Open AccessDynamic planning of distributed generation units in active distribution network
Author(s) -
Abapour Saeed,
Zare Kazem,
MohammadiIvatloo Behnam
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.1143
Subject(s) - distributed generation , computation , mode (computer interface) , computer science , mathematical optimization , function (biology) , linear programming , voltage , operational planning , integer programming , control theory (sociology) , engineering , renewable energy , algorithm , mathematics , control (management) , electrical engineering , management , evolutionary biology , artificial intelligence , economics , biology , operating system
This study proposes long‐term dynamic planning of distributed generation (DG) units in distribution networks considering active management (AM). In the AM operation mode, the distribution network equipment is controlled in real time based on the real‐time measurements of system parameters (voltage and current). The proposed model determines the optimal size, location and time of investment of DG units. The uncertainty of energy price and rate of load growth is handled using a scenario‐based modelling approach. To demonstrate the advantages of dynamic DG planning, its results are compared with those of static model. Also the results of optimal DG planning in active network are compared with those of the passive operation mode. In addition, a technical evaluation and comparison between conventional, passive and active networks have been performed. The proposed model is successfully applied to the 33‐bus radial distribution network. The results indicate that using AM, the cost function and losses are decreased effectively compared to the passive management. Computation of the optimal DG planning in AM environment is formulated as a mixed integer non‐linear problem which can be solved using commercial optimisation packages like as GAMS.