Open AccessOptimal multi‐configuration and allocation of SVR, capacitor, centralised wind farm, and energy storage system: a multi‐objective approach in a real distribution network
Author(s) -
Ahmadi Mikaeel,
Lotfy Mohamed Elsayed,
Danish Mir Sayed Shah,
Ryuto Shigenobu,
Yona Atsushi,
Senjyu Tomonobu
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2018.5057
Subject(s) - wind power , computer science , energy storage , real time computing , environmental economics , reliability engineering , engineering , electrical engineering , economics , power (physics) , physics , quantum mechanics
Extension of renewable energies in power system planning and operation especially distribution networks is not limited to power sustainability. It also encompasses many significant contributions such as eliminating electricity shortages by diversifying energy supply, improving reliability with power quality, reducing greenhouse gas emissions, and providing energy independence, which is the most crucial aspect for both developed and developing countries power sector. The extraction of such benefits in the best manner can be achieved by considering storage and control devices, aiding well‐configured electricity networks through competitive optimisation techniques. By taking such points into consideration, optimal multi‐configuration and allocation of step‐voltage regulators (SVRs), capacitor banks, and energy storage system along with centralised wind‐power generation integrating to distribution network are investigated and applied, using a novel and Pareto based epsilon multi‐objective genetic algorithm. The proposed methodology is applied to an extensive and real 162‐bus distribution network in Kabul city to validate its sturdiness. The simulations are performed in MATLAB ® environment with six configuration scenarios to compare the effect of multiple arrangements in the distribution network, and to discover the best configuration fulfilling the optimisation criteria with the objective functions being as power loss, voltage deviation, and violation cost.