学术
摘 要:
由于风、光的强随机性和负荷的多变性，风-光互补发电系统的频率稳定性变差．有功功率优化既能够保证发电系统频率的安全与稳定，又能够使其经济地运行．但它是一个带约束的非线性多极值优化问题，用传统的方法很难处理．随着风-光互补发电场规模的增大，频率控制的实时性变差．针对这些问题，建立了以发电成本最小为目标的频率优化控制的数学模型，提出了基于均匀设计和惰性变异的粒子群算法和基于多Agent的协调优化方法，并用于发电系统频率的优化控制．实验验证了频率优化控制模型的正确性，显示了改进的粒子群算法比标准的粒子群算法优化效果更好；另一实验显示，多Agent的协调优化方法比单一种群的粒子群算法更加高效、更加适合于大规模风-光互补发电系统的频率控制．[著者文摘]
文章出处:
《控制理论与应用》-2008年25卷1期 -155-158页
栏目信息:
文献标识码:
A
文章编号:
1000-8152（2008）01-0155-04
Frequency control of wind-photovoltaic hybrid power systems
ZHANG Bo-quan, YANG Yi-min, GAN Lu （1. Faculty of Computer, Guangdong University of Technology, Guangzhou Guangdong 510090, China; 2. Institute of Automation, Guangdong University of Technology, Guangzhou Guangdong 510090, China）
Abstract:
Because of strong randomicity of wind and photovoltaic resources and the variety of loads, the stability of frequency of wind-photovoltaic hybrid power systems becomes low. The optimization of active power not only ensures the safety and stability of frequency of power systems, but also makes it operating economically. However, it is a non-linear optimization problem with constraints and multi-extremum, so this problem is difficult to be solved by traditional methods. With the size of wind-photovoltaic hybrid power farm increasing, the performance of real-time control becomes worse. Based on the above analysis, a mathematical model with the minimal generation cost as objective is constructed. Also a new particle swarm optimization（PSO） based on uniform design and inertia mutation （UMPSO） and a multi-Agent based collaborative optimization method are brought forward, which are used to optimize and control the frequency of power system. Experiment verifies the correctness of the mathematical model and shows the higher effect of UMPSO than that of the standard one. Another experiment indicates that the multi-Agent based collaborative optimization method is preferable for larger power generation farm to the sole population PSO.[著者文摘]
Key words:
wind-photovoltaic hybrid power generation; frequency control; active power optimization; particle swarm optimization （PSO）; multi-Agent systems
基金资助:
国家自然科学基金重点资助项目（60534040）：广东省自然科学基金自由申请项目（05001819）.
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