulfill unsatisfied demands and the remaining energy are sent to storage devices.     

可再生能源分布式发电能量管理的复杂性研究

本文主要研究可再生能源分布式发电系统能量的优化与协调问题.基于系统能量管理中优化与协调控制问题的复杂性,提出采用基于Agent的建模与仿真方法进行研究.针对系统要求,以分散式发电系统的实体节点的映射封装Agent,提出了智能混合控制Agent的概念,建立了3层混合Agent的内部结构模型;并建立了以个体层、组织层和社会层构成的3层动态层级MAS体系结构,进一步辅以主导Agent和移动Agent辅助系统优化与决策,最终建立了一种新的面向再生能源的分散式发电系统能量管理的MAS宏观模型.最后应用有色Petri网对系统的优化与协调过程进行了动态模拟,验证了所提出的结构的正确性、合理性和有效性.     

Self-balancing dynamic scheduling of electrical energy for energy-intensive enterprises

Balancing production and consumption with self-generation capacity in energy-intensive enterprises has huge economic and environmental benefits. However, balancing production and consumption with self-generation capacity is a challenging task since the energy production and consumption must be balanced in real time with the criteria specified by power grid. In this article, a mathematical model for minimising the production cost with exactly realisable energy delivery schedule is formulated. And a dynamic programming (DP)-based self-balancing dynamic scheduling algorithm is developed to obtain the complete solution set for such a multiple optimal solutions problem. For each stage, a set of conditions are established to determine whether a feasible control trajectory exists. The state space under these conditions is partitioned into subsets and each subset is viewed as an aggregate state, the cost-to-go function is then expressed as a function of initial and terminal generation levels of each stage and is proved to be a staircase function with finite steps. This avoids the calculation of the cost-to-go of every state to resolve the issue of dimensionality in DP algorithm. In the backward sweep process of the algorithm, an optimal policy is determined to maximise the realisability of energy delivery schedule across the entire time horizon. And then in the forward sweep process, the feasible region of the optimal policy with the initial and terminal state at each stage is identified. Different feasible control trajectories can be identified based on the region; therefore, optimising for the feasible control trajectory is performed based on the region with economic and reliability objectives taken into account.     

基于猴群算法的混合动力汽车能量管理策略

混合动力汽车通常由两种不同的动力源驱动,对于驾驶员需求,如何分配动力源的输出,使得整个循环的耗油量达到最小是混合动力系统控制表示法需要解决的问题。实际汽车中各部件模型是非线性的,而且受到约束,结合猴群算法理论,提出了基于猴群算法的能量管理策略。对505及NEDC等标准工况进行了仿真,结果表明,猴群算法用很小的猴群就可以很快收敛到全局最优解,寻优能力强,可以实现低油耗。     

考虑不确定性因素的微电网经济运行的优化

本文针对微电网经济运行优化问题,考虑分布式可再生能源的输出功率具有随机性特点,提出了一种基于场景分解及场景策略聚合机制的优化方法.该方法针对所提出的优化问题中,统计信息不足以建立一个随机规划模型的难点,首先进行了场景分析,将原优化问题分解为多个场景子优化问题;其次引入场景束约束,将场景策略分为可容许策略、可实现策略和偶然策略,依照可用信息的进化机制,迭代修正每个场景子优化问题,并且采用动态规划方法求解每个场景子优化问题.然后,统计分析子优化问题最优解的主要特征和趋势,最终得到微电网经济运行优化问题的最优解.通过仿真验证了所提出方法的有效性.     

Smart energy pricing for demand‐side management in renewable energy smart grids

Smart grids are expected to provide various benefits to society by integrating advances in power engineering with recent developments in the field of information and communications technology. One of the advantages is the support to efficient demand‐side management (DSM), for example, changes in consumer demands for energy based on using incentives. Indeed, DSM is expected to help grid operators balance time‐varying generation by wind and solar units, and the optimization of their usage. This paper focuses on DSM considering renewable energy generation and proposes an auction, in which consumers submit bids to renewable energy usage plans. An additional model is introduced to allow consumers to compute their bid for a given usage plan. Both models have been extended to include energy storage devices. The proposed model is compared to a system with time‐varying pricing for energy, where it is shown to allow consumers to use more appliances, to lead to a larger profit, and to reduce the peak‐to‐average ratio of energy consumption. Finally, the impact of the use of energy storage in households and in the energy provider is also considered.     

基于遗传算法的超级电容RTG能量管理研究

为提高超级电容混合动力轮胎式集装箱门式起重机(Rubber Typed Gantry, RTG)系统的能量利用率和燃油的经济性,需要对混合动力能量系统进行有效的管理。提出了一种考虑系统能耗和非再生能量因素情况下,能分别确定混合动力系统中柴油发电机组最优输出功率和超级电容器组的最优输出功率的优化方法。建立了柴油发动机组和超级电容器组的数学模型。根据混合动力RTG关键特性参数和负载需求值,得出了负载需求曲线图。考虑发电机组燃油消耗的能量、来自电容器组的能量贡献和产生的非再生能量,构造了整体能耗的成本函数。并引入遗传算法(Genetic Algorithm, GA)进行求解,最后进行仿真和对比分析。结果表明,与传统的基于规则的控制策略相比,遗传算法优化后的能量管理系统,其能量消耗减少了35.9%。     

A decoupled controller for fuel cell hybrid electric power split

The power management of a hybrid system composed of a fuel cell, a battery and a DC/DC power converter is developed. A decoupled control strategy is proposed, aimed at balancing the power flow between the stack and the battery and avoiding electrochemical damage due to low oxygen concentration in the fuel cell cathode. The controller is composed of two components. The first controller regulates the compressor, and as consequence the oxygen supplied to the cathode, via a classic proportional–integral controller. The second controller optimally manages the current demanded by the fuel cell and battery via linear-quadratic control strategy acting on the converter. The closed-loop performance has been tested both in simulation and in real-time simulation using a microprocessor for the controller.     

Fixed frequency sliding mode control of renewable energy resources in DC micro grid

The rising cost of fossil fuels, their high depleting rate and issues regarding environmental pollution have brought the attention of the researchers towards renewable energy technologies. Different renewable energy resources like wind turbines, fuel cells and solar cells are connected to DC micro grid through controllable power electronic converters. In presence of these diverse generation units, robust controllers are required to ensure good power quality and to regulate grid voltage. This paper presents a sliding mode control based methodology to address the above mentioned challenges. The proposed technique keeps the switching frequency constant so that electromagnetic compatibility (EMC) issues can be solved with conventional filter design. Parallel operation of converter in DC micro gird is considered. Chattering reduction and power quality improvement by harmonic cancellation is proposed. A scaled down hardware for unregulated 11.5 V to 17.5 V input and 24V output is designed and tested. The experimental results show good performance of the controller under different loads and uncertain input voltage conditions. Moreover, the results show the robustness of the closed loop system to sudden variations in load conditions. Furthermore, a significant improvement in power quality is achieved by harmonic cancellation of chattering in the output of the converters.     

基于多搜索器优化算法的含可再生能源协同优化调度

考虑到风光不确定性给能源系统运行带来的影响,本文建立了含有燃气发电厂、热电联产装置、供热厂、风电场和光伏电场的热电数学模型,并提出了多搜索器优化算法,用于解决热电能量管理优化问题.混沌搜索具有随机性、遍历性和规律性,混沌理论的引入能够不断缩小优化变量的搜索空间,并不断提高搜索精度,从而有较高的搜索效率.算法中的双层搜索器的设置既能保证快速收敛到近似最优解,也能及时避免陷入局部最优解.通过标准函数和27机组热电系统的仿真结果表明,本方法具有较高收敛稳定性且收敛速度快,能够有效解决热电能量管理系统中的高度非线性、非光滑、非凸问题.     

Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP‐SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite‐horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time‐invariant causal state‐feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite‐horizon case, and for the same level of complexity as a discounted future‐cost controller, the shortest‐path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.     

基于测试和运行数据的新能源快速调频参数动态修正算法

随着新能源发电接入电力系统比例的不断提升,电力系统对新能源发电参与系统一次调频提出了具体要求.频率调差系数是评估新能源电站一次调频调节量的重要指标,系统对于新能源电站的调差率是预设的.然而系统发生频率波动时,无法预知新能源电站的运行状态,单一控制参数下新能源电站无法保证调频量满足系统需求.鉴于此,本文提出了基于调频量闭环控制的新能源电站一次调频控制参数动态修正方法,根据新能源电站的运行状态,动态调整其频率调差系数,使得新能源电站运行在不同状态下均可以满足调频要求,支撑系统频率稳定性.     

Real‐time optimal energy management for a fuel cell/battery hybrid system

An energy management strategy is proposed for a class of fuel cell/battery hybrid systems. In such hybrid systems, a fuel cell system is the main power source, and a lithium‐ion battery is the auxiliary power source. In order to manage the system power at the next moment in a reasonable way, a load current filter with bounded estimation errors is designed to estimate the load current. Then, a real‐time optimal energy management algorithm is proposed to optimize economy consumption of the hybrid system. By taking current change rate of the fuel cell and the state of charge into consideration and taking reasonable model simplifications, the optimization problem can be described as a quadratic programming problem. Then a general purpose solver is proposed to solve the quadratic programming problem based on the alternating direction method of multipliers. The efficiency of the proposed solver is much faster than computing interior point method or active set method. Simulation results in MATLAB/SIMULINK are carried out to validate the significant effectiveness and efficiency of the proposed management strategy.     

应用于光储系统中的改进型扰动观察MPPT算法

本文提出了一种改进的步长可变、稳态时无振荡的扰动观察MPPT算法,该改进算法在保持基本的扰动观察法简单以及便于实现特点的同时,还能停止稳态振荡并具备良好的追踪速度.该算法首先通过变步长实现快速跟踪最大功率点,当确定到达稳态之后,停止人工扰动,实现无振荡稳定输出功率,从而减少因振荡造成的功率损耗,提高了整个系统的效率.当...     

多源多变换微电网系统建模与动态稳定分析方法

微电网内部电力电子变换器接口型分布式电源广泛存在.电力电子接口微源具备与传统电力系统电源拓扑结构、控制方法和动态特性的差异性,控制方法的多样性、电力电子接口微源高渗透率将给低惯量微电网的协调控制和安全稳定运行带来严峻的挑战.多类型微源、多类型负荷在微电网内混合共存,可能引发源源耦合交互、负荷间交互以及源荷交互,不同特性的设备间相互作用将重新塑造区别与传统电力系统的动态响应特性,并诱发稳定性问题.本文首先归纳总结可再生能源渗透率不断提升下多源多变换微电网典型运行特性和存在的稳定性问题,详细阐述了含风光柴储多源多变换接口的微电网模块化动态建模方法.在此基础上,给出了用于微电网动态稳定分析的特征值分析方法和基本步骤.以珠海东澳岛多源多变换智能微电网为对象,基于提出的动态建模方法和动态稳定分析方法,给出建模与分析讨论结果.论文对多源多变换微电网系统动态稳定分析方法的论述可为微电网的广泛推广和应用提供建模和分析理论基础.     

Coordinating workload balancing and power switching in renewable energy powered data center

There has been growing concern about energy consumption and environmental impact of datacenters. Some pioneers begin to power datacenters with renewable energy to offset carbon footprint. However, it is challenging to integrate intermittent renewable energy into datacenter power system. Grid-tied system is widely deployed in renewable energy powered datacenters. But the drawbacks (e.g. Harmonic disturbance and costliness) of grid tie inverter harass this design. Besides, the mixture of green load and brown load makes powermanagement heavily depend on software measurement and monitoring, which often suffers inaccuracy. We propose DualPower, a novel power provisioning architecture that enables green datacenters to integrate renewable power supply without grid tie inverters. To optimize DualPower operation, we propose a specially designed power management framework to coordinate workload balancing with power supply switching. We evaluate three optimization schemes (LM, PS and JO) under different datacenter operation scenarios on our trace-driven simulation platform. The experimental results show that DualPower can be as efficient as grid-tied system and has good scalability. In contrast to previous works, DualPower integrates renewable power at lower cost and maintains full availability of datacenter servers.     

新能源电力系统控制与优化

新能源电力的规模化开发以及传统能源的清洁高效利用已成为当前能源革命的主题.目前,我国新能源电力比例持续增加,但受限于化石能源占主导的电源结构,未来很长一段时间内我国将处于混合能源时代.强随机波动性、间歇性以及不确定性使得规模化新能源电力的消纳问题日益严重,我国弃风弃光现象十分突出.深入认知电源侧、电网侧、负荷侧的基本特性,着重解决其控制与优化难题,是实现新能源电力系统安全高效运行的基本前提.本文围绕电源效应、电网响应、负荷响应,着重从电网友好型发电控制、基于多源互补的火力发电弹性控制、适应高比例新能源电力消纳的电网调度控制、需求侧资源特性与主动适应控制以及基于分布式能源的微电网控制5个方面阐述了新能源电力系统局部与全局控制中存在的关键科学问题.     

学习驱动的分布式异构混合流水车间批量流能效调度优化

本文研究了分布式异构混合流水车间批量流能效调度问题, 其中每个工厂的加工效率不同, 工件可以分割成若干子批进入加工系统. 以最大完成时间和总能耗为优化目标, 建立了混合整数规划模型. 本文提出了一种学习驱动的多目标进化算法, 包括学习驱动的全局搜索和局部搜索. 引入Q学习作为学习引擎, 以种群和非支配解集的评价作为环境反馈信号, 通过不断的学习来动态指导搜索操作的选择; 基于问题特征, 设计了算法的状态集、动作集和奖励机制. Q学习的引入能够及时感知当前搜索的状态, 减少搜索操作的盲目性, 提高搜索的效率. 通过对仿真数据集的测试, 表明所提出算法能够有效地求解分布式异构混合流水车间批量流能效调度问题.