Open AccessOptimal placement of DG units for the enhancement of MG networks performance using coalition game theory
Author(s) -
Karimizadeh Karvan,
Soleymani Soodabeh,
Faghihi Faramarz
Publication year - 2020
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.2019.0070
Subject(s) - particle swarm optimization , distributed generation , computer science , mathematical optimization , grid , smart grid , reduction (mathematics) , renewable energy , demand response , matlab , photovoltaic system , profit (economics) , power (physics) , electricity , engineering , electrical engineering , mathematics , operating system , physics , geometry , quantum mechanics , economics , microeconomics
Nowadays, the continuous growth of energy demand micro‐grid (MG) networks that use as distributed energy source. MGs are usually installed at locations, where access to the power grid is not economical due to the far distance and losses. To cost reduction in MG networks, it is necessary to optimize placement of distributed generation (DG) units in MG networks. In this study, the optimal allocation of DG units using craziness‐based particle swarm optimization (CRPSO) algorithm based on the game‐theoretic formulation strategy was proposed to decrease power supply costs. The objective function of optimization comprises of the cost of buying power, power loss, communication and load shedding. Load shedding is considered so that the cost of operation of MGs declined. The optimization includes the size, site and the optimal order of joined DG units (wind turbine and photovoltaic) in test system. The novelty of this research is that it determines the order of joined DG units in the coalition when the arrangement of seller MG after DG unit's placement is reshaped. The simulation results in the modified IEEE 33 buses system validated through the MATLAB. The simulation results show the reduction in the costs of operation and rise in the sellers' profit.