Probabilistic method for the operation of three-phase unbalanced active distribution networks

被引:30
|
作者
Mokryani, Geev [1 ]
Majumdar, Ankur [2 ]
Pal, Bikash C. [2 ]
机构
[1] Univ Bradford, Sch Elect Engn & Comp Sci, Bradford, W Yorkshire, England
[2] Imperial Coll London, Dept Elect & Elect Engn, London, England
基金
英国工程与自然科学研究理事会;
关键词
probability; distribution networks; active networks; optimisation; power system management; voltage control; power factor; photovoltaic cells; Monte Carlo methods; constraint theory; Pareto optimisation; three-phase unbalanced active distribution network; probabilistic multiobjective optimisation method; active network management scheme; ANM scheme; coordinated voltage control; adaptive power factor control; total energy loss; photovoltaic cell; network constraint; intermittent PV generation; probability density function; PDF; Monte Carlo simulation method; epsilon-constraint approach; fuzzy satisfying method; Pareto optimal set; IEEE 13-bus test feeder; IEEE 34-bus test feeder; REACTIVE POWER/VOLTAGE CONTROL; DYNAMIC-PROGRAMMING APPROACH; POWER-FLOW ANALYSIS; ALGORITHM; MICROGRIDS; MODEL;
D O I
10.1049/iet-rpg.2015.0334
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study proposes a probabilistic multi-objective optimisation method for the operation of three-phase distribution networks incorporating active network management (ANM) schemes including coordinated voltage control and adaptive power factor control. The proposed probabilistic method incorporates detailed modelling of three-phase distribution network components and considers different operational objectives. The method simultaneously minimises the total energy losses of the lines from the point of view of distribution network operators and maximises the energy generated by photovoltaic (PV) cells considering ANM schemes and network constraints. Uncertainties related to intermittent generation of PVs and load demands are modelled by probability density functions (PDFs). Monte Carlo simulation method is employed to use the generated PDFs. The problem is solved using -constraint approach and fuzzy satisfying method is used to select the best solution from the Pareto optimal set. The effectiveness of the proposed probabilistic method is demonstrated with IEEE 13- and 34-bus test feeders.
引用
收藏
页码:944 / 954
页数:11
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