Automation of Electric Power SystemsVol. 39, Issue 21, Pages: 73-80(2015)
作者机构:
浙江工业大学信息工程学院,浙江省,杭州市,310023
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DOI:
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Published:2015
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WANG Jing. [J]. Automation of electric power systems, 2015, 39(21): 73-80.
DOI:
WANG Jing. [J]. Automation of electric power systems, 2015, 39(21): 73-80.DOI:
基于改进型MMAS算法的微电源容量优化布址
摘要
以实现微网经济效益最大化和提高系统供电可靠性为策略
提出了一种基于改进型最大-最小蚁群系统(MMAS)算法的微电源容量优化布址方法。首先
建立了考虑不同环境因素下风/光/储孤岛微电源容量优化布址的多目标模型
定义了N-1故障可靠性指标
明确了能量过剩率、稳态可靠性指标以及各微电源的运行约束。其次
提出改进型MMAS算法
通过实现信息启发式因子和期望启发式因子的自适应调节
改善MMAS算法的收敛速度和精度。然后
设计了基于改进型MMAS的微电源容量优化布址流程
经过对评估时间内经济性和可靠性的综合评价
得到基于网络结构的微网最优容量和最优布址方案。最后
利用MATLAB编程实现了5节点孤岛系统的容量优化以及丹麦海岛中压微网系统的微电源布址
与相关案例的对比与分析验证了所述方法的可行性与正确性。
Abstract
An improved max-min ant system (MMAS) algorithm is presented for optimal siting and sizing of distributed generators (DGs) in a microgrid. The strategies of the optimization are to maximize the economic benefit and to guarantee stipulated reliability criteria. Firstly
a multi-objective model for configuration of the independent microgrid
which includes wind power
photovoltaic (PV) generation and energy storage devices in different environments
is developed. The rate of load power supply after a failure is defined. Meanwhile
the related factors
such as the energy excess rate
the steady rate of load power supply and DGs’ constraints in the microgrid
are considered. Secondly
an improved MMAS algorithm is proposed to improve the rate and precision of convergence by adaptively adjusting the information heuristic factor and expectation heuristic factor. Then
the flow chart of optimally siting and sizing of DGs is designed. Through an evaluation of economy and reliability during a specified period of time
a comprehensive approach is obtained. Finally
the optimization is programmed in MATLAB to achieve the optimal sizing of a five-bus island system and the optimal siting of the medium voltage microgrid on an island of Denmark. The advancement and feasibility of the improved algorithm are validated through comparison of relevant cases. This work is supported by National Natural Science Foundation of China (No. 51147010