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.     

基于扩展CNP的分布式多Agent协同系统

传统合同网协议的投标类型单一,不能准确表达参与任务分配的Agent的意图。结合应用环境的任务抢占机制需求,对合同网协议的投标类型进行适应性扩展,基于扩展合同网协议设计分布式多Agent协同系统,给出Agent程序实现方法、多Agent通信机制设计和协同机制应用实例。该系统具有良好的可扩展性,便于动态引入具有不同功能的Agent,为协同机制的研究提供了基础平台。     

基于Agent的分布式动态作业车间调度

Agent技术是分布式工业系统建模的一种重要方法．本文对Agent及多Agent技术进 行了简要总结,综述了Agent技术在制造作业车间调度中的应用研究概况,提出了一种基于 合同网协议投标机制的多Agent分布式动态作业车间调度方案．     

协作企业制造过程多Agent调度建模技术

构建了面向订单的基于WWW协作企业制造过程模型。通过车间调度的多Agent协作流程,建立车间调度多Agent实体的UML描述方式。采用合同网协议,把交货时间、生产成本和设备利用率等作为多Agent投标时的性能指标,形成分布式产品调度动态处理模型。最后通过一个实例来验证多Agent调度建模技术的可行性。     

一种基于投标结果修正的合同网协议

合同网协议是一种广泛应用于多Agent系统的合作协议. 针对传统的合同网协议, 提出一种基于投标结果修正的合同网协议, 并论述该协议的合作过程, 说明该协议简化了传统合同网协议的工作过程. 最后把该协议应用于一个风力/太阳能互补发电系统的实例中, 结果表明该协议的工作效率比传统合同网协议更高.     

基于改进合同网协议的多Agent协作模型

合同网是用于分布式问题求解环境下各Agent进行通信和协作的一种协议,各个Agent内部的知识、能力和策略在问题求解过程中总是动态变化的。为了能够得到实用性强且易于实现的协作模型,分析了传统经典合同网的缺点并进行了改进,利用熟人模型对投标者进行了合理的筛选。采用Agent的主动感知特性,改进了任务Agent接受任务的被动性。在投标过程中引人信任度、感知系数和活跃度等智能参数,防止了投标者在投标过程中的自私性,平衡了各个合同承担者的负载。通过实验证明,改进的合同网降低了网络通信量,提高了多Agent系统协作的效率。     

基于改进合同网协议的多Agent协作模型

合同网是用于分布式问题求解环境下各Agent进行通信和协作的一种协议,各个Agent内部的知识、能力和策略在问题求解过程中总是动态变化的。为了能够得到实用性强且易于实现的协作模型,分析了传统经典合同网的缺点并进行了改进,利用熟人模型对投标者进行了合理的筛选。采用Agent的主动感知特性,改进了任务Agent接受任务的被动性。在投标过程中引人信任度、感知系数和活跃度等智能参数,防止了投标者在投标过程中的自私性,平衡了各个合同承担者的负载。通过实验证明,改进的合同网降低了网络通信量,提高了多Agent系统协作的效率。     

改进的任务分配策略在WSN中的应用研究

Agent协调是多Agent系统发挥整体效能的保证,也是Agent系统的一个核心问题。合同网模型是多Agent系统中经典的协调策略,但仍有许多不足。主要对合同网中标策略进行改进,提出了一种带有精准度的任务分配策略。用精准度对Agent的投标值进行评价,使Agent的投标值更接近Agent的实际能力。并且对以往多属性任务分配策略中的能力和负载进行了更为详细的评价,对Agent的能力进行了详细的分析后,将任务完成质量的好坏和任务花费代价归为对Agent的能力大小的评价,而对负载的改进则是加入了对任务列表中任务数量的评价。最后以无线传感器网络为应用背景对提出的改进方法进行了仿真实验。     

基于代理的分布式网络实时取证模型及关键技术研究

该文在分析分布式网络犯罪入侵特点的基础上,提出基于移动Agent和静态Agent技术相结合的分布式网络实时取证系统模型MADBFS,它采用“分布取证、动态协调、区域监督、全局推理”概念模型,用一个类似树状的结构来构造分布式网络实时取证系统。并对MADRFS中证据完整性保护、证据信息可靠与高速获取、系统的健壮性与容忍性、动态协调移动Agent取证、Agent之间协调得相互关系等关键技术进行了探讨。     

多Agent系统中基于改进合同网模型的任务分配研究

任务分配是多Agent系统中研究的热.奴,合同网模型是关于多Agent系统中任务分配的经典策略,但传统的合同网模型存在很多不足。在引入基于信任度的招标策略和基于自适应的投标策略的基础上,主要针对传统合同网模型中标阶段存在的问题,从投标Agent的负载、能力和信任度3个方面进行综合考虑和权衡,提出了一种基于多属性评价中标策略的动态任务分配算法,从而有效地提高了任务分配和执行的效率。最后通过仿真实验验证了基于多属性评价中标策略的正确性和合理性。     

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...     

基于multi-Agent和Petri网的分布式风光互补系统

近年来随着“大机组、高电压、大电网”发供电模式的弊端日益显露, 分散式供电系统成为21世纪电力工业的发展方向.此外, 随着风力、太阳能等可再生能源的飞速发展及其发电效率的不断提高, 使研究风光互补系统的分布式供电有着重要意义. 文中提出一种基于multi-Agent的分布式风光互补混合供电系统, 将分散的风力、太阳能光伏发电装置组成分布式系统, 并采用有色Petri网建立了multi-Agent的协作模型. 通过模拟风力–太阳能的运行情况, 证明了分散控制策略的有效性.     

Energy management for multi-microgrid system based on model predictive control

To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid system, an energy optimization management method based on model predictive control is presented. The idea of decomposition and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is minimized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation, and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO) algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and significantly higher efficiency.     

An intelligent coordinator design for GCSC and AGC in a two-area hybrid power system

This study addresses the design procedure of an optimized fuzzy fine-tuning (OFFT) approach as an intelligent coordinator for gate controlled series capacitors (GCSC) and automatic generation control (AGC) in hybrid multi-area power system. To do so, a detailed mathematical formulation for the participation of GCSC in tie-line power flow exchange is presented. The proposed OFFT approach is intended for valid adjustment of proportional–integral controller gains in GCSC structure and integral gain of secondary control loop in the AGC structure. Unlike the conventional classic controllers with constant gains that are generally designed for fixed operating conditions, the outlined approach demonstrates robust performance in load disturbances with adapting the gains of classic controllers. The parameters are adjusted in an online manner via the fuzzy logic method in which the sine cosine algorithm subjoined to optimize the fuzzy logic. To prove the scalability of the proposed approach, the design has also been implemented on a hybrid interconnected two-area power system with nonlinearity effect of governor dead band and generation rate constraint. Success of the proposed OFFT approach is established in three scenarios by comparing the dynamic performance of concerned power system with several optimization algorithms including artificial bee colony algorithm, genetic algorithm, improved particle swarm optimization algorithm, ant colony optimization algorithm and sine cosine algorithm.     

A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is highlighted. This paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.      

面向动态能源资源协调的分布式原对偶优化方法

分布式优化在电力系统中发挥着越来越重要的作用。本文研究一类包含分布式发电机（DGs）和储能设备（ESs）的动态能源资源（DERs）协调问题,其目标是在满足局部耦合物理约束的前提下,使得总成本（包括发电成本, 储能成本和环境成本）最小化。首先,本文将动态DERs协调问题等价转换为更具一般性的分布式复合约束优化模型,并利用拉格朗日对偶理论分析得到原问题的对偶形式。 其次,提出一种新的分布式原对偶优化算法。特别地,所提算法使用局部常数步长,同时采用基于边的通信方式,这本质上区别于基于节点的一致性优化方法。最后,利用基于IEEE 39-bus系统的仿真实验进一步验证了所提算法在求解DERs协调问题上的有效性与可行性。     

A Novel Cascaded PID Controller for Automatic Generation Control Analysis With Renewable Sources

Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, automatic generation control (AGC) with diverse renewable sources and a modified-cascaded controller are presented in the paper. Also, a new hybrid scheme of the improved teaching learning based optimization-differential evolution (hITLBO-DE) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a proportional integral derivative (PID), integral double derivative (IDD) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load change.The suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as dead-band of, generation rate constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by ± 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.      

基于分布式深度强化学习的微电网实时优化调度

随着海量新能源接入到微电网中, 微电网系统模型的参数空间成倍增长, 其能量优化调度的计算难度不断上升. 同时, 新能源电源出力的不确定性也给微电网的优化调度带来巨大挑战. 针对上述问题, 本文提出了一种基于分布式深度强化学习的微电网实时优化调度策略. 首先, 在分布式的架构下, 将主电网和每个分布式电源看作独立智能体. 其次, 各智能体拥有一个本地学习模型, 并根据本地数据分别建立状态和动作空间, 设计一个包含发电成本、交易电价、电源使用寿命等多目标优化的奖励函数及其约束条件. 最后, 各智能体通过与环境交互来寻求本地最优策略, 同时智能体之间相互学习价值网络参数, 优化本地动作选择, 最终实现最小化微电网系统运行成本的目标. 仿真结果表明, 与深度确定性策略梯度算法(Deep Deterministic Policy Gradient, DDPG)相比, 本方法在保证系统稳定以及求解精度的前提下, 训练速度提高了17.6%, 成本函数值降低了67%, 实现了微电网实时优化调度.     

基于多Agent系统的分散发电调度规划

根据分散发电、分布式供能的特点提出了混合发电的数学模型,并将分布式多Agent系统的原理应用其中,对多种可再生能源混合发电的方式进行了探讨,提出了一种基于Agent框架下的单体分层、模块式结构.从宏观的角度对混合发电的问题进行了规划设计,赋予了Agent自主性和协调能力,有关联合目标的分解规划算法,在一定程度上克服了自然资源随机变化带来的不确定性.给出的算例说明该方法具有较强的实用性.