期刊
IET RENEWABLE POWER GENERATION
卷 13, 期 5, 页码 762-773出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rpg.2018.5057
关键词
genetic algorithms; voltage regulators; Pareto optimisation; wind power plants; energy storage; power distribution planning; centralised wind farm; energy storage system; multiobjective approach; renewable energies; MATLAB environment; Pareto based epsilon multiobjective genetic algorithm; step-voltage regulator allocation; optimisation techniques; power quality reliability; SVR; electricity networks; greenhouse gas emission reduction; distribution networks; centralised wind-power generation; capacitor banks; energy supply; electricity shortages; power sustainability; power system planning
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 MATLABA (R) 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.
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